8 November 2005 Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit
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Abstract
The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.
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Yibin Ying, Yibin Ying, Yande Liu, Yande Liu, Xiaping Fu, Xiaping Fu, Huishan Lu, Huishan Lu, } "Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit", Proc. SPIE 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality, 59960W (8 November 2005); doi: 10.1117/12.630461; https://doi.org/10.1117/12.630461
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