Study on Identification the Crack Feature of Fresh Jujube Using Hyperspectral Imaging
YU Ke-qiang1, ZHAO Yan-ru1, LI Xiao-li1, ZHANG Shu-juan2, HE Yong1*
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 2. College of Engineering, Shanxi Agricultural University, Taigu 030801, China
Abstract：Crack is one of the most important indicators to evaluate the quality of fresh jujube. Crack not only accelerates the decay of fresh jujube, but also diminishes the shelf life and reduces the economic value severely. In this study, the potential of hyperspectral imaging covered the range of 380～1 030 nm was evaluated for discrimination crack feature (location and area) of fresh jujube. Regression coefficients of partial least squares regression (PLSR), successive projection analysis (SPA) and principal component analysis (PCA) based full-bands image were adopted to extract sensitive bands of crack of fresh jujube. Then least-squares support vector machine (LS-SVM) discriminant models using the selected sensitive bands for calibration set (132 samples) were established for identification the prediction set (44 samples). ROC curve was used to judge the discriminant models of PLSR-LS-SVM, SPA-LS-SVM and PCA-LS-SVM which are established by sensitive bands of crack of fresh jujube. The results demonstrated that PLSR-LS-SVM model had an optimal effect (area=1, std=0) to discriminate crack feature of fresh jujube. Next, images corresponding to five sensitive bands (467, 544, 639, 673 and 682 nm) selected by PLSR were executed to PCA. Finally, the image of PC4 was employed to identify the location and area of crack feature through imaging processing. The results revealed that hyperspectral imaging technique combined with image processing could achieve the qualitative discrimination and quantitative identification of crack feature of fresh jujube, which provided a theoretical reference and basis for develop instrument of discrimination of crack of jujube in further work.
Key words：Hyperspectral imaging;Cracks of fresh jujube;Qualitative discrimination;Quantitative identification
余克强1，赵艳茹1，李晓丽1，张淑娟2，何 勇1* . 基于高光谱成像技术的鲜枣裂纹的识别研究 [J]. 光谱学与光谱分析, 2014, 34(02): 532-537.
YU Ke-qiang1, ZHAO Yan-ru1, LI Xiao-li1, ZHANG Shu-juan2, HE Yong1* . Study on Identification the Crack Feature of Fresh Jujube Using Hyperspectral Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(02): 532-537.
[1] LIU Guang-sheng，LIU Yuan-yuan，MA Shu-ping, et al(刘光生，刘源源，马树平，等). Journal of Shanxi Agriculture Sciences(山西农业科学), 2012, 40(2): 150. [2] Rajkumar P, Wang N, Elmasry G, et al. Journal of Food Engineering, 2012, 108: 194. [3] Gabriel A, Leiva V, Lu R F, et al. Journal of Food Engineering, 2013, 115: 91. [4] Mendoza F, Lu R F, Afriana D W, et al. Postharvest Biology and Technology, 2011, 62: 149. [5] Wang J, Nakano K, Ohashi S, et al. Biosystems Engineering, 2011, 108: 345. [6] Cho B K , Kim M S, et al. Postharvest Biology and Technology, 2013, 76: 40. [7] Wu D, Shi H, Wang S J, et al. Analytic Chimica Acta, 2012, 726: 57. [8] LI Jiang-bo, RAO Xiu-qin, YING Yin-bin, et al(李江波，饶秀勤，应义斌，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(8): 222. [9] Wu D, Sun D W. Talanta, 2013, 111: 39. [10] ZOU Hong-xia, QIN Feng, CHENG Ze-kai, et al(邹洪侠，秦 锋，程泽凯，等). Computer Technology and Development(计算机技术与发展), 2009, 19(6): 109. [11] Li X L, Nie P C, Qiu Z J, et al. Expert Systems with Applications, 2011, 38: 11149. [12] HUANG Wen-qian, CHEN Li-ping, LI Jiang-bo, et al(黄文倩，陈立平，李江波，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(1): 272. [13] Abbott J A, Lu R, Upchurch B L, et al. Horticultural Review, 1997，(20): 1. [14] ElMasry G, Wang N, Vigneault C, et al. LWT-Food Science and Technology, 2008, 41: 337. [15] He H J, Wu D, Sun D W. Innovative Food Science and Emerging Technologies, 2013, http://dx.doi.org/10.1016/j.ifset.2013.02.009.
