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Following Coffee Production from Cherries to Cup: Microbiological and Metabolomic Analysis of Wet Processing of Coffea arabica Sophia Jiyuan Zhang, a Florac De Bruyn, a Vasileios Pothakos, a Julio Torres, b Carlos Falconi, c Cyril Moccand, d Stefan Weckx, a Luc De Vuyst a a Research Group of Industrial Microbiology and Food Biotechnology, Faculty of Sciences and Bioengineering Sciences, Vrije Universiteit Brussel, Brussels, Belgium b Nestlé Ecuador, Quito, Ecuador c Plantsphere Laboratories, Quito, Ecuador d Nestlé Research Centre, Lausanne, Switzerland ABSTRACT A cup of coffee is the final product of a complex chain of operations. Wet postharvest processing of coffee is one of these operations, which involves a fermentation that inevitably has to be performed on-farm. During wet coffee pro- cessing, the interplay between microbial activities and endogenous bean metabo- lism results in a specific flavor precursor profile of the green coffee beans. Yet, how specific microbial communities and the changing chemical compositions of the beans determine the flavor of a cup of coffee remains underappreciated. Through a multiphasic approach, the establishment of the microbial communities, as well as their prevalence during wet processing of Coffea arabica, was followed at an experi- mental farm in Ecuador. Also, the metabolites produced by the microorganisms and those of the coffee bean metabolism were monitored to determine their influence on the green coffee bean metabolite profile over time. The results indicated that lac- tic acid bacteria were prevalent well before the onset of fermentation and that the fermentation duration entailed shifts in their communities. The fermentation dura- tion also affected the compositions of the beans, so that longer-fermented coffee had more notes that are preferred by consumers. As a consequence, researchers and coffee growers should be aware that the flavor of a cup of coffee is determined be- fore as well as during on-farm processing and that under the right conditions, lon- ger fermentation times can be favorable, although the opposite is often believed. IMPORTANCE Coffee needs to undergo a long chain of events to transform from coffee cherries to a beverage. The coffee postharvest processing is one of the key phases that convert the freshly harvested cherries into green coffee beans before roasting and brewing. Among multiple existing processing methods, the wet pro- cessing has been usually applied for Arabica coffee and produces decent quality of both green coffee beans and the cup of coffee. In the present case study, wet pro- cessing was followed by a multiphasic approach through both microbiological and metabolomic analyses. The impacts of each processing step, especially the fermenta- tion duration, were studied in detail. Distinct changes in microbial ecosystems, pro- cessing waters, coffee beans, and sensory quality of the brews were found. Thus, through fine-tuning of the parameters in each step, the microbial diversity and en- dogenous bean metabolism can be altered during coffee postharvest processing and hence provide potential to improve coffee quality. KEYWORDS Coffea arabica, amplicon sequencing, coffee bean fermentation, lactic acid bacteria, metabolomics, wet processing Citation Zhang SJ, De Bruyn F, Pothakos V, Torres J, Falconi C, Moccand C, Weckx S, De Vuyst L. 2019. Following coffee production from cherries to cup: microbiological and metabolomic analysis of wet processing of Coffea arabica. Appl Environ Microbiol 85:e02635-18. https://doi.org/10.1128/AEM .02635-18. Editor Johanna Björkroth, University of Helsinki Copyright © 2019 American Society for Microbiology. All Rights Reserved. Address correspondence to Luc De Vuyst, [email protected]. S.J.Z., F.D.B., and V.P contributed equally to this work. Received 31 October 2018 Accepted 5 January 2019 Accepted manuscript posted online 1 February 2019 Published FOOD MICROBIOLOGY crossm March 2019 Volume 85 Issue 6 e02635-18 aem.asm.org 1 Applied and Environmental Microbiology 6 March 2019 on May 2, 2020 by guest http://aem.asm.org/ Downloaded from

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Following Coffee Production from Cherries to Cup:Microbiological and Metabolomic Analysis of Wet Processingof Coffea arabica

Sophia Jiyuan Zhang,a Florac De Bruyn,a Vasileios Pothakos,a Julio Torres,b Carlos Falconi,c Cyril Moccand,d Stefan Weckx,a

Luc De Vuysta

aResearch Group of Industrial Microbiology and Food Biotechnology, Faculty of Sciences and Bioengineering Sciences, Vrije Universiteit Brussel, Brussels, BelgiumbNestlé Ecuador, Quito, EcuadorcPlantsphere Laboratories, Quito, EcuadordNestlé Research Centre, Lausanne, Switzerland

ABSTRACT A cup of coffee is the final product of a complex chain of operations.Wet postharvest processing of coffee is one of these operations, which involves afermentation that inevitably has to be performed on-farm. During wet coffee pro-cessing, the interplay between microbial activities and endogenous bean metabo-lism results in a specific flavor precursor profile of the green coffee beans. Yet, howspecific microbial communities and the changing chemical compositions of thebeans determine the flavor of a cup of coffee remains underappreciated. Through amultiphasic approach, the establishment of the microbial communities, as well astheir prevalence during wet processing of Coffea arabica, was followed at an experi-mental farm in Ecuador. Also, the metabolites produced by the microorganisms andthose of the coffee bean metabolism were monitored to determine their influenceon the green coffee bean metabolite profile over time. The results indicated that lac-tic acid bacteria were prevalent well before the onset of fermentation and that thefermentation duration entailed shifts in their communities. The fermentation dura-tion also affected the compositions of the beans, so that longer-fermented coffeehad more notes that are preferred by consumers. As a consequence, researchers andcoffee growers should be aware that the flavor of a cup of coffee is determined be-fore as well as during on-farm processing and that under the right conditions, lon-ger fermentation times can be favorable, although the opposite is often believed.

IMPORTANCE Coffee needs to undergo a long chain of events to transform fromcoffee cherries to a beverage. The coffee postharvest processing is one of the keyphases that convert the freshly harvested cherries into green coffee beans beforeroasting and brewing. Among multiple existing processing methods, the wet pro-cessing has been usually applied for Arabica coffee and produces decent quality ofboth green coffee beans and the cup of coffee. In the present case study, wet pro-cessing was followed by a multiphasic approach through both microbiological andmetabolomic analyses. The impacts of each processing step, especially the fermenta-tion duration, were studied in detail. Distinct changes in microbial ecosystems, pro-cessing waters, coffee beans, and sensory quality of the brews were found. Thus,through fine-tuning of the parameters in each step, the microbial diversity and en-dogenous bean metabolism can be altered during coffee postharvest processing andhence provide potential to improve coffee quality.

KEYWORDS Coffea arabica, amplicon sequencing, coffee bean fermentation, lacticacid bacteria, metabolomics, wet processing

Citation Zhang SJ, De Bruyn F, Pothakos V,Torres J, Falconi C, Moccand C, Weckx S, DeVuyst L. 2019. Following coffee productionfrom cherries to cup: microbiological andmetabolomic analysis of wet processing ofCoffea arabica. Appl Environ Microbiol85:e02635-18. https://doi.org/10.1128/AEM.02635-18.

Editor Johanna Björkroth, University of Helsinki

Copyright © 2019 American Society forMicrobiology. All Rights Reserved.

Address correspondence to Luc De Vuyst,[email protected].

S.J.Z., F.D.B., and V.P contributed equally to thiswork.

Received 31 October 2018Accepted 5 January 2019

Accepted manuscript posted online 1February 2019Published

FOOD MICROBIOLOGY

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A cup of coffee is the endpoint of a complex chain of events. This chain includespostharvest processing, roasting, and brewing. Postharvest processing consists of

several steps performed on-farm after the coffee cherries have been harvested, and ityields the green coffee beans that can be roasted (1, 2). During this processing, aninterplay between microbial activities and endogenous bean metabolism takes place,which results in a specific flavor precursor profile of the green coffee beans (2, 3) Coffeecherries can be processed in different ways (1). Wet processing is usually implementedfor Coffea arabica cherries to produce high-grade Arabica coffee.

During wet processing, the harvested coffee cherries are depulped, spontaneouslyfermented underwater, soaked, and dried (4, 5). The fermentation step aims to removethe mucilage that is firmly attached to the beans. This fermentation is performed bymicroorganisms that originate from the cherry surfaces, plantation environment, orprocessing equipment. As processing progresses, microbial communities grow due tovariable selective pressures from intrinsic (e.g., coffee cultivar and geography) andextrinsic (e.g., processing, handling, and operational practices) (3, 5–8) factors. Howthese factors shape these communities remains to be elucidated. Due to this uncer-tainty, researchers have already tried to standardize the fermentation process byadding selected microbial strains to the fermentation mass, without managing specificoperational practices (9–11). Since metabolites of microbial origin (such as organicacids) can be present on the green coffee beans, the mechanisms shaping the coffeeecosystem need to be better understood (3).

As coffee beans are still metabolically active during postharvest processing, theyrespond to various abiotic stresses, such as those caused by depulping at the start ofthe processing, anoxic and acidic conditions during underwater fermentation, anddrought stress upon drying (12, 13). These stress-related metabolic responses will alsochange the metabolite composition of the green coffee beans. Coffee bean stress ismarked by the evolution of, for instance, �-aminobutyric acid (GABA) and carbohydrateconcentrations (3, 14–16). However, the evolution of such compounds along the entirepostharvest processing chain has not been studied extensively.

During roasting, the chemical profiles of the green coffee beans, which are deter-mined not only by cultivar and geography but also by postharvest processing asdescribed above, transform into the characteristic coffee flavor (17–19). Ultimately,gauging the effect of postharvest processing on the coffee cup quality requires sensoryanalysis by a trained panel. However, reports on the relationship between the sensorydata and the fermentation process (postharvest effect) are scarce (7, 20).

Given this complex and interlinked postharvest processing of coffee, an integratedsystematic study of the relationship between the coffee processing microbiota, endog-enous bean metabolism, operational practices, and cup quality was necessary. There-fore, this study aimed to decode the complete wet processing chain of Arabica coffeeunder different operational practices, starting from the harvesting of the coffee cherriesthrough on-farm processing until coffee roasting and brewing (Fig. 1). This was tackledthrough a multiphasic approach, monitoring the coffee microbiota (on-farm microbi-ological analysis and high-throughput sequencing), the coffee bean composition(meta-metabolomics), and the final quality of the coffee brews (sensory analysis).

RESULTSMicrobial community dynamics during postharvest processing. (i) Microbial

community dynamics during pooling, depulping, and fermentation. The pooledcherries and depulped beans showed high counts of all microbial groups targeted,namely, lactic acid bacteria (LAB), acetic acid bacteria (AAB), enterobacteria, and yeasts(Fig. 2). Isolate identification and amplicon sequencing of targeted genes of thewhole-community DNA affirmed the community members of these groups belong tothe Acetobacteraceae (encompassing Acetobacter, Gluconobacter, and Kozakia), entero-bacteria, Leuconostoc pseudomesenteroides, Pichia kluyveri (particularly found by ampli-con sequencing), Hanseniaspora uvarum, and Candida quercitrusa (Fig. 3 and 4; see alsoTable S1 in the supplemental material). These high counts persisted once the depulped

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beans were submerged in the fermentation tank (particularly LAB and enterobacteria).From here on, LAB asserted their quantitative prevalence over other microbial groupsduring the standard fermentation (S). This prevalence was further developed during theextended fermentation (E) and was retained until the end of fermentation (F). Duringthis phase, a shift from L. pseudomesenteroides to lactobacilli, namely Lactobacillusvaccinostercus, Lactobacillus brevis, and Lactobacillus plantarum, occurred within theLAB communities. Conversely, the counts of enterobacteria and AAB showed a contin-uous decrease during the standard and extended fermentations. No major shiftsoccurred within the community compositions of these groups. The yeast counts andcommunities remained relatively stable during these phases, although Starmerellabacillaris (particularly found through isolate identification) became more pronouncedas fermentation progressed and Saccharomycopsis crataegensis was encountered oc-casionally. Lactococcus lactis was prevalent transiently during the standard fermenta-tion (12 to 24 h). Other species were sporadically encountered, such as Leuconostocfallax (at 16 h) and Pediococcus pentosaceus (at 48 h). Hence, the initial occurrence ofenterobacteria (and to a lesser degree, AAB) and the prevalence of Leuconostoc(accompanied by the transient occurrence of Lactococcus) characterized the standardfermentation. The continued but diminishing prevalence of Leuconostoc and the sub-sequent prevalence of Lactobacillus characterized the extended fermentation.

(ii) Microbial community dynamics during soaking. The viability of the microor-ganisms was retained and/or regained during soaking, since high counts of all microbialgroups targeted, particularly LAB, were found after 24 h of soaking (Fig. 2). The relativevalues of these counts were comparable to those found during fermentation. Leucono-stoc pseudomesenteroides and P. kluyveri were important community members, regard-less of soaking after the standard or extended fermentations (Fig. 3 and 4). Notably, a

FIG 1 (a) Overview of the Arabica coffee postharvest wet processing experiments. The abbreviations of the different samples are indicated as well. The darkblue line represents the standard fermentation process (S), and the light blue line represents the extended fermentation process (E). Sample codes starting withF correspond to fermentation samples, followed by a number corresponding to the fermentation duration in hours. Sample codes starting with SS denotesoaking samples after standard fermentation and sample codes starting with SE denote soaking samples after extended fermentation, followed by a numbercorresponding to the soaking duration in hours. Sample codes starting with DS denote drying samples after standard fermentation and sample codes startingwith DE denote drying samples after extended fermentation, followed by a number corresponding to the drying duration in hours. Samples SB and EB denotegreen coffee bean samples resulting from the standard fermentation process and the extended fermentation process, respectively. (b) Sampling schemesaccording to the type of analysis. The sampling points for each analysis type are colored based on their location in the processing chain. Gray sampling pointsdenote samples that were not included for analysis.

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higher abundance of Lactobacillus was found by amplicon sequencing when perform-ing soaking after the extended fermentation (reflecting its higher prevalence at the endof the extended fermentation). Minor variations were found for other communities,such as enterobacteria and Lactococcus. Within the yeast communities, P. kluyveri andH. uvarum (particularly found through isolate identification) emerged as prominentmembers during soaking after both the standard and extended fermentations.

(iii) Microbial community dynamics during drying. During sun drying after thestandard and extended fermentations, all microbial groups targeted either decreased inviability, as their counts decreased, or were present at �2.0 log CFU/g (Fig. 2). This wasin contrast with that at the end of soaking, i.e., there were relatively higher counts ofyeasts during soaking than during drying. The loss of viability was faster and morepronounced after the extended fermentation than after the standard fermentation.

Microbial ecology of the processing apparatus and processing environment.The microbial contamination of the processing apparatus (cherry storage bags,depulper exit shaft, empty fermentation tank, and empty soaking tank) and environ-mental samples (plantation soil, coffee tree flowers, coffee tree leaves, and freshcherries from the coffee trees) was generally soil or plant associated but was variablefor the different pieces of apparatus analyzed (Fig. 4). Notably, taxa that were foundextensively during fermentation (e.g., Leuconostoc, enterobacteria, and AAB) werefound in much lower relative abundances on the processing apparatus. These taxa weresporadically encountered in relatively high relative abundances in the coffee phyllo-sphere (e.g., that of coffee cherries that were attached to the coffee trees or of thecoffee leaves). Coffee cherries that were attached to the trees displayed microbialcounts that spanned a wide range (see Fig. S1). Microbial groups that were found in

FIG 2 Microbial counts during coffee cherry pooling, depulping, fermentation, soaking, and drying. The agar media used forenumeration are plate count agar (PCA) for total bacteria, modified de Man-Rogosa-Sharpe (MRS-S) agar for lactic acid bacteria,violet-red-bile-glucose (VRBG) agar for enterobacteria, yeast glucose (YG) agar for yeasts, and modified deoxycholate-mannitol-sorbitol(mDMS) agar for acetic acid bacteria. Counts of 2.0 log CFU/g indicate counts equal to or below this value. Sample abbreviations areas in the legend for Fig. 1.

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high counts during the harvesting-depulping interval were often present at �2.0 logCFU/g when analyzing these coffee cherries (e.g., LAB and enterobacteria). Differencesin community compositions of the different samples were elucidated by principal-component analysis (PCA) (see Fig. S2). The fermentation and soaking water samplesformed a cluster distinct from the environmental samples. This separation was sub-stantiated by network analysis, through which these environmental samples weredisjoined from all other types of samples and were connected with different microbialcommunities (see Fig. S3). The overall sequencing error rate of all amplicons was 0.04%.

Growth assessment of the epiphytic coffee cherry microbiota. All of the micro-bial groups followed were able to grow on the appropriate agar media when platingsamples from coffee cherries inside the sterile plastic bags used for an imitation of theharvesting-depulping interval of the coffee postharvest processing chain (see Fig. S4).However, the most rapid and most substantial increase (approximately 2.0 log CFU/ml)was that of the LAB communities, which was approximately 14-fold and 5-fold that ofthe AAB and yeast communities, respectively.

Microbial community dynamics in postfermentation waters. The LAB commu-nity profiles of the postfermentation water (PFW; mixture of fermentation water andwash water) samples were similar to those at the end of the standard fermentation (Fig.4; Fig. S2 and S3). These profiles were characterized by a relatively high prevalence ofLeuconostoc, Lactococcus, lactobacilli, enterobacteria, and Pichia. In contrast to thestandard fermentation profile, taxa unique to the PFW were found, notably, Clostridium.

Metabolite course in fermentation and soaking waters. (i) Metabolite course infermentation waters. The nonamino acid compounds quantified in the fermentationwater samples (W) were divided into three clusters according to hierarchical clusteringanalysis of the heatmap data (A1 to A3) based on their profiles during fermentation(Fig. 5). Cluster A1 compounds, represented by sucrose, citric acid, malic acid, andacetaldehyde, reached high concentrations after 12 to 24 h of fermentation and were

FIG 3 Isolate identification during coffee cherry pooling, depulping, fermentation, and soaking. Isolates are grouped into majormicrobial categories (acetic acid bacteria, lactic acid bacteria, and yeasts). Colors denote the different species identified, and the sizeof the dots is relative to the number of isolates picked up and identified. The number of isolates picked up and identified at each timepoint is represented inside each dot. Sample abbreviations are as in the legend for Fig. 1.

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depleted toward the end (F64). In comparison, cluster A3 compounds, represented byglucose and fructose, also reached the highest concentrations after 12 h of fermenta-tion (F12). Yet, these compounds remained at relatively high concentrations toward theend of the extended fermentation. For example, glucose and fructose concentrationsreached 3.6 and 3.1 mg/ml in F64W, respectively, whereas the concomitant sucroseconcentration was only 0.025 mg/ml. In contrast, cluster A2 compounds (lactic acid andmannitol) were characterized by their continuous accumulation throughout fermenta-tion. The accumulation of these compounds increased after 16 h of fermentation (F16),resulting in a 2- to 8-fold increase at the end of the extended fermentation (F64). As aresult, the most abundant compounds in F64W were lactic acid (8.2 mg/ml),5-ketogluconic acid (5.7 mg/ml), acetic acid (2.4 mg/ml), mannitol (1.9 mg/ml), ethanol(1.8 mg/ml), and glycerol (0.5 mg/ml). Compounds originating from the coffee plant,such as quinic acid, caffeine, trigonelline, and succinic acid, also displayed smallincrements in their concentrations. Chlorogenic acids (CGAs) were not found in thefermentation water samples.

The total free amino acid concentrations in the fermentation water samples almostdoubled to 1.02 mg/ml from the beginning toward the end of the extended fermen-tation (Fig. 6). The evolution profile of the amino acids was also divided into twoclusters based on hierarchical clustering analysis of the heatmap data (B1 and B2).Similar to cluster A2, the majority of the amino acids grouped in cluster B1 built upduring the standard and extended fermentations. Cluster B1 had GABA (0.30 mg/ml),asparagine (0.23 mg/ml), and alanine (0.18 mg/ml) as the most abundant compoundsat the end of the extended fermentation (F64). However, certain amino acids of clusterB2 (e.g., arginine, glutamine, valine, leucine, and isoleucine) decreased, especiallyduring the extended fermentation.

More than 100 volatile compounds were identified in the fermentation waters,among which, 50% were esters, 23% alcohols, 12% aldehydes, and 5% terpenes/terpenoids. The total aroma intensity in the fermentation waters increased 5 times in

FIG 4 Distribution of amplicon sequence variants (ASVs) of the V4 region of the 16S rRNA gene (bacteria) and the internal transcribed spacer (ITS1) region of the fungalribosomal transcribed unit (yeasts and molds) during coffee cherry pooling, depulping, fermentation, soaking, on the processing apparatus, in the processingenvironment, and in the postfermentation waters. Sample abbreviations are as in the legend for Fig. 1.

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F64 compared to that in F16. The production dynamics of these volatile compoundsallowed a grouping into three different clusters based on hierarchical clusteringanalysis of the heatmap data (C1 to C3) (Fig. 7). At the beginning of the fermentation,the compounds of cluster C3 already built up, e.g., 2,3-butanediol, 2/3-methylbutanal,

FIG 5 Hierarchical clustering analysis and heatmap visualization of selected quantified chemical compound profiles (column dendrogram) in fermentation andsoaking waters (FW and SW) (a) and coffee beans (B) (b) along the wet coffee processing chain. The absolute concentrations are summed and displayed on theright; a color key as a measure for the concentrations is also displayed on the right. Sample abbreviations are as in the legend for Fig. 1. dm, dry mass.

FIG 6 Hierarchical clustering analysis and heatmap visualization of free amino acid profiles (column dendrogram) in fermentation and soaking waters (FW andSW) (a) and coffee beans (B) (b) along the wet coffee processing chain. The total free amino acid concentrations are summed and displayed in green on the right;a color key as a measure for the concentrations is also displayed on the right. Sample abbreviations are as in the legend for Fig. 1. dm, dry mass.

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and ethyl 2/3-methylbutanoate. This was succeeded by the emergence of cluster C2compounds (especially after 16 h of fermentation; F16), among which, 48% were esters.The major esters, such as 3-methylbutyl acetate, isobutyl acetate, ethyl butanoate, ethyl2-hydroxypropanoate, and ethyl acetate, reached their highest aroma intensities at F64,with a 10-fold increment compared to that in F16, whereas ethyl hexanoate andisobutyl butanoate increased 26 and 33 times but displayed lower aroma intensities.The build-up of esters was accompanied by the accumulation of the corresponding(higher) alcohols (e.g., ethanol, 3-methyl-1-butanol, 2-methyl-1-butanol, 2-methyl-1-pentanol, and 2-methyl-1-propanol), which also displayed high aroma intensities, aswell as organic acids (e.g., acetic acid, propionic acid, butanoic acid, and pentanoicacid), as seen in cluster C2.

(ii) Metabolite course in soaking waters. The concentrations of the total nonaminoacid metabolites measured decreased to 5.2% of F16W and 1.2% of F64W for the standard(S0W) and extended (SE0W) fermentations, respectively (Fig. 5). After the standard fermen-tation, simple carbohydrates were still found in clusters A1 and A3 in relatively highconcentrations (glucose, 0.27 mg/ml; fructose, 0.13 mg/ml) at the start of soaking (S0),whereas both their concentrations were 0.10 mg/ml in SE0. In both cases, the simplecarbohydrates were completely consumed after 24 h of soaking. Concurrently, an accumu-lation of cluster A2 compounds was found during soaking, and high concentrations werefound when extended fermentation took place. Lactic acid (0.70 and 0.68 mg/ml), ethanol(0.14 and 0.24 mg/ml), acetic acid (0.10 and 0.17 mg/ml), and acetaldehyde (0.03 and0.05 mg/ml) were the major compounds in both S24W and SE24W, respectively, whereasmannitol (0.33 mg/ml) was only abundant in SE24W. In addition, trigonelline and caffeine,which originate from the coffee plant, accumulated at similar concentrations in both S24Wand SE24W, with 0.014 and 0.034 mg/ml in S24W and 0.019 and 0.037 mg/ml in SE24W,respectively. The total free amino acid concentrations also increased during soaking, butthey were 50 times diluted compared to the corresponding end-points of fermentation (Fig.6). Whereas asparagine remained as abundant in the soaking waters as in the fermentationwaters, glutamic acid and GABA were no longer found in the soaking waters. Instead, thehistidine and lysine concentrations were relatively high, albeit still at low levels (5 mg/liter).

FIG 7 Hierarchical clustering analysis and heatmap visualization of nontargeted aroma profiles (column dendrogram) in the fermentation and soaking waters(FW and SW). The aroma intensities normalized to the internal standard are summed and displayed in blue on the right (in arbitrary units [AU]); a color keyas a measure for the concentrations is also displayed on the right. Sample abbreviations are as in the legend for Fig. 1.

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Among the 65 volatile compounds found in the soaking waters, 35% were esters,20% alcohols, 14% ketones, 12% aldehydes, and 9% terpenes/terpenoids (Fig. 7). Afterthe standard fermentation, the total aroma intensity dropped to 27% of F16W afterwashing and remained at a similar level until the end of soaking. In comparison, thetotal aroma intensity at SE0W was 16% of F64W and increased to 22% toward the end.The major volatile compounds were still dominated by cluster C2 compounds, as in thefermentation step. The major esters and alcohols remained the same, but high inten-sities of these compounds were found after the extended fermentation compared tothat for the standard one. In comparison, the compounds in cluster C1, such asbutyl propanoate, butyl 2-propenoate, butyl butanoate, 1-butanol, and 3-hydroxy-2-butanone, occurred at similar aroma intensities in the soaking waters as in the fermen-tations. Among the terpenes/terpenoids, linalool (3,7-dimethyl-1,6-octadien-3-ol) re-mained as the most abundant compound, as in the fermentation water samples, whilethe other compounds stayed at lower aroma intensities.

(iii) Correlation analysis between the microbiota and volatile compounds. Thisanalysis indicated that esters and alcohols detected in the fermentation and soakingwaters, were positively correlated with the taxa Leuconostoc and Lactobacillus andnegatively correlated with enterobacteria (see Fig. S5). Some volatiles, in particular,cluster C3 compounds, were positively correlated with the enterobacteria.

(iv) Metabolite course in postfermentation waters. Compounds such as lacticacid, ethanol, fructose, acetic acid, and glucose that were present during fermentationwere also abundant in the PFW samples (see Fig. S6). However, butyric acid andpropionic acid were found in much higher concentrations during the storage ofpostfermentation waters, and their concentrations were inversely related to the con-centrations of the simple carbohydrates.

Temporal metabolic response of the coffee beans during postharvest process-ing. All coffee bean samples were rich in an array of coffee bean endogenous compounds,which contained, in decreasing order of concentrations, sucrose, 3-caffeoylquinic acid(3-CQA), caffeine, trigonelline, citric acid, malic acid, and quinic acid. Most of these com-pounds remained relatively stable during the entire coffee processing chain. In the follow-ing paragraphs, only the changes in the compounds targeted are featured (Fig. 5, 6, and 8).

(i) Metabolite profiling of coffee beans during fermentation. Corresponding to thecompounds of cluster A2 in the fermentation waters, most of these compounds were alsofound at increasing concentrations on the beans (B) (Fig. 5). For example, mannitol andlactic acid concentrations were 3-fold higher, whereas acetic acid, ethanol, and glycerolconcentrations were 2-fold higher in F64B than in F16B. After 64 h of fermentation, theconcentrations of ethanol, acetic acid, mannitol, and lactic acid in F64B were 4.6, 4.5, 3.6,and 2.2 g/kg, respectively. In addition, although the total simple carbohydrate contentsremained relatively stable, glucose and fructose concentrations changed constantly duringthe fermentation, peaking at F24 and F48. Sucrose concentrations evolved along anout-of-phase pattern compared with that of glucose and fructose, and reached peakconcentrations at F12 and F36. Among the proteinogenic amino acids targeted, all butcysteine were found on the beans (Fig. 6). The major amino acids, glutamic acid andasparagine, represented on average 55% of the total free amino acids throughout coffeeprocessing. The concentrations of some amino acids, e.g., glycine, methionine, threonine,and histidine, increased upon fermentation. GABA displayed the highest increase, with aconcentration that was 10-fold higher in F64B (353 mg/kg) than in F0B after 64 h offermentation. The concentrations of glycine, methionine, and histidine also increased,whereas the concentrations of the remaining amino acids remained relatively stable.

More than 170 volatile compounds were detected on the beans along the entireprocessing chain, among which, 26% were alcohols, 23% esters, 14% aldehydes, 7%terpenes/terpenoids, and 7% furans/furanones (Fig. 8). Among them, 70% were foundon the beans during fermentation. At the beginning, the major volatile compoundswere mainly aldehydes (e.g., 3-methylbutanal, 3-methyl-2-butenal, methional, andbenzaldehyde) and alcohols (e.g., 3-methyl-1-butanol, 5-methyl-1-hexanol, 1-octen-3-ol, and 3-methyl-1-propanol), which belonged to clusters D1 and D3. Some esters

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were also present, such as ethyl 2-methylbutanoate, 3-methylbutanoate, and acetate3-methylpentanoate. Other compounds, such as linalool, D-limonene, 2-pentylfuran,and 2-methoxy-3-(2-methylpropyl)pyrazine occurred at relatively high abundances. Asfermentation progressed, the total aroma intensities remained similar for the first 16 hof fermentation but doubled after 64 h (F64). As seen in cluster D1, existing and newlyappearing alcohols and aldehydes contributed to part of the increment, whereas theproduction of esters that were also abundant in the fermentation waters contributed toanother part. Towards the end of the extended fermentation, more ethyl estersappeared on the beans, such as ethyl acetate, ethyl 2-hydroxypropanoate, ethylbutanoate, ethyl benzene acetate, ethyl hexanoate, 3-methyl-1-butanoyl acetate, andethyl pentanoate. Among the terpenes/terpenoids, linalool and D-limonene were themajor compounds found on the beans, and their aroma intensities increased duringfermentation. However, the concentrations of other terpenes/terpenoids (e.g., citral,�-pinene, �-terpineol, and 3-carene) remained relatively stable.

(ii) Metabolite profiling of coffee beans during soaking. After washing, theconcentrations of mannitol and lactic acid in S0B and SE0B decreased to 20% of thosein F16B and F64B, whereas the concentrations of ethanol (60% and 95%, respectively)and acetic acid (86% and 69%, respectively) decreased to a lower extent in F16B andF64B. Due to the higher concentrations in F64B, these compounds remained 1 to 3times more abundant in SE0B than in S0B. At the end of soaking, these compoundsdiffused back into the soaking water after the extended fermentation, but the endpointSE24B still contained 1.4 times more lactic acid than S24B. The CGA concentrations aswell as the concentrations of trigonelline decreased slightly during the soaking step ofthe extended fermentation but remained relatively stable after the soaking step of thestandard fermentation. The GABA concentration in S0B was similar to that in F16B(143 mg/kg) and remained stable during soaking. In contrast, the GABA concentrationin SE0B (180 mg/kg) was only half of that in F64B, but it continuously increased to

FIG 8 Hierarchical clustering analysis and heatmap visualization of nontargeted aroma profiles (column dendrogram) in the coffee beans (B) duringfermentation, soaking, and drying. The aroma intensities normalized to the internal standard are summed and displayed in blue on the right (in arbitrary units[AU]); a color key as a measure for the concentrations is also displayed on the right. Sample abbreviations are as in the legend for Fig. 1.

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220 mg/kg during soaking. The total free amino acid concentrations decreased to 90%and 86% of the corresponding fermentation points in S0B and SE0B, respectively, withthe decreases in asparagine, aspartic acid, serine, leucine, and methionine concentra-tions mainly contributing.

A total of 110 volatile compounds were detected in the soaking beans, amongwhich, 33% were esters, 30% alcohols, and 10% aldehydes. The major aroma com-pounds on the soaking beans remained similar to those during fermentation, but thetotal aroma intensities dropped to 80% and 65% of the corresponding end points offermentation in S0B and SE0B, respectively. Compared to that in the standard fermen-tation, the soaking beans contained a higher abundance of esters (clusters D1 and D2)and reached a two times higher aroma intensity after the extended fermentation. Forinstance, compounds such as ethyl pentanoate, ethyl-2-hydroxypropanoate, and ethylhexanoate were 5 to 25 times higher in SE24B than in S24B. Other volatile compounds,such as 2-methyl-1-penten-3-ol, butyrolactone, linalool, 2-methyl-2-butenal, and 2/3-methylbutanal, were also found at higher intensities in SE24B than in S24B.

(iii) Correlation analysis between the microbiota and volatile compounds. Thisanalysis indicated that esters and alcohols (belonging to clusters D1 and D3) in thebeans during fermentation and soaking were positively correlated with the taxaLeuconostoc and Lactobacillus and negatively correlated with enterobacteria (in partic-ular, Pantoea, Klebsiella, and Tatumella) (Fig. S5).

(iv) Metabolite profiling of coffee beans during drying. During the drying step(D), the total concentrations of simple carbohydrates, aroma compounds, sugar alco-hols, and CGAs dropped slightly for both the standard and extended fermentations,except for some isomers of the CGAs. Citric acid and malic acid concentrationsincreased in the beans (Fig. 5). The evolution patterns of the glucose and fructoseconcentrations were similar but out of phase with the concentration pattern of sucrose.The ratios between certain isomers of CGAs also increased during drying, whereas theyremained stable during fermentation and soaking. For instance, the ratio of 4,5-diCQAto 5-CQA increased from 0.2 to 0.5, whereas the ratio of 4,5-diCQA to 3,5-diCQAincreased from 0.5 to 1.2 during drying in both processes. The resulting green coffeebeans finally contained different fat contents, with 11.0% � 0.2% and 9.5% � 0.5% instandard- and extended-processed green coffee beans (SB and EB), respectively. EBcontained significantly higher concentrations of glucose (4.7 times), fructose (2.5 times),lactic acid (5.0 times), and mannitol (1.4 times) than SB, whereas the concentrations oftrigonelline (0.8 times) and glycerol (0.4 times) were significantly lower in EB. Theconcentrations of many free amino acids, such as GABA, glutamic acid, aspartic acid,proline, valine, tyrosine, and glutamine, built up during the drying step, were accom-panied by a decrease of the concentrations of asparagine and alanine. The total aminoacid concentrations were higher in EB than in SB. The GABA concentrations continuedto increase, and the final concentration in EB (338 mg/kg) was more than 2 times thatin SB (144 mg/kg).

During drying, a total of 120 volatile compounds were detected, among which, 26%were alcohols, 17% esters, and 11% aldehydes. The major volatile compounds switchedto 1-hexanol, 3-methyl-1-butanol, 1-pentanol, and 2,3-butanediol, as well as hexanal,decane, and butyrolactone. Increases in the aroma intensities of furanones, ketones,and furans appeared once the drying started (green coffee bean smell), as seen incluster D4, represented by decane, 2-pentanone, 2,6-dimethylpyridine, butyrolactone,undecane, 2-ethyl-5-methyl furan, �-pinene, dihydro-5-methyl-2(3H)-furanone, and3-pentanone. Cluster D1 compounds were at much lower abundances during thedrying step. At the end of drying, the major esters mentioned above were still presentand in higher aroma intensities in EB than in SB. The aroma intensities of other volatilecompounds, such as linalool (12 times), ethyl-3-methylbutanoate (9 times), and 2,3-butanediol (5 times), were also higher in EB than in SB.

Sensory analysis of coffee beverages. The overall odor intensity and fruity flavorof the coffee beverages were found to be significantly discriminant (P � 0.05) between

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the coffees brewed from beans subjected to standard and extended fermentationsduring postharvest processing. Other attributes, including fruity odor, bitterness, acid-ity, burnt, earthy, rubber, malty, cereal, and cocoa/coffee flavor notes, were perceivedas different (0.05 � P � 0.50) (see Fig. S7). Specifically, the coffee from the standardfermentation processing had a higher overall odor intensity, body, and astringency andwas more bitter, burnt, earthy, rubber, and cocoa/coffee-like in flavor. The coffee fromthe extended fermentation processing was perceived as having a more fruity and floralodor and had a higher acidity, fruitiness, and maltiness flavor.

DISCUSSION

Coffee postharvest processing must have an impact on the composition of greencoffee beans and hence on the coffee cup quality (1–3). This paper unraveled five majormechanisms for how fermentation duration during wet coffee processing affects thecoffee microbiota, green coffee bean composition, and coffee beverage flavor.

(i) First, it showed that the plantation and processing environment and harvesting-depulping interval are important for the microbial evolution during coffee processing.Indeed, the coffee trees and the cherries that they carried were likely the sources of themicroorganisms found during coffee processing. Microbial taxa that were highly prev-alent during cherry harvesting and pooling (such as Leuconostoc, AAB, and enterobac-teria) or during the early stages of fermentation (particularly Leuconostoc) were fre-quent in the coffee phyllosphere. Taxa that were highly prevalent in the coffeephyllosphere but less so during further processing (such as soil-related taxa, encom-passing Bacillales, Rhizobiales, and Gammaproteobacteria) were less suited to the coffeeprocessing conditions. Thus, they were quickly outcompeted by more fastidious taxa,such as Leuconostoc, AAB, and enterobacteria. Hence, the sleeping taxa of the coffeephyllosphere became prevalent microbial members during processing (particularlyduring fermentation) as a result of changing environmental conditions. These fastidioustaxa were occasionally found on the apparatuses used for coffee processing. However,it is unlikely that these taxa originated from these pieces of apparatus, since they werealready highly prevalent on the coffee cherries before processing started and thesecherries came into contact with the apparatus. Thus, their presence on these pieces ofapparatus might be due to remnant DNA of previous cross-contaminations. The contactwith substrates was found to be the driving force for the development of microbialcommunities on apparatus surfaces, as shown before in a brewery context (21). Theswitch in microbiota at the onset of the coffee processing was reflected in the geneticdiversity of its different steps. The genetic diversity of bacterial populations duringfermentation and soaking differed significantly from that of the apparatus and envi-ronment. Concerning the fungal diversity, the prevalence of Pichia on some pieces ofapparatus (depulper, fermentation tank, and soaking tank) pushed the genetic diversityprofile closer toward processing (fermentation and soaking), making the distinctionbetween processing and environment (including apparatus) less clear.

The crucial factors facilitating the switch from soil- and plant-related taxa to LAB,AAB, and enterobacteria were the availability of simple carbohydrates (present in thesap exuding from cherries inside the storage bags) and the relatively mild conditions(pH and temperature) experienced on the cherries inside the bags. Before depulpingcan start, the cherries need to be amassed in sufficient amounts and are thereforepooled in bags awaiting further processing. Inside these bags, the cherries are tightlypacked and experience mechanical pressure. This causes the cherries to burst and toleak sap. As the coffee cherry mesocarp is rich in fermentable carbohydrates, such asglucose, fructose, and sucrose (3), the available sap was utilized by the epiphytic coffeecherry microbiota to initiate growth. When simulating the conditions of the harvesting-depulping interval for freshly harvested cherries, yeasts, AAB, and LAB could indeedgrow. Yet, none of these microbial groups grew as substantially or as quickly as the LAB.This indicated an aptness of LAB for rapid growth when simple fermentable carbohy-drates became available (rapid consumption and acidification) and when externalconditions were not too harsh. Moreover, the LAB not only were able to grow in

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circumstances similar to those experienced in the storage bags but also outgrew othermicrobial groups during the simulated harvesting-depulping period. This competitiveadvantage of LAB has been shown before in other food-related niches (22). Thecombination of the harvesting-depulping interval and the availability of fermentablecarbohydrates thus resulted in a prefermentation prevalence of microorganisms, inparticular, LAB. Moreover, the microbial counts on cherries inside the bags and ondepulped beans before fermentation were higher than reported previously (4, 23, 24).

(ii) Second, microbial community dynamics and intertwined microbial and endog-enous activities occurred during fermentation and soaking. Indeed, the evolution of thechemical profiles in the fermentation and soaking waters reflected a dynamic andcomplex nature of the wet coffee processing chain, mainly as a result of three factors:(i) the constant release of nutrients from mucilage into the fermentation waters, (ii) themicrobial activities present in the fermentation and soaking waters, and (iii) a minorexchange of compounds between microbial metabolites and compounds in the coffeebeans. Concerning the first factor, the release of compounds from the mucilageprovided the starting nutrients for the microorganisms in the fermentation tank.Mucilage is rich in simple carbohydrates, amino acids, caffeine, trigonelline, quinic acid,citric acid, and malic acid, some of which can be used for microbial growth and/orconcomitant metabolite production, as shown in the metabolite clusters A1, A2 (partly),and A3 of the present study (3, 18). The increasing concentrations of free amino acids(cluster B1) in the fermentation waters mostly came from the mucilage, with asparticacid and asparagine as key amino acids. Also, some plant-related volatile compounds,especially terpenes and terpenoids (e.g., linalool and D-limonene) as well as hexanal andhexenol, increased during fermentation. These compounds are related to the plantmetabolism (25). The nutrients released were continuously consumed by the microor-ganisms, resulting in intense microbial activities throughout both the fermentation andsoaking steps. At the same time, endogenous plant enzymes could degrade themucilage macromolecules and accelerate the release of compounds into the fermen-tation waters.

Concerning the second factor, at the onset of fermentation, the microorganismsconsumed sucrose (prior to glucose and fructose), followed by citric acid and malic acid.Correspondingly, the accumulation of microbial metabolites, especially mannitol, lacticacid, ethanol, and acetic acid (cluster A2 compounds), was proportional to the fermen-tation time. This fast consumption of nutrients at the beginning of fermentationsubstantiated the concomitant high microbial counts that resulted from the growthspurt of microorganisms during the harvesting-depulping interval. The increasingproduction of metabolites during the standard and extended fermentations confirmedthe continuous growth of LAB as well as a prevalent shift from Leuconostoc toLactobacillus upon extended fermentation. The metabolite profile was typical forheterofermentative LAB species of Leuconostoc and Lactobacillus. These LAB species,utilizing glucose via the phosphoketolase pathway, preferentially metabolize disaccha-rides (sucrose) through phosphorylase activity in plant substrates, whereas fructose ispreferentially or exclusively reduced to mannitol (22). The delayed consumption ofmalic acid and citric acid might indicate a shift in metabolism of straightforwardsubstrates (mono- and disaccharides) to a more specialized metabolism (of organicacids, alkaloids, and phenolics), as is also seen during vegetable and fruit fermentations(26, 27). Alternatively, the amino acids arginine and glutamine (cluster B2), followed byvaline, isoleucine, and leucine, were consumed toward the end of the extendedfermentation. This consumption of branched-chain amino acids was the result ofmicrobial activities (likely LAB, since they were highly prevalent during fermentation).This LAB-mediated breakdown of branched-chain amino acids and its relevance toflavor have been extensively documented for other fermented foods (28, 29). These aminoacids are converted via �-ketoacids into aldehydes, alcohols, and/or carboxylic acids. Theseintermediates, such as 2/3-methylbutanal, 2/3-methylbutanol, 2-methylpropanol, and theircorresponding carboxylic acids, were indeed found in the volatile profile in the fermenta-tion waters at increasing concentrations, and they contributed to a high percentage of the

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aldehydes and alcohols. In combination with these compounds, a low pH in the fermen-tation tank, and high concentrations of lactic acid, acetic acid, and ethanol, the esters weremost probably produced chemically (30). Esters occupied the majority of the volatilecompounds toward the end of the extended fermentation (cluster C2 compounds). Finally,GABA could be produced by Lactobacillus or Lactococcus during both standard andextended fermentations. The GABA-producing potential of highly prevalent members ofthis ecosystem, such as L. brevis, has been widely reported in fermented foods (31, 32). Themetabolism of glutamic acid into GABA might have resulted in a competitive advantagethrough better tolerance toward the acidic stress experienced during extended fermenta-tion (33–35).

Concerning the third factor, minor exchanges of compounds between waters andbeans occurred during fermentation and soaking. Compounds present at high concen-trations in the fermentation and soaking waters could be adsorbed onto the beans ortrapped between the coffee bean endosperm and parchment during fermentation.These compounds were partially retained on the beans after washing, which resultedin a significant drop of the concentrations and aroma intensities of both volatile andnonvolatile compounds on these beans. These compounds were then released into theclean soaking waters due to osmotic differences. For example, monosaccharide con-centrations were high at the end of the standard fermentation, and monosaccharideswere carried over to the soaking waters, providing the microorganisms with nutrientsto grow. Leuconostoc (high prevalence during soaking) could utilize these compoundsto generate lactic acid under low acidic stress. In comparison, lactic acid, mannitol, andacetic acid were present at high concentrations at the end of the extended fermenta-tion. These compounds were also carried over into the soaking waters and resulted ina build-up toward the end of soaking. The final concentrations of lactic acid in thesoaking waters were similar for both processes, although the origins of the lactic acidwere different (microbial versus carry over). In addition, the increase in caffeine andtrigonelline concentrations in both fermentation and soaking waters might have beendue to minor leakage from the beans. Therefore, a dynamic equilibrium occurredcontinuously during the fermentation and soaking steps. This equilibrium might beaffected by the submerging time, pH, bean-to-water ratio, and environmental temper-ature. These factors should be considered by coffee farmers in the future.

(iii) Third, the metabolic processes on the beans vary during drying. Compared tothe chemical profile changes in the fermentation and soaking waters, the changes inthe chemical profiles of the beans were similar but less pronounced during fermenta-tion, soaking, and drying. The metabolites from prolonged microbial activities were stillpresent after soaking and drying (lactic acid and esters). The leakage of caffeine andtrigonelline into the waters, however, had no obvious impact on the chemical com-position of the beans. When the beans entered the drying step, newly formed com-pounds, such as pyrazines and furans (cluster D4), due to the high-temperature drying(initiating Maillard reactions), gave the characteristic green coffee bean smell. At thesame time, the microbiota present on the beans switched toward a prevalence of moredrought-tolerant communities, in particular, yeasts, and the microbial viability de-creased concomitantly. This decrease was more pronounced after the extended fer-mentation due to the prolonged stress.

(iv) Fourth, a significant endogenous bean metabolism within the coffee beansoccurred besides microbial activities. As only the mature coffee cherries were har-vested, most metabolic events that happened in the hydrated coffee beans wererelated to seed germination (16). The removal of pulp initiated bean germination andexposed the beans to various abiotic stresses, e.g., anoxia and osmotic stress duringfermentation and soaking and dehydration during drying. Whereas GABA (indicator ofgeneral stress) built up in the beans at the fermentation and soaking steps, beingproduced by LAB, its retention percentage was still high at the start of the soaking step.This indicated that the majority of GABA must have been endogenous to the beans.Indeed, GABA has been reported as a signaling compound during hypoxia/anoxia andgermination, whereby it can be converted into succinate through the GABA shunt of

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the plant (36). This corresponded to the rise of succinic acid in the beans duringfermentation and soaking. During drying, the production of GABA was prominent in thecoffee beans, as has been shown previously (14, 37). As live beans usually switch to ananaerobic metabolism when submerged in water, glycolysis would still occur and resultin the production of pyruvate. However, due to the lack of oxygen, pyruvate wasconverted into ethanol by alcohol dehydrogenase and/or lactic acid by lactate dehy-drogenase. Indeed, beans generate more ethanol than lactic acid under anoxia (38).This corroborated the high ethanol and acetic acid concentrations in the coffee beans,even after the washing step following the extended fermentation. The ethanol con-centrations remained the same before and after washing, indicating that the ethanoloriginated from the endogenous bean metabolism. Furthermore, during drying, variousosmolytes (such as proline and GABA) accumulated in the beans to increase theosmotic potential of the cells, as plant cells usually do under drought stress (14, 37).

The off-phase evolution profile of sucrose, compared to the profiles of glucose andfructose, might be related to the carbohydrate and lipid remobilization in the live coffeebeans. Sucrose is the principal saccharide translocated in plants and seeds. Endogenousinvertases can hydrolyze sucrose to glucose and fructose, which can be taken upactively by the endosperm cells prior to utilization in metabolic pathways (39). Thiswould result in the dynamic off-phase evolution of these carbohydrates. At the sametime, coffee beans are a rich reservoir of lipids (mainly triglycerides). During beangermination, triglycerides are catabolized through �-oxidation to acetyl coenzyme A(acetyl-CoA) and then enter the glyoxylate cycle for sucrose biosynthesis (39). Duringthe extended fermentation, the beans remained hydrated for a longer time than duringthe standard fermentation, which might have resulted in an enhanced lipid catabolism,as the total simple carbohydrate concentrations remained similar during fermentationand soaking. This might result in the lower total lipid content reserve of green coffeebeans from the extended fermentation than of green coffee beans from the standardfermentation. A high glyoxylate cycle activity has been reported during fermentation,but less so during the drying step of wet coffee processing (16). This might explain thedecrease of total simple carbohydrates during the drying step, as the lipid catabolismmight not be as active during this step as during fermentation and soaking.

Abundant free amino acids, such as glutamic acid and asparagine, have beenreported in beans from different geographical locations (14, 40). These amino acids canbe used as transport molecules in the plant to provide nitrogen. The 11S storageprotein has been characterized as the major storage protein in coffee beans, whichaccounts for 45% of the total coffee bean proteins, and is relatively rich in glutamine,glycine, leucine, and glutamic acid. Endogenous protease activity has been shown ingreen coffee beans (41), which might explain the increase of free amino acids duringdrying and their higher concentrations after the extended fermentation. Furthermore,the latter process yielded green coffee beans with a lower total CGA content. Theprolonged water stress during the extended fermentation might have caused the fasteroxidation of polyphenols when the beans were reexposed to oxygen during drying, asreported previously (3). Oxidation of CGAs has already been shown during aerobicincubation of green coffee bean powder. This might be due to endogenous phenoloxidase activity or autooxidation (41). As these reactions were avoided as much aspossible during sample preparation (addition of ascorbic acid and EDTA to the beanextracts), it is unlikely that the changes in these compounds were due to theseprocesses. From the above-stated information, it is clear that by changing processparameters or the duration of the processing steps, the endogenous bean metabolismcould result in a specific compositional change.

(v) Finally, upon roasting, a series of decisive chemical transformations occur. Bothvolatiles and nonvolatiles can be precursors of flavor in the final coffee cup. Reducingsugars and amino acids are responsible for the formation of pyrazines through Maillardreactions and Strecker degradation and of furans and furanones through caramelization(42). Such compounds are likely to give rise to the characteristic coffee and toastednotes (43). The use of time of flight mass spectrometry (TOF-MS) in the present study

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enabled us to find more volatile compounds in the beans at the same time than withcommonly applied methods. However, a causal link between the volatile compoundsand the microbiota is challenging to draw, since many of these compounds can be ofmicrobial, endogenous bean metabolism, or chemical origin. Moreover, in comparisonwith previous reports, higher concentrations of esters and lower concentrations oforganic acids were found (19, 43, 44). Given the similar extraction tools used, this waslikely due to the different extraction protocols and detection techniques applied.Linalool, terpineol, citral, and D-limonene are less volatile than esters or aldehydes andprovide foods with citrus and floral notes. Fruity esters (mainly ethyl 3-methylbutanoate)were present in higher concentrations in green coffee beans from the extendedfermentation processing. As LAB increase the acidity in the fermentation water, theproduction of other (off-flavor) compounds might have been minimal. Also, undesirablecompounds might have been degraded or favorable ones deposited onto the beans.This might have resulted in the significantly higher fruity notes of the coffee beverages.Higher CGA and trigonelline concentrations in green coffee beans from the standardfermentation processing might contribute to the higher bitterness and astringency ofthe final coffee cup. In comparison, higher lactic acid concentrations in these greencoffee beans might contribute to higher acidity (41).

Consequently, the coffee beverages brewed from beans that were subjected todifferent fermentation durations were significantly different in specific attributes (inparticular, fruity flavors and overall intensity). Crucially, the coffee from beans of theextended fermentation processing did not harbor any quality defects. Some notes thatare favored by consumers were even more pronounced in this coffee. Yet, coffeetraders and scientists generally believe that such coffee is associated with qualitydefects. Indeed, the generation of off-flavors (described as fermented notes) and ofstinker beans (production of dimethyl sulfide and butyric acid) typically results fromcoffee beans that ferment too long (45, 46). However, the present study showed thatextended fermentation (favoring acidification by LAB) did not necessarily lead to thesedefects, given the right microorganisms (LAB) that were present and the good farmingpractices that were applied. The compounds responsible for the defects associated withextended fermentation are often of microbial origin, notably, enterobacterial or clos-tridial origin (47). Yet, the coffee from beans of extended fermentation processing thatcame into considerable contact with high concentrations of desirable microbial (LAB)metabolites and showed prolonged endogenous metabolic activity displayed moredesirable flavor notes. Therefore, the conditions prevailing during fermentation deter-mined the microbial profile obtained through fermentation. These conditions resultedin a prevalence of LAB and the absence of off-flavor-producing microbial communitiesand allowed the beans to display extensive endogenous metabolic activity. Effectively,the coffee fermentation ecosystem was shielded from off-flavor production, so that adesirable flavor precursor profile could develop. LAB could aid in this safeguardingthrough competition for space and nutrients and extensive acidification of the fer-menting mass.

When altering the conditions prevailing during fermentation (in casu, the PFW), theemergence of anaerobic taxa (notably, Clostridium) and their metabolites related tooff-flavors (notably, butyric acid) prevailed. Thus, it could be that some LAB membersof the ecosystem had a protective effect toward coffee quality during fermentation inwet coffee processing. The role of LAB in providing a stable microbial environment incomplex ecosystems has been shown previously, for instance, during cocoa beanfermentation (48). Thus, this protective effect translated into a shielding of coffee beansfrom microbiota producing the typical off-flavors of overfermentation and allowed thebeans to display their extensive endogenous metabolic activity. LAB could attain thisshielding by rapid nutrient depletion and acidification as well as through competitionfor nutrients and space. In contrast, when conditions were different, microorganismsproducing off-flavors occurred.

In conclusion, the present study monitored the evolution of microbial diversity,metabolites, and bean chemical profiling along the whole wet coffee processing chain

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and evaluated the sensory quality of the concomitant coffee beverages made. Acombination of multiphasic analytical techniques enabled a deeper understanding ofeach step during coffee processing, which comprised dynamic and complex interac-tions between microorganisms, coffee beans, and environmental and processing con-ditions. Notably, prevalent microbial groups were selected before fermentation, inparticular, during the pooling of the coffee cherries awaiting depulping. During fer-mentation, LAB emerged as the prevalent microbial group, and the LAB communitiespresent depended on the fermentation duration. The LAB communities and theirmetabolites produced shielded the ecosystem from unwanted members (i.e., membersassociated with quality defects, such as enterobacteria and clostridia) and allowed thecoffee beans to display their extensive endogenous metabolism. These dual develop-ments resulted in distinct sensory profiles of coffee beverages produced from beanssubjected to standard and extended fermentation processing. Whether the ecologyand the metabolite profiles of the coffee beans of the present study exhibited vari-ability or were conservative needs to be confirmed by investigating wet coffee pro-cessing in other geographies and with other coffee varieties. In time, knowledgegathered from such studies could help to drive coffee processing toward consistentpostharvest processes with controllable outcomes.

MATERIALS AND METHODSPostharvest wet processing experiments. Coffea arabica L. var. Typica coffee cherries were used for

the wet processing experiments carried out at a plantation near Nanegal (Nestlé Ecuador; latitude andlongitude coordinates, 0°11’25.8“N and 78°40’41.4”W, respectively; altitude, 1,329 m) in June to July 2015.This research station carries out reproducible coffee wet processing under strictly controlled conditionsand according to a standardized protocol. Approximately 300 kg of healthy and mature coffee cherrieswere handpicked. The cherries were pooled in bags of approximately 50 kg each between harvesting anddepulping. After mechanical depulping (UCBE 500; Penagos, Bucaramanga, Colombia), the coffee beans(approximately 150 kg) were submerged in clean water to ferment spontaneously in a concrete tank (1 mby 2 m by 2 m). Half of the beans were fermented for 16 h and then withdrawn (referred to as standardfermentation), while the other half was fermented for 64 h before withdrawal (referred to as extendedfermentation). Whereas 16 h is the standard practice for wet fermentation at the local plantation of thepresent study, this fermentation duration was extended to 64 h, which corresponded to a time pointwhen the decreasing pH became stable, to see the effect of prolonged fermentation on the microbialcommunity dynamics, metabolite profiles, and sensory quality of the coffee produced. After fermenta-tion, both sets of beans were washed, soaked for 24 h, and sun dried, as described previously (3). Thetemperature and pH of the water were monitored on-line during fermentation and soaking. Sampleswere taken at specific time points throughout the postharvest processing chain (pooled coffee cherries,coffee beans, fermentation waters, and soaking waters). One part was used for on-site culture-dependentmicrobiological analysis; the remaining part was immediately frozen at �20°C until further metageneticand meta-metabolomic analyses. Each sample was given a specific code, as follows (Fig. 1). Pooled coffeecherry and depulped bean samples were denoted PC and DB, respectively. Fermentation samples weredenoted F followed by a number (indicating the hours of the fermentation duration) and ended with Bfor beans or W for fermentation water. Soaking bean samples after standard and extended fermentationwere denoted SS and SE, respectively, followed by a number (indicating the hours of the soakingduration) and B. The corresponding soaking water samples were denoted in the same way but endingwith W. Drying bean samples after standard and extended fermentation were denoted DS and DE,respectively, followed by a number (indicating the hours of the drying duration) and B. The green coffeebeans obtained through standard and extended practices were denoted SB and EB, respectively.

Environmental samples of the plantation soil (approximately 20 g of soil from different places directlyunder the coffee trees), coffee tree flowers (approximately 20), coffee tree leaves (approximately 10), andfresh cherries (approximately 20 g at eight different times) from different coffee trees were taken as well.Finally, based on the local farm practices, the fermentation water was drained at the end of thefermentation process, after which clean water was added to wash the fermented beans. This mixture offermentation water and wash water, i.e., the PFW, was collected at the end of the washing step and keptin a clean tank for 24 to 48 h. PFW samples were taken at three separate occasions, denoted PFW1, PFW2,and PFW3.

Selective plating and enumeration. To follow the microbial community dynamics, five microbialgroups were targeted by using selective agar media and incubation conditions, which were performedin triplicates. The total aerobic microbiota was enumerated on plate count agar (PCA), LAB on modifiedde Man-Rogosa-Sharpe agar supplemented with 0.1% (wt/vol) sorbic acid to inhibit fungal growth(MRS-S) (49), AAB on modified deoxycholate-mannitol-sorbitol agar (mDMS) (50), enterobacteria onviolet-red-bile-glucose agar (VRBG) (51), and yeasts and molds on yeast-glucose agar (YG) (51). MRS-S andmDMS agar media were supplemented with 0.2% (wt/vol) cycloheximide and 0.005% (wt/vol) ampho-tericin B to inhibit fungi, and YG agar medium was supplemented with 0.3% (wt/vol) chloramphenicolto inhibit bacteria. All agar plates were incubated aerobically at room temperature for 72 h, except for

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VRBG (incubated aerobically at room temperature for 24 h) and MRS-S (incubated anaerobically at roomtemperature for 72 h). Anaerobic incubation in bags was achieved by using AnaeroGen 2.5-liter oxygenscavengers (Thermo Fisher, Waltham, MA). Depending on their origin in the processing chain, samplesconsisted of cherries, beans, or waters, which were serially diluted in sterile saline (0.85% [wt/vol] NaCl).Starting with 20 g of material, 10-fold dilutions were made and plated in triplicates. For each selectiveagar medium and time point, an appropriate dilution containing 30 to 300 colonies was used forenumeration. Counts are expressed from triplicate averages as log CFU/ml � standard deviation. Allculture media and their compounds were purchased from Merck (Darmstadt, Germany).

Isolate recovery and identification. After enumeration, 10 to 15 colonies were picked randomly(independent of their appearance, shape, or size) from MRS-S, mDMS, and YG agar media correspondingwith high dilutions. These colonies were subsequently recultured in the corresponding liquid medium.After incubation for 24 to 48 h, 1.8 ml was supplemented with glycerol (final concentration of 25%[vol/vol]) and frozen at �20°C until further analysis. Cultures of purified isolates were subjected to DNAextraction, dereplication by (GTG)5-PCR genomic fingerprinting with numerical clustering, and identifi-cation of cluster representatives by 16S rRNA gene and internal transcribed spacer (ITS) region sequenc-ing, as described previously (52). When species-level identity could not be resolved for acetic acidbacteria, the dnaK gene was sequenced using the primers dnaK-01-F and dnaK-02-R (53). The accessionnumbers of the reference sequences used for identification of the isolates can be found in Table S1 inthe supplemental material.

Surface swabbing. The surface-associated microbiota of the processing apparatus (cherry storagebags, depulper exit shaft, empty fermentation tank, and empty soaking tank) was assessed by swabbinga surface of 10 cm2 at different angles. Cotton swab tips were wetted with 1 ml of sterile saline beforeswabbing. Cells attached to the tips were dislodged by sequential washing with 9 ml of saline. Theresulting 10-ml cell suspensions were then microcentrifuged (10,000 � g for 10 min at 4°C) before DNAextraction.

DNA extraction and metagenetic analysis. DNA was extracted by either a total DNA extractionprotocol, combining enzymatic, chemical, and mechanical cell lysis followed by phenol-chloroform-isoamyl alcohol extraction and column purification, as described previously (3), or by a commercial kit(PowerSoil DNA isolation kit; Mobio, Hilden, Germany), depending on the level of contamination. TotalDNA extraction was used for the freshly harvested cherries, postharvest processing samples (pooledcherries, depulped beans, fermenting beans, and soaking beans), and PFW samples. A commercial kit forDNA extraction was used for the processing apparatus and environmental samples (plantation soil,coffee tree flowers, and coffee tree leaves). The V4 hypervariable region of the 16S rRNA gene of bacteriaand the ITS1 region of the 26S rRNA gene of yeasts and molds were amplified from the DNA extractsobtained, as described previously (3). A modification of this protocol was the incorporation of a mockcommunity during sequencing (HM-783D; BEI Resources, Manassas, VA). All amplicons generated by thetwo primer sets were sequenced in parallel. The first six nucleotides of every forward and reverse primerwere used to separate the data sets before bioinformatic analysis. Amplicon sequence variants (ASVs)were inferred from the high-throughput amplicon sequencing data by using the dada2 package (version1.6.0). These ASVs are an alternative for the coarser and less accurate operational taxonomic unit (OTU)clustering approach and can resolve biological differences of as little as one nucleotide (54). The filteringparameters (maxN � 0, truncQ � 2, rm.phix � TRUE, maxEE � 1, and truncLen � 230) were appliedbefore inputting the filtered reads into dada2’s parametric error model. The truncLen parameter was notapplied for the ITS1 reads, since the expected sequence length is variable for yeasts and molds. OnlyASVs with total abundances of �0.1% are reported. Taxonomy was assigned with the SILVA database(version 128) for the bacterial ASVs and with the UNITE database (version 01.12.2017) for the fungal ASVs.Bacterial ASVs that were not classified to the genus level were resolved by comparing them to theEZBioCloud database (55), and fungal ASVs were resolved by comparing them to the nucleotide databaseof the National Center for Biotechnological Information (NCBI; Bethesda, MD) with the basic localalignment search tool (BLASTN) algorithm (56).

Growth assessment of the epiphytic coffee cherry microbiota. To assess the potential of theepiphytic coffee cherry microorganisms to grow during the harvesting-depulping interval of the post-harvest processing chain, cherries were subjected to conditions mimicking their storage in large bags,which results in exudation of sap. Therefore, approximately 3 kg of freshly harvested cherries were putinside sterile plastic bags to avoid contamination from the environment. A mass of approximately 5 kgwas placed on top to simulate the mechanical pressure they undergo during pooling. This mechanicalpressure results from common piling of the cherries inside the bags. To follow the evolution of thegrowth of the main microbial groups, samples were taken and enumerated on MRS-S, mDMS, and YGagar media.

Meta-metabolomic analysis. (i) Sample preparation. Frozen samples of fermentation water pluscoffee beans, soaking waters, PFW, and the coffee beans from all aforementioned samples were thawedbefore use. In the case of the first samples mentioned, fermentation water and coffee beans wereseparated. All aqueous samples were microcentrifuged (19,400 � g for 15 min at 10°C) prior to analysis.In the case of coffee bean samples, the parchment and silver skin were removed, followed by cooling inliquid nitrogen. The beans were then milled with a coffee grinder (KG49; DeLonghi, Treviso, Italy) toobtain fine powders appropriate for extraction. Three different extraction conditions were applied,including water, 0.01 N hydrogen chloride (Merck), and 40% (vol/vol) methanol (Merck), as describedpreviously (3) with slight modifications, in that 0.2 g of powder was mixed with 5 ml of extractionsolvents containing 0.2% (wt/wt) ascorbic acid (Merck) and 0.2% (wt/wt) EDTA (Merck) to inhibitoxidation and enzyme activity, respectively. Each extraction was performed in triplicates.

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(ii) Moisture and total fat contents. The moisture content of the coffee beans from all samples wasmeasured by means of an oven method (57). Therefore, grinded bean powder was dried in an oven(T5042; Heraeus, Hanau, Germany) for 24 h until the mass did not vary. The total fat content of the greencoffee beans was determined by the Soxhlet method with tert-butyl methyl ether (Acros Organics, Geel,Belgium) at 40°C for 6 h (58).

(iii) Quantification of simple carbohydrates and sugar alcohols. Concentrations of simple carbo-hydrates (fructose, galactose, glucose, and sucrose) and sugar alcohols (arabitol, erythritol, glycerol,mannitol, myo-inositol, sorbitol, and xylitol) were quantified in triplicates with internal standardization byhigh-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD)using an ICS 3000 chromatograph equipped with a CarboPac PA-100 and CarboPac MA-1 column(Dionex, Sunnyvale, CA), respectively (3). The internal standard (IS) solution was prepared by adding20 mg of rhamnose (Merck) to 500 ml of acetonitrile (Merck). Both fermentation and soaking watersamples, as well as the aqueous extracts of coffee beans, were mixed with the IS solution at a 1:3 ratio,microcentrifuged (19,400 � g for 15 min at 10°C), and filtered (Chromafil 0.20-�m polytetrafluoroethyl-ene [PTFE] [in the case of simple carbohydrates] or polyethersulfone [in the case of sugar alcohols] filters;Macherey-Nagel, Düren, Germany) before injecting (10 �l) into the column.

(iv) Quantification of organic acids, alkaloids, and phenolics. The concentrations of organic acids(citric acid, fumaric acid, gluconic acid, isocitric acid, 5-ketogluconic acid, lactic acid, malic acid, oxalicacid, quinic acid, and succinic acid), alkaloids (caffeine and trigonelline), and phenolics (six CGAs [3-CQA,4-CQA, 5-CQA, 3,4-diCQA, 3,5-diCQA, and 4,5-diCQA], ferulic acid, and caffeic acid) in both fermentationand soaking water samples, as well as coffee bean extracts, were quantified in triplicates with external(organic acids) or internal (alkaloids and phenolics) standardization by ultraperformance liquid chroma-tography coupled to tandem mass spectrometry (UPLC-MS/MS) using an Acquity system equipped withan HSS T3 column (Waters, Milford, MA), as described previously (3) with minor modifications. The ISsolutions for alkaloids and CGAs were 1-ethyl-4-(methoxycarbonyl)pyridinium iodide (0.15 ng/ml; Merck)and rosmarinic acid (1.0 ng/ml; Merck), respectively. Both fermentation and soaking water samples, aswell as the extracts of the coffee beans, were mixed with the IS solutions, microcentrifuged (19,400 � gfor 15 min at 10°C), and filtered (Chromafil 0.20-�m PTFE filters) before injecting (10 �l) into the column.

(v) Quantification of free amino acids. The proteinogenic amino acids and one nonproteinogenicamino acid (GABA) were quantified in fermentation and soaking water samples, as well as in the acidicextracts of the coffee beans, in triplicates with internal standardization by high-performance liquidchromatography coupled to tandem mass spectrometry (HPLC-MS/MS) using an Alliance 2695 chro-matograph equipped with a Micromass Quattro Micro (Waters). The mobile phase, at a flow rate of1 ml/min, was composed of 1 mM formic acid and 1 mM ammonium formate at pH 4.0 with 5% (vol/vol)acetonitrile (eluant A) and 100% (vol/vol) acetonitrile with 2 mM formic acid (eluant B) (all chemicals fromMerck). Two eluant programs were used to achieve good separation for the 21 amino acids on an AstecChirobiotic T column (15 cm by 4.6 mm, 5-�m particle size; Sigma-Aldrich, St. Louis, MO). The first eluantprogram was used to quantify aspartic acid, arginine, cysteine, GABA, glutamic acid, glutamine, histidine,and lysine, for which the following gradient was applied: 0.0 to 10.0 min, linear from 40% to 100% eluantA; 10.0 to 26.0 min, isocratic at 100% eluant A; 26.0 to 27.0 min, linear from 100% eluant A to 40% eluantB; and 27.0 to 32.0 min, isocratic at 40% eluant A. The second eluant program was used to quantify allother compounds with a gradient as follows: 0.0 to 12.0 min, isocratic at 20% eluant A; 12.0 to 16.0 min,linear from 20% to 40% eluant A; 16.0 to 20.0 min, linear from 40% to 100% eluant A; 20.0 to 24.5 min,isocratic at 100% eluant A; 24.5 to 25.0 min, linear from 100% to 20% eluant A; and 25.0 to 30.0min, isocratic at 20% eluant A. L-2-Amino butyric acid (1.2 ng/ml; Merck) was used as the IS. All sampleswere microcentrifuged (19,400 � g for 15 min at 10°C) and filtered (Chromafil 0.20-�m PTFE filters)before injecting (10 �l) into the column.

(vi) Quantification of short-chain fatty acids and low-molecular-mass volatiles. Short-chain fattyacids (SCFAs; acetic acid, butyric acid, hexanoic acid, isobutyric acid, 3-methylbutyric acid, pentanoic acid,and propionic acid) and low-molecular-mass volatiles (acetaldehyde, ethanol, ethyl acetate, ethyl lactate,and isopentyl acetate) were quantified in fermentation and soaking water samples, as well as coffee beanextracts, in triplicates with external calibration (with inclusion of 1-butanol [Merck] as IS) by gaschromatography coupled to flame ionization detection (GC-FID) using a Focus GC chromatograph(Interscience, Breda, The Netherlands) equipped with a Stabilwax-DA column (Restek, Bellefonte, PA) anda FID-80 detector (Interscience), as described previously (3).

(vii) Volatile profiling via headspace/solid-phase microextraction gas chromatography–time offlight mass spectrometry. Nontargeted volatile profiling was conducted by headspace/solid-phasemicroextraction coupled to gas chromatography and TOF-MS (HS/SPME-GC-TOF-MS) in triplicates usinga Trace 1300 gas chromatograph (Thermo Fisher) equipped with a Stabilwax-MS column (Restek) andcoupled to a BenchTOF-HD mass spectrometer (Markes International, Llantrisant, Wales). For analysis ofthe coffee bean samples, 1.5 g of grinded beans was incubated in a 10-ml screw-top headspace vial at50°C for 10 min, followed by extraction with agitation at 250 rpm for 45 min using an SPME fiber(DVB/CAR/PDMS, 50/30 �m; Sigma-Aldrich). For analysis of the fermentation and soaking water samples,2 ml of liquid was incubated at 30°C for 15 min and extracted using the same fiber for 15 min. To eachsample, 10 �l of 10 ppm toluene-D8 solution (Sigma-Aldrich) was added, and the vials were placed in atray cooled to 4°C before analysis. The volatiles from the SPME fiber were thermally desorbed at 260°Cat splitless mode and resolved with a fused silica capillary column (Stabilwax-MS, 30 m by 0.25 mm, filmthickness of 0.25 �m; Restek) coated with polyethylene glycol. The GC oven temperature was pro-grammed as follows: initially 40°C for 5 min, increased to 130°C at 3°C/min, then increased to 250°C at8°C/min, and finally held at 250°C for 2 min. Helium gas was used as the carrier gas at a flow rate of

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1 ml/min. The TOF was scanned in the m/z range of 35 to 400 with a solvent delay of 2 min. The raw datawere deconvoluted by TOF-DS software (Markes), followed by identification of the peaks, which wasmainly based on the NIST (National Institute of Standard and Technology, Gaithersburg, MD) library andsupported by the Kovats index (59) and standards commercially available. The peak area of eachcompound identified was normalized to the peak area of the IS. In the case of the bean samples, the peakarea was further adjusted according to the moisture content of the coffee beans. Both values wereconsidered values for aroma intensity.

Roasting and sensory evaluation. The green coffee beans from the standard and extendedfermentation processing (250 g each) were roasted according to a standard protocol until the color of theroasted beans was consistent, namely, corresponding to a Color Test Neuhaus (CTN) of 90 (NeuhausNeotec, Ganderkesee, Germany). Coffee beverages were prepared with a Moccamaster coffee machine(Technivorm, Amerongen, The Netherlands) at a ratio of 50 g of roasted beans per liter of water. Theywere served at 70°C in Nespresso plastic cups (80 ml). A quantitative descriptive analysis (QDA) wasapplied to measure the intensity of 27 sensory attributes, covering odor, flavor, and texture. The sampleswere evaluated one at a time for all attributes by 12 trained panelists at the Nestlé Research Center(Vers-chez-les-Blanc, Switzerland).

Statistical analysis. Concerning the microbial identifications, a centered and rotated principal-component analysis (PCA) was performed on the joint V4 and ITS1 ASV covariance in R (version 3.4.2).A network based on the presence/absence of the ASVs with relative abundances of �5% in any samplewas created with the Yifan Hu proportional algorithm of Gephi (version 0.9.2). The heatmaps of themeta-metabolomics data were calculated, and these data were clustered using the package massageRand ggplot in RStudio (version 0.99.902). The distance metric was based on Pearson’s correlationcoefficient. The subsequent hierarchical clustering was performed based on the average distancebetween the points in the two clusters. The hierarchical clustering of the metabolite data of thefermentation and soaking water samples was based on the correlation matrix of the fermentation waterdata, whereas the clustering of the bean samples was based on the correlation matrix of the entire dataset. The scaling of each row was performed with the build-in function of heatmap.2. The scaling of thefermentation and soaking water samples was done separately because of the large differences inabsolute concentrations. A correlation matrix of the microbial communities (ASV data) and the meta-metabolomics data of the fermentation and soaking steps (processing waters and coffee beans) wasconstructed based on the Spearman’s rank correlation coefficients. Correlations with a P value of �0.05were visualized as a heatmap. Analysis of variance (ANOVA) was used for the QDA data of the sensoryevaluation. The results were considered statistically different when the P value was �0.05.

Accession number(s). The sequences are available at the European Nucleotide Archive (https://www.ebi.ac.uk/ena) under accession number PRJEB29145 (https://www.ebi.ac.uk/ena/data/view/PRJEB29145).

SUPPLEMENTAL MATERIALSupplemental material for this article may be found at https://doi.org/10.1128/AEM

.02635-18.SUPPLEMENTAL FILE 1, PDF file, 1 MB.

ACKNOWLEDGMENTSThis work was supported by the Research Council of the Vrije Universiteit Brussel

(SRP7 and IOF342 projects), the Hercules foundation (projects UABR09004 andUAB13002), and Nestec S.A., a subsidiary of Nestlé S.A.

We thank Charles Lambot for advice on the experimental design, María Isabel Larreafor helping in the preparation of the materials for the field experiments, Sander Wuytsfor the help with the bioinformatics analysis, Dominique Maes for advice on thestatistical analysis, and Wim Borremans for technical assistance with the analyticalapparatus.

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