ON-LINE DETECTION OF CERASUS HUMILIS FRUIT BASED ON VIS/NIR SPECTROSCOPY COMBINED WITH VARIABLE SELECTION METHODS AND GA-BP MODEL
| Autor: | Bin Wang, Shujuan Zhang, Junlin He, Lili Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mechanical Engineering
010401 analytical chemistry Vis nir spectroscopy Cerasus humilis Analytical chemistry Feature selection 02 engineering and technology 01 natural sciences Industrial and Manufacturing Engineering 0104 chemical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Line (text file) Food Science Mathematics |
| Zdroj: | INMATEH Agricultural Engineering. :199-210 |
| ISSN: | 2068-2239 2068-4215 |
| Popis: | In order to realize the rapid and non-destructive detection of fresh Cerasus Humilis’ (CH) classification, and promote the deep-processing of post-harvest fresh fruit and improve market competitiveness, this study proposed a nonlinear identification method based on genetic algorithm (GA) optimized back propagation (BP) neural network of different varieties of fresh CH fruit. “Nongda-4”, “Nongda-5”, and “Nongda-7” fresh CH fruit were selected as research objects to collect their visible/near-infrared spectral data dynamically. The original spectra were preprocessed by moving smoothing (MS) and standard normal variate (SNV) methods, for the characteristic wavelengths were extracted with four dimension-reducing methods, namely principal components analysis (PCA), competitive adaptive reweighed sampling (CARS), CARS-mean impact value (CARS-MIV), and random frog (RF) algorithm. Finally, the BP prediction models were established based on full-spectrum and characteristic wavelengths. At the same time, the GA optimization was used to optimize the initial weight and threshold of the BP neural network and compared with the partial least squares’ discrimination analysis (PLS-DA) linear model. Through comparing the MS (7)+SNV was proved to be the best preprocessing method, the CARS-MIV-GA-BP model had the best discriminant accuracy, the prediction set accuracy was 98.76%, of which the variety “Nongda-4” and “Nongda-5” recognition rate were 100%, the variety “Nongda-7” recognition rate was 96.29%. The results show that the GA can effectively optimize the initial weights and threshold randomization of the BP neural network, improve the discrimination accuracy of CH varieties, and the CARS-MIV algorithm can effectively reduce the number of input nodes of the BP neural network model, simplify the structure of BP neural network. This study provides a new theoretical basis for the detection of fresh CH fruit classification. |
| Databáze: | OpenAIRE |
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