Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)

Sim, Siong Fong and Min, Xuan Laura Chai and Amelia Laccy, Jeffrey Kimura (2018) Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR). Journal of Chemistry. pp. 1-8. ISSN 2090-9071

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Abstract

Fourier-transform infrared (FTIR) offers the advantages of rapid analysis with minimal sample preparation. FTIR in combination with multivariate approach, particularly partial least squares regression (PLSR), has been widely used for adulterant analysis. Limited study has been done to compare PLSR with other regression strategies. In this paper, we apply simple linear regression (SLR), multiple linear regression (MLR), and PLSR for prediction of lard in palm olein oil. Pure palm olein oil was adulterated with lard at different concentrations and subjected to analysis with FTIR. )e marker bands distinguishing lard and palm olein oil were determined using Fisher’s weights. )e marker regions were then subjected to regression analysis with the models verified based on 100 training/test sets. )e prediction performance was measured based on the percentage root mean square error (%RMSE). )e absorption bands at 3006 cm−1 , 2852 cm−1 , 1117 cm−1 , 1236 cm−1 , and 1159 cm−1 were identified as the marker bands. )e bands at 3006 and 1117 cm−1 were found with satisfactory predictive ability, with PLSR demonstrating better prediction yielding %RMSE of 16.03 and 13.26%, respectively.

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