Abstract
Soluble solids content (SSC) is the main internal quality index of navel orange, in order to detect the SSC of navel orange by the combination of visible/near infrared spectroscopy and variable selection method, 141 samples of calibration set and 47 samples of prediction set were used. The transportation speed of navel orange was 0.3 m/s. The visible/near infrared spectra of navel orange samples were collected online by a USB4000 micro spectrometer. Firstly, uninformative variable elimination (UVE) and genetic algorithm (GA) were used to prescreen the wavelength variables in the wavelength range of 650~950 nm, then competitive adaptive weighted sampling (CARS) and successive projections algorithm (SPA) were used to further screen the wavelength variables. Furthermore, partial least squares (PLS) method was used to establish the online prediction models of SSC of navel orange, and these prediction models were compared with the prediction model established using original spectra. The results indicate that,for SSC of navel orange, GA method is better than UVE method in pre screening, while CARS method is better than SPA method in variable selection. GA-CARS and GA-SPA combined variable selection method is better than the corresponding single variable selection methods CARS and SPA. GA-CARS method obtains the best results for SSC of navel orange among the above variable selection methods, with the correlation coefficients of PLS model of SSC of navel orange in calibration and prediction set of 0.933 and 0.824 respectively, and the root mean square errors of calibration and prediction set are 0.429% and 0.670%, respectively. The performance of GA-CARS-PLS model is better than that of PLS model established by original spectra, and the number of modeling wavelength variables reduces from 1 385 to 78, only accounting for 5.63% of the number of original wavelength variables. In conclusion, the combined variable selection method of GA-CARS can effectively screen the wavelength variables of SSC of navel orange, and improve the stability and prediction accuracy of the prediction model.
Publication Date
2-28-2020
First Page
89
Last Page
93
DOI
10.13652/j.issn.1003-5788.2020.02.016
Recommended Citation
Shui-quan, JIANG and Tong, SUN
(2020)
"Online detection of soluble solid content in navel orange based on visible/near infrared spectroscopy and variable selection,"
Food and Machinery: Vol. 36:
Iss.
2, Article 16.
DOI: 10.13652/j.issn.1003-5788.2020.02.016
Available at:
https://www.ifoodmm.cn/journal/vol36/iss2/16
References
[1] JIANG Hao,LU Jian-gang.Using an optimal CC-PLSR-RBFNN model and NIR spectroscopy for the starch content determination in corn[J].Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2018,196:131-140.
[2] PRIETO N,DUGAN M E R,JUREZ M,et al.Using portable near-infrared spectroscopy to predict pig subcutaneous fat composition and iodine value[J].Canadian Journal of Animal Science,2018,98(2):221-229.
[3] 陶瑞,史智佳,贡慧,等.傅里叶变换近红外光谱技术快速检测金枪鱼新鲜度[J].肉类研究,2017,31(4):43-49.
[4] LIU Yun,PENG Qing-wei,YU Jian-cheng,et al.Identification of tea based on CARS-SWR variable optimization of visible/near-infrared spectrum[J].Journal of the Science of Food and Agriculture,2020,100(1):371-375.
[5] WANG Yan-zeng,GUO Wen-chuan,ZHU Xin-hua,et al.Effect of homogenisation on detection of milk protein content based on NIR diffuse reflectance spectroscopy[J].International Journal of Food Science and Technology,2019,54(2):387-395.
[6] 雷晓晴,王秀丽,李耿,等.近红外光谱法快速测定当归中7种成分的含量[J].中草药,2019,50(16):3 947-3 954.
[7] 冼瑞仪,黄富荣,黎远鹏,等.可见和近红外透射光谱结合区间偏最小二乘法(iPLS)用于橄榄油中掺杂煎炸老油的定量分析[J].光谱学与光谱分析,2016,36(8):2 462-2 467.
[8] 刘燕德,翟建龙.脐橙可溶性固形物的在线近红外光谱检测[J].西北农林科技大学学报:自然科学版,2014,42(3):186-190.
[9] 韩东海,周恩洋,戚淑叶.苹果糖度在线检测降低杂散光影响研究[J].农业机械学报,2015,46(11):260-265.
[10] 郭成,梁梦醒,江明珠,等.在线检测无花果中可溶性固形物的近红外漫透射技术研究[J].江苏科技大学学报:自然科学版,2018,32(2):285-290,297.
[11] TIAN Xi,FAN Shu-xiang,LI Jiang-bo,et al.Comparison and optimization of models for SSC on-line determination of intact apple using efficient spectrum optimization and variable selection algorithm[J].Infrared Physics & Technology,2019,102:102979.
[12] XU Xiao,MO Jian-can,XIE Li-juan,et al.Influences of detection position and double detection regions on determining soluble solids content(SSC)for apples using on-line visible/near-infrared(Vis/NIR)spectroscopy[J].Food Analytical Methods,2019,12(9):2 078-2 085.
[13] SHEN F,ZHANG B,CAO C J,et al.On-line discrimination of storage shelf-life and prediction of post-harvest quality for strawberry fruit by visible and near infrared spectroscopy[J].Journal of Food Process Engineering,2018,41(7):e12866.
[14] 刘燕德,朱丹宁,吴明明,等.玉露香梨可溶性固形物近红外漫透射光谱在线检测[J].食品与机械,2016,32(10):115-119,163.
[15] 孙通,江水泉.基于可见/近红外光谱和变量优选的南水梨糖度在线检测[J].食品与机械,2016,32(3):69-72.
[16] 陆辉山,陈鹏强,闫宏伟,等.基于近红外光谱漫透射技术的苹果可溶性固形物含量在线检测[J].食品与机械,2015,31(3):40-42.
[17] 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.
[18] LEARDI R.Application of genetic algorithm-PLS for feature selection in spectral data sets[J].Journal of Chemometrics,2000,14(5/6):643-655.
[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] 陈斌,孟祥龙,王豪.连续投影算法在近红外光谱校正模型优化中的应用[J].分析测试学报,2007,26(1):66-69.