文章摘要
尚静,孟庆龙,张艳.基于紫外/可见光谱技术的李子硬度无损检测[J].包装工程,2020,41(3):51-56.
SHANG Jing,MENG Qing-long,ZHANG Yan.Nondestructive Detection of Firmness of Plums Based on UV/VIS Spectroscopy[J].Packaging Engineering,2020,41(3):51-56.
基于紫外/可见光谱技术的李子硬度无损检测
Nondestructive Detection of Firmness of Plums Based on UV/VIS Spectroscopy
投稿时间:2019-06-12  修订日期:2020-02-10
DOI:10.19554/j.cnki.1001-3563.2020.03.008
中文关键词: 李子  硬度  光谱技术  连续投影算法  竞争性自适应重加权算法  无损检测
英文关键词: plums  firmness  spectroscopy technology  successive projection algorithm  competitive adaptive reweighted sampling  nondestructive detection
基金项目:国家自然科学基金(61505036);贵州省科技计划(黔科合基础[2019]1010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);贵阳市科学技术局-贵阳学院科技专项资金(GYU-KYZ〔2019—2020〕PT05-01)
作者单位
尚静 贵阳学院贵阳 550005 
孟庆龙 贵阳学院贵阳 550005 
张艳 贵阳学院贵阳 550005 
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中文摘要:
      目的 探究采用紫外/可见光谱技术结合化学计量学预测李子硬度的可行性。方法 以“红”李子和“青”李子为研究对象,采用光谱采集系统获取李子样本的平均光谱;采用标准正态变换对原始光谱数据进行预处理,并利用连续投影算法(SPA)和竞争性自适应重加权算法(CARS)从全光谱的1024个波长中分别提取2个(513.04 nm和636.72 nm)和10个(230.01,244.67,274.71,287.66,290.90,300.59,311.78,423.08,515.39,631.31 nm)特征波长;分别建立基于全光谱和提取的特征波长预测李子硬度的误差反向传播(BP)网络模型。结果 将采用SPA和CARS特征波长选择方法提取的特征变量作为BP网络输入,明显提升了BP网络模型的运行效率,且SPA-BP网络模型具有相对较好的李子硬度预测能力(rp=0.695,预测样本集均方根误差为1.610 kg/cm2)。结论 采用紫外/可见光谱技术结合特征波长提取方法可实现李子硬度的快速无损检测。
英文摘要:
      The work aims to explore the feasibility of predicting firmness of plums by ultraviolet radiation/visible spectroscopy technology combined with chemometrics. The spectra acquisition system was used to collect the average spectral reflectance of “Red” and “Green” plums. The standard normal variation (SNV) was used to preprocess original spectral reflectance. Then the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) method were used to select characteristic wavelengths of 2 (513.04 nm and 636.72 nm) and 10 (230.01, 244.67, 274.71, 287.66, 290.90, 300.59, 311.78, 423.08, 515.39 and 631.31 nm) from 1024 wavelengths, respectively. An error back propagation (BP) network model was established based on full spectra and selected characteristic wavelengths for predicting the firmness of plums. The characteristic wavelengths extracted by SPA and CARS were used as the input of BP network model, which obviously improved the working efficiency of BP network model, and SPA-BP model had the best ability of predicting firmness of plums (rp=0.695, RMSEP=1.610 kg/cm2). Ultraviolet radiation/visible spectroscopy technology combined with the characteristic wavelength selection methods is effective for rapid nondestructive detection on firmness of plums.
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