Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

TLDR

The study evaluates hyperspectral imaging for detecting adulteration in red‑meat products. The authors collected line‑scanning hyperspectral images of lamb, beef, and pork in various states and compared handcrafted spectral/spatial features with deep CNN‑extracted features using SVM and CNN classifiers. The CNN classifier achieved 94.4 % overall accuracy with balanced F‑scores across all meat states, demonstrating that hyperspectral imaging combined with deep learning provides rapid, reliable, and non‑destructive detection of red‑meat adulteration.

Abstract

This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing).