[Research on optimization of model for detecting sugar content of navel orange by online near infrared spectroscopy].
Identifieur interne : 001631 ( Main/Exploration ); précédent : 001630; suivant : 001632[Research on optimization of model for detecting sugar content of navel orange by online near infrared spectroscopy].
Auteurs : Xu-Dong Sun [République populaire de Chine] ; Yong Hao ; Rong-Jie Gao ; Ai-Guo Ouyang ; Yan-De LiuSource :
- Guang pu xue yu guang pu fen xi = Guang pu [ 1000-0593 ] ; 2011.
English descriptors
- KwdEn :
- MESH :
- chemical , analysis : Carbohydrates.
- chemistry : Citrus sinensis.
- Calibration, Least-Squares Analysis, Models, Theoretical, Spectroscopy, Near-Infrared.
Abstract
The objective of the present research was to optimize the model of sugar content in navel orange for improving the detection presicion by the online near infrared spectroscopy. The reference wavelength was chosen by coefficient of variation of the different wavelengths in the calibration set in the wavelength range of 700.28 - 933.79 nm. Then the spectra were transformed into ratio specra. The absorbance and ration spectra were pretreated by different preprocessing methods. The models of sugar content were developed by partial least squares (PLS) and least squares support vector regression (LSSVR). The 30 unknown navel orange samples were applied to evaluate the performance of the models. By comparison of the predictive performances, the LSSVR model was the best among the models with the first derivative preprocessing and ration spectra. The correlation coeffiecient (R(P)) of the best model was 0.85, the root mean square error of prediction (RMSEP) was 0.41 Brix. The results suggested that it was feasible to improve the precision of online near infrared spectroscopy detecting sugar content in navel orange by the optimization of reference wavelengths, the first derivative preprocessing and LSSVR.
PubMed: 21800571
Affiliations:
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The reference wavelength was chosen by coefficient of variation of the different wavelengths in the calibration set in the wavelength range of 700.28 - 933.79 nm. Then the spectra were transformed into ratio specra. The absorbance and ration spectra were pretreated by different preprocessing methods. The models of sugar content were developed by partial least squares (PLS) and least squares support vector regression (LSSVR). The 30 unknown navel orange samples were applied to evaluate the performance of the models. By comparison of the predictive performances, the LSSVR model was the best among the models with the first derivative preprocessing and ration spectra. The correlation coeffiecient (R(P)) of the best model was 0.85, the root mean square error of prediction (RMSEP) was 0.41 Brix. The results suggested that it was feasible to improve the precision of online near infrared spectroscopy detecting sugar content in navel orange by the optimization of reference wavelengths, the first derivative preprocessing and LSSVR.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Jiangxi</li></region><settlement><li>Nanchang</li></settlement></list><tree><noCountry><name sortKey="Gao, Rong Jie" sort="Gao, Rong Jie" uniqKey="Gao R" first="Rong-Jie" last="Gao">Rong-Jie Gao</name><name sortKey="Hao, Yong" sort="Hao, Yong" uniqKey="Hao Y" first="Yong" last="Hao">Yong Hao</name><name sortKey="Liu, Yan De" sort="Liu, Yan De" uniqKey="Liu Y" first="Yan-De" last="Liu">Yan-De Liu</name><name sortKey="Ouyang, Ai Guo" sort="Ouyang, Ai Guo" uniqKey="Ouyang A" first="Ai-Guo" last="Ouyang">Ai-Guo Ouyang</name></noCountry><country name="République populaire de Chine"><region name="Jiangxi"><name sortKey="Sun, Xu Dong" sort="Sun, Xu Dong" uniqKey="Sun X" first="Xu-Dong" last="Sun">Xu-Dong Sun</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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