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MAP  >  Food Materials Research
2024 Volume 4
Article Contents
Abstract
INTRODUCTION
RESULTS
Extractable trans-resveratrol content in developing grape berries
Analysis of RNA-Seq data
GO and KEGG pathway enrichment analysis of DEGs
DEGs related to phenylalanine metabolism
Differentially expressed TF genes
Weighted gene co-expression network construction and identification of TF genes co-expressed with STSs
Validation of RNA-seq data by RT-qPCR
DISCUSSION
CONCLUSIONS
MATERIALS AND METHODS
Plant material
Extraction and analysis of resveratrol content
RNA extraction, transcriptome library construction and sequencing
RNA-seq data analysis
Validation of RNA-Seq data by RT-qPCR
ACKNOWLEDGMENTS
Conflict of interest
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References
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Fluorimetric determination of quinine using a portable system and digital image processing

  • 1.

    Instituto de Química, Universidade Federal da Bahia, Salvador, 40170-290, Brazil

  • 2.

    Sciware Systems, 07193, Bunyola, Spain

  • 3.

    Dep. Química, Univ. Illles Balears, 07122, Palma de Mallorca, Spain

More Information
  • The development of a portable device created by 3D printing for fluorimetric measurements is an efficient tool for analytical applications in situ or in the laboratory presenting a wide field of applications in the environmental and food field. This device uses a light-emitting diode (LED) as a radiation source and a digital microscope as a detector. Digital images obtained by the interaction between the radiation source and the sample were analyzed with the help of the YouCam software, the images were filed in JPEG format and processed with the Chemostat software.The entire system is connected to a notebook, which serves as an LED and detector power supply without the need for any additional power source. The proposed device was used for the determination in situ of quinine in water and beverage samples, respectively. For the validation of the developed system, the results obtained were compared with a conventional spectrofluorometer with a t-test at a 95% confidence level. The proposed system provides satisfactory precision and accuracy values. The proposed method presented detection and quantification limits of 0.99 and 1.46 mg·L−1, at a 95% confidence interval.

  • INTRODUCTION

    Grapevines are among the most widely grown and economically important fruit crops globally. Grapes are used not only for wine making and juice, but also are consumed fresh and as dried fruit[1]. Additionally, grapes have been increasingly recognized as an important source of resveratrol (trans-3, 5,4'-trihydroxystilbene), a non-flavonoid stilbenoid polyphenol that in grapevine may act as a phytoalexin. In humans, it has been widely reported that dietary resveratrol has beneficial impacts on various aspects of health[2, 3]. Because of the potential value of resveratrol both to grapevine physiology and human medicine, resveratrol biosynthesis and its regulation has become an important avenue of research. Similar to other stilbenoids, resveratrol synthesis utilizes key enzymes of the phenylpropanoid pathway including phenylalanine ammonia lyase (PAL), cinnamate 4-hydroxylase (C4H), and 4-coumarate-CoA ligase (4CL). In the final steps, stilbene synthase (STS), a type II polyketide synthase, produces trans-resveratrol from p-coumaroyl-CoA and malonyl-CoA, while chalcone synthase (CHS) synthesizes flavonoids from the same substrates[4, 5]. Moreover, trans-resveratrol is a precursor for other stilbenoids such as cis-resveratrol, trans-piceid, cis-piceid, ε-viniferin and δ-viniferin[6]. It has been reported that stilbenoid biosynthesis pathways are targets of artificial selection during grapevine domestication[7] and resveratrol accumulates in various structures in response to both biotic and abiotic stresses[8−12]. This stress-related resveratrol synthesis is mediated, at least partialy, through the regulation of members of the STS gene family. Various transcription factors (TFs) participating in regulating STS genes in grapevine have been reported. For instance, MYB14 and MYB15[13, 14] and WRKY24[15] directly bind to the promoters of specific STS genes to activate transcription. VvWRKY8 physically interacts with VvMYB14 to repress VvSTS15/21 expression[16], whereas VqERF114 from Vitis quinquangularis accession 'Danfeng-2' promotes expression of four STS genes by interacting with VqMYB35 and binding directly to cis-elements in their promoters[17]. Aided by the release of the first V. vinifera reference genome assembly[18], genomic and transcriptional studies have revealed some of the main molecular mechanisms involved in fruit ripening[19−24] and stilbenoid accumulation[8, 25] in various grapevine cultivars. Recently, it has been reported that a root restriction treatment greatly promoted the accumulation of trans-resveratrol, phenolic acid, flavonol and anthocyanin in 'Summer Black' (Vitis vinifera × Vitis labrusca) berry development during ripening[12]. However, most of studies mainly focus on a certain grape variety, not to investigate potential distinctions in resveratrol biosynthesis among different Vitis genotypes. In this study, we analyzed the resveratrol content in seven grapevine accessions and three berry structures, at three stages of fruit development. We found that the fruits of two wild, Chinese grapevines, Vitis amurensis 'Tonghua-3' and Vitis davidii 'Tangwei' showed significant difference in resveratrol content during development. These were targeted for transcriptional profiling to gain insight into the molecular aspects underlying this difference. This work provides a theoretical basis for subsequent systematic studies of genes participating in resveratrol biosynthesis and their regulation. Further, the results should be useful in the development of grapevine cultivars exploiting the genetic resources of wild grapevines.

    RESULTS

    Extractable trans-resveratrol content in developing grape berries

    For each of the seven cultivars, we analyzed resveratrol content in the skin, pulp, and seed at three stages of development: Green hard (G), véraison (V), and ripe (R) (Table 1). In general, we observed the highest accumulation in skins at the R stage (0.43−2.99 µg g−1 FW). Lesser amounts were found in the pulp (0.03−0.36 µg g−1 FW) and seed (0.05−0.40 µg g−1 FW) at R, and in the skin at the G (0.12−0.34 µg g−1 FW) or V stages (0.17−1.49 µg g−1 FW). In all three fruit structures, trans-resveratrol showed an increasing trend with development, and this was most obvious in the skin. It is worth noting that trans-resveratrol was not detectable in the skin of 'Tangwei' at the G or V stage, but had accumulated to 2.42 µg g−1 FW by the R stage. The highest amount of extractable trans-resveratrol (2.99 µg g−1 FW) was found in 'Tonghua-3' skin at the R stage.
    Table 1.  Resveratrol concentrations in the skin, pulp and seed of berries from different grapevine genotypes at green hard, véraison and ripe stages.
    StructuresSpeciesAccessions or cultivarsContent of trans-resveratrol (μg g−1 FW)
    Green hardVéraisonRipe
    SkinV. davidiiTangweindnd2.415 ± 0.220
    V. amurensisTonghua-30.216 ± 0.0410.656 ± 0.0432.988 ± 0.221
    Shuangyou0.233 ± 0.0620.313 ± 0.0172.882 ± 0.052
    V. amurensis × V. ViniferaBeibinghong0.336 ± 0.0761.486 ± 0.1771.665 ± 0.100
    V. viniferaRed Global0.252 ± 0.0510.458 ± 0.0571.050 ± 0.129
    Thompson seedless0.120 ± 0.0251.770 ± 0.0320.431 ± 0.006
    V. vinifera × V. labruscaJumeigui0.122 ± 0.0160.170 ± 0.0210.708 ± 0.135
    PulpV. davidiiTangwei0.062 ± 0.0060.088 ± 0.009nd
    V. amurensisTonghua-30.151 ± 0.0660.324 ±0.1040.032 ± 0.004
    Shuangyou0.053 ± 0.0080.126 ± 0.0440.041 ± 0.017
    V. amurensis × V. ViniferaBeibinghong0.057 ± 0.0140.495 ± 0.0680.087 ± 0.021
    V. viniferaRed Global0.059 ± 0.0180.159 ± 0.0130.027 ± 0.004
    Thompson seedless0.112 ± 0.0160.059 ± 0.020nd
    V. vinifera × V. labruscaJumeigui0.072 ± 0.0100.063 ± 0.0170.359 ± 0.023
    SeedV. davidiiTangwei0.096 ± 0.0140.169 ± 0.0280.049 ± 0.006
    V. amurensisTonghua-30.044 ± 0.0040.221 ± 0.0240.113 ± 0.027
    Shuangyound0.063 ± 0.0210.116 ± 0.017
    V. amurensis × V. ViniferaBeibinghongnd0.077 ± 0.0030.400 ± 0.098
    V. viniferaRed Global0.035 ± 0.0230.142 ± 0.0360.199 ± 0.009
    Thompson seedless−−−
    V. vinifera × V. labruscaJumeigui0.077 ± 0.0250.017 ± 0.0040.284 ± 0.021
    'nd' indicates not detected in samples, and '−' shows no samples are collected due to abortion.
     | Show Table
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    Analysis of RNA-Seq data

    To gain insight into gene expression patterns influencing resveratrol biosynthesis in 'Tangwei' and 'Tonghua-3', we profiled the transcriptomes of developing berries at G, V, and R stages, using sequencing libraries representing three biological replicates from each cultivar and stage. A total of 142.49 Gb clean data were obtained with an average of 7.92 Gb per replicate, with average base Q30 > 92.5%. Depending on the sample, between 80.47%−88.86% of reads aligned to the V. vinifera reference genome (Supplemental Table S1), and of these, 78.18%−86.66% mapped to unique positions. After transcript assembly, a total of 23,649 and 23,557 unigenes were identified as expressed in 'Tangwei' and 'Tonghua-3', respectively. Additionally, 1,751 novel transcripts were identified (Supplemental Table S2), and among these, 1,443 could be assigned a potential function by homology. Interestingly, the total number of expressed genes gradually decreased from the G to R stage in 'Tangwei', but increased in 'Tonghua-3'. About 80% of the annotated genes showed fragments per kilobase of transcript per million fragments mapped (FPKM) values > 0.5 in all samples, and of these genes, about 40% showed FPKM values between 10 and 100 (Fig. 1a). Correlation coefficients and principal component analysis of the samples based on FPKM indicated that the biological replicates for each cultivar and stage showed similar properties, indicating that the transcriptome data was reliable for further analyses (Fig. 1b & c).
    Figure 1.  Properties of transcriptome data of 'Tangwei' (TW) and 'Tonghua-3' (TH) berry at green hard (G), véraison (V), and ripe (R) stages. (a) Total numbers of expressed genes with fragments per kilobase of transcript per million fragments mapped (FPKM) values; (b) Heatmap of the sample correlation analysis; (c) Principal component analysis (PCA) showing clustering pattern among TW and TH at G, V and R samples based on global gene expression profiles.
    DownLoad: Full-Size Img PowerPoint

    GO and KEGG pathway enrichment analysis of DEGs

    By comparing the transcriptomes of 'Tangwei' and 'Tonghua-3' at the G, V and R stages, we identified 6,770, 3,353 and 6,699 differentially expressed genes (DEGs), respectively (Fig. 2a). Of these genes, 1,134 were differentially expressed between the two cultivars at all three stages (Fig. 2b). We also compared transcriptional profiles between two adjacent developmental stages (G vs V; V vs R) for each cultivar. Between G and V, we identified 1,761 DEGs that were up-regulated and 2,691 DEGs that were down-regulated in 'Tangwei', and 1,836 and 1,154 DEGs that were up-regulated or down-regulated, respectively, in 'Tonghua-3'. Between V and R, a total of 1,761 DEGs were up-regulated and 1,122 DEGs were down-regulated in 'Tangwei', whereas 2,774 DEGs and 1,287 were up-regulated or down-regulated, respectively, in 'Tonghua-3' (Fig. 2c). Among the 16,822 DEGs between the two cultivars at G, V, and R (Fig. 2a), a total of 4,570, 2,284 and 4,597 had gene ontology (GO) annotations and could be further classified to over 60 functional subcategories. The most significantly represented GO terms between the two cultivars at all three stages were response to metabolic process, catalytic activity, binding, cellular process, single-organism process, cell, cell part and biological regulation (Fig. 2d).
    Figure 2.  Analysis of differentially expressed genes (DEGs) at the green hard (G), véraison (V), and ripe (R) stages in 'Tangwei' (TW) and 'Tonghua-3' (TH). (a) Number of DEGs and (b) numbers of overlapping DEGs between 'Tangwei' and 'Tonghua-3' at G, V and R; (c) Overlap among DEGs between G and V, and V and R, for 'Tangwei' and 'Tonghua-3'; (d) Gene ontology (GO) functional categorization of DEGs.
    DownLoad: Full-Size Img PowerPoint
    We also identified 57 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were enriched, of which 32, 28, and 31 were enriched at the G, V, and R stages, respectively. Seven of the KEGG pathways were enriched at all three developmental stages: photosynthesis-antenna proteins (ko00196); glycine, serine and threonine metabolism (ko00260); glycolysis/gluconeogenesis (ko00010); carbon metabolism (ko01200); fatty acid degradation (ko00071); cysteine and methionine metabolism (ko00270); and valine, leucine and isoleucine degradation (ko00280) (Supplemental Tables S3−S5). Furthermore, we found that the predominant KEGG pathways were distinct for each developmental stage. For example, phenylpropanoid biosynthesis (ko00940) was enriched only at the R stage. Overall, the GO and KEGG pathway enrichment analysis showed that the DEGs in 'Tangwei' and 'Tonghua-3' were enriched for multiple biological processes during the three stages of fruit development.

    DEGs related to phenylalanine metabolism

    We then analyzed the expression of genes with potential functions in resveratrol and flavonoid biosynthesis between the two cultivars and three developmental stages (Fig. 3 and Supplemental Table S6). We identified 30 STSs, 13 PALs, two C4Hs and nine 4CLs that were differentially expressed during at least one of the stages of fruit development between 'Tangwei' and 'Tonghua-3'. Interestingly, all of the STS genes showed increasing expression with development in both 'Tangwei' and 'Tonghua-3'. In addition, the expression levels of STS, C4H and 4CL genes at V and R were significantly higher in 'Tonghua-3' than in 'Tangwei'. Moreover, 25 RESVERATROL GLUCOSYLTRANSFERASE (RSGT), 27 LACCASE (LAC) and 21 O-METHYLTRANSFERASE (OMT) DEGs were identified, and most of these showed relatively high expression at the G and V stages in 'Tangwei' or R in 'Tonghua-3'. It is worth noting that the expression of the DEGs related to flavonoid biosynthesis, including CHS, FLAVONOL SYNTHASE (FLS), FLAVONOID 3′-HYDROXYLASE (F3'H), DIHYDROFLAVONOL 4-REDUCTASE (DFR), ANTHOCYANIDIN REDUCTASE (ANR) and LEUCOANTHOCYANIDIN REDUCTASE (LAR) were generally higher in 'Tangwei' than in 'Tonghua-3'at G stage.
    Figure 3.  Expression of differentially expressed genes (DEGs) associated with phenylalanine metabolism. TW, 'Tangwei'; TH, 'Tonghua-3'. PAL, PHENYLALANINE AMMONIA LYASE; C4H, CINNAMATE 4-HYDROXYLASE; 4CL, 4-COUMARATE-COA LIGASE; STS, STILBENE SYNTHASE; RSGT, RESVERATROL GLUCOSYLTRANSFERASE; OMT, O-METHYLTRANSFERASE; LAC, LACCASE; CHS, CHALCONE SYNTHASE; CHI, CHALCONE ISOMERASE; F3H, FLAVANONE 3-HYDROXYLASE; FLS, FLAVONOL SYNTHASE; F3'H, FLAVONOID 3′-HYDROXYLASE; DFR, DIHYDROFLAVONOL 4-REDUCTASE; LAR, LEUCOANTHOCYANIDIN REDUCTASE; ANR, ANTHOCYANIDIN REDUCTASE; LDOX, LEUCOANTHOCYANIDIN DIOXYGENASE; UFGT, UDP-GLUCOSE: FLAVONOID 3-O-GLUCOSYLTRANSFERASE.
    DownLoad: Full-Size Img PowerPoint

    Differentially expressed TF genes

    Among all DEGs identified in this study, 757 encoded potential TFs, and these represented 57 TF families. The most highly represented of these were the AP2/ERF, bHLH, NAC, WRKY, bZIP, HB-HD-ZIP and MYB families with a total of 76 DEGs (Fig. 4a). We found that the number of downregulated TF genes was greater than upregulated TF genes at G and V, and 48 were differentially expressed between the two cultivars at all three stages (Fig. 4b). Several members of the ERF, MYB, WRKY and bHLH families showed a strong increase in expression at the R stage (Fig. 4c). In addition, most of the TF genes showed > 2-fold higher expression in 'Tonghua-3' than in 'Tangwei' at the R stage. In particular, a few members, such as ERF11 (VIT_07s0141g00690), MYB105 (VIT_01s0026g02600), and WRKY70 (VIT_13s0067g03140), showed > 100-fold higher expression in 'Tonghua-3' (Fig. 4d).
    Figure 4.  Differentially expressed transcription factor (TF) genes. (a) The number of differentially expressed genes (DEGs) in different TF families; (b) Number of differentially expressed TF genes, numbers of overlapping differentially expressed TF genes, and (c) categorization of expression fold change (FC) for members of eight TF families between 'Tangwei' and 'Tonghua-3' at green hard (G), véraison (V), and ripe (R) stages; (d) Heatmap expression profiles of the three most strongly differentially expressed TF genes from each of eight TF families.
    DownLoad: Full-Size Img PowerPoint

    Weighted gene co-expression network construction and identification of TF genes co-expressed with STSs

    We constructed a gene co-expression network using the weighted gene co-expression network analysis (WGCNA) package, which uses a systems biology approach focused on understanding networks rather than individual genes. In the network, 17 distinct modules (hereafter referred to by color as portrayed in Fig. 5a), with module sizes ranging from 91 (antiquewhite4) to 1,917 (magenta) were identified (Supplemental Table S7). Of these, three modules (ivory, orange and blue) were significantly correlated with resveratrol content, cultivar ('Tonghua-3'), and developmental stage (R). The blue module showed the strongest correlation with resveratrol content (cor = 0.6, p-value = 0.008) (Fig. 5b). KEGG enrichment analysis was carried out to further analyze the genes in these three modules. Genes in the ivory module were significantly enriched for phenylalanine metabolism (ko00360), stilbenoid, diarylheptanoid and gingerol biosynthesis (ko00945), and flavonoid biosynthesis (ko00941), whereas the most highly enriched terms of the blue and orange modules were plant-pathogen interaction (ko04626), plant hormone signal transduction (ko04075) and circadian rhythm-plant (ko04712) (Supplemental Fig. S1). Additionally, a total of 36 genes encoding TFs including in 15 ERFs, 10 WRKYs, six bHLHs, two MYBs, one MADs-box, one HSF and one TRY were identified as co-expressed with one or more STSs in these three modules (Fig. 5c and Supplemental Table S8), suggesting that these TFs may participate in the STS regulatory network.
    Figure 5.  Results of weighted gene co-expression network analysis (WGCNA). (a) Hierarchical clustering tree indicating co-expression modules; (b) Module-trait relationship. Each row represents a module eigengene, and each column represents a trait. The corresponding correlation and p-value are indicated within each module. Res, resveratrol; TW, 'Tangwei'; TH, 'Tonghua-3'; (c) Transcription factors and stilbene synthase gene co-expression networks in the orange, blue and ivory modules.
    DownLoad: Full-Size Img PowerPoint

    Validation of RNA-seq data by RT-qPCR

    To assess the reliability of the RNA-seq data, 12 genes determined to be differentially expressed by RNA-seq were randomly selected for analysis of expression via real-time quantitative PCR (RT-qPCR). This set comprised two PALs, two 4CLs, two STSs, two WRKYs, two LACs, one OMT, and MYB14. In general, these RT-qPCR results strongly confirmed the RNA-seq-derived expression patterns during fruit development in the two cultivars. The correlation coefficients between RT-qPCR and RNA-seq were > 0.6, except for LAC (VIT_02s0154g00080) (Fig. 6).
    Figure 6.  Comparison of the expression patterns of 12 randomly selected differentially expressed genes by RT-qPCR (real-time quantitative PCR) and RNA-seq. R-values are correlation coefficients between RT-qPCR and RNA-seq. FPKM, fragments per kilobase of transcript per million fragments mapped; TW, 'Tangwei'; TH, 'Tonghua-3'; G, green hard; V, véraison; R, ripe.
    DownLoad: Full-Size Img PowerPoint

    DISCUSSION

    Grapevines are among the most important horticultural crops worldwide[26], and recently have been the focus of studies on the biosynthesis of resveratrol. Resveratrol content has previously been found to vary depending on cultivar as well as environmental stresses[27]. In a study of 120 grape germplasm cultivars during two consecutive years, the extractable amounts of resveratrol in berry skin were significantly higher in seeded cultivars than in seedless ones, and were higher in both berry skin and seeds in wine grapes relative to table grapes[28]. Moreover, it was reported that total resveratrol content constantly increased from véraison to complete maturity, and ultraviolet-C (UV-C) irradiation significantly stimulated the accumulation of resveratrol of berry during six different development stages in 'Beihong' (V. vinifera × V. amurensis)[9]. Intriguingly, a recent study reported that bud sport could lead to earlier accumulation of trans-resveratrol in the grape berries of 'Summer Black' and its bud sport 'Nantaihutezao' from the véraison to ripe stages[29]. In the present study, resveratrol concentrations in seven accessions were determined by high performance liquid chromatography (HPLC) in the seed, pulp and skin at three developmental stages (G, V and R). Resveratrol content was higher in berry skins than in pulp or seeds, and were higher in the wild Chinese accessions compared with the domesticated cultivars. The highest resveratrol content (2.99 µg g−1 FW) was found in berry skins of 'Tonghua-3' at the R stage (Table 1). This is consistent with a recent study of 50 wild Chinese accessions and 45 cultivars, which reported that resveratrol was significantly higher in berry skins than in leaves[30]. However, we did not detect trans-resveratrol in the skins of 'Tangwei' during the G or V stages (Table 1). To explore the reason for the difference in resveratrol content between 'Tangwei' and 'Tonghua-3', as well as the regulation mechanism of resveratrol synthesis and accumulation during berry development, we used transcriptional profiling to compare gene expression between these two accessions at the G, V, and R stages. After sequence read alignment and transcript assembly, 23,649 and 23,557 unigenes were documented in 'Tangwei' and 'Tonghua-3', respectively. As anticipated, due to the small number of structures sampled, this was less than that (26,346) annotated in the V. vinifera reference genome[18]. Depending on the sample, 80.47%−88.86% of sequence reads aligned to a single genomic location (Supplemental Table S1); this is similar to the alignment rate of 85% observed in a previous study of berry development in Vitis vinifera[19]. Additionally, 1751 novel transcripts were excavated (Supplemental Table S2) after being compared with the V. vinifera reference genome annotation information[18, 31]. A similar result was also reported in a previous study when transcriptome analysis was performed to explore the underlying mechanism of cold stress between Chinese wild Vitis amurensis and Vitis vinifera[32]. We speculate that these novel transcripts are potentially attributable to unfinished V. vinifera reference genome sequence (For example: quality and depth of sequencing) or species-specific difference between Vitis vinifera and other Vitis. In our study, the distribution of genes based on expression level revealed an inverse trend from G, V to R between 'Tangwei' and 'Tonghua-3' (Fig. 1). Furthermore, analysis of DEGs suggested that various cellular processes including metabolic process and catalytic activity were altered between the two cultivars at all three stages (Fig. 2 and Supplemental Table S3−S5). These results are consistent with a previous report that a large number of DEGs and 100 functional subcategories were identified in 'Tonghua-3' grape berries after exposure to UV-C radiation[8]. Resveratrol biosynthesis in grapevine is dependent on the function of STSs, which compete with the flavonoid branch in the phenylalanine metabolic pathway. Among the DEGs detected in this investigation, genes directly involved in the resveratrol synthesis pathway, STSs, C4Hs and 4CLs, were expressed to significantly higher levels in 'Tonghua-3' than in 'Tangwei' during V and R. On the other hand, DEGs representing the flavonoid biosynthesis pathway were upregulated in 'Tangwei', but downregulated in 'Tonghua-3' (Fig. 3 and Supplemental Table S6). These expression differences may contribute to the difference in resveratrol content between the two cultivars at these stages. We note that 'Tangwei' and 'Tonghua-3' are from two highly diverged species with different genetic backgrounds. There might be some unknown genetic differences between the two genomes, resulting in more than 60 functional subcategories being enriched (Fig. 2d) and the expression levels of genes with putative roles in resveratrol biosynthesis being significantly higher in 'Tonghua-3' than in 'Tangwei' during V and R (Fig. 3). A previous proteomic study also reported that the expression profiles of several enzymes in the phenylalanine metabolism pathway showed significant differences between V. quinquangularis accession 'Danfeng-2' and V. vinifera cv. 'Cabernet Sauvignon' at the véraison and ripening stages[33]. In addition, genes such as RSGT, OMT and LAC involved in the production of derivatized products of resveratrol were mostly present at the G and V stages of 'Tangwei', potentially resulting in limited resveratrol accumulation. However, we found that most of these also revealed relatively high expression at R in 'Tonghua-3' (Fig. 3). Despite this situation, which does not seem to be conducive for the accumulation of resveratrol, it still showed the highest content (Table 1). It has been reported that overexpression of two grapevine peroxidase VlPRX21 and VlPRX35 genes from Vitis labruscana in Arabidopsis may be involved in regulating stilbene synthesis[34], and a VqBGH40a belonging to β-glycoside hydrolase family 1 in Chinese wild Vitis quinquangularis can hydrolyze trans-piceid to enhance trans-resveratrol content[35]. However, most studies mainly focus on several TFs that participate in regulation of STS gene expression, including ERFs, MYBs and WRKYs[13, 15, 17]. For example, VvWRKY18 activated the transcription of VvSTS1 and VvSTS2 by directly binding the W-box elements within the specific promoters and resulting in the enhancement of stilbene phytoalexin biosynthesis[36]. VqWRKY53 promotes expression of VqSTS32 and VqSTS41 through participation in a transcriptional regulatory complex with the R2R3-MYB TFs VqMYB14 and VqMYB15[37]. VqMYB154 can activate VqSTS9/32/42 expression by directly binding to the L5-box and AC-box motifs in their promoters to improve the accumulation of stilbenes[38]. In this study, we found a total of 757 TF-encoding genes among the DEGs, including representatives of the MYB, AP2/ERF, bHLH, NAC, WRKY, bZIP and HB-HD-ZIP families. The most populous family was MYB, representing 76 DEGs at G, V and R between 'Tangwei' and 'Tonghua-3' (Fig. 4). A recent report indicated that MYB14, MYB15 and MYB13, a third uncharacterized member of Subgroup 2 (S2), could bind to 30 out of 47 STS family genes. Moreover, all three MYBs could also bind to several PAL, C4H and 4CL genes, in addition to shikimate pathway genes, the WRKY03 stilbenoid co-regulator and resveratrol-modifying gene[39]. VqbZIP1 from Vitis quinquangularis has been shown to promote the expression of VqSTS6, VqSTS16 and VqSTS20 by interacting with VqSnRK2.4 and VqSnRK2.6[40]. In the present study, we found that a gene encoding a bZIP-type TF (VIT_12s0034g00110) was down-regulated in 'Tangwei', but up-regulated in 'Tonghua-3', at G, V and R (Fig. 4). We also identified 36 TFs that were co-expressed with 17 STSs using WGCNA analysis, suggesting that these TFs may regulate STS gene expression (Fig. 5 and Supplemental Table S8). Among these, a STS (VIT_16s0100g00880) was together co-expressed with MYB14 (VIT_07s0005g03340) and WRKY24 (VIT_06s0004g07500) that had been identified as regulators of STS gene expression[13, 15]. A previous report also indicated that a bHLH TF (VIT_11s0016g02070) had a high level of co-expression with STSs and MYB14/15[15]. In the current study, six bHLH TFs were identified as being co-expressed with one or more STSs and MYB14 (Fig. 5 and Supplemental Table S8). However, further work needs to be done to determine the potential role of these TFs that could directly target STS genes or indirectly regulate stilbene biosynthesis by formation protein complexes with MYB or others. Taken together, these results identify a small group of TFs that may play important roles in resveratrol biosynthesis in grapevine.

    CONCLUSIONS

    In summary, we documented the trans-resveratrol content of seven grapevine accessions by HPLC and performed transcriptional analysis of the grape berry in two accessions with distinct patterns of resveratrol accumulation during berry development. We found that the expression levels of genes with putative roles in resveratrol biosynthesis were significantly higher in 'Tonghua-3' than in 'Tangwei' during V and R, consistent with the difference in resveratrol accumulation between these accessions. Moreover, several genes encoding TFs including MYBs, WRKYs, ERFs, bHLHs and bZIPs were implicated as regulators of resveratrol biosynthesis. The results from this study provide insights into the mechanism of different resveratrol accumulation in various grapevine accessions.

    MATERIALS AND METHODS

    Plant material

    V. davidii 'Tangwei', V. amurensis × V. Vinifera 'Beibinghong'; V. amurensis 'Tonghua-3' and 'Shuangyou'; V. vinifera × V. labrusca 'Jumeigui'; V. vinifera 'Red Globe' and 'Thompson Seedless' were maintained in the grapevine germplasm resource at Northwest A&F University, Yangling, Shaanxi, China (34°20' N, 108°24' E). Fruit was collected at the G, V, and R stages, as judged by skin and seed color and soluble solid content. Each biological replicate comprised three fruit clusters randomly chosen from three plants at each stage. About 40−50 representative berries were separated into skin, pulp, and seed, and immediately frozen in liquid nitrogen and stored at −80 °C.

    Extraction and analysis of resveratrol content

    Resveratrol extraction was carried out as previously reported[8]. Quantitative analysis of resveratrol content was done using a Waters 600E-2487 HPLC system (Waters Corporation, Milford, MA, USA) equipped with an Agilent ZORBAX SB-C18 column (5 µm, 4.6 × 250 mm). Resveratrol was identified by co-elution with a resveratrol standard, and quantified using a standard curve. Each sample was performed with three biological replicates.

    RNA extraction, transcriptome library construction and sequencing

    Three biological replicates of each stage (G, V and R) from whole berries of 'Tangwei' and 'Tonghua-3' were used for all RNA-Seq experiments. Total RNA was extracted from 18 samples using the E.Z.N.A. Plant RNA Kit (Omega Bio-tek, Norcross, GA, USA). For each sample, sequencing libraries were constructed from 1 μg RNA using the NEBNext UltraTM RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA). The library preparations were sequenced on an Illumina HiSeq2500 platform (Illumina, San Diego, CA, USA) at Biomarker Technologies Co., Ltd. (Beijing, China).

    RNA-seq data analysis

    Raw sequence reads were filtered to remove low-quality reads, and then mapped to the V. vinifera 12X reference genome[18, 31] using TopHat v.2.1.0[41]. The mapped reads were assembled into transcript models using Stringtie v2.0.4[42]. Transcript abundance and gene expression levels were estimated as FPKM[43]. The formula is as follows:
    FPKM =cDNA FragmentsMapped Fragments(Millions)× Transcript Length (kb)
    Biological replicates were evaluated using Pearson's Correlation Coefficient[44] and principal component analysis. DEGs were identified using the DEGSeq R package v1.12.0[45]. A false discovery rate (FDR) threshold was used to adjust the raw P values for multiple testing[46]. Genes with a fold change of ≥ 2 and FDR < 0.05 were assigned as DEGs. GO and KEGG enrichment analyses of DEGs were performed using GOseq R packages v1.24.0[47] and KOBAS v2.0.12[48], respectively. Co-expression networks were constructed based on FPKM values ≥ 1 and coefficient of variation ≥ 0.5 using the WGCNA R package v1.47[49]. The adjacency matrix was generated with a soft thresholding power of 16. Then, a topological overlap matrix (TOM) was constructed using the adjacency matrix, and the dissimilarity TOM was used to construct the hierarchy dendrogram. Modules containing at least 30 genes were detected and merged using the Dynamic Tree Cut algorithm with a cutoff value of 0.25[50]. The co-expression networks were visualized using Cytoscape v3.7.2[51].

    Validation of RNA-Seq data by RT-qPCR

    RT-qPCR was carried out using the SYBR Green Kit (Takara Biotechnology, Beijing, China) and the Step OnePlus Real-Time PCR System (Applied Biosystems, Foster, CA, USA). Gene-specific primers were designed using Primer Premier 5.0 software (PREMIER Biosoft International, Palo Alto, CA, USA). Cycling parameters were 95 °C for 30 s, 42 cycles of 95 °C for 5 s, and 60 °C for 30 s. The grapevine ACTIN1 (GenBank Accession no. AY680701) gene was used as an internal control. Each reaction was performed in triplicate for each of the three biological replicates. Relative expression levels of the selected genes were calculated using the 2−ΔΔCᴛ method[52]. Primer sequences are listed in Supplemental Table S9.

    ACKNOWLEDGMENTS

    This research was supported by the National Key Research and Development Program of China (2019YFD1001401) and the National Natural Science Foundation of China (31872071 and U1903107).
  • The authors declare that they have no conflict of interest.
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  • Cite this article

    de Souza CT, Danchana K, Ferreira SLC, Cerdà V. 2024. Fluorimetric determination of quinine using a portable system and digital image processing. Food Materials Research 4: e001 doi: 10.48130/fmr-0023-0036
    de Souza CT, Danchana K, Ferreira SLC, Cerdà V. 2024. Fluorimetric determination of quinine using a portable system and digital image processing. Food Materials Research 4: e001 doi: 10.48130/fmr-0023-0036
    shu

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Fluorimetric determination of quinine using a portable system and digital image processing

  • 1.

    Instituto de Química, Universidade Federal da Bahia, Salvador, 40170-290, Brazil

  • 2.

    Sciware Systems, 07193, Bunyola, Spain

  • 3.

    Dep. Química, Univ. Illles Balears, 07122, Palma de Mallorca, Spain

  • Received: 12 July 2023
  • Revised: 21 August 2023
  • Accepted: 28 August 2023
  • Published online: 02 January 2024
Food Materials Research  4,  Article number: e001  (2024)  |  Cite this article

Abstract: The development of a portable device created by 3D printing for fluorimetric measurements is an efficient tool for analytical applications in situ or in the laboratory presenting a wide field of applications in the environmental and food field. This device uses a light-emitting diode (LED) as a radiation source and a digital microscope as a detector. Digital images obtained by the interaction between the radiation source and the sample were analyzed with the help of the YouCam software, the images were filed in JPEG format and processed with the Chemostat software.The entire system is connected to a notebook, which serves as an LED and detector power supply without the need for any additional power source. The proposed device was used for the determination in situ of quinine in water and beverage samples, respectively. For the validation of the developed system, the results obtained were compared with a conventional spectrofluorometer with a t-test at a 95% confidence level. The proposed system provides satisfactory precision and accuracy values. The proposed method presented detection and quantification limits of 0.99 and 1.46 mg·L−1, at a 95% confidence interval.

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    Introduction

    • Current trends in analytical monitoring include the miniaturization of equipment, the potential of in situ analysis, decrease in the amount of sample required and the reduction of organic components[1,2]. The development of new and miniaturized light sources in comparison with traditional light sources such as tungsten bulbs, has allowed the advance of inexpensive, miniaturized, and robust methods. An example of this is the LED, which has been coupled into portable microfluidic chips for fluorescent detection[3−5].

      Digital image analysis (DIA) has been widely used in analytical chemistry due to its ease of operation, portability, speed and good analytical performance[5−7]. Briefly, an image or video of colorimetric or luminescent reactions is converted into measurable data that is correlated with analyte concentration.

      One of the trends in analytical chemistry research is the development of user-friendly and universally accessible tools. Since the early 2000s, there has been a significant increase in the number of scientific publications employing scanners, digital cameras, webcams, and smartphones in chemical analysis methodologies. Despite their existence, there are few studies using digital microscopes to capture images[8]. Digital microscopes are image capture devices that can be connected directly to a computer or laptop. These devices allow the use of zoom for manual or automatic magnification of objects in an optical range of 50 to 1000x, which can be adjusted to different systems[9].

      Quinine is one of the main compounds in tonic, a popular carbonated drink in which quinine is used as a flavoring, due to the fact that it gives it its characteristic bitter taste. However, since at high doses it can cause side effects, the American FDA has limited its concentration to a maximum of 83 ppm (approximately four thousandths of that used medically for different medical treatments).

      Precisely because of its quinine content, tonic water provides some properties, such as: it is a digestive drink, inducing secretion and reflecting the salivary and gastric glands, it also exerts vascularization of the gastric mucosa.

      On the other hand, it also became the main compound used in the medical treatment of malaria, until other much more effective synthetic drugs, such as primaquine, chloroquine or quinacrine, replaced it. Quinine is however still used in the treatment of resistant malaria.

      In turn, it also provides other therapeutic benefits. For example, it is a recognized antipyretic, analgesic and antimalarial.

      A digital image-based fluorimetric method, using a 3D printed platform and a UV-LED camera made of biodegradable polylactic acid (PLA), for quantification of quinine in beverages was proposed by da Silva et al.[10], presenting detection limits and quantification of 6.9 × 10−8 and 1.39 × 10−5 mol·L−1. A smartphone was used as a detector and the 3D camera was irradiated by four LEDs for excitation of the molecule, requiring a more robust system for cooling and system power.

      This work presents the development of a fluorimetric system, based on the analysis of digital images, for the determination of quinine in commercial samples of tonic water; using a digital microscope as an image capture device and a monochromatic LED as a radiation source.

    Experimental procedure

      Reagents and solutions

    • Analytical grade reagents and doubly deionized water (DDI water, resistivity 18.2 MΩ) from a Milli-Q system (Millipore, Bedford, MA, USA) were used to prepare the solutions. An intermediate solution of 100 mg·L−1 of quinine was prepared by dissolving extra pure quinine sulfate dihydrate, C2OH24N2O2·2H2O (Scharlau, Spain) in sulfuric acid, H2SO4 (Scharlau, Spain) 0.05 mol·L−1 and analytical standards at concentrations of 2, 4, 6, 8 and 10 mg·L−1 were used in the construction of the analytical curve.

      Fluorimetric system

    • The proposed fluorimetric system consists of a device, printed with a 3D FormLabs2 stereolithographic printer, to accommodate the capture device, a cuvette and a radiation source, which is powered by the USB ports of a computer, using an adjustable voltage source.

      The electrical circuit shown in Fig. 1 was used to create the voltage source. When connected to the computer, allows control of the intensity of the radiation source. The switch may be used to turn off the light of the LED in order to obtain the dark current of the detector.

      Figure 1. 

      Fluorimetric system adjustable voltage source. (a) Schematic figure created with the Eagle program. (b) Board figure created with Eagle. (c) Photo of the electronic device.

      The design of the 3D device is shown in Fig. 2, and is used to support the digital microscope, the photometric cell and the light source was elaborated with the aid of the Rhinoceros 5 program. The LED was positioned perpendicular to the detector, i.e., as a source of radiation, aiming to excite the chemical compound under study, a 5 mm LED was used, with a wavelength of 346 nm.

      Figure 2. 

      Design of the 3D device using the fluorimetric system, elaborated with the aid of the Rhinoceros 5 program.

      A photograph of the 3D device is shown in Fig. 3, showing that a digital microscope, Knup KP-8012, was used to capture the images.

      Figure 3. 

      Photograph of the 3D fluorimetric system.

      Acquisition and processing of images

    • In the acquisition stage, with the help of YouCam software, the images were filed in JPEG format and processed with Chemostat software (version: V2 030918.d3e5d19)[11]; ROI on center pixel, size 100 × 100, coordinates x-230 and y-180.

      In possession of color data from the RGB space, the analytical signal was calculated from the intensities of the B (blue) channel and the vector norm processing was analyzed.

      Validation and application of the system

    • The proposed system was employed for the determination of quinine in tonic water samples commercially available in the city of Palma de Mallorca, Balearic Islands, Spain. Because it is a carbonated drink, the samples were subjected to agitation to eliminate the carbon dioxide present in the composition. Then, the samples were diluted 10 times, with a 0.05 mol·L−1 sulfuric acid solution and the readings were performed.

    Results and discussions

      Fluorometric system

    • In dilute H2SO4 solutions, quinine sulfate is present as a di-cationic species, which is quite stable and whose molecular structure is shown in Fig. 4. Thus, blank, analytical standards and samples were prepared in solutions of H2SO4 0.05 mol·L−1.

      Figure 4. 

      Structure of quinine sulfate in diluted H2SO4 solutions.

      Observing the spectra, obtained with a Perkin Elmer LS-50B luminescence spectrometer, it was noted that the maximum intensity of absorption and emission occurs at wavelengths of 347 and 452 nm, respectively (Fig. 5). In the proposed fluorimetric system, a conventional 5 mm LED was used as the source of radiation/excitation with a wavelength of approximately 346 nm.

      Figure 5. 

      Emission spectra of quinine.

      LEDs, whose suitability was investigated and validated, have interesting characteristics for application in portable fluorimetric systems, such as, for example, emission of monochromatic light, popularity, low cost and availability of colors.

      Smartphones, webcam and scanners are devices commonly used in digital image analysis[12−15]; although it exists, there is a limited number of studies that use a portable microscope for image capture[8,9]. The application of a USB microscope in the proposed fluorimetric system, presented satisfactory results, producing a better focusing of the image.

      Two factors were analyzed in order to apply the system: 1) The intensity of the radiation source; minimum and maximum; and 2) the use of a cover in the cuvette compartment to control the internal lighting. After analyzing the curves and analytical parameters presented, the conditions of maximum intensity of the radiation source and the use of a lid, printed in 3D, on the upper part of the cuvette holder were selected. Therefore, the procedure was quite simple: the sample and standards were place in the cuvette, which was then covered with the cover holding the LED, and a photo was taken.

      With the conditions defined, the images of white and pattern analytical data were obtained, as shown in Fig. 6.

      Figure 6. 

      Digital images of the blank and analytical standards.

      Mathematical processing of RGB space color data

    • After obtaining the intensities of the RGB channels, considering that the phenomenon of fluorescence is related to the emission of radiation and that quinine, in a diluted acid solution, presents a maximum intensity of emission at a wavelength of 452 nm, corresponding to the blue color in the CEI chromaticity diagram, the B channel intensities showed better correlation with the concentration variation as can be seen in Table 1. da Silva et al.[10], in their quinine quantification study in beverages, also observed that the B channel had the highest fluorescence intensity and was used in the determinations

      Table 1.  Mean RGB intensities for blanks and analytical standards in triplicate.

      Quinine/H2SO4 0.05 mol L−1
      (mg L−1)      		 R G B
      0 (Blank) 0 0 0
      0 (Blank) 0 0 0
      0 (Blank) 0 0 0
      2 0.29 0.26 24.25
      2 0.5 0.26 23.6
      2 0.42 0.24 24.31
      4 0.6 2.65 66.73
      4 0.57 2.92 66.87
      4 0.64 3.04 67.99
      6 0.6 10.01 104.05
      6 0.67 9.56 102.35
      6 0.66 9.67 103.41
      8 0.67 20.74 139.35
      8 0.65 20.84 139.99
      8 0.65 20.73 139.73
      10 0.66 34.5 170.52
      10 0.65 34.54 169.93
      10 0.68 34.19 170.65
      R: red; G: green; B: blue.

      Thus, the intensities of the blue channel were used in the construction of the analytical curve (Fig. 7). Seeking to verify the construction of the analytical signal, considering the contributions of the intensities of the R, G and B channels, the mathematical processing norm vector was evaluated.

      Figure 7. 

      Analytical curve for quinine determination, constructed from the blue channel intensity values.

      It is possible to observe, in Fig. 8, that the vector norm mathematical processing presented a behavior similar to the use of channel B intensities, without mathematical processing. Featuring a greater slope of the analytical curve and sensitivity, in terms of detection and quantification limits, a standard processing vector was used in the application of the fluorimetric system.

      Figure 8. 

      Analytical curve for quinine determination, built from the mathematical processing standard vector of RGB intensities values.

      Method validation

    • The analytical blank was included in the projection of the analytical curve and all curve concentrations were analyzed in triplicate, allowing a more robust regression and evaluation of curve linearity, among other analytical parameters. The adequacy of the fit of the analytical curve, constructed from the color data for the fluorimetric system for quinine determination, was confirmed by the correlation coefficient (R2) equal to 0.998.

      Linearity

    • The R2 of the analytical curve is commonly used to evaluate the linearity of a method, based on the analysis of its proximity to the value 1. However, the R value must be associated with the linear relationship between the concentration and the signal; in the evaluation of linearity the experimental and critical F values must be analyzed.

      Seeking to evaluate the linearity of the proposed method, the experimental F value, corresponding to the ratio between the residual variance and the pure squared error, given by Equation 1, and the tabulated critical F were compared.

      Fexp=(sy/xsy)2 (1)

      Since Sy/x is the residual standard deviation and Sy is the pure error, there are two statistical hypotheses: H0, called the null hypothesis, and the alternative hypothesis (H1). The null hypothesis must be rejected, at the α significance level studied, if Fexp exceeds the critical value at the α level, F (α, I - 2, I – L), where I is the number of calibration samples and L is the number of concentration levels.

      Considered the best indicator of linearity, suitable for statistically verifying whether the residual variance is greater than the pure squared error, derived from the study of samples in triplicate, the test was applied in the linearity analysis of the proposed fluorimetric method. An experimental F value, 4449, was observed, much higher than the tabulated critical F, 4.45, resulting in the rejection of the null hypothesis, thus having linearity in the dynamic range of work analyzed, LQ at 10 mg·L−1.

      Detection and qualification limits

    • The sensitivity of a method is commonly expressed in terms of the limits of detection (LD) and quantification (LQ). However, many works estimate the LD, minimum detectable quantities, applying the old IUPAC definition, based on the concentration of the analyte that gives a signal at least three times greater than the standard deviation of the blank signal, considering the slope of the curve analytics.

      In general terms, the most recent IUPAC recommendation defines the LD as a concentration level for which the risk of false negatives, also called β errors or Type II errors, has an associated β probability, as shown by Equation 2.

      LD=t(α,v)σc,0+t(β,v)σc,LD=3,3Sx/yA√1+h0+1I (2)

      Where, t(α,v)σc,0 + t(β,v)σc,LD are Student coefficients with v degrees of freedom and probability α and β, respectively, σc,0 and σc,LD are the concentration standard errors for blank and LD levels; A is the slope of the calibration curve, I is the number of calibration samples and Sy/x is the residual standard deviation. h0 can be obtained by assuming σc,0 = σc,LD, 95% confidence level (α = β = 0.05) and a large number of degrees of freedom. The same concept is applied in the calculation of LQ.

      The old definition significantly underestimates the values of LD and LQ, and the current recommendation often results in higher values. The proposed method presented detection and quantification limits of 0.99 and 1.46 mg·L−1, at a 95% confidence interval, with data obtained from vector norm mathematical processing.

      Precision

    • Precision provides the dispersion of measured values around a mean value, and its numerical value is estimated by the relative standard deviation (RSD), expressed in Equation 3, where s is the standard deviation and ¯x is the mean value of total number of samples.

      DRP=100S¯X (3)

      Precision was evaluated in terms of repeatability performing nine determinations on samples containing a concentration equivalent to the average concentration of the analytical curve, DPR values of 1.15% and 1.13% were obtained, as shown in Table 2.

      Table 2.  Accuracy of the method verified in terms of DRP%.

      Sample Repetitions (mg·L−1) DRP%
      Sample F 4.82 4.73 4.8 4.91 4.86 4.8 4.88 4.87 4.88 1.15
      Sample G 5.49 5.54 5.39 5.54 5.39 5.46 5.5 5.56 5.52 1.13
      Accuracy

    • Accuracy reflects the proximity between the measured value (xi) and a reference value considered true (Xv) and is related to the absolute error of a measurement. Accuracy was verified by comparing the results obtained from the proposed method, based on digital image analysis, with the results obtained from a PerkinElmer LS-50B luminescence spectrometer, wavelength of 346 nm, as shown in Table 3.

      Table 3.  Accuracy of the proposed method.

      Sample Proposed method
      (mg/100 g)      		 Luminescence PerkinElmer
      LS-50B (452 nm) (mg/100 g)
      A 55.0 ± 0.19 55.1 ± 0.01
      B 64.3 ± 0.02 64.2 ± 0.02
      C 47.8 ± 0.22 47.9 ± 0.02

      It was observed that there is no significant difference between the values measured with the proposed method and the values of the reference method.

      Application

    • The proposed method was applied in the determination of quinine in seven samples of tonic water, as shown in Table 4, and contents from 47.8 to 66.7 mg·L−1 were calculated.

      Table 4.  Quinine contents in commercial samples of tonic water, obtained in the application of the proposed method.

      Sample [Quinine] ± SD (mg·L−1)
      A 54.68 ± 0.29
      B 64.82 ± 0.24
      C 66.72 ± 0.23
      D 49.38 ± 0.38
      E 63.66 ± 0.25
      F 48.20 ± 0.40
      G 47.77 ± 0.43

      Decree No. 6871, of June 4, 2009, which regulates Law No. 8918 (July 1994), which provides for standardization, classification, registration, inspection, production and supervision of beverages, establishes in Art. 25 that 'Quinine tonic water is a soft drink that contains, obligatorily, three to seven milligrams of quinine or its salts, expressed in anhydrous quinine, per one hundred milliliters of beverage'. Thus, the calculated concentrations of the studied samples are within the standard established by current legislation.

    Conclusions

    • The fluorimetric system showed satisfactory performance for the determination of quinine in commercial samples of tonic water. A system presents possibilities for improvement, observing the calculated merit figures, from detailed studies of factors that influence the system; such as the focus magnification range of the digital microscope and the electric power supply current for the LED.

      The proposed method presents higher detection and quantification limits than those presented by da Silva et al.[10], 6.9 × 10−8 and 1.39 × 10−5 mol·L−1, but stands out for its simplicity and greater range linear working dynamics. Considering the high levels of quinine in the samples of interest, the proposed method is an alternative to the reference method.

      Acknowledgments

      • The authors acknowledge financial support from the Spanish Ministry of Economy and Competitiveness (MINECO) through Project CTQ-2016-77155-R co-financed by FEDER funds. Kaewta Danchana acknowledges the financial support from the Conselleria d'Innovacio, Recerca I Turisme of the Govern de les Illes Balears and of the Fons Social Europea (FPI/1867/2016). Brazilian authors thank the fellowship and support from the Brazilian agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (Finance Code 001), and Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB).

      Conflict of interest

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

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press on behalf of Nanjing Agricultural University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (8)  Table (4) References (15)
  • About this article
    Cite this article
    de Souza CT, Danchana K, Ferreira SLC, Cerdà V. 2024. Fluorimetric determination of quinine using a portable system and digital image processing. Food Materials Research 4: e001 doi: 10.48130/fmr-0023-0036
    de Souza CT, Danchana K, Ferreira SLC, Cerdà V. 2024. Fluorimetric determination of quinine using a portable system and digital image processing. Food Materials Research 4: e001 doi: 10.48130/fmr-0023-0036
    shu

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  • Introduction
  • Experimental procedure

  • Reagents and solutions

  • Fluorimetric system

  • Acquisition and processing of images

  • Validation and application of the system

  • Results and discussions

  • Fluorometric system

  • Mathematical processing of RGB space color data

  • Method validation

  • Linearity
  • Detection and qualification limits
  • Precision
  • Accuracy
  • Application
  • Conclusions
  • Acknowledgments
  • Conflict of interest
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