基于近红外光谱与PLS-DA的红枣品种识别研究

张景川; 张晓; 白铁成; 石鲁珍

doi:10.13386/j.issn1002-0306.2017.08.005

基于近红外光谱与PLS-DA的红枣品种识别研究

1. 塔里木大学机械电气化工程学院
2. 新疆维吾尔自治区普通高等学校现代农业工程重点实验室
3. 塔里木大学信息工程学院
4. 南疆农业信息化研究中心

基金项目:

国家自然科学基金项目(61501314;41561088;61462074)；应急管理项目(61640413)；

详细信息

作者简介:
张景川 (1977-) , 男, 硕士, 副教授, 研究方向:光学应用, E-mail:1483023190@qq.com。;

石鲁珍 (1972-) , 女, 硕士, 讲师, 研究方向:光谱分析, E-mail:shiluzhen08@yeah.net。;

中图分类号: O657.33;S665.1
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出版历程
- 收稿日期: 2016-10-17

Jujube species identification based on near infrared spectroscopy and PLS-DA

College of Mechanical Electrical Engineering, Tarim University, China Tarim University
Xinjiang Uygur Autonomous Region General Institutes of Higher Education, Key Lab of Modern Agriculture Engineering
College of Information Engineering
Agricultural Information Research Center in Southern Region of Xinjiang

摘要

摘要: 新疆红枣品种繁多,采后红枣在加工过程中需要将其他品种的红枣挑选出,本研究应用近红外光谱分析技术结合偏最小二乘判别分析（PLS-DA）法对新疆红枣品种进行判别。结果表明,采用一阶导数对原始光谱进行预处理,并使用方差分析法选择波长变量结合PLS-DA方法对校正样本建立判别分析模型,其验证集预测结果与实际分类结果的相关系数（RP）均大于0.92,预测标准偏差（RMSEP）都小于0.27,最后模型对验证集中的骏枣、灰枣和冬枣3个品种的识别率都为100%。该结果为新疆红枣品种快速识别提供理论依据。
- 近红外光谱 /
- PLS-DA /
- 品种识别 /
- 红枣 /
- 方差分析
Abstract: There are a variety of jujubes in Xinjang.It's necessary to pick out other varieties of jujubes in jujube processing.This research was conducted to attempt to discriminate jujube varieties by the method that near-infrared spectroscopy combined with partial least squares discriminant analysis ( PLS-DA) method. The discriminatory analysis models of jujube varieties was established by using first derivative ( FD) , the wavelength selection method of deviation analysis and partial least square discriminant analysis ( PLS-DA) .The experimental results showed that the correlation coefficient ( RP) of validation set predictions with the actual classification was greater than 0.92, the standard error of prediction ( RMSE) was less than 0.27.Finally, the model recognition rate for three varieties of Jun-jujube, Hui-jujube and Dong-jujube was 100%.The result offered theory evidences for the varieties identification of jujube in Xinjang.
- near infrared spectroscopy /
- PLS-DA /
- species identification /
- jujube /
- deviation analysis

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参考文献(16)

[1]	郭裕新, 单公华.中国枣[M].上海:上海科学技术出版社, 2010:1-3.
[2]	李林, 倪座山, 张文新, 等.新疆南疆地区红枣产业现状分析及发展战略思考[J].落叶果树, 2008 (3) :34-36.
[3]	彭云发.近红外光谱技术在南疆红枣品质快速无损检测中的应用研究[D].新疆:塔里木大学, 2015.
[4]	褚小立.化学计量学方法与分子光谱分析技术[M].北京:化学工业出版社, 2011:293-295.
[5]	彭云发, 詹映, 彭海根, 等.用遗传算法提取南疆红枣总糖的近红外光谱特征波长[J].食品工业科技, 2015 (3) :303-307.
[6]	彭云发, 罗华平, 王丽, 等.3种不同红枣水分检测方法的比较[J].江苏农业科学, 2016 (1) :308-310.
[7]	石鲁珍, 张景川, 蒋霞, 等.光谱测定南疆鲜冬枣VC含量方法的研究[J].塔里木大学学报, 2015 (4) :93-98.
[8]	周晶, 孙素琴, 李拥军, 等.近红外光谱和聚类分析法无损快速鉴别不同辅料奶粉[J].光谱学与光谱分析, 2009 (1) :110-113.
[9]	张宁, 张德权, 李淑荣, 等.近红外光谱结合SIMCA法溯源羊肉产地的初步研究[J].农业工程学报, 2008, 24 (12) :309-312.
[10]	杨忠, 任海青, 江泽慧.PLS-DA法判别分析木材生物腐蚀的研究[J].光谱学与光谱分析, 2008, 28 (4) :793-796.
[11]	郝勇, 孙旭东, 高荣杰, 等.基于可见近红外光谱与SIMCA和PLS-DA的脐橙品种识别[J].农业工程学报, 2010 (12) :373-377.
[12]	Eva Borràs, Joan Ferré, Ricard Boqué, et al.Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA) [J].Food Chemistry, 2016, 203 (14) :314-322.
[13]	Leonardo Valderrama, Patrícia Valderrama.Nondestructive identification of blue pen inks for documentoscopy purpose using i Phone and digital image analysis including an approach for interval confidence estimation in PLS-DA models validation[J].Chemometrics and Intelligent Laboratory Systems, 2016, 156 (8) :188-195.
[14]	Ewa Szymańska, Edoardo Saccenti, Age K Smilde, et al.Double-check:validation of diagnostic statistics for PLS-DA models in metabolomics studies[J].Metabolomics, 2012, 8 (1) :3-16.
[15]	Zewei Chen, Zhuoyong Zhang, Ruohua Zhu, et al.Diagnosis of patients with chronic kidney disease by using two fuzzy classifiers[J].Chemometrics and Intelligent Laboratory Systems, 2016, 153 (4) :140-145.
[16]	Torres, Carolina A, Lorenzo León, et al.Spectral fingerprints during sun injury development on the tree in Granny Smith apples:A potential non-destructive prediction tool during the growing season[J].Scientia Horticulturae, 2016, 209 (9) :165-172.