Non-destructive detection of moisture content in gherkin using hyperspectral imaging

Li Dan; He Jianguo; Liu Guishan; He Xiaoguang; Wang Songlei; Wu Longguo

near-infrared hyperspectral imaging technique was investigated for non-destructive determination of moisture content in gherkin. Multiplicative scatter correction and Savitzky-Golay smoothing were used to acquire the best pretreatment method in the spectral region between 900 nm and 1 700 nm. Optimal wavelengths were selected by regression coefficients of partial least-squares models. Prediction models were developed based on partial least squares method in the full wavelengths and optimal wavelengths. The results show that the best predictions are obtained with Savitzky-Golay smoothing spectral. Models of optimal wavelengths are better than models of full wavelengths for predicting the moisture content in gherkin, and the correlation coefficient and root mean square error of calibration and validation models are 0.86, 0.90 and 0.111, 0.156, respectively. Therefore, it's feasible to determinate the moisture content of gherkin using hyperspectral imaging technique.

1. School of Agriculture,Ningxia University,Yinchuan 750021,China

Received Date: 2013-11-12

Rev Recd Date: 2013-12-22

Publish Date: 2014-07-25

Key words:

Abstract: near-infrared hyperspectral imaging technique was investigated for non-destructive determination of moisture content in gherkin. Multiplicative scatter correction and Savitzky-Golay smoothing were used to acquire the best pretreatment method in the spectral region between 900 nm and 1 700 nm. Optimal wavelengths were selected by regression coefficients of partial least-squares models. Prediction models were developed based on partial least squares method in the full wavelengths and optimal wavelengths. The results show that the best predictions are obtained with Savitzky-Golay smoothing spectral. Models of optimal wavelengths are better than models of full wavelengths for predicting the moisture content in gherkin, and the correlation coefficient and root mean square error of calibration and validation models are 0.86, 0.90 and 0.111, 0.156, respectively. Therefore, it's feasible to determinate the moisture content of gherkin using hyperspectral imaging technique.

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Reference (19)

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Non-destructive detection of moisture content in gherkin using hyperspectral imaging

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