光谱学与光谱分析 |
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Determination of Acidity in Oranges Based on Emphatic Orthogonal Signal Correction and Principal Component Orthogonal Signal Correction |
YANG Fan, QIU Xiao-zhen, HAO Rui, GAO Fan, DU Wei, ZHANG Zhuo-yong* |
Department of Chemistry, Capital Normal University, Beijing 100048, China |
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Abstract Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with PC-OSC algorithm, EOSC algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity in 112 orange samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with EOSC algorithm were satisfactory and the EOSC algorithm was not as susceptible to overfitting the data as PC-OSC algorithm. The correlation between actual and predicted values of calibration samples (Rc) obtained by the EOSC acidity model was 0.888 0, and prediction samples (Rp) was 0.885 6. The RMSEP was 0.081 65. The results proved that the portable NIR analyzer combined with EOSC algorithm and GRNN can be a feasible tool for the determination of acidity in oranges.
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Received: 2011-05-22
Accepted: 2011-09-10
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Corresponding Authors:
ZHANG Zhuo-yong
E-mail: gusto2008@vip.sina.com
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[1] Hasim K, Serkan S, Ahmet C, et al. Microchemical Journal, 2009, 91: 187. [2] Yande L, Xudong S, Jianmin Z, et al. Mathematical and Computer Modelling 2010, 51: 1438. [3] Yande L, Xudong S, Aiguo O. LWT-Food Science and Technology, 2010, 43: 602. [4] Jagdish C T, Vivechana D, Byoung-Kwan C, et al. Spectrochimica Acta Part A, 2008, 71: 1119. [5] LU Wan-zhen(陆婉珍). Modern Near Infrared Spectroscopy Analytical Technology(Second Edition)(现代近红外光谱分析技术, 第2版). Beijing:China Petrochemical Press(北京:中国石化出版社),2007. [6] Svante W, Henrik A, Fredrik L, et al. Chemometrics and Intelligent Laboratory Systems, 1998, 44: 175. [7] Johan A W, Sijmen D J, Age K S. Chemometrics and Intelligent Laboratory Systems, 2001, 56: 13. [8] Robert N F, Huwei T, Steven D B. Chemometrics and Intelligent Laboratory Systems, 2002, 63: 129. [9] Peter B H, Jacky K, Jacques A, et al. Analytical Chemistry, 2009, 81: 7160. [10] Jiajin Z, Zhuoyong Z, Yuhong X, et al. Talanta, 2011, 83: 1401. [11] Galvo R K H, Araujo M C U, José G E, et al. Talanta, 2005, 67: 736. |
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