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食品研究与开发:2025,46(2):172-177
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基于偏振高光谱成像的南疆冬枣品质检测
(1.塔里木大学 机械电气化工程学院,新疆 阿拉尔 843300;2.新疆维吾尔自治区教育厅普通高等学校现代农业工程重点实验室,新疆 阿拉尔 84330)
Quality Detection of Winter Jujube from South Xinjiang Based on Polarized Hyperspectral Imaging
(1. College of Mechanical and Electronic Engineering,Tarim University,Alar 843300,Xinjiang,China;2. Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region,Alar 843300,Xinjiang,China)
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投稿时间:2023-08-06    
中文摘要: 该文以南疆冬枣为研究对象,同时选取红提葡萄作为验证对象,基于偏振高光谱检测技术,采集900~1 750 nm 冬枣和红提葡萄无偏高光谱和4 个偏振角(0°、45°、90°和135°)样本高光谱数据,将原始光谱进行包络线去除处理,使用竞争性自适应重加权算法进行数据降维,选择最有效的波长。以1 个无偏和4 个偏振方向反射率建立南疆冬枣含水率与可溶性固形物含量(soluble solid content,SSC)的包络线去除-偏最小二乘回归预测模型。与无偏高光谱建模对比,冬枣含水率和SSC 模型预测集相关系数,在偏振角(90°和135°)高光谱时有最优建模效果,其值分别为0.958 8、0.924 3,剩余预测偏差均大于2,红提葡萄建模效果类似。结果表明:部分偏振角高光谱建模精度优于无偏高光谱,冬枣含水率和SSC 都在偏振角(90°和135°)高光谱建模时精度最高。
Abstract:The hyperspectral data (900-1 750 nm) of non-polarization and at four polarization angles (0°,45°,90°,and 135°) were collected for winter jujube from South Xinjiang and red grapes (used for validation).The original spectra were processed by envelope removal. The competitive adaptive reweighted sampling algorithm was used for the dimension reduction of data,and the most effective wavelengths were selected. A partial least squares regression(PLSR) model was established with the reflectance data from one non-polarized and four polarized directions to predict the moisture content and soluble solid content(SSC) in winter jujube from South Xinjiang. Compared with non-polarized hyperspectral modeling,the models with data from spectral data with the polarization angles of 90° and 135° showed improved performance in predicting the moisture content and SSC of winter jujube,achieving correlation coefficients of 0.958 8 and 0.924 3,respectively,and remaining prediction deviations greater than 2. Similar results were obtained in the modeling for red grapes. The findings demonstrated that polarized hyperspectral imaging outperformed non-polarized hyperspectral imaging in modeling. The modeling with the data from hyperspectral imaging at the polarization angles of 90° and 135° had the highest accuracy in predicting the moisture content and SSC of winter jujube.
文章编号:202502022     中图分类号:    文献标志码:
基金项目:国家自然科学基金项目(11964030)
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