Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging

Hongzhe Jiang; Weidong Yuan; Yu Ru; Qing Chen; Jinpeng Wang; Hongping Zhou

doi:10.1016/j.saa.2022.121689

Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging

Spectrochim Acta A Mol Biomol Spectrosc. 2022 Dec 5:282:121689. doi: 10.1016/j.saa.2022.121689. Epub 2022 Jul 28.

Authors

Hongzhe Jiang¹, Weidong Yuan², Yu Ru³, Qing Chen³, Jinpeng Wang², Hongping Zhou³

Affiliations

¹ Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China. Electronic address: jianghongzhe@njfu.edu.cn.
² College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
³ Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

PMID: 35914356
DOI: 10.1016/j.saa.2022.121689

Abstract

Mutton kebab is an attractive type of meat product with high nutritional value, and is favored by consumers worldwide. However, mutton kebab is often subjected to adulteration due to its high price. Chicken, duck, and pork are frequently used as adulterated substitutes. The purpose of current study aims at developing a methodology based on hyperspectral imaging (HSI, 400-1000 nm) for identifying the authenticity of fresh and cooked mutton kebabs. Kebab samples were individually scanned using HSI system in their fresh and cooked states. Spectra of chicken, duck, pork, and mutton kebabs were first extracted from representative regions of interest (ROIs) identified in their calibrated hyperspectral images. After that, principal component analysis (PCA) was carried out, and results showed that the first three or two PCs were effective for identifying fresh or cooked samples of different meat species. Different effective modeling algorithms including k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) algorithms combined with different preprocessing methods were employed to develop classification models. Performances exhibited that PLS-DA models using raw spectra outperformed the KNN and SVM models, and the accuracies reached both 100 % in prediction sets for fresh and cooked meat kebabs, respectively. Moreover, compared to iteratively variable subset optimization (IVSO), random frog (RF), and successive projections algorithm (SPA) algorithms, the PC loadings successfully screened 14 and 8 effective wavelengths for fresh and cooked meat kebabs, respectively, from the complex original full-band wavelengths. The PC-PLS-DA models showed the optimal predicted performances with overall classification accuracies of 97.5 % and 100 %, sensitivity values of 1.00 and 1.00, specificity values of 0.97 and 1.00, precisions of 0.91 and 1.00, for fresh and cooked mutton kebabs, respectively. Furthermore, the visualization of classification maps confirmed the experimental results intuitively. Overall, it was evident that HSI showed immense potential to identify the authenticity of fresh and cooked mutton kebabs when substituted by different meats including chicken, duck, and pork.

Keywords: Authenticity; Chemometrics; Hyperspectral imaging; Meat species; Mutton kebabs; Visualization.

MeSH terms

Algorithms
Animals
Chickens
Discriminant Analysis
Feasibility Studies
Hyperspectral Imaging*
Least-Squares Analysis
Spectroscopy, Near-Infrared* / methods
Support Vector Machine