| 光谱学与光谱分析 |
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| Research on the Sugar Content Measurement of Grape and Berries by Using Vis/NIR Spectroscopy Technique |
| WU Gui-fang1,2,HUANG Ling-xia3*,HE Yong1* |
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China
2. College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, huhhot 010018, China
3. College of Animal Sciences, Zhejiang University, Hangzhou 310029, China |
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Abstract Aiming at the nonlinear correlation characteristic of Vis/NIR spectra and the corresponding sugar content of grape and berries, the Vis/NIR spectra of grape and berries were obtained by diffusion reflectance. A mixed algorithm was presented to predict sugar content of grape and berries. The original spectral data were processed using partial least squares (PLS), and three best principal factors were selected based on the reliabilities. The scores of these 3 principal factors would be taken as the input of the three-layer back-propagation artificial neural network (BP-ANN). Trained with the samples in calibration collection, the BP-ANN predicted the samples in prediction collection. The values of decision coefficient (r2), the root mean squared error of prediction (RMSEP), and bias were used to estimate the mixed model. The observed results using PLS-ANN (r2=0.908, RMSEP=0.112 and Bias=0.013) were better than those obtained by PLS (r2=0.863, RMSEP=0.171, Bias=0.024). The result indicted that the detection of internal quality of grape and berries such as sugar content by nondestructive determination method was very feasible and laid a solid foundation for setting up the sugar content forecasting model for grape and berries.
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Received: 2007-03-16
Accepted: 2007-06-18
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Corresponding Authors:
HUANG Ling-xia,HE Yong
E-mail: lxhuang@zju.edu.cn
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[1] HE Pu-chao(贺普超). Study of Grape(葡萄学). Beijing: China Agriculture Press(北京：中国农业出版社), 1999. 4.
[2] HE Yong, LI Xiao-li, SHAO Yong-ni(何 勇, 李晓丽, 邵咏妮). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(5): 850.
[3] Ann P, Nico S, Kathleen T, et al. Biosystems Engineering, 2002, 81(3): 305.
[4] WU Gui-fang, ZHU Deng-sheng, HE Yong(吴桂芳, 朱登胜, 何 勇). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2008, 28(6): 1260.
[5] Liu Fei, He Yong. Food Research International, 2008, 41(5): 562.
[6] CHEN Da, WANG Fang, SHAO Xue-guang, et al(陈 达, 王 芳, 邵学广, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(6): 672.
[7] Cen Haiyan, He Yong. Trends in Food Science & Technology, 2007, 18(2): 72.
[8] He Yong, Li Xiaoli, Deng Xunfei. Journal of Food Engineering, 2007, 79(4): 1238.
[9] SHAO Yong-ni, HE Yong(邵永妮, 何 勇). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2006, 25(6): 478.
[10] QI Xiao-ming, ZHANG Lu-da, DU Xiao-lin，et al(齐小明, 张陆达, 杜晓林, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2003, 23(5): 870.
[11] Cen Haiyan, He Yong, Huang Min. Journal of Agricultural and Food Chemistry. 2006, 54(20): 7437.
[12] Cen Haiyan, Bao Yidan, Huang Min, et al. Applied Optics，2006, 45(29): 7679. |
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