| NIR Hyperspectral Imaging Measurement of Sugar Content in Peach Using PLS Regression |
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| 作者姓名： | 郭峰 曹其新 Nagata Masteru Jasper Tallada |
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| 作者单位： | Research Institute of Robotics Shanghai Jiaotong Univ.，Research Institute of Robotics，Shanghai Jiaotong Univ.，Faculty of Agriculture，Miyazaki Univ.，Faculty of Agriculture，Miyazaki Univ.，Shanghai 200240，China，Shanghai 200240，China，Miyazaki 8892192，Japan，Miyazaki 8892192，Japan |
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| 摘 要： | Near infrared (NIR) hyperspectral imaging measurement of sugar content in peach was introduced. NIR spectral images (650～1 000 nm, resolution: 2 nm) of peach samples were captured with developed hyperspectral imaging setup. Partial least square (PLS) regression prediction model was developed to estimate the sugar content in peach; step-wise backward method was utilized to determine optimal wavelength subsets. Experimental results show that the calibration model with optimal wavelength subsets has a correlation coefficient of prediction of 0.97 and a standard error of prediction of 0.19, the prediction accuracy is higher than the calibration model applied over the whole wavelength, which proves that variable selection plays an important role in improving the prediction accuracy of PLS regression model.
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| 关 键 词： | 红外线 图象检测 糖含量检测 食品 |
| 文章编号： | 1007-1172（2007）05-0597-05 |
| 修稿时间： | 2006-09-15 |
NIR Hyperspectral Imaging Measurement of Sugar Content in Peach Using PLS Regression |
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| GUO Feng,CAO Qi-xin,Nagata Masteru,Jasper Tallada.NIR Hyperspectral Imaging Measurement of Sugar Content in Peach Using PLS Regression[J].Journal of Shanghai Jiaotong university,2007,12(5):597-601. |
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| Authors: | GUO Feng CAO Qi-xin Nagata Masteru Jasper Tallada |
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| Institution: | 1. Research Institute of Robotics, Shanghai Jiaotong Univ. , Shanghai 200240, China
2. Faculty of Agriculture, Miyazaki Univ. , Miyazaki 8892192, Japan |
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| Abstract: | Near infrared (NIR) hyperspectral imaging measurement of sugar content in peach was introduced. NIR spectral images (650～ 1 000 nm, resolution: 2 nm) of peach samples were captured with developed hyperspectral imaging setup. Partial least square (PLS) regression prediction model was developed to estimate the sugar content in peach; step-wise backward method was utilized to determine optimal wavelength subsets. Experimental results show that the calibration model with optimal wavelength subsets has a correlation coefficient of prediction of 0.97 and a standard error of prediction of 0.19, the prediction accuracy is higher than the calibration model applied over the whole wavelength, which proves that variable selection plays an important role in improving the prediction accuracy of PLS regression model. |
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| Keywords: | near infrared hyperspectral imaging system sugar content partial least square regression |
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