# Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
###### Abstract
The prediction model--Partial Least Square Regression Model (PLSR) coefficient of determination for prediction data \(R_{\text{P}}^{2}\) = 0.890, Root Mean Square Error (RMSEP) = 0.735, and Residual Predictive Deviation (RPD) = 2.733 were established for a* values. In another work, [PERSON] et al. [22] investigated the quality of pork meat for predicting the color and pH of salted meat. To reduce the dimensions of the HSI cube, PCA was used and the results show L* = 0.723, a* = 0.726, and pH = 0.86 for the coefficients of determination (\(R_{\text{cv}}^{2}\), cv = cross-validation) and \(RMSE_{\text{cv}}\) of 2.898, 1.408, and 0.073 respectively. Similarly, in 2020, [PERSON] et al. [23] experimented on an entire HSI cube of real salmon fillets through several feature descriptors such as Gray-level Co-occurrence Matrix (GLCM), variogram, Histograms of Oriented Gradients (HOG), and Local Binary Pattern (LBP). The obtained results showed HOG features outperformed with 97% accuracy for the red meat. In addition, [PERSON] et al. [24] performed the classification of multiple minced meat types (beef, mutton, and chicken). To perform better classification, myoglobin spectral features were used with the SVM algorithm which outperformed the mostly used method by achieving an overall accuracy of 88.8%. However, in most cases, traditional approaches applied to complex 3D HSI cube require several feature extraction steps [23], which reduces the effectiveness of the classification model [25]. Besides, they are also limited to the use of spectral information [26]. Irrespective of these methods, Convolutional Neural Network (CNN) allows both spatial and spectral information of HSI-system [15; 27]. For instance, [PERSON] et al. [6] proposed an in-depth model for beef, lamb, pork, and fat adulteration based on SVM, 1D and 3D CNN [28] for identification and classification of different meat types. The result indicates the 3D CNN model outperforms with 94.4%, as compared to the traditional techniques. Similarly, in another work, [PERSON] et al. [29] used 3D CNN to classify red-meat using snapshot HSI. Chunk meat classification and identification have been explored through CNN models and showed remarkable results as compared to the traditional methods. However, CNN has not yet been used for minced meat identification and classification. Therefore, this work proposes the identification of minced meat through classification using Deep Learning (DL) techniques, which follow the process of pixel-based classification of minced meat. This work also proposes the novel _isos-bestic_ point band reduction approach using myoglobin (Mb) pigments irrespective of traditional wavelength reduction techniques. In our previous work [24], we have utilized the absorption feature spectrum of minced meat to perform spectral classification and used the entire spectrum rather than reduced spectra. In a nutshell, the following steps are followed. 1. Collection and mincing of meat types using two cross blades. 2. HSI system is used for image acquisition and image correction. 3. Reduction of dimensionality using _Isos-bestic_ point in Mb pigments to retain maximum color information. 4. Classification of minced meat types using KNN, SVM, and 3D-CNN. The rest of the paper is structured as; Section 2, sampling process, and HSI-sensor data acquisition is explained with image correction and pre-processing. This section also explains the _Isos-bestic_ point wavelength reduction along with the classification techniques. Section 3, discuss the information found in the minced meat spectrum, alongside achieved classification results for both traditional and state-of-the-art DL methods. Finally, Section 4 concludes the work with future directions.