| In order to research rapid&non-destructive measurement method of the quality characteristics about navel oranges using near-infrared spectroscopy, predictive and control quality characteristics of total soluble sugar, total acidity, vitamin C, soluble solids in navel orange while storing, 420 navel orange samples were chosen as a sample collection, the denoising effect of 11 different popular spectroscopy pretreament approaches were compared, turned out the best decomposing levels of wavelet denoising, put forwarded the best means of wavelet of near-infrared spectroscopy denoising, established non-destructive near-infrared spectroscopy quantitative analysis model of quality characteristics about navel oranges in storage, established BP artificial neural network model of quality characteristics about navel oranges. The results are as follows:1. The best conventional pretreatment methods using near-infrared spectroscopy of total soluble sugar content is straight line subtraction (SLS); The predictive values correlation coefficient R of PLS calibration and validation model about different varieties were 0.9487 and 0.877, the root mean square error of cross-validation variance(RMSECV) were 0.776% and 0.6992%; while the R of single variety were 0.961 and 0.9626, RMSECV were 0.767% and 0.7769%; The best conventional pretreatment methods using near-infrared spectroscopy of total acidity content was multiplication scattering correction (MSC), the R of different varieties were 0.9268 and 0.894, RMSECV were 0.0355% and 0.0407%, and the R of single variety were 0.9663 and 0.9813, RMSECV were 0.0328% and 0.01705%; The best conventional pretreatment methods using near-infrared spectroscopy of vitamin C (Vc) content is first derivative+vector normalization (FD+VN), the R of different varieties were 0.9306 and 0.8689, RMSECV were 5.07mg/100g and 3.888mg/100g, and the R of single variety were 0.9392 and 0.9717, RMSECV were 2.02mg/100g and 1.8356mg/100g; The best conventional pretreatment methods using near-infrared spectroscopy of total soluble solids content is first derivative (FD), the R of different varieties were 0.9654 and 0.8952, RMSECV were 0.316% and 0.4262%, and the R of single variety were 0.9737 and 0.94, RMSECV were 0.282% and 0.36%.2. The best decomposing levels using near-infrared spectroscopy wavelet denoising of total soluble sugar was 6, PLS model predictive value R was 0.9231, RMSECV was 0.672%. The best decomposing levels using near-infrared spectroscopy wavelet denoising of total acidity was 3, PLS model predictive value R was 0.9371, RMSECV was 0.0334%. The best decomposing levels using near-infrared spectroscopy wavelet denoising of vitamin C is 3 PLS model predictive value R was 0.9632, RMSECV was 2.78mg/100g. The best decomposing levels using near-infrared spectroscopy wavelet denoising of soluble solids was 5, PLS model predictive value R was 0.9791, RMSECV was 0.292%.3. Wavelet packet transform was an effective method to denoise near-infrared spectroscopy of total soluble sugar, total acidity, vitamin C, soluble solids in navel orange. The best near infrared spectroscopy denoising wavelet of Vitamin C was db5, its R of PLS model predictive value was 0.9427, RMSECV was 2.02 mg/100g. The best near infrared spectroscopy denoising wavelet of soluble solids was db5, its R of PLS model predictive value was 0.968, RMSECV was 0.344%. The best near-infrared spectroscopy denoising wavelet of total soluble sugar was db6, its R of PLS model predictive value was 0.9431, RMSECV was 0.373%. The best near-infrared spectroscopy denoising wavelet of total acidity was db4, its R of PLS model predictive value was 0.9507, RMSECV was 0.0336%.4. In the BP artificial neural network model of quality characteristics and storage time, the hidden layer neurons number of the model for total soluble sugar was 60, the predictive value R of correction model about storage time was 0.864, validation model R was 0.88, The hidden layer neurons number of the model for total acidity was 50, the predictive value R of correction model about storage time was 0.984, validation model R was 0.9814. The hidden layer neurons number of the model for VC was 50, the predictive value R of correction model about storage time was 0.82, validation model R was 0.8648. The hidden layer neurons number of the model for soluble solids contents was 30, the predictive value R of correction model about storage time was 0.933, validation model R was 0.9343. The hidden layer neurons number of the model for sugar-acidity ratio was 60, the predictive value R of correction model about storage time was 0.89, validation model R was 0.90. The most notable quality index related to the storage time was total acidity.5. The hidden layer neurons number which was based on five indexes including total soluble sugar, total acidity, VC, soluble solids, sugar-acid ratio and optimized by the BP artificial neural network model was 8, the model was multi-element and changing with the storage time. Predictive value R about storage time of correction model was 0.98, the same one of validation model was 0.99.The forecasted effect of multi-element model was better than the one of single-element model. It should take the multi-element model to predict the storage time and the storage life. |
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