Portable near-infrared (NIR) spectrometer and chemometrics for rapid identification of butter cheese adulteration.

Artisanal cheeses are highly valued around the world for their distinct sensory characteristics, thus being prone to adulteration by substituting authentic material for cheaper products, such as vegetable oil. In this work, we developed a method based on a portable NIR spectrometer as a non-destructive and low-cost alternative to identify adulteration in butter cheese. Dataset consisted of authentic and intentionally adulterated cheeses in the laboratory and commercial cheeses, which were identified as authentic and adulterated with vegetable oil after analysis of the fatty acid profile. PLS-DA classification models identified adulterated samples with an accuracy of 94.44%. PLS prediction models showed excellent performance (RPD > 3.0) to predict the adulterant level. These results demonstrate that NIR spectra can be used to identify the replacement of authentic fat by soybean oil in butter cheese and that the developed models can be used to identify adulteration in external samples with good performance.

Rapid and non-destructive cinnamon authentication by NIR-hyperspectral imaging and classification chemometrics tools.

Cinnamon is a valuable aromatic spice widely used in pharmaceutical and food industry. Commonly, two-cinnamon species are available in the market, Cinnamomum verum (true cinnamon), cropped only in Sri Lanka, and Cinnamomum cassia (false cinnamon), cropped in different geographical origins. Thus, this work aimed to develop classification models based on NIR-hyperspectral imaging (NIR-HSI) coupled to chemometrics to classify C. verum and C. cassia sticks. First, principal component analysis (PCA) was applied to explore hyperspectral images. Scores surface displayed the high similarity between species supported by comparable macronutrient concentration. PC3 allowed better class differentiation compared to PC1 and PC2, with loadings exhibiting peaks related to phenolics/aromatics compounds, such as coumarin (C. cassia) or catechin (C. verum). Partial least square discriminant analysis (PLS-DA) and Support vector machine (SVM) reached similar performance to classify samples according to origin, with error = 3.3 % and accuracy = 96.7 %. A permutation test with p < 0.05 validated PLS-DA predictions have real spectral data dependency, and they are not result of chance. Pixel-wise (approach A) and sample-wise (approach B, C and D) classification maps reached a correct classification rate (CCR) of 98.3 % for C. verum and 100 % for C. cassia. NIR-HSI supported by classification chemometrics tools can be used as reliable analytical method for cinnamon authentication.