Identification of flavor modulating compounds that positively impact coffee quality

Identification of Compounds That Impact Consumer Flavor Liking of American-European Hazelnut Hybrids Using Nontargeted LC/MS Analysis

American-European (Corylus americana × Corylus avellana) hazelnut hybrids are being developed for the Midwest-growing region of the United States. However, an inadequate understanding of the compounds that impact the consumer acceptance of hazelnuts limits breeding programs. Nontargeted liquid chromatography/mass spectrometry (LC/MS) chemical profiles of 12 roasted hybrid hazelnut samples and the corresponding consumer flavor liking scores were modeled by orthogonal partial least squares with good fit and predictive ability (R2Y > 0.9, Q2 > 0.9) to identify compounds that impact nut liking. The five most predictive compounds (1-5) were negatively correlated to flavor liking, selected as putative markers, purified by multidimensional preparative LC/MS, structurally elucidated (nuclear magnetic resonance, MS), quantified, and validated for sensory relevance. Compound 1 was identified as 1″-O-3'-b-glucofuranosyl-1'-O-1-b-glucofuranosyl-(2,6-dihydroxyphenyl)-ethan-4-one. Compounds 2 and 4 were identified as rotamers of 2-(3-hydroxy-2-oxoindolin-3-yl) acetic acid 3-O-6'-galactopyranosyl-2″-(2″oxoindolin-3″yl) acetate, whereas compounds 3 and 5 were identified as rotamers of 1″-O-1'-b-glucofuranosyl-9-O-6'-b-glucopyranosyl-2″-(2″-oxoindolin-3″yl) acetate. Sensory evaluation determined that all compounds were characterized by bitterness and/or astringency. The sensory threshold values of compounds 1-5 were determined to be below the concentrations reported in 91, 83, 41, 25, and 41% of all 12 hybrid hazelnut samples, respectively, indicating they contributed to aversive flavor attributes.

Identification of potential quality markers in Indonesia's Arabica specialty coffee using GC/MS-based metabolomics approach

INTRODUCTION

The cupping test is a widely used method for quality assessment of Arabica coffee. However, the cupping test is limited by the low number of certified panelists and the low throughput. Therefore, an analytical-based quality assessment may be a promising tool to complement the cupping test. A present, there is no report investigating quality marker candidates, focusing only on "specialty" grade Arabica coffee from Indonesia.

OBJECTIVE

This study identified the potential quality marker(s) in Arabica Specialty coffee at different stages (green beans, roasted beans, and brewed coffee.

METHODS

The metabolite profiles of ten different Arabica specialty-grade coffees were analyzed with different cup scores using gas chromatography-mass spectrometry (GC/MS). From the ten samples, green coffee beans, roasted coffee beans, and brewed coffee were selected. In addition, an orthogonal projection to latent structure (OPLS) regression analysis was conducted to obtain a potential quality marker based on the variable importance in projection (VIP). The potential quality marker(s) were validated by GC/MS metabolome profiling and OPLS analysis of different sets of samples consisting of 35 Arabica specialty-grade coffee samples.

RESULTS

In Arabica coffee samples, the OPLS model of the three stages showed galactinol to have a high VIP score. Galactinol showed a consistent positive correlation with cup scores at all stages of coffee production (green beans, roasted beans, and brewed coffee). The correlation suggests galactinol is a potential quality marker after further validation using different samples.

CONCLUSION

GC/MS combined with OPLS regression analysis suggested galactinol as a quality marker and provide an early screening method for Arabica coffee quality that complements the cupping test performed by certified panelists.

Microbial Diversity of Anaerobic-Fermented Coffee and Potential for Inhibiting Ochratoxin-Produced Aspergillus niger

Coffee flavor considerably depends on the fermentation process, with contributing factors including fermentation temperature, oxygen concentration, and microbial diversity. Efficient controlling of the fermentation can improve the quality of coffee beverages. Therefore, several studies on coffee fermentation processes have been conducted in various regions. The objective of this study was to assess the microbial diversity of coffee beans undergoing anaerobic fermentation at various temperatures (4 °C or 37 °C) and fermentation durations (12 h or 36 h) using full-length 16S rRNA sequencing. This analysis aimed to evaluate the inhibitory effects of the fermented metabolites against ochratoxin-producing Aspergillus niger. From our results, Acetobacter was identified as the dominant microbial community at higher fermentation temperatures, whereas Leuconostoc and Gluconobacter were the dominant genera at lower temperatures. However, at lower temperatures, changes in microbial communities were relatively slow. This study expands our knowledge of the microbial diversity involved in the anaerobic fermentation of coffee beans in Taiwan. The findings of this study can be used in future research to cultivate microorganisms linked to the quality and improve the quality of coffee beverages through fermentation.

Identification of compounds that enhance bitterness of coffee brew

The bitterness perception of coffee is a key attribute that impacts consumer acceptance. Nontargeted liquid chromatography/mass spectrometry (LC/MS) flavoromics analysis was applied to identify compounds that enhance the bitter perception of roasted coffee brew. Orthogonal partial least squares (OPLS) analysis was used to model the comprehensive chemical profiles and sensory bitter intensity ratings of fourteen coffee brews with good fit and predictivity. Five compounds that were highly predictive and positively correlated to bitter intensity were selected from the OPLS model, further isolated, and purified using preparative LC fractionation. Sensory recombination testing demonstrated that five compounds significantly enhanced the bitter perception of coffee when presented as a mixture, but not when presented individually. In addition, a set of roasting experiments revealed the five compounds were generated during the coffee roasting process.

Infrared-Photoacoustic Spectroscopy and Multiproduct Multivariate Calibration to Estimate the Proportion of Coffee Defects in Roasted Samples

Infrared-photoacoustic spectroscopy (IR-PAS) and partial least squares (PLS) were tested as a rapid alternative to conventional methods to evaluate the proportion of coffee defects in roasted and ground coffees. Multiproduct multivariate calibration models were obtained from spectra of healthy beans of Coffea canephora and C. arabica (Arabica) and blends composed of defective and healthy beans of Arabica in different proportions. The blends, named selections, contained sour, black, broken, whole beans, skin, and coffee woods. Six models were built using roasted and ground coffee samples. The model was optimized through outlier evaluation, and the parameters of merit such as accuracy, sensitivity, limits of detection and quantification, the inverse of analytical sensitivity, linearity, and adjustment were computed. The models presented predictive capacity and high sensitivity in determining defects, all being predicted with suitable correlation coefficients (ranging from 0.7176 to 0.8080) and presenting adequate performance. The parameters of merit displayed promising results, and the prediction models developed for %defects can be safely used as an alternative to the reference method. Furthermore, the new method is fast, efficient, and suitable for in-line application in quality control industrial coffee processing.

Preliminary Analysis of Unsaturated Fatty Acid Profiles of Coffea arabica L., in Samples with a Denomination of Origin and Speciality of Oaxaca, Mexico

In February 2020, Coffea arabica L. grown on the coast and in the Southern Sierra Madre of the state of Oaxaca, Mexico obtained the denomination of origin. Which does not have data on color and chemical composition, the first associated with the degree of roasting and the second with lipids (17-18%), as the group of compounds responsible, in part, for flavor, consistency, and may contribute to health benefits. In the present work, color was determined on the CIE L*a*b* scale and the unsaturated fatty acids by Nuclear Magnetic Resonance (NMR) of ¹H and ¹³C in samples of medium roasted specialty coffee from the "Pluma" coffee-growing region, Oaxaca, Mexico. The average value of L* luminosity in ground coffee was 42.1 ± 0.1 reported for a light roast. Unsaturated fatty acids were quantified from the lipid fraction of the gr₁ ound grain by NMR ¹H and ¹³C, obtaining on average the highest abundance of linoleic (41.7 ± 0.5 by ₁ H and 41.24 ± 0.5 by ¹³C), followed by oleic (9.2 ± 0.2 by ¹H and 7.4 ± 0.2 by ¹³C) and linolenic (1.5 ± 0.1 by H and 1.1 ± 0.2 by ¹³C). This study indicates that ¹H and ¹³C NMR spectroscopy is a useful tool for the quantification of linolenic, linoleic, and oleic fatty acids by the method of key signal shifts of these acids found in lipid samples in roasted coffee grains.

The molecular mechanisms of quality difference for Alpine Qingming green tea and Guyu green tea by integrating multi-omics

Introduction

Harvest time represents one of the crucial factors concerning the quality of alpine green tea. At present, the mechanisms of the tea quality changing with harvest time have been unrevealed.

Methods

In the current study, fresh tea leaves (qmlc and gylc) and processed leaves (qmgc and gygc) picked during Qingming Festival and Guyu Festival were analyzed by means of sensory evaluation, metabolomics, transcriptomic analysis, and high-throughput sequencing, as well as their endophytic bacteria (qm16s and gy16s).

Results

The results indicated qmgc possessed higher sensory quality than gygc which reflected from higher relative contents of amino acids, and soluble sugars but lower relative contents of catechins, theaflavins, and flavonols. These differential metabolites created features of light green color, prominent freshness, sweet aftertaste, and mild bitterness for qmgc.

Discussion

Flavone and flavonol biosynthesis and phenylalanine metabolism were uncovered as the key pathways to differentiate the quality of qmgc and gygc. Endophytic bacteria in leaves further influence the quality by regulating the growth of tea trees and enhancing their disease resistance. Our findings threw some new clues on the tea leaves picking to pursue the balance when facing the conflicts of product quality and economic benefits.

Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning

This study examines the volatilome of good and oxidised coffee samples from two commercial coffee species (i.e., Coffea arabica (arabica) and Coffea canephora (robusta)) in different packagings (i.e., standard with aluminium barrier and Eco-caps) to define a fingerprint potentially describing their oxidised note, independently of origin and packaging. The study was carried out using HS-SPME-GC-MS/FPD in conjunction with a machine learning data processing. PCA and PLS-DA were used to extrapolate 25 volatiles (out of 147) indicative of oxidised coffees, and their behaviour was compared with literature data and critically discussed. An increase in four volatiles was observed in all oxidised samples tested, albeit to varying degrees depending on the blend and packaging: acetic and propionic acids (pungent, acidic, rancid), 1-H-pyrrole-2-carboxaldehyde (musty), and 5-(hydroxymethyl)-dihydro-2(3H)-furanone.

Targeted and Non-Targeted HPLC Analysis of Coffee-Based Products as Effective Tools for Evaluating the Coffee Authenticity

Coffee is a very popular beverage worldwide. However, its composition and characteristics are affected by a number of factors, such as geographical and botanical origin, harvesting and roasting conditions, and brewing method used. As coffee consumption rises, the demands on its high quality and authenticity naturally grows as well. Unfortunately, at the same time, various tricks of coffee adulteration occur more frequently, with the intention of quick economic profit. Many analytical methods have already been developed to verify the coffee authenticity, in which the high-performance liquid chromatography (HPLC) plays a crucial role, especially thanks to its high selectivity and sensitivity. Thus, this review summarizes the results of targeted and non-targeted HPLC analysis of coffee-based products over the last 10 years as an effective tool for determining coffee composition, which can help to reveal potential forgeries and non-compliance with good manufacturing practice, and subsequently protects consumers from buying overpriced low-quality product. The advantages and drawbacks of the targeted analysis are specified and contrasted with those of the non-targeted HPLC fingerprints, which simply consider the chemical profile of the sample, regardless of the determination of individual compounds present.

Targeted approaches to improve tomato fruit taste

Tomato (Solanum lycopersicum) is the most valuable fruit and horticultural crop species worldwide. Compared with the fruits of their progenitors, those of modern tomato cultivars are, however, often described as having unsatisfactory taste or lacking flavor. The flavor of a tomato fruit arises from a complex mix of tastes and volatile metabolites, including sugars, acids, amino acids, and various volatiles. However, considerable differences in fruit flavor occur among tomato varieties, resulting in mixed consumer experiences. While tomato breeding has traditionally been driven by the desire for continual increases in yield and the introduction of traits that provide a long shelf-life, consumers are prepared to pay a reasonable premium for taste. Therefore, it is necessary to characterize preferences of tomato flavor and to define its underlying genetic basis. Here, we review recent conceptual and technological advances that have rendered this more feasible, including multi-omics-based QTL and association analyses, along with the use of trained testing panels, and machine learning approaches. This review proposes how the comprehensive datasets compiled to date could allow a precise rational design of tomato germplasm resources with improved organoleptic quality for the future.

Conduction of a chemical structure-guided metabolic phenotype analysis method targeting phenylpropane pathway via LC-MS: Ginkgo biloba and soybean as examples

The phenylpropane pathway (PPP) is one of the most extensively investigated metabolic routes. This pathway biosynthesizes many important active ingredients such as phenylpropanoids and flavonoids that affect the flavor, taste and nutrients of food. How to elucidate the metabolic phenotype of PPP is fundamental in food research and development. In this study, we designed a structural periodical table filled with 103 metabolites produced from PPP. All of them especially the 62 structural isomers were qualified and quantified with high resolution and sensitivity via multiple reaction mode in liquid chromatography tandem triple quadrupole mass spectrometry. Ginkgo biloba and soybean were used as samples for the practical application of this method: The delicate spatial-temporal metabolic balance of PPP from ginkgo biloba has been first elucidated; It is first confirmed that the salt and draught stresses could redirect the biosynthesis trend of PPP to produce more isoflavones in soybean leaves.

Does Coffee Have Terroir and How Should It Be Assessed?

The terroir of coffee is defined as the unique sensory experience derived from a single origin roasted coffee that embodies its source. Environmental conditions such as temperature, altitude, shade cover, rainfall, and agronomy are considered the major parameters that define coffee terroir. However, many other parameters such as post-harvest processing, roasting, grinding, and brewing can combine to influence the perception of terroir. In this review, we discuss the contribution of these parameters and their influence on coffee terroir. Assessment of terroir requires defined sensory descriptors, as provided by the World Coffee Research Lexicon, and standardized roast level, grind size, and brew method. The choice of the post-harvest processing method is often environmentally dependent, suggesting that an inclusion into the coffee terroir definition is warranted. Coffee terroir is often not intentionally created but results from the contributions of the Coffea species and variety planted, environmental and agricultural parameters, and both the harvest and post-harvest method used. The unique combination of these parameters gives the consumer a unique cup of coffee, reminiscent of the place the coffee was produced.

Identification of subthreshold chlorogenic acid lactones that contribute to flavor instability of ready-to-drink coffee

Flavor instability of ready-to-drink (RTD) coffee during storage negatively impacts product quality. Untargeted liquid chromatography/mass spectrometry (LC/MS) analysis was applied to identify chemical compounds that degraded during storage and impacted the flavor attributes of RTD coffee. LC/MS chemical profiles of non-aged and aged coffee samples were modeled against the degree of difference sensory scores by orthogonal partial least squares with good fit (R²Y = 0.966) and predictive ability (Q² = 0.960). The top five predictive chemical features were subsequently purified by off-line multidimensional Prep-LC, revealing ten coeluting chlorogenic acid lactones (CGLs) compounds that were identified by LC/MS and nuclear magnetic resonance (NMR). The concentrations of eight CGLs significantly decreased in the coffee during the 4-month storage. Sensory recombination testing revealed the degradation of 3-O-caffeoyl-ɣ-quinide and 4-O-caffeoyl-ɣ-quinide significantly impacted the flavor stability of RTD coffee at subthreshold concentrations.

Identification of non-volatile compounds that impact consumer liking of strawberry preserves: Untargeted LC-MS analysis

Non-volatile compounds that impact the acceptability of strawberry preserves were investigated by untargeted LC-MS flavoromics analysis. Chemical profiles for fifteen strawberry preserves were modeled against consumer liking scores by orthogonal partial least squares (OPLS) with good fit (R²Y = 0.995) and predictive ability (Q² = 0.918). Four chemical compounds predictive of acceptability were identified, by accurate MS and NMR, as secoisolariciresinol monoglucoside, (+)-isolariciresinol monoglucoside, 1-hexanoyl-phloroglucinol-2-O-β-d-glucoside, and the novel compound decanoic acid-4-O-β-d-glucoside. Sensory recombination testing of preserve samples with added levels of the four predictive LC-MS compounds indicated perceivable sensory changes in the flavor profile. Female consumers significantly preferred the recombination preserve with added levels of both predictive GC-MS and LC-MS compounds as compared to the control preserve, demonstrating the applicability of the approach for understanding product liking.

Identification of volatile compounds correlated with consumer acceptability of strawberry preserves: Untargeted GC-MS analysis

Volatile compounds that impact the acceptability of strawberry preserves were investigated by untargeted GC-MS flavoromics analysis. Chemical profiles for fifteen strawberry preserves were modeled against consumer liking scores by orthogonal partial least squares (OPLS) with good fit (R²Y = 0.998) and predictive ability (Q² = 0.853). Ten highly predictive compounds were selected, nine of which were identified, and eight of which were quantified using tandem mass spectrometry. Sensory recombination difference testing revealed that the addition of predictive compounds to an average-liked preserve at levels found in the most-liked preserve induced a perceptible difference to frequent consumers of strawberry jams. One of the highly predictive compounds was identified (MS and synthesis) as a novel linalool oxide product 2-methyl-2-vinyl-tetrahydrofuran with a fruity, herbal-minty, piney aroma and a low odor threshold value.

Comprehensive analysis of quality characteristics in main commercial coffee varieties and wild Arabica in Kenya

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The contents of key non-volatile compounds, including chlorogenic acid, trigonelline, caffeine, and sucrose, vary significantly among Arabica cultivars in Kenya.

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Trigonelline is strongly associated with quality attributes of coffee brews.

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Pyrazines and thiols are major coffee flavor determinants in commercial cultivars in Kenya.

Coffee flavor is a complex commercial trait and its generation mechanisms remain largely unclear. Here, we investigated non-volatile and volatile compounds in the AA grade coffee beans of cultivated and wild Coffea arabica accessions in Kenya. An increased accumulation of trigonelline and sucrose along with a decreased accumulation of caffeine and 5-caffeoylquinic acid or chlorogenic acid (CGA) relative to wild Arabica contribute to the improved flavor of commercial varieties. Trigonelline was strongly associated with attributes of coffee brews such as pH, aroma intensity and antioxidant activity, suggesting that it is one of the main flavor precursors. Partial least squares discriminant analysis (PLS-DA) identified 18 volatiles that could potentially define flavor quality of coffee brews, with pyrazines and thiols as the major coffee flavor determinants.

Untargeted LC-MS based identification of Rebaudioside A degradation products impacting flavor perception during storage

Untargeted LC-MS flavoromics chemical profiling was used to identify compounds predictive of Rebaudioside A (Reb A) flavor instability in an acidified beverage after 6 weeks at 35 °C. High-quality orthogonal partial least squares analysis models were developed from the chemical data and d' values from tetrad sensory panel testing with good predictive ability. The top four highly predictive compounds were selected and identified as Reb A (negatively correlated) and three Reb A degradation compounds (positively correlated), which included a rearrangement, hydration, and an epoxidation/rearrangement of Reb A, termed compounds 1, 2, and 3, respectively. The concentrations of compounds 1-3 in the aged beverages were determined to be below the sensory recognition threshold values. However, sensory recombination testing of compounds 1-3 as a tertiary mixture revealed a perceivably significant flavor change that was aligned with the aged beverage.

Identification of Non-Volatile Compounds Generated during Storage That Impact Flavor Stability of Ready-to-Drink Coffee

Coffee brew flavor is known to degrade during storage. Untargeted and targeted LC/MS flavoromics analysis was applied to identify chemical compounds generated during storage that impacted the flavor stability of ready-to-drink (RTD) coffee. MS chemical profiles for sixteen RTD coffee samples stored for 0, 1, 2, and 4 months at 30 °C were modeled against the sensory degree of difference (DOD) scores by orthogonal partial least squares (OPLS) with good fit and predictive ability. Five highly predictive untargeted chemical features positively correlated to DOD were subsequently identified as 3-caffeoylquinic acid, 4-caffeoylquinic acid, 5-caffeoylquinic acid, 3-O-feruloylquinic acid, and 5-O-feruloylquinic acid. The increase in the six acidic compounds during storage was confirmed by sensory recombination tests to significantly impact the flavor stability of RTD coffee during storage. A decrease in pH, rather than an increase in total acidity, was supported to impact the coffee flavor profile.

Metabolomics-Based Approach for Coffee Beverage Improvement in the Context of Processing, Brewing Methods, and Quality Attributes

Coffee is a worldwide beverage of increasing consumption, owing to its unique flavor and several health benefits. Metabolites of coffee are numerous and could be classified on various bases, of which some are endogenous to coffee seeds, i.e., alkaloids, diterpenes, sugars, and amino acids, while others are generated during coffee processing, for example during roasting and brewing, such as furans, pyrazines, and melanoidins. As a beverage, it provides various distinct flavors, i.e., sourness, bitterness, and an astringent taste attributed to the presence of carboxylic acids, alkaloids, and chlorogenic acids. To resolve such a complex chemical makeup and to relate chemical composition to coffee effects, large-scale metabolomics technologies are being increasingly reported in the literature for proof of coffee quality and efficacy. This review summarizes the applications of various mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based metabolomics technologies in determining the impact of coffee breeding, origin, roasting, and brewing on coffee chemical composition, and considers this in relation to quality control (QC) determination, for example, by classifying defected and non-defected seeds or detecting the adulteration of raw materials. Resolving the coffee metabolome can aid future attempts to yield coffee seeds of desirable traits and best flavor types.

Exploring Chemical Markers Related to the Acceptance and Sensory Profiles of Concentrated Liquid Coffees: An Untargeted Metabolomics Approach

In this study, we aimed to apply an untargeted LC/QTOF-MS analysis for the identification of compounds that positively and negatively affect the acceptance of coffee beverages from liquid coffee concentrates (CLCs) before and after storage. The metabolomic results were integrated with physicochemical and sensory parameters, such as color, pH, titratable acidity, and oxygen contents, by a bootstrapped version of partial least squares discriminant analysis (PLS-DA) to select and classify the most relevant variables regarding the rejection or acceptance of CLC beverages. The OPLS-DA models for metabolite selection discriminated between the percent sensory acceptance (the Accepted group) and rejection (the Rejected group). Eighty-two molecular features were considered statistically significant. Our data suggest that coffee sample rejection is associated with chlorogenic acid hydrolysis to produce ferulic and quinic acids, consequently generating methoxybenzaldehydes that impact the perceived acidity and aroma. Furthermore, acceptance was correlated with higher global scores and sweetness, as with lactones such as feruloyl-quinolactone, caffeoyl quinolactone, and 4-caffeoyl-1,5-quinolactone, and significant oxygen levels in the headspace.

Relationship between the Chemical Composition and the Biological Functions of Coffee

Coffee is a Rubiaceae coffee plant ranked as the first of the three most important beverages in the world, with effects including lowering blood sugar, protecting the liver, and protecting the nerves. Coffee contains many chemical components, including alkaloids, phenolic acids, flavonoids, terpenoids, and so on. Chemical components in coffee are the basis of its biological function and taste. The chemical components are the basis of biological activities and form the characteristic aroma of coffee. The main chemical components and biological activities of coffee have been extensively studied, which would provide a relevant basis and theoretical support for the further development of the coffee industry.

Widely Targeted Metabolomics Analysis Reveals Great Changes in Nonvolatile Metabolites of Oolong Teas during Long-Term Storage

As a semifermented tea, oolong is exceedingly popular worldwide for its elegant, flowery aroma and mellow, rich taste. However, recent marketing trends for old oolong teas and their chemical quality largely remain unexplored. In this study, we applied widely targeted metabolomics using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) combined with multivariate analysis to investigate the chemical change of oolong teas in the aging process. With the increasing of store time, most nongalloylated catechins; tannins, including TFs and proanthocyanidins; flavonols and glycosylated flavonols; amino acids and their derivatives; nucleotides and their derivatives; and lots of alkaloids and phospholipids declined, while most fatty acids and organic acids increased, and galloylated catechins, GA, and caffeine were almost stable. The result also suggested that approximately seven years (but not an infinite extension) was a special period for oolong tea storage, which brings about excellent taste.

Acids in coffee: A review of sensory measurements and meta-analysis of chemical composition

Coffee contains a variety of organic acids (OAs) and chlorogenic acids (CGAs) that contribute to overall sensory properties. Large variations in preparation and measurement methodology across the literature complicate interpretation of general trends. Here, we perform a systematic review and meta-analysis of the published literature to elucidate the concentrations of OAs and CGAs in both Coffea arabica (arabica) and Coffea canephora (robusta), for both green coffee and roasted coffee at multiple roast levels. A total of 129 publications were found to report acid concentration measurements, yielding 8,634 distinct data points. Analysis of the full data set reveals several trends. First, roasted robusta has considerably more acidic compounds than arabica with 2 to 5 times as much total OAs, and much larger amounts of formic and acetic acid. As for CGAs, in both arabica and robusta 5-CQA is the major component, and progressive roasting decreases the concentration of all CGAs. The total amount of CGA present was more dependent on roast level than the type of coffee (arabica vs. robusta). Overall, this meta-analysis suggests that the increases in certain OAs with roast level might play more of a role in the sensory profile of dark roast coffees than previously suspected.

Gas chromatography/mass spectrometry-based metabolite profiling of coffee beans obtained from different altitudes and origins with various postharvest processing

INTRODUCTION

Coffee is a popular beverage because of its pleasant aroma and distinctive flavor. The flavor of coffee results from chemical transformations influenced by various intrinsic and extrinsic factors, including altitude, geographical origin, and postharvest processing. Despite is the importance of grading coffee quality, there is no report on the dominant factor that influences the metabolomic profile of green coffee beans and the correlated metabolites for each factor.

OBJECTIVE

This study investigated the total metabolite profile of coffees from different altitudes and coffees subjected to different postharvest processing.

METHOD

Arabica green coffee beans obtained from different geographical origins and different altitudes (400 and 800 m) and produced by different postharvest processes (dry, honey, and washed process) were used in this study. Coffee samples obtained from altitudes of 400-1600 m above sea level from various origins that were produced by the washed method were used for further study with regard to altitudes. Samples were subjected to gas chromatography/mass spectrometry (GC/MS) analysis and visualized using principal component analysis (PCA) and orthogonal partial least squares (OPLS) regression analysis.

RESULTS

The PCA results showed sample separation based on postharvest processing in PC1 and sample separation based on altitude in PC2. A clear separation between samples from different altitudes was observed if the samples were subjected to the same postharvest processing method, and the samples were of the same origin. Based on this result, OPLS analysis was conducted using coffee samples obtained from various altitudes with the same postharvest processing. An OPLS model using altitude as a response variable and 79 metabolites annotated from the GC/MS analysis as an explanatory variable was constructed with good R² and Q² values.

CONCLUSION

Postharvest processing was found to be the dominant factor affecting coffee metabolite composition; this was followed by geographical origin and altitude. The metabolites glutamic acid and galactinol were associated with the washed and honey process, while glycine, lysine, sorbose, fructose, glyceric acid, and glycolic acid were associated with the dry process. Two metabolites with high variable influence on projection scores in the OPLS model for altitude were inositol and serotonin, which showed positive and negative correlations, respectively. This is the first study to report characteristic coffee metabolites obtained from different altitudes.

Chemical ingredients characterization basing on 1H NMR and SHS-GC/MS in twelve cultivars of Coffea arabica roasted beans

This work aimed to study the composition differences of roasted beans between 12 coffee cultivars (Catimor 7963, HIBRIDO DE TIMOR, Ruiru 11, Castillo, DTARI 296, DTARI 366, DTARI 392, DTARI 585, SL28, SL34, Catuai-Amarelo and Catuai-Vermelho) from Bourbon-Typica group and Introgressed group under subtropical humid monsoon climate. The water-soluble compounds of roasted coffee beans were characterized by proton nuclear magnetic resonance spectroscopy (¹H NMR), and the aroma components were analyzed by static headspace gas chromatography mass spectrometry (SHS-GC/MS). In total, 20 water soluble compounds and 43 volatile compounds were identified. Both water-soluble and volatile compounds are rich in acidic substances, and the content varied depending on the cultivars. Furthermore, principal component analysis (PCA) clustered 12 coffee cultivars into four groups. The four different chemically defined clusters of Arabica cultivars produced by chemical differences cannot reflect the traditional grouping based on introgressed, and it is one-sided to judge coffee quality based on lineage. These results give further insight into the quality characteristics of different coffee cultivars, which is of great significance for guiding the adjustment of cultivars' structure and the breeding of new cultivars.

Chromatographic Fingerprinting Strategy to Delineate Chemical Patterns Correlated to Coffee Odor and Taste Attributes

Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor-taste integration.

Identification of coffee compounds that suppress bitterness of brew

Untargeted LC-MS flavoromic profiling was utilized to identify compounds that suppress bitterness perception of coffee brew. The chemical profiles of fourteen brew samples and corresponding perceived bitterness intensities determined by descriptive sensory analysis were modeled by orthogonal partial least squares (OPLS) with good fit (R²Y > 0.9) and predictive ability (Q² > 0.9). Ten chemical markers that were highly predictive and negatively correlated to bitter intensity were subsequently purified by multi-dimensional preparative LC-MS to conduct sensory recombination testing and/or confirm compound identifications by NMR. Three of the ten compounds evaluated, namely 4-caffeoylquinic acid, 5-caffeoylquinic acid, and 2-O-β-d-glucopyranosyl-atractyligenin were identified as bitter modulators in coffee, and significantly decreased the perceived bitterness intensity of the brew.

Identification of Compounds that Negatively Impact Coffee Flavor Quality Using Untargeted Liquid Chromatography/Mass Spectrometry Analysis

Untargeted liquid chromatography/mass spectrometry (LC/MS) flavoromics analysis was carried out on 18 coffee brews ranging in Specialty Coffee Association (SCA) cup scores. Six compounds highly predictive of low cup score were isolated from coffee using multidimensional preparative LC/MS and further evaluated by sensory recombination analysis with certified SCA quality graders. A significant decrease in cup score was demonstrated with four of the six compounds when added to a specialty coffee brew. High-resolution mass spectrometry and mono- and bidimensional nuclear magnetic resonance experiments were used to successfully elucidate four of the structures as 16α,17-dihydroxy-ent-kauran-19-oic acid (compound 1), its diglycosidic compound 16α,17-dihydroxy-ent-kauran-19-diglycoside (compound 2), 16α,17,18-trihydroxy-ent-kauran-19-oic acid (compound 5), and 16α-hydroxy-17-ent-kauren-19-oic acid (compound 6). All four ent-kaurane diterpene compounds were endogenous to green coffee beans, providing direct chemical indicators of low-quality coffee.

Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression

BACKGROUND

Coffee is a raw material of global interest. Due to its relevance, this work evaluated the performance of calibration models constructed from spectral data obtained using near-infrared spectroscopy (FT-NIR) to determine the pH values and acidity in coffee beans in a practical and non-destructive way. Partial least squares regression was used during the calibration and the cross-validation to optimize the number of latent variables. The predictive capacity of the spectral pre-processing methods was also accessed.

RESULTS

The results obtained showed that the best methods of pre-processing were the first derivative for the pH variable and the standard normal variate for the acidity, which produced models with correlations of 0.78 and 0.92, ratios of prediction to deviation of 2.061 and 2.966 and biases of -0.00011 and -0.152 to test set validation, respectively. The average errors between predicted and experimental values were lower than 7%.

CONCLUSIONS

FT-NIR was successfully applied to predict properties related to the quality of coffee. The method was demonstrated to be a fast and non-destructive tool which allows the rapid inline evaluation of samples facilitating industrial and commercial processing. © 2020 Society of Chemical Industry.