|本期目录/Table of Contents|

[1]刘 军,吴梦婷,谭正林,等.近红外光谱无损检测技术中数据的分析方法概述[J].武汉工程大学学报,2017,39(05):496-502.[doi:10. 3969/j. issn. 1674-2869. 2017. 05. 001]
 LIU Jun,WU Mengting,TAN Zhenglin,et al.Overview of Data Analysis Methods in Near-Infrared Spectroscopy Nondestructive Testing[J].Journal of Wuhan Institute of Technology,2017,39(05):496-502.[doi:10. 3969/j. issn. 1674-2869. 2017. 05. 001]
点击复制

近红外光谱无损检测技术中数据的分析方法概述(/HTML)
分享到:

《武汉工程大学学报》[ISSN:1674-2869/CN:42-1779/TQ]

卷:
39
期数:
2017年05期
页码:
496-502
栏目:
机电与信息工程
出版日期:
2017-12-19

文章信息/Info

Title:
Overview of Data Analysis Methods in Near-Infrared Spectroscopy Nondestructive Testing
文章编号:
20170516
作者:
刘 军13吴梦婷13谭正林2李 威13
1. 智能机器人湖北省重点实验室(武汉工程大学),湖北 武汉 430205; 2. 湖北经济学院烹饪与营养学系,湖北 武汉 430205; 3. 武汉工程大学计算机科学与工程学院,湖北 武汉 430205
Author(s):
LIU Jun13WU Mengting13TAN Zhenglin2LI Wei13
1.Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology), Wuhan 430205, China; 2.Department of Cuisine and Nutrition Education, Hubei University of Economics, Wuhan 430205, China; 3. School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China
关键词:
近红外光谱无损检测数据分析方法
Keywords:
near-infrared spectroscopy nondestructive testing data analysis methods
分类号:
R857.3
DOI:
10. 3969/j. issn. 1674-2869. 2017. 05. 001
文献标志码:
A
摘要:
近红外光谱无损检测技术可用于品种鉴别与农产品的定性或者是定量的分析工作. 本文介绍了近红外光谱的基本原理及各类近红外光谱分析方法. 近红外光谱无损检测技术中数据分析方法是通过光谱定量分析找到光谱以及对应浓度的内在关系,建立相应的数学模型. 这些方法主要有偏最小二乘回归、主成分分析法、BP神经网络算法、支持向量机、K最近邻分类算法和线性判别分析法等. 通过这些分析模型的对比,研究表明:支持向量机将是近红外光谱数据分析方法未来一个重要的研究方向.
Abstract:
Near-infrared spectroscopy nondestructive testing technology can be used for variety identification and the qualitative or quantitative analysis of agricultural products. The basic principle of near-infrared spectroscopy and the methods of near-infrared spectrum analysis were introduced. The data analysis methods in near-infrared nondestructive testing technology aim at finding the relationship between the spectrum and the corresponding concentration through the quantitative analysis of the spectrum, and establishing the corresponding mathematical model, which mainly include partial least squares regression, principal component analysis, back propagation artificial neural network, support vector machine(SVM), K-Nearest neighbor classification algorithm and linear discriminant analysis. The comparison result of these analytical models show that SVM method may be a future research direction in near infrared spectrum data analysis.

参考文献/References:

[1] 黄瑞娟. 红外光谱技术在食品检测中的应用[J]. 现代测量与实验室管理,2015(1):9-14. HUANG R J. Application of infrared spectroscopy in food detection [J]. Advanced Measurement and Laboratory Management, 2015(1):9-14. [2] MODAREST F, ARAGHINEJAD S. A comparative assessment of support vector machines, probabilistic neural networks, and k-nearest neighbor algorithms for water quality classification[J]. Water Resources Management, 2014, 28(12): 4095-4111. [3] 谭正林,金国粱,吴梦婷,等. 奶油中人工色素检测方法概述[J]. 食品安全质量检测学报, 2017,8(2):468-474. TAN Z L, JIN G L, WU M T, et al. Overview on detection methods of artificial pigments in cream [J]. Journal of Food Safety & Quality, 2017, 8(2):468-474. [4] 章海亮,罗微,杜焱喆. PCA和SPA的近红外光谱识别白菜种子品种研究[J]. 光谱学与光谱分析,2016,36(11):3536-3541. ZHANG H L, LUO W, DU Y Z. Discrimination of varieties of cabbage with near infrared spectra based on PCA and SPA[J]. Spectroscopy and Spectral Analysis, 2016,36(11):3536-3541. [5] MAUER L J, CHERNYSHOVA A A, HIATT A N, et al. Melamine detection in infant formula powder using near-and mid-infrared spectroscopy[J]. Journal of Agricultural and Food Chemistry. 2009, 57(10): 3974-3980. [6] 阎吉祥,王茜蒨,黄志文,等. 基于主成分分析和人工神经网络的激光诱导击穿光谱塑料分类识别方法研究[J]. 光谱学与光谱分析,2012(12):3179-3182. YAN J X,WANG Q Q, HUANG Z W, et al. Classification of plastics with laser-induced breakdown spectroscopy based on principal component analysis and artificial neural network model[J]. Spectroscopy and Spectral Analysis, 2012(12): 3179-3182. [7] 刘洪林. 基于近红外光谱技术(NIRS)对工夫红茶含水率、游离态氨基酸、茶多酚品质成分评价研究[J]. 食品工业科技,2016,37(12):67-70. LIU H L. Research to moisture content,free form amino acids, polyphenols quality ingredients of Kungfu black tea by near infrared spectroscopy[J]. Science and Technology of Food Industry, 2016,37(12):67-70. [8] 徐可欣,苗静,曹玉珍,等. 基于二维相关近红外谱参数化及BP神经网络的掺杂牛奶鉴别[J]. 光谱学与光谱分析,2013,33(11):3032-3035. XU K X,MIAO J, CAO Y Z, et al. Identification of adulterated milk based on two-dimensional correlation near-infrared spectra parameterization and BP neural network[J]. Spectroscopy and Spectral Analysis, 2013, 33(11):3032-3035. [9] WANG Y S, LI Q Y. Application study on assistive movement training using BP neural network[C]//Internation Conrerence on Computational Science and Engineering. Paris: Atlantis Press, zeger karssen, 2015:274-277. [10] MUTLU A C, BOYACI I H, GENIS H E, et al. Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks[J]. European Food Research & Technology,2011, 233(2): 267-274. [11] 白京,彭彦昆,王文秀. 基于可见近红外光谱玉米种子活力的无损检测方法[J]. 食品安全质量检测学报,2016,7(11):4472-4477. BAI J, PENG Y K, WANG W X. Discrimination of vitality of maize seeds based on near visible infrared spectroscopy [J]. Journal of Food Safety and Quality, 2016, 7(11):4472-4477. [12] 王小燕,王锡昌,刘源,等. 基于SVM算法的近红外光谱技术在鱼糜水分和蛋白质检测中的应用[J]. 光谱学与光谱分析,2012,32(9):2418-2421. WANG X Y, WANG X C, LIU Y, et al. Application of near infrared spectroscopy technique based on support vector machine in the measurement of moisture and protein contents in surimi [J]. Spetroscopy and Spectral Analysis, 2012, 32(9):2418-2421. [13] 张海云,彭彦昆,王伟,等. 基于光谱技术和支持向量机的生鲜猪肉水分含量快速无损检测[J]. 光谱学与光谱分析,2012,32(10):2794-2798. ZHANG H Y, PENG Y K, WANG W, et al. Rapid nondestructive detection of water content in fresh pork based on spectroscopy technique combined with support vector machine [J]. Spetroscopy and Spectral Analysis, 2012,32(10):2794-2798. [14] 吴习宇,赵国华,祝诗平. 近红外光谱分析技术在肉类产品检测中的应用研究进展[J]. 食品工业科技,2014,35(1):371-374,380. WU X Y, ZHAO G H, ZHU S P. Study on the application of near infrared spectroscopy in the meat quality evaluation[J]. Science and Technology of Food Industry, 2014,35(1):371-374,380. [15] 陈彬,刘阁,张贤明. 连续投影算法的润滑油中含水量的近红外光谱分析[J]. 红外与激光工程,2013(12):3168-3174. CHEN B, LIU G, ZHANG X M. Analysis on near infrared spectroscopy of water content in lubricating oil using successive projections algorithm [J]. Infrared and Laser Engineering, 2013(12):3168-3174. [16] ZHANG N, SHETTY D. An effective LS-SVM based approach for surface roughness prediction in machined surfaces [J]. Neurocomputing, 2016, 198: 35-39. [17] 乔延江,徐冰,王星,等. 基于遗传算法的多目标最小二乘支持向量机在近红外多组分定量分析中的应用[J]. 光谱学与光谱分析,2014(3):638-642. QIAO Y J, XU B, WANG X, et al. Genetic algorithm based multi-objective least square support vector machine for simultaneous determination of multiple components by near infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 2014(3):638-642. [18] JI J, ZHANG C S, GUI Y L, et al. New observations on the application of LS-SVM in slope system reliability analysis[J]. Journal of Computing in Civil Engineering, 2016(10):601-602. [19] 刘应东,牛惠民. 基于K-最近邻图的小样本KNN分类算法[J]. 计算机工程,2011,37(9):198-200. LIU Y D,NIU H M. KNN classification algorithm based on K-nearest neighbor graph for small sample [J]. Computer Engineering, 2011, 37(9):198-200. [20] 倪力军,钟霖,张鑫,等. 近红外光谱结合非线性模式识别方法进行牛奶中掺假物质的判别[J]. 光谱学与光谱分析,2014(10):2673-2678. NI L J,ZHONG L,ZHANG X, et al. Identification of adulterants in adulterated milks by near infrared spectroscopy combined with non-linear pattern recognition methods [J]. Spectroscopy and Spectral Analysis, 2014 (10):2673-2678. [21] 伍学千,廖宜涛,樊玉霞,等. 连续投影算法在猪肉PH值无损检测中的应用[J]. 农业工程学报, 2010,26(增刊):379-383. WU X Q, LIAO Y T, FAN Y X, et al. Application of successive projections algorithm to nondestructive determination of pork PH value [J]. Transactions of the Chinese Society of Agricultural Engineering 2010, 26 (Suppl.): 379-383. [22] CHENG J H, SUN D W, PU H, et al. Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle[J]. Food Chemistry, 2016(197): 855-863. [23] 应义斌,介邓飞,谢丽娟,等. 近红外光谱变量筛选提高西瓜糖度预测模型精度[J]. 农业工程学报,2013,29(12):264-270. YING Y B, JIE D F, XIE L J, et al. Improving accuracy of prediction model for soluble solids content of watermelon by variable selection based on near-infrared spectroscopy[J]. Transaction of the Chinese Society of Agricultural Engineering, 2013, 29(12):264-270. [24] 王福杰,吴远远,陆辉山,等. 基于近红外光谱技术的老陈醋可溶性固形物定量分析[J]. 中国酿造, 2016(8):69-72. WANG F J, WU Y Y,LU H S, et al. Quantitative analysis of soluble solids content in mature vinegar based on near infrared spectroscopy technology [J]. China Brewing, 2016(8):69-72. [25] 孙剑伟,王宏涛. 基于BP神经网络和SVM的分类方法研究[J]. 软件,2015,36(11):96-99. SUN J W, WANG H T. Research on the classification method based on BP neural network and SVM[J]. Software, 2015,36(11):96-99. [26] 冯先成,李寒,周密,等. 基于前馈神经网络的智慧城市空巢老人识别[J]. 武汉工程大学学报,2015,37(10):36-39. FENG X C, LI H, ZHOU M, et al. Recognition of empty-nest elders in intelligent city based on feedforward neural network[J]. Journal of Wuhan Institute of Technology, 2015,37(10):36-39.

相似文献/References:

备注/Memo

备注/Memo:
收稿日期:2017-05-20 基金项目:湖北省食品药品监督管理局项目(201610+13); 湖北省智能机器人重点实验室开放基金( HBIR 201608);武汉工程大学研究生创新基金(CX2016063) 作者简介:刘 军,博士,副教授. E-mail:liujun@wit.edu.cn
更新日期/Last Update: 2017-10-26