The objective of this work is the identification of cheese maturation time using attenuated total reflection Fourier transform spectroscopy (FTIR-ATR) data and analytical measurements associated with chemometrics techniques. Minas artisanal cheeses were collected from 8 producers matured at times 1, 7, 14, 21, 28, 45 and 60 days, fractioned in rind and crumb for moisture, water activity (aw), dry in fat extract, pH, acidity, ashes, protein, extent and depth of proteolysis, color and identification of functional groups in FTIR-ATR. Principal component analysis (PCA), multiple linear regression (MLR) and partial least squares (PLS) techniques were performed on the dataset. PCA was used to assess differences and similarities of the samples. The best results to predict the maturation time were using MLR for analytical measurements in the portion of the crumb, with coefficient of determination (R2) = 0.92, root mean square error of validation (RMSEV) = 5.4 days and PLS for FTIR-ATR with R2 = 0.92, RMSEV = 5.3 days. The results indicated that the FTIR-ATR was adequate to predict maturation times. Although, it does not eliminate the need for more detailed chemical analysis.

Mid-infrared spectroscopy (MID), chemical composition and physicochemical characteristics associated with chemometrics were used to rapidly detect and quantify the amount of cow’s milk added in buffalo’s milk. A total of 165 samples, divided into buffalo’s milk, buffalo’s milk added with cow’s milk (10 to 90%) and cow’s milk were evaluated to obtain fat, protein, lactose, total and defatted solids, urea, pH, acidity, cryoscopic index and band absorbances in the spectra associated with principal component analysis (PCA), multiple linear regression (MLR) and partial least squares regression (PLS). The treatments were separated into groups by PCA, allowing the classification of samples. MLR and PLS models were able to predict cow’s milk contents in buffalo’s milk. MID and results of the analytical measures studied when associated with chemometrics are efficient in the rapid quantitative detection of adulteration in buffalo’s milk.

The objective of this study was to use Fourier transform infrared (FTIR) spectroscopy combined with multivariate analysis to identify adulterations in raw milk and in samples from producers. Five levels of concentration of sodium bicarbonate, sodium hydroxide, hydrogen peroxide, starch, sucrose and urea were used. A total of 620 samples previously adulterated, frozen and lyophilized were analyzed in FTIR-attenuated total reflection (ATR) equipment and 15 peaks of the spectra were obtained. With the multiple linear regression method for samples adulterated with sodium bicarbonate, sucrose and urea, a coefficient greater than 75% was obtained, and with artificial neural networks all adulterated samples obtained a percentage of correctness greater than 76.6%, making it possible to identify adulterants from 0.1%. Of the 249 samples of producers analyzed, 2.4% were adulterated. With the use of FTIR allied to the multivariate analysis as a screening method, it was possible to obtain a satisfactory classification for the adulterated samples in this study.