Determination of coffee origins by integrated metabolomic approach of combining multiple analytical data

Analysis of volatile and nonvolatile compounds in decaffeinated and regular coffee prepared under various roasting conditions

This study investigated the effect of various roasting conditions on regular and decaffeinated green beans. Regular and decaffeinated green beans from Guatemala, Brazil, Ethiopia, and Colombia were prepared under light, medium, and dark roasting conditions. Analysis of the decaffeination-induced changes in nonvolatile compounds revealed that decaffeinated green coffee beans had significantly lower concentrations of trigonelline (25%) and total carbohydrates (16%) but a higher chlorogenic acid content (10-14%) than regular green coffee beans (bothp < 0.05). Flavor differences between regular and decaffeinated coffee were investigated by analysis of the volatile and nonvolatile compounds in roasted coffee beans. From the odor impact ratio values, 3-ethyl-2,5-dimethyl pyrazine, 5-methyl furfural, and guaiacol were primarily responsible for coffee flavor. 3-Ethyl-2,5-dimethyl pyrazine had 58% lower concentration in decaffeinated coffee than in regular coffee. This study is valuable in providing the chemical composition of decaffeinated coffee and way to improve the quality of decaffeinated coffee.

1H NMR-based approach to determine the geographical origin and cultivation method of roasted coffee

A comprehensive study of 603 roasted arabica coffee samples using NMR fingerprinting and multivariate data analysis was performed to differentiate coffee samples according to their geographical origin and cultivation method. Both lipophilic and hydrophilic coffee metabolites were recorded using 1H NMR spectroscopy, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was applied. Coffee samples were fist differentiated according to their continents of origin followed by discrimination of coffee samples from Brazil, Ethiopia, and Colombia from coffee samples originating from another continent. Discrimination of coffee samples according to their continent of origin and additional assignment to the countries Brazil and Ethiopia were successful. However, an unambiguous separation of Colombian coffee samples from coffee samples of another continent (other than South America) was not possible. Also, differentiation of organically and conventionally produced coffee samples by using 1H NMR and PCA-LDA was not achieved.

A Systematic Analysis of the Correlation between Flavor Active Differential Metabolites and Multiple Bean Ripening Stages of Coffea arabica L

Coffee cherries contain a crucial flavor-precursor and chemical substances influencing roasted bean quality, yet limited knowledge exists on metabolite changes during cherry ripening. Our study identified 1078 metabolites, revealing 46 core differential metabolites using a KEGG pathway analysis. At the GF vs. ROF stage, amino acid synthesis dominated; ROF vs. BRF featured nucleotide catabolism; BRF vs. PRF exhibited glycoside and flavonoid synthesis; and PRF vs. PBF involved secondary metabolite synthesis and catabolism. The PRF stage emerged as the optimal cherry-harvesting period. A correlation analysis identified core differential metabolites strongly linked to taste indicators, suggesting their potential as taste markers. Notably, nucleotides and derivatives exhibited significant negative correlations with glycosides and flavonoids during ripening. This research systematically analyzed flavor and active substances in green coffee beans during cherry ripening, offering valuable insights into substance formation in Coffea arabica L.

Metabolomics as a tool for geographic origin assessment of roasted and green coffee beans

Coffee is widely consumed across the globe. The most sought out varieties are Arabica and Robusta which differ significantly in their aroma and taste. Furthermore, varieties cultivated in different regions are perceived to have distinct characteristics encouraging some producers to adopt the denomination of origin label. These differences arise from variations on metabolite content related to edaphoclimatic conditions and post-harvest management among other factors. Although sensory analysis is still standard for coffee brews, instrumental analysis of the roasted and green beans to assess the quality of the final product has been encouraged. Metabolomic profiling has risen as a promising approach not only for quality purposes but also for geographic origin assignment. Many techniques can be applied for sample analysis: chromatography, mass spectrometry, and NMR have been explored. The data collected is further sorted by multivariate analysis to identify similar characteristics among the samples, reduce dimensionality and/or even propose a model for predictive purposes. This review focuses on the evolution of metabolomic profiling for the geographic origin assessment of roasted and green coffee beans in the last 21 years, the techniques that are usually applied for sample analysis and also the most common approaches for the multivariate analysis of the collected data. The prospect of applying a wide range of analytical techniques is becoming an unbiased approach to determine the origin of different roasted and green coffee beans samples with great correlation. Predictive models worked accurately for the geographic assignment of unknown samples once the variety was known.

Medium Roasting and Brewing Methods Differentially Modulate Global Metabolites, Lipids, Biogenic Amines, Minerals, and Antioxidant Capacity of Hawai'i-Grown Coffee (Coffea arabica)

In the United States, besides the US territory Puerto Rico, Hawai'i is the only state that grows commercial coffee. In Hawai'i, coffee is the second most valuable agricultural commodity. Health benefits associated with moderate coffee consumption, including its antioxidant capacity, have been correlated to its bioactive components. Post-harvest techniques, coffee variety, degree of roasting, and brewing methods significantly impact the metabolites, lipids, minerals, and/or antioxidant capacity of brewed coffees. The goal of our study was to understand the impact of roasting and brewing methods on metabolites, lipids, biogenic amines, minerals, and antioxidant capacity of two Hawai'i-grown coffee (Coffea arabica) varieties, "Kona Typica" and "Yellow Catuai". Our results indicated that both roasting and coffee variety significantly modulated several metabolites, lipids, and biogenic amines of the coffee brews. Furthermore, regardless of coffee variety, the antioxidant capacity of roasted coffee brews was higher in cold brews. Similarly, total minerals were higher in "Kona Typica" cold brews followed by "Yellow Catuai" cold brews. Hawai'i-grown coffees are considered "specialty coffees" since they are grown in unique volcanic soils and tropical microclimates with unique flavors. Our studies indicate that both Hawai'i-grown coffees contain several health-promoting components. However, future studies are warranted to compare Hawai'i-grown coffees with other popular brand coffees and their health benefits in vivo.

Paper Spray Mass Spectrometry as an Effective Tool for Differentiating Coffees Based on Their Geographical Origins

With the rising trend of valuing flavor complexity of coffees, means to distinguish the properties of individual coffee sources is vital to the sustainable growth of the coffee industry. Herein, paper spray mass spectrometry (PS-MS), a simple technique with little sample preparation, was used to collect mass data from aqueous extracts of coffees from various sources. Thereafter, principal component analysis and linear discriminant analysis were used to successfully classify coffee samples (with 80-100 % accuracy) from various studies including the differentiations of Arabica and Robusta coffees, Arabica coffees from different countries, Robusta coffees from different geographical locations, and Arabica coffees from different locations within the same province in Thailand. With further insight from significant test via Fisher weight determination, this method was proved to be practical for differentiating coffees based on types and geographical origins, thus paving the way for broader applications.

A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed.

SNP markers identification by genome wide association study for chemical quality traits of coffee (Coffea spp.) Germplasm

BACKGROUND

Coffee quality is an important selection criterion for coffee breeding. Metabolite profiling and Genome-Wide Association Studies (GWAS) effectively dissect the genetic background of complex traits such as metabolites content (caffeine, trigonelline, and 5-caffeoylquinic acid (5-CQA)) in coffee that affect quality. Therefore, it is important to determine the metabolic profiles of Coffea spp. genotypes. This study aimed to identify Single Nucleotide Polymorphisms (SNPs) within Coffea spp. genotypes through GWAS and associate these significant SNPs to the metabolic profiles of the different genotypes.

METHODS AND RESULTS

A total of 1,739 SNP markers were obtained from 80 genotypes using the DArTseq™ method. Caffeine, trigonelline, and 5-CQA content were determined in coffee leaves using Ultra-Performance Liquid Chromatography/tandem mass spectrometry (UPLC-MS/MS) analyses. The GWAS was carried out using the Genome Association and Prediction Integrated Tool (GAPIT) software and a compressed mixed linear model. Finally, a total of three significant SNP markers out of ten were identified. One SNP, located in the coffee chromosome (Chr) 8, was significantly associated with caffeine. The two remaining SNPs, located in Chr 4 and 5, were significantly associated with trigonelline and six SNPs markers were associated with 5-CQA in Chr 1, 5 and 10, but these six markers were not significant.

CONCLUSIONS

These significant SNP sequences were associated with protein ubiquitination, assimilation, and wall receptor kinases. Therefore, these SNPs might be useful hits in subsequent quality coffee breeding programs.

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.

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Secondary metabolites are molecules with unlimited applications that have been gaining importance in various industries and studied from many angles. They are mainly used for their bioactive capabilities, but due to the improvement of sensibility in analytical chemistry, they are also used for authentication and as a quality control parameter for foods, further allowing to help avoid food adulteration and food fraud, as well as helping understand the nutritional value of foods. This manuscript covers the examples of secondary metabolites that have been used as qualitative and authentication molecules in foods, from production, through processing and along their shelf-life. Furthermore, perspectives of analytical chemistry and their contribution to metabolite detection and general perspectives of metabolomics are also discussed.

Review of Analytical Methods to Detect Adulteration in Coffee

As one of the most consumed beverages in the world, coffee plays many major socioeconomical roles in various regions. Because of the wide coffee varieties available in the marketplaces, and the substantial price gaps between them (e.g., Arabica versus Robusta; speciality versus commodity coffees), coffees are susceptible to intentional or accidental adulteration. Therefore, there is a sustaining interest from the producers and regulatory agents to develop protocols to detect fraudulent practices. In general, strategies to authenticate coffee are based on targeted chemical profile analyses to determine specific markers of adulterants, or nontargeted analyses based on the “fingerprinting” concept. This paper reviews the literature related to chemometric approaches to discriminate coffees based on nuclear magnetic resonance spectroscopy, chromatography, infrared/Raman spectroscopy, and array sensors/indicators. In terms of chemical profiling, the paper focuses on the detection of diterpenes, homostachydrine, phenolic acids, carbohydrates, fatty acids, triacylglycerols, and deoxyribonucleic acid. Finally, the prospects of coffee authentication are discussed.

Untargeted HILIC-MS-Based Metabolomics Approach to Evaluate Coffee Roasting Process: Contributing to an Integrated Metabolomics Multiplatform

An untargeted metabolomics strategy using hydrophilic interaction chromatography-mass spectrometry (HILIC-MS) was developed in this work enabling the study of the coffee roasting process. Green coffee beans and coffee beans submitted to three different roasting degrees (light, medium, and strong) were analyzed. Chromatographic separation was carried out using water containing 10 mM ammonium formate with 0.2 % formic acid (mobile phase A) and acetonitrile containing 10 mM ammonium formate with 0.2 % formic acid (mobile phase B). A total of 93 molecular features were considered from which 31 were chosen as the most statistically significant using variable in the projection values. 13 metabolites were tentatively identified as potential biomarkers of the coffee roasting process using this metabolomic platform. Results obtained in this work were complementary to those achieved using orthogonal techniques such as reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) since only one metabolite was found to be common between HILIC-MS and RPLC-MS platforms (caffeoylshikimic acid isomer) and other between HILIC-MS and CE-MS platforms (choline). On the basis of these results, an untargeted metabolomics multiplatform is proposed in this work based on the integration of the three orthogonal techniques as a powerful tool to expand the coverage of the roasted coffee metabolome.

Gas Chromatography-Mass Spectrometry and Single Nucleotide Polymorphism-Genotype-By-Sequencing Analyses Reveal the Bean Chemical Profiles and Relatedness of Coffea canephora Genotypes in Nigeria

The flavor and health benefits of coffee (Coffea spp.) are derived from the metabolites that accumulate in the mature bean. However, the chemical profiles of many C. canephora genotypes remain unknown, even as the production of these coffee types increases globally. Therefore, we used Gas Chromatography-Mass Spectrophotometry to determine the chemical composition of C. canephora genotypes in Nigeria—those conserved in germplasm repositories and those cultivated by farmers. GC-MS revealed 340 metabolites in the ripe beans, with 66 metabolites differing (p-value < 0.05) across the represented group. Univariate and multivariate approaches showed that the ‘Niaouli’ genotypes could be clearly distinguished from ‘Kouillou’ and ‘Java’ genotypes, while there was almost no distinction between ‘Kouillou’ and ‘Java,’. Varietal genotyping based on bean metabolite profiling was synchronous with that based on genome-wide Single Nucleotide Polymorphism analysis. Across genotypes, the sucrose-to-caffeine ratio was low, a characteristic indicative of low cup quality. The sucrose-to-caffeine ratio was also highly correlated, indicative of common mechanisms regulating the accumulation of these compounds. Nevertheless, this strong correlative link was broken within the ‘Niaouli’ group, as caffeine and sucrose content were highly variable among these genotypes. These ‘Niaouli’ genotypes could therefore serve as useful germplasm for starting a Nigerian C. canephora quality improvement breeding program.

GC/MS based metabolite profiling of Indonesian specialty coffee from different species and geographical origin

INTRODUCTION

The consumption of high quality coffee such as specialty Arabica and fine Robusta coffee is increasing steadily in recent years. Development of single origin coffee is an important strategy to maintain coffee quality, grade and high cupping score. Indonesia is a top exporting country for Arabica coffee with high variety of specialty coffees from different origins. Despite its long standing reputation in global coffee market, very few is known about the variability among Indonesian specialty coffees.

OBJECTIVES

This study aims to observe metabolite variability among Indonesian coffees from different species and geographical origins by means of non-targeted GC/MS metabolite profiling.

METHODS

Sixty-four compounds were tentatively identified from 16 green and roasted coffee beans from different species and cultivation areas in Indonesia and were subjected to principal component analysis (PCA). Ten Specialty Arabica coffee and five Fine Robusta representing all important high quality coffees of Indonesia were also analyzed independently to further classify Indonesian coffee according to their origin.

RESULTS

PCA results of 16 green and roasted coffee beans of different species and cultivation areas showed that samples were separated along PC1 based on different roasting condition (green and roasted) with 52.9% variance and were separated along PC2 based on different species with 19.3% variance. The result from this study showed the clustering of samples based on three major cultivation areas in Indonesia (western, central, eastern part). Metabolites showing higher concentration in Sulawesi, Papua, Flores and Sumatra samples were glycerol, glucuno-1,5-lactone, gluconic acid and sorbitol. A clear distinction in galactitol and galactinol concentration between all samples from eastern part of Indonesia and western and middle part of Indonesia was also observed.

CONCLUSIONS

Our results showed that each region (western, central and eastern part of Indonesia) has signature compounds that may serve as discriminant markers for coffee authentication. This is the first report on the classification of Indonesian specialty coffee based on their metabolic profiles and can act as a basis for marker identification for routine procedures in industry.

Differentiating the geographical origin of Ethiopian coffee using XRF- and ICP-based multi-element and stable isotope profiling

To test the potential of different analytical tools to determine the geographical origin of Ethiopian coffee, 103 green arabica coffee samples from four coffee regions in Ethiopia were subjected to multi-elements and δ¹³C, δ¹⁵N and δ¹⁸O determinations. Multi-elements were determined by using inductively coupled plasma (ICP)- and X-ray fluorescence spectrometry (XRF)-based techniques, and δ¹³C, δ¹⁵N and δ¹⁸O were determined by using elemental analyzer-isotope ratio mass spectrometry. Using linear discriminant analysis, XRF-based multi-elements with and without δ¹³C appeared to be most effective in discriminating the geographical origin of coffee, giving higher classification accuracy (89 and 86%, respectively) than ICP-based multi-elements with and without stable isotopes (80%, each). These results demonstrate the potential of XRF-based multi-element profiling as a relatively fast and low-cost tool to trace the geographical origin of Ethiopian coffee. All together this study offers the proof of concept for a promising method that, upon standardization, could be used for coffee provenance authentication and fraud detection.

A non-targeted metabolomic approach based on reversed-phase liquid chromatography-mass spectrometry to evaluate coffee roasting process

In this work, a non-targeted metabolomics approach based on the use of reversed-phase liquid chromatography coupled to a high-resolution mass spectrometer has been developed to provide the characterization of coffee beans roasted at three different levels (light, medium, and dark). In this way, it was possible to investigate how metabolites change during the roasting process in order to identify those than can be considered as relevant markers. Twenty-five percent methanol was selected as extracting solvent since it provided the highest number of molecular features. In addition, the effect of chromatographic and MS parameters was evaluated in order to obtain the most adequate separation and detection conditions. Data were analyzed using both non-supervised and supervised multivariate statistical methods to point out the most significant markers that allow group discrimination. A total of 24 and 33 compounds in positive and negative ionization modes, respectively, demonstrated to be relevant markers; most of them were from the hydroxycinnamic acids family. Graphical abstract ᅟ.

Untargeted Metabolomics Analysis of Eggplant (Solanum melongena L.) Fruit and Its Correlation to Fruit Morphologies

Eggplant is one of the most widely cultivated vegetables in the world and has high biodiversity in terms of fruit shape, size, and color. Therefore, fruit morphology and nutrient content become important considerations for both consumers and breeders who develop new eggplant-based products. To gain insight on the diversity of eggplant metabolites, twenty-one eggplant accessions were analyzed by untargeted metabolomics using GC-MS and LC-MS. The dataset of eggplant fruit morphologies, and metabolites specific to different eggplant fruit accessions were used for correlation analysis. Untargeted metabolomics analysis using LC-MS and GC-MS was able to detect 136 and 207 peaks, respectively. Fifty-one (51) metabolites from the LC-MS analysis and 207 metabolites from the GC-MS analysis were putatively identified, which included alkaloids, terpenes, terpenoids, fatty acids, and flavonoids. Spearman correlation analysis revealed that 14 fruit morphologies were correlated with several metabolites. This information will be very useful for the development of strategies for eggplant breeding.

The integration of multi-platform MS-based metabolomics and multivariate analysis for the geographical origin discrimination of Oryza sativa L

For the authentication of white rice from different geographical origins, the selection of outstanding discrimination markers is essential. In this study, 80 commercial white rice samples were collected from local markets of Korea and China and discriminated by mass spectrometry-based untargeted metabolomics approaches. Additionally, the potential markers that belong to sugars & sugar alcohols, fatty acids, and phospholipids were examined using several multivariate analyses to measure their discrimination efficiencies. Unsupervised analyses, including principal component analysis and k-means clustering demonstrated the potential of the geographical classification of white rice between Korea and China by fatty acids and phospholipids. In addition, the accuracy, goodness-of-fit (R²), goodness-of-prediction (Q²), and permutation test p-value derived from phospholipid-based partial least squares-discriminant analysis were 1.000, 0.902, 0.870, and 0.001, respectively. Random Forests further consolidated the discrimination ability of phospholipids. Furthermore, an independent validation set containing 20 white rice samples also confirmed that phospholipids were the excellent discrimination markers for white rice between two countries. In conclusion, the proposed approach successfully highlighted phospholipids as the better discrimination markers than sugars & sugar alcohols and fatty acids in differentiating white rice between Korea and China.

Comparison of Attenuated Total Reflectance Mid-Infrared, Near Infrared, and 1H-Nuclear Magnetic Resonance Spectroscopies for the Determination of Coffee's Geographical Origin

The sensorial properties of Colombian coffee are renowned worldwide, which is reflected in its market value. This raises the threat of fraud by adulteration using coffee grains from other countries, thus creating a demand for robust and cost-effective methods for the determination of geographical origin of coffee samples. Spectroscopic techniques such as Nuclear Magnetic Resonance (NMR), near infrared (NIR), and mid-infrared (mIR) have arisen as strong candidates for the task. Although a body of work exists that reports on their individual performances, a faithful comparison has not been established yet. We evaluated the performance of ¹H-NMR, Attenuated Total Reflectance mIR (ATR-mIR), and NIR applied to fraud detection in Colombian coffee. For each technique, we built classification models for discrimination by species (C. arabica versus C. canephora (or robusta)) and by origin (Colombia versus other C. arabica) using a common set of coffee samples. All techniques successfully discriminated samples by species, as expected. Regarding origin determination, ATR-mIR and ¹H-NMR showed comparable capacity to discriminate Colombian coffee samples, while NIR fell short by comparison. In conclusion, ATR-mIR, a less common technique in the field of coffee adulteration and fraud detection, emerges as a strong candidate, faster and with lower cost compared to ¹H-NMR and more discriminating compared to NIR.

Modern analytical methods for the detection of food fraud and adulteration by food category

This review provides current information on the analytical methods used to identify food adulteration in the six most adulterated food categories: animal origin and seafood, oils and fats, beverages, spices and sweet foods (e.g. honey), grain-based food, and others (organic food and dietary supplements). The analytical techniques (both conventional and emerging) used to identify adulteration in these six food categories involve sensory, physicochemical, DNA-based, chromatographic and spectroscopic methods, and have been combined with chemometrics, making these techniques more convenient and effective for the analysis of a broad variety of food products. Despite recent advances, the need remains for suitably sensitive and widely applicable methodologies that encompass all the various aspects of food adulteration. © 2017 Society of Chemical Industry.

Food Fingerprinting: Metabolomic Approaches for Geographical Origin Discrimination of Hazelnuts (Corylus avellana) by UPLC-QTOF-MS

Ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was used for geographical origin discrimination of hazelnuts (Corylus avellana L.). Four different LC-MS methods for polar and nonpolar metabolites were evaluated with regard to best discrimination abilities. The most suitable method was used for analysis of 196 authentic samples from harvest years 2014 and 2015 (Germany, France, Italy, Turkey, Georgia), selecting and identifying 20 key metabolites with significant differences in abundancy (5 phosphatidylcholines, 3 phosphatidylethanolamines, 4 diacylglycerols, 7 triacylglycerols, and γ-tocopherol). Classification models using soft independent modeling of class analogy (SIMCA), linear discriminant analysis based on principal component analysis (PCA-LDA), support vector machine classification (SVM), and a customized statistical model based on confidence intervals of selected metabolite levels were created, yielding 99.5% training accuracy at its best by combining SVM and SIMCA. Forty nonauthentic hazelnut samples were subsequently used to estimate as realistically as possible the prediction capacity of the models.

Classification of Coffee Beans by GC-C-IRMS, GC-MS, and (1)H-NMR

In a previous work using (1)H-NMR we reported encouraging steps towards the construction of a robust expert system for the discrimination of coffees from Colombia versus nearby countries (Brazil and Peru), to assist the recent protected geographical indication granted to Colombian coffee in 2007. This system relies on fingerprints acquired on a 400 MHz magnet and is thus well suited for small scale random screening of samples obtained at resellers or coffee shops. However, this approach cannot easily be implemented at harbour's installations, due to the elevated operational costs of cryogenic magnets. This limitation implies shipping the samples to the NMR laboratory, making the overall approach slower and thereby more expensive and less attractive for large scale screening at harbours. In this work, we report on our attempt to obtain comparable classification results using alternative techniques that have been reported promising as an alternative to NMR: GC-MS and GC-C-IRMS. Although statistically significant information could be obtained by all three methods, the results show that the quality of the classifiers depends mainly on the number of variables included in the analysis; hence NMR provides an advantage since more molecules are detected to obtain a model with better predictions.

Isotopic and Elemental Composition of Roasted Coffee as a Guide to Authenticity and Origin

This study presents the stable isotopic and elemental compositions of single-origin, roasted coffees available to retail consumers. The δ(13)C, δ(15)N, and δ(18)O compositions were in agreement with those previously reported for green coffee beans. The δ(15)N composition was seen to be related to organic cultivation, reflected in both δ(2)H and δ(18)O compositions. The δ(13)C composition of extracted caffeine differed little from that of the bulk coffee. Stepwise discriminant analysis with jackknife tests, using isotopic and elemental data, provided up to 77% correct classification of regions of production. Samples from Africa and India were readily classified. The wide range in both isotopic and elemental compositions of samples from other regions, specifically Central/South America, resulted in poor discrimination between or within these regions. Simpler X-Y and geo-spatial plots of the isotopic data provided effective visual means to distinguish between coffees from different regions.

Identification of 3-methylbutanoyl glycosides in green Coffea arabica beans as causative determinants for the quality of coffee flavors

The quality of coffee green beans is generally evaluated by the sensory cupping test, rather than by chemical compound-based criteria. In this study, we examined the relationship between metabolites and cupping scores for 36 varieties of beans, using a nontargeted LC-MS-based metabolic profiling technique. The cupping score was precisely predicted with the metabolic information measured using LC-MS. Two markers that strongly correlated with high cupping scores were determined to be isomers of 3-methylbutanoyl disaccharides (3MDs; 0.01-0.035 g/kg of beans) by spectroscopic analyses after purification, and one of them was a novel structure. Further, both the 3MDs were determined to be precursors of 3-methylbutanoic acid that enhance the quality of coffee. The applicability of 3MDs as universal quality indicators was validated with another sample set. It was concluded that 3MDs are the causative metabolites determining beverage quality and can be utilized for green bean selection and as key compounds for improving the beverage quality.

Espresso beverages of pure origin coffee: mineral characterization, contribution for mineral intake and geographical discrimination

Espresso coffee beverages prepared from pure origin roasted ground coffees from the major world growing regions (Brazil, Ethiopia, Colombia, India, Mexico, Honduras, Guatemala, Papua New Guinea, Kenya, Cuba, Timor, Mussulo and China) were characterized and compared in terms of their mineral content. Regular consumption of one cup of espresso contributes to a daily mineral intake varying from 0.002% (sodium; Central America) to 8.73% (potassium; Asia). The mineral profiles of the espresso beverages revealed significant inter- and intra-continental differences. South American pure origin coffees are on average richer in the analyzed elements except for calcium, while samples from Central America have generally lower mineral amounts (except for manganese). Manganese displayed significant differences (p<0.05) among the countries of each characterized continent. Intercontinental and inter-country discrimination between the major world coffee producers were achieved by applying canonical discriminant analysis. Manganese and calcium were found to be the best chemical descriptors for origin.

Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolomics for comparison of caffeinated and decaffeinated coffee and its implications for Alzheimer's disease

Findings from epidemiology, preclinical and clinical studies indicate that consumption of coffee could have beneficial effects against dementia and Alzheimer’s disease (AD). The benefits appear to come from caffeinated coffee, but not decaffeinated coffee or pure caffeine itself. Therefore, the objective of this study was to use metabolomics approach to delineate the discriminant metabolites between caffeinated and decaffeinated coffee, which could have contributed to the observed therapeutic benefits. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolomics approach was employed to characterize the metabolic differences between caffeinated and decaffeinated coffee. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed distinct separation between the two types of coffee (cumulative Q² = 0.998). A total of 69 discriminant metabolites were identified based on the OPLS-DA model, with 37 and 32 metabolites detected to be higher in caffeinated and decaffeinated coffee, respectively. These metabolites include several benzoate and cinnamate-derived phenolic compounds, organic acids, sugar, fatty acids, and amino acids. Our study successfully established GC-TOF-MS based metabolomics approach as a highly robust tool in discriminant analysis between caffeinated and decaffeinated coffee samples. Discriminant metabolites identified in this study are biologically relevant and provide valuable insights into therapeutic research of coffee against AD. Our data also hint at possible involvement of gut microbial metabolism to enhance therapeutic potential of coffee components, which represents an interesting area for future research.

Discrimination of white ginseng origins using multivariate statistical analysis of data sets

Background

White ginseng (Panax ginseng Meyer) is commonly distributed as a health food in food markets. However, there is no practical method for distinguishing Korean white ginseng (KWG) from Chinese white ginseng (CWG), except for relying on the traceability system in the market.

Methods

Ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry combined with orthogonal partial least squares discrimination analysis (OPLS-DA) was employed to discriminate between KWG and CWG.

Results

The origins of white ginsengs in two test sets (1.0 μL and 0.2 μL injections) could be successfully discriminated by the OPLS-DA analysis. From OPLS-DA S-plots, KWG exhibited tentative markers derived from ginsenoside Rf and notoginsenoside R3 isomer, whereas CWG exhibited tentative markers derived from ginsenoside Ro and chikusetsusaponin Iva.

Conclusion

Results suggest that ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry coupled with OPLS-DA is an efficient tool for identifying the difference between the geographical origins of white ginsengs.

Metabolomics in plant environmental physiology

Changes in plant metabolism are at the heart of plant developmental processes, underpinning many of the ways in which plants respond to the environment. As such, the comprehensive study of plant metabolism, or metabolomics, is highly valuable in identifying phenotypic effects of abiotic and biotic stresses on plants. When study is in reference to analysing samples that are relevant to environmental or ecologically based hypotheses, it is termed 'environmental metabolomics'. The emergence of environmental metabolomics as one of the latest of the omics technologies has been one of the most critically important recent developments in plant physiology. Its applications broach the entire landscape of plant ecology, from the understanding of plant plasticity and adaptation through to community composition and even genetic modification in crops. The multitude of novel studies published utilizing metabolomics methods employ a variety of techniques, from the initial stages of tissue sampling, through to sample preservation, transportation, and analysis. This review introduces the concept and applications of plant environmental metabolomics as an ecologically important investigative tool. It examines the main techniques used in situ within field sites, with particular reference to sampling and processing, and those more appropriate for use in laboratory-based settings with emphasis on secondary metabolite analysis.

Selection of discriminant markers for authentication of Asian palm civet coffee (Kopi Luwak): a metabolomics approach

Kopi Luwak, an exotic Indonesian coffee, is made from coffee berries that have been eaten by the Asian palm civet ( Paradoxurus hermaphroditus ). Despite being known as the world's most expensive coffee, there is no reliable, standardized method for determining its authenticity. GC-MS-based multimarker profiling was employed to explore significant metabolites as discriminant markers for authentication. Extracts of 21 coffee beans ( Coffea arabica and Coffea canephora ) from three cultivation areas were analyzed and subjected to multivariate analyses, principal component analysis, and orthogonal projection to latent structures discriminant analysis. Citric acid, malic acid, and the inositol/pyroglutamic acid ratio were selected for further verification by evaluating their differentiating abilities against various commercial coffee products. The markers demonstrated potential application in the differentiation of original, fake Kopi Luwak, regular coffee, and coffee blend samples with 50 wt % Kopi Luwak content. This is the first report to address the selection and successful validation of discriminant markers for the authentication of Kopi Luwak.

(13)C NMR-based metabolomics for the classification of green coffee beans according to variety and origin

(13)C NMR-based metabolomics was demonstrated as a useful tool for distinguishing the species and origins of green coffee bean samples of arabica and robusta from six different geographic regions. By the application of information on (13)C signal assignment, significantly different levels of 14 metabolites of green coffee beans were identified in the classifications, including sucrose, caffeine, chlorogenic acids, choline, amino acids, organic acids, and trigonelline, as captured by multivariate analytical models. These studies demonstrate that the species and geographical origin can be quickly discriminated by evaluating the major metabolites of green coffee beans quantitatively using (13)C NMR-based metabolite profiling.

A personalized life: biomarker monitoring from cradle to grave

Considering the holy grail of future medical treatment being personalized medicines, biomarker research will become more and more the focus for attention not only to develop new medical treatment regimes, based on changes in biomarker patterns, but also for nutritional advice to guarantee a lifelong optimized health condition. The current review gives an outline of how personalized medicine can become established for actual medical treatment using new biomarker concepts. Starting from the development of biomarker research using mainly immunological techniques, the review gives an overview about biomarkers of prediction evolved and focuses on new methodology for the identification of biomarkers using hyphenated analytical techniques like metabolomics and lipidomics. The actual use of multivariate statistical methods in combination with metabolomics and lipidomics is discussed not only for medical treatment but also for precautionary risk identification in human biomonitoring studies.

Metabolomics for measuring phytochemicals, and assessing human and animal responses to phytochemicals, in food science

Metabolomics, comprehensive metabolite analysis, is finding increasing application as a tool to measure and enable the manipulation of the phytochemical content of foods, to identify the measures of dietary intake, and to understand human and animal responses to phytochemicals in the diet. Recent applications of metabolomics directed toward understanding the role of phytochemicals in food and nutrition are reviewed.