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Article

Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy

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Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia
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BRI Research Institute, Jl. Harsono RM No. 2, Ragunan, Passsar Minggu, Jakarta 12550, Indonesia
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Department of Chemical Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia
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Department of Electronics Engineering, FICT, BUITEMS, Quetta 87300, Pakistan
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High Performance Cloud Computing Centre (HPC), Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Theodoros Varzakas
Foods 2021, 10(10), 2405; https://doi.org/10.3390/foods10102405
Received: 16 August 2021 / Revised: 20 September 2021 / Accepted: 21 September 2021 / Published: 11 October 2021
Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as “Pure (80 samples)” and “Adulterated (90 samples)”. Lard was mixed with the ratio of 10–50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700–1800 cm−1 and 2800–3000 cm−1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence. View Full-Text
Keywords: food adulteration; halal authentication; Fourier transform infrared (FTIR) spectroscopy; principal component analysis (PCA); chemometric methods; multiclass support vector machine (M-SVM) food adulteration; halal authentication; Fourier transform infrared (FTIR) spectroscopy; principal component analysis (PCA); chemometric methods; multiclass support vector machine (M-SVM)
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MDPI and ACS Style

Siddiqui, M.A.; Khir, M.H.M.; Witjaksono, G.; Ghumman, A.S.M.; Junaid, M.; Magsi, S.A.; Saboor, A. Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy. Foods 2021, 10, 2405. https://doi.org/10.3390/foods10102405

AMA Style

Siddiqui MA, Khir MHM, Witjaksono G, Ghumman ASM, Junaid M, Magsi SA, Saboor A. Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy. Foods. 2021; 10(10):2405. https://doi.org/10.3390/foods10102405

Chicago/Turabian Style

Siddiqui, Muhammad A., Mohd H.M. Khir, Gunawan Witjaksono, Ali S.M. Ghumman, Muhammad Junaid, Saeed A. Magsi, and Abdul Saboor. 2021. "Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy" Foods 10, no. 10: 2405. https://doi.org/10.3390/foods10102405

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