| 光谱学与光谱分析 |
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| Determination of Soluble Solids Content in Nanfeng Mandarin by Vis/NIR Spectroscopy and UVE-ICA-LS-SVM |
| SUN Tong, XU Wen-li, HU Tian, LIU Mu-hua* |
| Optics-Electronics Application of Biomaterials Lab，Jiangxi Agricultural University，Nanchang 330045，China |
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Abstract The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350~1 000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate that Vis/NIR spectroscopy combined with UVE-ICA-LS-SVM is suitable for assessing SSC of Nanfeng mandarin, and the precision of prediction is high. UVE-ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.
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Received: 2013-04-29
Accepted: 2013-07-18
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Corresponding Authors:
LIU Mu-hua
E-mail: suikelmh@sina.com
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