光谱学与光谱分析 |
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Determination of Tomato’s SSC and TS Based on Diffuse Transmittance Spectroscopy |
WANG Fan, LI Yong-yu*, PENG Yan-kun, ZHENG Xiao-chun |
College of Engineering, China Agricultural University, National Research and Development Center for Agro-processing Equipment, Beijing 100083, China |
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Abstract In order to meet the demands for rapid and safe nondestructive testing of fruit and vegetable quality,tomato detection system with a special circular light source was built based on the visible / near infrared diffuse transmission principle. Taking soluble solids content (SSC) and total sugar (TS) as the internal quality index, the prediction of 58 tomato samples was carried out by using this system. First, we collected the spectral data of four points for each tomato. Second, Savitzky-Golay smooth(SG-Smooth), standard normal variable transformation(SNV), multiplication scattering correction(MSC), first derivative (FD) and other methods were used to process the original spectral curve before the partial least squares regression(PLSR) model was established. Finally, we validated the established model. The results show that the correlation coefficient (r) of calibration and prediction of the SSC prediction model -are 0.995 6 and 0.976 0 when using 10 point SG-smooth, and the root mean square error of calibration and prediction are 0.052 4% and 0.082 3%. The partial least square regression (PLSR)model, with respect to the first derivative (FD) spectra, provides better prediction performance for total sugar of tomato, with correlation coefficient (r) of calibration of 0.969 1 and 0.972 9, and prediction, root mean standard error of 0.423 8% and 0.454 9%. In the experimental verification of the prediction model, the relationship of SSC between predicted and true value is 0.985 5, root mean square error is 0.066 3°Brix, the relationship of TS between predicted and true value is 0.944 9 while root mean square error is 0.571 5%. The results show that the content of soluble solids and total sugar in tomato can be realized by using visible / near infrared diffuse reflectance spectroscopy. It provides a real-time, nondestructive and rapid detection method for the evaluation of the internal quality of tomato, and provides a theoretical basis for its online grading.
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Received: 2015-10-14
Accepted: 2016-02-10
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Corresponding Authors:
LI Yong-yu
E-mail: yyli@cau.edu.cn
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[1] Nicolai,Beullens B M,Bobelyn K, et al. Postharvest Biology and Technology, 2007,46(2): 99. [2] ZHANG Peng,LI Jiang-kuo,MENG Xian-jun, et al(张 鹏,李江阔,孟宪军,等). Food Science(食品科学),2011,32(6):191. [3] HAN Dong-hai, CHANG Dong, SONG Shu-hui, et al(韩东海,常 冬,宋曙辉,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报),2013, 7(44):174. [4] Xu Huirong,Qi Bing,Sun Tongfu,et al. J. Food Eng., 2011, 72(6): 22. [5] CAI Jian-rong,TANG Ming-jie,Lü Qiang,et al(蔡健荣,汤明杰,吕 强,等). Food Science(食品科学), 2009, 4: 250. [6] ZHOU Zhu, LI Xiao-yu, GAO Hai-long, et al(周 竹,李小昱,高海龙,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(11): 237. [7] Xie Lijuan, Ying Yibin, Ying Tiejin, et al. Analytica Chimica Acta, 2007, 584(2): 379. [8] Dorais M, Ehret D L, Papadopoulos A P. Phytochemistry Reviews, 2008, 7(2): 231. [9] JIN Tong-ming(金同铭). Instrumentation Analysis Monitoring(仪器仪表与分析监测),1997b,13(3): 49. [10] MA Lan, XIA Jun-fang, ZHANG Zhan-feng, et al(马 兰,夏俊芳,张战锋,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2009, 25(10):350. [11] Katherine Flores, María-Teresa Sánchez, Dolores Pérez-Marín, et al. Journal of Food Engineering 2009, 91: 311. [12] ZHANG Ruo-yu, RAO Xiu-qin, GAO Ying-wang, et al(张若宇,饶秀勤,高迎旺,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2013, 29(23): 247. [13] Dubois M, Gilles K, Hamilton J K, et al. Nature, 1951, 28(7):167. [14] Fraser D G, Künnemeyer R. Postharvest Biology and Technology, 2001, 22(3): 191. |
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