光谱学与光谱分析 |
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Determination of Acidity and Vitamin C in Apples Using Portable NIR Analyzer |
YANG Fan, LI Ya-ting, GU Xuan, MA Jiang, FAN Xing, WANG Xiao-xuan, 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 kernel Isomap algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity and vitamin C in six kinds of apple samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with kernel Isomap algorithm were satisfactory. The correlation between actual and predicted values of calibration samples (Rc) obtained by the acidity model was 0.999 4, and for prediction samples (Rp) was 0.979 9. The root mean square error of prediction set (RMSEP) was 0.055 8. For the vitamin C model, Rc was 0.989 1, Rp was 0.927 2, and RMSEP was 4.043 1. Results proved that the portable NIR analyzer can be a feasible tool for the determination of acidity and vitamin C in apples.
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Received: 2010-11-10
Accepted: 2011-02-02
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Corresponding Authors:
ZHANG Zhuo-yong
E-mail: gusto2008@vip.sina.com
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[1] Park B, Abbott J A, Lee K J, et al. Transactions of the ASAE, 2003, 46(6): 1721. [2] Mehinagic E, Royer G, Symoneaux R, et al. Postharvest Biology and Technology, 2004, 34(3): 257. [3] Zude M, Herold B, Roger J M, et al. Journal of Food Engineering, 2006, 77(2): 254. [4] QING Zhaoshen, JI Baoping, Zude M. Journal of Food Engineering, 2007, 82(1): 58. [5] Manabu K, Shinji H, Iori H, et al. Computers and Chemical Engineering, 2001, 25(7): 1103. [6] Ustun B, Melssen W J, Oudenhuijzen M, et al. Analytica Chimica Acta, 2005, 544(1): 292. [7] Choi H, Choi S. Electronics Letters, 2004, 40(25): 1612. [8] Galvo R K H, Araujo M C U, José G E, et al. Talanta, 2005, 67(4): 736. [9] Kulkami S G, Chaudhary A K, Nandi S, et al. Biochemical Engineering Journal, 2004, 18(3): 193. [10] Celikoglu H B. Mathematical and Computer Modelling, 2006, 44(7): 640. |
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