Detection of Meat Adulteration Using Spectroscopy-Based Sensors

Abstract

Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific.

Detection of food fraud of meat products from the different brands by application of histological methods

“…Multispectral and hyperspectral imaging is a powerful, nondestructive, noninvasive, rapid, and reliable meat authentication technology (Aredo, Velásquez, & Siche, 2017; Cheng, Nicolai, & Sun, 2017). Many researchers have used this technique to detect meat product adulteration (Fengou, Lianou, Tsakanikas, Mohareb, & Nychas, 2021; Huang, Liu, & Ngadi, 2014; Jiang, Cheng, & Shi, 2020; Jiang, Ru, Chen, Wang, & Xu, 2021; Kamruzzaman, Makino, & Oshita, 2015; Mendez, Mendoza, Cruz‐Tirado, Quevedo, & Siche, 2019).…”

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

“…Furthermore, the same trend was observed for sensitivity and specificity (94.0% in both cases). Previous researchers reported that SVMs could result in the development of robust regression and classification models for poultry products [31,58]. SVM and QSVM models were more suitable for MSI spectral data, with SVM linear classifiers presenting the best separation of data's hyperplane [31].…”

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

“…Sensory tests have been unable to meet the need whilst other detection methods present high technical requirements and complex operation with the improvement in adulteration methods. In recent years, spectral technology has been widely used in adulteration detection of meat and meat products due to its simple and fast operation and non-destructive characteristics [ 15 , 16 ]. Near-infrared hyperspectral imaging (NIR-HSI) is a three-dimensional information acquisition technology combining spectral and image technology [ 17 ].…”