The Potential of Spectroscopic Techniques in Coffee Analysis—A Review

Smart E-Tongue Based on Polypyrrole Sensor Array as Tool for Rapid Analysis of Coffees from Different Varieties

Due to the lucrative coffee market, this product is often subject to adulteration, as inferior or non-coffee materials or varieties are mixed in, negatively affecting its quality. Traditional sensory evaluations by expert tasters and chemical analysis methods, although effective, are time-consuming, costly, and require skilled personnel. The aim of this work was to evaluate the capacity of a smart electronic tongue (e-tongue) based on a polypyrrole sensor array as a tool for the rapid analysis of coffees elaborated from beans of different varieties. The smart e-tongue device was developed with a polypyrrole-based voltammetric sensor array and portable multi-potentiostat operated via smartphone. The sensor array comprised seven electrodes, each doped with distinct counterions to enhance cross-selectivity. The smart e-tongue was tested on five Arabica coffee varieties (Typica, Bourbon, Maragogype, Tabi, and Caturra). The resulting voltammetric signals were analyzed using principal component analysis assisted by neural networks (PCNN) and cluster analysis (CA), enabling clear discrimination among the coffee samples. The results demonstrate that the polypyrrole sensors can generate distinct electrochemical patterns, serving as "fingerprints" for each coffee variety. This study highlights the potential of polypyrrole-based smart e-tongues as a rapid, cost-effective, and portable alternative for coffee quality assessment and adulteration detection, with broader applications in the food and beverage industry.

Evaluating the Impact of Green Coffee Bean Powder on the Quality of Whole Wheat Bread: A Comprehensive Analysis

The current investigation focuses on the effect of different concentrations of green coffee bean powder (GCBp) on the physicochemical, microbiological, and sensory characteristics of whole wheat bread (WWB). C1 bread formulation (containing 1% GCBp) exhibited the highest loaf volume, suggesting optimal fermentation. Moisture analysis revealed minor alterations in the moisture retention attributes of the bread formulations. Impedance analysis suggested that C1 exhibited the highest impedance with a high degree of material homogeneity. Swelling studies suggested similar swelling properties, except C5 (containing 5% GCBp), which showed the lowest swelling percentage. Furthermore, color and microcolor analysis revealed the highest L* and WI in C1. Conversely, higher concentrations of GCBp reduced the color attributes in other GCBp-containing formulations. FTIR study demonstrated an improved intermolecular interaction in C1 and C2 (containing 2% GCBp) among all. No significant variation in the overall textural parameters was observed in GCBp-introduced formulations, except C2, which showed an improved gumminess. Moreover, the TPC (total phenolic content) and microbial analysis revealed enhanced antioxidant and antimicrobial properties in GCBp-incorporated formulations compared to Control (C0, without GCBp). The sensory evaluation showed an enhanced appearance and aroma in C1 compared to others. In short, C1 showed better physicochemical, biological, and sensory properties than the other formulations.

Predicting the evolution of pH and total soluble solids during coffee fermentation using near-infrared spectroscopy coupled with chemometrics

Currently, coffee fermentation is visually operated, which results in incomplete or excessive processes and coffees with undesirable characteristics. In front of it, pH and total soluble solids (TSS) have been shown to be good fermentation indicators, although this requires rapid, accurate, and chemical-free measurement techniques such as NIR spectroscopy. However, the complexity of the NIR spectra requires optimization steps in which variable selection techniques simplify profiles and subsequent models. This work tests a new covering array feature selection (CAFS) approach on NIR spectra to optimize prediction models in coffee samples during fermentation. Spectral profiles in the range 1100-2100 nm were extracted from coffee beans (Typica, Caturra, and Catimor varieties) raw and during fermentation (4, 8, 12, 16, 20, and 24 h). Partial least-squares regressions (PLSR) were performed using full spectra using a five-fold cross-validation strategy for training and validation. The relevant wavelengths were then selected using the β coefficients, the important projection of variables (VIP), and the CAFS method. Finally, optimized models were performed using the relevant wavelengths and compared among these using their statistical metrics. The models performed using the selected variables (22-47) of CAFS showed the best performance in predicting pH (R 2 = 0.825-0.903, RMSE = 0.096-0.158, RPD = 6.33-10.38) and TSS (R 2 = 0.865-0.922, RMSE = 0.688-1.059, RPD = 0.94-1.45) compared to the other methods. These findings suggest that simple and efficient models could be performed and implemented in routine analysis due to the maximum coverage and minimum cardinality of CAFS.

Identifying the origin of Yemeni green coffee beans using near infrared spectroscopy: a promising tool for traceability and sustainability

Yemeni smallholder coffee farmers face several challenges, including the ongoing civil conflict, limited rainfall levels for irrigation, and a lack of post-harvest processing infrastructure. Decades of political instability have affected the quality, accessibility, and reputation of Yemeni coffee beans. Despite these challenges, Yemeni coffee is highly valued for its unique flavor profile and is considered one of the most valuable coffees in the world. Due to its exclusive nature and perceived value, it is also a prime target for food fraud and adulteration. This is the first study to identify the potential of Near Infrared Spectroscopy and chemometrics-more specifically, the discriminant analysis (PCA-LDA)-as a promising, fast, and cost-effective tool for the traceability of Yemeni coffee and sustainability of the Yemeni coffee sector. The NIR spectral signatures of whole green coffee beans from Yemeni regions (n = 124; Al Mahwit, Dhamar, Ibb, Sa'dah, and Sana'a) and other origins (n = 97) were discriminated with accuracy, sensitivity, and specificity ≥ 98% using PCA-LDA models. These results show that the chemical composition of green coffee and other factors captured on the spectral signatures can influence the discrimination of the geographical origin, a crucial component of coffee valuation in the international markets.

Production of oyster mushroom (Pleurotus ostreatus) from some waste lignocellulosic materials and FTIR characterization of structural changes

In this study, oyster (Pleurotus ostreatus) mushroom was cultivated from hazelnut branches (HB) (Corylus avellana L.), hazelnut husk (HH), wheat straw (WS), rice husk (RH) and spent coffee grounds (CG). Hazelnut branch waste was used for the first time in oyster mushroom cultivation. In the study, mushrooms were grown by preparing composts from 100 to 50% mixtures of each waste type. Yield, biological activity, spawn run time, total harvesting time and mushroom quality characteristics were determined from harvested mushroom caps. In addition, chemical analysis of lignocellulosic materials (extractive contents, holocellulose, α-cellulose, lignin and ash contents) were carried out as a result of mushroom production and their changes according to their initial amounts were examined. In addition, the changes in the structure of waste lignocellulosic materials were characterized by FTIR analysis. As a result of the study, 172 g/kg yield was found in wheat straw used as a control sample, while it was found as 255 g/kg in hazelnut branch pruning waste. The highest spawn run time (45 days) was determined in the compost prepared from the mixture of hazelnut husk and spent coffee ground wastes. This study showed that HB wastes can be used for the cultivation of oyster mushroom (P. ostreatus). After mushroom cultivation processes, holocelulose and α-cellulose content rates decreased while ash contents increased. FTIR spectroscopy indicated that significant changes occurred in the wavelengths regarding cellulose, hemicellulose and lignin components. Most significant changes occurred in 1735, 1625, 1510, 1322 and 1230 wavelengths.

Chemical analysis and NIR spectroscopy in the determination of the origin, variety and roast time of Mexican coffee

Coffee is a product whose quality and price are associated with its geographical, genetic and processing origin; therefore, the development of analytical techniques to authenticate the above mentioned is important to avoid adulteration. The objective of this study was to compare conventional analytical methods with NIR technology for the authentication of roasted and ground coffee samples from different producing regions in Mexico (origins) and different varieties. A second objective was to determine, under the same processing conditions, if roasting times can be differentiated by using this technology. A total of 120 samples of roasted and ground commercial coffee were obtained from the states of Chiapas, Oaxaca, Tabasco and Veracruz in Mexico, 30 locally available samples per state. Samples from Veracruz included three different varieties, grown on the same farm and processed under the same conditions. One of these varieties was selected to evaluate the chemical composition of samples roasted at 185 °C using four different roasting times (15, 17, 19 and 21 min). Samples from different producing regions showed significant differences (P < 0.05) in fat content (from 7.45 ± 0.42% in Tabasco to 18.40 ± 2.95% in Chiapas), which was associated with the altitude of coffee plantations (Pearson's r = 0.96). The results indicate that NIR technology generates sufficient useful information to authenticate roasted and ground coffee from different geographical origins in Mexico and different varieties from the same coffee plantation, with similar results to those obtained by conventional analytical methods.

Quality Assessment of Ground Coffee Samples from Greek Market Using Various Instrumental Analytical Methods, In Silico Studies and Chemometrics

Coffee is one of the most widely consumed beverages worldwide due to its sensory and potential health-related properties. In the present comparative study, a preparation known as Greek or Turkish coffee, made with different types/varieties of coffee, has been investigated for its physicochemical attributes (i.e., color), antioxidant/antiradical properties, phytochemical profile, and potential biological activities by combining high-throughput analytical techniques, such as infrared spectroscopy (ATR-FTIR), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and in silico methodologies. The results of the current study revealed that roasting degree emerged as the most critical factor affecting these parameters. In particular, the L* color parameter and total phenolic content were higher in light-roasted coffees, while decaffeinated coffees contained more phenolics. The ATR-FTIR pinpointed caffeine, chlorogenic acid, diterpenes, and quinic esters as characteristic compounds in the studied coffees, while the LC-MS/MS analysis elucidated various tentative phytochemicals (i.e., phenolic acids, diterpenes, hydroxycinnamate, and fatty acids derivatives). Among them, chlorogenic and coumaric acids showed promising activity against human acetylcholinesterase and alpha-glucosidase enzymes based on molecular docking studies. Therefore, the outcomes of the current study provide a comprehensive overview of this kind of coffee preparation in terms of color parameters, antioxidant, antiradical and phytochemical profiling, as well as its putative bioactivity.

Diuretics in Different Samples: Update on the Pretreatment and Analysis Techniques

Diuretics are drugs that promote the excretion of water and electrolytes in the body and produce diuretic effects. Clinically, they are often used in the treatment of edema caused by various reasons and hypertension. In sports, diuretics are banned by the World Anti-Doping Agency (WADA). Therefore, in order to monitor blood drug concentration, identify drug quality and maintain the fairness of sports competition, accurate, rapid, highly selective and sensitive detection methods are essential. This review provides a comprehensive summary of the pretreatment and detection of diuretics in various samples since 2015. Commonly used techniques to extract diuretics include liquid-liquid extraction, liquid-phase microextraction, solid-phase extraction, solid-phase microextraction, among others. Determination methods include methods based on liquid chromatography, fluorescent spectroscopy, electrochemical sensor method, capillary electrophoresis and so on. The advantages and disadvantages of various pretreatment and analytical techniques are elaborated. In addition, future development prospects of these techniques are discussed.

Monitoring Chemical Changes of Coffee Beans During Roasting Using Real-time NIR Spectroscopy and Chemometrics

Variations occurring in coffee beans during roasting are ascribable to several chemical-physical phenomena: to quickly track the whole process and to ensure its reproducibility, a process analytical technology (PAT) approach is needed.

In this study, a method combining in-line Fourier transform near-infrared (FT-NIR) spectroscopy and chemometric modelling was investigated to get real-time and practical knowledge about the roasting effects on coffee’s chemical-physical composition. In-line spectra were acquired by inserting a NIR probe into a laboratory coffee roaster, running twenty-four roasting experiments, planned spanning different coffee species (Arabica and Robusta), four roasting temperature settings (TS1–TS4) and times (650–1580 s).

Multivariate curve resolution-alternate least squares (MCR-ALS) was used to model the chemical-physical changes occurring during the roasting process, and information about maximum rate, acceleration and deceleration of the process was obtained, also highlighting potential effects due to the different roasting temperatures and coffee varieties.

The proposed approach provides the groundwork for direct real-time implementation of rapid, non-invasive automated monitoring of the roasting process at industrial scale.

Conformational Changes in the Structure of Dough and Bread Enriched with Pumpkin Seed Flour

Pumpkin seed flour is a promising raw material for use in the technology of various bakery products. It has a high biological value and valuable amino acid profile. During the technological process of making bread, there are conformational changes in the protein structure. The purpose of the study was to determine the effect of pumpkin seed flour on conformational changes in the structure of protein substances of dough and bread from wheat flour by near-infrared reflection spectroscopy. The protein profile changed to complete when replacing 10% or more of wheat flour because the score for all amino acids was higher than 100%. The utilitarian coefficient indicates the same balance of amino acids in proteins of all samples. As the percentage of substitution increases, the number of amino acids used for anabolic purposes decreases, and these are more fully utilized by the body.

Near-infrared spectroscopy as a green technology to monitor coffee roasting

Wet chemistry methods are traditionally used to evaluate the quality of a coffee beverage and its chemical characteristics. These old methods need to be replaced with more rapid, objective, and simple analytical methods for routine analysis. Near-infrared spectroscopy is an increasingly popular technique for nondestructive quality evaluation called a green technology. Our study aimed to apply near-infrared spectroscopy to evaluate the quality of coffee samples of different origin (Brazil, Guatemala, Peru, and Kongo). Particularly, we analyzed the roasting time and its effect on the quality of coffee. The colorimetric method determined a relation between the coffee color and the time of roasting. Partial least squares regression analysis assessed a possibility of predicting the roasting conditions from the near-infrared spectra. The regression results confirmed the possibility of applying near-infrared spectra to estimate the roasting conditions. The correlation between the spectra and the roasting time had R2 values of 0.96 and 0.95 for calibration and validation, respectively. The root mean square errors of prediction were low – 0.92 and 1.05 for calibration and validation, respectively. We also found a linear relation between the spectra and the roasting power. The quality of the models differed depending on the coffee origin and sub-region. All the coffee samples showed a good correlation between the spectra and the brightness (L* parameter), with R2 values of 0.96 and 0.95 for the calibration and validation curves, respectively. According to the results, near-infrared spectroscopy can be used together with the chemometric analysis as a green technology to assess the quality of coffee.

Special Issue on “Feature Review Papers in Section Food Processes”

The successive growth of the population, degradation of the natural environment, and development of civilization diseases force a continuous increase in the production of high-quality food [...]

Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees

The Specialty Coffee Association (SCA) sensory analysis protocol is the methodology that is used to classify specialty coffees. However, because the sensory analysis is sensitive to the taster’s training, cognitive psychology, and physiology, among other parameters, the feasibility of instrumental approaches has been recently studied for complementing such analyses. Spectroscopic methods, mainly near infrared (NIR) and mid infrared (FTIR—Fourier Transform Infrared), have been extensively employed for food quality authentication. In view of the aforementioned, we compared NIR and FTIR to distinguish different qualities and sensory characteristics of specialty coffee samples in the present study. Twenty-eight green coffee beans samples were roasted (in duplicate), with roasting conditions following the SCA protocol for sensory analysis. FTIR and NIR were used to analyze the ground and roasted coffee samples, and the data then submitted to statistical analysis to build up PLS models in order to confirm the quality classifications. The PLS models provided good predictability and classification of the samples. The models were able to accurately predict the scores of specialty coffees. In addition, the NIR spectra provided relevant information on chemical bonds that define specialty coffee in association with sensory aspects, such as the cleanliness of the beverage.