Spectrometry is practical for rapid analysis of fruit maturity. RBF LSSVR model is effective for Vis-NIR calibration. However, nonlinear effect of the high-dimensional spectrum data influences the model prediction accuracy. A fuzzy optimization strategy was proposed to improve performance of RBF LSSVR models in Vis-NIR determination of pomelo maturity. A linguistic iterative mode was introduced. The input variables were features extracted by fuzzy transform PCA. The output was a polynomial formula. In application, the maturity of pomelo was recorded as indices of L*, a* and b*. The fuzzy transformed data was input to the fuzzy optimized LSSVR model for calibration, validation and test. Parameter uncertainty was also investigated to examine the advantage of the proposed strategy. Results showed that the fuzzy optimized model gained appreciate predictions on the indices. This strategy is effective for implementation of Vis-NIR LSSVR models and expected for rapid prediction of fruit maturity.

Near-infrared (NIR) data containing spectral response information for detecting target composition are sparsely implied in spectral frequency sequence. Spectral feature information should be extracted using computer-oriented chemometric methods. An Internet of Things (IoT) framework constructed with NIR calibration platform needs some advanced algorithm architectures to realize intelligent analysis. A feedback convolutional neural network (CNN) architecture, including three repeated segments of convolution, pooling, and flattening, is designed in this article for multiple extraction of spectral features from one-dimensional NIR data. An error-feedback iteration mechanism is proposed in the model training process to optimize convolution filters of each segment. Multisegment features are fused successively to ease the sparse information issue. Fusion data are further used to train the calibration models with a parametric-scaling fully connected network to determine the suitable numbers of hidden and output nodes. The adaptive network structure has the advantage of obtaining optimal prediction results from fused feature data. The proposed feedback CNN architecture based on feature information fusion is applied to the NIR rapid quantitative detection of selenium content in paddy rice samples. Experimental results showed that the fusion of multisegment features can enhance the ability of spectral information extraction. The optimal model based on fused feature data performs better than models based on separate feature data of each segment. The feedback convolutional network for information fusion can be applied in the NIR collaborative IoT framework for rapid detection spectroscopy to ensure high-confidence NIR analysis in the artificial intelligence performance of IoT.

The algorithmic scheme of the BPN-DL framework (details of eachH_kare presented in the hexagonal box).

Pomelo is an important agricultural product in southern China. Near-infrared hyperspectral imaging (NIRHI) technology is applied to the rapid detection of pomelo fruit quality. Advanced chemometric methods have been investigated for the optimization of the NIRHI spectral calibration model. The partial least squares (PLS) method is improved for non-linear regression by combining it with the kernel Gaussian radial basis function (RBF). In this study, the core parameters of the PLS latent variables and the RBF kernel width were designed for grid search selection to observe the minimum prediction error and a relatively high correlation coefficient. A deep learning architecture was proposed for the parametric scaling optimization of the RBF-PLS modeling process for NIRHI data in the spectral dimension. The RBF-PLS models were established for the quantitative prediction of the sugar (SU), vitamin C (VC), and organic acid (OA) contents in pomelo samples. Experimental results showed that the proposed RBF-PLS method performed well in the parameter deep search progress for the prediction of the target contents. The predictive errors for model training were 1.076% for SU, 41.381 mg/kg for VC, and 1.136 g/kg for OA, which were under 15% of their reference chemical measurements. The corresponding model testing results were acceptably good. Therefore, the NIRHI technology combined with the study of chemometric methods is applicable for the rapid quantitative detection of pomelo fruit quality, and the proposed algorithmic framework may be promoted for the detection of other agricultural products.