文章摘要
尚静,张艳,孟庆龙.紫外/可见光谱技术无损检测苹果的挤压损伤[J].包装工程,2019,40(13):25-30.
SHANG Jing,ZHANG Yan,MENG Qing-long.Detection of Pressed Damage on Apples Based on UV/VIS Spectroscopy[J].Packaging Engineering,2019,40(13):25-30.
紫外/可见光谱技术无损检测苹果的挤压损伤
Detection of Pressed Damage on Apples Based on UV/VIS Spectroscopy
投稿时间:2019-03-18  修订日期:2019-07-10
DOI:10.19554/j.cnki.1001-3563.2019.13.004
中文关键词: 光谱技术  无损检测  模式识别  损伤苹果
英文关键词: spectroscopy technology  nondestructive detection  pattern recognition  pressed apple
基金项目:国家自然科学基金(61505036);贵州省科技厅联合基金(黔科合LH字[2014]7174号);贵州省教育厅青年科技人才成长项目(黔教合KY字[2018]290);贵州省普通高等学校工程研究中心项目(黔教合KY字[2016]017);贵阳市财政支持贵阳学院学科与硕士点建设项目【SY-2019】
作者单位
尚静 贵阳学院贵阳 550005 
张艳 贵阳学院贵阳 550005 
孟庆龙 贵阳学院贵阳 550005 
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中文摘要:
      目的 通过紫外/可见光谱技术结合模式识别算法,建立挤压损伤苹果的Fisher识别模型、K最近邻(KNN)识别模型和偏最小二乘判别分析(PLS-DA)识别模型。方法 以挤压损伤苹果和无损苹果为研究对象,采用光谱仪采集2种苹果的光谱反射率,综合比较不同光谱预处理方法(二阶微分(SD)、标准正态变换(SNV)和多元散射校正(MSC))对各模型识别效果的影响,并利用主成分分析方法(PCA)对预处理后的光谱数据进行降维,并提取能反映损伤苹果的特征光谱。结果 采用主成分分析法选择了累计贡献率超过99%的前7个主成分(P1—P7)作为特征光谱数据,有效地实现了光谱数据的降维;二阶微分对光谱反射率预处理的效果最好;3种判别模型均能满足实际要求,且SD+Fisher和SD+PLS-DA识别模型对校正集和预测集样本的总正确识别率均高达100%。结论 研究结果有助于实现挤压损伤苹果的快速识别。
英文摘要:
      The work aims to establish the discriminant models of Fisher, K nearest neighbor (KNN) and partial least-square discriminant analysis (PLS-DA) for the pressed apples based on ultravioletradiation/visible spectroscopy technology combined with pattern recognition algorithm. With pressed apple and intact apple as the study object, the optical fiber spectrum system was used to acquire the spectral reflectance of these two kinds of apple. The effect of different spectral pretreatment methods (Second Derivation (SD), Standard Normal Variation (SNV) and Multi-Scatter Calibration (MSC)) on the recognition effect of each model was comprehensively compared, and the principal component analysis (PCA) was applied for the dimensionality reduction of the pretreated spectral data, and the characteristic spectrum that could reflect the pressed apple was extracted. The results showed that the first 7 principal components (P1—P7) with cumulative contribution rate of 99% were selected as the characteristic spectral data by the principal component analysis, and the dimensionality reduction of the spectral data was well realized. The preprocessing effect of Second Derivation on spectral reflectivity was the best, and three models could all meet the practical requirements, especially both SD+Fisher and SD+PLS-DA models had the optimal recognition performance with a total correct recognition rate of 100% for the samples of calibration set and prediction set. The research results are conductive to the fast recognition of pressed apples.
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