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

Maria Lucimar da Silva Medeiros; Adriano Freitas Lima; Mônica Correia Gonçalves; Helena Teixeira Godoy; Douglas Fernandes Barbin

doi:10.1016/j.foodchem.2023.136461

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

Food Chem. 2023 Nov 1:425:136461. doi: 10.1016/j.foodchem.2023.136461. Epub 2023 May 26.

Authors

Maria Lucimar da Silva Medeiros¹, Adriano Freitas Lima², Mônica Correia Gonçalves³, Helena Teixeira Godoy⁴, Douglas Fernandes Barbin⁵

Affiliations

¹ Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil. Electronic address: m229269@dac.unicamp.br.
² Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil.
³ Agrifood Science and Technology Center, Federal University of Campina Grande, Pombal, PB, Brazil.
⁴ Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil. Electronic address: helenatg@unicamp.br.
⁵ Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, SP, Brazil. Electronic address: dfbarbin@unicamp.br.

PMID: 37285626
DOI: 10.1016/j.foodchem.2023.136461

Abstract

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.

Keywords: Authentication; Dairy fraud; Handmade cheeses; Industry 4.0; Non-destructive technologies.

MeSH terms

Butter* / analysis
Cheese* / analysis
Chemometrics
Food Contamination / analysis
Least-Squares Analysis
Plant Oils / analysis
Soybean Oil / analysis

Substances

Butter
Plant Oils
Soybean Oil