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Agtron method is widely used in the industry to determine roasting degrees in whole and ground coffee but it suffers from some inconveniences associated with unavailability of equipment, high cost, and lack of reproductive results. This study investigates the feasibility to determine roasting degrees in coffee beans and ground specialty coffees using near-infrared (NIR) spectroscopy combined with multivariate calibration based on partial least squares (PLS) regression. Representative data sets were considered to cover all Agtron roasting profiles for whole and ground coffees. Proper development of models with outlier evaluation and complete validation using parameters of merit such as accuracy, adjust, residual prediction deviation, linearity, analytical sensitivity, and limits of detection and quantification are presented to prove their performance. The results indicated that predictive chemometric models, for intact coffee beans and ground coffee, could be used in the coffee industry as an alternative to Agtron, thus digitalizing the roasting quality control.

The chemical complexity of coffee influences the sensory evaluation of the beverage, the main method used to define the quality of the coffee. In view of the subjectivity that method offers, we propose the association of an instrumental method with multivariate calibration (PLS and GA-SVR) to predict the quality of arabica coffee as support for sensory analysis. Arabica coffee samples were submitted to sensory evaluation using the Specialty Coffee Association (SCA) protocol and HS-SPME-GC/MS analysis. The models presented RMSEp results from 0.20 to 0.25, within the evaluation range the quality levels of sensory attributes (0.25). For the fragrance/aroma attribute, a value of R2p equal to 0.8503 was reached. 15 volatile compounds were identified as responsible for predicting the quality of arabica coffee, among which, 1-nonadecene was first reported as an impact compound in the prediction of important sensory attributes.

The near-infrared spectrometry combined with the one-class classification method was applied as quality control of the agroforestry-grown specialty coffee. A total of 34 samples were analyzed in this study. Spectral data were obtained using a NIR portable and different pre-treatment strategies for baseline correction were evaluated. Unsupervised pattern recognition (PCA and HCA) techniques were performed. The construction of the classification model was carried out using the dd -SIMCA algorithm with 19 samples acquired directly from producers that are recognized for the best quality control of the specialty type coffee. In order to test the model, 15 samples of non-specialty type, obtained in local markets, were evaluated. The classification model with the highest correct classification rate (CCR) scored 100% and 87% in the validation and test groups, respectively. The results demonstrated that the application of this strategy was successful in verifying the authenticity of specialty type agroforestry-grown coffee samples.

The objective of this study was to evaluate the volatile composition and the sensory effect of the presence of Quaker beans in natural specialty coffee beverage and, consequently, to confront the requirement of the Specialty Coffee Association regarding the total absence of Quaker beans in a natural specialty coffee batch. Sensory analysis and volatile composition were performed for three different colorations of Quaker beans, added separately to natural specialty coffee samples at seven different concentrations. Beans with color equal to or above Agtron 82.8 negatively affected the sensory characteristics of natural specialty coffee only from the presence of 7 Quaker beans in one cup (65 beans). Through the analysis of volatile composition, volatile compounds formed during roasting were identified in Quaker beans from precursors present in raw immature beans. Therefore, the color and sensory characteristics of Quaker are a consequence of the chemical composition of an immature bean.

In view of the possibility of diversifying metabolic routes promoted by fermentation, this study proposed a new processing method for coffee, which consists of adapting a technique already consolidated in winemaking, carbonic maceration. The assay occurred under anaerobic conditions with different time and temperature fermentation. The aim of this study was to determine the differences in coffee characteristics (sensorial, chemical, and microbial) after carbonic maceration and fermentation. Specialty Coffee Association protocol, nuclear magnetic resonance, and denaturing gradient gel electrophoresis were used in these analyzes. A significant functional relationship between global score and temperature (38 °C), for the fermentation time of 96 h was observed. Bacterial diversity and sensory characteristics had a positive correlation. Furthermore, trigonelline, formic acid, hydroxymethylfurfural, lipids, and γ-butyrolactone also contributed to score and sensory quality of coffee beverage. Thus, our data show consistent factors to infer on the microbiological action on the sensory quality of coffee beverage.

Immature coffee cherries produce roast coffees with lower hedonic scores than those produced from mature cherries, but variation in volatile and sensory characteristics over a range of maturities is not well studied. In this work, cherries from two coffee cultivars (Caturra, Catimor) were sorted into seven maturity stages from fully immature (Stage 1, green) to fully overripe (Stage 7, purple). Volatile profiles of Stage 1 roast coffee had lower concentrations of carbohydrate degradation products and higher concentrations of N-heterocycles and phenols. Differences in volatiles among Stage 2 (partially immature, yellow-green) and subsequent stages were insignificant (p > 0.05) or else minor. Principle component analysis of the volatile data set also distinguished Stage 1 from other stages. Similarly, a trained cupping panel reported significantly lower sensory scores for Stage 1 as compared to Stages 2–7, but few differences among Stages 2–7. Thus, partially mature and overripe cherries may be appropriate for specialty coffee.

Coffee cup quality, determined by the sensory attributes evaluated by professional tasters, is a decisive factor for evaluating coffee, with the “Specialty Coffee Association of America” (SCAA) classification being nowadays considered the most suitable. P anels of trained coffee tasters are used by the industry to describe and evaluate beverage quality, but those evaluations can be subjective and time demanding. Recent studies have demonstrated the potential of spectroscopy-based methods for establishing parameters of quality in the analysis of food products, including coffee. Thus, the aim of this study was to evaluate the potential of ATR-FTIR and chemometrics to discriminate espresso coffees with different sensory characteristics reported by a panel of coffee tasters. The results showed good consistency among coffee tasters. PLS-DA models based on spectroscopic data were able to classify samples according to sensory attributes, confirming the potential of FTIR and chemometrics in coffee quality evaluation.

Untargeted LC/MS flavoromic profiling was utilized to identify compounds that positively impact coffee quality. The chemical profiles of eighteen coffee samples and the corresponding Specialty Coffee Association (SCA) cup scores were modeled by orthogonal partial least squares (OPLS) analysis with good fit and predictive ability (R2Y > 0.9, Q2 > 0.9). Four highly predictive chemical compounds positively correlated to cup score were subsequently isolated and purified (>90%) by multi-dimensional preparative LC/MS fractionation. Sensory recombination analysis by certified SCA Q-graders (n = 5) confirmed three out of four compounds significantly increased cup score when added to a control coffee (p