## 2 Related Work  
Because of the growing demand for high-quality meat products, assessing meat quality factors has become an important step in the manufacturing process. Traditional meat quality assessments are based on chemical, physical, and sensory methods that have proved to be destructive, time-consuming, laborious, and costly. As a result, a more dependable, accurate, cost-effective, and non-destructive method is desperately needed. Recently, deep learning models have been effectively employed in meat quality assessment tasks. A brief review of meat quality assessment techniques available in the literature are described as follows:  
A unique method for identifying spoiled meat using semiconductor gas sensors and image processing techniques was presented by [PERSON] et al. [17]. The authors utilized a gas sensor array and a camera equipped with image processing that used a grey level co-occurrence matrix to detect gases released by decaying meat. The respective responses were analyzed using ANN in order to classify the degree of spoilage. The suggested technique yielded a high success rate, up to 82% accuracy. The authors concluded that the method may eventually supersede the need for human senses in the classification of meat. [PERSON] et al. [18] presented a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to build the intricate relationship between pork freshness and near-infrared spectroscopy (NIRS). The presented approach can automatically extract features from the input spectra and can be used as an end-to-end model to streamline the modeling procedure. With a classification accuracy of 93.72%, the 1D-SE-ResNet performed the best when compared to five widely used classification models. The authors concluded that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection. A study by [PERSON] et al. [2] examined CNNs' efficacy in assessing chicken quality. Cropping, Otsu thresholding, and RGB-to-binary conversion were performed. Images of both fresh and rotten chicken meat were used to train the CNN model Ayam6 Net. The authors reported that Ayam6 Net yields the highest accuracy of 92.9% when compared to other CNN models (AlexNet, VGGNet, and GoogLeNet). A machine-learning-based approach for meat freshness prediction was suggested by [PERSON] et al. [19]. Two models were employed in their work to categorize the meat sample as fresh, half-fresh, and spoiled classes. The misclassification cost metric was used to assess the models' performance. The authors concluded that the Resnet model performed relatively well in terms of the cost of misclassification and attained an accuracy of over 90%. [PERSON] et al. [20] developed a machine learning model for beef cut classification. Both single and multiple modality features from the beef samples were employed. This resulted in a better classification model. Furthermore, an investigation of various machine learning classifiers (such as SVM, RF, and LDA) were performed. The authors concluded that the model with multiple modality feature fusion results in a prediction accuracy of above 90%. Classification of beef quality using artificial intelligence was suggested by [PERSON] et al. [21]. They developed an application in a client-server system for real-time purposes. The client can take a picture of beef using a mobile phone and send it to the server. Analysis of the received image to evaluate the freshness and marbling of the beef is performed on the server side. The obtained results are sent back to the client. [PERSON] et al. [22] investigated the efficacy of pre-trained CNN models for meat classification. Three different datasets were used in this work. A total of 422 images (129 of mutton, 144 of pork, and 156 of beef) were utilized. From the experimental findings, they concluded that Resnet-50 model outperforms Resnet-101 with F1-score of 95.96%. Assessment of red meat quality using machine learning and deep learning techniques was performed in [PERSON] et al. [23].