Combining mid infrared spectroscopy and paper spray mass spectrometry in a data fusion model to predict the composition of coffee blends

rIDIMS: A novel tool for processing direct-infusion mass spectrometry data

Metabolomics using mass spectrometry-only (MS) analysis either by continuous or intermittent direct infusion (DIMS) and ambient ionization techniques (AMS) has grown in popularity due to their rapid, high-throughput nature and the advantage of performing fast analysis with minimal or no sample pretreatments. But currently, end-users without programming knowledge do not find applications with Graphical User Interface (GUI) specialized in processing DIMS or AMS data. Specifically, there is a lack of standardized workflow for processing data from limited sample sizes and scans from different total ion chronograms (TIC).To address this gap, we present rIDIMS, a browser-based application that offers a straightforward and fast workflow focusing on high-quality scan selection, grouping of isotopologues and adducts, data alignment, binning, and filtering. We also introduce a novel function for selecting TIC scans that is reproducible and statistically reliable, which is a feature particularly useful for studies with limited sample sizes. After processing in rIDIMS, the result is exported in an HTML report document that presents publication-quality figures, statistical data and tables, ready to be customized and exported. We demonstrate rIDIMS functionality in three cases: (i) Classification of coffee bean species through the chemical profile obtained with Mass Spec Pen; (ii) Public repository DIMS data from lipid profiling in monogenic insulin resistance syndromes, and (iii) Lipids for lung cancer classification. We show that our implementation facilitates the processing of AMS and DIMS data through an easy and intuitive interface, contributing to reproducible and reliable metabolomic investigations. Indeed, rIDIMS function asa user-friendly GUI based Shiny web application for intuitive use by end-users (available at https://github.com/BioinovarLab/rIDIMS).

Spectrochemical differentiation in endometriosis based on infrared spectroscopy advanced data fusion and multivariate analysis

Endometriosis is a common benign gynecological condition characterized by the growth of endometrial gland and stroma located outside the uterine cavity, which the current approaches for its detection are invasive and expensive, limiting their clinical utility. There is a need for cost-effective and minimally invasive approaches to facilitate the diagnosis of this disease. Attenuated total reflection Fourier-transform infrared and near infrared spectroscopy combined with multivariate classification were applied as a new tool to analyze blood plasma samples from women with endometriosis (n = 41) and healthy individuals (n = 34). In addition, the use of advanced data fusion strategies and multivariate analysis techniques improved the classification models and facilitated diagnostics segregation of both sample categories in a fast and non-destructive way, generating high levels of accuracy, sensitivity and specificity. 2D correlation analysis revealed strong positive correlations between the spectrochemical biomarkers identified in both IR regions. To the best of our knowledge, this is the first study demonstrating the efficacy of a new tool for fast and non-invasive diagnosis of endometriosis using blood plasma samples analyzed with IR spectroscopy combined with multivariate classification.

Multiplatform Path-ComDim study of Capixaba, indigenous and non-indigenous Amazonian Canephora coffees

Integrating diverse measurement platforms can yield profound insights. This study examined Brazilian Canephora coffees from Rondônia (Western Amazon) and Espírito Santo (southeast), hypothesizing that geographical and climatic differences along botanical varieties significantly impact coffee characteristics. To test this, capixaba, indigenous, and non-indigenous Amazonian canephora coffees were analyzed using nine distinct platforms, including both spectroscopic techniques and sensory evaluations, to obtain results that are more informative and complementary than conventional single-method analyses. By applying multi-block Path-ComDim analysis to the multiple data sets, we uncovered crucial correlations between instrumental and sensory measurements. This integrated approach not only confirmed the hypothesis but also demonstrated that combining multiple data sets provides a more nuanced understanding of coffee profiles than traditional single-method analyses. The results underscore the value of multiplatform approaches in enhancing coffee quality evaluation, offering a more detailed and comprehensive view of coffee characteristics that can drive future research and improve industry standards.

An overview on the Brazilian Coffea canephora scenario and the current chemometrics-based spectroscopic research

This review explores the historical, botanical, sensory, and quality aspects of Coffea canephora, with a focus on Brazil's rise as a producer of specialty canephora coffees in the Amazon region, Espírito Santo, and Bahia. Brazil has gained global recognition through the first geographical indications for canephora: Matas de Rondônia for robusta amazônico coffee and Espírito Santo for conilon coffee. Despite this, comprehensive insights into how variety, terroir, environmental conditions, and cultivation practices influence the chemical and sensory attributes of Brazilian canephora remain underdeveloped compared to well-studied arabica coffee. Producers and researchers are working to elevate canephora coffees to higher market levels, despite technological, production, and perception challenges stemming from its historical reputation for poor quality. Ensuring the sustainability of Amazonian canephora coffee without deforestation is particularly challenging due to the need to verify practices across numerous small-scale farms. There is also a critical need for standardized production and tasting protocols for Brazilian canephora, leveraging local expertise and professional cuppers to ensure consistent quality and reliable sustainability claims. Significant opportunities exist in valuing the production chain of geographically unique canephora coffees, which could increase specialty exports, enhance economic prospects for local farmers, and support Amazon preservation. Recognizing and marketing these coffees as premium products with unique flavor profiles can boost their global appeal. Another challenge lies in establishing new specialty standards for soluble coffee from specialty canephora to meet consumer demands for convenience without compromising taste or ethical standards. In such a scenario, several analytical methods have been suggested to identify high-quality variants, combating their stigmatization. The potential of spectroscopy techniques and chemometrics-based data science is highlighted in confirming coffee quality, authenticity, traceability, and geographical origin, enhancing model interpretation and predictive accuracy through synergistic and complementary information. Non-targeted spectroscopic analyses, providing comprehensive spectral fingerprints, are contrasted with targeted analyses. Overall, this review offers valuable insights for the coffee scientific community, exporters, importers, roasters, and consumers in recognizing the potential of Brazilian canephora coffees.

Development of machine learning models using multi-source data for geographical traceability and content prediction of Eucommia ulmoides leaves

Rapid and scientific quality evaluation is a hot topic in the research of food and medicinal plants. With the increasing popularity of derivative products from Eucommia ulmoides leaves, quality and safety have attracted public attention. The present study utilized multi-source data and traditional machine learning to conduct geographical traceability and content prediction research on Eucommia ulmoides leaves. Explored the impact of different preprocessing methods and low-level data fusion strategy on the performance of classification and regression models. The classification analysis results indicated that the partial least squares discriminant analysis (PLS-DA) established by low-level fusion of two infrared spectroscopy techniques based on first derivative (FD) preprocessing was most suitable for geographical traceability of Eucommia ulmoides leaves, with an accuracy rate of up to 100 %. Through regression analysis, it was found that the preprocessing methods and data blocks applicable to the four chemical components were inconsistent. The optimal partial least squares regression (PLSR) model based on aucubin (AU), geniposidic acid (GPA), and chlorogenic acid (CA) had a residual predictive deviation (RPD) value higher than 2.0, achieving satisfactory predictive performance. However, the PLSR model based on quercetin (QU) had poor performance (RPD = 1.541) and needed further improvement. Overall, the present study proposed a strategy that can effectively evaluate the quality of Eucommia ulmoides leaves, while also providing new ideas for the quality evaluation of food and medicinal plants.

Electrochemiluminescence enhanced by isolating ACQphores in imine-linked covalent organic framework for organophosphorus pesticide assay

Most of covalent organic frameworks (COFs) are non or weakly emissive due to either the molecular thermal motion-mediated energy dissipation or the aggregation-caused quenching (ACQ) effect. Herein, we synthesize an imine-linked COF (TFPPy-TPh-COF) with high electrochemiluminescence (ECL) emission and the capability of eliminating the ACQ effect and further construct an ECL sensor for malathion detection. The imine-linked COF is obtained by the condensation reaction of (1,1':3',1″-terphenyl)-4,4″-diamine (TPh) and 1,3,6,8-tetrakis(p-formylphenyl)pyrene (TFPPy), and it has higher ECL efficiency than TFPPy aggregates due to the separation of ACQ luminophores (i.e., TFPPy) from each other by TPh and the restriction of intramolecular motions of TFPPy and TPh to reduce the nonradiative decay. The efficient quenching of ECL is achieved by electrochemiluminescence resonance energy transfer (ERET) from the excited state of the TFPPy-TPh-COF to zeolite imidazolate framework-8 (ZIF-8) and the steric hindrance of ZIF-8. Acetylcholinesterase (AChE) can enzymatically hydrolyze acetylcholine (ACh) to generate acetic acid. The resultant acetic acid can trigger the dissolution of ZIF-8 to produce an enhanced ECL signal. Malathion as an organophosphorus pesticide serves as an AChE inhibitor to prevent the production of acetic acid, inducing the decrease of ECL signal. This sensor displays a limit of detection (LOD) of 2.44 pg/mL and a wide dynamic detection range of 0.01-1000 ng/mL. Furthermore, it can be used to detect other organophosphates pesticides (e.g., methidathion, chlorpyrifos, and paraoxon) and measure malathion in real samples (i.e., pakchoi, lettuce, and apples).

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Discrimination of Filter Coffee Extraction Methods of a Medium Roasted Specialty Coffee Based on Volatile Profiles and Sensorial Traits

An untargeted gas chromatography-mass spectrometry (GC-MS) approach combined with sensory analysis was used to present the effects of different extraction methods (i.e., Pure Brew, V60, AeroPress, and French Press) on specialty graded Coffea arabica from Kenya. Partial Least Square Discriminant analysis and hierarchical clustering were applied as multivariate statistical tools in data analysis. The results showed good discrimination and a clear clustering of the groups of samples based on their volatile profiles. Similarities were found related to the filter material and shape used for the extraction. Samples extracted with paper filters (V60 and AeroPress) resulted in higher percentages of caramel-, and flowery-related compounds, while from metal filter samples (Pure Brew and French Press), more fruity and roasted coffees were obtained. Discriminant analysis allowed the identification of eight compounds with a high VIP (variable important in projection) discriminant value (i.e., >1), with 2-furanmethanol being the main feature in discrimination. Sensorial analyses were carried out through an expert panel test. The main evaluations revealed the French Press system as the lowest-scored sample in all the evaluated parameters, except for acidity, where its score was similar to V60. In conclusion, the data obtained from GC-MS analyses were in line with the sensorial results, confirming that the extraction process plays a fundamental role in the flavor profile of filter coffee beverages.

Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion

Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm^-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (R_p: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.

SHS-GC-MS applied in Coffea arabica and Coffea canephora blend assessment

Considering the great economic significance of Coffea arabica (arabica) associated with the lower production cost of C. canephora (conilon), blends of these coffees are commercially available to reduce costs and combine sensory attributes. Thus, analytical tools are required to ensure consistency between real and labeled compositions. In this sense, chromatographic methods based on volatile analysis using static headspace-gas chromatography-mass spectrometry (SHS-GC-MS) and Fourier transform infrared (FTIR) spectroscopy associated with chemometric tools were proposed for the identification and quantification of arabica and conilon blends. The peak integration from the total ion chromatogram (TIC) and extracted ion chromatogram (EIC) was compared in multivariate and univariate scenarios. The optimized partial least squares (PLS) models with uninformative variable elimination (UVE) and chromatographic data (TIC and EIC) have similar accuracy according to a randomized test, with prediction errors between 3.3% and 4.7% and R_p² > 0.98. There was no difference between the univariate models for the TIC and EIC, but the FTIR model presented a lower performance than GC-MS. The multivariate and univariate models based on chromatographic data had similar accuracy. For the classification models, the FTIR, TIC, and EIC data presented accuracies from 96% to 100% and error rates from 0% to 5%. Multivariate and univariate analyses combined with chromatographic and spectroscopic data allow the investigation of coffee blends.

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

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

Paper Spray Mass Spectrometry as an Effective Tool for Differentiating Coffees Based on Their Geographical Origins

With the rising trend of valuing flavor complexity of coffees, means to distinguish the properties of individual coffee sources is vital to the sustainable growth of the coffee industry. Herein, paper spray mass spectrometry (PS-MS), a simple technique with little sample preparation, was used to collect mass data from aqueous extracts of coffees from various sources. Thereafter, principal component analysis and linear discriminant analysis were used to successfully classify coffee samples (with 80-100 % accuracy) from various studies including the differentiations of Arabica and Robusta coffees, Arabica coffees from different countries, Robusta coffees from different geographical locations, and Arabica coffees from different locations within the same province in Thailand. With further insight from significant test via Fisher weight determination, this method was proved to be practical for differentiating coffees based on types and geographical origins, thus paving the way for broader applications.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Direct authentication and composition quantitation of red wines based on Tri-step infrared spectroscopy and multivariate data fusion

A robust data fusion strategy integrating Tri-step infrared spectroscopy (IR) with electronic nose (E-nose) was established for rapid qualitative authentication and quantitative evaluation of red wines using Cabernet Sauvignon as an example. The chemical fingerprints of four types of wines were thoroughly interpreted by Tri-step IR, and the defined spectral fingerprint region of alcohol and sugar was 1200-950 cm^-1. The wine types were authenticated by IR-based principal component analysis (PCA). Furthermore, ten quantitative models by partial least squares (PLS) were built to evaluate alcohol and total sugar contents. In particular, the model based on the fusion datasets of spectral fingerprint region and E-nose was superior to the others, in which RMSEP reduced by 47.95% (alcohol) and 79.90% (total sugar), r_p increased by 11.95% and 43.47%, and RPD >3.0. The developed methodology would be applicable for mass screening and rapid multi-chemical-component quantification of wines in a more comprehensive and efficient manner.

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV-Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

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.

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

Coffee quality is determined by several factors and, in the chemometric domain, the multi-block data analysis methods are valuable to study multiple information describing the same samples. In this industrial study, the Common Dimension (ComDim) multi-block method was applied to evaluate metabolite fingerprints, near-infrared spectra, sensory properties, and quality parameters of coffee blends of different cup and roasting profiles and to search relationships between these multiple data blocks. Data fusion-based Principal Component Analysis was not effective in exploiting multiple data blocks like ComDim. However, when a multi-block was applied to explore the data sets, it was possible to demonstrate relationships between the methods and techniques investigated and the importance of each block or criterion involved in the industrial quality control of coffee. Coffee blends were distinguished based on their qualities and metabolite composition. Blends with high cup quality and lower roasting degrees were generally differentiated from those with opposite characteristics.

Mid-Infrared Spectroscopy as a Valuable Tool to Tackle Food Analysis: A Literature Review on Coffee, Dairies, Honey, Olive Oil and Wine

Nowadays, food adulteration and authentication are topics of utmost importance for consumers, food producers, business operators and regulatory agencies. Therefore, there is an increasing search for rapid, robust and accurate analytical techniques to determine the authenticity and to detect adulteration and misrepresentation. Mid-infrared spectroscopy (MIR), often associated with chemometric techniques, offers a fast and accurate method to detect and predict food adulteration based on the fingerprint characteristics of the food matrix. In the first part of this review the basic concepts of infrared spectroscopy, sampling techniques, as well as an overview of chemometric tools are summarized. In the second part, recent applications of MIR spectroscopy to the analysis of foods such as coffee, dairy products, honey, olive oil and wine are discussed, covering a timespan from 2010 to mid-2020. The literature gathered in this article clearly reveals that the MIR spectroscopy associated with attenuated total reflection acquisition mode and different chemometric tools have been broadly applied to address quality, authenticity and adulteration issues. This technique has the advantages of being simple, fast and easy to use, non-destructive, environmentally friendly and, in the future, it can be applied in routine analyses and official food control.

Spatial Distribution of Endogenous Molecules in Coffee Beans by Atmospheric Pressure Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a promising chemical imaging method. Among various endogenous molecules, mapping the concentration and the spatial distribution of specific compounds in the coffee bean tissue is of tremendous significance in its function research, as these compounds are critical to grading coffee beans at the molecular level, determining the geographical origin, and optimizing storage conditions of coffee beans. In this paper, we established an atmospheric pressure (AP) matrix-assisted laser desorption/ionization (MALDI) MSI method for the microscopic distribution analysis of endogenous molecules, for example, sucrose, caffeine, and caffeoylquinic acid, in the coffee bean endosperm. Experiments were done on the differences between coffee beans from eight countries. Principal component analysis (PCA) was performed using IMAGEREVEAL software. The results showed that the chemical composition and relative content of coffee beans from different origins are different. Our work provides a detection method that may be used for coffee bean quality identification, efficient use, product traceability, and product counterfeiting.

A data fusion model merging information from near infrared spectroscopy and X-ray fluorescence. Searching for atomic-molecular correlations to predict and characterize the composition of coffee blends

This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm-1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.

Review of Analytical Methods to Detect Adulteration in Coffee

As one of the most consumed beverages in the world, coffee plays many major socioeconomical roles in various regions. Because of the wide coffee varieties available in the marketplaces, and the substantial price gaps between them (e.g., Arabica versus Robusta; speciality versus commodity coffees), coffees are susceptible to intentional or accidental adulteration. Therefore, there is a sustaining interest from the producers and regulatory agents to develop protocols to detect fraudulent practices. In general, strategies to authenticate coffee are based on targeted chemical profile analyses to determine specific markers of adulterants, or nontargeted analyses based on the “fingerprinting” concept. This paper reviews the literature related to chemometric approaches to discriminate coffees based on nuclear magnetic resonance spectroscopy, chromatography, infrared/Raman spectroscopy, and array sensors/indicators. In terms of chemical profiling, the paper focuses on the detection of diterpenes, homostachydrine, phenolic acids, carbohydrates, fatty acids, triacylglycerols, and deoxyribonucleic acid. Finally, the prospects of coffee authentication are discussed.