Metabolomic insights into the Arabica-like flavour of stenophylla coffee and the chemistry of quality coffee

Stenophylla coffee, an undomesticated species from Upper West Africa, is of commercial interest due to its high heat tolerance and Arabica-like flavour. To investigate the chemical basis of flavour similarity, we analysed unroasted coffee bean samples using liquid chromatography-mass spectrometry (LC-MS) and applied metabolomics approaches to compare chemical profiles. We report similarities between Arabica and stenophylla in the relative levels of several key compounds linked to coffee flavour, including caffeine, trigonelline, sucrose and citric acid. Differences in their chemical profiles were also observed, especially in their diterpenoid and hydroxycinnamic acid profiles. We report the additional novel finding that theacrine occurs in stenophylla, which is the first record of this alkaloid in coffee beans. For stenophylla, the dissimilarities in chemical compound composition (compared to Arabica) may offer opportunities for a better understanding of the chemical basis of high-quality coffee and sensory diversification.

Chemometric Classification and Bioactivity Correlation of Black Instant Coffee and Coffee Bean Extract by Chlorogenic Acid Profiling

Chlorogenic acids (CGAs) play a key role in defining the quality and functionality of coffee products. CGA fingerprints of black instant coffee (BIC) and coffee bean extract (CBE) were profiled using ultra-performance liquid chromatography-mass spectrometry and analyzed by chemometrics. A total of 25 CGAs were identified. The BICs yielded higher levels of major CGAs than the CBEs. Furthermore, chemometrics methods, including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), successfully classified the CBEs and the BICs. In vitro cellular antioxidant activity and viability assays between coffee products further confirmed the relationship between phenolic compounds and bioactivities. Compared to the CBEs, the BICs provided higher cellular toxicity and oxidant activity for hepatocellular carcinoma G2 (HepG2) cells. These results demonstrated that CGAs and their derivatives could be markers for studying coffee-related products. This study revealed the unique phenolic profiles of various coffee products, highlighting the differences between whole beans and soluble coffee.

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis-linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia.

Impact of different roasting conditions on the chemical composition, antioxidant activities, and color of Coffea canephora and Coffea arabica L. samples

This work aimed to evaluate the physicochemical changes during the roasting process of Robusta and Arabica coffee. The highest content of total phenolics was detected in roasted coffee at temperatures of 135 °C/20.20 min, 210 °C/9.02 min, 210 °C/11.01 min, and 220 °C/13.47 min for both species. Robusta coffee showed greater antioxidant activity compared to Arabica coffee, except for the profiles at 230 °C/17.43 min and 275 °C/7.46 min that did not differ between samples by the DPPH and FRAP methods. For Arabica coffee, the antioxidant activity was independent of the roasting profile used. Robusta coffee presented higher values of the indexes b* (intensity of yellow vs blue), c* (chroma) and hue, being characterized as lighter and with greater chroma and hue. The highest levels of caffeoylquinic acid (5-CQA) were observed in Robusta coffee. Arabica coffee had lower trigonelline values. Caffeic acid and hydroxymethylfurfural were identified only in Robusta coffee. However, the results provided solid knowledge for the design of general properties and chemical compounds generated from binomials of roasting time and temperature that are little used in the world market.

Specialty and high-quality coffee: discrimination through elemental characterization via ICP-OES, ICP-MS, and ICP-MS/MS of origin, species, and variety

BACKGROUND

This study aimed to establish the elemental profiling and origin combined with the genetic asset of coffee samples collected from major coffee-producing countries. A total of 76 samples were analysed for 41 elements using inductively coupled plasma-optical emission spectroscopy (ICP-OES), inductively coupled plasma-mass spectrometry (ICP-MS), and inductively coupled plasma-triple quadrupole mass spectrometry (ICP-MS/MS). The mineral composition of the silver skin detachment during the roasting process was also evaluated to verify the loss of minerals during roasting, differences in composition with beans, and between species.

RESULTS

Application of linear discriminant analysis provided models with an accuracy of 93.3% for continents, 97.8% for countries of cultivation, and 100% for species. Discrimination between Arabica, Canephora coffee, and Eugenoides, and different varieties of Arabica species were identified in both models with calcium (Ca), barium (Ba), cadmium (Cd), rubidium (Rb), and strontium (Sr) as significant discriminant elements. Rb, Sr, sulphur (S), and thulium (Tm) were significant discriminant elements in both models for geographical distinction at different scales. Most of the elements had significantly higher values in silver skin than those in roasted coffee at different magnitudes, with exceptions of P and Rb.

CONCLUSION

In summary, determination of mineral elements, processed by multivariate statistical analysis, was demonstrated to be discriminant for different coffee species. Linear discriminant analysis of the elemental analysis of samples from the seven major producing countries provided a reliable prediction model. Elemental analysis of major and minor elements is relatively easy and can be used together with other traceability systems and sensory evaluations to authenticate the origin of roasted coffee, different species, and varieties. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Discrimination of Green Coffee (Coffea arabica and Coffea canephora) of Different Geographical Origin Based on Antioxidant Activity, High-Throughput Metabolomics, and DNA RFLP Fingerprinting

The genus Coffea is known for the two species C. arabica (CA) and C. canephora (CC), which are used to prepare the beverage coffee. Proper identification of green beans of coffee varieties is based on phenotypic and phytochemical/molecular characteristics. In this work, a combination of chemical (UV/Vis, HPLC-DAD-MS/MS, GC-MS, and GC-FID) and molecular (PCR-RFLP) fingerprinting was used to discriminate commercial green coffee accessions from different geographical origin. The highest content of polyphenols and flavonoids was always found in CC accessions, whereas CA showed lower values. ABTS and FRAP assays showed a significant correlation between phenolic content and antioxidant activity in most CC accessions. We identified 32 different compounds, including 28 flavonoids and four N-containing compounds. The highest contents of caffeine and melatonin were detected in CC accessions, whereas the highest levels of quercetin and kaempferol derivatives were found in CA accessions. Fatty acids of CC accessions were characterized by low levels of linoleic and cis octadecenoic acid and high amounts of elaidic acid and myristic acid. Discrimination of species according to their geographical origin was achieved using high-throughput data analysis, combining all measured parameters. Lastly, PCR-RFLP analysis was instrumental for the identification of recognition markers for the majority of accessions. Using the restriction enzyme AluI on the trnL-trnF region, we clearly discriminated C. canephora from C. arabica, whereas the cleavage performed by the restriction enzymes MseI and XholI on the 5S-rRNA-NTS region produced specific discrimination patterns useful for the correct identification of the different coffee accessions. This work extends our previous studies and provides new information on the complete flavonoid profile, combining high-throughput data with DNA fingerprinting to assess the geographical discrimination of green coffee.

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Authentication of Coffee Blends by 16-O-Methylcafestol Quantification Using NMR Spectroscopy

In 2019, a coffee chain in Taiwan was found to be mixing relatively cheap Robusta beans into products marketed as 100% Arabica. Many studies show 16-OMC is a remarkable marker to distinguish Robusta from Arabica beans, and nuclear magnetic resonance (NMR) is a convenient and efficient technique for 16-OMC quantification. Here, a 500 MHz NMR was employed to determine the content of 16-OMC in coffee for adulterate evaluation. A total of 118 samples were analyzed including products from the coffee chain, raw materials (single coffee beans), and other commercial products. The contents of 16-OMC in single Robusta beans were between 1005.55 and 3208.32 mg/kg and were absent from single Arabica beans. The surveillance results indicate that 17 out of 47 blend products claiming to contain 100% Arabica had 16-OMC quantifications in the range of 155.74–784.60 mg/kg. Furthermore, all 17 products were produced by the same coffee chain. We confirmed that coffee chain adulterated Arabica with Robusta in parts of their products, which claimed to include 100% Arabica. Moreover, this work highlights the free form of 16-OMC was esterified by coffee instantly. The decomposition products of 16-OMC were observed obviously in green Robusta while the mechanisms remain unclear. Future research should focus more on these aspects to further increase our understanding of these mechanisms.

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.

Front-face synchronous fluorescence spectroscopy: a rapid and non-destructive authentication method for Arabica coffee adulterated with maize and soybean flours

This article describes a novel front-face synchronous fluorescence spectroscopy (FFSFS) method for the fast and non-invasive authentication of ground roasted Arabica coffee adulterated with roasted maize and soybean flours. The detection was based on the different composition of fluorescent Maillard reaction products and caffeine in roasted coffee and cereal flours. For each roasted maize or soybean adulterant flour (5-40 wt%), principal component analysis coupled with linear discriminant analysis (PCA-LDA) was used for qualitative discrimination. Quantitative prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square regression (PLSR), followed by fivefold cross-validation and external validation. The PLSR models produced suitable results, with the determination coefficient of prediction (R p 2) > 0.9, root mean square error of prediction (RMSEP) < 5%, relative error of prediction (REP) < 25% and residual predictive deviation (RPD) > 3. The limits of detection (LOD) were both 10% for roasted maize and soybean flours. Most relative errors for the prediction of simulated blind samples were between -30% and + 30%. The benefits of this strategy are simplicity, rapidity, and non-destructive detection. However, owing to the high similarity between roasted coffee and roasted cereal flours and the influence of the roasting degree on fluorescent Maillard reaction products, its application is limited to the preliminary screening of roasted coffee with the same roasting degree, adulterated with relatively large amounts of roasted cereal flours which are roasted to analogous color to the coffee.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00003-022-01396-8.

The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans

Arabica coffee beans are sold at twice the price, or more, compared to Robusta beans and consequently are susceptible to economically motivated adulteration by substitution. There is a need for rapid, non-destructive, and efficient analytical techniques for monitoring the authenticity of Arabica coffee beans in the supply chain. In this study, multispectral imaging (MSI) was applied to discriminate roasted Arabica and Robusta coffee beans and perform quantitative prediction of Arabica coffee bean adulteration with Robusta. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, built using selected spectral and morphological features from individual coffee beans, achieved 100% correct classification of the two coffee species in the test dataset. The OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. MSI analysis has potential as a rapid screening tool for the detection of fraud issues related to the authenticity of Arabica coffee beans.

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.

Current strategies to guarantee the authenticity of coffee

As they become more health conscious, consumers are paying increasing attention to food quality and safety. In coffee production, fraudulent strategies to reduce costs and maximize profits include mixing beans from two species of different economic value, the addition of other substances and/or foods, and mislabeling. Therefore, testing for coffee authenticity and detecting adulterants is required for value assessment and consumer protection. Here we provide an overview of the chromatography, spectroscopy, and single-nucleotide polymorphism-based methods used to distinguish between the major coffee species Arabica and Robusta. This review also describes the techniques applied to trace the geographical origin of coffee, based mainly on the chemical composition of the beans, an approach that can discriminate between coffee-growing regions on a continental or more local level. Finally, the analytical techniques used to detect coffee adulteration with other foods and/or coffee by-products are discussed, with a look at the practice of adding pharmacologically active compounds to coffee, and their harmful effects on health.