Using near-infrared spectroscopy in the classification of white and iberian pork with neural networks

The visible/near-infrared spectrum consists of overtones and combination bands of the fundamental molecular absorptions found in the visible and near-infrared region. The analysis of the spectrum might be difficult because overlapping vibrational bands may appear nonspecific and poorly resolved. Nevertheless, the information it could be retrieved from the analysis of the spectrum might be very useful for the food industry producers, consumers, and food distributors because the meat could be classified based on the spectrum in several aspects such as the quality, tenderness, and kind of meat. This paper applies Mutual Information theory and several classification models (Radial Basis Function Neural Networks and Support Vector Machines) in order to determine the breed of pork meat (Iberian or White) using only as input the infrared spectrum. First, the more relevant wavelengths from the spectrum will be chosen, then, those wavelengths will be the input data to design the classifiers. As the experiments will show, the proposed techniques, when applied with a correct design methodology are capable of obtaining quality results for this specific problem.