Coffee Authentication Research Articles

Portable fluorescence spectroscopy for single-origin coffee authentication with 365 nm wavelength as excitation source

Specialty coffee with geographical indications (GI) labels has been regarded as one of the most expensive commercial coffee in Indonesia. In this research, we utilize a new approach to classify specialty coffee from Indonesia with different geographical origins using portable fluorescence spectroscopy. For samples, 80 coffee samples with 1 gram of each sample were provided: 40 samples of Gayo arabica coffee (origin: Gayo, Aceh), and 40 samples of Java Mocha robusta coffee (origin: Java). All coffee samples were subjected to the following sample preparation: roasting (at 200°C for 15 min), grinding, and sieving using mesh size 40 to obtain samples with a homogenous size of 420 micrometers. The extraction of coffee samples was done by using hot distilled water. After extraction and dilution, coffee samples were injected into the 10 mm standard cell. The fluorescence spectral acquisition was performed by using a GoyaLab fluorescence spectrometer. This spectrometer has four LED lamps with 365 nm peaks used as excitation sources. The principal component analysis (PCA) of preprocessed spectral data results showed that most coffee samples could be differentiated into two different clusters. The proposed method is easy, and affordable to be adopted by many coffee industries in Indonesia.

An AI-powered e-nose system using a density-based clustering method for identifying adulteration in specialty coffees

Coffee is a valuable commodity with diverse varieties and unique specifications, particularly in Indonesia, a coffee-producing country. Ensured classification and quality of coffee throughout the supply chain are crucial for industry stakeholders, particularly in terms of flavor and aroma. This study focuses on the authenticity of coffee, particularly specialized coffee beans from microclimates that may be impacted by climate change. Advanced technologies, such as electronic nose (E-Nose) systems paired with machine learning techniques, play a significant role in achieving accurate and efficient authentication. Furthermore, the research addresses adulteration issues, such as blending different types of coffee beans, which can discourage farmers from participating in coffee cultivation. In this study, we develop an authentication method leveraging the olfactory characteristics of coffee aroma, emphasizing the unique interplay between microclimate conditions and flavor profiles. The clustering method of density-based spatial clustering of applications with noise (DBSCAN) technique is used to authenticate tropical climate specialty coffee and recognize adulterated specialty coffee beans. In our case study, adulteration in Arabica and Luwak specialty coffees was successfully detected using the DBSCAN technique combined with dimensionality reduction methods. This work has demonstrated a novel method for identifying specialty coffee authenticity and adulteration using the E-Nose system paired with machine learning techniques.

Coffee authentication via targeted metabolomics and machine learning: Unveiling origins and their discriminating biochemicals

Coffee is an export commodity that is prone to fraudulent practices. Therefore, this study presents a novel approach to authenticate coffee origins using targeted metabolomics with gas chromatography-tandem mass spectrometry (GC-MS/MS) and machine learning models. A total of 200 coffee samples from different harvest years and areas from Indonesia were extracted using the derivatisation method and then analysed for their metabolite profiles. Several supervised machine-learning models were tested to classify coffee origins and discover their potential markers. The study found various metabolite features spanning diverse chemical classes, encompassing sugar alcohols, carbohydrates, amino acids, organic acids, fatty acids, and phenols. Random forest (RF) and partial least squares discriminant analysis (PLS-DA) were among the most accurate models in predicting the origin of coffee from several classes in the validation dataset. The accuracy of both models is in the range of 91%–100%. Furthermore, this study proposes a new strategy for determining “intersection features” as the set of features that are important and common to both RF and PLS-DA models, thereby providing a robust selection of coffee origin markers. Overall, the approach and findings of this study have far-reaching implications for coffee authentication.

Government Policy on Gayo Women's Coffee Cooperative Community in Bener Meriah Regency, Aceh Province, Indonesia

The Gayo Women's Coffee Cooperative (KOKOWA GAYO) is Southeast Asia's first women's cooperative to trade Gayo Arabica coffee to the international market. The marketing is to Europe and the United States through cooperation agreements to export coffee commodities with three international investors: Fairtrade, Ecocert, and Cafe Feminino Foundation. The annual contract to export Gayo Arabica coffee commodity is in the amount of 20 containers. This cooperative has 500 farmers trained to break the marketing chain from loan sharks to increase the income and economic welfare of the coffee farmers. The decision-making made by women in this cooperative requires government involvement in terms of policies that support the export of coffee commodities to international markets. Yet, there is still miscommunication between the government and the KOKOWAGAYO. Because of these problems, a data analysis was carried out using radical feminism theory and structural functionality. The research paradigm used was the critical paradigm with a qualitative methodological approach. The result of this study indicates that they carried out KOKOWAGAYO, a women's cooperative. Also, miscommunication occurred regarding government regulations on policies issued by the cooperative because it independently exported coffee commodities to international markets, and the government has issued regulations or policies related to the safeguarding of Gayo Arabica coffee authenticity through a Decree of Origin (SKA) and the Gayo Coffee Protection Society (MPKG).

New genetic markers for 100% arabica coffee demonstrate high discriminatory potential for InDel-HRM-based coffee authentication

Food authenticity is crucial in today's society, given the heightened consumer awareness and attention to the products they consume. Reliable and efficient techniques are needed to quickly detect potential food adulterations that can negatively impact product quality and economic value. Coffee, a globally traded agricultural product, holds immense economic importance, with an estimated value of USD 83 billion. It is widely consumed and recognized as a functional food that provides minerals (K, Mg, Mn, Cr), niacin, and antioxidants. However, the preferred coffee species, Coffea arabica, known for its superior drink quality, is often adulterated with Coffea canephora (Robusta and Conilon) beans, even in 100% Arabica coffee. To distinguish between these two coffee species, a comprehensive study was conducted using a robust approach to identify differences in Single-Ortholog Copy (SOC) based on InDel regions in these gene pairs. These differences were validated using a meticulous methodology that considered variations in amplicon size: electrophoretic profile, and high-resolution melting (HRM). The innovative combination of InDels and HRM resulted in highly distinctive HRM profiles, outperforming SNP-based methods previously used. The targeted InDel approach utilized in this study facilitated precise quantification of Coffea species beans with a detection sensitivity of 0.5%. The study's findings establish the reliability and accuracy in distinguishing between the two coffee species, showcasing the valuable application of InDels for quality control and ensuring the authenticity of coffee beans. This pioneering research contributes to the advancement of authenticity verification methods for both imported and exported coffee beans, as well as in future studies that require significant genetic differences between these species, such as C. arabica and C. canephora.

Using HPLC with In-Column Derivatization to Authenticate Coffee Samples

Coffee is one of the world's most popular beverages, with the global coffee capsule market worth over USD 4 billion and growing. The incidence of coffee fraud is estimated to be up to one in five coffees being contaminated with cheaper blends of coffee. Given the worsening extent of climate change, coffee crop yields are harder to maintain, while demand is increasing. The 2021 Brazil frost delaying or destroying many coffee crops is an example. Hence, the incidence of coffee fraud is expected to increase, and as the market becomes more complex, there needs to be faster, easier, and more robust means of real-time coffee authentication. In this study, we propose the use of novel approaches to postcolumn derivatization (termed herein as in-column derivatization) to visualize the antioxidant profiles of coffee samples, to be later used as indicators for authentication purposes. We propose three simple mathematical similarity metrics for the real-time identification of unknown coffee samples from a sample library. Using the CUPRAC assay, and these metrics, we demonstrate the capabilities of the technique to identify unknown coffee samples from within our library of thirty.

Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder

SummaryCoffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta‐adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.

IMPACT OF THE HARVESTING YEAR ON THE POSSIBLE AUTHENTICATION OF GEOGRAPHICAL ORIGIN OF GREEN COFFEA ARABICA USING PROFILE OF VOLATILES

Authentication of coffee is highly required. This study aimed to understand the relative abundance of volatiles of green coffee harvested during two years. Using volatiles (GC-MS) and Linear Discriminant Analysis, we focused on the geographical origin identification. We analyzed samples of green Coffea arabica from Africa, Central America, and South America, harvested in 2018 and the same samples harvested in 2019. A total of 215 different volatiles were detected. Based on their chemical structure and the functional chemical group, they were divided into categories: furan derivates, aldehydes, ketones, alcohols, organic acids, hydrocarbons (alkanes, alkenes, alkynes, aromatic hydrocarbons), terpenoids, heterocyclic compounds, nitriles, amines. Green Arabica contained mostly organic acids and esters, aldehydes, hydrocarbons, and alcohols. We observed significant differences in aromatic hydrocarbons and furan derivates by comparing the volatiles profiles of African coffee beans collected in 2018 and 2019. The profile of Central American samples (both years) was homogenous; thus, no significant differences were observed. The aroma profile of South American coffees had significant differences in aromatic hydrocarbons and alkanes (p-value < 0.05). Rao’s approximation and Bartlett’s test proved a significant difference between 3 continents by applying LDA. More than 94% of the variability between Africa, Central, and South America coffees harvested in 2018 was explained by organic acids and esters, alkenes, aldehydes, and ketones. By adding samples from 2019, LDA calculations reduced input parameters to aldehydes and ketones, organic acids and esters, alkenes, terpenoids, and aldehydes. These appear to be useful for geographical authentication regardless of the year of harvesting.

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.

Lab-made 3D printed electrochemical sensors coupled with chemometrics for Brazilian coffee authentication

Analytical assurance of coffees’ geographical indication (GI) authenticity is essential for producers and consumers. In this way, chemometric methods, electrochemical techniques, and 3D printed sensors become attractive to assure the coffee’s quality. These sensors are low-cost, fast, and simple, with the possibility of miniaturization and portability. Therefore, 3D printed electrodes with chemometrics were used to classify-three Brazilian coffees from regions with GI. Further, Au/Gpt-PLA electrodes with partial least squares regression were used to detect the blending of GI coffee with traditional coffee. Soft independent modelling of class analogies coupled with cyclic voltammetry had the best performance, with 91–95% accuracy, specificity of 94–100%, and 80–83% sensitivity. Furthermore, the calibration models detected and quantified traditional coffee in all three coffees from regions with GI. The detection limits ranged from 1.4 to 10% (w/w), and quantification 4.6–32%, depending on the specific coffee.

NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends.

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.

How Does LC/MS Compare to UV in Coffee Authentication and Determination of Antioxidant Effects? Brazilian and Middle Eastern Coffee as Case Studies.

Coffee is a popular beverage owing to its unique flavor and diverse health benefits. The current study aimed at investigating the antioxidant activity, in relation to the phytochemical composition, of authenticated Brazilian green and roasted Coffea arabica and C. robusta, along with 15 commercial specimens collected from the Middle East. Ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-ESI–HRMS) and UV spectrometry were employed for profiling and fingerprinting, respectively. With the aid of global natural product social molecular networking (GNPS), a total of 88 peaks were annotated as belonging to different chemical classes, of which 11 metabolites are reported for the first time in coffee seeds. Moreover, chemometric tools showed comparable results between both platforms, with more advantages for UV in the annotation of roasting products, suggesting that UV can serve as a discriminative tool. Additionally, antioxidant assays coupled with the UHPLC-ESI–HRMS dataset using partial least-squares discriminant analysis (PLS-DA) demonstrated that caffeoylquinic acid and caffeine were potential antioxidant markers in unroasted coffee versus dicaffeoyl quinolactone and melanoidins in roasted coffee. The study presents a multiplex metabolomics approach to the quality control of coffee, one of the most consumed beverages.

Vibrational spectroscopy in combination with chemometrics as an emerging technique in the authentication of coffee: A review

Vibrational spectroscopy in combination with chemometrics as an emerging technique in the authentication of coffee: A review

Tracing commercial coffee quality by infrared spectroscopy in tandem with pattern recognition approaches

Nowadays, coffee has a broad highlight in the international commerce and its qualities are certified according to the Global Quality Program developed by specialized coffee agencies. Expert tasters provide a score classifying the coffees as Gourmet, Superior, Traditional and Inferior. However, due to relatively high-cost, possibility of misclassification, difficulty of large-scale analysis, and mainly to the subjectivity generated by the tasters, the international commerce requires a fast and reliable analysis. Fourier Transform Infrared Spectroscopy (FTIR) is a widely disseminated technique in laboratories routine for direct analysis besides successfully applied to measure the coffee quality. In the present work, coffee FTIR spectra were assisted by chemometric tools based on pattern recognition such as principal component analysis (PCA) and linear discriminant analysis (LDA) to find a suitable classification model for authentication purposes. Spectral predictors did not favor the discrimination of coffee samples. LDA performance was greatly improved using the PCs obtained from PCA as input variables. Several models were fitted from these PCs, so that the optimal classifier achieved 95 % accuracy in the overall predictions. This model also showed sensitivities in the range 83–100 % and specificities ranged from 93 to 100 %, indicating that the tuned model is trustworthy to successfully evaluate the commercial coffee authenticity. The developed method is simple, rapid, and environmentally friendly, since not demanding any sample preparation and does not producing waste after analysis. Its application can help to protect coffee market and companies by product quality certification.

Non-targeted high-performance liquid chromatography with ultraviolet and fluorescence detection fingerprinting for the classification, authentication, and fraud quantitation of instant coffee and chicory by multivariate chemometric methods

Non-targeted strategies based on high-performance liquid chromatography with ultraviolet detection (HPLC-UV) and fluorescence detection (HPLC-FLD) fingerprints were evaluated to accomplish the classification and authentication of instant coffee (40 samples), instant decaf coffee (26 samples), and chicory (22 samples, including both ground and instant), as well as to detect and quantify frauds based on chicory adulteration by multivariate chemometric methods. HPLC-UV and HPLC-FLD fingerprints were simultaneously obtained with a HPLC-UV-FLD instrument, and they proved to be excellent chemical descriptors for the classification of coffee and decaf coffee against chicory samples by partial least squares regression-discriminant analysis (PLS-DA). In contrast, HPLC-UV fingerprints improved the classification results when addressing coffee against decaf coffee samples (94.4% classification rate in comparison to 83.3% for HPLC-FLD fingerprints). Besides, the proposed methodologies resulted to be excellent to detect and quantify fraud levels in coffee and decaf coffee samples adulterated with chicory by using partial least squares (PLS) regression, exhibiting good calibration linearities, calibration errors, and prediction errors. In this case, improved capabilities were observed with HPLC-FLD fingerprints, providing better PLS calibration linearities (R2 > 0.999), lower calibration errors (≤0.8%), and similar to better prediction errors (2.9–3.2%) in comparison to HPLC-UV fingerprints.

Authenticity Assessment and Fraud Quantitation of Coffee Adulterated with Chicory, Barley, and Flours by Untargeted HPLC-UV-FLD Fingerprinting and Chemometrics.

Coffee, one of the most popular drinks around the world, is also one of the beverages most susceptible of being adulterated. Untargeted high-performance liquid chromatography with ultraviolet and fluorescence detection (HPLC-UV-FLD) fingerprinting strategies in combination with chemometrics were employed for the authenticity assessment and fraud quantitation of adulterated coffees involving three different and common adulterants: chicory, barley, and flours. The methodologies were applied after a solid–liquid extraction procedure with a methanol:water 50:50 (v/v) solution as extracting solvent. Chromatographic fingerprints were obtained using a Kinetex® C18 reversed-phase column under gradient elution conditions using 0.1% formic acid aqueous solution and methanol as mobile phase components. The obtained coffee and adulterants extract HPLC-UV-FLD fingerprints were evaluated by partial least squares regression-discriminants analysis (PLS-DA) resulting to be excellent chemical descriptors for sample discrimination. One hundred percent classification rates for both PLS-DA calibration and prediction models were obtained. In addition, Arabica and Robusta coffee samples were adulterated with chicory, barley, and flours, and the obtained HPLC-UV-FLD fingerprints subjected to partial least squares (PLS) regression, demonstrating the feasibility of the proposed methodologies to assess coffee authenticity and to quantify adulteration levels (down to 15%), showing both calibration and prediction errors below 1.3% and 2.4%, respectively.

Classification of Lampung robusta Specialty Coffee According to Differences in Cherry Processing Methods Using UV Spectroscopy and Chemometrics

The postharvest processing factors including cherry processing methods highly influence the final quality of coffee beverages, especially in the composition of several coffee metabolites such as glucose, fructose, the amino acid (glutamic acid), and chlorogenic acids (CGA) as well as trigonelline contents. In this research, UV spectroscopy combined with chemometrics was used to classify a ground roasted Lampung robusta specialty coffee according to differences in the cherry processing methods. A total of 360 samples of Lampung robusta specialty coffee with 1 g of weight for each sample from three different cherry processing methods were prepared as samples: 100 samples of pure dry coffee (DRY), 100 samples of pure semi-dry coffee (SMD), 100 samples of pure wet coffee (WET) and 60 samples of adulterated coffee (ADT) (SMD coffee was adulterated with DRY and WET coffee). All samples were extracted using a standard protocol as explained by previous works. A low-cost benchtop UV-visible spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, Waltham, MA, USA) was utilized to obtain UV spectral data in the interval of 190–400 nm using the fast scanning mode. Using the first three principal components (PCs) with a total of 93% of explained variance, there was a clear separation between samples. The samples were clustered into four possible groups according to differences in cherry processing methods: dry, semi-dry, wet, and adulterated. Four supervised classification methods, partial least squares–discriminant analysis (PLS-DA), principal component analysis–linear discriminant analysis (PCA-LDA), linear discriminant analysis (LDA) and support vector machine classification (SVMC) were selected to classify the Lampung robusta specialty coffee according to differences in the cherry processing methods. PCA-LDA is the best classification method with 91.7% classification accuracy in prediction. PLS-DA, LDA and SVMC give an accuracy of 56.7%, 80.0% and 85.0%, respectively. The present research suggested that UV spectroscopy combining with chemometrics will be highly useful in Lampung robusta specialty coffee authentication.

Novel authentication approach for coffee beans and the brewed beverage using a nuclear-based species-specific marker coupled with high resolution melting analysis

Coffee is a very popular and highly traded commodity that is targeted by fraudulent practices, affecting global production and economy. A common practice involves the substitution of the more expensive Arabica coffee with the less popular Robusta. Various physiochemical and DNA-based technologies have been used for coffee authentication; yet detection of adulterants in coffee still faces limitations in terms of the raw material used, the level of processing, and the sensitivity of available analytical tools. Additionally, DNA-based methodologies cannot be successfully used in the presence of strong inhibitors in brewed coffee. Herein, we designed an Internal Transcribed Spacer region 2 (ITS2)-based marker that allowed coffee authentication, even from the brewed beverage, when coupled with High Resolution Melting (HRM) analysis. The developed methodology could effectively detect differences in Arabica/Robusta admixtures, as low as 1% (v/v) DNA content. Direct species identification from the brewed beverage was enabled by the use of an inhibitor-tolerant DNA polymerase with enhanced sensitivity, without prior DNA purification. In the context of food safety, this novel approach could advance coffee authentication methods for safeguarding coffee producers and consumers.

Critical Review of Analytical and Bioanalytical Verification of the Authenticity of Coffee.

The driving factors for the commercial adulteration of coffee are reviewed. Methods have been assessed for the identification of the most common materials used to adulterate coffee by dilution, to establish the geographic origins, the genotypes of beans, and to assess the authenticity of Kopi Luwak coffee. The literature was surveyed manually and electronically from 1820 to 2018. A flow diagram has been developed to summarize the best approaches to deal with the authentication of coffee. Encouragement is given to the interlaboratory validation of spectroscopic approaches, the exploration of civet cat deoxyribonucleic acid for the identification of Kopi Luwak, and the development of appropriately large and well-curated datasets of authenticity information across multiple techniques. The current analytical difficulties in the authentication of coffee are highlighted and suggestions made to improve the situation.

Green and Roasted Coffee Authentication: Species, Origin, and Diluent Methods of Analysis.

Green and Roasted Coffee Authentication: Species, Origin, and Diluent Methods of Analysis.