A conjunction of sn-2 fatty acids and overall fatty acid composition combined with chemometric techniques increase the effectiveness of lard detection in fish feed

Idris, Mohamed Haniff Hanafy and Manaf, Yanty Noorzianna and Mohd Desa, Mohd Nasir and Mohd Hashim, Amalia and Abdullah Sani, Muhamad Shirwan and Mohd Zaki, Nor Nadiha and Yuswan, Mohd Hafis and Kamaruddin, Mohd Salleh and Yusof, Yus Aniza and Mustafa, Shuhaimi and Hassan, Mohd Sukri (2021) A conjunction of sn-2 fatty acids and overall fatty acid composition combined with chemometric techniques increase the effectiveness of lard detection in fish feed. Chemometrics and Intelligent Laboratory Systems, 213. pp. 1-10. ISSN 0169-7439 E-ISSN 1873-3239

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Abstract

Fish oil is a common source of fat in fish feed production. However, there is a tendency to substitute fish oil with other fats such as lard to reduce production costs. Thus, an efficient method for lard detection is highly needed for fish feed's authenticity. In this study, sn-2 fatty acids (sn-2 FAs) and fatty acid (FA) compositions were incorporated with chemometric techniques namely Principal Component Analysis (PCA), Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA), and Orthogonal Partial Least Square-Regression (OPLS-R) to identify lard adulteration in the fish feeds. The inclusion of sn-2 FAs into PCA model 2 exhibited a preferable variation pattern relative to PCA model 1. The PCA identified C14:0, C18:0, C18:2, C18:3, C20:0 sn-2 C16:0, sn-2 C18:0, sn-2 C18:1, and sn-2 C18:2 were the most significant FAs to discriminate the fish feeds. The inclusion of sn-2 FA composition improved the OPLS-DA model 2 performance by providing more significant class discrimination between lard-adulterated, and non-adulterated fish feeds as compared to OPLS-DA model 1. The OPLS-DA model 2 identified C18:0, C18:2, C18:3, and sn-2 C16:0 FAs as markers of lard adulteration with an increment in the value of the coefficient of determination (R2) and decrement in the Root Mean Square Error of Estimation (RMSEE) and Root Mean Standard of Cross-Validation (RMSECV) values. The Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron-Artificial Neural Network (MLP-ANN), and internal and external validations corroborated the OPLS-DA model 2 and OPLS-R model 2 performances. Therefore, the incorporation of sn-2 FA and FA compositions coupled with the chemometric techniques had improved the detection and quantification of lard adulteration in fish feeds. © 2021 Elsevier B.V.

Item Type:	Article (Journal)
Additional Information:	7834/89540
Uncontrolled Keywords:	Chemometrics; Fish feeds; Gas chromatography-mass spectrometry; Lard adulteration; Machine learning; sn-2 fatty acid
Subjects:	S Agriculture > SF Animal culture S Agriculture > SH Aquaculture. Fisheries. Angling > SH151 Aquaculture - Fish Culture
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	International Institute for Halal Research and Training (INHART)
Depositing User:	Muhamad Shirwan Abdullah Sani
Date Deposited:	26 Apr 2021 08:56
Last Modified:	16 Jul 2021 15:42
URI:	http://irep.iium.edu.my/id/eprint/89540

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