Application of partial least squares discriminant analysis for discrimination of palm oil / Mas Ezatul Nadia Mohd Ruah ... [et al.]

• Main Authors: Mohd Ruah, Mas Ezatul Nadia (Author), Rasaruddin, Nor Fazila (Author), Sim, Siong Fong (Author), Jaafar, Mohd Zuli (Author)
• Format: Book
• Published: Research Management Institute (RMI), 2014.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/12799/1/AJ_MAS%20EZATUL%20NADIA%20MOHD%20RUAH%20SRJ%2014%201.pdf
• Keywords: Palm oil; Nutrition. Foods and food supply

This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm-1 to 4000 cm-1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oil by implementing Partial Least Square Discriminant Analysis (PLS-DA), Learning Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardised before developing the classification models. The classification model was validated by finding the value of percentage correctly classified using test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as t-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLS-DA classifier of the standardised data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.