Search
Submit Manuscript
2.3CiteScore
Advanced Search
MAP  >  Beverage Plant Research
2022 Volume 2
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
Supplementary information
Rights and permissions
References
About this article
Next Previous
ARTICLE   Open Access    

Physicochemical difference of coffee beans with different species, production areas and roasting degrees

  • 1.

    Jiahe Foods Industry Co., Ltd, Suzhou 215000, China

  • 2.

    Suzhou Kingmao Coffee Co., Ltd, Suzhou 215000, China

More Information
  • In recent decades, the demand for coffee has seen a continuous increase, and the aroma and flavor of coffee has been widely studied. The current research chose coffee beans of two species (Coffea arabica and C. canephora) from five production areas (Brazil, India, Indonesia, Uganda and Vietnam) with four different roasting degrees (medium light, medium, medium dark and dark), to investigate the difference on physicochemical properties. The results showed that Arabica coffee beans had higher concentrations of fat and organic acids, and total amount of volatile compounds, whereas Robusta beans had higher concentrations of protein. With the increase of roasting degree, the concentrations of protein, fat, organic acids, and the total amount of volatile compounds of coffee beans increased, while the concentrations of chlorogenic acid compounds decreased. The discriminant analysis indicated that the tested coffee beans could be clearly discriminated by species and roasting degrees, but not by production area. The results of this research conclude the physicochemical difference of Arabica and Robusta beans with different roasting degrees. The results can provide a theoretical basis for coffee bean selection for the relevant industries.

  • 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
    DownLoad: CSV

    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.
  • Supplemental Table1 Amount of Volatile Compounds of Each Coffee Bean.
  • [1] Yu F, Dong W, Hu R, Long Y, Zong Y, et al. 2021. Effects of different roasting speeds on the volatile components of Xinglong coffee beans. Chinese Journal of Tropical Agriculture 42:11

    Google Scholar

    [2] Mahmud MMC, Shellie RA, Keast R. 2020. Unravelling the relationship between aroma compounds and consumer acceptance: coffee as an example. Comprehensive Reviews in Food Science and Food Safety 19:2380−420 doi: 10.1111/1541-4337.12595

    CrossRef   Google Scholar

    [3] Davis AP, Govaerts R, Bridson DM, Stoffelen P. 2006. An annotated taxonomic conspectus of the genus Coffea (Rubiaceae). Botanical Journal of the Linnean Society 152:465−512 doi: 10.1111/j.1095-8339.2006.00584.x

    CrossRef   Google Scholar

    [4] Toledo PRAB, Pezza L, Pezza HR, Toci AT. 2016. Relationship between the different aspects related to coffee quality and their volatile compounds. Comprehensive Reviews in Food Science and Food Safety 15:705−19 doi: 10.1111/1541-4337.12205

    CrossRef   Google Scholar

    [5] Zhi M, Xu W, Wu L, Pan H, Huang W, et al. 2021. Detection and evaluation of flavor substances of roasted coffee beans in different regions. Quality and Safety of Agro-Products 3:84−90

    Google Scholar

    [6] de Melo Pereira GV, de Carvalho Neto DP, Magalhães AI Jr, Vásquez ZS, Medeiros ABP, et al. 2019. Exploring the impacts of postharvest processing on the aroma formation of coffee beans – A review. Food Chemistry 272:441−52 doi: 10.1016/j.foodchem.2018.08.061

    CrossRef   Google Scholar

    [7] de Luca S, de Filippis M, Bucci R, Magrì AD, Magrì AL, et al. 2016. Characterization of the effects of different roasting conditions on coffee samples of different geographical origins by HPLC-DAD, NIR and chemometrics. Microchemical Journal 129:348−61 doi: 10.1016/j.microc.2016.07.021

    CrossRef   Google Scholar

    [8] Kučera L, Papoušek R, Kurka O, Barták P, Bednář P. 2016. Study of composition of Espresso coffee prepared from various roast degrees of Coffea arabica L. coffee beans. Food Chemistry 199:727−35 doi: 10.1016/j.foodchem.2015.12.080

    CrossRef   Google Scholar

    [9] Cui D, Liu Y, Chen Y, Feng X, Lu Y, et al. 2020. Application of SPME-GC-TOFMS, E-nose, and sensory evaluation to investigate the flavor characteristics of Chinese Yunnan coffee at three different conditions (beans, ground powder, and brewed coffee). Flavour and Fragrance Journal 35:541−60 doi: 10.1002/ffj.3597

    CrossRef   Google Scholar

    [10] Li N, Zhang F, Li M, Lin J. 2019. Effect of different roasting degree on the aroma components of robusta coffee beans. Beverage Industry 22(01):49−55

    Google Scholar

    [11] Nakilcioğlu-Taş E, Ötleş S. 2019. Physical characterization of arabica ground coffee with different roasting degrees. Annals of the Brazilian Academy of Sciences 91:e20180191 doi: 10.1590/0001-3765201920180191

    CrossRef   Google Scholar

    [12] Hu H, Song H, Wang J, Lin T, Liu X, et al. 2018. Determination of 6 kinds of chlorogenic acid by high performance liquid chromatography. Journal of Food Safety & Quality 9:1634−43

    Google Scholar

    [13] Duan L, Qiu B, Dai L, Zhang S, Mei L, Kang W. 2018. Seven organic acids contents in catimor coffee detected by HPLC. Chinese Journal of Tropical Agriculture 38:100−4

    Google Scholar

    [14] Pazmiño-Arteaga JD, Chagolla A, Gallardo-Cabrera C, Ruiz-Márquez AF, González-Rodríguez AT, et al. 2019. Screening for green coffee with sensorial defects due to aging during storage by MALDI-ToF mass fingerprinting. Food Analytical Methods 12:1571−76 doi: 10.1007/s12161-019-01485-9

    CrossRef   Google Scholar

    [15] Wang Y. 2018. The Analysis of Components of Xinglong Coffee with Different Degree of Roasting and the Effect of Roasting Process on Quality, Dissertation. East University of Heilongjiang, China. pp. 20–22
    [16] Zanin RC, Viegas MC, Smrke S, Yeretzian C, Kurozawa LE, et al. 2021. The role of ultrasound-assisted emulsification of roasted coffee oil on aroma profile in spray-dried microparticles and its dynamic release by PTR-ToF–MS. European Food Research and Technology 247:865−78 doi: 10.1007/s00217-020-03670-1

    CrossRef   Google Scholar

  • Cite this article

    Liu X, Fei Y, Wang W, Lei S, Cheng C, et al. 2022. Physicochemical difference of coffee beans with different species, production areas and roasting degrees. Beverage Plant Research 2: 7 doi: 10.48130/BPR-2022-0007
    Liu X, Fei Y, Wang W, Lei S, Cheng C, et al. 2022. Physicochemical difference of coffee beans with different species, production areas and roasting degrees. Beverage Plant Research 2: 7 doi: 10.48130/BPR-2022-0007
    shu

Figures(4)  /  Tables(6)

Download PDF

Article Metrics

Article views(6370) PDF downloads(1290)

Other Articles By Authors

ARTICLE   Open Access    

Physicochemical difference of coffee beans with different species, production areas and roasting degrees

  • 1.

    Jiahe Foods Industry Co., Ltd, Suzhou 215000, China

  • 2.

    Suzhou Kingmao Coffee Co., Ltd, Suzhou 215000, China

  • Received: 31 December 2021
  • Accepted: 17 March 2022
  • Published online: 12 April 2022
Beverage Plant Research  2,  Article number: 7  (2022)  |  Cite this article

Abstract: In recent decades, the demand for coffee has seen a continuous increase, and the aroma and flavor of coffee has been widely studied. The current research chose coffee beans of two species (Coffea arabica and C. canephora) from five production areas (Brazil, India, Indonesia, Uganda and Vietnam) with four different roasting degrees (medium light, medium, medium dark and dark), to investigate the difference on physicochemical properties. The results showed that Arabica coffee beans had higher concentrations of fat and organic acids, and total amount of volatile compounds, whereas Robusta beans had higher concentrations of protein. With the increase of roasting degree, the concentrations of protein, fat, organic acids, and the total amount of volatile compounds of coffee beans increased, while the concentrations of chlorogenic acid compounds decreased. The discriminant analysis indicated that the tested coffee beans could be clearly discriminated by species and roasting degrees, but not by production area. The results of this research conclude the physicochemical difference of Arabica and Robusta beans with different roasting degrees. The results can provide a theoretical basis for coffee bean selection for the relevant industries.

    HTML

    INTRODUCTION

    • In recent decades, the demand for coffee has been continuously increasing, and the global consumption increased by 1%−2% every year[1]. According to the statistics of the International Coffee Organization, the annual market value of coffee is about 200 billion US dollars, and it is expected that future consumption will continue to drive the demand for coffee[2]. Coffee is a tropical plant, belonging to Rubiaceae. There are 124 species of coffee in the genus Coffea[3], but only two species − Arabica (C. arabica) and Robusta (C. canephora) are commercially traded in the international market. Arabica coffee grows well in a cool climate and at high altitude (1,000−2,100 m) while Robusta grows better in a hot and humid climate at lower altitude (100−1,000 m)[4]. At present, the main coffee production areas are located between 25° north latitude and 30° south latitude, among which Brazil, Indonesia, India, Uganda, and Vietnam are the main production areas worldwide[5].

      Recently, many studies have been carried out on how the quality of coffee beans are affected by species, geographical origin, postharvest processing and production processing[4,6], Luca et al.[7] characterized the effects of different roasting conditions on coffee of different geographical origin by high performance liquid chromatograph (HPLC) with photo-diode array detector, near infrared spectrum instrument (NIRS) and stoichiometry, and concluded that different roasting conditions and geographical origin influenced the properties of roasted coffee beans. And the aroma and flavor of coffee beans has been widely studied, and many conclusions obtained. Kučera et al.[8] analyzed espresso coffee with different roasting degrees (light, medium, medium dark and dark) by ultra-performance liquid chromatography-tandem mass spectrometry. The obtained raw data were analyzed using multivariate statistics to assess the difference between each degree of baking. Cui et al.[9] found that coffee made from the coffee beans of two species exhibited a different, special flavor, as Arabica beans exhibited a 'baked' flavor and Robusta beans exhibited a 'sweet' flavor. The sensory properties, especially the flavor compounds of coffee beans are obviously influenced by production area[5]. In addition, roasting speed and roasting degree also have a significant effect on the aroma components of coffee beans[1,10].

      As well as sensory properties, physicochemical properties of roasted coffee beans are also affected during the production procedure[11]. However, little research has focussed on the physicochemical properties, and at the same time excluded the influence of species, production area and roasting degree. Furthermore, most recent research has focused on the coffee beverage rather than coffee beans. This research chose two species of coffee beans (Arabica and Robusta beans) from five production areas (Brazil, India, Indonesia, Uganda and Vietnam), and roasted them to four different degrees (medium light, medium, medium dark and dark), to investigate the difference on physicochemical properties, such as the concentrations of protein, fat, organic acids, chlorogenic acid compounds (CACs) and the composition of volatile compounds (VCs). The results of this research can provide a theoretical basis for coffee bean selection for the relevant industry.

    MATERIALS AND METHODS

      Materials

    • Arabica and Robusta beans produced in five areas were used, and the details are shown in Table 1. Sulfuric acid, hydrochloric acid and sodium hydroxide were analytical pure grade and from Wuxi Zhanwang Chemical Reagent Co., Ltd (Wuxi, China). Acetonitrile and methanol were chromatographically pure grade and from TEDIA Co., Ltd, USA. The water used in all experiments was ultrapure water.

      Table 1.  Dat for 40 coffee beans used in the study.

      Production area/regionSpeciesRoasting degree
      Medium lightMediumMedium darkDark
      Brazil/Espirito SantoArabica
      Robusta
      India/KarnatakaArabica
      Robusta
      Indonesia/SumatraArabica
      Robusta
      Uganda/ElgonArabica
      Robusta
      Vietnam/Lam DongArabica
      Robusta

      Methods

      Sample preparation

    • The coffee bean were reasted with 7 grade fire to four roasting degrees (medium light, medium, medium dark and dark) using a roaster (Tino Probat Inc., Germany), then ground to 0.9 mm powder after 24 h storage. The pictures of the forty samples are shown in Table 1.

      Protein and fat determination

    • The experiments were carried out in accordance with the method in GB 5009.5—2016 and GB 5009.6—2016, and using Automatic Kieldahl apparatus and a digestion furnace (Hanon Advanced Technology Group Co., Ltd, China).

      Organic acid determination

    • Sample preparation: For exact details please refer to the operation procedure in GB 5009.157—2016. The samples were weighed to 1−2 g in a 50 mL volumetric flask. Then, 30 mL 0.1% phosphoric acid solution was added, followed by ultrasonic treatment for 20 min. Next, 0.1% phosphoric acid solution was added to the volume scale. The flask is then shaken and filtered using a 0.22 μm organic membrane. Samples were then stored at 4 °C in the dark.

      HPLC with an ultraviolet detector (1260, Agilent Technologies Co., Ltd) and a chromatographic column (CAPCELL PAK C18 MG II, 4.6 mm × 250 mm, 5 μm, OSAKA SODA CO., Ltd) was used to determine the acid concentrations, and the determined parameters are shown in Table 1.

      CACs determination

    • Sample preparation: Please refer to the operation procedure in Hu et al.'s research[12]. Samples of 1−2 g were weighed in a 50 mL brown volumetric flask. Then, 30 mL methanol-0.1% phosphoric acid (50:50, V/V) solution, was added followed by ultrasonic treatment for 20 min. Methanol-0.1% phosphoric acid (50:50, V/V) solution was then added to the volume scale. The mixture was shaken and then filtered using a 0.22 μm organic membrane. Samples were stored at 4 °C in the dark.

      HPLC was used to determine the CACs concentration, and the determined parameters are shown in Table 2.

      Table 2.  Chromatographic parameters for organic acid and CACs determination.

      ParametersOrganic acidsCACs
      Speed0.3 mL/min1.0 mL/min
      Column temperature40 °C30 °C
      Wavelength210 nm327 nm
      Moving PhaseMethanol (A)-0.1% phosphoric acid solution (B)
      Elution program0.00~20.00 min:
      10%A-90%B      		0.00~20.00 min:
      20%A-80%B
      20.01~25.00 min: 100%A20.01~45.00 min:
      35%A-65%B
      25.01~35.00 min: 10%A-90%B45.01~55.00 min:
      40%A-60%B
      55.01~60.00 min:
      20%A-80%B
      VCs determination

    • Firstly, 0.3 g of sample was weighed in a 20 mL headspace sampler, the samples were then analysed by Ultra-fast E-nose (Heracles NEO, Alpha MOS, France)[13], and the parameters are shown in Table 3.

      Table 3.  E-nose parameters for VCs determination.

      ParametersValue
      Incubation temperature60 °C
      Incubation duration30 min
      Injection volume5000 μL
      Injection speed125 μL/s
      Injector temperature200 °C
      Injection time45 s
      Trapping temperature40 °C
      Trapping duration50 s
      Split10 mL/min
      Final temperature250 °C
      Initial temperature50 °C (20 s)
      Temperature program1 °C/s−80 °C (20 s)
      3 °C/s−250 °C (40 s)
      Acquisition duration167 s
      Fid temperature260 °C
      FID gain12
      Statistical analysis

    • Experimental samples were measured three times for each experiment. Data was processed by ExcelTM and Minitab19. One-way analysis of variance (ANOVA) and statistical difference (Fisher Test) was performed at p < 0.05 using Minitab19. Principal Components Analysis (PCA) was performed by Origin 2017. Graphs were made by Origin 2017 and Photoshop CS5.

    RESULTS AND DISCUSSION

      Protein and fat

    • Protein and fat are important components of coffee beans, fats especially play an important role in the sensory attribute of coffee[14]. In addition, protein and fat are also important substances in the Maillard reaction during coffee roasting, which can contribute to the formation of pleasant flavor and color of roasted coffee beans. The results of protein and fat determination are shown in Table 4 and Fig. 1. The protein concentration of Arabica beans with different roasting degrees from all production areas was significantly lower than that of Robusta beans, as the average protein concentration of Arabica beans was about 14.3 g/100 g while that of Robusta beans was about 18.5 g/100 g. Thus, Robusta beans from Brazil, India, Indonesia, Uganda and Vietnam may have more protein than Arabica beans. With the increase of roasting degrees, it is obvious that the protein concentration of Arabica and Robusta beans from five production areas increased. It may also be closely related to the decrease in moisture content. Separating the samples into two groups according to species, a one-way ANOVA was performed at p < 0.05 using Minitab19 for each group. Arabica beans from Brazil and India, and Robusta beans from India and Vietnam had higher protein concentration, while both Arabica and Robusta beans from Indonesia and Uganda had lower protein concentration. Thus, production area may also have a significant influence on the protein concentration of coffee beans, which needs further investigation.

      Table 4.  Protein and fat concentration of each coffee bean (g/100 g).

      SampleProteinFat
      Brazil-A-114.85 ± 0.07jkl6.3 ± 0.28cdefghijk
      Brazil-A-215 ± 0.14ijk5.85 ± 0.49fghijklm
      Brazil-A-315.25 ± 0.07ij6.7 ± 0.14bcdefghi
      Brazil-A-415.75 ± 0.07i7.95 ± 0.49b
      India-A-114.6 ± 0.14jklm5.15 ± 0.49jklmno
      India-A-214.7 ± 0.42jklm5.6 ± 0.42hijklmn
      India-A-314.6 ± 0.28jklm5.6 ± 0.28hijklmn
      India-A-415.05 ± 0.07ijk7.15 ± 0.07bcdefg
      Indonesia-A-113.35 ± 0.07op4.5 ± 0.57mno
      Indonesia-A-213.6 ± 0nop6.2 ± 0.14defghijkl
      Indonesia-A-314.15 ± 0.21lmn6.8 ± 0.42bcdefgh
      Indonesia-A-414.5 ± 0.14jklm7.6 ± 0.14bcd
      Uganda-A-112.55 ± 0.07q5.3 ± 0.57ijklmno
      Uganda-A-212.85 ± 0.07pq6.75 ± 0.92bcdefghi
      Uganda-A-313.1 ± 0.14pq7.95 ± 0.64b
      Uganda-A-413.15 ± 0.07pq10.05 ± 0.35a
      Vietnam-A-114.07 ± 0.21mno6.35 ± 0.52cdefghijk
      Vietnam-A-214.45 ± 0.25klm7.36 ± 0.36bcde
      Vietnam-A-314.69 ± 0.13jklm7.74 ± 0.06bc
      Vietnam-A-414.85 ± 0.07jkl9.46 ± 0.35a
      Brazil-R-117.78 ± 0.3efg4.07 ± 0.3o
      Brazil-R-218.36 ± 0.19def5 ± 0.21jklmno
      Brazil-R-318.8 ± 0.15cd4.87 ± 0.33klmno
      Brazil-R-418.93 ± 0.05bcd5.84 ± 0.21fghijklm
      India-R-119.04 ± 0.08bcd4.85 ± 0.02lmno
      India-R-219.52 ± 0.02abc5.3 ± 0.24ijklmno
      India-R-319.66 ± 0.01ab5.92 ± 0.07efghijklm
      India-R-419.86 ± 0.1a6.19 ± 0.25defghijkl
      Indonesia-R-117.02 ± 0.12h4.33 ± 0.16no
      Indonesia-R-217.34 ± 0.37gh5.72 ± 0.33ghijklmn
      Indonesia-R-317.57 ± 0.19gh5.99 ± 0.21efghijklm
      Indonesia-R-418.37 ± 14def5.99 ± 7.1efghijklm
      Uganda-R-117.44 ± 0.09gh6.15 ± 0.17defghijkl
      Uganda-R-217.68 ± 0.03fgh5.49 ± 0.3hijklmno
      Uganda-R-317.82 ± 0.35efg6.45 ± 0.47cdefghij
      Uganda-R-418.48 ± 0.17de6.39 ± 0.01cdefghij
      Vietnam-R-118.47 ± 0.17de5.52 ± 0.28hijklmno
      Vietnam-R-219.39 ± 0.07abc5.57 ± 0.35hijklmn
      Vietnam-R-319.45 ± 0.24abc6.02 ± 0.14efghijkl
      Vietnam-R-419.94 ± 0.16a7.32 ± 0.09bcdef
      *A: Arabica bean; R: Robusta bean
      1: medium light; 2: medium; 3: medium dark; 4: dark
      Letter code: samples with the same letter code are not significantly different (p < 0.05)

      Figure 1.  Protein and fat concentration of coffee beans (g/100 g). (a) & (b), protein concentration; (c) & (d), fat concentration. Blue represents Arabica beans, orange represents Robusta beans. 1, medium light; 2, medium; 3, medium dark; 4, dark.

      The fat concentration of coffee beans was generally positively correlated to the roasting degree, which was also closely associated with the decrease of moisture content. And, fat concentration of Arabica beans was obviously higher than that of Robusta beans. Thus, roasting degree and species may be significant factors that affect the fat concentration of coffee beans. According to Fig. 1d, there was no significant difference among fat concentration of coffee beans from each production area. Thus, production area might not influence the fat concentration of coffee beans.

      Organic acid and chlorogenic acid compounds

    • During the roasting of coffee beans, organic acids are produced while CACs are broken down. Similar to fat and protein, organic acids are also important compounds in the sensory properties of coffee. As they determine the pH value, which is related to the acidity of coffee[13]. The results of organic acids and CACs are shown in Table 5. One-way ANOVA was performed at p < 0.05 using Minitab19 in each species of coffee beans, and the results are shown in Fig. 2.

      Table 5.  Organic acids and CACs concentration of each coffee bean (g/100 g).

      SampleOrganic acidsChlorogenic acids compounds
      Tartaric
      acid      		Malic
      acid      		Citric
      acid      		Succinic
      acid      		Fumaric
      acid      		TotalNeochlorogenic
      acid      		Chlorogenic
      acid      		Cryptochlorogenic
      acid      		Isochlorogenic
      acid A      		Isochlorogenic
      acid B      		Isochlorogenic
      acid C      		Total
      Brazil-A-13.48 ± 0.240.46 ± 0.150.44 ± 0.061.37 ± 0.270 ± 05.75 ± 0.71ghijklmn0.45 ± 0.020.97 ± 0.080.58 ± 0.070.03 ± 0.010.06 ± 0.020.06 ± 0.012.16 ± 0.2de
      Brazil-A-23.44 ± 0.230.48 ± 0.110.64 ± 0.062.13 ± 0.280 ± 06.67 ± 0.21cdefg0.33 ± 00.67 ± 0.020.38 ± 0.020.02 ± 00.03 ± 00.03 ± 01.45 ± 0.01fghi
      Brazil-A-33.97 ± 0.130.53 ± 0.060.69 ± 0.062.49 ± 0.290 ± 07.67 ± 0.04bcd0.22 ± 00.41 ± 0.010.28 ± 0.020 ± 00.02 ± 0.010.02 ± 00.94 ± 0.04jkl
      Brazil-A-44.18 ± 0.230.64 ± 0.051.04 ± 0.071.94 ± 0.110 ± 07.8 ± 0.36bc0.13 ± 00.22 ± 0.010.15 ± 0.010 ± 00 ± 00 ± 00.5 ± 0.02nop
      India-A-12.61 ± 0.250.52 ± 0.040.49 ± 0.041.59 ± 0.050 ± 05.2 ± 0.12hijklmnop0.71 ± 0.11.42 ± 0.040.85 ± 0.070.05 ± 0.010.08 ± 0.010.08 ± 0.013.19 ± 0.24ab
      India-A-23.58 ± 0.110.55 ± 0.060.8 ± 0.040.49 ± 0.090 ± 05.41 ± 0.3ghijklmnop0.38 ± 0.050.74 ± 0.070.48 ± 0.10.02 ± 00.03 ± 0.010.03 ± 01.68 ± 0.23fgh
      India-A-33.62 ± 0.050.73 ± 0.071.01 ± 0.112.77 ± 0.010 ± 08.13 ± 0.25b0.27 ± 0.020.49 ± 0.020.32 ± 0.020.01 ± 00.02 ± 00.02 ± 01.13 ± 0.07ijk
      India-A-43.75 ± 0.060.81 ± 0.12.6 ± 0.072.77 ± 0.250 ± 09.93 ± 0.49a0.15 ± 00.27 ± 0.010.19 ± 0.020 ± 00.01 ± 0.010 ± 0.010.62 ± 0.03lmnop
      Indonesia-A-12.9 ± 0.280.33 ± 0.011.08 ± 0.061.03 ± 0.050 ± 0.015.33 ± 0.39ghijklmnop0.67 ± 0.021.51 ± 0.080.81 ± 0.020.05 ± 0.010.09 ± 0.010.09 ± 0.013.22 ± 0.14a
      Indonesia-A-23.53 ± 0.170.59 ± 0.081.05 ± 0.061.11 ± 0.070.02 ± 0.016.29 ± 0.27defghij0.5 ± 0.011.11 ± 0.020.65 ± 0.070.03 ± 0.010.06 ± 0.010.05 ± 02.41 ± 0.12cd
      Indonesia-A-33.9 ± 0.160.51 ± 0.040.71 ± 0.151.47 ± 0.040 ± 06.57 ± 0.38cdefgh0.31 ± 0.010.58 ± 0.010.36 ± 0.010.01 ± 00.03 ± 00.03 ± 01.32 ± 0.04hij
      Indonesia-A-44.87 ± 0.230.73 ± 0.071.01 ± 0.041.19 ± 0.060 ± 07.8 ± 0.39bc0.15 ± 00.25 ± 0.010.17 ± 00 ± 00 ± 0.010 ± 0.010.58 ± 0.01lmnop
      Uganda-A-12.92 ± 0.190.57 ± 0.111.47 ± 0.241.52 ± 0.240 ± 0.016.48 ± 0.78cdefghi0.6 ± 01.28 ± 00.74 ± 00.04 ± 00.07 ± 00.07 ± 02.79 ± 0.02bc
      Uganda-A-23.07 ± 0.110.55 ± 0.151.81 ± 0.232.2 ± 0.160.01 ± 0.017.62 ± 0.64bcde0.4 ± 0.010.86 ± 0.040.49 ± 0.010.02 ± 00.04 ± 00.04 ± 01.85 ± 0.07ef
      Uganda-A-33.47 ± 0.080.55 ± 0.152.04 ± 0.132.5 ± 0.220.01 ± 0.018.56 ± 0.4ab0.26 ± 0.010.51 ± 00.32 ± 00.02 ± 00.02 ± 00.02 ± 01.14 ± 0.02ijk
      Uganda-A-43.57 ± 0.110.48 ± 0.10.61 ± 0.041.7 ± 0.140.02 ± 06.38 ± 0.38cdefghij0.14 ± 0.010.24 ± 0.020.17 ± 0.010 ± 00 ± 00 ± 00.56 ± 0.04lmnop
      Vietnam-A-11.28 ± 0.110.32 ± 0.051.55 ± 0.132.21 ± 0.180.01 ± 05.36 ± 0.46ghijklmnop0.48 ± 0.041.18 ± 0.080.59 ± 0.020.04 ± 00.08 ± 0.010.08 ± 02.45 ± 0.15cd
      Vietnam-A-21.46 ± 0.050.3 ± 0.041.27 ± 0.083.1 ± 0.010.02 ± 06.14 ± 0.09fghijkl0.29 ± 0.010.65 ± 0.020.38 ± 0.040.02 ± 00.03 ± 00.04 ± 0.011.41 ± 0.08ghi
      Vietnam-A-31.73 ± 0.060.25 ± 0.031 ± 0.013.2 ± 0.070.01 ± 0.016.19 ± 0.18efghijk0.17 ± 0.010.35 ± 0.020.23 ± 0.040.01 ± 00.02 ± 00.02 ± 00.8 ± 0.07klmno
      Vietnam-A-41.97 ± 0.11.11 ± 0.040.79 ± 0.033.41 ± 0.040.03 ± 0.017.3 ± 0.21bcdef0.11 ± 0.020.19 ± 0.020.13 ± 0.020 ± 00 ± 00 ± 00.42 ± 0.06op
      Brazil-R-12.19 ± 0.120.1 ± 0.010.39 ± 0.030.14 ± 0.020 ± 0.012.83 ± 0.18s0.65 ± 0.041.43 ± 0.040.8 ± 0.020.05 ± 00.09 ± 00.09 ± 0.013.11 ± 0.11ab
      Brazil-R-22.66 ± 0.380.39 ± 0.040.27 ± 0.040 ± 00.01 ± 0.013.33 ± 0.37rs0.39 ± 0.030.78 ± 0.050.48 ± 0.020.03 ± 00.04 ± 00.04 ± 01.75 ± 0.1efg
      Brazil-R-33.04 ± 0.10.73 ± 0.070.16 ± 0.040.41 ± 0.020 ± 04.33 ± 0.24nopqr0.16 ± 00.29 ± 0.010.2 ± 0.010 ± 00 ± 0.010 ± 0.010.66 ± 0.01lmnop
      Brazil-R-43.8 ± 0.250.3 ± 0.040.09 ± 0.020.38 ± 00 ± 04.58 ± 0.27mnopqr0.08 ± 0.010.13 ± 0.010.09 ± 0.010 ± 00 ± 00 ± 00.29 ± 0.03p
      India-R-13.06 ± 0.210.28 ± 0.040.28 ± 0.050 ± 00 ± 03.62 ± 0.3qrs0.5 ± 0.010.99 ± 0.030.63 ± 0.040.06 ± 0.020.1 ± 00.09 ± 02.36 ± 0.1d
      India-R-23.44 ± 0.190.85 ± 0.090.23 ± 0.030 ± 00 ± 04.52 ± 0.24mnopqr0.29 ± 00.57 ± 0.040.36 ± 0.020.02 ± 0.010.04 ± 00.04 ± 01.33 ± 0.08hij
      India-R-33.15 ± 0.211.01 ± 0.030.22 ± 0.010.4 ± 0.030.02 ± 0.014.8 ± 0.15klmnopq0.21 ± 00.39 ± 0.030.26 ± 0.010.01 ± 00.03 ± 0.010.02 ± 00.92 ± 0.06jklm
      India-R-43.87 ± 0.191.26 ± 0.140.26 ± 0.060.5 ± 0.040.02 ± 05.91 ± 0.43fghijklm0.12 ± 0.010.21 ± 0.010.14 ± 00.01 ± 00.01 ± 00.01 ± 00.51 ± 0.03mnop
      Indonesia-R-12.74 ± 0.220.32 ± 0.030.68 ± 0.050.34 ± 0.060.01 ± 0.014.09 ± 0.37pqrs0.5 ± 0.020.99 ± 0.020.62 ± 0.020.05 ± 00.09 ± 00.09 ± 0.012.32 ± 0.08d
      Indonesia-R-23.14 ± 0.150.43 ± 0.020.47 ± 0.070.23 ± 00.01 ± 0.014.27 ± 0.21opqr0.33 ± 0.010.68 ± 0.050.45 ± 0.060.03 ± 0.010.08 ± 0.010.05 ± 0.011.63 ± 0.14fgh
      Indonesia-R-33.63 ± 0.110.47 ± 0.010.29 ± 0.020.33 ± 0.020.01 ± 0.014.73 ± 0.15lmnopqr0.14 ± 0.020.25 ± 0.020.16 ± 0.010.01 ± 00.01 ± 00.01 ± 00.58 ± 0.05lmnop
      Indonesia-R-43.66 ± 3.780.16 ± 0.480.22 ± 0.60.38 ± 1.440.01 ± 0.0044.43 ± 0.37nopqr0.1 ± 0.30.17 ± 0.5770.12 ± 0.3530 ± 0.0110 ± 0.0240 ± 0.0240.4 ± 1.289op
      Uganda-R-13.27 ± 0.150.31 ± 0.040.54 ± 0.060.25 ± 0.040.01 ± 04.38 ± 0.29nopqr0.5 ± 0.011.08 ± 0.050.67 ± 0.070.05 ± 0.010.08 ± 0.010.08 ± 02.46 ± 0.15cd
      Uganda-R-23.52 ± 0.20.25 ± 0.130.45 ± 0.040.3 ± 0.020.02 ± 04.53 ± 0.06mnopqr0.39 ± 0.030.77 ± 0.060.5 ± 0.060.03 ± 0.010.05 ± 0.010.05 ± 01.78 ± 0.18efg
      Uganda-R-33.9 ± 0.330.16 ± 0.010.11 ± 0.010.37 ± 0.080.02 ± 04.56 ± 0.41mnopqr0.1 ± 0.120.35 ± 0.020.27 ± 0.060 ± 00.02 ± 00.01 ± 0.010.76 ± 0.05klmno
      Uganda-R-44.01 ± 0.170.25 ± 0.130.11 ± 0.010.41 ± 0.030.02 ± 0.014.8 ± 0.07klmnopq0.07 ± 0.020.15 ± 0.010.1 ± 0.010 ± 00 ± 00 ± 00.32 ± 0.03p
      Vietnam-R-13.96 ± 0.080.16 ± 0.080.46 ± 0.070.38 ± 0.040 ± 04.96 ± 0.1jklmnopq0.7 ± 0.021.42 ± 0.050.88 ± 0.050.08 ± 00.15 ± 0.010.14 ± 03.36 ± 0.14a
      Vietnam-R-23.94 ± 0.210.5 ± 0.040.2 ± 00.48 ± 0.040.01 ± 0.015.13 ± 0.13ijklmnop0.3 ± 0.010.54 ± 0.010.37 ± 0.020.02 ± 00.08 ± 00 ± 01.31 ± 0.04hij
      Vietnam-R-34.69 ± 0.140.81 ± 0.130.19 ± 0.020.5 ± 0.060 ± 06.19 ± 0.35efghijk0.2 ± 00.36 ± 00.24 ± 00.01 ± 00.02 ± 00.01 ± 0.010.85 ± 0.02klmn
      Vietnam-R-44.92 ± 0.120.5 ± 0.010.14 ± 0.010 ± 00 ± 05.56 ± 0.13ghijklmno0.07 ± 00.12 ± 00.08 ± 00 ± 00 ± 00 ± 00.28 ± 0.01p
      *A: Arabica bean; R: Robusta bean;
      1: medium light, 2: medium, 3: medium dark; 4: dark;
      Letter code: Samples with the same letter code are not significantly different (P < 0.05).

      Figure 2.  Organic acid and CACs concentration of coffee beans (g/100 g). (a) & (b), organic acid concentration; (c) & (d), CACs concentration. Blue represents Arabica beans, Orange represents Robusta beans. 1, medium light; 2, medium; 3, medium dark; 4, dark.

      According to Fig. 2a, for most coffee beans, the total organic acid concentration increased with the increase of roasting degree. However, when the roasting degree of Arabica beans from Uganda and Robusta beans from Vietnam was dark, the concentration of total organic acids fell significantly, which corresponds to the results of Wang[15]. And the significant decrease of total concentration was mainly as a result of the decease of citric acid and succinic acid, which are easily decomposed at high temperature[8]. Between the groups of different species, the value of Robusta beans was lower, and the value was significantly different (Fig. 2b). While the value of Arabica was not different from each other. Thus, production area may influence the organic acid concentration of Robusta beans.

      For the concentration of CACs of each coffee beans, it had an obvious negative correlation with roasting degree. With regard to production area, there was no significant influence on the CACs concentration of two species of coffee beans (Fig. 2d). Also, the value of Arabica beans was similar to that of Robusta. Therefore, the concentration of CACs may only be influenced by roasting degree.

      Volatile compounds

    • VCs are the main contributors to the odor of coffee, usually related to roasted coffee oil[16]. VCs can be composed of various chemical compounds, such as aldehydes, esters, ketones, alcohols, hydrocarbons, phenols, carboxylic acids, pyridine, pyrazines, furans and so on. The result of each volatile compound of each sample are shown in Supplemental Table S1. The total amount of VCs of coffee beans are shown in Fig. 3. For different species of coffee beans, there were some unique compounds. Arabica beans contained furfural and butyraldehyde isopropyl ester, and Robusta beans contained methylheptenone. Also, the total amount of VCs were also different. As the value of Arabica beans was higher than that of Robusta beans, the total amount of VCs of most coffee beans increased with the increase of roasting degree. As for the production area, there was no significant difference between each species of coffee beans (Fig. 3b).

      Figure 3.  Total amount of VCs of different coffee beans. Blue represents Arabica beans, Orange represents Robusta beans. 1, medium light; 2, medium; 3, medium dark; 4, dark.

      Principal component analysis

    • PCA was performed on five indexes (protein concentration, fat concentration, total organic acid concentration, total CACs concentration and total amount of VCs) of 40 samples, and the results are shown in Table 6. Three principal components were extracted, and the cumulative variance contribution rate was 93.1%. Based on PC1 and PC2, a discriminant analysis was performed among the 40 samples, shown in Fig. 4. The results clearly showed that Arabica bean and Robusta bean, and the beans with different roasting degrees were discriminated by PC1 and PC2. While coffee beans from the same production area did not cluster together clearly. Thus, species and roasting degrees can distinguish coffee beans better than production area, which corresponds to the above results. Coffee beans produced in different areas may have the same or similar origin. In addition, Brazil, India, Indonesia, Uganda and Vietnam are located on a similar latitude. So, the same species of coffee bean from these production areas is mostly likely to show similar characteristics.

      Table 6.  Loading table of each index of coffee bean

      IndexPC1PC2PC3
      Protein−0.390.613−0.162
      Fat0.4890.2490.501
      Total organic acids0.501−0.097−0.806
      Total CACs0.272−0.740.118
      Total VCs0.532−0.0660.241
      RVC (%)60.126.56.6
      Cumulative RVC (%)93.1

      Figure 4.  Discriminant results of coffee beans.

    CONCLUSIONS

    • In this research, coffee beans of two species (Arabica beans and Robusta beans) from five different production areas (Brazil, India, Indonesia, Uganda and Vietnam) with four different roasting degrees (medium light, medium, medium dark and dark), and a total of 40 samples, were used. The protein, fat, organic acids, CACs and VCs were measured to investigate the difference among the samples. The results suggested that coffee beans of two species had obviously different substance concentrations, as Arabica coffee beans had higher concentrations of fat and organic acids, and total amount of VCs, while Robusta beans had higher concentrations of protein. Due to the increasing loss of moisture, the concentration of protein, fat, organic acids, and the total amount of VCs of coffee beans increased with the increase of roasting degree. The concentration of CACs was lower, while the roasting degree of coffee beans was higher, as CACs decomposed at high temperature. Compared to species and roasting degree, production area influenced only part of the index. The concentration of protein of two species of coffee beans and the concentration of organic acids of Robusta beans were affected significantly by production area. For further conclusions on the influence of production area on coffee beans, further research is required. Based on the results of the measurement of protein, fat, organic acids, CACs and VCs, PCA was performed to illustrate the correlation among species, production area and roasting degree with coffee beans. The discriminant analysis indicated that species and roasting degrees could differentiate the coffee beans clearly. While production area could not differentiate the coffee beans well.

      The results of this research conclude the difference between Arabica and Robusta beans, and coffee beans with different roasting degrees from different production areas. It can provide a theoretical basis for coffee bean selection for the relevant industry.

      Conflict of interest
      • The authors declare that they have no conflict of interest.
      Supplementary information
      Rights and permissions
      • Copyright: © 2022 by the author(s). Exclusive Licensee Maximum Academic Press, Fayetteville, GA. 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 (4)  Table (6) References (16)
  • About this article
    Cite this article
    Liu X, Fei Y, Wang W, Lei S, Cheng C, et al. 2022. Physicochemical difference of coffee beans with different species, production areas and roasting degrees. Beverage Plant Research 2: 7 doi: 10.48130/BPR-2022-0007
    Liu X, Fei Y, Wang W, Lei S, Cheng C, et al. 2022. Physicochemical difference of coffee beans with different species, production areas and roasting degrees. Beverage Plant Research 2: 7 doi: 10.48130/BPR-2022-0007
    shu

Catalog

  • Html

  • Introduction
  • Materials and methods

  • Materials

  • Methods

  • Sample preparation
  • Protein and fat determination
  • Organic acid determination
  • Cacs determination
  • Vcs determination
  • Statistical analysis
  • Results and discussion

  • Protein and fat

  • Organic acid and chlorogenic acid compounds

  • Volatile compounds

  • Principal component analysis

  • Conclusions
  • Conflict of interest
  • Supplementary information
  • 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