Abstract
Sugar content (SC) is one of the important internal qualities of Nanshui pears. In this research, Visible/near infrared (Vis/NIR) spectroscopy was used to detect SC of Nanshui pears on-line. Transmission speed of Nanshui pears was 0.3 m/s, and USB4000 spectrometer was used to acquire the spectra of Nanshui pear samples in the wavelength range of 470～1 150 nm. Then three variable selection methods were used to select sensitive wavelength variables, and partial least squares (PLS) was used to develop calibration models of SC for Nanshui pears, also performance of calibration models was compared. The results indicate that Vis/NIR spectroscopy combined with variable selection method is feasible for on-line detection of SC for Nanshui pears. Competitive adaptive reweighted sampling (CARS) method is superior to uninformative variable elimination (UVE) and successive projections algorithm (SPA) methods. CARS method can simplify calibration model and improve performance of calibration model. The correlation coefficients in prediction and root mean square errors of prediction (RMSEPs) of full-PLS and CARS—PLS models of SC for Nanshui pears are 0.940,0.951 and 0.467%,0.420%, respectively.
Publication Date
3-28-2016
First Page
69
Last Page
72
DOI
10.13652/j.issn.1003-5788.2016.03.014
Recommended Citation
Tong, SUN and Shuiquan, JIANG
(2016)
"On-line detection of sugar content of Nanshui pears by Vis/NIR spectroscopy and variable selection methods,"
Food and Machinery: Vol. 32:
Iss.
3, Article 14.
DOI: 10.13652/j.issn.1003-5788.2016.03.014
Available at:
https://www.ifoodmm.cn/journal/vol32/iss3/14
References
[1] Li Jun-hui, Danao MGC, Chen Shih-fang, et al. Prediction of starch content and ethanol yields of sorghum grain using near infrared spectroscopy[J]. Journal of Near Infrared Spectroscopy, 2015, 23(2): 85-92.
[2] 冯光, 杨亚春, 宋丰顺, 等. 近红外反射技术建立合肥地区精米直链淀粉含量测定模型[J]. 核农学报, 2012, 26(6): 942-946.
[3] Huang Lin, Zhao Jie-wen, Zhang Yan-hua, et al. Simultaneous multi-component analysis of pork meat during bacterial spoiling process by FT—NIR evaluated with a non-linear algorithm[J]. Analytical Methods, 2012, 4(11): 3 816-3 823.
[4] 黄伟, 杨秀娟, 曹志勇, 等. 近红外反射光谱快速检测滇南小耳猪肉中水分、粗脂肪及粗蛋白含量的研究[J]. 中国畜牧杂志, 2015, 51(7): 73-77.
[5] Wu Di, Chen Xiao-jing, Shi Pin-yan, et al. Determination of alpha-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination[J]. Analytica Chimica Acta, 2009, 634(2): 166-171.
[6] Ye Meng-qi, Gao Zheng-peng, Zhao Li, et al. Rapid detection of volatile compounds in apple wines using FT—NIR spectroscopy[J]. Food Chemistry, 2016, 190: 701-708.
[7] 张海东, 李贵荣, 李若诚, 等. 近红外光谱结合极限学习机和GA—PLS算法检测普洱茶茶多酚含量[J]. 激光与光电子学进展, 2013, 50(4): 180-186.
[8] 赵杰文, 毕夏坤, 林颢, 等. 鸡蛋新鲜度的可见—近红外透射光谱快速识别[J]. 激光与光电子学进展, 2013, 50(5): 209-216.
[9] 程文宇, 管骁, 刘静. 近红外光谱技术检测液态奶中微量三聚氰胺的可行性研究[J]. 食品与机械, 2015, 31(1): 71-74, 81.
[10] Sun Tong, Lin Hong-jian, Xu Hui-rong, et al. Effect of fruit moving speed on predicting soluble solids content of 'Cuiguan' pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biology and Technology, 2009, 51(1): 86-90.
[11] 韩东海, 常冬, 宋曙辉, 等. 小型西瓜品质近红外无损检测的光谱信息采集[J]. 农业机械学报, 2013, 44(7): 174-178.
[12] Li Jiang-bo, Huang Wen-qian, Zhao Chun-jiang, et al. A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy[J]. Journal of Food Engineering, 2013, 116(2): 324-332.
[13] 欧阳爱国, 谢小强, 刘燕德. 苹果可溶性固形物近红外在线光谱变量优选[J]. 农业机械学报, 2014, 45(4): 220-225.
[14] Moller S M, Travers S, Bertram H C, et al. Prediction of postharvest dry matter, soluble solids content, firmness and acidity in apples (cv. Elshof) using NMR and NIR spectroscopy: a comparative study[J]. European Food Research and Technology, 2013, 237(6): 1 021-1 024.
[15] Jie Deng-fei, Xie Li-juan, Rao Xiu-qin, et al. Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system[J]. Postharvest Biology and Technology, 2014, 90: 1-6.
[16] Zhao Xin, Zhu Qi-bing, Huang Min, et al. An IGA—PLSP method for FT—NIR wavelength selection for measuring soluble solid content of citrus fruits[J]. Analytical Methods, 2013, 5(18): 4 811-4 817.
[17] 孙通, 许文丽, 胡田, 等. 基于UVE—ICA和支持向量机的南丰蜜桔可溶性固形物可见—近红外检测[J]. 光谱学与光谱分析, 2013, 33(12): 3 235-3 239.
[18] 张纯, 张海东, 江水泉. 用混合线性分析法建立苹果糖度近红外光谱预测模型[J]. 食品与机械, 2006, 22(6): 83-85, 126.
[19] Li Hong-dong, Liang Yi-zeng, Xu Qing-song, et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J]. Analytica Chimica Acta, 2009, 648(1): 77-84.
[20] Centner V, Massart D L, Denoord O E, et al. Elimination of uninformative variables for multivariate calibration[J]. Analytical Chemistry, 1996, 68(21): 3 851-3 858.
[21] Galvo R K H, Araújo M C U, Fragoso W D, et al. A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemometrics and Intelligent Laboratory Systems, 2008, 92(1): 83-91.