Authentication and discrimination of new Brazilian Canephora coffees with geographical indication using a miniaturized near-infrared spectrometer.

New Brazilian Canephora coffees (Conilon and Robusta) of high added value from specific origins have been protected by geographical indication to guarantee their origin and quality. Recently, benchtop near-infrared (NIR) spectroscopy combined with chemometrics has demonstrated its usefulness to discriminate them. It was the first study, however, and therefore the possibility exists to develop a new portable NIR method for this purpose. This work assessed a miniaturized NIR as a cheaper spectrometer to discriminate and authenticate new Brazilian Canephora coffees with certified geographical origins and to differentiate them from specialty Arabica. Discriminant chemometric and class modeling techniques have been applied and have obtained good predictive ability on external test sets. In addition, models with similar classification purpose were compared with those obtained in previous research carried out with benchtop NIR for the same samples, obtaining comparable results. In this context, the portable method was used as a laboratory technique and has the advantage of being cheaper than benchtop NIR spectrometer. Furthermore, it brings a high possibility to be implemented in small coffee cooperatives, industries or control agencies in the future that do not have high economic resources.

Discrimination of Robusta Amazônico coffee farmed by indigenous and non-indigenous people in Amazon: comparing benchtop and portable NIR using ComDim and duplex.

Robusta Amazônico is the name given to the Amazonian coffee that has been becoming popular and has recently been registered as a geographical indication in Brazil. It is produced by indigenous and non-indigenous coffee producers in regions that are geographically very close to one another. There is a need to authenticate whether coffee is truly produced by indigenous people and near-infrared (NIR) spectroscopy is an excellent technique for this. To meet the substantial trend towards NIR spectroscopy miniaturization, this work compared benchtop and portable NIR instruments to discriminate Robusta Amazônico samples using partial least squares discriminant analysis (PLS-DA). To ensure the results to be fairly comparable and, at the same time, to guarantee representative selection of both training and test set for the discriminant analysis, a sample selection strategy based on coupling ComDim multi-block analysis and the duplex algorithm was applied. Different pre-processing techniques were tested to create multiple matrices to be used in ComDim, as well as to build the discriminant models. The best PLS-DA model for benchtop NIR provided an accuracy of 96% for the test samples, while for the portable NIR the correct classification rate was 92%. It was demonstrated that portable NIR provides similar results to benchtop NIR for coffee origin classification by performing an unbiased sample selection strategy.

Proof-of-concept on the effect of human milk storage time: Lipid degradation and spectroscopic characterization using portable near-infrared spectrometer and chemometrics.

Human milk (HM) modifications over time represent an important issue. This work proposed to evaluate the changes in HM during one-year storage through total lipids (TL) degradation and portable near-infrared (NIR) spectrometer combined with chemometrics. Colostrum, transition, and mature stages were obtained from donors and considered in the raw and pasteurized forms. Principal component analysis in TL content showed changes in the mature stages for both forms after 75 days. Multivariate curve resolution with alternating least squares in NIR spectral data reveals a decrease in protein and triacylglycerol contents while an increase in free fatty acids (palmitic acid) contents were observed through the storage after around 5-6 months. Therefore, more than 5-6 months of storage suggest possible biochemical changes in the HM nutritional composition. Moreover, the chemometrics investigation was crucial in extracting information, bringing coherent results, and helping to understand the chemical changes in human milk during storage.

Near-infrared spectroscopy and multivariate calibration as an alternative to the Agtron to predict roasting degrees in coffee beans and ground coffees.

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.

Multivariate classification for the direct determination of cup profile in coffee blends via handheld near-infrared spectroscopy.

Professional cupping is a reliable methodology for the coffee industry and its professionals. However, it faces barriers for its implementation on an industrial scale. To date, no study has determined a coffee cup profile using a handheld near-infrared (NIR) spectrometer. Therefore, the aim of this study was to evaluate directly cup profiles in roasted and ground coffee blends via handheld NIR spectroscopy combined with partial least squares with discriminant analysis (PLS-DA), in an industrial case study. The sensitivity and specificity of the model obtained ranged from 91 to 100%, 84-100%, and 73-95% in the training, prediction, and internal cross-validation sets, respectively. These results are therefore promising for the industrial reality and the methodology could assist coffee professionals in their decisions during cup evaluation in further tests. The proposed method is viable for the direct determination of cup profile at industrial scale since it is portable, fast, simple, robust, and less expensive compared to the benchtops equipment.

Brazilian Coffee Blends: A Simple and Fast Method by Near-Infrared Spectroscopy for the Determination of the Sensory Attributes Elicited in Professional Coffee Cupping.

The diversity of compounds and variations in the aroma and flavor of ground and roasted coffee make the sensory evaluation by the "cupping test" a complex task to be performed. A total of 217 commercial coffee samples classified as different beverage type and with different roast degrees were evaluated by official cuppers in the "cupping test" and the responses for sensory attributes were used to verify the correlation to the near-infrared (NIR) spectra. Chemometric models based on partial least squares (PLS) were built for the powder fragrance, drink aroma, acidity, bitterness, flavor, body, astringency, residual flavor, and overall quality. The parameters of merit such as accuracy, fit, linearity, residual prediction deviation, sensitivity, analytical sensitivity, limits of detection, and quantification were evaluated. All sensory attributes were predicted with adequate values according to the parameters of merit. The proposed method, when compared to the "cupping test," is an alternative to the determination of the coffee sensory attributes. The results demonstrated that the use of NIR associated with chemometrics is efficient and recommended for the prediction of sensorial attributes of coffee by means of the direct analysis of roasted and ground samples, and without any additional preparation, it is a promising tool for the coffee industry. PRACTICAL APPLICATION: This study has shown potential use of near-infrared (NIR) spectroscopy coupled with a chemometric tool for the prediction of sensory attributes of commercial coffees. Prediction models for powder fragrance, drink aroma, acidity, bitterness, flavor, body, astringency, residual flavor, and overall quality were built and showed good predictive capacity. The use of NIR allows rapid analysis (1 min or less per sample), and it was possible to evaluate all sensory attributes directly in roasted and ground coffee, without beverage preparation.

Systematic study on the extraction of antioxidants from pinhão (araucaria angustifolia (bertol.) Kuntze) coat.

Food by-products containing bioactive substances have attracted attention due to the possibility of adding values to residues of the food industry. In this work, the extraction of phenolic compounds from pinhão seed coats (Araucaria angustifolia (Bertol.) Kuntze) using a central composite rotatable design was applied to obtain prediction models for the extract volume yield, total phenolic content, total phenolic acids and total flavonoids. Principal component analysis and hierarchical cluster analysis were implemented showing an evident poor effect of the temperature on phenolic compounds extraction, which is in accordance with the prediction model obtained by the experimental design for total phenolic acids. Volume yield presented a high positive correlation with extraction temperature, followed by solvent composition. Scanning electron microscopy showed that higher temperatures and lower ethanol percentages resulted in highly defibrillated pinhão coats that retained more extract after the extraction process, leading to lower volume yield percentages.