Abstract
Puffy fruit and soluble solid content (SSC) are important indexes for evaluating the quality of citrus. The feasibility was discussed for detecting Puffing disease and SSC of intact citrus simultaneously by online visible-near infrared (visible-NIR) transmittance spectroscopy. The spectra were recorded with the integration time of 100 ms in the wavelength range of 350~1 150 nm when the samples were conveyed at the speed of five samples per second. The feasibility of simultaneous and online detection of puffiness fruit and SSC for intact citrus simultaneously was discussed by visible-near infrared transmittance spectroscopy. The response properties of visible-NIR spectra for normal fruit, mild and severe puffiness fruit were analyzed. Then least squares support vector machine (LSSVM) and discrimination partial least square (DPLS) were developed for discrimination of puffiness fruit and health citrus. At the same time, the optimal soluble solids content model of citrus was conducted by partial least squares regression methods. Other 35 samples without developing calibration models were applied to evaluate precision of online sorting. The classification rate was 100% for identifying puffiness fruit, and the accuracy of sorting SSC for health pears was 97%. The results showed that simultaneous detection of puffiness and SSC were feasible by visible-NIR transmittance spectroscopy.
Publication Date
11-28-2016
First Page
116
Last Page
121
DOI
10.13652/j.issn.1003-5788.2016.11.026
Recommended Citation
Yande, LIU; Mingming, WU; Xudong, SUN; and Danning, ZHU
(2016)
"Simultaneous detection of puffiness and soluble solids content for intact citrus by online spectroscopy,"
Food and Machinery: Vol. 32:
Iss.
11, Article 26.
DOI: 10.13652/j.issn.1003-5788.2016.11.026
Available at:
https://www.ifoodmm.cn/journal/vol32/iss11/26
References
[1] 吕强, 何绍兰, 刘斌, 等. 班菲尔脐橙可溶性固形物近红外光谱特征谱区选择[J]. 农业机械学报, 2012(S1): 211-214.
[2] CAYUELA J A. Vis/NIR soluble solids prediction in intact oranges (Citrus sinensis L.) cv. Valencia Late by reflectance[J]. Postharvest Biology and Technology, 2008, 47(1): 75-80.
[3] TEWARI J C, DIXIT V, CHO B K, et al. Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2008, 71(3): 1 119-1 127.
[4] CAYUELA J A, WEILAND C. Intact orange quality prediction with two portable NIR spectrometers[J]. Postharvest Biology and Technology, 2010, 58(2): 113-120.
[5] LIU Yan-de, SUN Xu-dong, OUYANG Ai-guo. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN[J]. LWT-Food Science and Technology, 2010, 43(5): 602-607.
[6] MAGWAZA L S, OPARA U L, TERRY L A, et al. Prediction of‘Nules Clementine’mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopy[J]. Postharvest Biology and Technology, 2012, 74: 1-10.
[7] MAGWAZA L S, LANDAHL S, CRONJE P J R, et al. The use of Vis/NIRS and chemometric analysis to predict fruit defects and postharvest behaviour of ‘Nules Clementine’mandarin fruit[J]. Food Chemistry, 2014, 163: 267-274.
[8] 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.
[9] JAMSHIDI B, MINAEI S, MOHAJERANI E, et al. Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges[J]. Computers and Electronics in Agriculture, 2012, 85: 64-69.
[10] WANG Ai-chen, XIE Li-juan. Technology using near infrared spectroscopic and multivariate analysis to determine the soluble solids content of citrus fruit[J]. Journal of Food Engineering, 2014, 143: 17-24.
[11] LIU C, YANG S X, DENG L. Determination of internal qualities of newhall navel oranges based on nir spectroscopy using machine learning[J]. Journal of Food Engineering, 2015, 161: 16-23.
[12] LORENTE D, ESCANDELL-MONTERO P, CUBERO S, et al. Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit[J]. Journal of Food Engineering, 2015, 163: 17-24.
[13] SANKARAN S, EHSANI R. Visible-near infrared spectroscopy based citrus greening detection: Evaluation of spectral feature extraction techniques[J]. Crop Protection, 2011, 30(11): 1 508-1 513.
[14] JAMES P Reed, DESMOND Devlin, SANDRA R R Esteves, et al. Integration of NIRS and PCA techniques for the process monitoring of a sewage sludge anaerobic digester[J]. Bioresource Technology, 2013, 133(4): 398-404.
[15] NEJADGHOLI I, BOLIC M. A comparative study of PCA, SIMCA and Cole model for classification of bioimpedance spectroscopy measurements[J]. Computer in Biology and Medicine, 2015, 63: 42-51.
[16] CHAUCHARD F, COGDILL R, ROUSSEL S, et al. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes[J]. Chemometrics and Intelligent Laboratory Systems, 2004, 71(2): 141-150.
[17] MARCO F Ferro, SIMONE C Godoy, ANNELISE E Gerbase, et al. Non-destructive method for determination of hydroxyl value of soybean polyol by LS-SVM using HATR/FT-IR[J]. Analytica Chimica Acta, 2007, 9(6): 114-119.
[18] 郝勇, 孙旭东, 高荣杰. 基于可见/近红外光谱与SIMCA和PL S-DA的脐橙品种识别[J]. 农业工程学报, 2010, 26(12): 373-377.
[19] 孙旭东, 刘燕德, 李轶凡, 等. 鸭梨黑心病和可溶性固形物含量同时在线检测研究[J]. 农业机械学报, 2016, 47(1): 227-233.