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Principal Components-Artificial Neural Networks for Predicting SSC and Firmness of Fruits based on Near Infrared Spectroscopy

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2007 ASAE Annual Meeting  073057.(doi:10.13031/2013.23177)
Authors:   Xiaping Fu, Yibin Ying, Huirong Xu, Ying Zhou
Keywords:   NIRS, PCA, BP-ANN, SSC, firmness

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for components analysis of many materials. Principal component analysis (PCA) and artificial neural networks using back-propagation algorithm (BP-ANN) were used to establish nonlinear model for the prediction of soluble solid content (SSC) and firmness of peach and loquat fruits from NIR spectral data. The first ten principal components extracted from original spectra and spectra after multiplicative scattering correction (MSC) were used as input nodes of BP-ANN. TANSIG and LOGSIG transfer functions and two to nine neurons were considered for the hidden layer of the network. For peaches, the best results were R train=0.940 and R test= 0.900 for SSC; R train=0.701 and R test =0.453 for firmness. For loquats, the best results were R train=0.962 and R test= 0.893 for SSC; R train=0.812 and R test =0.624 for firmness. The results of this study show that combination of PCA and BP-ANN is feasible for predicting fruit quality based on NIRS. For further researches, factors such as the number of neurons for input layer, the number of hidden layers, other learning algorithms and so on could be studied to improve modeling performance and predicting accuracy.

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