Citation: MA Wen-Qiang, ZHANG Man, LI Yuan, LI Min-Zan, YANG Li-Ling, ZHU Zhan-Jiang, CUI Kuan-Bo. Detection and Grading Method of Walnut Kernel Quality Based on Hyperspectral Image[J]. Chinese Journal of Analytical Chemistry, ;2020, 48(12): 1737-1746. doi: 10.19756/j.issn.0253-3820.191289 shu

Detection and Grading Method of Walnut Kernel Quality Based on Hyperspectral Image

1.
Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;
2.
Agricultural Mechanization Institute, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China;
3.
Soil Fertilizer and Agricultural Water Saving Research Institute, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China

Corresponding author: ZHANG Man, cauzm@cau.edu.cn
Received Date: 16 August 2019
Revised Date: 14 October 2020

Fund Project: This work was supported by the Project of Renovation Capacity Building for the Young Sci-Tech Talents Sponsored by Xinjiang Academy of Agricultural Sciences (No. xjnkq-2019007), the National Natural Science Foundation of China (No.31701319) and the Science and Technology Innovation Key Cultivation Special Project of Xinjiang Academy of Agricultural Sciences (No.xjkcpy-004).

Hyperspectral imaging technology enables rapid non-destructive inspection and grading of various agricultural products. In this work, the research on the quality detection method of walnut kernel based on hyperspectral image was carried out. The combination of spectrum and image information was used to realize the prediction of protein and fat content and the classification of the integrity and color of walnut kernel. The "Wen 185" walnuts, which were produced from Xinjiang, were shelled and prepared by different grades of integrity and color. Then the hyperspectral image of each sample was measured in the range of 862.9-1704.02 nm and 382.19-1026.66 nm by Gaia hyperspectral imager. After that, the color difference, fat content and protein content of samples were measured. Multivariate scatter correction and standard normalized variate were used to pre-processing the original spectral information. And the feature bands were screened by the method, which combined competitive adaptive re-weighting sampling (CARS) and correlation coefficient method (CCM ) algorithm, for the three parameters of protein content, fat content and total color difference of walnut kernel samples. Six feature bands related to protein content and 7 feature bands related to fat content were screened out. The internal quality parameter prediction model of the full spectrum band and the characteristic spectrum band were established by partial least squares regression (PLSR) algorithm. Compared with the full-spectrum band, the verification set coefficient (R²) of the feature band protein content prediction model increased from 0.66 to 0.91, and the mean square error (RMSEP) decreased from 1.37% to 0.78%. The verification set coefficient (R²) of the feature band fat content prediction model increased from 0.83 to 0.93, and the RMSEP decreased from 0.98% to 0.47%. It showed that the selected characteristic bands effectively reduced the complexity of the full spectrum information and improved the quality of modeling. In terms of appearance quality, the feature bands associating with the color difference were selected to be 402.5 and 689.2 nm. The full-spectral spectrum, RGB spectrum, characteristic spectrum and the combination of spectral and image information were used to establish the walnut appearance quality classification model by decision tree, K-nearest neighbor and support vector machine algorithm. It showed that the feature bands modeling greatly reduced the interference of redundant information, improved the modeling efficiency, and the classification accuracy were also significantly higher than the RGB bands by comparison. The adding image statistical feature parameter to the feature bands and RGB bands could further improve the accuracy of classification model which had the highest classification accuracy rate reached to 98.6% by decision tree algorithm. In terms of classification algorithm, the decision tree algorithm had obvious advantages in classification accuracy and calculation speed when the number of input variables was less. The used of hyperspectral technology could realize the internal quality detection and appearance classification of walnut kernels, which provided a new theoretical basis for the application of non-destructive testing of walnut kernel quality.
- Walnut kernel,
- Hyperspectral image,
- Nondestructive testing,
- Classification,
- Feature band
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