Search
Submit Manuscript
3.3CiteScore
Advanced Search
MAP  >  Food Innovation and Advances
2023 Volume 2
Article Contents
Abstract
Introduction
Dynamics of the microbiomes associated with medicinal plants under abiotic stress
Deciphering the role(s) and underlying mechanisms by which beneficial micro-organisms aid medicinal plant resistance to abiotic stress
Drought stress
Salt stress
Heavy metal-stress
Nutrient deficiency
Beyond stress tolerance: the role of beneficial microbes in enhancing medicinal plant quality under abiotic stress
Concluding remarks and future perspectives
Author contributions
Data availability
Acknowledgments
Conflict of interest
Rights and permissions
References
About this article
Next Previous
ARTICLE   Open Access    

Discrimination capacity analysis of FTIR-PCA and EEM-PARAFAC on dandelion tissues extracts

  • College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Processing Technology and Quality Control in Shandong Province, Taian 271018, China

More Information
  • Dandelion root contains triterpenoids, polyphenols and flavonoids, dandelion leaf is rich in polyphenols, flavonoids, flavonoids glycosides, and dandelion flower mainly contains flavonoids, among other substances. These different substance content leads to specific benefits and function effects of each part. Fourier transform infrared spectroscopy, three-dimensional fluorescence spectroscopy and related multivariate statistical methods are widely used to determine sample characteristics, but limited research focuses on the substance difference and characteristics in dandelion tissues. In this paper, Fourier transform infrared spectra-principal component analysis and three-dimensional fluorescence spectroscopy-parallel factor analysis were conveyed to analyze dandelion stem, leaf, root and flower tissue extracts, for determining the substance species and content difference among dandelion tissues and evaluating the discrimination capacity of these analysis methods. The Fourier transform infrared spectroscopy of root was distinct from others, and the two principal component models could distinguish dandelion stem and flower, but failed to differentiate leaf and root; while the excitation and emission matrix showed that stem and flower, leaf and root had similar intensity band distribution but different fluorescence intensity, and the parallel factor analysis results proved that one- and three-component models cannot differentiate the tissues of stem and flower, leaf and root, since the fluorescent compounds (polyphenol, flavonoid etc.) structure and content were similar in different tissues. These results indicated that Fourier transform infrared-principal component analysis might be a useful method when various fluorescent compounds exist.

  • Introduction

    Medicinal plants serve as the primary reservoirs of numerous essential natural compounds, playing a crucial role in human health through disease prevention and treatment. Prominent examples, such as artemisinin from Artemisia annua, tanshinone from Salvia miltiorrhiza, and ginsenosides from Panax ginseng, contribute significantly to human well-being[1]. The importance of these botanical resources has been further accentuated in the aftermath of the recent COVID-19 pandemic, highlighting the increasing reliance of humanity on these botanical resources[2]. However, like all plant species, medicinal plants encounter diverse environmental stresses during their growth. Particularly, extreme environmental stresses such as drought, flooding and heat stress, resulting from the global climate changes, pose huge challenges to plant growth[3,4]. These abiotic stresses increase the uncertainty in the yield and quality of medicinal plants, profoundly impacting downstream industrial production and human health. Thus, it is imperative to enhance the resilience of medicinal plant to environmental fluctuations.

    Plants have evolved multiple strategies to cope with these challenges, which have been widely documented at the physiological and molecular levels. For instance, plants adapt to drought conditions by elongating roots, developing lateral roots, and enhancing the expression of aquaporins in order to facilitate water resource utilization[5]. Under salt-induced osmotic stress, plants employ physiological mechanisms such as actively secreting salt through salt glands[6] or passively excluding Na+ from entering cells through the plasma membrane[7] to enhance adaptability. To counter heavy-metal stresses, plants can activate reactive oxygen species-mediated antioxidant systems or regulate the expression of stress-related transcription factors, such as bZIPs, MYBs, AP2, and DREN2B, through miRNA modulation[8,9]. Beyond these intrinsic strategies, plant-associated microorganisms, acting as the second genome of plants (if the organellar genomes of plants are not considered as separate entities), also play a crucial role in plant growth, development, and adaptation to environmental stresses. For instance, Li et al.[10] showed that the salt-recruited beneficial soil bacteria could effectively enhance plant adaptability to salt stress. This theory also applies to the endophytic microorganisms, as endophytic bacterial incubation has been demonstrated to not only enhance plant tolerance to abiotic stresses, but also increase the accumulation of secondary metabolites[11,12]. Moreover, the link between microbial communities and plant nutrient uptake, yield formation, and stress adaptation is well-established[13,14]. However, to date, the significance and functional mechanisms of these microorganisms in modulating adverse environmental fluctuations within medicinal plants remain to be fully elucidated.

    Here, we systematically survey the responses of plant-associated microorganisms, encompassing both rhizospheric and endophytic communities, to common abiotic stresses experienced by plants, and review the crucial role of these organisms in facilitating plant adaptation and promoting the synthesis of medicinal compounds. The present study aims to provide essential insights for future cultivation of medicinal plants in the face of climate change scenarios.

    Dynamics of the microbiomes associated with medicinal plants under abiotic stress

    Abiotic stresses such as drought, salinity, and heavy metals have been demonstrated to affect both plant growth and development, as well as the associated rhizospheric or endophytic microbial communities, owing to their intricate interplay[15]. Such influence arises partly from the direct environmental effects, such as alterations in soil moisture and salinity, which subsequently affect the diversity and composition of soil microorganisms[16]. Equally importantly, plant physiological and metabolic responses to stresses indirectly shape the assembly of these microbial communities, particularly via the secretion of key compounds into the rhizosphere zone by root system[17]. Indeed, numerous studies have reported the microbial responses to abiotic stresses, with most studies revealing predominantly negative or insignificant impacts. For instance, via meta-analysis, Quiroga et al.[18] demonstrated that drought stress significantly decreased the abundance of rhizosphere microorganisms, particularly fungi, without significantly affecting phyllosphere microorganisms. Similarly, Signorini et al.[19] showed that copper (Cu), instead of cadmium (Cd), negatively affects the α-diversity of rhizosphere bacteria in a dose-dependent manner. These adverse effects underscore the sensitivity of microbial communities to environmental perturbations. In the context of medicinal plants, understanding these interactions is particularly crucial given their unique natural habitats and the specific metabolic compounds which they produce.

    The diversity and patterns of assembly of microbial communities are key indicators of ecosystem function and biotic interactions, which are intimately connected to the fluctuations within the environment. In the case of the rhizosphere microbiome, limited research indicates a potentially pronounced negative effect of drought stress on the rhizobacterial diversity of medicinal plant rhizospheres, but a potential benefit to fungal communities[20,21]. Comparable results have been observed under heavy-metal stress, with a study on Tamarix ramosissima revealing that bacterial diversity was more negatively affected by heavy metal stress than fungal diversity[22]. Such an influence of salt stress is more complex, as both positive[23], negative[24], and negligible effects[25] have been documented. This variability is likely attributable to differences in the salt tolerance among plant species and the specific conditions of stress exposure, such as duration and intensity. Additionally, soil nutrients are speculated to be a key factor in shaping rhizosphere microbiome assembly. For example, Su et al. found that variations in nutrients such as potassium (K), magnesium (Mg), copper (Cu), and iron in the core planting area of citrus determine rhizosphere microorganism compositions, which in turn influence the synthesis of citrus terpenes[26]. Similarly, research on Glehnia littoralis demonstrates that varied soil nutrients act as key influencers at various developmental stages, with pH and available phosphorus (P) positively impacting bacterial and fungal communities during early development stages, while nitrate enhances these communities in the middle and late growth stages[27]. Interestingly, N deficiency may enhance the bacterial diversity, as seen in Lycium barbarum[28]. While studies on the relationship between other soil nutrients and the assembly of rhizosphere or endophytic microbial communities in medicinal plants are limited, it is hypothesized that P deficiency might reduce bacterial diversity but increase fungal diversity. This could strengthen the role of deterministic processes in shaping bacterial community assembly, as suggested by research in rice plants[29]. Additionally, a study on ginseng has identified soil total K as a key driver of the temporal dynamics of core-enriched bacterial communities in bulk and rhizosphere soils[30]. Thus, further research is needed to fully understand the impact of P or K deficiency on rhizosphere microbial communities in medicinal plants.

    Notably, these stresses would also reshape the composition and structure of rhizosphere microbial communities, while the responses may vary according to plant type and stress intensity. For instance, an examination of Atractylodes lancea has demonstrated that severe drought stress decreases the relative abundance of Proteobacteria phylum, whereas moderate stress leads to an increase of species of this phylum[21]. In the case of Stevia rebaudiana experiencing moderate drought stress, bacteria belonging to the phyla of Actinobacteria, δ-Proteobacteria, Planctomycetes, and Chloroflexi were mainly enriched[31]. The enrichment of these microbes are partly associated with functions related to carbohydrate metabolism, lipid and secondary metabolite metabolism, which may be helpful in osmotic stress regulation since all of these compounds have been documented to play such a function[32−34]. Under salt stress, specific microbes like those from the orders Micrococcales and Bacillales have been observed to be enriched in some studies while others suggest no significant shifts in the dominant bacterial phyla[23,35]. These examples suggest that the selective enrichment of microbes under stresses is substantially influenced by the host, which can attract and recruit beneficial bacteria via releasing specific root exudates. Notably, root exudates, comprising a diverse array of compounds such as carbohydrates, organic acids, amino acids, and secondary metabolites, are well-documented to be capable of enhancing plant tolerance to abiotic stress by attracting specific beneficial microorganisms within the rhizosphere acting as chemical signals[36,37]. As evidence in support of this, Pan et al.[38] demonstrated that the assembly of rhizosphere bacteria under drought and salt stress is accompanied by the accumulation of rhizosphere compounds such as organic acids, growth hormones, and sugars. Additionally, a study on Limonium sinense indicated that plants can attract and recruit beneficial bacteria through organic acids secreted from roots, promoting plant growth under salt stress[39]. This selective enrichment is further exemplified under low-N stress, where the secretion of C-containing compounds from roots may alter the C-N ratio in rhizosphere soil, thus recruiting specific rhizosphere bacteria to improve N use efficiency. This can lead to an increase in the relative abundance of Acidobacteriota and Myxococcota phyla, as well as ammonia-oxidizing bacteria such as Nitrosospira[28,40]. In addition, theanine secreted by tea plants has been shown to significantly changed the structure of the rhizosphere microbiota, selectively shaping rhizosphere microbial assembly[41].

    Alterations in the network structure of rhizosphere microorganisms when subjected to stress conditions are equally importance, reflecting the complex interactions among microbial species and the ecological stability of the community. For example, studies on the assembly of rhizosphere bacterial communities in Stevia rebaudiana under drought stress have revealed simplified and unstable networks, yet also highlighted an increase in positive interactions and a strengthened deterministic assembly process[42]. Similar findings have been documented in salt-stressed Cynomorium songaricum[43], which further suggests that plants may be employing adaptive strategies to enhance their resilience to stresses. Additionally, N stress could reduce the number of edges in microbial occurrence networks, thereby altering network complexity and potentially increasing the contribution of deterministic processes to community assembly[40,42]. These changes in network complexity provide insights into microbial community adaptation and response to environmental pressures.

    While the number of case studies remains limited, we can discern certain general trends that characterize how these internal microbial communities react and adapt to various environmental stimuli. For example, studies in Codonopsis pilosula indicated increased Shannon and Chao indices of root endophytes[44,45]. Notably, the bacterial community showed a significant increase in Rhizobiales, while the fungal community was enriched with Hypocreales[44]. Similar to those rhizosphere microbes, these microbes were implicated in the modulation of key sugar components that function in drought resistance. Similarly, based on research in model plants, it is reasonable to hypothesize that salt stress could lead to a significant reduction in endophyte diversity. There may, concurrently be a recruitment of microorganisms which function in energy and carbohydrate metabolism, which are essential for the plant adaptation to saline conditions[10,46]. These cases again highlight the beneficial roles of specific microbial taxa enriched under stress conditions, can provide enhanced support for plant acclimation. Moreover, different from the response of rhizosphere microorganisms, Ma et al.[47] revealed a decrease in endophytic fungal diversity specifically in heavy metal-treated Symphytum officinale, with bacterial diversity being less affected. Additionally, the occurrence-network of these endophytes were also altered by heavy-metal stress, with the modular interactions of bacteria strengthened but those of fungi weakened, respectively. Heavy metal pollution also selectively affects microbial compositions by favoring microorganisms with specific functions, such as heavy metal transport and detoxification[47]. This is exemplified in studies on Sedum alfredii, a metal hyperaccumulating plant, which specifically recruits Cd/Zinc-resistant bacteria to enhance its own tolerance to heavy metals[48]. Moreover, under heavy metal stresses, both endophyte and rhizosphere microbiomes were dominated by members of the phylum Proteobacteria, including genera such as Pseudomonas and Serratia[49,50], further supporting the aforementioned findings. These findings underscore the importance of understanding the complex dynamics of plant-microbe interactions under stress and highlight the potential for manipulating these relationships to improve plant resilience and stress tolerance.

    Collectively, these findings highlight an evolutionary strategy where plants and their associated microbiomes co-evolve to mitigate the negative effects of abiotic stresses and such interaction shapes the assembly features of microbial communities. Future research should delve into the mechanisms by which medicinal plants and their microbiomes co-evolve under stress, which could potentially inform strategies for enhancing their resilience and optimizing their cultivation.

    Deciphering the role(s) and underlying mechanisms by which beneficial micro-organisms aid medicinal plant resistance to abiotic stress

    In the face of hostile and unpredictable environments, plants must develop robust adaptive capabilities to ensure their survival and reproductive success. The plant-associated microbiome, often referred to as the plant's second genome, plays a pivotal role in facilitating these adaptive responses. Through a long history of co-evolution with their host, these microorganisms have formed symbiotic relationships that significantly contribute to the plant ability to withstand diverse environmental stresses[51]. This relationship extends to medicinal plants as well, where mounting evidence suggests that inoculating rhizospheric or endophytic microbes can enhance their growth and development, even under stressful conditions[17,52], as listed in Table 1. Crucially, such microbial interventions have been observed to mitigate oxidative damage caused by various abiotic stress, triggering a cascade of physiological and molecular responses that enhance the plant resilience. The modulation of reactive oxygen species, enhancement of antioxidant systems, and regulation of stress-responsive genes are among the molecular events influenced by these beneficial microbes[53]. Understanding the intricate mechanisms by which these microorganisms confer medicinal plants with abiotic stress resistance is essential for advancing our knowledge of plant-microbe interactions and optimizing cultivation practices in the face of environmental adversity.

    Table 1.  Role of beneficial microorganisms in aiding host plant resisting abiotic stresses.
    Stress type Strain/microbes Medicinal plant Mechanistic function Ref.
    Drought AMF Pelargonium graveolens, Glycyrrhiza uralensis; Camellia sinensis Activation of antioxidant systems Xie et al.[54]; Amiri et al.[55]; Wang et al.[56]
    Poncirus trifoliate; Osmotic regulation Wu et al.[57]
    Nicotiana tabacum;
    Cinnamomum migao    		 Activation of antioxidant systems;
    osmotic regulation    		 Begum et al.[58];
    Yan et al.[59]
    Ephedra foliata Boiss Activation of antioxidant systems; osmotic regulation; regulating IAA, GA and ABA levels Al-Arjani et al.[60]
    Poncirus trifoliate; Regulating root development;
    regulating IAA and ABA levels    		 Liu et al.[61];
    Zhang et al.[62]
    Glycyrrhiza uralensis Regulating aquaporin expression; regulating ABA level Xie et al.[63]
    DES
    		 Lycium ruthenicum, Glycyrrhiza uralensis Regulating root development, nutrient uptake and regulating soil microbiome assembly He et al.[20]; He et al.[64]; He et al.[65]
    Glycyrrhiza uralensis Regulating IAA production and ACCD activity Ahmed et al.[66]
    DSE+ Trichoderma viride Astragalus mongholicus Regulating soil microbial assembly He et al.[67]
    Bacillus sp. Glycyrrhiza uralensis; Activation of antioxidant systems Xie et al.[68]
    Bacillus sp. Trigonella foenum-graecum Enhancing ACCD activity;
    beneficial microbial colonization    		 Barnawal et al.[69]
    Bacterial combination/
    syncoms    		 Astragalus mongholicus Activation of antioxidant systems Lin et al.[70]
    Mentha piperita; Hyoscyamus niger Activation of antioxidant systems Chiappero et al.[71]; Ghorbanpour et al.[72]
    Mentha pulegium Activation of antioxidant systems;
    regulation of ABA and flavonoid levels    		 Asghari et al.[73]
    Ociumum basilicm Osmotic regulation Heidari et al.[74]
    AM fungi and PGPB Lavandula dentata Regulation of IAA levels and ACCD activity Armada et al.[75]
    Trigonella foenum-graecum Activation of antioxidant systems; osmotic regulation; regulation of JA levels Yue et al.[76]
    Echinacea purpurea Regulation of nutrient uptake Attarzadeh et al.[77]
    Glycyrrhiza Regulation of nutrient uptake; beneficial microbial colonization Hao et al.[78]
    Myrtus communis Regulation of nutrient uptake; antioxidant system activation Azizi et al.[79]
    Salt stress AMF Chrysanthemum morifolium Regulation of nitrogen uptake Wang et al.[80]
    Ocimum basilicum Activation of antioxidant systems; osmotic regulation; regulation of the K+/ Na+ ratio Abd-Allah and Egamberdieva[81]
    Trifoliate orange Enhanced aquaporin expression Cheng et al.[82]
    Ocimum basilicum Activation of antioxidant systems Yilmaz et al.[83]
    DES Artemisia ordosica Activation of antioxidant systems; IAA production; regulation of the K+/ Na+ ratio Hou et al.[84]
    Trichoderma asperellum; Priestia endophytica Lycium chinense;
    Trigonella foenum-graecum    		 Regulating nitrogen uptake and assimilation Yan et al.[85];
    Sharma et al.[86]
    Streptomyces sp. Glycyrrhiza uralensis Activation of antioxidant systems Li et al.[87]
    Glutamicibacter sp. Limonium sinense Activation of antioxidant systems; osmotic regulation; regulation of the K+/ Na+ ratio; promotion of flavonoid synthesis Qin et al.[88]
    Bacillus sp.;
    Streptomyces sp.; Azotobacter sp.    		 Limonium sinense; Glycyrrhiza glabra; Iranian Licorice Activation of antioxidant systems; osmotic regulation; regulation of the K+/ Na+ ratio Xiong et al.[39];
    Qin et al.[89];
    Mousavi et al.[90];
    Mousavi et al.[91]
    Paenibacillus sp. Panax ginseng Activation of antioxidant systems; osmotic regulation; regulation of ABA level Sukweenadhi et al.[92]
    Achromobacter sp. Catharanthus roseus Activation of antioxidant systems;
    enhanced ACCD activity    		 Barnawal et al.[93]
    Brachybacterium sp. Chlorophytum borivilianum Regulation of IAA level, ABA level and ACCD activity Barnawal et al.[94]
    Bacterial combination/ Syncoms Bacopa monnieri; Galega officinalis Regulation of the K+/ Na+ ratio Pankaj et al.[95]; Egamberdieva et al.[96]
    Phyllanthus amarus;
    Coriandrum sativum    		 Activation of antioxidant system Joe et al.[97];
    Al-Garni et al.[98]
    Bacopa monnieri; Salicornia sp.; Capsicum annuum Osmotic regulation Bharti et al.[99]; Razzaghi Komaresofla et al.[100];
    Sziderics et al.[101]
    Coriandrum sativum Regulation of the K+/ Na+ ratio;
    activation of antioxidant systems    		 Rabiei et al.[102]
    Glycyrrhiza uralensis Activation of antioxidant systems;
    regulation of nutrient uptake    		 Egamberdieva et al.[103]
    Mentha arvensis Activation of antioxidant systems; regulation of nutrient uptake; regulation of the K+/ Na+ ratio; regulation of ACCD activity and siderophore production Bharti et al.[104]
    Medicago sativa Regulation of the IAA level Saidi et al.[105]
    Pistacia vera Regulation of the K+/ Na+ ratio; regulating IAA level, ACCD activity and siderophore production Khalilpour et al.[106]
    Fungi and PGPB Ocimum sanctum Activation of antioxidant systems;
    regulation of the ACCD activity    		 Singh et al.[107]
    Acacia gerrardii Regulation of the nutrient uptake;
    regulation of the K+/ Na+ ratio    		 Hashem et al.[108]
    Artemisia annuaitalic;
    Sesamum indicum    		 Activation of antioxidant systems; osmotic regulation Arora et al.[109]; Khademian et al.[110]
    Heavy metal stress Halomonas sp Ligusticum chuanxiong Reduction of the heavy-metal uptake and regulating rhizosphere microbial assembly Li et al.[111]
    Piriformospora sp. Piriformospora indica Improving tolerance; regulation of rhizosphere microbial assembly Rahman et al.[112]
    Rhizobia Robinia pseudoacacia Improving tolerance;
    regulation of rhizosphere microbial assembly    		 Fan et al.[113]
    Sphingomonas sp. Sedum alfredii Activation of antioxidant systems Pan et al.[114]
    Burkholderia sp. Sedum alfredii Regulating translocation ability Chen et al.[115]
    Leifsonia sp. Camellia sinensis Regulation of rhizosphere microbial assembly Jiang et al.[116]
    Pseudomonas sp. Solanum nigrum Regulation of nutrient uptake; recruiting beneficial bacteria Chi et al.[117]
    Microbial inoculant Panax quinquefolium;
    Salvia miltiorrhiza    		 Reduction of heavy-metal uptake;
    regulation of rhizosphere microbial assembly    		 Cao et al.[118];
    Wei et al.[119]
    Nutrient deficiency AMF Glycyrrhiza uralensis Regulation of P and K uptake; improving nutrient utilization Chen et al.[120]
    Bacillus sp. Mentha arvensis Improving P solubilization Prakash and Arora[121]
    Bacillus sp. Camellia sinensis Regulation of K utilization Pramanik et al.[122]
    Serratia sp. Achyranthes aspera Improving P solubilization, IAA level and siderophore production Devi et al.[123]
    Bacterial combination/syncoms Angelica dahurica Regulation of nutrient uptake Jiang et al.[40]
    Astragalus mongolicus Regulation of nutrient uptake; regulation of rhizosphere microbial assembly Shi et al.[124]
    Camellia sinensis Regulating root development;
    Regulation of the N uptake    		 Xin et al.[125]
    Glycyrrhiza uralensis Regulation of nutrient uptake, IAA level and siderophore production Li et al.[126]
    Heat stress Soil suspension Atractylodes lancea Recruiting specific endophytic bacterial Wang et al.[127]
    Chilling stress Fungi and PGPB Ocimum sanctum Regulation of nutrient uptake and the ACCD activity; osmotic regulation Singh et al.[128]
    Flooding stress Bacterial
    combination/syncoms    		 Ocimum sanctum Regulation of the ACCD activity Barnawal et al.[93]
     | Show Table
    DownLoad: CSV

    Drought stress

    Diverse microorganisms can form symbiotic relationships with plants and provide benefits under drought stress conditions. Among these, arbuscular mycorrhizal (AM) fungi are particularly acknowledged for their critical role in enhancing plant drought tolerance. For instance, extensive studies have demonstrated the positive role of AM fungi in enhancing stress tolerance in plants such as Glycyrrhiza uralensis[63,129], Ephedra foliata[60], and Poncirus Trifoliate[57] under drought stress. This benefits mainly evident in the increased growth rate, photosynthesis rate as well as the higher water content. Mechanistically, AM fungi enhance plant drought resistance by activating antioxidant defense systems[59,60,130] and facilitating the synthesis of osmotic substances such as sucrose and proline[57]. AM fungal incubation can also improve water absorption of G. uralensis through the upregulation of expression of PIP gene that related to aquaporin[63]. Interestingly, studies on Poncirus trifoliate have demonstrated that AM fungi incubation increases root endogenous hormone (e.g. abscisic acid, indole-3-acetic acid and brassinosteroids) levels, and thus promotes root hair density and diameter[61,62]. Such an effect is partly associated with the down-regulation of root auxin efflux carriers (PtPIN1 and PtPIN3) and up-regulation of root auxin influx carriers (PtABCB19 and PtLAX2) under drought stress. Moreover, via combined transcriptome and metabolome analysis, Xie et al.[54] demonstrated that AM-induced plant drought tolerance was primarily linked to the accumulation of root phenolic compounds and flavonoids. In addition to AM fungi, dark septate endophytes (DSE) have also been demonstrated to enhance biomass formation and root development in medicinal plants under drought stress, as seen in G. uralensis[20,65], and Lycium ruthenicum[64]. DSE appear to regulate plant drought tolerance by direct plant interactions and by enriching specific composition of soil microbial communities, including AM fungi, gram-negative bacteria, and actinomycetes. Bacterial inoculants also exhibit positive effects on plant drought tolerance via varying mechanisms. For instance, inoculation with Bacillus sp.[68,76] or Pseudomonas sp.[72] can mitigate lipid oxidation by enhancing the antioxidant enzyme activity or the oxidant content in the host plant. These beneficial bacteria also contribute to osmotic adjustment by increasing proline or soluble carbohydrates levels[72,74]. Furthermore, certain ACC deaminase-containing bacteria can enhance plant resistance to drought by lowing ethylene levels[69], while the activation of hormone-mediated defense pathways, such as those involving jasmonic acid (JA), is equally important[76].

    The co-inoculation of diverse microorganisms, moreover, more effectively enhances plant tolerance due to their mutualistic interaction. For instance, the combined inoculation of DSE and Trichoderma viride has been shown to markedly enhance the growth and drought tolerance of Astragalus mongholicus[67]. Further evidence of the benefits of microbial co-inoculation comes from studies on Astragalus mongholicus[70], Mentha piperita[71], and M. pulegium[73], where various bacterial combinations have been documented to exert positive effects. These synergistic interactions are particularly prominent in bacterial-fungal combinations, which mitigate oxidative damage induced by drought stress by enhancing nutrient uptake and accumulation, while also reducing ethylene levels[78,131]. These findings suggest the importance of microbial diversity and the potential for mutualistic relationships between different microorganisms in improving plant resilience. The importance of microbial diversity is further demonstrated by a previous study showing significantly improved growth and drought resistance in Atractylodes lancea when incubated with an entire soil microbial community[21]. The enhanced growth and drought tolerance observed in plants inoculated with these microbial combinations highlight the potential of harnessing microbial diversity and synergies for boosting plant stress tolerance.

    Salt stress

    Salt stress is a widespread abiotic stressor that significantly influences plant growth and quality worldwide[132]. While it induces osmotic stress in plants similar to drought stress, the primary damage derives from ion imbalance and toxicity, which can severely impair plant productivity[133]. In the context of plant-microorganism interactions, various strategies are employed to mitigate this complex osmotic stress. Similar to their role in addressing drought stress, AM fungi represent a key strategy for mitigating salt stress in medicinal plants. Via meta-analysis, previous studies have shown that plants inoculated with AM fungi, either alone or in combination with other bacteria, exhibit better performance under salt stress compared to non-mycorrhizal plants[134,135]. This beneficial effect has been observed in medicinal plants such as Artemisia annua[109], Acacia gerrardii[108], and Ocimum basilicum[81]. The improved resilience of these medicinal plants is largely attributed to several factors. Crucially, AM fungi enhances nutrient uptake, leading to increased levels of essential elements such as N, P, and microelements while reducing the concentrations of harmful ions such as Na+ and Cl−[80,110]. This also results in a more favorable potassium/sodium (K+/Na+) ratio. Equally important is the activation of antioxidant enzyme activities contributes to the enhanced stress tolerance. Additionally, a study on peanut demonstrated that combined inoculation of AM and Lactobacillus plantarum under salt stress modulated signal transduction pathways, including those involving phytohormone synthesis and mitogen-activated protein kinases[136]. This finding highlights the underlying molecular mechanisms involved in microorganism-regulated plant salt tolerance, similar to that observed in the salt tolerance promoted by DES in G. glabra[137]. Moreover, Trichoderma has also been shown to positively impact medicinal plant health, primarily through enhanced nutrient, especially N, absorption. This is accompanied by increased activities of N assimilation enzymes, including nitrate reductase, nitrite reductase, and glutamine synthetase[85].

    Notably, N-fixing rhizobia and actinomycetes have garnered considerable interest for their roles in producing antioxidants and osmoregulatory substances[87,91]. Rhizobia enhance plant salt tolerance by improving N absorption and assimilation, while actinomycetes exhibit significant resilience to water shortage and osmotic stress. Similarly, other plant growth-promoting rhizobacteria (PGPRs) also exhibit great potential in enhancing plant salt tolerance. For instance, bacteria such as Bacteroidetes, Bacillus, and Pseudomonas can activate the entire defence system of medicinal plants under salt stress by facilitating ion transport, boosting antioxidant production, and aiding in osmotic regulation[39,96,138]. Furthermore, the synergistic effects of co-inoculating these beneficial bacteria warrant significant consideration. For instance, co-inoculation of rhizobia and Pseudomonas or Bacillus species can effectively mitigate hydrogen peroxide production under salt stress by enhancing N content, antioxidant capacity, and free radical scavenging ability[97,103]. Incorporating multiple beneficial bacteria or a combination of growth-promoting bacteria and fungi may further strengthen plant capacity to tolerant salt stress through diverse regulatory mechanisms. Specifically, these beneficial microbes primarily elevate the K+/Na+ ratio by promoting K+ absorption and thus inhibiting Na+ uptake, which is also accompanied by the activation of antioxidant systems and the absorption of other nutrients to synergistically improve plant resistance[100]. Additionally, these bacteria can support plant growth under saline conditions through the production of extracellular polysaccharide, ACC deaminase or siderophores (iron carriers)[99,106].

    Heavy metal-stress

    When addressing heavy metal pollution, plant-microorganism systems primarily adopt two strategies: inhibiting the absorption of heavy metals by plants and improving plant tolerance to these metals, aiming to mitigate the associated negative effects. For example, Li et al.[111] demonstrated that Halomonas sp. could reduce the bioavailability of cadmium and phoxim in soil and plants, thereby improving rhizome biomass and reducing oxidative damage. Similarly, microbial inoculant have been found to reduce the accumulation of heavy metals in the roots of medicinal plants such as Panax quinquefolium[118] and Salvia miltiorrhiza[119], thus enhancing their survival efficiency. These findings highlight the potential of microbial inoculation as a sustainable and eco-friendly strategy for mitigating heavy metal pollution in agricultural ecosystems. Furthermore, microbial inoculation not only facilitates metal sequestration but also enhances plant tolerance to heavy metals by modulating plant physiology and biochemistry through various mechanisms. For instance, Chen et al.[115] demonstrated that Sphingomonas inoculation confers Sedum alfredii tolerance to cadmium stress and reduces oxidative damage in vivo through organic acids secretion. Additionally, the inoculation with multiple bacterial species has been shown to promote plant tolerance to aluminum stress by upregulating stress-related gene expression, such as AtAIP, AtALS3, and AtALMT1, and by recruiting aluminum-resistant and growth-promoting bacteria[116,138]. Comparable effects have been observed under arsenic stress, where beneficial microorganisms like Bacillus sp., Nitrospira sp., and Microbacterium sp., contributing to plant growth and tolerance under heavy metal stress[118]. Conversely, some beneficial bacteria enhance the transport efficiency of heavy metals in plants, promoting their accumulation in aboveground parts while simultaneously improving plant tolerance[112,139]. This is largely attributed to the reassembly of the rhizosphere microbiome, particularly the enrichment of beneficial microorganisms such like Mesorhizobium and Streptomyces genera[112,113]. These findings suggest that microbial inoculants aid in managing and mitigating the negative effects of heavy metals by cultivating a more robust and diverse microbial community. Thus, harnessing the beneficial interactions between plants and microorganisms can enhance plant resilience to heavy metal stress and promote ecosystem health.

    Nutrient deficiency

    Beneficial rhizobacteria are well-established for their critical role in enhancing nutrient uptake and utilization efficiency in plants. Investigations on Astragalus mongolicus[124] and G. uralensis[126] have elucidated that inoculating plants with rhizobacteria combinations can increase the mineral nutrients uptake, thus enhancing biomass production under low-nutrient conditions. Similar findings have been recorded in Angelica dahurica and Camellia sinensis, where the application of synthetic microbial communities (Syncoms) markedly improved N uptake and assimilation, concomitant with the theanine synthesis, under low-N conditions[40,125]. Moreover, bacteria possessing P-solubilizing properties may act as pivotal regulator of plant growth under low-P conditions. These bacteria facilitate root development and aboveground biomass formation by elevating rhizosphere soil P content through rhizosphere acidification and phosphatase excretion into the rhizosphere[121,140]. Notably, AM fungi has exhibited significant efficacy in enhancing P nutrient absorption, which typically resulting in reduced soil P levels and enhanced plant P uptake, consequently promoting plant growth under low-P conditions[120,141]. Furthermore, studies on Achyranthes aspera have highlighted the plant growth-promoting capabilities of Serratia sp., which produce siderophores under iron-deficient conditions[123]. Additionally, their roles in P solubilization and indole-3-acetic acid (IAA) production further contribute to plant growth. Given the significant contributions of beneficial rhizobacteria to plant nutrient uptake and growth enhancement, it is evident that harnessing these microbial interactions can develop innovative strategies to address global nutrient heterogeneity and to establish more resilient agricultural systems.

    Additionally, microbial inoculation has been reported to enhance the resilience of medicinal plants under other stress conditions, including chilling[127], heat[128], and waterlogging[93] stresses. The beneficial effects are primarily attributed to processes such as decreased ethylene production, improved nutrient absorption, and the recruitment of specific bacteria. Together with the above instances, it is evident that these beneficial microorganisms can significantly impact plant physiology and biochemistry processes via soil nutrient remobilization, hormone level regulation and reshaping of microbial community (Fig. 1). Furthermore, while research on medicinal plants is limited, studies in model crops such as Arabidopsis[142] and Oryza sativa[143] suggest that beneficial microorganisms can enhance plant tolerance by triggering induced systemic resistance (ISR), which stimulates the plant defense system, leading to the upregulation of genes involved in hormone regulation and stress responses, such as dehydrin (DHN), glutathione S-transferase (GST) and responsive to desiccation 29B (RD29B). These events comprehensively highlighted the pivotal role of employing beneficial microorganisms in advancing sustainable agricultural development under the challenging global climate changes. Thus, it is imperative to further investigate the efficacy of different types of microorganisms or their synthetic in mitigating diverse abiotic stresses as well as the underlying mechanisms. This will enable the development of corresponding microbial solutions for agricultural production under specific stress.

    Figure 1.  Mechanistic insights into how beneficial microbes enhance medicinal plant resilience to abiotic stress.
    DownLoad: Full-Size Img PowerPoint

    Under abiotic stress conditions, diverse microorganisms in the rhizosphere or within endophytic compartments—such as fungi and bacteria—play a crucial role in enhancing the adaptability of medicinal plants. These microorganisms can improve nutrient absorption and metabolism by enhancing soil nutrient availability. Additionally, their ability to produce auxin or ACC deaminase enzymes influences plant hormone production, which subsequently affects downstream resistance events. Furthermore, microbial inoculations can trigger induced systemic resistance (ISR) and then enhance the overall resilience of medicinal plants by altering their antioxidant system, osmotic regulation, and hormone balance. Under specific stress conditions, such as heavy metal pollution, these beneficial microorganisms can re-assemble the microbial community further to amplify their positive effects on the host plant.

    Beyond stress tolerance: the role of beneficial microbes in enhancing medicinal plant quality under abiotic stress

    In addition to growth, development, and stress adaptability, the variation in specific secondary metabolites in medicinal plants is equally important, as these compounds dictate their medicinal quality and economic value[144]. Previous studies have shown that under abiotic stresses, the levels of these compounds can either increase or decrease. The decreases may result from metabolic disruptions under severe stress condition, while increases are primarily attributed to the concentration effect resulting from reduced plant biomass, especially under drought conditions[145]. This prevalent trade-off between plant growth and differentiation complicates the simultaneous enhancement of both plant stress adaptation and medicinal compound concentration. Hence, given plant-microbe interactions, it is imperative to further comprehend the effects of beneficial microbial inoculation on the content of valuable compounds in medicinal plants and their potential mechanisms.

    Notably, numerous previous studies have illustrated that the single or combined inoculation of these microorganisms effectively alters the accumulation of specific metabolites under abiotic stresses, while also promoting plant growth (Table 2). For example, under drought stress, favorable results have been observed with the inoculation of DES[20], AM fungi[63], and Bacillus amyloliquefaciens[76] on the contents of glycyrrhizic acid, glycyrrhizin and liquiritin in Glycyrrhiza uralensis, alongside enhanced activities of key enzymes involved in licorice synthesis pathway. Similarly, investigations on Nicotiana tabacum and Pelargonium graveolens have indicated that AMF treatment significantly enhanced both the content and yield of essential oil[55,58]. Furthermore, studies have documented the positive effects of co-inoculation of two or more bacteria on secondary metabolite production in various medicinal plants under drought stress. This includes increased astragaloside IV and calycosin-7-glucoside content in Astragalus mongholicus[70], essential oil contents in Mentha pulegium[73], and tropane alkaloids in Hyoscyamus niger[72]. Such beneficial effects have also been recorded when plants were subjected to salt stress. For example, an examination of Artemisia annua showed that combined treatment with AM fungi and beneficial bacteria enhances both artemisinin accumulation and host salt resistance[109]. Similar results have been reported for specific metabolites in other medicinal plants, such as saponin[99], flavonoid[88], and glycyrrhizic acid[91]. Mechanistically, these effects may be attributed to an increase in plant sugar content under osmotic stress[91,92,107], as sugars play a central role as precursors for secondary C metabolism. Additionally, microbial inoculation may modulate plant hormone balance through ISR, thereby influencing the synthesis of secondary metabolites biosynthesis[146]. This was also evidenced by Yue et al.[76], who highlighted the role of JA in Bacillus sp.-promoted flavonoid and terpenoid synthesis.

    Table 2.  Role of beneficial microorganisms in the contents of main medicinal compounds in medicinal plants subjecting to abiotic stresses.
    Stress type Strain/microbes Medicinal plant Contribution Ref.
    Drought AMF Glycyrrhiza uralensis
    		 Improving glycyrrhizin and liquiritin production;
    up-regulation of the expression of key genes (e.g. squalene synthase (SQS1), β-amyrin synthase (β-AS)
    and cytochrome P450 monooxygenases (CYP88D6
    and CYP72A154)    		 Orujei et al.[129];
    Xie et al.[63]
    Nicotiana tabacum/ Pelargonium graveolens Enhancement of oil content; up-regulation of the expression of key genes Begum et al.[58]; Amiri et al.[55]
    DSE Glycyrrhiza uralensis Elevation of glycyrrhizin and glycyrrhizic acid content; regulation of the N and P content He et al.[147]
    Bacillus sp. Glycyrrhiza uralensis Enhancement of the total flavonoids, total polysaccharide and glycyrrhizic acid content; up-regulation of the expression of key enzymes (e.g. lipoxygenase and phenylalanine ammonia-lyase); simulation of JA-synthesis Xie et al.[68];
    Yue et al.[76]
    Bacterial combination/ Syncoms Astragalus mongholicus Enhancement of the astragaloside IV and calycosin-7-glucoside content Lin et al.[70]
    Mentha pulegium Improving phenolic, flavonoid and oxygenated monoterpenes production Asghari et al.[73]
    Hyoscyamus niger Improving tropane alkaloid production Ghorbanpour et al.[72]
    Salt AMF Glycyrrhiza glabra Elevation of the glycyrrhizin and terpenoid precursors production Amanifar et al.[148]
    Priestia endophytica Trigonella foenum-graecum Improving phenolic compounds and trigonelline synthesis and N fixation Sharma et al.[86]
    Azotobacter sp. Glycyrrhiza glabra Enhancement of the glycyrrhizic acid and glabridin production Mousavi et al.[91]
    Bacterial combination/ syncoms Artemisia annua Enhancement of the artemisinin production; improving N and P contents Arora et al.[109]
    Foeniculum vulgare Enhancement of the essential oil production Mishra et al.[149]
    Bacopa monnieri Enhancement of the bacoside A production Pankaj et al.[95]
    AM fungi and PGPB Sesamum indicum Improving phenolic, flavonoid, sesamin and sesamolin production Khademian et al.[110]
    Mentha arvensis Enhancement of essential oil content Bharti et al.[150]
    Heavy-metal stress Microbial inoculant Panax quinque folium Enhancement of the ginsenoside production; regulation of the rhizo-microbial structure and composition Cao et al.[118]
    Microbial inoculant Salvia miltiorrhiza Elevation of the total tanshinones content; recruiting beneficial microorganisms Wei et al.[119]
    N-deficiency Bacterial combination Angelica dahurica Enhancement of the furanocoumarin production Jiang et al.[40]
    Astragalus mongolicus Enhancement of the flavonoids, saponins, and polysaccharides contents Shi et al.[124]
    P-deficiency AMF Hypericum perforatum Enhancement of the glycyrrhizic acid, liquiritin, isoliquiritin, and isoliquiritigenin contents; regulation of the nutrient absorption Lazzara et al.[151]
    Polygonum cuspidatum Enhancement of the chrysophanol, emodin, polydatin, and resveratrol contents; regulation of the nutrient absorption Deng et al.[141]
    Nutrient-deficiency AMF Glycyrrhiza uralensis Elevation of the isoliquiritin and isoliquiritigenin content; enhancement of the P, K and microelements absorption Chen et al.[120]
    Heat stress Soil suspension Atractylodes lancea Enhancement of the hinesol, β-eudesmol, atractylon and atractylodin content, up-regulation of the expression of key genes (e.g. 1-deoxy-D-xylulose 5-phosphate synthase (DXS), farnesyl diphosphate synthase (FPPS), 3-hydroxy-3-methylglutaryl-coenzyme (HMGR)); enrichment of specific beneficial bacteria Wang et al.[127]
     | Show Table
    DownLoad: CSV

    The promotion of nutrient absorption by microbial incubation is as also considerable with prior studies having identified correlations between N and P contents and the terpenoid synthesis in G. uralensis[147] and Artemisia annua[109]. Additionally, alkaloid levels can be actively regulated by exogenous bacterial inoculation under salt stress, attributed to the induction of N fixation mechanisms in plants, thereby enhancing trigonelline biosynthesis[86]. Therefore, given the nutrient uptake-promoting effects of these microorganisms and the critical roles of N and P in secondary metabolite synthesis, these microorganisms are anticipated to have a substantial impact on the metabolic regulation of plants under nutritional stresses. Indeed, researchers have found that Syncoms derived from bacteria isolated under N-deficient conditions significantly improve the N utilization efficiency of Angelica dahurica and enhance the synthesis of furanocoumarins[40]. This stimulatory function is linked to the capacity of functional bacteria to enhance nutrient utilization. Supportively, N-fixing bacteria such as Bacillus and Arthrobacter have been shown to enhance nutrient uptake and accumulation, as well as elevate the flavonoid, saponin, and polysaccharide contents in Astragalus mongolicus[124]. An investigation on tea plants demonstrated that synthetic communities lacking N-fixing bacteria could not effectively facilitate theanine synthesis[125], further demonstrating the critical role of N in microbial regulation of secondary metabolism. P absorption and utilization are equally important for the synthesis of C-based secondary metabolites, which serve as vital components for acetyl-CoA, glyceraldehyde-3-phosphate, as well as isopentenyl pyrophosphate (IPP)/dimethylallyl diphosphate (DMAPP), the common substrate for both the methylerythritol phosphate (MEP) and mevalonate (MVA) pathways[152]. Such results have been extensively documented with AM inoculation, which enhances terpenoid production through enhaced P uptake. For instance, AM has been shown to effectively increase the levels of hypericin and pseudohypericin in Hypericum perforatum[151], as well as chrysophanol, emodin, polydatin, and resveratrol concentrations in Polygonum cuspidatum[141], under low phosphorus conditions. Moreover, a study on Mentha arvensis revealed that Bacillus can increase essential oil production by solubilizing P[121], indicating that combining nutrients with microbial agents can more effectively enhance both the yield and quality of medicinal plants.

    Furthermore, there is evidence from limited studies documenting the beneficial effects of microbial inoculation on the quality of medicinal plants under other stress conditions. For example, the application of microbial inoculants significantly increased the accumulation of total ginsenosides in Panax quinquefolium[118] and total tanshinones in Salvia miltiorrhiza[119] under heavy metal stress, offering new approaches for cultivating medicinal plants in contaminated regions. Furthermore, a study on heat stress has demonstrated the remarkable resilience conferred by inoculation with soil microbial communities, along with the accumulation of volatile medicinal compounds in the roots of Atractylodes lancea[127]. This observed enhancement was accompanied by the upregulation of key genes such as farnesyl diphosphate synthase (FPPS), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), and 1-deoxy-d-xylulose 5-phosphate synthase (DXS), shedding light on the molecular mechanisms underlying microbial-mediated stress response and secondary metabolite production. Importantly, these investigations have identified simultaneous changes in the structure and composition of rhizosphere and endophytic microbial communities. These changes would selectively recruit microbes with specialized functions, thereby enhancing secondary metabolic processes under stress conditions. These findings not only deepen our understanding of plant-microbe interactions, but also hold significant implications for the sustainable production of high-quality medicinal plants in the face of environmental challenges. Notably, these elevated secondary metabolites may also be helpful in enhancing host plant resistance to abiotic stresses. However, evidence linking microbes, secondary metabolites and medicinal plant resistance is largely lacking and warrants further investigations in the future.

    Concluding remarks and future perspectives

    As the second genome of plants, plant-associated microorganisms, including rhizospheric and endophytic ones, will respond to environmental stresses correspondingly with plants and thereby critically contribute to plant adaptability. Faced with these stresses, microorganisms respond by altering their community diversity and assembly processes in a manner that is mainly mediated by root exudates. As a result, the specific core microorganisms recruited under stresses will favour host coping with the environmental stresses, as well as the improved quality formation of medicinal plants. Such results provide novel insights for cultivating high-quality medicinal plants under stressful conditions. The underlying mechanisms are multifaceted, encompassing the modulation of plant nutrient utilization and hormone production associated with the microbial functions such as nitrogen fixation and ACCD enzyme activity. These microorganisms also play a role as inducers of ISR in plants, thus stimulating their defense against various stresses. Equally important is the re-assembly of the microbial community structure through interaction with other microorganisms, thus enhancing the overall resilience of the plant-microbiome system. In the future, it is imperative to strength the research into the specific microbial communities associated with medicinal plants, which includes the development of agent or bio-fertilizer products associated with these beneficial microorganisms or the Syncoms, thus ensuring the robust growth and quality of medicinal plants in the face of increasingly challenging environmental conditions.

  • The authors confirm contribution to the paper as follows: study conception and design: Sun Y, Fernie AR; literatures collection and analysis: Sun Y, Yuan H; draft manuscript preparation: Sun Y, Fernie AR. All authors approved the final version of the manuscript.

  • Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

    • Acknowledgments

    This work was supported by the National Natural Science Foundation of China (32101842), the Open Fund of Jiangsu Key Laboratory for the Research and Utilization of Plant Resources (JSPKLB202303) and the Max Planck Society.

  • The authors declare that they have no conflict of interest.

  • [1]

    González-Castejón M, Visioli F, Rodriguez-Casado A. 2012. Diverse biological activities of dandelion. Nutrition Reviews 70:534−47

    doi: 10.1111/j.1753-4887.2012.00509.x

    CrossRef   Google Scholar

    [2]

    Law SK, Lo C, Han J, Chuanshan X, Leung A. 2021. Traditional Chinese herbal, "Dandelion" and its applications on skin-care. Traditional and Integrative Medicine 6:152−57

    doi: 10.18502/tim.v6i2.6794

    CrossRef   Google Scholar

    [3]

    Menke K, Schwermer M, Felenda J, Beckmann C, Stintzing F, et al. 2018. Taraxacum officinale extract shows antitumor effects on pediatric cancer cells and enhance mistletoe therapy. Complementary Therapies in Medicine 40:158−64

    doi: 10.1016/j.ctim.2018.03.005

    CrossRef   Google Scholar

    [4]

    Park S, Kim DS, Wu X, Yi QJ. 2018. Mulberry and dandelion water extracts prevent alcohol-induced steatosis with alleviating gut microbiome dysbiosis. Experimental Biology and Medicine 243:882−94

    doi: 10.1177/1535370218789068

    CrossRef   Google Scholar

    [5]

    Xue Y, Zhang S, Du M, Zhu MJ. 2017. Dandelion extract suppresses reactive oxidative species and inflammasome in intestinal epithelial cells. Journal of Functional Foods 29:10−18

    doi: 10.1016/j.jff.2016.11.032

    CrossRef   Google Scholar

    [6]

    Mahboubi M, Mahboubi M. 2020. Hepatoprotection by dandelion (Taraxacum officinale) and mechanisms. Asian Pacific Journal of Tropical Biomedicine 10:1−10

    doi: 10.4103/2221-1691.273081

    CrossRef   Google Scholar

    [7]

    Williams CA, Goldstone F, Greenham J. 1996. Flavonoids, cinnamic acids and coumarins from the different tissues and medicinal preparations of Taraxacum officinale. Phytochemistry 42:121−27

    doi: 10.1016/0031-9422(95)00865-9

    CrossRef   Google Scholar

    [8]

    Domitrović R, Jakovac H, Romić Ž, Rahelić D, Tadić Ž. 2010. Antifibrotic activity of Taraxacum officinale root in carbon tetrachloride-induced liver damage in mice. Journal of Ethnopharmacology 130:569−77

    doi: 10.1016/j.jep.2010.05.046

    CrossRef   Google Scholar

    [9]

    Yarnell E, Abascal K. 2009. Dandelion (Taraxacum officinale and T mongolicum). Integrative Medicine 8:35−38

    Google Scholar

    [10]

    Hu C. 2018. Taraxacum: Phytochemistry and health benefits. Chinese Herbal Medicines 10:353−61

    doi: 10.1016/j.chmed.2018.08.003

    CrossRef   Google Scholar

    [11]

    Jia XX, Li S, Han DP, Chen RP, Yao ZY, et al. 2022. Development and perspectives of rapid detection technology in food and environment. Critical Reviews in Food Science and Nutrition 62:4706−25

    doi: 10.1080/10408398.2021.1878101

    CrossRef   Google Scholar

    [12]

    Sainani KL. 2014. Introduction to principal components analysis. PM& R 6:275−78

    doi: 10.1016/j.pmrj.2014.02.001

    CrossRef   Google Scholar

    [13]

    Carrascal LM, Galván I, Gordo O. 2009. Partial least squares regression as an alternative to current regression methods used in ecology. Oikos 118:681−90

    doi: 10.1111/j.1600-0706.2008.16881.x

    CrossRef   Google Scholar

    [14]

    Abbas O, Zadravec M, Baeten V, Mikuš T, Lešić T, et al. 2018. Analytical methods used for the authentication of food of animal origin. Food Chemistry 246:6−17

    doi: 10.1016/j.foodchem.2017.11.007

    CrossRef   Google Scholar

    [15]

    Jamwal R, Amit, Kumari S, Sharma S, Kelly S, et al. 2021. Recent trends in the use of FTIR spectroscopy integrated with chemometrics for the detection of edible oil adulteration. Vibrational Spectroscopy 113:103222

    doi: 10.1016/j.vibspec.2021.103222

    CrossRef   Google Scholar

    [16]

    Måge I, Böcker U, Wubshet SG, Lindberg D, Afseth NK. 2021. Fourier-transform infrared (FTIR) fingerprinting for quality assessment of protein hydrolysates. LWT 152:112339

    doi: 10.1016/j.lwt.2021.112339

    CrossRef   Google Scholar

    [17]

    Sazonova S, Grube M, Shvirksts K, Galoburda R, Gramatina I. 2019. FTIR spectroscopy studies of high pressure-induced changes in pork macromolecular structure. Journal of Molecular Structure 1186:377−83

    doi: 10.1016/j.molstruc.2019.03.038

    CrossRef   Google Scholar

    [18]

    Vera M, Cruz S, Boleda MR, Mesa J, Martín-Alonso J, et al. 2017. Fluorescence spectroscopy and parallel factor analysis as a dissolved organic monitoring tool to assess treatment performance in drinking water trains. Science of The Total Environment 584-585:1212−20

    doi: 10.1016/j.scitotenv.2017.01.184

    CrossRef   Google Scholar

    [19]

    Bro R, Vidal M. 2011. EEMizer: Automated modeling of fluorescence EEM data. Chemometrics and Intelligent Laboratory Systems 106:86−92

    doi: 10.1016/j.chemolab.2010.06.005

    CrossRef   Google Scholar

    [20]

    He XS, Fan QD. 2016. Investigating the effect of landfill leachates on the characteristics of dissolved organic matter in groundwater using excitation–emission matrix fluorescence spectra coupled with fluorescence regional integration and self-organizing map. Environmental Science and Pollution Research 23:21229−37

    doi: 10.1007/s11356-016-7308-7

    CrossRef   Google Scholar

    [21]

    Callejón RM, Amigo JM, Pairo E, Garmón S, Ocaña JA, et al. 2012. Classification of Sherry vinegars by combining multidimensional fluorescence, parafac and different classification approaches. Talanta 88:456−62

    doi: 10.1016/j.talanta.2011.11.014

    CrossRef   Google Scholar

    [22]

    Pan Z, Li RH, Cui YY, Wu XJ, Zhang YY, et al. 2021. A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy 245:118948

    doi: 10.1016/j.saa.2020.118948

    CrossRef   Google Scholar

    [23]

    Wu P, Ben T, Zou H, Chen Y. 2022. PARAFAC modeling of dandelion phenolic compound fluorescence relation to antioxidant properties. Journal of Food Measurement and Characterization 16:2811−19

    doi: 10.1007/s11694-022-01389-z

    CrossRef   Google Scholar

    [24]

    Mohsin GF, Schmitt FJ, Kanzler C, Hoehl A, Hornemann A. 2019. PCA-based identification and differentiation of FTIR data from model melanoidins with specific molecular compositions. Food Chemistry 281:106−13

    doi: 10.1016/j.foodchem.2018.12.054

    CrossRef   Google Scholar

    [25]

    Mooi E, Sarstedt M, Mooi-Reci I. 2018. Principal component and factor analysis. In Market Research: The Process, Data, and Methods Using Stata. Singapore: Springer. pp. 265−311. https://doi.org/10.1007/978-981-10-5218-7_8
    [26]

    Bro R. 1997. PARAFAC. Tutorial and applications. Chemometrics and Intelligent Laboratory Systems 38:149−71

    doi: 10.1016/S0169-7439(97)00032-4

    CrossRef   Google Scholar

    [27]

    Harshman RA, Lundy ME. 1994. PARAFAC: Parallel factor analysis. Computational Statistics & Data Analysis 18:39−72

    doi: 10.1016/0167-9473(94)90132-5

    CrossRef   Google Scholar

    [28]

    Durak T, Depciuch J. 2020. Effect of plant sample preparation and measuring methods on ATR-FTIR spectra results. Environmental and Experimental Botany 169:103915

    doi: 10.1016/j.envexpbot.2019.103915

    CrossRef   Google Scholar

    [29]

    Lever J, Krzywinski M, Altman N. 2017. Principal component analysis. Nature Methods 14:641−42

    doi: 10.1038/nmeth.4346

    CrossRef   Google Scholar

    [30]

    Murphy KR, Stedmon CA, Graeber D, Bro R. 2013. Fluorescence spectroscopy and multi-way techniques. PARAFAC. Analytical Methods 5:6557−66

    doi: 10.1039/c3ay41160e

    CrossRef   Google Scholar

    [31]

    Murphy KR, Stedmon CA, Wenig P, Bro R. 2014. OpenFluor - an online spectral library of auto-fluorescence by organic compounds in the environment. Analytical Methods 6:658−61

    doi: 10.1039/C3AY41935E

    CrossRef   Google Scholar

    [32]

    Walker SA, Amon RMW, Stedmon C, Duan S, Louchouarn P. 2009. The use of PARAFAC modeling to trace terrestrial dissolved organic matter and fingerprint water masses in coastal Canadian Arctic surface waters. Journal of Geophysical Research: Biogeosciences 114:G00F06

    doi: 10.1029/2009jg000990

    CrossRef   Google Scholar

  • Cite this article

    Li G, Zou H, Chen Y. 2023. Discrimination capacity analysis of FTIR-PCA and EEM-PARAFAC on dandelion tissues extracts. Food Innovation and Advances 2(4):247−254 doi: 10.48130/FIA-2023-0026
    Li G, Zou H, Chen Y. 2023. Discrimination capacity analysis of FTIR-PCA and EEM-PARAFAC on dandelion tissues extracts. Food Innovation and Advances 2(4):247−254 doi: 10.48130/FIA-2023-0026
    shu

Figures(6)

Download PDF

Article Metrics

Article views(2865) PDF downloads(451)

Other Articles By Authors

ARTICLE   Open Access    

Discrimination capacity analysis of FTIR-PCA and EEM-PARAFAC on dandelion tissues extracts

  • College of Food Science and Engineering, Shandong Agricultural University, Key Laboratory of Food Processing Technology and Quality Control in Shandong Province, Taian 271018, China

  • Received: 19 May 2023
  • Accepted: 08 September 2023
  • Published online: 25 October 2023
Food Innovation and Advances  2,  2023, 2(4): 247−254  |  Cite this article

Abstract: Dandelion root contains triterpenoids, polyphenols and flavonoids, dandelion leaf is rich in polyphenols, flavonoids, flavonoids glycosides, and dandelion flower mainly contains flavonoids, among other substances. These different substance content leads to specific benefits and function effects of each part. Fourier transform infrared spectroscopy, three-dimensional fluorescence spectroscopy and related multivariate statistical methods are widely used to determine sample characteristics, but limited research focuses on the substance difference and characteristics in dandelion tissues. In this paper, Fourier transform infrared spectra-principal component analysis and three-dimensional fluorescence spectroscopy-parallel factor analysis were conveyed to analyze dandelion stem, leaf, root and flower tissue extracts, for determining the substance species and content difference among dandelion tissues and evaluating the discrimination capacity of these analysis methods. The Fourier transform infrared spectroscopy of root was distinct from others, and the two principal component models could distinguish dandelion stem and flower, but failed to differentiate leaf and root; while the excitation and emission matrix showed that stem and flower, leaf and root had similar intensity band distribution but different fluorescence intensity, and the parallel factor analysis results proved that one- and three-component models cannot differentiate the tissues of stem and flower, leaf and root, since the fluorescent compounds (polyphenol, flavonoid etc.) structure and content were similar in different tissues. These results indicated that Fourier transform infrared-principal component analysis might be a useful method when various fluorescent compounds exist.

    HTML

    Introduction

    • Dandelion is a perennial herb of compositae family, native to Europe and widely grown in temperate regions of the northern hemisphere[1]. In Chinese traditional medicine books, its effects of dispelling wind-heat, detoxification and detumescence, diuresis, defecation, jaundice, liver and gallbladder detoxification ability are comprehensively described in detail, and as a medicinal plant, it has anti-inflammatory, anti-oxidation, anti-tumor, antibacterial, hypoglycemic, liver and gallbladder, regulation of gastrointestinal microecology and other therapeutic effects[2−4], so it has been widely investigated as an ingredient in the health care industry for various foods.

      Dandelion is rich in a variety of bioactive substances, including polyphenols (trans-p-hydroxyphenylpropofol, trans-p-hydroxyphenylacrolein, p-hydroxybenzoic acid, p-hydroxyphenylpropionic acid, protocatechualdehyde), flavonoids (rutin and quercetin), polysaccharides and triterpenoids, etc. Moreover, the types and content distribution of functional components in different tissues are different[5], leading to different roles. According to the existing research results, dandelion leaves are rich in caffeic acid, coumarin, chlorogenic acid, flavonoid glycosides, and chicoric acid; dandelion roots contain triterpenoids, chlorogenic acid, caffeic acid, rutin, and vanillic acid; dandelion flowers are rich in flavonoids, free luteolin, chelonethylene glycol, and chrysol[6−8]. It is widely believed that dandelion leaves can be used as a gallbladder, diuretic, and bitter digestive irritant, while the root is also used as an alternative to help relieve skin diseases, treat digestive disorders, increase bile flow, and can promote appetite[9,10]. Considering the differences in the types and distribution of compounds, a more effective way of dandelion utilization is to divide the whole plant into different parts for individual bioactivity compound extraction and specific beneficial effects evaluation. The results of compound identification and differentiation in dandelion tissue using different spectral techniques and multivariate statistical techniques have not yet been systematically studied.

      Spectroscopic techniques used for compound identification mainly include infrared spectroscopy (different chemical functional groups absorb different frequencies of infrared light, the optical technology can detect the vibration and rotation of molecular bonds, and can be used for chemical fingerprint identification, chemical imaging and chemical structure analysis) and fluorescence spectrum (each organic compounds has a separate maximum emission / excitation wavelength at different wavelengths, parallel factor analysis (PARAFAC) was used to process the excitation-emission matrices (EEMs) and determine the characteristics of samples)[11].

      Fourier transform infrared spectroscopy (FTIR) is a vibration spectroscopy technology based on the mathematical processing of Fourier transform, which has been widely used in food quality control, food structure and function research due to its characteristics of high speed, high accuracy and resolution. Studies have shown that FTIR spectrum combined with a variety of analysis methods, such as principal component analysis (PCA, a statistical technique to reduce the dimension of data, can use less dimensions to describe the change of data, but still contains most information[12]) and partial least squares regression (PLS-R, a multiple linear regression analysis, reduce the raw data to less and maximizing the explained variance to detect the relationship between predictor and response variables[13]). It can accurately and rapidly predict edible oil adulteration, distinguish coffee bean samples from different origins, detect meat and meat product adulteration, monitor biochemical, microbial spoilage and shelf life, and determine protein and lipid structure changes[14−17].

      Three-dimensional fluorescence spectrum (known as excitation-emission matrix) could quickly determine the fluorescence intensity of a substance in different excitation wavelength and emission wavelength ranges, and mixtures can be directly detected and distinguished by the different fluorophores structures of each compound and the corresponding three-dimensional fluorescence spectral information[18]. Parallel factor analysis is an efficient method to decompose fluorescence excitation-emission matrices into their underlying chemical components, this analysis method can lead to the mathematical identification and quantification of independently varying fluorophores (individual component with fluorescent group) from the complex, obtain excitation and emission spectra and their corresponding concentration or content[19]. Three-dimensional fluorescence spectroscopy combined with parallel factor analysis has the advantages of high sensitivity, good selection performance and no damage to samples, and has been widely used in the detection and identification of chemical components in vinegar, wine and other foods as well as water quality assessment[20−22].

      Without sample pretreatment and chromatographic conditions optimization, or time-consuming mass spectrum processing and compound identification, the Fourier transform infrared spectroscopy and three-dimensional fluorescence spectrum combined with corresponding analytical methods have been widely used in the extraction and classification of plant species, tissues and functional compounds. The spectroscopy technologies have been used by thousands of researchers for commercial or academic purposes, but their application scope and analysis results have not yet been systematically analyzed, which also limits the mining and comparison of spectral data and hinders the correct technical selection. In this paper, dandelion roots, stems, flowers and leaves were obtained and prepared for water extraction preparation, the FTIR spectroscopy combined with PCA analysis, and three-dimensional fluorescence spectroscopy combined with PARAFAC analysis were used to establish corresponding models to identify the extracts of dandelion tissues, so as to compare the classification results of different tissues (roots, stems, flowers and leaves) of dandelion. Based on the research of substance species and content distribution in dandelion tissues, this paper tried to fill the gap between molecular spectroscopy discrimination results and substance differences, without comparing individual compound contents in each tissue. In addition, the discrimination capacity difference between Fourier transform infrared spectroscopy and three-dimensional fluorescence spectrum were discussed, and the internal relationships between molecular bond vibrations and rotations spectrum and fluorescent compounds structure and content contour were also investigated. In summary, this paper aims to compare the Fourier transform infrared spectroscopy and fluorescence spectroscopy analysis results systematically.

    Materials and methods

      Materials

    • Dried dandelion plant was purchased from a local pharmacy, and stored in sealed light resistant packaging at 4 °C before use. Formic acid, deuterium oxide for infrared spectrum and three-dimensional fluorescence spectrum were of chromatographic grade and obtained from Shanghai Maclin Biochemical Co., Ltd. (Shanghai, China). Deionized water used to prepare solutions was 18 MΩ and purified using an ultra-pure water system.

      Preparation of dandelion tissue extracts

    • Different dandelion tissue extracts were prepared according to previous research literature[23]. The roots, stems, flowers and leaves of the whole dried dandelion were cut out and oven-dried separately at 60 °C until the dry weight was constant. The same tissues from different plants were milled, and the resulting particles were mixed and screened using a 60-mesh sieve. Six grams of root, stem, flower and leaf powder was mixed with 60 mL of 1‰ formic acid water solution separately, and vortexed at 20 °C for 2 h. After centrifugation at 8,000 r/min for 15 min, the supernatants of different dandelion tissues were taken and filtered through a 0.22 μm nylon filter to yield the crude extract, and all the processed extracts were stored at 4 °C in the dark.

      Fourier transform infrared spectroscopy acquisition

    • The obtained tissue extracts were freeze-dried, 0.4 g of the samples were dissolved in 5 mL of deuterium oxide, then filtered with a needle filter of 0.22 μm before testing. About 3 mL of liquid was placed in a diamond ATR module, and the FTIR spectrum was collected by Bruker Tensor 27 Fourier Infrared spectrometer. Instrument parameters were set as follows: the wavelength range was 4,000−400 cm−1, with a spectrum resolution of 1 cm−1, the scan number was set as 32, the temperature was 25 °C, and the spectrum of deuterium oxide was used as the blank sample[15, 16].

      Principal component analysis

    • The principal component analysis was performed in the window between 3,900 and 400 cm−1. Baseline were first corrected for all samples, then the spectra were normalized (all spectra were centered on the mean, and the mean absorbance was calculated and then subtracted from the spectrum. The FTIR spectra were scaled to make the sum squared deviation over the indicated wavelengths equal one.) and smoothed with polynomial 2nd order using the Savitzky-Golay-algorithm[24]. Then the covariance matrix of normalized spectra was computed to identify the variables with respect to others. The eigenvectors and eigenvalues of the covariance matrix were calculated and ordered by eigenvectors values in descending order, to determine the proper principal components in order of significance. Recast the data along the axes of principal components using the eigenvectors of the covariance matrix, which could be done by multiplying the transpose of the original data set[25].

      PCA was used to decompose the data matrix, and covariance data matrices were used to calculate the principal components (PCs). The principal components of PC1 to PC3, and their corresponding PC loadings were calculated. The PCA analysis was conveyed by the additional automatic application following the above steps, and the results were illustrated using the software of Origin 9.0 (Origin Lab, Northampton, USA).

      Three-dimensional fluorescence spectrum acquisition

    • The processed extracts of root, stem, flower and leaf obtained from whole dried dandelion were diluted 20 times to obtain the tested liquid for three-dimensional fluorescence spectrum measurement. The fluorescence analysis was performed using the PerkinElmerLS55 system with 1 cm quartz colorimeter, the device was validated with deionized water, the Raman and Rayleigh peaks were measured and used to correct the original spectrum. Then the tested extracts of different tissues were placed at the excitation wavelength of 200−400 nm and the emission wavelength of 220−600 nm to obtain the three-dimensional fluorescence spectrum. The excitation interval was set at 10 nm, the voltage was set as 700 V, and the excitation and emission slit width were both 20 nm.

      Parallel factor analysis

    • Parallel factor analysis statistically decomposes the three-dimensional fluorescence spectrum into individual fluorescence components and a residual matrix. The individual fluorescence components are directly proportional to the component concentration in the sample and could be converted into actual concentration when the excitation and emission of each component are known[19, 26].

      xijk=∑Fn=1ainbjnckn+εijk

      where, xijk is the fluorescence intensity of the ith dandelion extraction at the kth excitation and jth emission wavelength, ain is directly proportional to the concentration of the nth fluorophore in the ith sample, bjn and ckn are estimates of emission and excitation spectra of nth fluorophore at wavelength j and k. F is the number of components, and εijk is the residual matrix.

      In this paper, the PARAFAC analysis was conveyed using the N-way program[26] following the steps of blank spectrum subtracting, outliers removed, limiting the Raman scattering, data normalization, and parallel factor analysis model establishing using the inner automatic functions. After subtracting deuterium oxide spectrum manually, and loading the total three-dimensional fluorescence spectrum containing 379 × 21 intensity readings, the program first eliminated Rayleigh and Raman scattering peaks of each scan centered on the respective scattering peak by excising portions (10 and 20 nm at each excitation wavelength). The PARAFAC model was then established with default PARAFAC constraints, no negative values in concentration, emission and excitation wavelength were applied to process the data. The PARAFAC model was tested from one to five component by means of fitting values, core consistency, and split-half quality calculation. Samples with high leverage (the elements on the diagonal of the hat matrix of the score matrix) or high sum-squared residual were removed until no samples were assessed as outliers by default set, and the PARAFAC model of proper component number was identified.

      Statistical analysis

    • All the tissue extracts of dandelion root, stem, flower and leaf for FTIR, and three-dimensional fluorescence spectrum acquisition were repeated three times, and the data were expressed as mean ± standard deviation. The PCA analysis was carried out using the additional application in the software of Origin 9.0, and the PARAFAC analysis was conveyed by the N-ways program.

    Results

      FTIR spectroscopy measurement and principal component analysis

    • The Fourier transform infrared (FTIR) spectroscopy of dandelion tissues extracts of root, stem, flower and leaf were measured respectively, and their absorbance spectrum was recorded. As shown in Fig. 1, the FTIR of dandelion stem, flower and leaf have the same peak location wavelength and similar fingerprint, the dandelion root had two distinct peak wavelengths between 2,500−1,750 cm−1 and 3,500−2,750 cm−1. The absorbance spectrum indicated that stem, flower and leaf might have the same compounds with different content, while the substances in root extracts were different.

      Figure 1. 

      Fourier transform infrared spectroscopy of dandelion tissue extracts.

      Since the differences in absorbance spectra are due to compound species and contents of compounds in different tissues, the identification efficiency was evaluated by principal component analysis (PCA). As shown in Fig. 2, two main components of PC1 (factor 1, 59%) and PC2 (factor 2, 38%) were extracted according to the above steps in the method section with the cumulative variance contribution rate of 97%, which indicated that the two-component model could explain the total 97% of the absorbance spectroscopy difference determined by the dandelion tissues, and could be used to distinguish the test dandelion tissues. Although obvious difference existed between the three repeated experiments of stem, flower and leaf tissue extracts, the PCA maps showed the Fourier transform infrared spectroscopy characteristics of dandelion tissues. Dandelion leaf, represented by green points, accounted for the comparatively higher values in PC1 and PC2 dimensions, ranging from 0.5 to 0.8 and −0.2 to 0 respectively; the dandelion stems (marked as blue points) values varied in the moderate range, which changed in the range of −0.1 to 0.3 in the PC1 axis and −0.4 to −0.1 in the PC2 axis. For the dandelion flowers in the red group, its values were of the comparatively lower range, showing a narrow area in the PC1 coordinate (−0.2 to 0) and a large area in the PC2 coordinate (−0.3 to −0.6). The dandelion root (black points) group showed a different tendency compared to the other tissues, with a smaller repeat difference and high values in both PC1 and PC2. According to the PCA analysis results, dandelion tissue extract could be divided into three groups, dandelion root group and dandelion leaf were not well disguised from each other.

      Figure 2. 

      Score cluster plot with top two principal components (PCs) for different dandelion tissues.

      Fluorescence spectrum measurement

    • Different dandelion tissue extracts were scanned to obtain raw three-dimensional fluorescence spectra (excitation-emission matrices, EEMs) in the excitation range of 200−400 nm and the emission range of 220−600 nm. As shown in Fig. 3, where a, b, c, and d correspond to the spectrum of dandelion root, flower, stem, and leaf extracts respectively. The fluorescence spectra of dandelion tissue extracts showed different fluorescence fingerprints with one or two relatively intense bands, the fluorescence spectra of dandelion root and leaf had one strong band and maximum excitation / emission wavelengths of about 260 nm / 370 nm with obvious intensity difference, while the dandelion stem and flower fluorescence spectrum had two intense bands at 220 nm / 370 nm and 260 nm / 370 nm, and the excitation wavelength at 220 nm had higher fluorescence intensity compared to the ones at 260 nm. In addition, the fluorescence intensity of all the tissues had a fluorescent band around 260 nm / 370 nm, and the obtained three-dimensional fluorescence spectrum could be divided into two distinct groups with different intensity bond distribution and fluorescence intensity.

      Figure 3. 

      Three-dimensional fluorescence spectra of dandelion tissue extracts. (a) Root, (b) flower, (c) stem, (d) leaf).

      PARAFAC analysis

    • The PARAFAC model was established from component number 1-5 after removing Rayleigh and Raman scattering from the original fluorescence spectra. In order to determine appropriate component numbers, the residual sum of the square, core consistence and interaction number were compared and evaluated. As shown in Fig. 4, the residual sum of the squares decreased as more components were selected. The core consistency analysis of the model reaches 100 at the first component and decreased in the second, third, fourth, and fifth component models, while the interaction number remained at the lowest level at one- component model, followed by the second and third component models. Considering all the test indicators, one and three component models were identified for the fluorescence dataset based on the higher residual sum of the square and lower interaction number. The core consistence retained some variability, while the four and five component models were rejected due to their lower residual sum of square and higher interaction number. Although one and three components were selected to model, it does not indicate that only one or three types of fluorophores were present in these extracts[27].

      Figure 4. 

      Analysis diagram of the parallel factor model. (a) Square residual, (b) core consistence, (c) interaction number.

      Figure 5 showed the excitation and emission spectrum and calculated concentrations determined by the one component PARAFAC model. The maximum excitation wavelength of the first component was about 260 nm, and the maximum emission wavelength was about 370 nm, and the obtained concentration of dandelion flower extract was the highest, followed by tissues of stem, leaf and root. In addition, the obtained concentration of dandelion flower and stem, root and leaf overlapped with each other, this result indicated that one-component model cannot distinguish tissues of flower and leaf tissue, as well as root and leaf. Then the three-component model was established and shown in Fig. 6. The first component represented by the black line had a maximum excitation wavelength at about 260 nm and a maximum emission wavelength at about 370 nm, which were the same as the one component model, the second component marked with the red line had a maximum excitation wavelength at about 270 nm and a maximum emission wavelength at about 370 nm, while the third component of the blue line had a maximum excitation wavelength at about 230 nm and a maximum emission wavelength at about 350 nm. The concentration distribution was the same as the component one model, for the stem tissue had the highest concentration, followed by tissues of flower, leaf, and root respectively. While the second component had the opposite tendency, the concentrations of root and leaf were higher compared to the flower and stem, and the concentration distribution of the third component was the same as the first, where the flower and stem concentration had a higher value, than root and leaf.

      Figure 5. 

      Results of one component PARAFAC model of dandelion tissue extracts. (a) Excitation specta, (b) emission spectra, (c) relative concentration of dandelion tissue extract.

      Figure 6. 

      Results of three component PARAFAC model of dandelion tissue extracts (a-b were (a) Excitation spectra, (b) emission spectra, (c) - (e) relative concentrations of dandelion tissue extracts in components I−III.

    Discussion

    • Fourier transform infrared spectroscopy with principal component analysis, and three-dimensional fluorescence coupled with parallel factor analysis have been used to identify plant species and origin or determine the effects of processing on food quality. In this paper, these two methods were used to process spectrum data, and evaluate the component characteristics of different dandelion tissues.

      Fourier transform infrared spectroscopy coupled with PCA analysis could well distinguish the stem and flower, but failed to discriminate the root and leaf tissue. Former research proved that dandelion root, leaf, and flower contain polyphenols and flavonoids, while dandelion root contains unique triterpenoids, and dandelion leaf contains unique flavonoid glycosides[5]. The peak list contains O-H aromatic (3,400 cm−1), C-H aromatic (2,900 cm−1), C-H aliphatic (2,800 cm−1), C=O (1,743 cm−1), C=C (1,640 cm−1), C=C aromatic (1,550 cm−1), C-O (1,100 cm−1), C-H alkanes (1,450 cm−1) and C-N (1,240 cm−1), these functional groups indicated that phenolic acids, alcohols, esters, carboxylic acids widely exist in the extracts of various dandelion tissues, which is also proven by former research on FTIR analysis results[28]. The FTIR spectrum includes absorption, reflection, emission, or photoacoustic spectrum, and all the substance species and content difference contribute to the spectrum characteristics. On the other hand, PCA summarises the obtained data features, without reference to prior knowledge about whether the samples come from the same dandelion tissues, or the species and content of polyphenol flavonoids, triterpenoids and lavonoid glycosides[29]. Therefore, the dandelion origin, experiment error, and even repeat number could all impact the PCA analysis results. As shown in Fig. 2, two dimensional principal component analysis (PCA) was used to analyze the difference of extracts from different tissues of dandelion. The obtained information is the trend of the point pattern relative to the other patterns, the close point distance of tissue sample reflects the higher similarity among tissues, while the far sample distance of each point represents a comparatively obvious difference[12]. In this case, it is a combination of similarity and difference in FTIR spectral patterns. The higher repeat difference might be related to the lower classical PCA efficiency, it implies that the PCA model failed to classify different tissues. Increasing the number of repeated experiments could improve the discriminant efficiency and the robustness of the model, but the calculated principal component values and the distribution in the score cluster graph will not change.

      Three-dimensional fluorescence spectrum showed all the fluorescent compound fingerprints at specific excitation and emission wavelengths, with various bond distribution and fluorescence intensity. Based on the available fluorescence data and former research, it could be referred that polyphenols show fluorescent peaks at 220 nm / 370 nm, with a higher maximum intensity in flower extracts, than the ones of stem, leaf and root[23, 30]. Although other compounds of flavonoids, triterpenoids and lavonoid glycosides have unique fluorescence absorption and emission, their maximum wavelength of excitation and emission are closed to typical polyphenol, and cannot be detected or discriminated by the fluorescence scan[31].

      In order to compare and distinguish the fluorescence spectra of different dandelion tissues, the PARAFAC method was then used to extract the characteristic components and corresponding spectral features. When compared to the PARAFAC models, the obtained concentrations of leaf and root, as well as the stem and flower tissues overlapped with each other, in both one- (Fig. 5c) and three- (Fig. 6c−e) component models. This result indicated that calculated one- and three-component models had similar efficiency, since we cannot distinguish dandelion tissues in box plot of one component or all three components. On the other hand, the calculated excitation and emission spectra of the first component in the one-component model were the same as the third component in the three-component model, and both fluorescence spectra referred to the existence of classical phenolic compounds. The fluorescence spectrum only reflects the fluorescent component characteristics, this why the PARAFAC method has only successfully determined dissolved organic matter and fingerprint in wastewater or water processing[32].

      Based on the results and discussion, it seems that the FTIR-PCA analysis had a better discrimination capacity, since all the substances including fluorescent components and non-fluorescent components could be taken into account, while the fluorescence spectrum does not have enough resolution for discrimination substances with similar structures with close excitation and emission peak wavelengths. By systematically comparing the above methods, this paper gives the limitation and potential application in plant tissue discrimination, as well as the processing effects, plant origin, and compound evaluation in the food industry.

    Conclusions

    • Fourier transform infrared spectroscopy combined with principal component analysis, and three-dimensional fluorescence spectroscopy combined with parallel factor analysis were used to distinguish different dandelion tissue extracts. Results indicated that FTIR-PCA analysis could well discriminate tissues of stem and flower from leaf and root, while three-dimensional fluorescence spectrometry with PARAFAC analysis cannot differentiate the tissues of leaf from root, and stem from flower, for the concentrations overlapping with each other. This paper demonstrates that both methods could distinguish samples without prior knowledge of the substance type and content, and FTIR-PCA might be more suitable when fluorescent components exist in various amounts among different samples.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Li G, Zou H; data collection: Li G; analysis and interpretation of results: Li G, Zou H; draft manuscript preparation: Li G, Chen Y. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • All data generated or analyzed during this study are included in this published article.

      Acknowledgments

      • This work was supported by funding: 'Innovation Project of Shandong Province Agricultural Application Technology', No 2130106.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press on behalf of China Agricultural University, Zhejiang University and Shenyang 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 (6)  References (32)
  • About this article
    Cite this article
    Li G, Zou H, Chen Y. 2023. Discrimination capacity analysis of FTIR-PCA and EEM-PARAFAC on dandelion tissues extracts. Food Innovation and Advances 2(4):247−254 doi: 10.48130/FIA-2023-0026
    Li G, Zou H, Chen Y. 2023. Discrimination capacity analysis of FTIR-PCA and EEM-PARAFAC on dandelion tissues extracts. Food Innovation and Advances 2(4):247−254 doi: 10.48130/FIA-2023-0026
    shu

Catalog

  • HTML

  • Introduction
  • Materials and methods

  • Materials

  • Preparation of dandelion tissue extracts

  • Fourier transform infrared spectroscopy acquisition

  • Principal component analysis

  • Three-dimensional fluorescence spectrum acquisition

  • Parallel factor analysis

  • Statistical analysis

  • Results

  • FTIR spectroscopy measurement and principal component analysis

  • Fluorescence spectrum measurement

  • PARAFAC analysis

  • Discussion
  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgments
  • Conflict of interest
  • Rights and permissions
  • About this article
logo

Contents

Policies

Services

Partnerships

© 2025 Maximum Academic Press, except Open Access articles. MAP is a member of ALPSP, COPE, CrossRef, cOAlition S, OASPA and STM.

Privacy Policy  |  Site Map  |  Terms and Conditions

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return