Mid infrared spectroscopy for comparative analysis of fermented arabica and robusta coffee

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.

Conilon coffee: A critical review and bibliometric analysis for the agri-food industry

This study includes a bibliometric analysis and literature review on Conilon coffee and its relevant aspects for the food industry, focusing on its chemical constituents, the application of fermentation as a processing method, and the reuse of waste generated during processing. Relevant articles were selected through a bibliometric analysis of titles indexed in the Web of Science (WoS) and Google Scholar databases. Conilon coffee cultivation, especially in the Brazilian states of Espírito Santo, Rondônia and Bahia, has played a growing role in the global economy, with research focused on strategies to increase productivity, reduce costs and improve nutritional and bioactive quality. The use of agricultural waste as substrates for seedlings and genetic manipulation to develop clonal cultivars are showing promise, although some options may increase the heavy metal content in plants. This review enabled the identification of the main chemical constituents of Conilon coffee and an assessment of their contributions to the product's sensory attributes and bioactive properties. It was clear that the choice of fermentation conditions changes the sensory characterization of coffee, potentially benefiting the overall rating of the beverage. This review also suggests that Conilon coffee residues contain volatile compounds of considerable commercial value, so they should be subjected to extraction methods and subsequently preserved. Acid hydrolysis and microencapsulation can be alternatives for extracting and preserving compounds of interest from Conilon coffee. This work contributes to deepening knowledge about the challenges faced by Conilon coffee and the search for alternatives to increase its market value.

Challenges in coffee fermentation technologies: bibliometric analysis and critical review

Advancements in coffee processing technologies have led to improved efficiency in field operations, but challenges still exist in their practical implementation. Various alternatives and solutions have been proposed to enhance processing efficiency and address issues related to safety, standardization, and quality improvement in coffee production. A literature review using SciMAT and ScientoPy software highlighted advancements in fermentation tanks and the emergence of novel fermentation methodologies. However, these innovations lack sufficient scientific evidence. Researchers are now focusing on systematic approaches, such as controlled fermentations and evaluating the influence of microorganisms and process conditions on sensory attributes and coffee composition. Brazil is the leader in coffee bean fermentation research, but the number of published papers in the field has recently decreased. Despite this, efforts continue to improve process control and optimize product quality. The study emphasizes the need for further innovation in coffee fermentation technologies to increase efficiency, sustainability, and profitability while minimizing environmental impact. Implementing these advancements promises a more sustainable and quality-driven future for the coffee industry.

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.

Application of ATR-FTIR for Green Arabica Bean Shelf-Life Determination in Accelerated Storage

Coffee bean oxidation is associated with enzymatic and non-enzymatic browning, the degradation of desirable aromatic compounds, the development of undesirable flavors, increased susceptibility to microbial spoilage, and volatile compound losses. This study investigated natural dry process (DP) and honey process (HP) green coffee beans stored in GrainPro® bags for 0, 5, 10, and 20 days under accelerated storage conditions at 30 °C, 40 °C, and 50 °C with relative humidity of 50%. A kinetic model was used to estimate the shelf life of the green coffee beans. DP recorded durability of 45.67, 29.9, and 24.92 days at 30 °C, 40 °C, and 50 °C, respectively, with HP 60.34, 38.07, and 19.22 days. Partial least squares (PLS) analysis was performed to build the models in order to predict the shelf life of coffee based on peroxide (PV) and thiobarbituric acid reactive substances (TBARS) values. In terms of prediction with leave-one-out cross-validation (LOOCV), PLS provided a higher accuracy for TBARS (R2 = 0.801), while PV was lower (R2 = 0.469). However, the auto-prediction showed good agreement among the observed and predicted values in both PV (R2 = 0.802) and TBARS (R2 = 0.932). Based on the variable importance of projection (VIP) scores, the ATR-FTIR peaks as 3000-2825, 2154-2150, 1780-1712, 1487-2483, 1186-1126, 1107-1097, and 1012-949 cm-1 were identified to be the most related to PV and TBARS on green coffee beans shelf life. ATR-FITR showed potential as a fast and accurate technique to evaluate the oxidation reaction that related to the loss of coffee quality during storage.

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.

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.

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.

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

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.