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

• Published in: Food chemistry Vol. 425; p. 136461
• Main Authors: Medeiros, Maria Lucimar da Silva, Freitas Lima, Adriano, Correia Gonçalves, Mônica, Teixeira Godoy, Helena, Fernandes Barbin, Douglas
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.11.2023
• Subjects: Authentication; Dairy fraud; Handmade cheeses; Industry 4.0; Non-destructive technologies; Non-destructive technologies; Industry 4.0; Handmade cheeses; Dairy fraud; Authentication
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2023.136461

[Display omitted] •We used a low-cost NIR spectrometer to identify of adulteration in butter cheese.•PCA applied to spectral data separated fraudulent samples with > 20% soybean oil.•PLS-DA models allowed identification of authentic samples with 94.44% accuracy.•PLS-DA models showed good performance to identify tampering in real situations.•Percentage of adulteration can be estimated with excellent accuracy. 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.