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基于高光谱成像技术的鲜枣裂纹的识别研究
引用本文:余克强,赵艳茹,李晓丽,张淑娟,何勇. 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(2): 532-537. DOI: 10.3964/j.issn.1000-0593(2014)02-0532-06
作者姓名:余克强  赵艳茹  李晓丽  张淑娟  何勇
作者单位:1. 浙江大学生物系统工程与食品科学学院,浙江 杭州 310058
2. 山西农业大学工学院,山西 太谷 030801
基金项目:国家“十二五”科技支撑计划课题项目(2011BAD21B04)和国家自然科学基金项目(31071332)资助
摘    要:裂纹是衡量鲜枣品质的重要指标之一,果皮裂纹加速鲜枣的腐烂,导致鲜枣货架期的缩短,严重降低鲜枣的经济价值。采用高光谱成像技术在380~1 030 nm波段范围内对鲜枣裂纹的位置及大小信息特征进行快速识别。选用偏最小二乘回归(PLSR)、连续投影法(SPA)和全波段图像主成分分析(PCA),得到鲜枣裂纹相关的敏感波段。然后利用选取的鲜枣裂纹的敏感波段对建模集的132个样本建立最小二乘支持向量机(LS-SVM)判别模型,并对预测集的44个样本进行判别。对PLSR-LS-SVM,SPA-LS-SVM和PCA-LS-SVM判别模型采用ROC曲线进行评判,得出PLSR-LS-SVM模型对鲜枣裂纹定性判别的结果(area=1,std=0)最佳。选取PLSR回归系数挑选出的5条鲜枣裂纹敏感波段(467,544,639,673和682 nm)对应的单波段图像进行主成分分析,其中将主成分PC4的图像结合图像处理技术,最终识别出鲜枣裂纹的位置、大小信息。结果表明,采用高光谱成像技术结合光谱图像处理可以实现鲜枣裂纹定性判别和定量识别的研究,为进一步开发相关仪器的研究提供理论方法和依据。

关 键 词:高光谱成像技术  鲜枣裂纹  定性判别  定量识别   
收稿时间:2013-05-06

Study on Identification the Crack Feature of Fresh Jujube Using Hyperspectral Imaging
YU Ke-qiang,ZHAO Yan-ru,LI Xiao-li,ZHANG Shu-juan,HE Yong. Study on Identification the Crack Feature of Fresh Jujube Using Hyperspectral Imaging[J]. Spectroscopy and Spectral Analysis, 2014, 34(2): 532-537. DOI: 10.3964/j.issn.1000-0593(2014)02-0532-06
Authors:YU Ke-qiang  ZHAO Yan-ru  LI Xiao-li  ZHANG Shu-juan  HE Yong
Affiliation:1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China2. 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.
Keywords:Hyperspectral imaging  Cracks of fresh jujube  Qualitative discrimination  Quantitative identification
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