Deep learning in a sensor array system based on the distribution of volatile compounds from meat cuts using GC-MS analysis

Generally, people distinguish the type of meat by looking at the color, texture, and even aroma of meat. These three methods have less effective approaches to distinguish the types of meat from meat cuts. Some researchers analyze the differences in the aroma of meats by using laboratory equipment, which is gas chromatography-mass spectrometry (GC-MS). This tool is mostly accurate, but it requires some time to determine the meat types completely. Moreover, the analysis process using GC-MS is also complicated. Nowadays, the electronic nose (e-nose) is a promising technology because it has a faster process of identifying various food types with reasonable production costs. Hence, the development of an e-nose for distinguishing volatile compounds from some meat types is appealing. Not only to determine the type of meat, but this study can also differentiate the part of the body from the meat, which has never been done by previous researchers. GC-MS was used as ground truth for the e-nose system, which helped the results to meet the standards. To achieve the objective in differentiating two meat cuts from three types of meat, this study uses statistical parameters for extraction feature, PCA for reducing the dimension, and deep learning. Furthermore, to get more improvements from the previous researches, this study aims to optimize the parameters of deep learning. The result of the proposed method was compared to several machine learning algorithms that were used in previous studies, i.e., k-nearest neighbor (k-NN), support vector machine (SVM), Multi-Layer Perceptron (MLP), and basic deep learning. The experimental results showed that e-nose could detect meat cuts for 120 s, and the proposed method provides a significant improvement.