Multivariate regression methods with infrared spectroscopy to detect the falsification of traditional butter
Journal Title: Invention Journal of Research Technology in Engineering & Management - Year 2018, Vol 2, Issue 7
Abstract
Recently, food safety and guaranteed of food marks have become more important subjects of foodstuff production and the marketing of processed foods. This paper demonstrates the ability of Mid Infrared spectroscopy coupled with multivariate regression tools to detect vegetable butter (as adulterant) in a binary mixture with traditional cow’s butter. Blends of traditional cow’s butter with different percentages of vegetable butter were measured using Attenuated Total Reflectance-Fourier Transform Mid Infrared Spectroscopy (ATRFTMIR). Spectral and reference data were firstly analyzed by principal component analysis (PCA) to check outliers samples; and improve the robustness of the prediction models to be established. Multivariate regression methods as Principal component regression (PCR) and Partial least square regression (PLSR) were used to establish calibration model. Excellent correlation between ATR-FTMIR analysis and studied butter blends was obtained R2 = 0.99; with Root Mean Square Errors of Prediction < 3.04, Limit of Detection 9.12% (By PCR) and 6.06% (by PLSR), and Relative Prediction Errors as low as 3.13.
Authors and Affiliations
W. Terouzi
Keywords
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- EP ID EP363568
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