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• W2946522182 abstract "Aims: To detect the adulterant in edible oil rapidly.&#x0D; Study Design: Authenticity and adulteration detection in edible oils are the increasing challenges for researchers, consumers, industries and regulatory agencies. Traditional approaches may not be the most effective option to combat against adulteration in edible oils as that’s are complex, laborious, expensive, require a high degree of technical knowledge when interpreting data and produce hazardous chemical. Consequently, a cost effective, rapid and reliable method is required.&#x0D; Place and Duration of the Study: The experiment was conducted jointly in the laboratory of the Department of Food Technology and Rural Industries, Bangladesh Agricultural University, Mymensingh and the Institute of Food Science and Technology, BCSIR, Dhaka.&#x0D; Methods: In this study, Fourier Transform Infrared spectroscopy coupled with multivariate analysis was used for adulteration detection in sunflower and rice bran oil. Sunflower oil was adulterated with soybean oil in the range of 10-50% (v/v) and rice bran oil was adulterated with palm oil in the range of 4-40% (v/v) at approximately 10% and 5% increments respectively. FTIR spectra were recorded in the wavenumber range of 4000-650cm-1.&#x0D; Results: FTIR spectra data in the whole spectral range and reduced spectral range were used to develop a partial least square regression (PLSR) model to predict the level of adulteration in sunflower and palm oils. Good prediction model was obtained for all PLSR models with a coefficient of determination (R2) of >= 0.985 and root mean square errors of calibration (RMSEC) in the range of 0-1.7325%.&#x0D; Conclusion: The result suggested that FTIR spectroscopy associated with multivariate analysis has the great potential for a rapid and non-destructive detection of adulteration in edible oils laborious conventional analytical techniques." @default.
• W2946522182 created "2019-05-29" @default.
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• W2946522182 date "2019-05-22" @default.
• W2946522182 modified "2023-09-24" @default.
• W2946522182 title "Detection of Adulteration in Edible Oil Using FT-IR Spectroscopy and Machine Learning" @default.
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• W2946522182 doi "https://doi.org/10.9734/ijbcrr/2019/v26i130085" @default.
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