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This research (purchase required) presents a nanozyme-based colorimetric sensor array for effective coffee variety discrimination by analyzing characteristic components.  Nanozymes are nano-scale molecules (for example, some metal oxides) that can catalyse biological reactions in an analogous way to enzymes.  

The authors report the development of an optimized sensor array containing 13 nanozymes.  No details of the reference samples or Machine Learning training are reported in the public abstract.  They report that their sensor enabled the discrimination of coffee key compounds (no details given in the abstract) within the concentration range of 0.8 μmol/L to 100 μmol/L. The sensing mechanism involves coffee components modulating nanozyme peroxidase-like activity through electron transfer and hydrophobic interactions.

They report that this technology successfully distinguished two main coffee categories and their subtypes with high accuracy.

They also report the development of a mobile phone app based on their sensor.  It achieved identification of coffee varieties and the detection of semi-quantitative adulteration levels. The conclude that this portable platform demonstrates significant potential for commercial coffee quality monitoring, providing a reliable tool for authenticity verification in the coffee industry.

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n this paper (open access) the authors used of solid-phase microextraction (SPME)-gas chromatography-time-of-flight mass spectrometry (GC/Q-ToF-MS) combined with chemometrics to detect key differences between adulterated and non-adulterated ground roast coffee. They drilled into these differences and found two potential chemical markers for common adulterants.

They compared the aroma profiles of ground roasted coffee with some commonly used adulterants (ground roasted barley, corn and soybean). The SPME fibre collected and concentrated the headspace volatiles. Non-adulterated and adulterated samples were distinguished after applying some chemometric tools (principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA)) on the obtained chromatographic data. Two volatile compounds (1H-imidazole-4-methanol and benzene-2-(1,3-butadienyl)-1,3,5-trimethyl) were identified as potential markers for the determination of adulterants (ground roasted barley, corn or soybean) in ground roasted coffee (p-value cut-off<0.001 and fold change (FC) cut-off>10). Also, 2-furanmethanol and 2-formyl-1-methylprrrole were found as marker candidates for roasted coffee powder.

The authors tested this approach and were able to detect selected herbal adulterants (5% w/w) found in ground coffee.

Authenticity tests for coffee tend to focus on the variety (Arabica vs Rustica) or adulteration of roasted ground coffee (e.g. with chicory).  There has been relatively little focus on authenticating the origin of green beans, for example to underpin Fair Trade traceability.

Proteomics has previously shown differences among cultivars.  This paper (subscription required) built on previous studies that had showed that long-term adaptation to a distinct climate (associated with the geographical location), are likely to significantly affect various metabolic processes and thus protein profiles.  Most proteins in beans are likely to be enzymes, such as oxidases and peroxidases. Previous researchers had identified 531 proteins in C. arabica cultivars in high-altitude African and low-altitude South American samples. Further analysis pointed out that only a few proteins were significantly different between them, plausibly corresponding to the concentration of certain compounds (e.g., flavonoids) alongside the adaptation to the environmental niches (e.g., colder climate or predominant pathogens). Post-harvest processing modifies proteomic profile.

This study used a combination of proteomic profiling with linear discriminant analysis for the classification of the geographical origin of green specialty coffee beans from well-known harvesting regions in Central America, South America, Africa, and Asia. Out of 1596 identified proteins, the authors selected the top 30 target markers ranked by ANOVA. They report that the model's prediction performance using leave-one-out cross-validation reached 85.3 %, with the lowest accuracy in the prediction rate for Asian samples. Model performance and prediction sensitivity to random states were tested using 5-fold cross-validation. After 20 iterations, the model performance slightly decreased to 84.0 %. Specificity and sensitivity confirmed that the model appears to be reliable at distinguishing Asian and African samples.

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Chlorogenic acids (CGAs) are phenolic compounds found in plant-based foods including coffee. This study (open access) aimed to evaluate the profile of three CGAs (5-CGA, 4-CGA, and 3-CGA )in medium-roasted Coffea arabica L. and Coffea canephora Pierre ex A.Froehner beans originating from diverse geographical regions.

The researchers reported that 5-CGA was the predominant compound across all samples analyzed.  C. canephora samples contained significantly higher and more variable levels of CGAs compared to C. arabica samples.

Statistical analysis using ANOVA, combined with Duncan, Tukey, and Dunn post hoc tests, confirmed species-related differences in CQAs content. Additionally, violin plots provided a clear visualization of these distinctions. Principal Component Analysis (PCA) further indicated that the geographical origin of the samples may influence the accumulation of chlorogenic acids.

The authors conclude that both botanical species and environmental factors influence the CGA composition of coffee. Understanding such variability could both give a useful authenticity marker and could guide the development of value-added coffee-based products tailored to consumer preferences and health-related expectations.

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In this study (open access) a non-targeted method of headspace-solid phase microextraction with gas chromatography coupled to mass spectrometry (HS-SPME-GC–MS) was developed to achieve the characterization, classification, and authentication of different coffee samples according to geographical production region, and variety (arabica/robusta). Moreover, decaffeinated and non-decaffeinated instant coffee samples were analyzed. Some samples of chicory, a potential coffee adulterant, were also been included. The GC–MS fingerprints were used to classify and characterise the analyzed coffees using principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and partial least squares (PLS) regression.

185 reference samples were used.  42 were chicory (a typical coffee substitute or adulterant), 96 were coffee from three geographical production regions (Vietnam, Cambodia, and Costa Rica) and species (Arabica, Robusta, and Arabica-Robusta mixture), and 47 samples were soluble coffee (decaffeinated and non-decaffeinated). The chicory samples were purchased from Barcelona supermarkets and the coffee samples were from Vietnam, Cambodia and Costa Rica local supermarkets. Each paired PLS-DA model was built using 70 % of samples randomly selected from each group as the calibration set while the remaining 30 % of the samples were employed as the prediction set.  The authors compared models generated using two different GC columns and operating conditions.

The authors tested their model on mixtures prepared in-house from the same reference set:: Vietnamese Arabica Coffee adulterated with Vietnamese Robusta Coffee and Vietnamese Robusta Coffee adulterated with chicory.  They reported that the model could classify adulteration levels down to 15%.

(image from the paper)

Fluorescence spectroscopy utilizing benchtop and portable spectrometers with light-emitting diodes (LEDs) as a fixed excitation source has been used as a method for detecting food adulteration in various products, including honey, extra virgin olive oil, tea, and coffee  It is cost-effective, rapid, and sensitive, allowing for intact measurement. LED-based fluorescence spectroscopy is fast, accurate, and cheaper than using a laser.. Recent advancements in semiconductor technology have enabled the delivery of LEDs with commercially available wavelengths ranging from 370 to 470 nm, exhibiting significant light intensity.

In this paper (purchase required), the authors used the technique to develop a classification model to detect ground soy in ground-roasted Arabica coffee, and to differentiate Robusta and Liberica varieties.  The abstract gives no details of the reference samples used to construct or validate the model but it was limited to 2024 season samples harvested in Indonesia.

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The authors of this paper (open access) used Fourier-Transform Ion-Cyclotron Resonance Mass Spectrometry (FT-ICR) to accurately profile and classify thousands of chemical components within different types of coffee.  They are making their list of components publicly available alongside the paper.

Their reference set of 130 coffees were purchased at market, rather than being of traceable origin, but were verified by documentation from the manufacturers along with morphological examination and classification using the German standard NMR method.

From this list of markers, the authors investigated those with the potential to discriminate based on the complex Maillard reactions of roasting processes and those that could discriminate coffee varieties.  They propose a group of 25 tryptophan conjugates of hydroxycinnamic acids that could be measured by conventional high-resolution LC-MS and used as specific markers for rustica coffee vs arabica.