[Determination of soluble solids content in navel oranges by Vis/NIR diffuse transmission spectra combined with CARS method].
Identifieur interne : 001148 ( Main/Exploration ); précédent : 001147; suivant : 001149[Determination of soluble solids content in navel oranges by Vis/NIR diffuse transmission spectra combined with CARS method].
Auteurs : Tong Sun [République populaire de Chine] ; Wen-Li Xu ; Jin-Long Lin ; Mu-Hua Liu ; Xiu-Wen HeSource :
- Guang pu xue yu guang pu fen xi = Guang pu [ 1000-0593 ] ; 2012.
English descriptors
- KwdEn :
- MESH :
- chemistry : Citrus sinensis, Fruit.
- methods : Spectrophotometry, Infrared.
- Algorithms, Least-Squares Analysis, Models, Theoretical, Quality Control, Solubility.
Abstract
Soluble solids content (SSC) is one of important internal quality index for navel oranges. In the present study, visible/near infrared (Vis/NIR) diffuse transmission spectra of navel oranges were acquired using a QualitySpec spectrometer in the wavelength range of 350-1 000 nm, and CARS (competitive adaptive reweighted sampling) was used to select important variables related with SSC of navel oranges from spectra data, then was compared with other variable selection methods such as uninformative variables elimination (UVE) and successive projections algorithm (SPA). Finally, partial least squares (PLS) regression was used to develop calibration model for SSC of navel oranges using the 38 selected variables, and the calibration model was used to predict the SSC of 75 samples in the prediction set. The results indicate that CARS method is superior to other variable selection methods such as UVE and SPA, and can select the important variables for SSC efficiently. The calibration model of SSC developed by CARS-PLS is superior to that model developed by full-spectrum PLS, the correlation coefficient (r) and root mean square error (RMSE) in the calibration and prediction sets are 0.948, 0.347% and 0.917, 0.394%, respectively. So, Vis/NIR diffuse transmission spectra combined with CARS method is feasible to assess soluble solids content of navel oranges, and CARS method can simplify the prediction model and improve model prediction precision.
PubMed: 23427541
Affiliations:
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In the present study, visible/near infrared (Vis/NIR) diffuse transmission spectra of navel oranges were acquired using a QualitySpec spectrometer in the wavelength range of 350-1 000 nm, and CARS (competitive adaptive reweighted sampling) was used to select important variables related with SSC of navel oranges from spectra data, then was compared with other variable selection methods such as uninformative variables elimination (UVE) and successive projections algorithm (SPA). Finally, partial least squares (PLS) regression was used to develop calibration model for SSC of navel oranges using the 38 selected variables, and the calibration model was used to predict the SSC of 75 samples in the prediction set. The results indicate that CARS method is superior to other variable selection methods such as UVE and SPA, and can select the important variables for SSC efficiently. The calibration model of SSC developed by CARS-PLS is superior to that model developed by full-spectrum PLS, the correlation coefficient (r) and root mean square error (RMSE) in the calibration and prediction sets are 0.948, 0.347% and 0.917, 0.394%, respectively. So, Vis/NIR diffuse transmission spectra combined with CARS method is feasible to assess soluble solids content of navel oranges, and CARS method can simplify the prediction model and improve model prediction precision.</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="He, Xiu Wen" sort="He, Xiu Wen" uniqKey="He X" first="Xiu-Wen" last="He">Xiu-Wen He</name><name sortKey="Lin, Jin Long" sort="Lin, Jin Long" uniqKey="Lin J" first="Jin-Long" last="Lin">Jin-Long Lin</name><name sortKey="Liu, Mu Hua" sort="Liu, Mu Hua" uniqKey="Liu M" first="Mu-Hua" last="Liu">Mu-Hua Liu</name><name sortKey="Xu, Wen Li" sort="Xu, Wen Li" uniqKey="Xu W" first="Wen-Li" last="Xu">Wen-Li Xu</name></noCountry><country name="République populaire de Chine"><region name="Jiangxi"><name sortKey="Sun, Tong" sort="Sun, Tong" uniqKey="Sun T" first="Tong" last="Sun">Tong Sun</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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