Discrimination Of Lard And Other Edible Fats After Heating Treatments Using Partial Least Square Regression (plsr), Principal Component Regression (pcr) And Linear Support Vector Machine Regression (svmr).

Nor Aishah Mohd Salleh; Mohd Sukri Hassan

doi:10.1088/1742-6596/1366/1/012114

Publication:
Discrimination Of Lard And Other Edible Fats After Heating Treatments Using Partial Least Square Regression (plsr), Principal Component Regression (pcr) And Linear Support Vector Machine Regression (svmr).

dc.contributor.author	Nor Aishah Mohd Salleh	en_US
dc.contributor.author	Mohd Sukri Hassan	en_US
dc.date.accessioned	2024-05-28T04:35:43Z
dc.date.available	2024-05-28T04:35:43Z
dc.date.issued	2019
dc.date.submitted	23/5/2020
dc.description	Volume 1366 2nd International Conference on Applied & Industrial Mathematics and Statistics 23–25 July 2019, Kuantan, Pahang, Malaysia	en_US
dc.description.abstract	Discrimination between lard and other edibles fats is a challenging task for halal determination especially after the fats were heated at high temperature for a long period. In this study, three multivariate regression models such as partial least square regression (PLSR), principal component regression (PCR) and support vector machine regression (SVMR) were applied to evaluate the spectral data of FTIR (n=195) obtained from lard, chicken, beef, mutton and vegetable fats after heated at different conditions (120-240°C and 0.5-3 hrs). The regression of the Y-binary matrix was used to discriminate lard (as 1) and the others edibles fats (as 0). Kennard Stone (KS) algorithm selected a subset of the training set (n=145) and test set (n=50). The test set was used to validate the prediction ability of the suggested models. The obtained results showed the ability of the three proposed models to discriminate the heated lard simultaneously. The values of the R2 , adjusted R2 , root-mean-square error (RMSE) and root mean-square error of validation (RMSEV) showed a good results under Basic ATR correction transformation as PLSR (0.984, 0.977, 0.052 and 0.062); PCR (0.974, 0.971, 0.067 and 0.070), and SVMR (0.971, 0.959, 0.087 and 0.102) respectively. However, when using mean square error (MSE), it gives lower prediction error for PLSR (0.006), PCR (0.007) and SVMR (0.015). The results showed that PLSR as the best model for discrimination spectral data of lard and other edible fats after heating treatments for halal determination.	en_US
dc.identifier.doi	10.1088/1742-6596/1366/1/012114
dc.identifier.epage	12
dc.identifier.issn	1742-6596
dc.identifier.spage	1
dc.identifier.uri	https://iopscience.iop.org/article/10.1088/1742-6596/1366/1/012114
dc.identifier.uri	https://oarep.usim.edu.my/handle/123456789/5929
dc.identifier.volume	1366
dc.language.iso	en_US	en_US
dc.publisher	IOP Publishing Ltd.	en_US
dc.relation.ispartof	Journal of Physics: Conference Series	en_US
dc.title	Discrimination Of Lard And Other Edible Fats After Heating Treatments Using Partial Least Square Regression (plsr), Principal Component Regression (pcr) And Linear Support Vector Machine Regression (svmr).	en_US
dc.type	Article	en_US
dspace.entity.type	Publication

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Discrimination Of Lard And Other Edible Fats After Heating Treatments Using Partial Least Square Regression (plsr), Principal Component Regression (pcr) And Linear Support Vector Machine Regression (svmr).