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Vol. 169, Issue 4, April 2014, pp. 55-60

 

Bullet

 

Detection of Isoflavones Content in Soybean Based on Hyperspectral
Imaging Technology
 

Tan Kezhu, Chai Yuhua, Song Weixian, Cao Xiaoda

College of Electrical and Information, Northeast Agricultural University, 59 Mucai Street, Harbin, 150030, China
Tel.: +86 045155191383, fax: +86 045155191383

E-mail: tankezhu@yeah.net

 

Received: 10 February 2014 /Accepted: 7 April 2014 /Published: 30 April 2014

Digital Sensors and Sensor Sysstems

 

Abstract: Because of many important biological activities, Soybean isoflavones which has great potential for exploitation is significant to practical applications. Due to the conventional methods for determination of soybean isoflavones having long detection period, used too many reagents, couldn’t be detected on-line, and other issues, we propose hyperspectral imaging technology to detect the contents of soybean isoflavones. Based on the 40 varieties of soybeans produced in Heilongjiang province, we get the spectral reflection datum of soybean samples varied from the soybean’s hyperspectral images which are collected by the hyperspectral imaging system, and apply high performance liquid chromatography (HPLC) method to determine the true value of the selected samples of isoflavones. The feature wavelengths for isoflavones content prediction (1516, 1572, 1691, 1716 and 1760 nm) were selected based on correlation analysis. The prediction model was established by using the method of BP neural network in order to realize the prediction of soybean isoflavones content analysis. The experimental results show that, the ANN model could predict isoflavones content of soybean samples with of 0.9679, the average relative error is 0.8032 %, and the mean square error (MSE) is 0.110328, which indicates the effectiveness of the proposed method and provides a theoretical basis for the applications of hyerspectral imaging in non-destructive detection for interior quality of soybean.

 

Keywords: Soybean isoflacones, Hyperspectral images, Nondestructive examination, BP neural network.

 

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