# Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
###### Abstract  
Minced meat substitution is one of the most common forms of food fraud in the meat industry. Recently, Hyperspectral Imaging (HSI) has been used for the classification and identification of minced meat types. However, conventional methods are based only on spectral information and ignore the spatial variability of the data. Moreover, these methods first tend to reduce the size of the data, which to some extent ignores the abstract level information and does not preserve the spatial information. Therefore, this work proposes a novel _Iso-bestic_ wavelength reduction method for the different minced meat types, by retaining only Myoglobin pigments (Mb) in the meat spectra. A total of 60 HSI cubes are acquired using Fx 10 Hyperspectral sensor. For each HSI cube, a set of preprocessing schemes is applied to extract the Region of Interest (ROI) and spectral preprocessing, i.e., Golay filtering. Later, these preprocessed HSI cubes are fed into a 3D-Convolutional Neural Network (3D-CNN) model for nonlinear feature extraction and classification. The proposed pipeline outperformed several state-of-the-art methods, with an overall accuracy of 94.0%. minced meat; classification; deep learning; myoglobin spectral features; _isos-bestic_ wavelength reduction +
Footnote †: journal: Medical  
As earlier explained, HSI has been used for many real-world applications, and one of them is minced meat identification and classification. Minced meat is enriched with nutritional values such as iron and protein, which contains all the essential vitamins required for human growth. Minced meat is widely used in a variety of food products such as meatballs, burgers, and sausages, etc. Therefore, minced meat identification to prevent substitution is getting more attention from the research community [8; 9]. Accurate identification of minced meat not only minimizes the issues (risk) of human health (gastric cancer, allergies, blood pressure, and type-2 diabetes) [10], but it also helps in investigating substitution fraud [11; 12]. Substitution is the most common form of fraud due to its incredible impact on economic gain, i.e., inexpensive meat will give more revenue than expensive meat, which ultimately affects consumer religion, wealth, and lifestyle [13; 14]. HSI processing used traditional Artificial Intelligence (AI) and Machine Learning (ML) methods to prevent minced meat frauds [15]. These methods are faster and efficient [16; 17] than laboratory-based control measures such as pH meter, Drip-loss, and water holding capacity (WHC) [18]. For instance, [PERSON] et al. [16] introduced a study to differentiate and classify between four types of lamb muscles. The principal component analysis (PCA) was applied to reduce the dimensions of the HSI system. For the selected wavelengths, linear discrimination analysis outperformed the linear least mean squares classifier, multilayer perceptron with scaled conjugate gradient, variant of the Support Vector Machine (v-SVM), \(K\)-Nearest Neighbor (KNN), and Logistic Regression (LR). [PERSON] et al. [19] proposed a method to classify the marbling of 35 beef samples. The sample types include _longissimus dorsi_ and are examined through the HSI sensor. For better classification, each muscle was examined through the Decision Tree (DT). Similarly, [PERSON] et al. [20] used the DT approach to identify and classify chicken meat based on the REP Tree method and achieved an overall precision of 77.20%. Minced meat identification process usually include color, pH and texture parameters [17; 21; 22; 23] analysis. For example, [PERSON] et. al. [21] used quality parameters such as; Moisture Content (MC), and the color of beef meat was examined for (0-75 s) through microwave heating treatment. Color parameters (L*, a*), and their related Maygppin (Mb) percentage were investigated for the mean spectrum of beef samples.