Spectroscopy and Spectral Analysis, Volume. 42, Issue 10, 3052(2022)
Non-Destructive Detection and Visualization of Soybean Moisture Content Using Hyperspectral Technique
NIR Hyperspectral imaging technology was used to detect soybean moisture content rapidly and non-destructively and realized the visualization of soybean moisture content. A total of 96 soybean samples of hyperspectral images in the region of 900~2 500 nm were acquired, and the moisture content of each soybean sample was measured by the direct drying method. The average spectral information of the region of interest(ROI)of the image was extracted by HSI Analyzer software, representing the sample's spectral information. The SPXY algorithm was used to divide the sample calibration set and prediction set, and the spectral data in the band range of 938 to 2 215 nm were retained. The spectral's pretreatment was analyzed, such as Moving Average, Smoothing S-G, Baseline, Normalize, Standard Normal Variate(SNV), Multiple Scattering Correction(MSC)and Detrending, and the PLSR model established after Normalize pretreatment had the best effect. The characteristic wavelengths were selected by successive projections algorithm(SPA), competitive adaptive reweighted sampling(CARS)and uninformative variable elimination(UVE). 14,16 and 29 characteristic wavelengths were selected by SPA, CARS and UVE, accounting for 6.5%, 7.4% and 13.4% of the total wavelengths. The prediction models were established for the spectra and characteristic wavelengths of 938~2 215 nm, and the model with better effect was combined with the Normalize method. Compared with the 14 prediction models established, it was found that the modeling and prediction effect of characteristic wavelengths selected by the SPA algorithm was good, and the Normalize-SPA-PCR model was optimized. The values of
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Cheng-qian JIN, Zhen GUO, Jing ZHANG, Cheng-ye MA, Xiao-han TANG, Nan ZHAO, Xiang YIN. Non-Destructive Detection and Visualization of Soybean Moisture Content Using Hyperspectral Technique[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3052
Category: Research Articles
Received: Aug. 11, 2021
Accepted: Nov. 11, 2021
Published Online: Nov. 23, 2022
The Author Email: JIN Cheng-qian (412114402@qq.com)