Spectroscopic and Chromatographic Fingerprints for Discrimination of Specialty and Traditional Coffees by Integrated Chemometric Methods

Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning

In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-dimensional correlation spectroscopy (2D-COS), and multivariate statistical analysis were employed to rapidly distinguish and compare three different primary processed Arabica coffee beans. The results showed that the sun-exposed processed beans (SPB) exhibited the highest total color difference value and the largest pore size. Meanwhile, the wet-processed beans (WPB) retained the most bound and immobilized water in green and roast coffee beans. The FT-IR data analysis results indicated that the functional group composition was similar across the three different primary processed coffee beans, while significant differences in structural characteristics were observed in 2D-COS. The multivariate statistical analysis demonstrated that the orthogonal partial least squares-discriminant analysis model could effectively distinguish the different types of coffee beans. The machine learning results indicated that the six models could rapidly identify different samples of primary processed coffee beans. Notably, the SNV-Voting model demonstrated superior predictive performance, with an average precision, recall, and F1-score of 88.67%, 88.67%, and 0.88 for three primary processing coffee beans, respectively.

Chemical and sensorial profile of Coffea arabica cultivars fermented by different post-harvest processing methods

Climatic conditions, genotypes, and post-harvest processing methods influence coffee quality. Microbial fermentation during post-harvest processing has sparked researchers' interest due to the modulation of the sensory characteristics of coffee. However, the influence of microbial fermentation on different coffee genotypes has been little investigated. The objective of this study was to evaluate the chemical and sensory changes of Coffea arabica cultivars caused by different post-harvest processing and fermentations. Catucaí 785 and Catucaí Açú cultivars had better sensory evaluation than the Arara cultivar in the two years of sampling. However, dry fermentation proved to be a promising alternative to improve the sensory analysis of the Arara cultivar. Inoculation of Saccharomyces pastorianus yielded positive results in the chemical and sensory of the Catucaí Açú cultivar. Each coffee genotype is capable of expressing unique sensory and chemical attributes depending on the different post-harvest processing and fermentations applied.

Metabolomics as a tool for geographic origin assessment of roasted and green coffee beans

Coffee is widely consumed across the globe. The most sought out varieties are Arabica and Robusta which differ significantly in their aroma and taste. Furthermore, varieties cultivated in different regions are perceived to have distinct characteristics encouraging some producers to adopt the denomination of origin label. These differences arise from variations on metabolite content related to edaphoclimatic conditions and post-harvest management among other factors. Although sensory analysis is still standard for coffee brews, instrumental analysis of the roasted and green beans to assess the quality of the final product has been encouraged. Metabolomic profiling has risen as a promising approach not only for quality purposes but also for geographic origin assignment. Many techniques can be applied for sample analysis: chromatography, mass spectrometry, and NMR have been explored. The data collected is further sorted by multivariate analysis to identify similar characteristics among the samples, reduce dimensionality and/or even propose a model for predictive purposes. This review focuses on the evolution of metabolomic profiling for the geographic origin assessment of roasted and green coffee beans in the last 21 years, the techniques that are usually applied for sample analysis and also the most common approaches for the multivariate analysis of the collected data. The prospect of applying a wide range of analytical techniques is becoming an unbiased approach to determine the origin of different roasted and green coffee beans samples with great correlation. Predictive models worked accurately for the geographic assignment of unknown samples once the variety was known.

A Systematic Mapping Study of Coffee Quality throughout the Production-to-Consumer Chain

Coffee is one of the most consumed beverages in the world and is crucial in the economy of many developing countries. The search to improve coffee quality comes from many fronts, as do the many ways to measure quality and the factors that affect it. Several techniques are used to measure the different metrics to assess coffee quality, across different types of coffee samples and species, and throughout the entire process from farm to cup. In this work, we conducted a systematic mapping study of 1,470 articles to identify the aspects of quality that are the most important in the scientific literature to evaluate coffee throughout the processing chain. The study revealed that cup quality and biochemical composition are the most researched quality attributes. The main objective of the reviewed studies is the correlation between different quality measurements. The most used techniques are the analytical chemistry methods. The most studied species is Coffea arabica. The most used sample presentation is green coffee. The postharvest stage is the most researched, in which quality control receives more attention. In the preharvest stage, management practices stand out. Finally, the most used type of research was the evaluation research.

Effects of green coffee bean flour fortification on the chemical and nutritional properties of gluten-free cake

The objective of this study was the application of green coffee bean flour in gluten-free cakes with different percentages (4, 8 and 15% (w/w)), to evaluate the optimal value for fortification, and the products were characterized based on their centesimal composition and bioactive compounds (caffeine and total phenolic compounds). Significant differences (p ≤ 0.05) were observed in the content of lipids, total dietary fiber, insoluble fiber, energy values, sodium, caffeine, and total phenolics, mainly in the product in which 15% (w/w) green coffee bean flour was added. Caffeine content was only detected and quantified in products with > 8% (w/w) green coffee bean flour, whereas the total phenolic content was detected and quantified in products with > 4% (w/w) green coffee bean flour. Thus, fortification of these products with 15% green coffee bean flour promoted a higher content of total dietary fiber and lower content of lipids, calories, sodium, and increased bioactive compounds. Thus, green coffee bean flour is an excellent alternative for the production of innovative foods.

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.

The Potential of Spectroscopic Techniques in Coffee Analysis—A Review

This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.

Non-destructive authentication of Gourmet ground roasted coffees using NIR spectroscopy and digital images

The growing demand for excellent-quality coffees allied with their symbolic aestheticization that add value to the products favor the adulteration practices and consequently economic losses. So, this work proposes the suitability of NIR spectroscopy and Digital Images (from CACHAS) coupled with one-class classification methods for the non-destructive authentication of Gourmet ground roasted coffees. For this, Gourmet coffees (n = 44) were discriminated from Traditional (n = 36) and Superior (n = 10) by directly analyzing their powder without any sample preparation. Then, OC-PLS and dd-SIMCA were used to construct the models. dd-SIMCA using offset correction for NIR and RGB histogram for CACHAS achieved the best results, correctly recognizing all the 90 samples in both the training and test sets. Therefore, the proposed methodologies can be useful for both the consumers and regulatory agencies because it confirms the elevated standards of excellence of Brazilian specialty coffees, preventing fraudulent labeling, besides following the Principles of Green Analytical Chemistry.

Determination of Caffeine and Hydrogen Peroxide Antioxidant Activity of Raw and Roasted Coffee Beans Around Habru Woreda, Ethiopia Using UV-Vis Spectroscopy

Background

Coffee is a well-known beverage that is widely used around the world. Despite the wide use of coffee in Ethiopia, there is a lack of extensive studies addressing the issues related to the caffeine content and hydrogen peroxide antioxidant activity of varieties of coffee types, particularly in Habru woreda, Ethiopia.

Objective

This study aimed to determine the caffeine content and hydrogen peroxide antioxidant activity of raw and roasted coffee beans collected directly from Habru woreda, North Wollo zone of Ethiopia.

Methods

The study was conducted in Bohoro, Girana, and Wurgisa kebeles of Habru woreda, Ethiopia, by collecting 500 g of green beans of Arabica coffee without considering their variety. Then, the collected beans were divided into raw and roasted coffee to perform aqueous and dichloromethane extraction of their caffeine content and hydrogen peroxide antioxidant activity using UV-Vis spectrophotometry.

Results

The amounts of caffeine in aqueous and dichloromethane extraction were in the range of 124.01−191.27 ppm and 145.15−200.09 ppm in raw and roasted coffees, respectively. Using the IC₅₀ value, the hydrogen peroxide scavenging activity of the aqueous phase coffee bean extracts in Bohoro raw, Bohoro roasted, Wurgisa raw, Wurgisa roasted, Girana raw, and Girana roasted coffee were 32.17 ppm, 11.69 ppm, 26.14 ppm, 3.12 ppm, 24.83 ppm, and 11.06 ppm, respectively, while that of ascorbic acid was 6.91 ppm.

Conclusion

The study showed that the highest amount of caffeine in both aqueous and dichloromethane solvent extraction was found in Bohoro’s raw and roasted coffee beans. Also, the amounts of caffeine in all coffee bean samples were safe and the antioxidant activity was excellent. In most of the samples, significant variations in the concentration of caffeine in raw and roasted coffee bean samples were observed in the two extraction solvents.