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.

Effect of the food matrix on the capacity of flavor enhancers in intensifying salty taste.

The effect of the flavor enhancers monoammonium glutamate (MAG), monosodium glutamate (MSG), disodium guanylate (GMP), and disodium inosinate (IMP) on intensifying salty taste in food matrices (shoestring potatoes, requeijão cheese, and beef burgers) with a reduction in the amount of sodium chloride (NaCl) present was evaluated. Experiments were conducted using a central composite rotational design with two variables: the concentrations of flavor enhancer and NaCl added in the food matrix. The effect of IMP was not significant (P > 0.05) on the intensity of salty taste in any of the matrices analyzed. GMP presented lower performance compared to MAG and MSG in intensifying the salty taste of the treatments, regardless of the reduction of NaCl. Compared to MSG and GMP, MAG showed greater efficiency in intensifying the salty taste in requeijão cheese and beef burger with a reduction of 25%, 50%, 75%, and 100% of NaCl. MSG presented higher efficiency compared to MAG and GMP when applied in shoestring potatoes for all reductions of NaCl tested (25%, 50%, and 75%). The ability of flavor enhancers to improve the salty taste depends on the effect of the flavor enhancer, the complexity of the food matrix, and the reduction of NaCl in foods. PRACTICAL APPLICATIONS: The complexity of the food matrix plays a significant role in the perception of salty taste in sodium-reduced products. In these products, sodium reduction may affect the taste enhancer's effect of enhancing salty taste. Therefore, this study broadens the knowledge of the effects of flavor enhancers on different foods, as well as the ability to enhance salty taste in food matrices with NaCl reduction. Moreover, it provides information on how to reduce the sodium content in these matrices while maintaining the same perception of salty taste as a conventional matrix.

Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network.

In this work, we assessed the use of confocal Raman microscopy and artificial neural network as a practical method to assess and quantify adulteration of fluid milk by addition of whey. Milk samples with added whey (from 0 to 100%) were prepared, simulating different levels of fraudulent adulteration. All analyses were carried out by direct inspection at the light microscope after depositing drops from each sample on a microscope slide and drying them at room temperature. No pre- or posttreatment (e.g., sample preparation or spectral correction) was required in the analyses. Quantitative determination of adulteration was performed through a feed-forward artificial neural network (ANN). Different ANN configurations were evaluated based on their coefficient of determination (R2) and root mean square error values, which were criteria for selecting the best predictor model. In the selected model, we observed that data from both training and validation subsets presented R2>99.99%, indicating that the combination of confocal Raman microscopy and ANN is a rapid, simple, and efficient method to quantify milk adulteration by whey. Because sample preparation and postprocessing of spectra were not required, the method has potential applications in health surveillance and food quality monitoring.