Discrimination between arabica and robusta green coffee varieties according to their chemical composition

Effect of organic acid-soaking and sonication on the formation of volatile compounds and α-dicarbonyl compounds in Robusta coffee

In this study, the effects of organic acid-soaking (malic, citric, tartaric, and succinic acid) and sonication on the formation of flavor and α-dicarbonyl compounds in Robusta (C. canephora syn. Coffea robusta) green beans were investigated. A total of 20 volatile compounds were identified in Robusta coffee. Furfural and 5-methyl furfural, two dominant volatile compounds in Arabica coffee, increased after organic acid pretreatment. In Robusta coffee processed from 3% malic acid-soaked coffee beans, furfural and 5-methyl furfural increased by 90.99% and 24.92%, respectively, compared to the control. In Robusta coffee processed from 3% malic acid-sonicated (280 W, 1 h) coffee beans, furfural and 5-methyl furfural increased by 236.03% and 114.77%, respectively. α-Dicarbonyls (glyoxal, methylglyoxal, and diacetyl) were significantly (p < 0.05) decreased in all Robusta coffees after organic acid pretreatment. In Robusta coffee processed from coffee beans soaked and sonicated in tartaric acid solution, the α-dicarbonyls decreased by up to 44% and 58%, respectively, compared to the control. This study suggested the pretreatment methods to enhance the flavor substances and reduce the α-DCs in Robusta coffee.

Smartphone-Based Electrochemical Biosensor for On-Site Nutritional Quality Assessment of Coffee Blends

This work aimed to develop an easy-to-use smartphone-based electrochemical biosensor to quickly assess a coffee blend's total polyphenols (Phs) content at the industrial and individual levels. The device is based on a commercial carbon-based screen-printed electrode (SPE) modified with multi-walled carbon nanotubes (CNTs) and gold nanoparticles (GNPs). At the same time, the biological recognition element, Laccase from Trametes versicolor, TvLac, was immobilized on the sensor surface by using glutaraldehyde (GA) as a cross-linking agent. The platform was electrochemically characterized to ascertain the influence of the SPE surface modification on its performance. The working electrode (WE) surface morphology characterization was obtained by scanning electron microscopy (SEM) and Fourier-transform infrared (FT-IR) imaging. All the measurements were carried out with a micro-potentiostat, the Sensit Smart by PalmSens, connected to a smartphone. The developed biosensor provided a sensitivity of 0.12 μA/μM, a linear response ranging from 5 to 70 μM, and a lower detection limit (LOD) of 2.99 μM. Afterward, the biosensor was tested for quantifying the total Phs content in coffee blends, evaluating the influence of both the variety and the roasting degree. The smartphone-based electrochemical biosensor's performance was validated through the Folin-Ciocâlteu standard method.

Improvement of Robusta coffee aroma with l-leucine powder

BACKGROUND

l-Leucine powder (LP) was added to green Robusta coffee beans in order to reduce the difference in flavour between Robusta and Arabica coffee. l-Leucine was selected as an additive based on the Maillard reaction. The pre-treatment method conducted in this study was a short soaking (M1) or spraying procedure (M2), then LP was added at varying levels up to 3% (w/w, 30 g kg^-1 ). All samples were roasted (240 °C for 15 min) and extracted using an espresso machine. Volatile compounds were analysed by solid-phase microextraction-gas chromatography-mass selective detection.

RESULTS

Thirty volatile compounds (six pyrroles, eight pyrazines, three phenols, nine furans, two ketones, two aldehydes) were analysed. In 15 coffee samples, the levels of total volatile compounds (based on peak area ratios) ranged from 8.9 (M1-1) to 15. Non-treated Robusta had higher levels of bitter aroma compounds than Arabica coffee, and Robusta coffee had lower levels of bitter aroma compounds when pre-treated with LP. The sum of bitter volatiles (phenols, pyrroles, pyrazines) was lowest in M1-5 (3% LP), M2-1 (1% LP; both dried at 50 °C for 15 min) and M2-7 (3% LP, dried at 70 °C for 15 min) compared with non-treated Robusta (P < 0.05).

CONCLUSION

From the results of this study it can be shown that pre-treatment with LP can improve the flavour of Robusta. © 2023 Society of Chemical Industry.

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.

Natural Compounds for Counteracting Nonalcoholic Fatty Liver Disease (NAFLD): Advantages and Limitations of the Suggested Candidates

The booming prevalence of nonalcoholic fatty liver disease (NAFLD) in adults and children will threaten the health system in the upcoming years. The "multiple hit" hypothesis is the currently accepted explanation of the complex etiology and pathophysiology of the disease. Some of the critical pathological events associated with the development of NAFLD are insulin resistance, steatosis, oxidative stress, inflammation, and fibrosis. Hence, attenuating these events may help prevent or delay the progression of NAFLD. Despite an increasing understanding of the mechanisms involved in NAFLD, no approved standard pharmacological treatment is available. The only currently recommended alternative relies on lifestyle modifications, including diet and physical activity. However, the lack of compliance is still hampering this approach. Thus, there is an evident need to characterize new therapeutic alternatives. Studies of food bioactive compounds became an attractive approach to overcome the reticence toward lifestyle changes. The present study aimed to review some of the reported compounds with beneficial properties in NAFLD; namely, coffee (and its components), tormentic acid, verbascoside, and silymarin. We provide details about their protective effects, their mechanism of action in ameliorating the critical pathological events involved in NAFLD, and their clinical applications.

Development of a fast and simple method to identify pure Arabica coffee and blended coffee by Infrared Spectroscopy

ABSTRACT

Coffee is the second most consumed beverage in the world and Brazil is the biggest coffee producer. The main coffee species are Arabica (Coffea arabica) and Robusta (Coffea canephora). Arabica presents superior sensorial characteristics, as flavor and aroma, and Robusta is less expensive to produce. Pure Arabica coffee presents a market share of 70% and Arabic and Robusta are mixed to produce blended coffee. In this work, a fast and simple method to identify Arabica and blended coffee was proposed. The samples were analyzed by Infrared Spectroscopy in the mid and near-infrared regions and the spectra were used to develop a discriminant method. Using the method, the purity varied from 99.44 to 99.94% for pure Arabica coffees. To evaluate the method, the samples were characterized by gas chromatography coupled to mass spectrometry. It was possible to identify Arabica and blended coffee with high accuracy, in one minute, without complex analyses or sample preparations. The method is useful when Arabica is blended with more than 20% of Robusta and the practical application of the method can be extended to all coffee producers and distributors to ensure quality and to identify frauds or blended coffees and pure Arabica coffees.

GRAPHICAL ABSTRACT

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13197-021-05176-4.

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.

Use of non-volatile compounds for the classification of specialty and traditional Brazilian coffees using principal component analysis

Coffee beans contain different volatile and non-volatile compounds that are responsible for their flavor and aroma. Herein, principal component analysis (PCA) was employed to correlate the non-volatile composition of specialty and traditional coffees with drink quality. The quantified non-volatile compounds included caffeine, chlorogenic acid, caffeic acid, and nicotinic acid in both types of coffee samples, while 5-hydroxymethylfurfural was only quantified in the specialty coffee samples. The most abundant compounds present in specialty coffees were associated with the aroma and flavor, affording a high drink quality. In traditional coffees, the most abundant compounds included nicotinic acid and caffeine, indicating a stronger roasting process, loss of sensory characteristics, and blended formulations. PCA showed a distinction between the traditional and specialty coffees such that a relationship between the contents of the compounds in each type of coffee, quality, and classification could be established.

Development of a handheld liquid extraction pen for on-site mass spectrometric analysis of daily goods

We present a handheld liquid extraction pen (LEP) combined with a self-sustaining electrospray ionization platform for ambient mass spectrometry within a laboratory-independent workspace. The LEP enables direct sampling from various surfaces and textures, independent of sample shape without precise sample positioning or dedicated sample preparation. The combination of liquid extraction of analytes through the pen and electrospray ionization (ESI) opens a broad field of applications. Qualitative and semi-quantitative analysis is presented for pesticides, plasticizers and drugs which were analyzed from representative consumer goods, such as fruits, toys and pills. Food authentication via metabolomic fingerprinting and multivariate statistics is demonstrated for the analysis of fish fillets and coffee. The LEP source uses a rechargeable battery to power a compressor. Ambient air is used for solvent nebulization in ESI. Through a pressure pump with integrated solvent reservoir, a solvent flow through the LEP and ESI source is generated. Measurement times of more than three hours are possible. The ion source is adaptable to any kind of mass spectrometer equipped with an atmospheric pressure interface. Measurements were performed on orbital trapping instruments and on a miniature mass spectrometer. Coupled to the miniaturized mass spectrometer, the completely portable LEP-MS instrument has dimensions of 48.4 × 27.0 × 18.0 cm (l × w × h).

Coffee and Yeasts: From Flavor to Biotechnology

Coffee is one of the most consumed beverages in the world, and its popularity has prompted the necessity to constantly increase the variety and improve the characteristics of coffee as a general commodity. The popularity of coffee as a staple drink has also brought undesired side effects, since coffee production, processing and consumption are all accompanied by impressive quantities of coffee-related wastes which can be a threat to the environment. In this review, we integrated the main studies on fermentative yeasts used in coffee-related industries with emphasis on two different directions: (1) the role of yeast strains in the postharvest processing of coffee, the possibilities to use them as starting cultures for controlled fermentation and their impact on the sensorial quality of processed coffee, and (2) the potential to use yeasts to capitalize on coffee wastes—especially spent coffee grounds—in the form of eco-friendly biomass, biofuel or fine chemical production.

Arabica and Conilon coffee flowers: Bioactive compounds and antioxidant capacity under different processes

This study presents innovative research for comparison of the effect of the different dehydration techniques and methods of extraction on the antioxidant potential and bioactive compounds of Conilon and Arabica coffee flowers. The compounds were analyzed by high performance liquid chromatography and the antioxidant capacity evaluated by the 2,2'-azinobis (3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide assays. Among the compounds evaluated, trigonelline, gallic acid, chlorogenic acid, and caffeine were identified, with trigonelline and caffeine being those with the highest concentration. The investigated factors significantly influenced the profile of the bioactive compounds identified, and the antioxidant capacity. The 92 °C infusion of freeze-dried Conilon coffee flowers, in general, showed greater antioxidant capacity by ABTS and DPPH assays, as well as total phenolic content. Lyophilization had a positive influence on maintaining the content of phenolic compounds and antioxidant capacity of the samples. Coffee flowers proved to be a potential raw material for making tea-like drinks.

Reliable Discrimination of Green Coffee Beans Species: A Comparison of UV-Vis-Based Determination of Caffeine and Chlorogenic Acid with Non-Targeted Near-Infrared Spectroscopy

Species adulteration is a common problem in the coffee trade. Several attempts have been made to differentiate among species. However, finding an applicable methodology that would consider the various aspects of adulteration remains a challenge. This study investigated an ultraviolet–visible (UV-Vis) spectroscopy-based determination of caffeine and chlorogenic acid contents, as well as the applicability of non-targeted near-infrared (NIR) spectroscopy, to discriminate between green coffee beans of the Coffea arabica (Arabica) and Coffea canephora (Robusta) species from Java Island, Indonesia. The discrimination was conducted by measuring the caffeine and chlorogenic acid content in the beans using UV-Vis spectroscopy. The data related to both compounds was processed using linear discriminant analysis (LDA). Information about the diffuse reflectance (log 1/R) spectra of intact beans was determined by NIR spectroscopy and analyzed using multivariate analysis. UV-Vis spectroscopy attained an accuracy of 97% in comparison to NIR spectroscopy’s accuracy by selected wavelengths of LDA (95%). The study suggests that both methods are applicable to discriminate reliably among species.

Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling

In this work, different chemometric tools were compared to classify n = 26 conventional (CONV) and n = 19 organic (ORG) coffees from the main Brazilian producing regions based on the chemical composition, physicochemical properties, and antioxidant activity. Principal component analysis separated ORG and CONV coffees but the distinction among the producing regions of Brazilian coffee was not possible. Partial least squares discriminant analysis classified all ORG and CONV coffees in the external validation. Similarly, linear discriminant analysis was able to discriminate 100% and 81% of ORG and CONV coffees in the external validation, respectively, in which total phenolic content (TPC), ferric reducing antioxidant activity, and caffeic acid were the main discriminant variables. Overall 100% of samples from Paraná, Minas Gerais, and blended samples were correctly classified, where TPC, flavonoids, inhibition of lipid peroxidation, caffeic acid, pH, and soluble solids were the main discriminant variables. Support vector machines classified 95% ORG and 88% CONV, 100% Coffea arabica, and 88% and 78% coffees produced in São Paulo and Minas Gerais. k-Nearest neighbors was effective in distinguishing 100% CONV, 89% ORG, 100% coffees from São Paulo, and 100% C. arabica coffees. Overall, HPLC data and simple physicochemical parameters allied to chemometrics were effective in authenticating the cultivation system and the botanical origin of Brazilian coffees. PRACTICAL APPLICATION: Coffee adulteration is a serious problem in the food chain as some fraudsters replace coffee powder by other cheaper products. In the case of organic coffee, this scenario is even worse as still there is not a universal method to differentiate conventionally grown coffee from its organic counterpart. In addition, Brazilian coffee is produced in different regions and the commercial value varies. Therefore, we analyzed some physicochemical, chemical, and antioxidant properties of Brazilian coffees from distinct origins and classified the samples using chemometrics. Our approach seems to be interesting for quality control purposes.

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.

Efficacy of Arabica Versus Robusta Coffee in Improving Weight, Insulin Resistance, and Liver Steatosis in a Rat Model of Type-2 Diabetes

The effects of chronic coffee exposure in models of type 2 diabetes mellitus (T2D) models is scarcely studied, and the efficacy of the main coffee species has never been compared. We tested the hypothesis that long-term consumption of arabica and robusta coffee may differentially delay and affect T2D development in Zucker diabetic fatty rats. Three study groups received either chow mixed with arabica or robusta instant coffee (1.8% w/w) or unsupplemented chow food for 10 weeks. Both coffee species reduced liver triglyceride content and area under the curve of fasting and postprandial insulin. At study end, plasma adiponectin, total cholesterol and high density lipoprotein levels were higher in the robust group compared with both arabica and control groups. The liver gene expression of Glucose-6-phosphatase, catalytic subunit (G6pc) and Mechanistic target of rapamycin (mTOR) in robusta and Cpt1a in both coffee groups was downregulated. In conclusion, long-term consumption of both coffee species reduced weight gain and liver steatosis and improved insulin sensitivity in a rat model of T2D. Robusta coffee was seemingly superior to arabica coffee with respect to effects on lipid profile, adiponectin level and hepatic gene expression.

Convolutional neural network for hyperspectral data analysis and effective wavelengths selection

Fusion of spectral and spatial information has been proved to be an effective approach to improve model performance in near-infrared hyperspectral data analysis. Regardless, most of the existing spectral-spatial classification methods require fairly complex pipelines and exact selection of parameters, which mainly depend on the investigator's experience and the object under test. Convolutional neural network (CNN) is a powerful tool for representing complicated data and usually works with few "hand-engineering", making it an appropriate candidate for developing a general and automatic approach. In this paper, a two-branch convolutional neural network (2B-CNN) was developed for spectral-spatial classification and effective wavelengths (EWs) selection. The proposed network was evaluated by three classification data sets, including herbal medicine, coffee bean and strawberry. The results showed that the 2B-CNN obtained the best classification accuracies (96.72% in average) when compared with support vector machine (92.60% in average), one dimensional CNN (92.58% in average), and grey level co-occurrence matrix based support vector machine (93.83% in average). Furthermore, the learned weights of the two-dimensional branch in 2B-CNN were adopted as the indicator of EWs and compared with the successive projections algorithm. The 2B-CNN models built with wavelengths selected by the weight indicator achieved the best accuracies (96.02% in average) among all the examined EWs models. Different from the conventional EWs selection method, the proposed algorithm works without any additional retraining and has the ability to comprehensively consider the discriminative power in spectral domain and spatial domain.

Caffeine Consumption through Coffee: Content in the Beverage, Metabolism, Health Benefits and Risks

Caffeine (1,3,7-trimethylxanthine) is the most consumed psychoactive substance in the world, acting by means of antagonism to adenosine receptors, mainly A1 and A2A. Coffee is the main natural source of the alkaloid which is quite soluble and well extracted during the brew’s preparation. After consumption, caffeine is almost completely absorbed and extensively metabolized in the liver by phase I (cytochrome P450) enzymes, mainly CYP1A2, which appears to be polymorphically distributed in human populations. Paraxanthine is the major caffeine metabolite in plasma, while methylated xanthines and methyluric acids are the main metabolites excreted in urine. In addition to stimulating the central nervous system, caffeine exerts positive effects in the body, often in association with other substances, contributing to prevention of several chronic diseases. The potential adverse effects of caffeine have also been extensively studied in animal species and in humans. These aspects will be approached in the present review.

A simple and versatile authenticity assay of coffee products by single-nucleotide polymorphism genotyping

Interspecific single-nucleotide polymorphisms (SNPs) in the rbcL DNA barcode have been strictly validated and adopted as a designed SNP genotyping maker to discriminate between two major coffee species, Coffea arabica and C. canephora, and to estimate the mixing ratio of DNA from C. arabica/C. canephora in this study. The SNP genotyping is applicable to not only green (unroasted) coffee beans, but also processed coffee products (roasted coffee beans and instant coffee powder), in which genomic DNA is degraded, because the genotyping developed in this study requires only 10 copies of 63-bp-long DNA fragments of rbcL gene. The authenticity assay established in this study has several advantages: a high versatility to DNA sample conditions; simple and rapid procedures (only two steps; DNA extraction and SNP genotyping); the feasibility in coffee business for practical use to prevent false advertising and provide quality control. Abbreviations: SNP: single-nucleotide polymorphism; SBS: single base substitution; ISR: intergenic spacer region; INDEL: insertion-deletion.

Non-destructive analysis of sucrose, caffeine and trigonelline on single green coffee beans by hyperspectral imaging

Hyperspectral imaging (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addition, the intra-bean distribution of coffee constituents was analysed in Arabica and Robusta coffees on a large sample set from 12 countries, using a total of 260 samples. Individual green coffee beans were scanned by reflectance HSI (980–2500 nm) and then the concentration of sucrose, caffeine and trigonelline analysed with a reference method (HPLC-MS). Quantitative prediction models were subsequently built using Partial Least Squares (PLS) regression. Large variations in sucrose, caffeine and trigonelline were found between different species and origin, but also within beans from the same batch. It was shown that estimation of sucrose content is possible for screening purposes (R² = 0.65; prediction error of ~ 0.7% w/w coffee, with observed range of ~ 6.5%), while the performance of the PLS model was better for caffeine and trigonelline prediction (R² = 0.85 and R² = 0.82, respectively; prediction errors of 0.2 and 0.1%, on a range of 2.3 and 1.1% w/w coffee, respectively). The prediction error is acceptable mainly for laboratory applications, with the potential application to breeding programmes and for screening purposes for the food industry. The spatial distribution of coffee constituents was also successfully visualised for single beans and this enabled mapping of the analytes across the bean structure at single pixel level.

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NIR hyperspectral imaging was applied to green coffee beans on a single seed basis.

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Sucrose, caffeine and trigonelline were analysed from single beans using HPLC-MS.

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PLS regression models were built to quantify for the coffee constituents.

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Prediction models were applied to single pixels to visualise the compound distribution.

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HSI was shown to be useful for rapid non-destructive screening of single coffee beans.

Implementation of chemometrics in quality evaluation of food and beverages

Conventional methods for food quality evaluation based on chemical or microbiological analysis followed by traditional univariate statistics such as ANOVA are considered insufficient for some purposes. More sophisticated instrumental methods including spectroscopy and chromatography, in combination with multivariate analysis-chemometrics, can be used to determine food authenticity, identify adulterations or mislabeling and determine food safety. The purpose of this review is to present the current state of knowledge on the use of chemometric tools for evaluating quality of food products of animal and plant origin and beverages. The article describes applications of several multivariate techniques in food and beverages research, showing their role in adulteration detection, authentication, quality control, differentiation of samples and comparing their classification and prediction ability.

Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee

Due to the differences in terms of both price and quality, the availability of effective instrumentation to discriminate between Arabica and Robusta coffee is extremely important. To this aim, the use of multispectral imaging systems could provide reliable and accurate real-time monitoring at relatively low costs. However, in practice the implementation of multispectral imaging systems is not straightforward: the present work investigates this issue, starting from the outcome of variable selection performed using a hyperspectral system. Multispectral data were simulated considering four commercially available filters matching the selected spectral regions, and used to calculate multivariate classification models with Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA. Proper strategies for the definition of the training set and the selection of the most effective combinations of spectral channels led to satisfactory classification performances (100% classification efficiency in prediction of the test set).

Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors

Hyperspectral sensors represent a powerful tool for chemical mapping of solid-state samples, since they provide spectral information localized in the image domain in very short times and without the need of sample pretreatment. However, due to the large data size of each hyperspectral image, data dimensionality reduction (DR) is necessary in order to develop hyperspectral sensors for real-time monitoring of large sets of samples with different characteristics. In particular, in this work, we focused on DR methods to convert the three-dimensional data array corresponding to each hyperspectral image into a one-dimensional signal (1D-DR), which retains spectral and/or spatial information. In this way, large datasets of hyperspectral images can be converted into matrices of signals, which in turn can be easily processed using suitable multivariate statistical methods. Obviously, different 1D-DR methods highlight different aspects of the hyperspectral image dataset. Therefore, in order to investigate their advantages and disadvantages, in this work, we compared three different 1D-DR methods: average spectrum (AS), single space hyperspectrogram (SSH) and common space hyperspectrogram (CSH). In particular, we have considered 370 NIR-hyperspectral images of a set of green coffee samples, and the three 1D-DR methods were tested for their effectiveness in sensor fault detection, data structure exploration and sample classification according to coffee variety and to coffee processing method. Principal component analysis and partial least squares-discriminant analysis were used to compare the three separate DR methods. Furthermore, low-level and mid-level data fusion was also employed to test the advantages of using AS, SSH and CSH altogether. Graphical Abstract Key steps in hyperspectral data dimenionality reduction.

(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.

Arabica and robusta coffees: identification of major polar compounds and quantification of blends by direct-infusion electrospray ionization-mass spectrometry

Considering that illegal admixture of robusta coffee into high-quality arabica coffee is an important task in coffee analysis, we evaluated the use of direct-infusion electrospray ionization-mass spectrometry (ESI-MS) data combined with the partial least-squares (PLS) multivariate calibration technique as a fast way to detect and quantify arabica coffee adulterations by robusta coffee. A total of 16 PLS models were built using ESI± quadrupole time-of-flight (QTOF) and ESI± Fourier transform ion cyclotron resonance (FT-ICR) MS data from hot aqueous extracts of certified coffee samples. The model using the 30 more abundant ions detected by ES+ FT-ICR MS produced the most accurate coffee blend percentage prediction, and thus, it was later successfully employed to predict the blend composition of commercial robusta and arabica coffee. In addition, ESI± FT-ICR MS analysis allowed for the identification of 22 compounds in the arabica coffee and 20 compounds in the robusta coffee, mostly phenolics.

Dipstick test for DNA-based food authentication. Application to coffee authenticity assessment

This paper reports DNA-based food authenticity assays, in which species identification is accomplished by the naked eye without the need of specialized instruments. Strongly colored nanoparticles (gold nanoparticles) are employed as reporters that enable visual detection. Furthermore, detection is performed in a low-cost, disposable, dipstick-type device that incorporates the required reagents in dry form, thereby avoiding multiple pipetting and incubation steps. Due to its simplicity, the method does not require highly qualified personnel. The procedure comprises the following steps: (i) PCR amplification of the DNA segment that flanks the unique SNP (species marker); (ii) a 15 min extension reaction in which DNA polymerase extends an allele-specific primer only if it is perfectly complementary with the target sequence; (iii) detection of the products of the extension reaction within a few minutes by the naked eye employing the dipstick. No purification is required prior to application of the extension products to the dipstick. The method is general and requires only a unique DNA sequence for species discrimination. The only instrument needed is a conventional thermocycler for PCR, which is common equipment in every DNA laboratory. As a model, the method was applied to the discrimination of Coffea robusta and arabica species in coffee authenticity assessment. As low as 5% of Robusta coffee can be detected in the presence of Arabica coffee.

Quantification of the Robusta fraction in a coffee blend via Raman spectroscopy: proof of principle

Among the 100 different known Coffea species, Coffea arabica L. (Arabica) and Coffea canephora Pierre (Robusta) are the only two of commercial interest. They differ in a range of agronomic, genetic, and chemical properties. Due to the significant price difference between Arabica and Robusta, there is an economic incentive to illicitly replace Arabica with Robusta. Therefore, it is crucial to have accurate methods to determine the Robusta-to-Arabica-ratio in blends. This paper presents the proof of principle of a new and fast approach to determine the Robusta fraction in a blend based on Raman spectroscopy. The oils of two references (a pure Robusta and pure Arabica coffee) and six blends thereof consisting of different Robusta and Arabica fractions were extracted using a Soxhlet system. The solutes were analyzed by means of Raman spectroscopy without further workup. Using the intensity ratio between two Raman peaks, one characteristic for kahweol and one characteristic for fatty acids, allowed determinination of the Robusta content in a given mixture. The intensity ratio is linearly dependent on the Robusta content of the compound. Above a Robusta content of 75 wt %, kahweol was not detectable. The Raman data are in agreement with results obtained from the very time-consuming multistep DIN 10777 procedures based on HPLC.

Feasibility study on chemometric discrimination of roasted Arabica coffees by solvent extraction and Fourier transform infrared spectroscopy

In this feasibility study, Fourier transform infrared (FTIR) spectroscopy and chemometric analysis were adopted to discriminate coffees from different geographical origins and of different roasting degrees. Roasted coffee grounds were extracted using two methods: (1) solvent alone (dichloromethane, ethyl acetate, hexane, acetone, ethanol, or acetic acid) and (2) coextraction using a mixture of equal volume of the solvent and water. Experiment results showed that the coextraction method resulted in cleaner extract and provided a greater amount of spectral information, which was important for sample discrimination. Principal component analysis of infrared spectra of ethyl acetate extracts for dark and medium roast coffees showed separated clusters according to their geographical origins and roast degrees. Classification models based on soft independent modeling of class analogy analysis were used to classify different coffee samples. Coffees from four different countries, which were roasted to dark, were 100% correctly classified when ethyl acetate was used as a solvent. The FTIR-chemometric technique developed here may serve as a rapid tool for discriminating geographical origin of roasted coffees. Future studies involving green coffee beans and the use of larger sample size are needed to further validate the robustness of this technique.

Discrimination of green arabica and Robusta coffee beans by Raman spectroscopy

This paper presents an approach that may be applied as an accurate and rapid tool for classifying coffee beans on the basis of the specific kahweol content. Using Fourier-transform Raman spectroscopy with 1064 nm excitation it is possible to monitor the characteristic Raman bands of kahweol in green coffee beans without chemical and physical processing of the beans. The procedure was optimized on the basis of 83 and 125 measurements of whole and ground beans, respectively, using coffee samples of two different species, Coffea arabica L. and Coffea canephora L. (var. Robusta), and different origins (Asia, Africa, and South America). The relative contribution of the kahweol in individual beans can be determined quantitatively by means of a component analysis of the spectra, yielding a spectral kahweol index (σka) that is proportional to the relative content of kahweol in a coffee bean. The reproducibility of the spectroscopic measurement and analysis was found to be 3.5%. Individual beans of the same type and origin reveal a scattering of the σka values. Nevertheless, an unambiguous distinction between Arabica and Robusta samples is possible on the basis of single-bean measurements as the σka values are greater than and less than 10 for Arabica and Robusta coffees, respectively. Measurements of whole and ground beans afforded very similar results, despite the heterogeneous distribution of kahweol within a bean. Unlike conventional analytical techniques, the single-bean sensitivity of the present approach may also allow for a rapid detection of unwanted admixtures of low-value Robusta coffee to high-quality and more expensive Arabica coffee.

Mixture resolution according to the percentage of robusta variety in order to detect adulteration in roasted coffee by near infrared spectroscopy

Near infrared spectroscopy (NIRS), combined with multivariate calibration methods, has been used to quantify the robusta variety content of roasted coffee samples, as a means for controlling and avoiding coffee adulteration, which is a very important issue taking into account the great variability of the final sale price depending on coffee varietal origin. In pursuit of this aim, PLS regression and a wavelet-based pre-processing method that we have recently developed called OWAVEC were applied, in order to simultaneously operate two crucial pre-processing steps in multivariate calibration: signal correction and data compression. Several pre-processing methods (mean centering, first derivative and two orthogonal signal correction methods, OSC and DOSC) were additionally applied in order to find calibration models with as best a predictive ability as possible and to evaluate the performance of the OWAVEC method, comparing the respective quality of the different regression models constructed. The calibration model developed after pre-processing derivative spectra by OWAVEC provided high quality results (0.79% RMSEP), the percentage of robusta variety being predicted with a reliability notably better than that associated with the models constructed from raw spectra and also from data corrected by other orthogonal signal correction methods, and showing a higher model simplicity.