An embedded system based on DSP platform and PCA-SVM algorithms for rapid beef meat freshness prediction and identification

Statistical and artificial intelligence learning algorithms such as Principal Component Analysis (PCA) and Support Vector Machine (SVM) are widely used in many meat quality assessment applications to classify and predict the freshness of beef meat. This paper presents the implementation of the PCA and SVM algorithms on an embedded system based on a Digital Signal Processor (DSP). A dataset of eighty-one hue, saturation, and intensity (HSI) beef meat images was used. The PCA is used as a projection and prediction model where the SVM is used for the classification and identification of the beef meat. Results obtained from PCA projection model on a desktop system using Matlab software show the projection of three groups which represent the degree of beef meat freshness during the days of cold storage. A perfect prediction of the new unknown samples was obtained by PCA prediction model. A successful rate 100% of classification and identification was obtained by SVM. The PCA and SVM methods were implemented on the C6678 multi-core DSP. The implementation results of these algorithms were similar as those obtained by Matlab software. The processing time of the algorithms measured on the embedded system was lower compared to the desktop system. The embedded platforms based on DSP as portable tools can be used to predict or identify the sample beef meat freshness anywhere and in real-time.