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

Muhammad Aadil Siddiqui,Muhammad Junaid,Abdul Saboor,Saeed Ahmed Magsi,Gunawan Witjaksono,Mohd Haris Md Khir,Ali Shaan Manzoor Ghumman

doi:10.3390/foods10102405

Abstract

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.

Highlights

The verification of authenticity and the detection of adulterants are critical aspects of food control, in high-value items
The developed workflow for further investigating the lard adulteration was carried out using a three-stage process
Samples were divided into two classes, ‘Haram’ and ‘Halal’, for multiclass support vector machine (M-Support Vector Machine (SVM)) classification

Summary

Introduction

The verification of authenticity and the detection of adulterants are critical aspects of food control, in high-value items. Manual inspection, which is highly impacted by subjective variables, is used frequently in quality evaluation. As a result, detecting pork in a variety of food items has become a major research topic in many countries, in those where religious laws restrict the eating of pig products. Food adulterations may only financially impact a part of the population, but others may be more seriously affected [2,3,4] due to food poisoning, their religious views [5,6], etc. Some of the food tampering has been poisonous, for instance, such as the addition of sawdust to make white bread [7,8], the melamine adulteration of formula milk [7,9,10], the mixing of oil for engines with oil for human consumption in

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