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Evaluation of butter oil adulteration with soybean oil by FT-MIR and FT-NIR spectroscopies and multivariate analyses

Pereira, Cristina Guimarães, Leite, Antonio Iranaldo Nunes, Andrade, Jonathan, Bell, Maria José Valenzuela, Anjos, Virgílio
Lebensmittel-Wissenschaft + [i.e. und] Technologie 2019 v.107 pp. 1-8
adulterated products, butter oil, calibration, chemometrics, data collection, least squares, models, near-infrared spectroscopy, prediction, principal component analysis, product authenticity, quality control, rapid methods, soybean oil, spectral analysis, unsaturated fatty acids
The use of Fourier-transform mid-infrared (FT-MIR) and near-infrared (FT-NIR) spectroscopies associated with multivariate modeling was explored to obtain a rapid method that can detect adulteration of butter oil (BO) with soybean oil (SO). A total of 11 treatments comprising original BO, SO and their mixtures (10–90%, w/w) were analyzed. The substitution of BO by SO in the samples resulted in clear changes in the spectra intensities and positioning. The second and first derivative of MIR and NIR spectra, respectively, pointed out differentiations mainly in regions associated to unsaturated fatty acids. Principal component analyses could explore the similarities and differences among the samples, validating the spectral observations. Partial least squares models using both MIR and NIR spectral data allowed the prediction of the percentage of BO adulteration with SO with high accuracy, low relative errors and determination coefficients of the global fit above 0.9 for all calibration and validation datasets. Therefore, the spectroscopic tools applied in our work, when associated with chemometric approaches, showed good performance in the detection of BO adulterations. They can be of great importance in the development of new methodologies applied to quality control and authenticity of foods.