REVIEW   Open Access    

Effect of natural plant extracts on the quality of meat products: a meta-analysis

More Information
  • Natural plant extracts (NPE) from some organs of plants are rich in bioactive substances. They have special nutritional characteristics with strong antioxidant and antimicrobial activities. The potential of NPEs to maintain and improve the quality of meat products has attracted attention due to concerns about the safety hazards of synthetic food additives. This paper extensively reviewed the application of NPE in meat processing, and systematically analyzed the comprehensive effects of different NPE using meta-analysis. Fourty-eight articles from 23 countries were studied with standard mean deviation (SMD) using random effect model, and 28 indexes were isolated. Results showed that NPE can reduce the pH value of meat products, improve antioxidant capacity, reduce the degree of oxidation and inhibit microbial growth. In addition, it was found that NPE had a significant impact on the quality of meat products. This meta-analysis provides quantitative evidence to explain how NPE affects meat quality, and helps to better understand the role of NPE in meat processing.
  • South Africa is the center of origin and diversity of gladiolus or sword lily, Gladiolus × hybridus Rodigas (Iridaceae), although species are also native in the Mediterranean (Italy, the Arabian Peninsula) and into the Russian Federation[1]. It is a major cut flower in the floriculture industry (ranked in the top ten cut flower crops), used as a line flower in line-mass designs[2] and sold in mixed or single cultivar bunches at retail farmer's markets or wholesale commercial floral markets, respectively. Gladiolus have been in the top ten cut flowers in Dutch auctions since 1958, with ~1 M gladioli stems/year sold[3]. In 2018, gladiolus corm production in the Netherlands was 637 ha and floral spike production 153 ha[4]. In 2020, the US value of cut flower gladiolus was US$14.83M (w, wholesale farmgate value)[5], while Chinese production of gladiolus is increasing[6]. In China, gladiolus production covered 3,300 ha in 2014 which made it the second-most grown geophyte in China after Lilium[7]. It is also grown as an ornamental garden plant (non-hardy, tender perennial in northern latitudes, USDA Z3-4)[8].

    Gladiolus species are geophytic with corms (compressed stems) for underground storage organs[9]. In commercial production, gladioli are planted as 3−5 year-old corms, capable of flowering[10,11]. Gladioli are vegetatively propagated via daughter corms and/or cormels in commercial production. Daughter corms arise above the current season's mother corm; all corms and cormels arise from the daughter corm's basal plate and consist of an enlarged stem axis with nodes and internodes with dry, scale-like leaves forming a protective 'tunic'[12]. Thus, gladiolus have tunicate corms. Classically, one daughter corm is generated/year by the mother corm but cormel numbers vary from one to hundreds/corm, depending on the cultivar[12]. Cormels differ from daughter corms, being smaller and arising directly from the basal plate[12]). After corm/cormel sprouting, each propagule produces adventitious and contractile roots, the latter of which are thick fleshy roots which pull the seedling or corm deeper into the ground[13]. Seedlings form both root types and a small corm within 1−4 weeks post-germination immediately pulls the corm below the soil surface (N. Anderson & R. Eperjesi, 2019, unpublished data).

    Gladiolus production is for either cut flower (floral design) purposes or for use as a garden annual/perennial[14], depending on the USDA Hardiness Zone since most are frost- and cold-sensitive in northern latitudes[8]. The floral spike (defined as the central stem with all individual florets)[1] is cut when petal coloration starts in the lowest floret but before it reaches anthesis[15], commercially referred to as Stage 2[11,16]. The florets open acropetally or from the base upwards in a linear fashion over time, 1x/day[10]. All commercial cut flower and garden cultivars are non-fragrant, although ten or more wild species have fragrance, including G. orchidiflorus (Anderson, 1999, unpublished data), G. tristis and G. recurvus[17].

    Gladiolus stems are bunched in 5, 7 or 10 stems/bunch[16]. There are four grades (1−4) for minimum stem length 80−115 cm), minimum flower diameter (6.25−8.75 cm), stem strength (15°), stem deviation curvature from vertical (5−10 cm), and the minimum number of flowers or florets / stem (6−12)[10,11,16]. The four major flower color classes of gladiolus are blue, yellow, red, and green although whites are commonly produced as well as novelty types with varying petal shapes, ruffled edges, etc.[16]. Gladiolus floral spikes can be stored dry or in floral preservative at 3−4 °C, 90% relative humidity for 2 to 3 weeks[10,16] At room temperature (20−22 °C), expected vase life is 7 d minimum. Gladiolus may have ethylene (C2H4) sensitivity during postharvest storage[16,18] necessitating treatment with either silver thiosulfate (STS) or 1-Methylcyclopropene (1-MCP)[1921]. Ethylene response may reduce flower life by aborting unopened flower buds[21]. Prevention of ethylene buildup likewise increases postharvest longevity of gladiolus[18]. Gladiolus stem tips are negatively geotropic and are predominantly shipped/stored upright to prevent stem tip bending away from gravity[21].

    Without floral preservatives, gladiolus may have shortened postharvest life due to lack of water from occlusions of basal stem cuts and microbial plugging of the xylem[2, 10,19]. Sucrose (20%; overnight)[21,22] or cobalt[23] pulsing, as well as floral preservatives increase vase life of flower spikes as high as 12.3 d, although the range in vase life of a stem is 6−10 d[21]. Previous gladiolus cut flower postharvest research reported that higher temperatures during production decreased cut stem fresh weights, but the opposite was found with higher CO2 levels[24].

    Gladiolus breeding is primarily accomplished by amateur breeders in gladiolus societies, e.g. the North American Gladiolus Council[11], one private sector company (Breck's Holland)[25] and one public sector breeding program (University of Minnesota, USA)[8,2628]. Many new cultivars are released each year using divergent ancestries[11,29]. In recent decades, significant corm production, postharvest and physiological research on gladiolus has been conducted in Brazil (Universidade Federal de Santa Maria, Santa Maria), Pakistan (The University of Agriculture, Faisalabad and Peshawar), India (Indian Agriculture Research Institute, New Delhi; University of Agricultural Science, Bangalore), Egypt (Kafrelsheikh University, Kafr El-Sheikh; Agricultural Research Center, Giza), Poland (University of Agriculture, Kraków), the Czech Republic (Mendelova zemědělská a lesnická univerzita v Brně, Brno) and Italy (Universita degli Studi di Bari, Bari). However, much of the production and postharvest techniques to achieve saleable product aren't translated into public or private sector breeding programs to aid in the advancement of the crop. To unite physiological and breeding/genetic research efforts, the University of Minnesota flower breeding program is developing cut flower cultivars with unique floral colors and patterns, along with cold tolerance for USDA Z3-4[8], rapid generation cycling (RGC)[21], dwarf types for potted plant and container production, and vegetative or seed-propagated F1 hybrids. Most would be new traits for this crop and provide unique opportunities for postharvest testing to aid in breeding and selection. Thus, the objective of this research was to test a sample of advanced cut flower selections within the breeding program with varying ancestry, plant stature, and floral traits to establish baseline data for future breeding and selection for development of a gladiolus cut flower crop ideotype.

    This study was conducted at the public sector University of Minnesota Gladiolus Breeding & Genetics Program, involving greenhouse, laboratory, and field facilities in Saint Paul and Rosemount, Minnesota, USA.

    Thirteen cut flower gladiolus genotypes were tested in this experiment. Eleven clonal genotypes (numbered selections, GL-1 to GL-11; Table 1) were hybrids or inbreds derived from the University of Minnesota breeding program plus two commercial named comparisons ('Beatrice', 'Manhattan'). Genotypes GL-1 to GL-11 were hybrids or inbreds produced from controlled crossings or selfs, respectively, in the St. Paul campus greenhouses (44°59'17.8" N lat., −93°10'51.6" W long.) during 2006−2016 or as open-pollinated (OP) seedlings in field trials. The OP seedlings were most likely inbreds, due to self compatibility operating in tetraploid gladioli. Seedling growouts to flowering (1−5 years) for subsequent clonal evaluation occurred in breeder field trials, Rosemount, MN (44°42'58.2'' N, −93°5'54.9" W)[27,28]. The short stature genotypes did not require staking or additional support in the field production (Fig. 1) whereas the taller ones did if the stems were left to completely flower (Fig. 2).

    Table 1.  Hybrid gladioli of dwarf (< 90 cm) or tall stature (> 90 cm; Breck's Holland[25]) tested for field performance data (wk 22 planting dates), averaged over three years (2019, 2021, and 2022) grown under standard commercial field production trials in Rosemount, MN, USA for: number of weeks to visible bud date (VBD; VBD wk. no. – planting wk. no.), number of weeks to flowering (flowering wk. no. – planting wk. no.), number of weeks to termination of flowering (termination wk. no. – planting wk. no.), plant height (cm), plant width (cm), number of leaves, and flower petal type, flower color or petal markings.
    GenotypeNo. wks
    to VBD
    No. wks to flowering, terminationPlant height
    (cm)
    Plant width
    (cm)
    No. of
    leaves
    Flower petal type, flower color or petal markings
    Dwarf stature (< 90 cm)
    GL-11013 ab, 1580.0 b23.08Slightly ruffled, peach, white venation
    GL-21215 b, 1957.0 a13.09Hooded lt. pink/creamy white, yellow throat
    Tall stature (> 90 cm)
    GL-31213 ab, 1591.5c44.06Ruffled red w/white throat
    GL-41012 a, 1698.0 c20.56Fuchsia-red w/white streaked venation
    GL-51112 a, 14101.0 cd26.56Dark orange
    GL-61113 ab, 16115.0 d36.08Hooded cream w/yellow throat, red venation
    GL-71113 ab, 14111.0 d228Red
    GL-81112 a, 1693.0 c25.08Ruffled, red w/white throat
    GL-91012 a, 14112.0 d60.08Ruffled red w/yellow throat
    GL-101014 ab, 16117.2 de16.58Ruffled peach w/blotch (eye)
    GL-111214 ab, 16121.0 de53.08Ruffled orange w/white throat
    'Beatrice'1113 ab, 17104.5 cd27.68Ruffled pink picotee, white w/yellow throat
    'Manhattan'1012 a, 17100.5 cd43.58Red
    Significance0.782 ns0.034 *, 0.195 ns0.001 ***0.158 ns0.166 ns
    Flowering termination week number was determined when > 50% of the flowers had senesced. Significance (p-values) were determined from univariate ANOVAs and mean separations derived from Tukey's Honestly Significantly Difference (HSD) test at α = 0.05.
     | Show Table
    DownLoad: CSV
    Figure 1.  Production field planting with an example nonlodging gladiolus genotype (GL-1). Scale: bar = 6 cm.
    Figure 2.  Gladiolus cut flower stem lodging in the field production trials (GL-4), requiring staking or use of support mechanisms. Scale: bar = 6 cm.

    Prior to the present study, these genotypes were tested for field performance data for three years (2019, 2021, and 2022) when grown under standard commercial field production trials; planting occurred during wk 22 (starting wk number). The tested genotypes were phenotypically categorized by stem length of either dwarf (< 90 cm) or tall statures (> 90 cm)[25] as well as for important production and postharvest traits, including visible bud date (VBD) week number, flowering week number, termination (of flowering) week number, plant height (cm), plant width (cm), number of leaves, flower petal type, flower color and petal markings (Table 1). Flowering termination week number was determined when > 50% of the flowers/stem had senesced.

    In 2022, 3- to 5-year-old mature (capable of flowering) corms of the 13 selected genotypes for postharvest testing were grown in the fields. Corms were in the size grade ranges of 2.5 cm (Number 3) to 3.8 cm (Number 1), which ensured that all were capable of flowering[30]. As many as n = 30−100 clonal ramets of each genotype were grown for evaluation.

    Cultural conditions for the cut flower gladiolus trial were similar to those used for other herbaceous annuals and perennials in the University of Minnesota breeder field[31], located at the University of Minnesota Rosemount Research and Outreach Center, Rosemount, MN, USA. In week 22 (29 May 2022), the n = 30−100 clonal ramets (corms) per accession were planted in spaced rows (7.6 cm on center or On Center (O.C.) within rows; 61.0 cm among rows) in a trenched system, completely randomized design. Corm depth burial was 7.6−10.2 cm, as per recommendations[30]. Field plots were fertilized with urea (56 kg/ha actual N, preplant granular) with hand weeding, mechanical tilling, and pre-emergent herbicide chemical applications for weed control (Fortress®, Isoxaben + Dithiopyr granular; 22.7 kg/0.4 ha; Amvac Chemical Corp., Bluffton, SC, USA). Overhead boom irrigation was used to supplement intermittent rainfall to ensure average precipitation of 2.54 cm/wk.

    Cut flower harvest occurred during wk 37 (2022), once all of the genotypes were at flowering stage with sufficient numbers of stems available for the postharvest study. Harvest was at Stage 2, when color was showing in the petals of the lower flowers[10,16]. Stems were cut in early morning (0700−0800 HRS), with 1/3 of the lower leaves were removed, followed by placement directly into standard 25 cm cooler buckets with deionized water. One genotype was placed in each cooler bucket (38.1 cm × 18 cm or 15" × 8"; www.koehlerdramm.com/pr/COOLER-BUCKET-15-X-8-BLACK/42576); once sufficient stems were harvested, the floral buckets were placed into shade for transport to the St. Paul campus once all the harvesting had occurred. Stems were immediately stored in a dark, walk-in cooler (3−5 °C) until the postharvest experiment began in < 24 h.

    Unlike previous studies where the stems were recut to the standard 75 cm length[19,32], the inclusion of dwarf stature (< 90 cm) types necessitated using different stem lengths (Fig. 3). Thus, each stem was recut (2 cm removed) prior to the start of the postharvest experiment[24].

    Figure 3.  Example cut tall (left) vs dwarf (right) glad stem lengths. Scale: bar = 14 cm.

    The postharvest experiment was conducted during wks 37−38 (2022) in the laboratory at standard conditions of 24 h continuous light (10 µmol·sec−1·m−2) at 21 °C. Two solution treatments were tested: tested with two treatment solutions deionized, distilled water (DDW) and Floral Life floral preservative (FLFP; FloraLife Crystal Clear Flower Food 300® floral preservative; https://shop.floralife.com/) applied as continuous vase solutions. There were n = 6 (< 6 in some genotypes) replications/treatment solution/genotype, making a total of 13 genotypes × 2 treatments × 6 replications = 156 experimental units. Due to the size of the stems, large pedestal vases were used (24.765 cm, Syndicate Sales; https://directfloral.com/syndicate-sales-975-pedestal-vase-fiesta-assortment) and filled with 1.5 L of solution/vase. Vases were arranged in a completely randomized design (CRD) on the lab bench for the duration of the experiment; the experiment was conducted for 9 d.

    During the course of the experiment, the following parameters were measured, either at the beginning, ending or during the experiment: inflorescence cut stem length (cm), total no. of floret buds/stem, inflorescence internode length (cm), total no. (%) opened flowers, day 0 stem fresh weight (FW; g), day 9 stem FW (g), ΔFW (g; day 9 FW – day 0 FW), day 9 dry weight (DW; g), % water, 1st flower diameter (cm), 3rd flower diameter (cm), beginning and final pH, ΔpH, solution volume used per stem (ml), number of flowers senesced/day in days 1–9, total number of flowers senesced in days 1−9, and the number of saleable days (when the 5th floret from the base wilted; Fig. 4).

    Figure 4.  Stage when 50% of the gladiolus flowers/stem (occurring on genotype GL-8 on day 9) are commercially classified as 'wilted' or 'dead' [24]. Scale: bar = 3 cm.

    Data were analyzed with univariate general linear model Analysis of Variance (ANOVA) along with mean separations using Tukey's Honestly Significance Difference (HSD) tests at α = 0.05 (Statistical Package for the Social Sciences, SPSS, version 22, University of Chicago, Chicago, IL, USA). Repeated measures ANOVA were used for parameters measured > 1x/stem. Pearson's correlations (r) of all traits were performed. Chi-square (χ2) tests for equal distribution (1:1:1:1:1:1:1:1:1; df = 8; χ2 = 15.507) of the mean number of flowers senesced/day/genotype in days 1−9 and the total number of flowers sensed over the postharvest experiment period (days 1−9) were calculated.

    All genotypes reached VBD within 10-12 wks from planting (Table 1) and were not significantly different. The range of VBD was within a 3-week range of calendar weeks 32 (GL-1, -4, -9, -10, 'Manhattan') to 34 (GL-2, -3, -11); other genotypes were at week 33. Significant differences were found, however, for flowering calendar week number, ranging from weeks 34 to 37 (Table 1) with the differences ranging from 12 (GL-4, -5, -8, -9, 'Manhattan') to 15 weeks from planting (GL-2; Table 1). Interestingly, GL-2 is a dwarf stature type that took significantly longer to flower than many other genotypes. In contrast, the number of weeks to flowering termination was not significant, with a range of 14−19 wks (Table 1). Genotypes have a flowering date range of 14 wks (98 d) to 19 wks (133 d).

    Plant heights were significantly different among genotypes and ranged from 57 cm or Minimum Length Grade 4+ (GL-2) to 121 cm or Minimum Length Grade 1 (GL-11; Table 1)[16], with the dwarf stature types being significantly shorter than the tall statue types. All adhered to the Stem Strength Grades 1−4 of 15° and fell within the Stem Deviation Curvature of Grade 1 < 5 cm[16]. The significantly tallest genotypes were GL-10 and GL-11 at 117.2 and 120 cm, respectively. Plant width ranged from 13 cm (GL-2, dwarf stature) to 60 cm (GL-9, tall stature; Table 1), although none were not significantly different. Likewise, the number of leaves was insignificant and unrelated to plant statue, despite ranging from 6 (GL-3, -4, -5) to 9 leaves (GL-2; Table 1).ong the numerous and divergent genotypes tested, the phenotypic traits of importance for cut flower use, only flowering week and plant height were significantly different; all other traits were insignificant (Table 1).

    Since the gladiolus inflorescence cut stem lengths and numbers of flower buds (florets) per inflorescence (Fig. 3) varied due to varying stem lengths among the dwarf vs tall statures (stem lengths had to be long enough to stand in the preservative solution), there were significant differences within and among treatments (DDW, FLFP) and among most genotypes (Table 2). The interaction of genotype × treatment was not significant. As expected, the shortest two sets of inflorescences in both treatments (DDW, FLFP) had significantly shorter cut stem lengths than all other genotypes, all of which were classified as tall stature types. The significantly tallest inflorescence cut stem lengths occurred in GL-3 for both treatments and would be ranked as Grade 3 for Minimum Length (82 and 91.5 cm, FLFP and DDW, respectively; Table 2)[16]. Most of the other tall stature genotypes overlapped for inflorescence cut stem lengths. As would be expected, inflorescence cut stem lengths were significantly and positively correlated with all traits except for no. flowers senesced/day, Σ no. flowers senesced, final pH, and ΔpH (Table 3).

    Table 2.  Mean inflorescence cut stem length (cm), total no. of floret buds/stem, inflorescence internode length (cm), total no. (%) opened flowers in dwarf and tall stature gladiolus genotypes tested with two treatment solutions applied as continuous vase solutions.
    GenotypeInflorescence cut
    stem length (cm)
    Total no. floret
    buds/stem
    Inflorescence internode
    length (cm)
    Total no. (%) opened flowers
    DDWFLFPPooledDDWFLFPPooled
    Dwarf stature (< 90 cm)
    GL-143.5a42.5a7.7a5.3ab5.8a-c5.1ab (66%)
    GL-2z40.2a37.0a9.5a-c4.2a3.9a2.89a (28%)
    Tall stature (> 90 cm)
    GL-391.5d82.0d17.8g4.8a5.0ab12.2de (68%)
    GL-457.4b64.8bc10.1a-d5.8a-c6.3bd6.2a-c (62%)
    GL-5x65.8bc56.8b13.2e-f4.5a4.8a2.8a (22%)
    GL-665.3bc71.7c11.9c-f5.8a-c5.7a-c6.9a-c (59%)
    GL-7w62.0b73.3c12.9d-f4.9ab5.6a-c5.5ab (43%)
    GL-869.8bc68.3bc10.2a-e6.5cd7.1d9.0cd (88%)
    GL-9w60.0b57.7b9.0ab6.9d6.2bd8.1b-d (90%)
    GL-10w67.0bc69.0bc10.6a-e6.5cd6.3bd10.0c-e (94%)
    GL-11y76.0c68.0bc14.0f5.4a-c4.9ab12.8e (91%)
    'Beatrice'73.5c69.2bc12.3c-f5.9a-c5.7a-c7.2bc (58%)
    'Manhattan'61.8b67.0bc8.3ab7.5d8.4d7.6bc (92%)
    Significancev
    Genotype (G)F = 17.31***F = 13.26***F = 8.29***F = 6.48***
    Treatment (T)F = 13.12***F = 0.63nsF = 3.76*F = 1.33ns
    G × TF = 1.38nsF = 0.66nsF = 0.64nsF = 1.88*
    DDW = deionized, distilled water; FLFP = Floral Life floral preservative or Pooled if treatments were not significantly different. There were n = 6 replications/treatment solution/genotype unless noted otherwise; mean separations within columns based on Tukey's Honestly Significantly Difference (HSD) test at α = 0.05.
     | Show Table
    DownLoad: CSV
    Table 3.  Correlation matrix for the postharvest cut flower traits examined in dwarf and tall stature gladiolus genotypes tested with two treatment solutions applied as continuous vase solutions.
    Day 0
    FW
    Day 9
    FW
    ΔFWDay 9
    DW
    %
    water
    Inflor. cut
    stem length
    Σ no. flw
    buds/stem
    Inflor. internode
    length
    Σ no.
    open flws
    Σ % open
    flws
    Floret
    1 dia.
    Floret 3
    dia.
    No. flws
    senesced
    /day
    Σ no. flws
    senesced
    No. saleable
    days
    Final pHΔpHSol'n vol/stem
    Day 0 FW1.0
    Day 9 FW0.89**1.0
    ΔFW−0.21*0.24**1.0
    Day 9 DW0.91**0.88**0.091.0
    % water−0.010.18*0.26**−0.25**1.0
    Inflor. cut stem length0.81**0.8**0.060.78**0.031.0
    Σ no. flw buds/stem0.68**0.64**−0.070.49**0.24**0.64**1.0
    Inflor. internode length−0.050.030.160.16−0.24**0.21*−0.58**1.0
    Σ no. open flws0.58**0.31**−0.43**0.45**−0.34**0.53**0.39**0.051.0
    Σ % open flws0.23*−0.01−0.39**0.21*−0.51**0.27*−0.18*0.46**0.80**1.0
    Floret 1 dia.0.39**0.37**0.120.49**−0.180.36**0.010.36**0.31**0.32**1.0
    Floret 3 dia.0.35**0.34**0.070.47**−0.180.39**0.010.37**0.34**0.33**0.88**1.0
    No. flws senesced/day−0.07−0.11−0.03−0.04−0.11−0.01−0.010.010.090.060.020.011.0
    Σ no. flws senesced−0.03−0.08−0.010.01−0.14−0.010.02−0.020.030.00−0.01−0.020.94**1.0
    No. saleable days−0.33**−0.020.46**−0.20*0.29**−0.25**−0.34**0.17−0.62**−0.36**−0.19*−0.25**0.010.031.0
    Final pH0.21−0.02−0.53**0.08−0.190.090.070.030.160.130.15−0.050.020.04−0.081.0
    Δ pH−0.27−0.170.21−0.260.12−0.010.03−0.03−0.13−0.13−0.11−0.330.01−0.010.010.131.0
    Sol'n vol/stem0.75**0.79**−0.220.86**−0.090.76**0.460.380.330.240.350.15−0.19−0.230.100.220.191.0
    DDW = deionized, distilled water; FLFP = Floral Life floral preservative or Pooled if treatments were not significantly different.
     | Show Table
    DownLoad: CSV

    An example of the floret opening stage on Day 0, the start of the experiment are shown in Fig. 5. The mean Σ number of floret buds/stem varied significantly across genotypes but not treatments, ranging from 7.7 (GL-1, short stature) Grade 3 flower number/stem to 17.8 Grade 1 flower number/stem (GL-3, tall stature; Table 2)[16]. The interaction of genotype × treatment was not significant. This trait was positively and significantly correlated with Σ no. open flowers (r = 0.39), but negatively and significantly correlated with inflorescence internode length (r = −0.58), Σ % open flowers (r = −0.18) and no. saleable days (r = −0.34; Table 3).

    Figure 5.  Example cut gladiolus stems (stage 2) at day 0, the beginning of the experiment, for all six replications of one genotype. Scale: bar = 3 cm.

    The mean inflorescence internode length ranged from 3.9 cm (GL-2, FLFP treatment) to 7.5 cm ('Manhattan', DDW; Table 2). Genotypes and treatments were significant whereas the genotype x treatment interaction was not. This trait was significantly and positively correlated only with reproductive traits, i.e., Σ % open flowers, floret 1 diameter, floret 3 diameter (Table 3). Inflorescence internode length is not a function of, nor correlated with stature, as several significantly shorter internode lengths occurred in both the short and tall statures, whereas only the significantly longest internodes occurred in the tall stature genotypes (Table 2).

    The Σ number and Σ percent of opened flowers/inflorescence at the end of the experiment, ranged from 2.8% and 22% (GL-5) to 12.8% (GL-11) and 94% (GL-10; Table 2), respectively. The significantly lowest percentages of opened flowers/inflorescence occurred in both short (GL-2, 28%) and taller stature (GL-5, 22%) genotypes. An example of the flower opening/closing on Day 9 is shown for a single stem (Fig. 6) versus all stems within a genotype (Fig. 7). In some instances, flowers never opened in both solution treatments (Fig. 8). Genotypes differed significantly although treatments did not but their interaction was significant (Table 2). Both traits were significantly and positively correlated with each other (r = 0.8) as well as each trait with floret 1 and 3 diameters, but negatively and significantly correlated with the number of saleable days (r = −0.62, r = −0.36, respectively; Table 3).

    Figure 6.  Gladiolus stem post-stage when > 50% of the gladiolus flowers have wilted (GL-11 rep 1 on day 9). Scale: bar = 3 cm.
    Figure 7.  Set of six replicate gladiolus stems (GL-6 stems all reps) at the end of the experiment on day 9. Scale: bar = 3 cm.
    Figure 8.  Example of gladiolus flowers failing to open completely (GL-6 rep 2 stem on day 9). Note: This genotype often produced a secondary flowering shoot (left). Scale: bar = 3 cm.

    As would be expected, Day 0 stem FWs were not significantly different among treatments since the experiment had not yet commenced. However, genotypes were very highly significantly different, ranging from 9.9 g (GL-2, short stature) to 54.9 g (GL-3, tall stature; Table 4). The interaction of genotype × treatment was not significant. Day 0 FW were positively and significantly correlated with day 9 FW (r = 0.89) and DW (r = 0.91), inflorescence cut stem length (r = 0.81), Σ number of flower buds/stem (r = 0.68), Σ number of open flowers (r = 0.58), Σ % open flowers (r = 0.23), floret 1 diameter (r = 0.39), floret 3 diameter (r = 0.35), and solution volume/stem (r = 0.75; Table 3). Day 0 FW was significantly but negatively correlated with the number of saleable days (r = −0.33; Table 3); all other trait correlations were not significant.

    Table 4.  Mean day 0 stem fresh weight (FW; g), day 9 stem FW (g), ΔFW (g; day 9 FW – day 0 FW), day 9 dry weight (DW; g), % water in dwarf and tall stature gladiolus genotypes tested with two treatments applied as continuous vase solutions.
    GenotypeDay 0 stem FW (g)Day 9 stem FW (g)ΔFW (g)Day 9 DW (g)% Water
    PooledDDWFLFPDDWFLFPDDWFLFPDDWFLFP
    Dwarf stature (< 90 cm)
    GL-115.8ab13.9ab14.6ab−2.8a-d−0.2b-d1.9a2.0a75.8b75.4b
    GL-2z9.9a8.7a9.9a−3.2a-d1.8cd1.2a0.9a75.2b82.2bc
    Tall stature (> 90 cm)
    GL-3y54.9g47.7g50.1−15.1a3.0cd5.0e-g5.5fg80.9bc80.2bc
    GL-421.9a-c17.7a-c27.3b-e−1.6b-d2.8cd2.5ab3.4a-d75.6b77.8bc
    GL-5x24.3a-d30.1c-e25.6b-e1.3cd5.6d2.3ab1.2a85.8c90.9cd
    GL-638.4d-f30.6c-e46.9g−5.9ab6.7d3.7b-e4.5c-g78.6bc82.4bc
    GL-7w44.4e-g37.4d-f53.6g−6.2a8.2d5.1e-g5.9g76.0bc80.2bc
    GL-822.2a-c29.4b-e42.2e-g−12.4a9.5d4.4c-g5.1e-g74.1b78.7c
    GL-9w27.4b-d16.3a-c19.1a-c−11.4a−7.9a3.2a-d3.8b-e66.7a67.2a
    GL-10w29.6b-e24.1a-d31.1c-e−1.6b-d−2.2b-d3.3a-d4.2b-g76.1bc76.0bc
    GL-11y34.2c-f27.2b-e31.4c-e−6.6a3.8cd4.3c-g3.7b-e72.8ab71.1ab
    'Beatrice'46.9fg42.5e-g54.5g−2.2a-d5.3d5.1e-g5.8g78.7c80.7bc
    'Manhattan'30.8b-e25.4b-e31.3c-e−3.2a-d−1.6b-d3.1a-d3.7b-e78.0c78.6c
    Significancev
    Genotype (G)F = 15.54***F = 20.99***F = 5.45***F = 14.39***F = 21.80***
    Treatment (T)F = 0.52nsF = 22.42***F = 125.45***F = 43.82***F = 19.69***
    G × TF = 1.45nsF = 1.48nsF = 4.99***F = 1.47nsF = 1.54ns
    DDW = deionized, distilled water; FLFP = Floral Life floral preservative or Pooled if treatments were not significantly different.
    There were n = 6 replications/treatment solution/genotype unless noted otherwise; mean separations within columns based on Tukey's Honestly Significantly Difference (HSD) test at α = 0.05.
     | Show Table
    DownLoad: CSV

    Day 9 stem FWs were very highly significantly different for both genotypes and treatments, but not for their interaction (Table 4), ranging from 8.7 g (GL-2, DDW) to 54.5 g ('Beatrice', FLFP). This range was slightly lower than the range for Day 0, as illustrated by the ΔFW wherein most genotypes had negative ΔFW (−0.2 to −15.1). The exceptions occurred primarily in the FLFP treatment in both dwarf and tall stature genotypes; the only positive ΔFW in the DDW treatment was GL-5 (ΔFW = 1.3; Table 4). Day 9 FW were significantly and positively correlated with all traits except inflorescence internode length, Σ % open flowers, number of flowers senesced/day, Σ number of flowers senesced, final pH, and Δ pH (Table 3).

    The percent water ranged from 66.7% (GL-9, DDW) to 90.9% (GL-5, FLFP; Table 4), based on fresh weight – dry weight differences. The lowest percent water occurred in tall stature genotypes instead of the dwarf genotypes. The lowered level of water in some genotypes, e.g. GL-9 might indicate increased levels of fibers in the stem stalks and/or leaves but would need to be studied further to identify the root cause of depressed percent water.

    The 1st flower (floret) diameters were very highly significant for genotypes and treatments but only highly significantly different for their interaction (genotype x treatment; Table 5). The mean range of flower diameters for the 1st flower was 4.1–7.8 cm for DDW (miniature) and 4.6–8.1 cm for FLFP treatments (miniature). While most means overlapped in significance, there were genotypes with the significantly smallest (GL-6) and largest 1st flower diameters (GL-7 to GL-11, 'Beatrice' and 'Manhattan') in the DDW treatment (Table 5). Comparatively, the FLFP treatment smallest 1st flower diameters were GL-4 and GL-6, as opposed to the significantly largest diameters occurring in GL-3, GL-8 to GL-11, 'Beatrice' and 'Manhattan'. Thus, GL-6 consistently had the smallest 1st flower diameter in both treatments, whereas GL8 to GL-11, 'Beatrice', and 'Manhattan' consistently had the significantly largest 1st flower diameters (Table 5). The 1st flower diameter was significantly and positively correlated with all other traits except for ΔFW, % water, Σ number of flower buds/stem, number of flowers senesced/day, Σ number of flowers senesced, final pH, ΔpH, and solution vol./stem (Table 3).

    Table 5.  Mean 1st flower diameter (cm), 3rd flower diameter (cm), final pH, ΔpH, solution volume used per stem (ml) in dwarf and tall stature gladiolus genotypes tested with two treatment solutions applied as continuous vase solutions.
    Genotype1st Flower diameter (cm)3rd Flower diameter (cm)Final pH (ΔpH)Sol'n vol./stem (ml)
    DDWFLFPDDWFLFPDDWFLFPDDWFLFP
    Dwarf stature (< 90 cm)
    GL-15.5a-d6.5d-g4.8a5.4a-d4.9 (3.4)3.5 (0.6)1.73.3
    GL-2z5.0a-d5.0a-d4.2a6.2b-e4.9 (3.4)4.2 (−0.09)53.2
    Tall stature (> 90 cm)
    GL-3y7.5g7.2fg6.2b-e5.6a-d4.9 (3.5)4.2 (−0.1)3535
    GL-45.0a-d4.8ab5.1a-c4.7a5.3 (3.1)4.1 (−0.04)16.816.7
    GL-5x5.8b-e6.0c-f5.2a-c5.3a-d4.2 (4.2)4.1 (−0.02)2820
    GL-64.1a4.6ab3.9a4.1a4.9 (3.5)4.2 (−0.1)21.721.7
    GL-7w7.5g5.0a-d5.5a-d5.5a-d4.9 (3.4)4.8 (−0.7)2030
    GL-87.5g7.2fg6.8de6.3b-e5.1 (3.3)4.1 (−0.1)3025
    GL-9w7.8g6.8e-g6.3b-e5.8a-d5.1 (3.3)4.2 (−0.1)6.75
    GL-10w7.2fg7.7g6.1b-e7.3e5.0 (3.4)4.2 (−0.1)6.710.3
    GL-11y7.0fg6.5d-g6.2b-e6.0b-e5.2 (3.2)4.1 (−0.05)3030
    'Beatrice'6.8e-g7.8g6.1b-e7.8e4.4 (3.9)4.5 (−0.4)6050.8
    'Manhattan'7.0fg8.1g6.0b-e7.0e5.2 (3.2)4.1 (−0.05)6060
    Genotypes Pooled4.92 (3.45)b4.24 (−0.1)a24.7ab23.9a
    Significancev
    Genotype (G)F = 15.51***F = 8.19***F = 0.22nsF = 1.10ns
    Treatment (T)F = 20.59***F = 14.66***F = 13.87***F = 1.91*
    G × TF = 2.27**F = 2.51**F = 0.91nsF = 0.96ns
    z n = 4 reps. y n = 2 reps. x n = 5 reps. w n = 3 reps. v *** p < 0.001, ** p < 0.01, * p < 0.05, ns not significant.
    DDW = deionized, distilled water; FLFP = Floral Life floral preservative or Pooled if treatments were not significantly different.
    There were n = 6 replications/treatment solution/genotype unless noted otherwise; mean separations within columns based on Tukey's Honestly Significantly Difference (HSD) test at α = 0.05.
     | Show Table
    DownLoad: CSV

    Similar to the 1st flower diameters, the 3rd flower diameters were also very highly significant for genotypes and treatments but only highly significantly different for their interaction (genotype × treatment; Table 5). The mean range of flower diameters for the 3rd flower in the DDW treatment was 3.9 cm (GL-6; miniature) to 6.8 cm (GL-8; small) and 4.1 (GL-6; miniature) to 7.8 cm ('Beatrice'; miniature) in the FLFP. Similar to 1st flowers, GL-6 also displays genetic stability across treatments for the smallest 3rd flower diameters (Table 5).

    Final pH of the vase solution treatments was not significantly different among genotypes or genotype x treatment interaction but was very highly significantly different for treatment (Table 5). While the beginning pH of the water was pH = 8.38, prior to adding floral preservative to FLFP, as soon as it was added the FLFP starting pH decreased to pH = 4.07. With the exception of ΔFW (r = −0.53), all final pH correlations with other traits were nonsignificant and nearly zero (Table 3).

    Final pH values for the DDW treatment reduced significantly from the beginning (pH = 8.38) with a pooled mean of pH = 4.92 and ranging from pH = 4.2 (GL-5) to pH = 7.8 ('Beatrice') across genotypes although the means were not significantly different (Table 5). In most cases of DDW where the final pH was the highest, the inflorescence cut stem lengths were significantly longer and total number of floret buds/stem were significantly higher (Table 2). Thus, the inflorescence cut stem length and/or total number of floret buds/stem may be inferred to require additional solution changes during the test period to eliminate potentially higher levels of phloem unloading. Pooled mean final pH values for the FLFP treatment was pH = 4.24, significantly lower than that of DDW (Table 5). Specific genotype pH ranged from pH = 3.5 (GL-1) to pH = 4.8 (GL-7), although they did not differ significantly.

    Solution volume used / stem (uptake) were not significantly different for genotypes or genotype × treatment interactions but significantly different for treatments (Table 5). Pooled genotypic means were 24.7 ml (DDW), with significantly more solution volume used / stem than FLFP (23.9 ml) although they overlapped (Table 5). Four traits had positively and highly significant correlations with solution volume used / stem: day 0 FW (r = 0.75), day 9 FW (r = 0.79), day 9 DW (r = 0.86), and inflorescence cut stem length (r = 0.76; Table 3); all other traits were not correlated.

    The ANOVAs for mean number of flowers senesced/day (days 1−9) and Σ number of flowers senesced (days 1-9) showed significance for genotypes, but not for treatments or their interactions (Table 6). The mean number of flowers senesced/day ranged from 0.1 (GL-5, GL-7) to 0.6 (GL-8; Table 6). GL-2, -3, -5, -7, and -11 all had significantly less flowers senesced/day than GL-4, -8, and 'Manhattan'; the remaining genotypes all overlapped. For the Σ number of flowers senesced (days 1-9), mean values ranged from 0.85 (GL-5) to 6.8 (GL-8; Table 3). GL-2, -5, -11, and 'Beatrice' had significantly lower number of flowers senesced over the 9-day period than GL-4, -8, and 'Manhattan' (Table 3). Lower numbers of senescing flowers/day or in total would be ideal traits to breed and select for to enhance postharvest longevity, instead of higher numbers (faster senescence). Neither trait was not significantly correlated with any other trait excepting each other (r = 0.94; Table 3). All Chi-square 2) tests for equal distribution (1:1:1:1:1:1:1:1:1; df = 8; χ2 = 15.507) of the mean number of flowers senesced/day/genotype in days 1−9 and the total number of flowers sensed over the postharvest experiment period (days 1−9) in dwarf and tall stature gladiolus genotypes were not significant (Table 7), indicating that the rate of flower senescing per day or in total was the same (linear), regardless of genotype.

    Table 6.  Mean number of flowers senesced/day in days 1−9, total number of flowers sensed in days 1-9, and number of saleable days (when the 5th floret from the base wilted) in dwarf and tall stature gladiolus genotypes tested with two treatment solutions applied as continuous vase solutions.
    GenotypeNo. of
    flowers
    senesced/day
    (days 1−9)
    Total no.
    of flowers
    senesced
    in days 1−9
    No. of
    saleable days
    PooledPooledDDWFLFP
    Dwarf stature (<90 cm)
    GL-10.35ab3.05ab8.0e8.0e
    GL-2z0.15a1.10a7.5b-e8.0e
    Tall stature (>90 cm)
    GL-3y0.25a2.25ab5.5a7.0b-e
    GL-40.50b4.65b7.7c-e7.5b-e
    GL-5x0.10a0.85a7.0b-e8.0e
    GL-60.45ab3.85ab7.8c-e8.0e
    GL-7w0.10a1.10a6.0a8.0e
    GL-80.60b6.80b6.7bc7.8c-e
    GL-9w0.35ab3.15ab7.0b-e6.0a
    GL-10w0.40ab3.2ab6.7bc6.7bc
    GL-11y0.25a2.25a3.0a4.5a
    'Beatrice'0.35ab2.95a7.8c-e8.0e
    'Manhattan'0.50b4.5b8.0e7.3b-e
    Significancev
    Genotype (G)F = 2.446**F = 1.98*F = 9.98***
    Treatment (T)F = 0.55nsF = 0.69nsF = 4.47**
    G × TF = 0.69nsF = 0.73nsF = 1.69*
    z n = 4 reps. y n = 2 reps. x n = 5 reps. w n = 3 reps. v *** p < 0.001, ** p < 0.01, * p < 0.05, ns not significant.
    DDW = deionized, distilled water; FLFP = Floral Life floral preservative or Pooled if treatments were not significantly different.
    There were n = 6 replications/treatment solution/genotype unless noted otherwise; mean separations within columns based on Tukey's Honestly Significantly Difference (HSD) test at α = 0.05.
     | Show Table
    DownLoad: CSV
    Table 7.  Frequencies and Chi-square 2) tests for equal distribution (1:1:1:1:1:1:1:1:1; df = 8; χ2 = 15.507) of the mean number of flowers senesced/day/genotype in days 1−9 and the total number of flowers sensed over the postharvest experiment period (days 1−9) in dwarf and tall stature gladiolus genotypes tested with two treatment solutions applied as continuous vase solutions.
    GenotypeTreatmentMean no. of flowers senesced/day/genotypeχ2 (sig.)
    Day 1Day 2Day 3Day 4Day 5Day 6Day 7Day 8Day 9
    Dwarf stature (< 90 cm)
    GL-1DDW0.000.000.000.670.170.670.500.830.33.82nsz
    FLFP0.000.000.001.000.170.500.671.000.43.07ns
    GL-2DDW0.000.170.000.330.170.330.170.330.25.92ns
    FLFP0.000.170.000.170.000.170.000.170.17.51ns
    Tall stature (> 90 cm)
    GL-3DDW0.000.170.500.500.330.330.170.330.304.51ns
    FLFP0.170.330.170.330.330.330.170.330.204.90ns
    GL-4DDW0.000.000.670.831.000.830.501.000.501.50ns
    FLFP0.000.170.500.670.670.830.671.000.501.77ns
    GL-5DDW0.000.000.170.170.170.170.170.170.106.90ns
    FLFP0.000.000.000.170.000.170.170.170.107.51ns
    GL-6DDW0.000.000.830.330.830.830.671.000.501.79ns
    FLFP0.000.000.500.330.670.500.500.670.403.28ns
    GL-7DDW0.170.170.330.330.330.330.330.170.204.90ns
    FLFP0.000.000.000.000.000.000.000.000.009.00ns
    GL-8DDW0.830.500.830.671.000.501.001.000.700.43ns
    FLFP0.170.500.500.501.000.500.500.500.502.08ns
    GL-9DDW0.000.500.170.500.500.330.500.500.303.61ns
    FLFP0.330.500.330.500.330.500.330.500.403.10ns
    GL-10DDW0.000.170.500.500.500.500.500.500.403.28ns
    FLFP0.000.170.500.500.500.500.500.500.403.28ns
    GL-11DDW0.000.330.170.170.170.170.170.170.206.17ns
    FLFP0.330.500.330.330.330.330.330.330.303.85ns
    'Beatrice'DDW0.000.000.170.500.830.500.500.670.403.28ns
    FLFP0.000.000.001.000.170.170.001.330.304.04ns
    'Manhattan'DDW0.000.000.670.500.830.170.830.830.402.53ns
    FLFP0.000.170.670.670.830.831.001.000.601.16ns
    z ns not significantly different from the null hypothesis that the number of flowers senesced/day do not differ.
    DDW = deionized, distilled water; FLFP = Floral Life floral preservative.
     | Show Table
    DownLoad: CSV

    The number of saleable days (when the 5th floret from the base wilted) in dwarf and tall stature gladiolus genotypes tested with two treatment solutions ranged from 3 d (GL-11) to 8 d (GL-1, 'Manhattan') for DDW and 4.5 d (GL-11) to 8 d (GL-1, -2, -5, -6, -7, 'Beatrice') for FLFP vase treatment solutions (Table 6). The lowest number of saleable days were significantly lower than the highest values found, indicating significant genetic differences among the germplasm tested with particularly different results among the two comparison cultivars. The number of saleable days was negatively but significantly correlated with eight traits (day 0 FW, day 9 DW, inflorescence cut stem length, Σ number of flower buds/stem, Σ number of open flowers, Σ % open flowers, floret 1 diameter, floret 3 diameter) or positively and significantly correlated with two traits (ΔFW, % water; Table 3). These negative or positive significant correlations will be important to direct future breeding efforts and select for enhanced postharvest life.

    VBD occurred within a tight 3-week window among all genotypes, regardless of stem height (Table 1). When categorizing flowering time by the North American Gladiolus Council classifications, GL-4, -5, -8, -9, and 'Manhattan' are midseason (84 d); GL-1, -3, -6, -7, -10, -11, and 'Beatrice' are late (91-99 d) flowering; GL-2 is very late (> 100 d)[11]. While early types exist in the breeding program, by chance they were not selected for this study. Future research into earlier VBD types might reveal faster leaf unfolding rates; potential genotypes to research would be our cycle 1 RGC which flower in the first year in < 1 yr from seed[8,26,27]. Classification of genotypes by flowering date ranged from late (14 wks or 98 d) to very late (19 wks or 133 d; Table 1)[9,10].

    The significant differences in plant height were such that the tested genotypes were categorized from Grades 1-4 for Minimum Length Grade to 117.2 (GL-10) - 121 cm (GL-11; Table 1)[16]. Since taller genotypes exist, both in the UMN breeding program (137 cm is the tallest found; Anderson, 2021, unpublished data) and elsewhere (183 cm)[33], the significance of plant height differences could be further accentuated, although stems taller than GL-10 and -11 (Grade 1) would exceed the grading standards.

    Among the numerous and divergent genotypes tested for phenotypic traits of importance for cut flower use, only flowering week and plant height were significantly different; all other traits were not significant (Table 1). If this germplasm sampling is an accurate reflection of gladiolus cut flower genotypes, then future breeding and selection efforts should be focused on these two traits without regards to the others (no. weeks to VBD, flowering termination week number, plant width, no. leaves).

    Short- and tall-stature genotypes differed significantly for inflorescence cut stem length, matching plant height findings which infers that most of the plant height is influenced by the inflorescence length, rather than leaf internode lengths. Plant height would not need further analyses, rather only measurements of inflorescence cut stem lengths. Since inflorescence cut stem lengths were significantly and positively correlated with most traits (Day 0, 9 FW, Day 9 DW, Σ no. flower. buds/stem, inflorescence internode length, Σ no. or % open flowers, 1st and 3rd flower diameter, solution volume/stem), they may be linked traits to aid in co-selection all traits. Future research will determine whether the traits share similar single nucleotide polymorphisms (SNPs) or map to a single chromosome which would aid in marker-assisted selection.

    Since the Σ number of floret buds/stem varied significantly among genotypes, it could be a heritable trait for increased production capacity/stem. This trait hasn't been examined in previous postharvest studies[19,24,32], but is a critical trait of floriferousness that gladiolus breeding programs would want to breed and select for increased 'flower power'[34].

    Unlike what might be expected, inflorescence internode length is not correlated with stature (Table 3), since several significantly shorter internode lengths occurred in both the short and tall statures. However, only the significantly longest internodes occurred in the tall stature genotypes (Table 2) which may mean a threshold internode length has to be reached before this is correlated with plant stature. The ideal internode length could vary, depending on the flower size (miniature or < 6.3 cm to giant > 14 cm) and flower number, as long as stem strength is adequate[35].

    The Σ number and Σ percent of opened flowers/inflorescence of 2.8 (22%; GL-5) to 12.8 (GL-11) and 94% (GL-10; Table 2), respectively, exceeded the range of previous reports[19]. In some cases, the lower values were due to flowers which would not open (Fig. 8), regardless of solution treatment. Previous research found varying opened flowers/inflorescence in 'White Prosperity' (36.3%−84.1%) under various treatment solutions in two experiments[19], while other studies did not record this trait[24,32]. Both traits are important to assess salability and flower power for cut flower usage.

    Previous research did not find significant genotypic differences for FWs among 'American Beauty' and 'Snow Princess'[24], although this lack of significant FWs may be due to either the low number of genotypes tested or similar responses among the two cultivars. Thus, the increased number of genotypes in the present study have greater genetic diversity and provide new insights into FW levels. Day 0 stem FWs of both dwarf stature genotypes (GL-1, GL-2) overlapped with several tall stature types (GL-4, GL-5, and GL-8), which was unexpected.

    Day 9 stem FWs differed significantly among genotypes and treatments (Table 4) with a wide range in expression (8.7 g, GL-2, DDW to 54.5 g, 'Beatrice', FLFP). In previous research, FWs and ΔFW changed significantly within 'White Prosperity', based on post-harvest solution treatments, although all ΔFW were positive in most of the silver-based treatments except for tap water, 0.01, and 0.1 mg·L−1 nano-silver continuous vase solutions[19].

    The 1st flower diameters classified the many of the genotypes as miniature[10,11,16,35]. GL-6 consistently had the smallest 1st flower diameter in both treatments, whereas GL-8 and GL-11, 'Beatrice', and 'Manhattan' respectively, consistently had the significantly largest 1st flower diameters (Table 5). For all genotypes tested, the 1st flower diameters were smaller than those previously reported for 'American Beauty' (11.18 cm; small) and 'Snow Princess' (11.16 cm; small)[11,24] but similar in dimensions to 'White Prosperity' (6.5−9.1 cm; miniature to small)[11,19]. These differences could be either genetic, environmental or physiological with less reserved carbohydrates available for the 1st or basal floret[36]. Genotypic stability for the 1st flower diameter exhibited by the tested genotypes make them valuable germplasm for breeding purposes.

    The 3rd flower diameters of 3.9 cm (GL-6; miniature) in the DDW treatment, to 7.8 cm ('Beatrice'; miniature) in FLFP, were all smaller floral diameters than reported for 'American Beauty' (9.98 cm; small) and 'Snow Princess' (9.74 cm; small)[11,16]. GL-6 also displays genetic stability across treatments for the smallest 3rd flower diameters, regardless of solution treatment (Table 5). This genetic stability, regardless of preservative solutions is of value for future breeding efforts.

    As would be expected, final pH differed by treatment solutions. The final pH values were consistently lowest in all genotypes treated with FLPP (Table 5), as would be expected with floral preservatives[11,19,24,32,35]. It would be important to maintain current recommendations of floral preservatives to maximize gladiolus postharvest life by ensuring the solution pH most closely matches that of cell pH.

    While previous studies have not reported measuring ending pH for treatments or gladioli genotypes, our data provide an insight into the ability of cut gladiolus to decrease solution pH without added floral preservatives. These findings were completely unexpected and show the resilience of cut gladiolus as a cut flower crop for floral designs[2]. The implications of inflorescence cut stem lengths and total number of floret buds/stem on final pH are important considerations for future breeding and selection of the cut flower crop.

    Solution treatments had little to no effect on solution volume used per stem, despite having floral preservatives recommended to increase gladiolus vase life[21]. The highest solution volume used per stem of 60 ml ('Beatrice', 'Manhattan') matched similar levels for 'Friendship' over the same treatment period of 9 d[32]. Solution uptake volumes for other genotypes were lower than that of 'Friendship'. 'American Beauty' and 'Snow Princess' had slightly higher levels of solution volume used per stem (71.68−77.28 ml) over a 12-d period than our results[24].

    The range of saleable days was surprisingly similar despite not having floral preservative in one of the treatments (DDW). However, since the consumer vase life expectancy is 6−10 d[21], any genotypes with < 6 d vase life would not be recommended as cut gladioli.

    The similar rate of flower senescing per day or in total was the same (linear) and independent of genotype. This demonstrates consistent flower aging, regardless of vase solutions, across the postharvest test environment which will benefit the grower, distributor, wholesaler, retailer as well as the consumer. To the best of our knowledge, the heritability of these traits are unknown.

    Since genotype effects were significant for all traits examined except for final pH and solution volume/stem (Table 2), a wide range of genetic variation exists across the dwarf vs. tall stature types for the remaining traits tested, indicating potential for continued breeding, selection, and improvement of cut flower gladiolus for the floricultural industry. Genotypes were either midseason, late or very late in flowering time; it had been expected that the dwarf or short stature types would have been earlier flowering. The lack of early flowering in these types may be due to slowed leaf unfolding or floral scape development despite the significantly shorter stem lengths; future research could clarify these developmental rates to be equalized across stem length (plant and inflorescence height). Surprisingly, GL-11 was taller than the two cultivars and classified as Minimum Length Grade 1. Leaf number variation (ranging from 6 to 9) was unexpected and may have genetic heritability which would impact selection for earlier flowering due to increased leaf unfolding time in those genotypes with higher leaf numbers. Floral preservative versus the control (no floral preservative) had significant effects on all traits except for total number of floret buds/stem, total number (%) of opened flowers, day 0 stem FW, number of flowers senesced / day (days 1−9), and total number of flowers senesced in days 1−9. Thus, the recommended incorporation of floral preservative to maximize floret opening, life (d), and overall performance warrants its continued use with this crop, although our study suggests that, for some genotypes, changing the vase solution > 1x/week would be warranted. However, the decrease in solution pH for the DDW treatment was unexpected and warrants further study on the content of phloem unloading in cut gladiolus. Heritability of all traits included herein should be studied in programmed crosses, coupled with molecular marker creation to aid in selection. To the best of our knowledge, only cold tolerance heritability has been studied in gladiolus[8]. While several genes have been identified at the molecular level, e.g. UPSTREAM OF FLOWERING LOCUS C (UFC) and FLOWERING LOCUS C EXPRESSOR (FLX)[25], the gibberellin receptor gene[37], and two ubiquitin promoters (GUBQ2, GUBQ4)[38], the gladiolus genome has yet to be sequenced, GWAS and marker-assisted selection have yet to be created and implemented to complement classic gladiolus breeding programs. Data from this study and others will be used to formulate a new cut flower gladiolus crop ideotype to direct breeding and selection efforts for public- and private-sector gladiolus breeding programs, similar to other cut flower floricultural crops such as perennial flax[39,40] and chrysanthemum[41].

    Funding in support of this publication was from the Minnesota Gladiolus Society and the Minnesota Agricultural Experiment Station.

  • The author declares that there is no conflict of interest.

  • Supplemental Table S1 Details of meta-analysis.
  • [1]

    OECD and Food and Agriculture Organization of the United Nations. 2021. OECD-FAO Agricultural Outlook. https://doi.org/10.1787/19428846-en

    [2]

    Ding S, Post MJ, Zhou G. 2021. Perspectives on cultured meat. Food Materials Research 1:3

    doi: 10.48130/FMR-2021-0003

    CrossRef   Google Scholar

    [3]

    Ben Braïek O, Smaoui S. 2021. Chemistry, Safety, and Challenges of the Use of Organic Acids and Their Derivative Salts in Meat Preservation. Journal of Food Quality 2021:6653190

    doi: 10.1155/2021/6653190

    CrossRef   Google Scholar

    [4]

    Ashar N, Ali S, Asghar B, Hussnain F, Nasir J, et al. 2022. Application of ultrasound-assisted cooking temperature for improving physicochemical and sensory properties of broiler meat. Food Materials Research 2:16

    doi: 10.48130/FMR-2022-0016

    CrossRef   Google Scholar

    [5]

    Echegaray N, Hassoun A, Jagtap S, Tetteh-Caesar M, Kumar M, et al. 2022. Meat 4.0: principles and applications of industry 4.0 technologies in the meat industry. Applied Sciences 12(14):6986

    doi: 10.3390/app12146986

    CrossRef   Google Scholar

    [6]

    ur Rahman U, Sahar A, Ishaq A, Aadil RM, Zahoor T, et al. 2018. Advanced meat preservation methods: A mini review. Journal of Food Safety 38(4):e12467

    doi: 10.1111/jfs.12467

    CrossRef   Google Scholar

    [7]

    Li M, Lin S, Wang R, Gao D, Bao Z, et al. 2022. Inhibitory effect and mechanism of various fruit extracts on the formation of heterocyclic aromatic amines and flavor changes in roast large yellow croaker (Pseudosciaena Crocea). Food Control 131(1):108410

    doi: 10.1016/J.FOODCONT.2021.108410

    CrossRef   Google Scholar

    [8]

    Ranjha MMAN, Kanwal R, Shafique B, Arshad RN, Irfan S, et al. 2021. A Critical Review on Pulsed Electric Field: A Novel Technology for the Extraction of Phytoconstituents. Molecules 26(16):4893

    doi: 10.3390/molecules26164893

    CrossRef   Google Scholar

    [9]

    Shah MA, Bosco SJD, Mir SA. 2014. Plant Extracts as Natural Antioxidants in Meat and Meat Products. Meat Science 98(1):21−33

    doi: 10.1016/j.meatsci.2014.03.020

    CrossRef   Google Scholar

    [10]

    Kumar P, Kaur S, Goswami M, Singh S, Sharma A, et al. 2021. Antioxidant and antimicrobial efficacy of giloy (Tinospora Cordifolia) stem powder in spent hen meat patties under aerobic packaging at refrigeration temperature (4 ± 1 °C). Journal of Food Processing and Preservation 45(10):e15772

    doi: 10.1111/JFPP.15772

    CrossRef   Google Scholar

    [11]

    Li F, Zhu Y, Li S, Wang P, Zhang R, et al. 2021. A strategy for improving the uniformity of radio frequency tempering for frozen beef with cuboid and step shapes. Food Control 123(5):107719

    doi: 10.1016/J.FOODCONT.2020.107719

    CrossRef   Google Scholar

    [12]

    Li S, Zhang Y, Liu S. 2022. Effect of Refrigeration on the Collagen and Texture Characteristics of Yak Meat. Canadian Journal of Animal Science. 102(1):175−83

    doi: 10.1139/CJAS-2021-0059

    CrossRef   Google Scholar

    [13]

    Bassey AP, Chen Y, Zhu Z, Odeyemi OA, Frimpong EB, et al. 2021. Assessment of quality characteristics and bacterial community of modified atmosphere packaged chilled pork loins using 16S RRNA amplicon sequencing analysis. Food Research International 145(7):110412

    doi: 10.1016/J.FOODRES.2021.110412

    CrossRef   Google Scholar

    [14]

    Daszkiewicz T, Kondratowicz J. 2020. Fatty acid profile and sensory properties of roe deer meat after modified atmosphere storage. Italian Journal of Food Science 32(3):645−53

    doi: 10.14674/IJFS-1599

    CrossRef   Google Scholar

    [15]

    Jaspal MH, Ijaz M, Haq HAU, Yar MK, Asghar B, et al. 2021. Effect of oregano essential oil or lactic acid treatments combined with air and modified atmosphere packaging on the quality and storage properties of chicken breast meat. LWT 146(7):111459

    doi: 10.1016/J.LWT.2021.111459

    CrossRef   Google Scholar

    [16]

    Behera SS, El Sheikha AF, Hammami R, Kumar A. 2020. Traditionally fermented pickles: How the microbial diversity associated with their nutritional and health benefits? Journal of Functional Foods 70(7):103971

    doi: 10.1016/J.JFF.2020.103971

    CrossRef   Google Scholar

    [17]

    Yim DG, Ali M, Nam KC. 2020. Comparison of meat quality traits in salami added by nitrate-free salts or nitrate pickling salt during ripening. Food Science of Animal Resources 40(1):11−20

    doi: 10.5851/KOSFA.2019.E61

    CrossRef   Google Scholar

    [18]

    Hygreeva D, Pandey MC, Radhakrishna K. 2014. Potential applications of plant based derivatives as fat replacers, antioxidants and antimicrobials in fresh and processed meat products. Meat Science 98(1):47−57

    doi: 10.1016/J.MEATSCI.2014.04.006

    CrossRef   Google Scholar

    [19]

    Awad AM, Kumar P, Ismail-Fitry MR, Jusoh S, Ab Aziz MF, et al. 2021a. Green extraction of bioactive compounds from plant biomass and their application in meat as natural antioxidant. Antioxidants 10(9):1465

    doi: 10.3390/ANTIOX10091465

    CrossRef   Google Scholar

    [20]

    Munekata PES, Rocchetti G, Pateiro M, Lucini L, Domínguez R, et al. 2020. Addition of plant extracts to meat and meat products to extend shelf-life and health-promoting attributes: An overview. Current Opinion in Food Science 31(2):81−87

    doi: 10.1016/J.COFS.2020.03.003

    CrossRef   Google Scholar

    [21]

    Smaoui S, Hlima HB, Mtibaa AC, Fourati M, Sellem I, et al. 2019. Pomegranate peel as phenolic compounds source: Advanced analytical strategies and practical use in meat products. Meat Science 158(12):107914

    doi: 10.1016/J.MEATSCI.2019.107914

    CrossRef   Google Scholar

    [22]

    Alirezalu K, Pateiro M, Yaghoubi M, Alirezalu A, Peighambardoust SH, et al. 2020. Phytochemical constituents, advanced extraction technologies and techno-functional properties of selected mediterranean plants for use in meat products: A comprehensive review. Trends in Food Science & Technology 100(6):292−306

    doi: 10.1016/J.TIFS.2020.04.010

    CrossRef   Google Scholar

    [23]

    Beya MM, Netzel ME, Sultanbawa Y, Smyth H, Hoffman LC. 2021. Plant-based phenolic molecules as natural preservatives in comminuted meats: A review. Antioxidants 10(2):263

    doi: 10.3390/ANTIOX10020263

    CrossRef   Google Scholar

    [24]

    da Silva BD, Bernardes PC, Pinheiro PF, Fantuzzi E, Roberto CD. 2021. Chemical composition, extraction sources and action mechanisms of essential oils: Natural preservative and limitations of use in meat products. Meat Science 176(6):108463

    doi: 10.1016/J.MEATSCI.2021.108463

    CrossRef   Google Scholar

    [25]

    Efenberger-Szmechtyk M, Nowak A, Czyzowska A. 2021. Plant extracts rich in polyphenols: Antibacterial agents and natural preservatives for meat and meat products. Critical Reviews in Food Science and Nutrition 61(1):149−78

    doi: 10.1080/10408398.2020.1722060

    CrossRef   Google Scholar

    [26]

    Zangeneh M, Banaeian N, Clark S. 2021. Meta-analysis on energy-use patterns of cropping systems in Iran. Sustainability 13(7):3868

    doi: 10.3390/SU13073868

    CrossRef   Google Scholar

    [27]

    Orzuna-Orzuna J, Dorantes-Iturbide G, Lara-Bueno A, Mendoza-Martínez G, Miranda-Romero L, et al. 2021. Effects of dietary tannins' supplementation on growth performance, rumen fermentation, and enteric methane emissions in beef cattle: A meta-analysis. Sustainability 13(13):7410

    doi: 10.3390/SU13137410

    CrossRef   Google Scholar

    [28]

    Liu Z, Feng L, He Y, Yuan S, Xu C. 2022. The association between vitamin D and Hashimoto Thyroiditis: An up-to-date systematic review and meta-analysis. Food Materials Research 2:9

    doi: 10.48130/FMR-2022-0009

    CrossRef   Google Scholar

    [29]

    Abhijith A, Dunshea FR, Warner RD, Leury BJ, Ha M, et al. 2020. A meta-analysis of the effectiveness of high, medium, and low voltage electrical stimulation on the meat quality of small ruminants. Foods 9(11):1587

    doi: 10.3390/FOODS9111587

    CrossRef   Google Scholar

    [30]

    Herremans S, Vanwindekens F, Decruyenaere V, Beckers Y, Froidmont E. 2020. Effect of dietary tannins on milk yield and composition, nitrogen partitioning and nitrogen use efficiency of lactating dairy cows: A meta-analysis. Journal of Animal Physiology and Animal Nutrition 104(5):1209−18

    doi: 10.1111/JPN.13341

    CrossRef   Google Scholar

    [31]

    Lean IJ, Thompson JM, Dunshea FR. 2014. A meta-analysis of zilpaterol and ractopamine effects on feedlot performance, carcass traits and shear strength of meat in cattle. PLoS ONE 9(12):115904

    doi: 10.1371/JOURNAL.PONE.0115904

    CrossRef   Google Scholar

    [32]

    Higgins JPT, Thompson SG, Deeks JJ, Altman DG. 2003. Measuring Inconsistency in Meta-Analyses. BMJ 327(7414):557−60

    doi: 10.1136/BMJ.327.7414.557

    CrossRef   Google Scholar

    [33]

    Hedges LV. 1981. Distribution theory for glass's estimator of effect size and related estimators. Journal of Educational Statistics 6:107−28

    doi: 10.3102/10769986006002107

    CrossRef   Google Scholar

    [34]

    Madebo MP, Zheng Y, Jin P. 2022. Melatonin-Mediated Postharvest Quality and Antioxidant Properties of Fresh Fruits: A Comprehensive Meta-Analysis. Comprehensive Reviews in Food Science and Food Safety 21(4):3205−26

    doi: 10.1111/1541-4337.12961

    CrossRef   Google Scholar

    [35]

    Borenstein M, Hedges LV, Higgin JPT, Rothstein HR. 2009. Introduction to Meta-Analysis. UK: John Wiley & Sons. 421 pp. https://doi.org/10.1002/9780470743386

    [36]

    Torres RNS, Moura DC, Ghedini CP, Ezequiel JMB, Almeida MTC. 2020. Meta-Analysis of the Effects of Essential Oils on Ruminal Fermentation and Performance of Sheep. Small Ruminant Research 189(8):106148

    doi: 10.1016/J.SMALLRUMRES.2020.106148

    CrossRef   Google Scholar

    [37]

    Littell JH, Corcoran J, Pillai V. 2009. Systematic Reviews and Meta-Analysis. UK: Oxford University Press. 210 pp. https://doi.org/10.1093/ACPROF:OSO/9780195326543.001.0001

    [38]

    Sterne JAC, Harbord RM. 2004. Funnel Plots in Meta-Analysis. The Stata Journal 4(2):127−41

    doi: 10.1177/1536867X0400400204

    CrossRef   Google Scholar

    [39]

    Ioannidis J, Thomas A. 2007. The appropriateness of asymmetry tests for publication bias in meta-analyses: A large survey. CMAJ 176(8):1091−96

    doi: 10.1503/CMAJ.060410

    CrossRef   Google Scholar

    [40]

    Al-Juhaimi FY, Almusallam IA, Mohamed Ahmed IA, Ghafoor K, Babiker EE. 2020. Potential of Acacia Nilotica fruit flesh extract as an anti-oxidative and anti-microbial agent in beef burger. Journal of Food Processing and Preservation 44(7):e14504

    doi: 10.1111/JFPP.14504

    CrossRef   Google Scholar

    [41]

    Hastaoğlu E, Vural H, Can ÖP. 2021. Effects of Thymol and Rosemary Essential Oils and Red Beet Extract on Low-Nitrite and Carmine-Free Beef Mortadella. Journal of Food Processing and Preservation 45(10):e15855

    doi: 10.1111/jfpp.15855

    CrossRef   Google Scholar

    [42]

    Wu J, Li R, Zhang M, Shan K, Jia X, et al. 2021. Microbiota changes on the surface of pig carcasses during refrigerated transportation and marketing. Food Materials Research 1:4

    doi: 10.48130/FMR-2021-0004

    CrossRef   Google Scholar

    [43]

    Rothstein HR, Sutton AJ, Borenstein M. 2005. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments. UK: John Wiley & Sons. pp. 356. https://doi.org/10.1002/0470870168

    [44]

    Sterne JAC, Sutton AJ, Ioannidis JPA, Terrin N, Jones DR, et al. 2011. Recommendations for Examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ 343:d4002

    doi: 10.1136/BMJ.D4002

    CrossRef   Google Scholar

    [45]

    Jiang J, Xiong YL. 2016. Natural antioxidants as food and feed additives to promote health benefits and quality of meat products: A review. Meat Science 120(10):107−17

    doi: 10.1016/J.MEATSCI.2016.04.005

    CrossRef   Google Scholar

    [46]

    Zhang W, Xiao S, Samaraweera H, Lee EJ, Ahn DU. 2010. Improving functional value of meat products. Meat Science 86(1):15−31

    doi: 10.1016/J.MEATSCI.2010.04.018

    CrossRef   Google Scholar

    [47]

    Hagerman AE, Riedl KM, Jones GA, Sovik KN, Ritchard NT, et al. 1998. High molecular weight plant polyphenolics (Tannins) as biological antioxidants. Journal of Agricultural and Food Chemistry 46(5):1887−92

    doi: 10.1021/JF970975B

    CrossRef   Google Scholar

    [48]

    Domínguez R, Gullón P, Pateiro M, Munekata PES, Zhang W, et al. 2020. Tomato as potential source of natural additives for meat industry. A Review. Antioxidants 9(1):73

    doi: 10.3390/ANTIOX9010073

    CrossRef   Google Scholar

    [49]

    Torres-Martínez BDM, Vargas-Sánchez RD, Torrescano-Urrutia GR, Esqueda M, Rodríguez-Carpena JG, et al. 2022. Pleurotus genus as a potential ingredient for meat products. Foods 11(6):779

    doi: 10.3390/FOODS11060779

    CrossRef   Google Scholar

    [50]

    Yin Z, Liu F, Gu X, Zhang L, Ma Y, et al. 2022. A comparison of hepatic lipid metabolism and fatty acid composition in muscle between Duroc × Landrace × Yorkshire and Tibetan pigs from three regions. Food Materials Research 2:7

    doi: 10.48130/FMR-2022-0007

    CrossRef   Google Scholar

    [51]

    Mireles Arriaga AI, de Guanajuato U, Ruiz-Nieto JE, Juárez Abraham MR, Mendoza Carrillo M, et al. 2017. Functional restructured meat: applications of ingredients derived from plants. Revista Vitae 24(3):196−204

    doi: 10.17533/UDEA.VITAE.V24N3A05

    CrossRef   Google Scholar

    [52]

    Ferysiuk K, Wójciak KM. 2020. Reduction of Nitrite in Meat Products through the Application of Various Plant-Based Ingredients. Antioxidants 9(8):711

    doi: 10.3390/ANTIOX9080711

    CrossRef   Google Scholar

    [53]

    Vuorela S, Salminen H, Mäkelä M, Kivikari R, Karonen M, et al. 2005. Effect of plant phenolics on protein and lipid oxidation in cooked pork meat patties. Journal of Agricultural and Food Chemistry 53(22):8492−97

    doi: 10.1021/JF050995A

    CrossRef   Google Scholar

    [54]

    Zhou T, Gao H, Xing B, Bassey A, Yang L, et al. 2022. Effect of heating temperature and time on the formation of volatile organic compounds during reactions between linoleic acid and free amino acids or myofibrillar proteins. International Journal of Food Science & Technology 57:7644−52

    doi: 10.1111/ijfs.16107

    CrossRef   Google Scholar

    [55]

    Kumar Y, Yadav DN, Ahmad T, Narsaiah K. 2015. Recent trends in the use of natural antioxidants for meat and meat products. Comprehensive Reviews in Food Science and Food Safety 14(6):796−812

    doi: 10.1111/1541-4337.12156

    CrossRef   Google Scholar

    [56]

    Chan WK, Faustman C, Yin M, Decker EA. 1997. Lipid oxidation induced by oxymyoglobin and metmyoglobin with involvement of H2O2 and superoxide anion. Meat Science 46(2):181−90

    doi: 10.1016/S0309-1740(97)00014-4

    CrossRef   Google Scholar

    [57]

    Yogesh K, Ali J. 2012. Antioxidant potential of thuja (Thuja Occidentalis) cones and peach (Prunus Persia) seeds in raw chicken ground meat during refrigerated (4 ± 1 °C) storage. Journal of Food Science and Technology 51(8):1547−53

    doi: 10.1007/S13197-012-0672-5

    CrossRef   Google Scholar

    [58]

    Tyagi T, Agarwal M. 2017. Antioxidant properties and phenolic compounds in methanolic extracts of Eichhornia Crassipes. Research Journal of Phytochemistry 11(2):85−89

    doi: 10.3923/RJPHYTO.2017.85.89

    CrossRef   Google Scholar

    [59]

    Ruan C, Kong J, He X, Hu B, Zeng X. 2022. Interaction between polyphenols and amyloids: from the view of prevention of protein misfolding disorders related diseases. Food Materials Research 2:2

    doi: 10.48130/FMR-2022-0002

    CrossRef   Google Scholar

    [60]

    Komes D, Belščak-Cvitanović A, Horžić D, Rusak G, Likić S, et al. 2011. Phenolic composition and antioxidant properties of some traditionally used medicinal plants affected by the extraction time and hydrolysis. Phytochemical Analysis 22(2):172−80

    doi: 10.1002/PCA.1264

    CrossRef   Google Scholar

    [61]

    Ozsoy N, Candoken E, Akev N. 2009. Implications for degenerative disorders: antioxidative activity, total phenols, flavonoids, ascorbic acid, β-carotene and β-tocopherol in Aloe Vera. Oxidative Medicine and Cellular Longevity 2(2):99−106

    doi: 10.4161/OXIM.2.2.8493

    CrossRef   Google Scholar

    [62]

    Wanasundara UN, Shahidi F. 1998. Antioxidant and pro-oxidant activity of green tea extracts in marine oils. Food Chemistry 63(3):335−42

    doi: 10.1016/S0308-8146(98)00025-9

    CrossRef   Google Scholar

    [63]

    Engel E, Ratel J, Bouhlel J, Planche C, Meurillon M. 2015. Novel approaches to improving the chemical safety of the meat chain towards toxicants. Meat Science 109(11):75−85

    doi: 10.1016/J.MEATSCI.2015.05.016

    CrossRef   Google Scholar

    [64]

    Moure A, Cruz JM, Franco D, Domı́nguez JM, Sineiro J, et al. 2001. Natural antioxidants from residual sources. Food Chemistry 72(2):145−71

    doi: 10.1016/S0308-8146(00)00223-5

    CrossRef   Google Scholar

    [65]

    Miranda S, Vilches P, Suazo M, Pavez L, García K, et al. 2020. Melatonin triggers metabolic and gene expression changes leading to improved quality traits of two sweet cherry cultivars during cold storage. Food Chemistry 319(7):126360

    doi: 10.1016/J.FOODCHEM.2020.126360

    CrossRef   Google Scholar

    [66]

    Yun Z, Gao H, Chen X, Chen Z, Zhang Z, et al. 2021. Effects of hydrogen water treatment on antioxidant system of litchi fruit during the pericarp browning. Food Chemistry 336(1):127618

    doi: 10.1016/J.FOODCHEM.2020.127618

    CrossRef   Google Scholar

    [67]

    Balogh Z, Gray JI, Gomaa EA, Booren AM. 2000. Formation and Inhibition of Heterocyclic Aromatic Amines in Fried Ground Beef Patties. Food and Chemical Toxicology 38(5):395−401

    doi: 10.1016/S0278-6915(00)00010-7

    CrossRef   Google Scholar

    [68]

    Jongberg S, Tørngren MA, Gunvig A, Skibsted LH, Lund MN. 2013. Effect of green tea or rosemary extract on protein oxidation in bologna type sausages prepared from oxidatively stressed pork. Meat Science 93(3):538−46

    doi: 10.1016/J.MEATSCI.2012.11.005

    CrossRef   Google Scholar

    [69]

    Álvarez D, Delles RM, Xiong YL, Castillo M, Payne FA, et al. 2011. Influence of Canola-Olive Oils, Rice Bran and Walnut on Functionality and Emulsion Stability of Frankfurters. LWT - Food Science and Technology 44(6):1435−42

    doi: 10.1016/J.LWT.2011.01.006

    CrossRef   Google Scholar

    [70]

    Gao H, Lu Z, Yang Y, Wang D, Yang T, et al. 2018. Melatonin treatment reduces chilling injury in peach fruit through its regulation of membrane fatty acid contents and phenolic metabolism. Food Chemistry 245(4):659−66

    doi: 10.1016/J.FOODCHEM.2017.10.008

    CrossRef   Google Scholar

    [71]

    Wang F, Zhang X, Yang Q, Zhao Q. 2019. Exogenous melatonin delays postharvest fruit senescence and maintains the quality of sweet cherries. Food Chemistry 301(12):125311

    doi: 10.1016/J.FOODCHEM.2019.125311

    CrossRef   Google Scholar

    [72]

    Saleh E, Morshdy AE, El-Manakhly E, Al-Rashed S, F Hetta H, et al. 2020. Effects of olive leaf extracts as natural preservative on retailed poultry meat quality. Foods 9(8):1017

    doi: 10.3390/FOODS9081017

    CrossRef   Google Scholar

    [73]

    Fernández-López J, Zhi N, Aleson-Carbonell L, Pérez-Alvarez JA, Kuri V. 2005. Antioxidant and antibacterial activities of natural extracts: application in beef meatballs. Meat Science 69(3):371−80

    doi: 10.1016/J.MEATSCI.2004.08.004

    CrossRef   Google Scholar

    [74]

    Mueller M, Hobiger S, Jungbauer A. 2010. Anti-inflammatory activity of extracts from fruits, herbs and spices. Food Chemistry 122(4):987−96

    doi: 10.1016/J.FOODCHEM.2010.03.041

    CrossRef   Google Scholar

    [75]

    Lemay M. 2006. Anti-Inflammatory Phytochemicals: In Vitro and Ex Vivo Evaluation. In Phytochemicals, eds. Meskin MS, Bidlack WR, Randolph RK. Boca Raton, FL: CRC Press. pp. 41−60. https://doi.org/10.1201/9781420005905

    [76]

    Yoshino K, Higashi N, Koga K. 2006. Antioxidant and Antiinflammatory Activities of Oregano Extract. Journal of Health Science 52(2):69−173

    doi: 10.1248/JHS.52.169

    CrossRef   Google Scholar

    [77]

    Sharma N, Biswas S, Al-Dayan N, Alhegaili AS, Sarwat M. 2021. Antioxidant role of kaempferol in prevention of hepatocellular carcinoma. Antioxidants 10(9):1419

    doi: 10.3390/ANTIOX10091419

    CrossRef   Google Scholar

    [78]

    Kang F, Zhang S, Chen D, Tan J, Kuang M, et al. 2021. Biflavonoids from Selaginella Doederleinii as Potential Antitumor Agents for Intervention of Non-Small Cell Lung Cancer. Molecules 26(17):5401

    doi: 10.3390/MOLECULES26175401

    CrossRef   Google Scholar

    [79]

    Chen YC, Chia Y, Huang BM. 2021. Phytochemicals from Polyalthia species: Potential and implication on anti-oxidant, anti-inflammatory, anti-cancer, and chemoprevention activities. Molecules 26(17):5369

    doi: 10.3390/MOLECULES26175369

    CrossRef   Google Scholar

    [80]

    Martínez-Graciá C, González-Bermúdez CA, Cabellero-Valcárcel AM, Santaella-Pascual MS, Frontela-Saseta C. 2015. Use of herbs and spices for food preservation: advantages and limitations. Current Opinion in Food Science 6(12):38−43

    doi: 10.1016/J.COFS.2015.11.011

    CrossRef   Google Scholar

  • Cite this article

    Zhou T, Wu J, Zhang M, Ke W, Shan K, et al. 2023. Effect of natural plant extracts on the quality of meat products: a meta-analysis. Food Materials Research 3:15 doi: 10.48130/FMR-2023-0015
    Zhou T, Wu J, Zhang M, Ke W, Shan K, et al. 2023. Effect of natural plant extracts on the quality of meat products: a meta-analysis. Food Materials Research 3:15 doi: 10.48130/FMR-2023-0015

Figures(9)  /  Tables(1)

Article Metrics

Article views(5514) PDF downloads(827)

REVIEW   Open Access    

Effect of natural plant extracts on the quality of meat products: a meta-analysis

Food Materials Research  3 Article number: 15  (2023)  |  Cite this article

Abstract: Natural plant extracts (NPE) from some organs of plants are rich in bioactive substances. They have special nutritional characteristics with strong antioxidant and antimicrobial activities. The potential of NPEs to maintain and improve the quality of meat products has attracted attention due to concerns about the safety hazards of synthetic food additives. This paper extensively reviewed the application of NPE in meat processing, and systematically analyzed the comprehensive effects of different NPE using meta-analysis. Fourty-eight articles from 23 countries were studied with standard mean deviation (SMD) using random effect model, and 28 indexes were isolated. Results showed that NPE can reduce the pH value of meat products, improve antioxidant capacity, reduce the degree of oxidation and inhibit microbial growth. In addition, it was found that NPE had a significant impact on the quality of meat products. This meta-analysis provides quantitative evidence to explain how NPE affects meat quality, and helps to better understand the role of NPE in meat processing.

    • Meat is an important source of high-quality protein and essential fatty acids. In recent years, the consumption of meat products has increased greatly. According to the statistical results of the Organization for Economic Co-operation and Development (OECD)[1], the world's per capita meat consumption reached 42.4 kg in 2021, although there is still a large gap between developed and developing countries. Due to different traditions, cultures, religions and other factors, the main types of edible meat differ among countries. However, no matter which kind of meat, consumers' demand always focuses on nutrition, safety and sensory quality[2]. Freshness, long-term storage without deterioration, and no chemical preservatives are the most attractive factors for consumers[3]. pH, color, texture, antioxidant capacity, oxidation degree, microbial composition and other indicators are closely related to meat quality[4]. Maintaining the good quality of meat products could enhance consumers' purchasing desire, maintain the unique nutritional composition of meat products, and avoid food waste and potential economic losses[5,6]. Therefore, meat quality has become the key to restrict the development of the meat industry. The challenge to maintain the quality of meat products brings great interest in using plant extracts in meat products.

      There are many bioactive substances from plants, such as polyphenols, carotenoids and alkaloids[7]. The natural plant extract (NPE) refers to something rich in bioactive substances extracted from different organs of plants such as roots, stems, leaves, flowers, fruits and seeds by using different solvents and extraction methods[8,9]. Most of them have special nutritional properties and strong antioxidant activity. Added to the food system, they could ameliorate food quality, improve nutritional quality, inhibit microbial reproduction, prolong shelf life, enhance flavor and control the formation of environmental pollutants. Although many techniques have been studied for food preservation, such as refrigeration[1012], modified atmosphere storage[1315] and pickling[16,17], meat products with high fat content inevitably undergo oxidative rancidity. It has led to the widespread use of food additives, especially preservatives. Although the safety has been recognized through long-term research, consumers are worried about the possible safety hazards. Thus, people pay attention to natural plant extracts[18], which have a wide range of sources, and large space for selection, and rarely involve ethical issues. Therefore, the application of natural plant extracts in meat processing has become a hotspot of research.

      Many papers have reviewed the advantages of plant extracts for meat products comprehensively and systematically, including the extraction methods and antioxidant properties[19], preservative effect[20], and special effects of a certain or a class of extracts[2125]. However, most of reviews have certain limitations and subjectivity, and do not fully consider the nature of meat products. They usually focused on storage or nutritional quality, but ignored the impact of natural plant extracts on the sensory and texture of meat. In addition, the lack of quantitative evidence is also one of the weaknesses of traditional reviews. Meta-analysis is a quantitative data mining method, which expands the sample size by reorganizing the data reported in different studies, so as to obtain more reliable results[26]. Meta-analysis accurately explore the source of heterogeneity through group analysis[27,28], which helps to identify potential content that needs further deep study. Meta-analysis is widely used in clinical medicine and biological research[29], but it has not been fully used in food science research. This paper summarizes the application of NPE in meat products in the last three years, and combined with the basic principles of meta-analysis, the purpose was to comprehensively evaluate the impact of NPE addition on the quality of meat products, including physical, chemical, sensory, nutritional and microbial indicators. In addition, we examined the heterogeneity of the response by meta-analysis to determine the factors that lead to the observed variability in the response variables. The findings are expected to contribute to explaining how NPE improve the quality of meat products.

    • A comprehensive literature search was carried out in the databases of 'Web of Science' and 'Elsevier' to determine the research on the application of natural plant extracts in meat processing and its impact on the quality of meat products. The scope of the included research was expanded by the method of reference tracing. In all databases, the keywords 'natural, plant extract, meat' were used. Between 2020 and 2022, 613 scientific publications were published. Referring to the method of Orzuna-Orzuna et al.[27], these publications were screened in two steps. First, the title and abstract were used for selection, excluding articles that raise animals, reviews, and unmeasured variables of interest. Then the following issues were considered[30,31]: (1) natural plant extracts were used in the processing; (2) pH, color, texture, oxidation index and microbial index were measured; (3) the studies have appropriate control and experimental groups; (4) the publications contain figures for analysis; (5) peer-reviewed journal articles were written in English; (6) experimental design was employed (rotating or continuous); (7) least squares means of the control and experimental groups were measured with variability (standard error or standard deviation); and (8) sample size was used.

    • According to the selection criteria, 48 articles were included in the database for final analysis, and the number of articles included in different indicators was different. The response variables extracted for the meta-analysis include pH, L*, a*, b*, antioxidant activity-DPPH, antioxidant activity-AA, antioxidant activity-ABTS, antioxidant activity-FRAP, metal chelating capacity-BHA, peroxide value (PV), total volatile base nitrogen (TVB-N), thiobarbituric acid reactant (TBARS), total bacterial count (TVC), total mesophilic (TMVC), psychrophilic bacteria, lactic acid bacteria, pseudomonas, enterobacteriaceae, yeast and mold , moisture content, moisture activity, water holding capacity (WHC), extrusion loss, cooking loss, hardness, toughness, cohesiveness, elasticity, and chewiness. In addition, in order to investigate which aspects of meat products are more affected by all reported natural plant extracts, the results of different studies are combined through data consolidation.

      Other data were collected as much as possible, such as the characteristics of published studies (author, year of publication), the product form of meat, the source of natural extracts, and the number of repetitions. The article references contained in the dataset are listed in Supplemental Table S1. The mean, standard deviation (SD) and number of repetitions of each treatment were extracted from these articles. When the article introduces the SD of each experimental group, these values are directly used in the meta-analysis. If SD is not reported, it is calculated by standard error[32].

    • The data involved in meta-analysis were analyzed using Review Manager Software (version 5.4.1). Response variables were analyzed through the standardized mean difference (SMD), also called effect size (ES), the difference between the means of the experimental and control groups was standardized using the SD of the groups with and without NPE[33]. The heterogeneity is tested by formula (1) and the model is selected for meta-analysis[34]. i2 represents the proportion of inter study variation observed (due to real heterogeneity rather than accidental observation), Q is the standardized weighted sum of squares of each study variation, and df is the degree of freedom.

      i2=100%×QdfQ (1)

      i2 range from 0 to 100%. Values close to 25% represent low heterogeneity, close to 50% represent moderate heterogeneity, and close to 75% represent high heterogeneity in the study[35]. When i2 is greater than 50%, the random effect model is used to perform the analysis, otherwise, the fixed effect model is used, and Tukey test was used to detect the difference between the treatment groups[32].

    • The publication bias was evaluated by funnel plot[36]. When it was asymmetric, it was considered that there was a bias (p < 0.10)[37,38]. However, for the index that the number of articles included in the study is less than ten, the test of publication bias is not carried out, which may lead to false positive statements[39].

    • When the overall effect of a certain type of index is significant (p < 0.05), select an appropriate single index (the number of studies available for analysis is greater than 5) for in-depth analysis, and take the source of meat as the main classification basis for subgroup analysis, which is generally divided into five categories: fish (aquatic products), pork, beef, chicken, lamb[27].

    • From 2019 to 2022, online searches using two databases of scientific publications returned 613 publications. After selecting and excluding duplicate papers according to the criteria, 189 full-text articles were evaluated. Finally, 48 articles (Supplemental Table S1) were used to obtain quantitative data for meta-analysis. Descriptive statistics for meta-analysis are shown in Table 1.

      Table 1.  Descriptive statistical results of all indicators included in MA.

      Parameter MPQNMeanMedianMinimumMaximumSD
      ConNFEConNFEConNFEConNFEConNFE
      pH
      pH4026.145.855.866.104.624.816.897.540.44190.3588
      Color
      a*15010.4311.829.229.58−0.971.6133.8645.278.69478.9012
      b*14914.7614.7812.4912.64−0.43−2.1838.5428.207.00307.3735
      L*15849.2147.0948.8747.4920.189.3376.0078.0611.729811.4085
      Texture
      Chewiness2039.1124.8267.2026.1414.6715.101654.001675.0028.729011.0752
      Cohesiveness170.630.560.570.490.220.180.770.760.09960.1320
      CL, %3634.2034.0750.7048.173.827.7477.3179.2426.559626.3236
      Elasticity233.673.493.453.990.510.496.616.962.53012.4518
      Hardness, N1914.1714.2020.6310.242.344.8955.3567.6221.932722.9578
      Moisture, %3357.3449.0464.3755.5431.6329.4373.6973.7313.888111.5185
      PL, %1022.981.3322.9820.0422.9820.0422.9820.041.00000.2700
      Water activity180.950.930.970.940.880.871.000.990.03980.0383
      WHC, %946.3540.8736.7026.9536.7023.3065.6672.3714.486323.6829
      Antioxidant capacity
      DPPH, IC50 (μg)2041.51161.1221.2248.744.8411.1845.17228.0310.955884.4908
      Oxidation index
      Pv, mmol/kg184.511.811.131.031.130.537.114.472.65711.3670
      TBARs, mg/kg4026.145.850.721.230.140.1819.0321.980.44190.3588
      TVB-N, mg/100g32.8021.0217.1032.6910.9528.5731.2655.761.88277.6660
      Microbial index, log10 cfu/g
      Enterobacteriaceae345.263.363.482.501.340.995.814.202.49281.4109
      Enterococcus31.680.000.840.000.000.001.680.001.35000.0000
      LAB336.405.994.983.624.073.006.556.541.92511.6709
      Micrococcus/
      staphylococcus
      45.193.985.193.985.193.985.193.980.04000.0300
      Mold and yeast73.612.713.432.592.131.714.723.461.38630.9415
      Pseudomonas247.966.706.386.316.386.316.386.311.75110.7401
      Psychrotrophic135.814.765.104.191.300.005.375.102.37131.8038
      TAC127.156.927.156.927.156.927.156.920.11000.1200
      TAMB127.087.057.087.057.087.057.087.050.09000.0400
      TMB96.525.297.056.035.974.407.056.080.75412.1113
      TPC217.205.895.483.883.353.246.825.372.75111.9788
      MPQ: Meat product quality; N: number of comparisons; SD: standard deviation; Con: control; NFE: Natural plant extracts; CL: cooking loss; PL: press loss; WHC: Water holding capacity; DPPH: DPPH radical scavenging activity; Pv: Peroxide value; TBARs: Thiobarbituric acid reactant; TVB-N, Volatile base nitrogen; LAB: lactic acid bacteria; TAC: Total aerobic cryophage; TAMB: Total aerobic mesophilic bacteria; TMB; Total mesophilic bacteria; TPC: Total plate count.

      The included studies were conducted in 23 different countries (Supplemental Table S1). The sources of raw meat could be divided into six types, of which pork accounts for 40.0%, beef for 30.4%, chicken for 17.4%, fish for 10.9%, and mutton and rabbit meat for 2.2%. On the other hand, the sources of extracts are diverse, including thyme, rosemary, basil and other plants used as spices, as well as blueberries, grapes and other fruits, broccoli, cabbage and other vegetables, and quebracho Colorado wood. The main bioactive substances in different extracts are different, but they could generally be summarized as polyphenols, flavonoids, anthocyanins, tannins and alkaloids.

    • Figure 1 shows the effects of NPE on the pH, color, texture, antioxidant properties, oxidation degree and microbial growth of meat products. In general, the addition of NPE to meat products had a significant impact on the quality of meat products (SMD −0.73, 95% CI [−1.00, −0.45], sample size = 1682, i2 = 99%, p < 0.00001), but had no significant impact on color (SMD −0.08, 95% CI [−1.84, 1.67], sample size = 457, i2 = 56%, p = 0.93) and texture (SMD −0.20, 95% CI [−0.44, 0.04], sample size = 185, i2 = 82%, p = 0.10). However, it is worth noting that although there is no significant difference between the two indicators on the whole, specific trends could be found according to the forest plot, such as 'chewiness' and 'press loss' are obviously more inclined to the experimental group.

      Figure 1. 

      The forest plot on the effect of NPE addition on the quality of meat products. The forest plot was extracted by RevMan Software (Version 5.4.1). The first author and year of publication is listed in the first column. (Complete list can be referred to from the References). CI, confidence interval; IV, inverse variance; S.D., standard deviation; std, standard. Vertical line in last column indicates no effect line, horizontal line indicates individual study—where the length determined by sample size. Diamond symbol indicates overall effect tendency. p-values following Chi2 stands for heterogeneity, whereas the p-value following Z stands for statistical significance.

      Except for color and texture, significant differences were found in all other indicators (p < 0.05). Specifically, adding NPE to meat products can reduce the pH of meat products (SMD -0.29, 95% CI [−0.34, −0.23], sample size = 402, p < 0.00001), improve antioxidant capacity (SMD 119.61, 95% CI [82.87, 156.95], sample size = 20, p < 0.00001), reduce the oxidation degree of meat products (SMD −5.98, 95% CI [−10.49, −1.48], sample size = 446, i2 = 97%, p = 0.009) and inhibit microbial growth (SMD −0.87, 95% CI [−1.40, −0.34], sample size = 172, i2 = 99%, p = 0.001). It is found that the different indicator subgroups are related to redox, which may indicate that the role of NPE in improving the quality of meat products is based on its antioxidant properties.

    • Figure 2 shows the effect of adding NPE on the pH of meat products from different raw materials. The summary results of meta-analysis show that addition of NPE reduces the pH (SMD −0.23, 95% CI [−0.32, −0.13], sample size = 402, study = 51; i2 = 100%; p < 0.00001). Four studies have reported the effect of NPE on the pH of fish meat products. Meta-analysis showed that the pH of all fish meat products decreased after the addition of NPE (SMD −0.26, 95% CI [−0.28, −0.24], sample size = 33, study = 4; i2 = 55%; p < 0.00001).

      Figure 2. 

      The forest plot on the effect of NPE on pH of meat products produced with different raw materials.

      In the subgroups of pH of products from other raw meat, the addition of NPE significantly reduced the pH of beef products (SMD −0.28, 95% CI [−0.50, −0.07], sample size = 128, study = 16; i2 = 100%; p = 0.008). However, high heterogeneity was observed in the results. Among the 16 studies, the results of Al-Juhaimi et al.[40] and Hastaoğlu et al.[41] showed that the addition of NPE had no significant effect on the pH of beef products, and its weight share was 0.8%, 0.7%, 2.2% and 2.2% respectively, 30.5% of which was the total weight of the subgroup.

      In selected studies, the addition of NPE reduced the pH of pork products (SMD −0.16, 95% CI [−0.28, −0.04], sample size = 108, study = 20, i2 = 99%, p = 0.008). The overall effect showed that the addition of NPE had a significant effect on the pH of pork products, but still showed high heterogeneity. Eight studies crossed the invalid boundary.

      In the study on the pH of other meat products, the addition of NPE significantly reduced the pH of chicken products (SMD −0.23, 95% CI [−0.44, −0.02], sample size = 128, study = 9, i2 = 100%, p = 0.003), and also significantly affected the pH of mutton products (SMD −0.34, 95% CI [−0.41, −0.27], sample size = 5, study = 1, p < 0.00001).

      Taken together, the pH of fish, beef, pork, chicken and mutton products will be significantly reduced after adding NPE. The heterogeneity among different subgroups is low, and there is no significant difference (i2 = 49.9%, p = 0.09), indicating that NPE may delay the oxidation of meat products, thus showing a lower pH, and this effect is independent of the type of meat.

    • According to the analysis, NPE in meat products are based on their antioxidant properties, so it is necessary to further study their antioxidant properties in different meat products and the oxidation degree with or without NPE. TBARS and TVB-N were selected as the main analysis dimensions according to the number of studies available for analysis.

      Figure 3 shows the effect of adding NPE on TBARS values of different meat products. The summary results of meta-analysis showed that NPE had a significant impact on the TBARS value of meat products (SMD −0.93, 95% CI [−1.65, −0.20], sample size = 304, study = 36, i2 = 100%, p = 0.01). However, the results of different subgroups have certain heterogeneity, in other words, the antioxidant capacity of NPE in different raw meat may be different (i2 = 64.5%, p = 0.02).

      Figure 3. 

      Forest plot on the effect of NPE on TBARS of meat products produced with different raw materials.

      Specifically, in beef products (SMD −0.85, 95% CI [−1.10, −0.60], sample size = 107, study = 11, i2 = 100%, p < 0.00001) and chicken products (SMD −0.47, 95% CI [−0.61, −0.33], sample size =128, study = 12, i2 = 99%, p < 0.00001), addition of NPE has a significant impact on the TBARS value. However, there was no significant difference among meat products prepared with fish (SMD −0.30, 95% CI [−0.68, 0.08], sample size = 27, study = 4, i2 = 99%, p = 0.12), pork (SMD −0.24, 95% CI [−0.56, 0.08], sample size = 31, study = 6, i2 = 99%, p = 0.15) and mutton (SMD −0.64, 95% CI [−1.37, 0.10], sample size = 11, study = 3, i2 = 100%, p = 0.09). The above results may indicate that NPE has different antioxidant capacity in different raw meat, and it may also indicate that some NPE play a role in meat products independent of antioxidant capacity to enhance flavor, color, and taste.

      Volatile base nitrogen, another important indicator for the degree of oxidation in meat products is shown in Fig. 4. Subgroup analysis was not conducted due to the small number of studies available for analysis. However, the overall effect showed that the addition of NPE could significantly reduce the TVB-N value of meat products (SMD −18.32, 95% CI [−23.11, −13.54], sample size = 26, study = 4, i2 = 100%, p < 0.00001). The four studies included in the analysis showed consistency. In all studies, the TVB-N values of the groups treated with NPE were lower than control without NPE.

      Figure 4. 

      Forest plot on the effect of NPE on TVBN of meat products produced with different raw materials.

    • The growth of microorganisms has an important impact on the sensory, safety and shelf life of meat products[42]. The analysis includes several indicators closely related to the growth of microorganisms. However, due to the difference in categories of microorganisms in different meat products and storage methods in different studies, there is a small number of studies for further analysis of each indicator, and only the total number of colonies is used for in-depth analysis.

      The results of TPC showed that the addition of NPE could inhibit the growth of microorganisms, which was manifested by significantly reduced TPC of meat products (SMD 0.90, 95% CI [0.30, 1.49], sample size = 21, study = 6, i2 = 99%, p = 0.003) (Fig. 5). This result is confirmed by pH results above. NPE may prevent the pH rise of meat products through antioxidant effect, thus creating an adverse growth environment for microorganisms. It may also inhibit the growth of microorganisms through antibacterial effect, so that meat products show a low pH.

      Figure 5. 

      The forest plot on the effect of NPE on total plate count (TPA) of meat products produced with different raw materials.

    • The funnel plot was used to detect the publication bias of indicators. Publication bias is a measure in meta-analysis. The more obvious the processing effect of the study, the easier it is to be published, which leads to publication bias[43]. In addition, poor experimental design, reporting bias and errors also lead to publication bias[34]. Sterne et al.[44] believe that large heterogeneity (i2 > 75%) will affect the detection of publication bias. The publication bias detection of this meta-analysis is shown in Fig. 6. It can be seen from Fig. 6a that the oxidation index shows an obvious asymmetric trend, which may be due to the error effect caused by too few studies available for meta-analysis. The texture index also shows an obvious asymmetric trend, but it is mainly located in the triangle of 95% confidence interval, indicating that the publishing bias is small. The publication bias detection of meta-analysis for pH and TBARS is shown in Fig. 6b & c. The beef and chicken groups show asymmetry, which may be caused by the high heterogeneity. In addition, the points of the funnel plot accumulate at the top of the plot, indicating a low-risk bias.

      Figure 6. 

      Funnel plot of studies to detect the publication bias for the selected parameters. (a) Overall effect. (b) pH. (c) TBARS.

    • NPE come from a wide range of sources and are rich in active ingredients. Rational use of NPE extracted from low value parts such as pericarp, seeds and rhizomes is of great significance to the food industry[19,45]. Most of the isolated NPE with antioxidant effect are polyphenols with metastable antioxidant properties or secondary metabolites with conjugated double bonds[46]. Their main active components are polyphenols, flavonoids, phenolic diterpenes and tannins[47]. Many studies have been done on the extraction, separation, identification and application of NPE in meat products[48,49]. The results showed that based on its antioxidant properties (Fig. 7), it played a role in delaying oxidation, controlling pollutants, inhibiting microorganisms, alternating nitrate, enhancing flavor and improving quality in meat products[19]. However, relevant studies also show some heterogeneity, which indicates that the effect of NPE on meat products may be diversified. In addition, few qualitative or descriptive review articles are reported in peer-reviewed computable journals. There is no statistical review on the comprehensive quality impact of NPEs on different meat products. The present study evaluated the impact on different indicators, the consistency of evaluation results and their relationship.

      Figure 7. 

      Effect of NPE based on antioxidant properties on meat product quality.

      With high nutritional value, meat products provide human beings with proteins, lipids, minerals, vitamins and other trace elements necessary for growth and development[45,50]. However, rich nutrients also make it easy to deteriorate. It is generally believed that bacterial growth and lipid oxidation are the main reasons for the degradation of meat quality[51]. Storage and processing are the critical points to control the quality of meat. However, the potential safety hazards of using artificial preservatives, the cumbersome and uneconomical treatment of freezing, curing, air drying and other treatments, as well as the impact on the sensory quality of meat, have led people to focus on NPE[52].

      As for the mechanism of NPE in meat products, inhibition of lipid and protein oxidation is the basis (Fig. 8). Due to the free radical chain reaction, the reactive oxygen species and metal ions cause oxidative damage to meat protein[53], and also produce unpleasant flavour substances such as malondialdehyde through oxidation reaction[54,55]. In addition, hemoglobin and myoglobin also promote lipid oxidation in meat products[56]. Here, antioxidants react with free radicals to form stable inactive products[57]. According to relevant studies, antioxidants could be divided into two categories according to their action modes. One is called broken chain antioxidant compounds, which react directly with lipid free radicals by providing hydrogen atoms, and the other loses catalytic function by combining with metal ions[58]. NPE might have the function of two kinds of antioxidants at the same time because it contains a variety of active ingredients (Fig. 9). For example, phenolic acids and phenolic diterpenes have a strong ability to provide hydrogen atoms, while flavonoids and other phenolic compounds are considered to be able to chelate with metal ions[5961].

      Figure 8. 

      Possible mechanism of NPE improving meat product quality.

      Figure 9. 

      Antioxidant mechanism of common bioactive substances in NPE.

      Evidence has shown that synthetic antioxidants (butyl hydroxyanisole, butyl hydroxytoluene, propyl gallate and tert butyl hydroquinone) have potential genotoxicity, and high-dose use might even cause cancer[62]. However, there is little evidence that NPE has adverse effects on meat products or human body. NPE could not only prevent the oxidation of lipids and proteins, but also maintain the normal texture, color, taste and flavor of meat products, and avoid the destruction of vitamins and the formation of toxins[63,64]. In particular, ascorbic acid, anthocyanins, carotenoids, dehydroascorbic acid, glutathione, phenols and flavonoids in NPE are recognized antioxidants[65,66]. Some studies showed that addition of NPE to the formula has excellent antioxidant potential. If appropriate NPE were supplemented, and appropriate treatment or processing methods were applied, meat products will show strong antioxidant properties[67].

      In this meta-analysis, the summary results of pH, antioxidant properties, oxidation degree and microbial growth showed that the addition of NPE significantly delayed the oxidation of meat products but had no effect on color and texture. Some earlier studies indicate that NPE can protect the color of meat products by delaying the oxidation of hemoglobin[68]. However, after processing, the effect of its ingredients on color may be greater than that of hemoglobin. As for the impact on texture, earlier studies indicated that NPE changed the water retention capacity of meat products, thereby affecting other texture indicators[69]. The difference among these studies could be due to the fact that the antioxidant properties of NPE are not as strong as those of natural active substances artificially separated and purified, or that the color and texture of meat products themselves do not change significantly during the reported storage period.

      NPE based on phenols, flavonoids and tannins show the ability to limit and scavenge reactive oxygen species[70,71], and are fully used in different forms of meat products. For example, olive leaf essential oil rich in polyphenols can inhibit the microbial growth of fresh poultry and prolong its shelf life[72]. Garlic extract containing flavonoids and sulfur compounds can effectively inhibit lipid oxidation of sausage[10]. Rosemary extract rich in rat tail oxalic acid and terpene dienes can effectively reduce the TBARS value of beef balls during 12 d of storage[73]. When NPE with strong antioxidant capacity are added to meat products, they still have antioxidant potential, and based on their antioxidant properties.

      Different NPE extraction methods might have an impact on its antioxidant effect. Awad et al.[19] summarized the common NPE extraction schemes, including traditional water extraction, alcohol extraction, soxhlet extraction and emerging supercritical fluid extraction, ultrasonic assisted extraction, subcritical water extraction, microwave-assisted extraction and real-time pressure drop extraction. The application of various extraction technology combinations can play a synergistic effect.

      Many studies have analyzed the impact of NPE on human health. Rosemary extract (rosmarinic acid) has anti-inflammatory activity by stimulating the secretion of interleukin-10[74]. Origanum extract controls stress gastritis and hypersensitivity by inhibiting the secretion of cyclooxygenase-2 (COX-2) in epithelial cancer cells[75,76]. Kaempferol, a aglycone flavonoid abundant in Aloe Vera and peaking spurge, could prevent hepatocellular carcinoma by controlling oxidative stress caused by reactive oxygen species[77]. In addition, some flavonoids from moss can upregulate caspase-3/cleaved-caspase-3 and induce apoptosis of A549 cells by inhibiting the expression of XIAP and Survivin[78]. Luteolin and other flavonoids could selectively reduce the vitality of cancer cells and change some signal pathways, thus playing an anti-cancer effect in the human body[79]. The health effects of these NPE on human body have been very clear, but considering that they are often added to meat products, whether the final products are still beneficial to human health remains to be further studied.

      Consumer demand for natural preservatives is the main driving force for the research on the application of NPE in meat products. However, the bad taste and color of some NPE might adversely affect the sensory properties of products, so the combination of multiple NPE may be a feasible way. In addition, another challenge in the application of NPE is interaction with different meat products, which leads to the fact that some compounds play an effective antioxidant effect in vitro even at low concentrations, but they need to be used in high concentrations in meat products[80]. In particular, phenols and carotenoids can bind to meat protein or lipids, so the structure-activity relationship and dose effect are worthy of attention[23].

    • Consumers' preference for natural ingredients makes them prefer meat products that use NPE as preservatives and nutritional enhancers rather than synthetic compounds. Therefore, many studies have applied NPE to improve the quality of meat products. The present study indicates that NPE has a positive effect on the quality of meat products. The addition of NPE reduces the pH of meat products, improves antioxidant capacity, delays product oxidation and inhibits microbial reproduction. Specifically, NPE reduce the pH, peroxide value, TBARS and TVB-N value of meat products, inhibit the growth of bacteria such as Enterobacteriaceae, Enterococcus, micrococcus/staphylococcus and Pseudomonas, reduce total microbial cryophase and total plate count, and increase DPPH free radical scavenging activity. NPE effectively protect meat products, reduce the degree of lipid and protein oxidation, and prolong the shelf life without changing the basic properties of meat products, such as color and texture. The results might help to better understand the role of NPE in meat processing and it offers an advantageous method to quantitatively analyze how food components affect food matrix. Given the numerous and intricate sources of NPE, it is essential to classify it based on biochemical makeup and do a thorough investigation. The dose-effect relationship between the addition of NPE and the quality of meat products still need more study. Further investigation should be done in the future on the association between meat products containing NPE and human health from the standpoint of metabolic pathways, taking food safety concerns into consideration.

      • This study was funded by the Jiangsu Innovative Group of Meat Nutrition and Biotechnology.

      • The authors declare that they have no conflict of interest. Chunbao Li is the Editorial-Board member of Food Materials Research, who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial-Board member and his research groups.

      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press on behalf of Nanjing Agricultural University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (9)  Table (1) References (80)
  • About this article
    Cite this article
    Zhou T, Wu J, Zhang M, Ke W, Shan K, et al. 2023. Effect of natural plant extracts on the quality of meat products: a meta-analysis. Food Materials Research 3:15 doi: 10.48130/FMR-2023-0015
    Zhou T, Wu J, Zhang M, Ke W, Shan K, et al. 2023. Effect of natural plant extracts on the quality of meat products: a meta-analysis. Food Materials Research 3:15 doi: 10.48130/FMR-2023-0015

Catalog

  • About this article

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return