Open Access

ARTICLE

Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

Muhammad Aadil Siddiqui^1,*, M. H. Md Khir¹, Zaka Ullah², Muath Al Hasan², Abdul Saboor³, Saeed Ahmed Magsi¹

1 Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar, 32610, Malaysia
2 College of Engineering, Al Ain University, Al Ain, 64141, United Arab Emirates
3 High Performance Cloud Computing Centre (HPC), Universiti Teknologi Petronas, Seri Iskandar, 32610, Malaysia

* Corresponding Author: Muhammad Aadil Siddiqui. Email:

Computers, Materials & Continua 2023, 75(2), 2859-2871. https://doi.org/10.32604/cmc.2023.034164

Received 07 July 2022; Accepted 11 October 2022; Issue published 31 March 2023

Abstract

One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm⁻¹. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm⁻¹, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R² value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology.

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Keywords

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