NIR spectroscopy fruit quality detection algorithm based on the least angle regression model

摘要

To enhance the effectiveness and precision of fruit internal quality detection, the determination model of internal quality of fruit based on the least angle regression (LAR) is proposed. Compared with existing nonlinear and linear models, i.e., least squares support vector machines (LS-SVM) and partial least squares (PLS) regression (the proposed LAR model generates the best prediction results and performs better than conventional PLS. In an aspect of computational complexity, LAR and PLS are better than LS-SVM model. In aspect of interpretability, the proposed LAR is superior to the PLS model. Although the precision rate of LAR is worse than LS-SVM, it has advantages for model realisation, computation complexity, and interpretability over LS-SVM. Thus the proposed LAR model can be applied effectively in the determination of the internal quality of fruit-based on NIR (Near-infrared) spectroscopy.