| 光谱学与光谱分析 |
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| Temperature Compensation for Portable Vis/NIR Spectrometer Measurement of Apple Fruit Soluble Solids Contents |
| WANG Jia-hua1, QI Shu-ye2, TANG Zhi-hui3, JIA Shou-xing3, LI Yong-yu4* |
1. School of Food Science & Engineering, Xuchang University, Xuchang 461000，China
2. College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China
3. Institute of Machinery Equipment, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China
4. College of Engineering, China Agricultural University, Beijing 100083, China |
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Abstract Visible (Vis)/near infrared (NIR) spectroscopy has been used successfully to measure soluble solids content (SSC) in fruit. However, for practical implementation, the NIR technique needs to be able to compensate for fruit temperature fluctuations, as it was observed that the sample temperature affects the NIR spectrum. A portable Vis / NIR spectrometer was used to collect diffused transmittance spectra of apples at different temperatures (0～30 ℃). The spectral data of apple at 20 ℃ was used to develop a norm partial least squares (PLS) model. Slope/ bias technique was found to well suits to control the accuracy of the calibration model for SSC concerning temperature fluctuations. The correctional PLS models were used to predict the SSC of apple at 0, 10 and 30 ℃, respectively. The correctional method was found to perform well with Q values of 0.810, 0.822 and 0.802, respectively. When no precautions are taken, the Q value on the SSC may be as small as 0.525～0.680. The results obtained highlight the potential of portable Vis/NIR instruments for assessing internal quality of fruits on site under varying weather conditions.
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Received: 2011-10-09
Accepted: 2012-01-19
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Corresponding Authors:
LI Yong-yu
E-mail: yyli@cau.edu.cn
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[1] Nicola B M, Beullensa K, Bobelyna E, et al. Postharvest Biology and Technology, 2007, 46: 99.
[2] HAN Dong-hai, WANG Jia-hua(韩东海, 王加华). Chinese Journal of Lasers(中国激光), 2008, 35(8): 1123.
[3] SUN Xu-dong, HAO Yong, GAO Rong-jie, et al(孙旭东, 郝 勇, 高荣杰, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(5): 1230.
[4] ZHU Wei-xing, JIANG Hui, CHEN Quan-sheng, et al(朱伟兴, 江 辉, 陈全胜, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(10): 129.
[5] Wu P, Siesler H W. Journal of Near Infrared Spectroscopy, 1999, 7: 65.
[6] Hansen W G, Wiedemann S A C C, Snieder M. et al. Journal of Near Infrared Spectroscopy, 2000, 8: 125.
[7] CHANG Min, PENG Dan, XU Ke-xin(常 敏, 彭 丹, 徐可欣). Acta Optica Sinica(光学学报), 2007, 27(6): 1080.
[8] Peirs A, Scheerlinck N, Nicolaǐ B M. Postharvest Biology and Technology, 2003, 30: 233.
[9] Wülfert F, Kok W T, Smilde A K. Anal. Chem., 1998, 70: 1761.
[10] Maeda H, Ozaki Y, Tanaka M, et al. Journal of Near Infrared Spectroscopy, 1995, 3: 191.
[11] Bossart R, Grabinski J. US. Patent, 6480795, 2002.
[12] Devlin R M. Plant Physiology. 3rd ed. Boston: Willard Grant Press, 1975. 46.
[13] Hutchings J B. Food Color and Appearance. 2nd ed. Gaithersburg: Aspen Publishers Inc., 1999. 473.
[14] Polessello A, Giangiacomo R. Crit. Rev. Food Sci. Nutr., 1981, 18: 203.
[15] Banwell C N. Fundamentals of Molecular Spectroscopy. Berkshire: McGraw-Hill, 1983. 338. |
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