Coffee Adulteration Research Articles

Polyphenolic profiling of coffee beverages by Liquid Chromatography-High-Resolution mass Spectrometry for classification and characterization

The importance of monitoring the presence of bioactive compounds as food attributes for sample classification and characterization is increasing. In this study, targeted Liquid Chromatography coupled with High-Resolution Mass Spectrometry (LC-HRMS) was employed to analyze the chemical profile of polyphenolic compounds as the source of information for the characterization and classification of 306 commercial coffee samples. Coffee holds a distinguished position as one of the most widely popular beverages globally but also one of the most easily adulterated. Regrettably, in recent times, instances of coffee adulteration have been on the rise. Consequently, implementing rigorous quality control measures for coffee becomes imperative to guarantee its quality. The results obtained in this work confirm that the proposed chemical profiles serve as excellent descriptors for sample characterization and classification through the implementation of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), achieving classification rates higher than 83.3% in PLS-DA validation. Moreover, the proposed LC-HRMS polyphenolic approach was employed to identify and measure adulteration levels in coffee samples using partial least squares (PLS) regression with prediction errors below 7.8%.

DETECTION OF WHITE RICE ADULTERANTS IN LAMPUNG ROBUSTA COFFEE EXTRACT USING FT-IR METHOD BY FINGERPRINT ANALYSIS AND PRINCIPAL COMPONENT ANALYSIS

Lampung Province has 3 areas that produce coffee: West Lampung, Tanggamus, and Waykanan, which can produce 51,484 tons of coffee annually. The high demand for coffee and limited harvest are some factors that cause the addition of adulterants to coffee products such as corn, soybeans, rye, coffee beans, and rice. This study aims to detect rice adulterants in Lampung robusta coffee using the FTIR spectrophotometric method combined with chemometric analysis using the PCA (Principal Component Analysis) method. FTIR spectrophotometric method combined with chemometric analysis using PCA (Principal Component Analysis) method. The PCA method was validated using the cross-validation method. Samples were taken from 3 Lampung coffees on the market. The extraction process was carried out on Lampung coffee and Lampung rice from 3 regions using 96% ethanol. After concentrating, the coffee and rice extracts were measured using FTIR and read in the 4000-650 cm-1 wave number range. Different wave number curves were obtained between coffee and rice. A pre-processing chemometric analysis was performed, which resulted in the classification of Robusta coffee and rice using PC1 and PC2 (50% and 24%). The results of PCA analysis showed that PC1 and PC2 in the three Lampung coffee samples, samples A and C, did not contain adulterants, while sample B was suspected of containing white rice. Keywords: Alduteran, FTIR, Chemometrics, PCA, Lampung Robusta Coffee.

Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication.

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.

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.

Benchtop (60 MHz) proton NMR spectroscopy for quantification of 16-O-methylcafestol in lipophilic extracts of ground roast coffee

We present a method for analysing the lipophilic fraction extracted from ground coffee beans using 60 MHz proton (1H) NMR spectroscopy. In addition to the triglycerides from coffee oil, spectral features are seen from a range of secondary metabolites, such as various diterpenes. We demonstrate quantitation of a peak attributed to one such compound, 16-O-methylcafestol (16-OMC), which is of interest as a coffee species marker. It is present in low concentrations (<<50 mg/kg) in Coffea arabica L. (‘Arabica’) beans, but in orders of magnitude greater concentrations in other coffees, in particular the other commercially grown species C. canephora Pierre ex A. Froehner (commonly known as ‘robusta’). A series of coffee extracts spiked with 16-OMC analytical standard are used to establish a calibration, and to estimate 16-OMC concentrations in a range of different coffees (Arabicas and blends with robustas). To validate the method, values obtained are compared with an analogous quantitation method that uses high field (600 MHz) NMR spectroscopy.•Quantitation of 16-O-methylcafestol in ground roast coffee extracts using benchtop (60 MHz) NMR spectroscopy•Validated by comparison with quantitative high field (600 Mz) NMR spectroscopy•Detection limit is sufficient for discovering adulteration of Arabica coffee with non-Arabica species

A cost-effective and rapid method base on high-performance thin-layer chromatography in evaluating the roasted coffee adulteration

A cost-effective and rapid method base on high-performance thin-layer chromatography in evaluating the roasted coffee adulteration

Solid-state NMR spectroscopy of roasted and ground coffee samples: Evidences for phase heterogeneity and prospects of applications in food screening

The advancement in the use of spectroscopic techniques to investigate coffee samples is of high interest especially considering the widespread problems with coffee adulteration and counterfeiting. In this work, the use of solid-state nuclear magnetic resonance (NMR) is investigated as a means to probe the various chemically-distinct phases existent in roasted coffee samples and to detect the occurrence of counterfeiting or adulterations in coffee blends. Routine solid-state 1H and 13C NMR spectra allowed the distinction between different coffee types (Arabica/Robusta) and the evaluation of the presence of these components in coffee blends. On the other hand, the use of more specialized solid-state NMR experiments revealed the existence of phases with different molecular mobilities (e.g., associated with lipids or carbohydrates). The results illustrate the usefulness of solid-state NMR spectroscopy to examine molecular mobilities and interactions and to aid in the quality control of coffee-related products.

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.

Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee

Food systems are negatively affected by food frauds with food recalls challenging the system's sustainability and consumer confidence in food safety. Coffee, an economically important commodity is frequently adulterated for economic gains, thereby requiring fast and reliable detection techniques. Of the various tracing strategies, spectroscopic techniques have seen considerable commercial success but rely heavily on human-engineered features. Thus, this study aims to evaluate feasibility of deep chemometrics (i.e., convolutional neural network, CNN) for coffee adulterant quantification in comparison to standard chemometrics approaches (i.e., partial least squares, PLS; and interval-PLS, iPLS). Commercial ‘espresso’ coffee was admixed with chicory, barley, and maize (0–25%, w/w) and subjected to Fourier Transformed-Near Infrared (FT-NIR) spectral analysis. The results confirmed the feasibility of CNN algorithm for adulterant quantification from FT-NIR spectra with excellent performances (R 2 > 0.98). Furthermore, CNN with Data augmentation (DA) with either autoscaling (AS) and/or standard normal variate (SNV) pre-treatment showed better prediction performances with RMSEP (0.76–0.82%) and BIASP (−1.00 × 10 −2 to −1.00 × 10 −1 %) that were better to comparable to those of PLS and/or iPLS models (0.72 < % RMSEP <3.045; −7.98 × 10 −2 < % BIASP <8.63 × 10 −2 ) for the adulterants tested. The study showed that deep learning algorithms can be potential alternatives to standard methods with little to no human interference for feature extraction during real-time applications of spectroscopic tools targeted to overcome food fraud crisis. • Deep chemometric algorithm tested for coffee adulteration prediction. • Convolutional neural network led to better performance than chemometrics. • Outlier removal and data augmentation benefitted the CNN algorithm. • CNN can be a feasible alternative for FT-NIR data analysis.

An ultrasensitive microfluidic chip-based immunoassay for multiplex determination of 11 PDE-5 inhibitors in adulterated health foods

Multiclass chemical contamination of food has aroused growing attention due to the increasingly co-occurrence of multiple contaminations, such as illegal additives (sildenafil and tadalafil adulterants) in health food. In the present study, an ultrasensitive multiplex microfluidic chip-based assay towards sildenafil and tadalafil adulterants was developed. Dramatically enhanced sensitivity and assay convenience were obtained based on our prepared brightly fluorescent signal reporters, effective microfluidic chip-based analyte enrichment strategy and a mobile detection platform. Under optimal conditions, this method could detect 11 sildenafil and tadalafil adulterants with low limits of detection (0.027–0.066 ng/mL) and low cut-off values (2.0–9.0 ng/mL). High accuracy and precision were validated due to the reasonable recoveries (94.3%~99.8% for tadalafil, 87.8~112% for sildenafil) and acceptable coefficient of variation (2.81–11.2%). Comparison experiments between this work and LC-MS/MS analysis found good agreement in the simultaneous screening of total sildenafil and tadalafil adulterants in capsule, tea leaves, instant coffee and health wine samples. This new assay format can be extensively employed in ultrasensitive field detection of multiple PDE-5 inhibitors within minutes, while also providing an easy-to-operate and low-cost multiplex detection protocol.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

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.

Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee.

Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis—PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America).

Penggunaan UV-Vis Spektroskopi dan Kemometrika untuk Uji Keaslian Kopi Codot Lampung

Codot coffee from Tanggamus, Lampung is one of Indonesian specialty coffee with a very limited production. In this research, an authentication study for the Codot ground roasted coffee was conducted using UV-vis spectroscopy and chemometrics. A total of 330 samples of pure and adulterated Codot coffee was prepared. The adulterated Codot coffee samples were intentionally created by adding a regular coffee (non-Codot coffee) into pure Codot coffee samples with three levels of adulterations: low (10-20%), medium (30-40%), and high level (50-60%). All samples were 0,29 mm in particle size. The extraction procedure was performed with hot distilled water (98°C). The spectral data of coffee samples were acquired using a benchtop UV-visible spectrometer in the range of 190-1100 nm using a transmittance mode. The result showed that the pure and adulterated samples could be discriminated along PC1 and PC2 axis. The classification model was developed using LDA with 90,91% of accuracy could be obtained. The LDA model was used to classify the new samples and resulted in a sensitivity (SEN) of 100%, specificity (SPEC) of 76,67%, precision (PREC) of 78,13%, and accuracy (ACC) of 87,27% could be obtained. Using PLS regression, a PLS model was developed to quantify the percentages of Codot coffee adulteration and resulted in high of coefficient of determination both in calibration and validation (R2kal = 0,99 and R2val = 0,98). These results showed that UV-vis spectroscopy and chemometrics are suitable for authentication of Codot specialty coffee with RMSEP = 2,68% and RPD in prediction of 6,49.&#x0D; &#x0D; Keywords: authentication, LDA, PCA, PLS regression, UV-vis spectroscopy

SIGHTS TO AUTHENTICATION AND ADULTERATION OF THE COFFEE IN GLOBAL ASPECT

Coffee presents a valuable food product for several reasons, covering areas like a source of health benefits substances for the human body, brain energizing/stimulating product and its social aspect raising from coffee consumption. This study aims to summarise the most common practices used in coffee adulteration and the methods focused to confirm the authenticity of the coffee, the quality, and price determinants according to a place of origin and/or selection between Robusta versus Arabica coffee beans. Nowadays, several scientific approaches are used for this purpose, based on chemical or molecular analysis. Literature and the data presented in this article were collected from studies published in scientific journals. On the other hand, the article introduces how to protect customer\s rights in the global coffee trade.

Varietal Classification of Selected Green Coffee Beans (Coffea arabica L. and Coffea canephora Pierre ex A.Froehner) Using Image Processing Software

The study created a classification model that sorts green coffee beans based on its variety, primarily ‘Arabica’ and ‘Robusta’ to address coffee adulteration. A total of 1500 green coffee beans were obtained from Cavite, Philippines, allotting 70% and 30% of it for training and testing dataset, respectively. These were then captured through the fabricated image acquisition setup. Image processing techniques were performed to extract the features of the beans namely color, size, shape, and crack using ImageJ software. Fourier analysis was mainly performed for the extraction of the bean shape information. After performing t-test, 3, 4, and 7 parameters of color, size, and shape features were found to be significant, respectively. The 14 features were used in creating 7 classifications setups of different feature combinations through discriminant analysis. The model that yielded the highest accuracy (99%) is the combination of color and size features. This exceeded the subjective varietal classification accuracy (98%) performed by a coffee expert. Thus, the variety of green coffee beans were feasibly classified through image analysis and the created model had surpassed the traditional performance of sorting green coffee beans.

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.

HPLC-Based Chemometric Analysis for Coffee Adulteration.

Coffee is one of the top ten most adulterated foods. Coffee adulterations are mainly performed by mixing other low-value materials into coffee beans after roasting and grinding, such as spent coffee grounds, maize, soybeans and other grain products. The detection of adulterated coffee by high performance liquid chromatography (HPLC) is recognized as a targeted analytical method, which carbohydrates and other phenolic compounds are usually used as markers. However, the accurate qualitation and quantitation of HPLC analyses are time consuming. This study developed a chemometric analysis or called non-targeted analysis for coffee adulteration. The HPLC chromatograms were obtained by direct injection of liquid coffee into HPLC without sample preparation and the identification of target analytes. The distinction between coffee and adulterated coffee was achieved by statistical method. The HPLC-based chemometric provided more characteristic information (separated compounds) compared to photospectroscopy chemometric which only provide information of functional groups. In this study, green Arabica coffee beans, soybeans and green mung beans were roasted in industrial coffee bean roaster and then ground. Spent coffee ground was dried. Coffee and adulterants were mixed at different ratio before conducting HPLC analysis. Principal component analysis (PCA) toward HPLC data (retention time and peak intensity) was able to separate coffee from adulterated coffee. The detection limit of this method was 5%. Two models were built based on PCA data as well. The first model was used to differentiate coffee sample from adulterated coffee. The second model was designed to identify the specific adulterants mixed in the adulterated coffee. Various parameters such as sensitivity (SE), specificity (SP), reliability rate (RLR), positive likelihood (+LR) and negative likelihood (−LR) were applied to evaluate the performances of the designed models. The results showed that PCA-based models were able to discriminate pure coffee from adulterated sample (coffee beans adulterated with 5%–60% of soybeans, green mung beans or spent coffee grounds). The SE, SP, RLR, +LR and −LR for the first model were 0.875, 0.938, 0.813, 14.1 and 0.133, respectively. In the second model, it can correctly distinguish the adulterated coffee from the pure coffee. However, it had only about a 30% chance to correctly determine the specific adulterant out of three designed adulterants mixed into coffee. The SE, RLR and −LR were 0.333, 0.333 and 0.667, respectively, for the second model. Therefore, HPLC-based chemometric analysis was able to detect coffee adulteration. It was very reliable on the discrimination of coffee from adulterated coffee. However, it may need more work to tell discern which kind adulterant in the adulterated coffee.

Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics.

Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1–30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R2c: ≥ 0.99, standard error of calibration (SEC) of 0.39–0.82, and standard error of prediction (SEP) of 0.45–0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.

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.