近红外光谱分析技术在花生原产地溯源中的应用

doi:10.7506/spkx1002-6630-201306037

食品科学 ›› 2013, Vol. 34 ›› Issue (6): 167-170.doi: 10.7506/spkx1002-6630-201306037

近红外光谱分析技术在花生原产地溯源中的应用

张龙1,2，潘家荣1，朱诚1,*

1.中国计量学院生命科学学院，浙江杭州 310018；2.浙江大学生命科学学院，植物生理学与生物化学国家重点实验室，浙江杭州 310058

收稿日期:2011-12-01 修回日期:2013-02-01 出版日期:2013-03-25 发布日期:2013-03-01
通讯作者: 张龙 E-mail:10907017@zju.edu.cn
基金资助:
“十一五”国家科技支撑计划：食品安全关键技术--粮油、蔬果等安全控制技术的研究;浙江省重点科技创新团队--农产品安全标准与检测技术创新团队

Tracing Geographical Origin of Peanuts by Near Infrared Spectroscopy

ZHANG Long 1,2，PAN Jia-rong1，ZHU Cheng1,*

1. College of Life Sciences, China Jiliang University, Hangzhou 310018, China；2. State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China

Received:2011-12-01 Revised:2013-02-01 Online:2013-03-25 Published:2013-03-01

摘要/Abstract

摘要： 采用近红外光谱结合化学计量学方法对不同省份来源的花生样品进行溯源研究。首先花生近红外光谱通过标准正态变换加去趋势化预处理降噪、主成分分析降维和小波转换降噪降维两种处理，然后结合线性判别分析、贝叶斯判别分析和k最近邻分析3种判别模型对不同省份来源地花生进行判别。通过分析最优判别组合结果表明，小波转换结合k最近邻分析对花生产地分类效果最好，原始正确分类率为100.0%；交叉验证正确分类率为55.9%。初步实现了花生产地判别，但模型的性能仍有待提高。

关键词: 花生, 近红外光谱, 原产地, 主成分分析, 小波转换, 判别分析

Abstract: Near infrared spectroscopy combined with chemometrics was used to trace geographical origin of peanuts from different provinces of China. The original spectra were subjected to noise reduction by standard normal variate (SNV) and de-trend (DT) transformation (SNV+DT), and dimension reduction by principal component analysis; wavelet transform (WT) was also used for noise reduction and data compression. The extracted features were processed with linear discriminant analysis, Bayes discriminant analysis and k-nearest neighbors to discriminate the geographical origin of peanuts produced in different provinces of China. The results showed that WT and k-nearest neighbors present the best classification. Approximately 100.0% samples were correctly classified in original test, and 55.9% samples were correctly classified in cross-validation test. As a result, the geographical origin of peanuts from different provinces of China was discriminated. However, the performance of the discrimination models still needs to be improved.

Key words: peanut, near infrared spectroscopy, geographical origin, principal component analysis, wavelet transform, discriminant analysis

中图分类号:

TS201.2

张龙1,2，潘家荣1，朱诚1,*. 近红外光谱分析技术在花生原产地溯源中的应用[J]. 食品科学, 2013, 34(6): 167-170.

参考文献

[1] 宋玉智. 我国出口花生质量安全分析. 农业经济, 2011, 6:92.
[2] 中华人民共和国主席令第9 号. 中华人民共和国食品安全法[S].
[3] 严衍禄，赵龙莲。近红外光谱分析基础与应用。北京：中国轻工业出版社，2005.
[4] CEN HY, HE Y. Theory and application of near infrared reflectance spectroscopy in determination food quality. Trends in Food Science and Technology, 2007, 18: 72-83.
[5] 赵杰文, 陈全胜, 张海东. 近红外光谱分析技术在茶叶鉴别中的应用研究[J]. 光谱学与光谱分析, 2006, 26(9): l601-1604.
[6] 赵海燕, 郭波莉, 魏益民, 张波, 孙淑敏, 严军辉, 张磊. 近红外光谱对小麦产地来源的判别分析. 中国农业科学, 2011, 44(7):1451-1456.
[7] 陈永明, 林萍, 何勇. 基于遗传算法的近红外光谱橄榄油产地鉴别方法研究. 光谱学与光谱分析. 2009, 29(3): 671-674.
[8] 李勇, 魏益民, 潘家荣, 郭波莉. 基于FTIR指纹光谱的牛肉产地溯源技术研究. 光谱学与光谱分析, 2009, 29(13): 647-651.
[9] ZHU XR, LI SF, SHAN Y, ZHANG ZY, LI GY, SU DL, LIU F. (2010). Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics, Journal of Food Engineering, 101: 92 - 97.
[10] 庄小丽, 相玉红, 强洪, 张卓勇, 邹明强, 张孝芳. 近红外光谱和化学计量学方法用于橄榄油品质分析与掺杂量检测.光谱学与光谱分析, 2009, 30(4): 933-936.
[11] COPPA M, FERLAY A, LEROUX C, JESTIN M, CHILLIARD Y, MARTIN B, ANDUEZA D. Prediction of milk fatty acid composition by near infrared reflectance spectroscopy. International Dairy Journal, 2010, 20: 182-189.
[12] 肖昕, 李晓方. 应用近红外透射光谱法测定水稻种子直链淀粉含量的初步研究. 中国农业科学, 2004, 37(11): 1709-1712.
[13] 王海莲, 万建民, 万向元, 胡培松, 翟虎渠. 稻米脂肪含量近红外光谱分析技术研究. 中国农业科学, 2005, 38 (08): 1540-1546.
[14] 李君霞, 张洪亮, 严衍禄, 闵顺耕, 李自超. 水稻蛋白质近红外定量模型的创建及在育种中的应用. 中国农业科学, 2006, 39(04): 836-841.
[15] 刘辉军, 吕进, 张维刚, 陈华才. 茶叶中茶多酚含量的近红外光谱检测模型研究. 红外技术29(7): 429-432.
[16] 宋丽华, 刘立峰, 陈焕英, 穆国俊, 卢萍萍. 花生籽仁蛋白质含量近红外光谱模型的建立. 中国农学通报, 2011, 27(15): 85-89.
[17] 禹山林, 朱雨杰, 闵平, 杨庆利, 曹玉良, 王传堂, 刘旭, 周学秋. 傅立叶近红外漫反射非破坏性测定花生种子主要脂肪酸含量. 花生学报, 2010, 39(1): 11-14.
[18] BARNES RJ, DHANOA MS, LISTER SJ. Correction to the description of standard normal variate (SNV) and de-trend (DT) transformations in practical spectroscopy with applications in food and beverage analysis, second ed.. J. Near Infrared Spectrosc., 1993, 1: 185–186.
[19] BARNES RJ, DHANOA MS, LISTER SJ. Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra. Appl. Spectrosc., 1989, 43, 772-777.
[20] COVER TM, HART PE. Nearest Neighbor Pattern Classification. Institute of Electrical and Electronics Engineers Transactions on Information Theory, 1967, 13(1): 21-27.
[21] LEUNG AKM, CHAU FT, GAO JB, SHIH TM. Application of wavelet transform in infrared spectrometry: spectral compression and library search. Chemometrics Intell. Lab. Syst., 1998, 43: 69.
[22] KACHIGAN SK. 1986. Statistical Analysis. Radius Press, New York.
[23] TAMHANE AC, IORDACHE C, MAH RSH. A Bayesian approach to gross error detection in chemical process data. Part I: Model development. Chemometrics and Intelligent Laboratory Systems, 1988, 4: 33–45.
[24] ARMSTRONG N, HIBBERT DB. An introduction to Bayesian methods for analyzing chemistry data: Part 1: An introduction to Bayesian theory and methods Original Research Article Chemometrics and Intelligent Laboratory Systems, 2009, 97(2): 194-210.
[25] KITTLER J, ROLI F. Multiple Classifier System, Second International Workshop, MCS 2001 Cambridge, UK, July 2 – 4, 2001, Proceedings, Lecture Notes in Computer Science, vol. 2096, Springer, Berlin, Heidelberg, New York, 2001, ISBN 3-540-42284-6.
[26] DERDE MP, BUYDENS L, GUNS C, MASSART DL, HOPKE PK. Comparison of rule-building expert systems with pattern recognition for the classification of analytical data. Analytical Chemistry, 1987, 59(14): 1868–1871.
[27] 张俊民, 蔡凤歧, 何同康. 中国的土壤. 商务印书馆, 1995.

近红外光谱分析技术在花生原产地溯源中的应用

Tracing Geographical Origin of Peanuts by Near Infrared Spectroscopy

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刘玉兰，舒垚，孙国昊，马宇翔，姜元荣，陈宁. 花生品种对花生酱风味及综合品质的影响[J]. 食品科学, 2021, 42(9): 15-21.
[2]	程超，夏兰欣，杜芬妮，李伟，田成. 基于9 种风味物质的利川红SPME优化及PLS-DA风味识别[J]. 食品科学, 2021, 42(6): 215-222.
[3]	梁慧珍，许兰杰，余永亮，谭政委，杨红旗，董薇，李磊，李春明，刘新梅，张收良. 红花籽油中脂肪酸组成评价与分析[J]. 食品科学, 2021, 42(6): 244-249.
[4]	蔡玮琦，左雯雯，陆杨，黄胜良，李存玉，郑云枫，彭国平. 红糖、白砂糖、赤砂糖与黑糖的HPLC指纹图谱与化学模式识别[J]. 食品科学, 2021, 42(6): 250-255.
[5]	周红菲，吴志华，张英，陈红兵. 质谱法分析烘焙对花生过敏原Ara h 1潜在致敏性的影响[J]. 食品科学, 2021, 42(3): 1-6.
[6]	王娅，张存存，付玉叶，张凡，王颉，王文秀. 融合图谱特征信息的明虾挥发性盐基氮含量无损检测[J]. 食品科学, 2021, 42(2): 283-290.
[7]	周慧敏，张顺亮，郝艳芳，陈松，潘晓倩，吴倩蓉，李素，朱宁，乔晓玲，赵冰. HS-SPME-GC-MS-O结合电子鼻对坨坨猪肉主体风味评价分析[J]. 食品科学, 2021, 42(2): 218-226.
[8]	王贺，赵玉红，杨凯. HS-SPME-GC-MS结合电子鼻对10 个品系红松籽油挥发性物质分析比较[J]. 食品科学, 2021, 42(18): 178-184.
[9]	王俊娟，李欣芮，陈成，孙善峰，刘桂蓉，车会莲. 花生主要过敏原Ara h 1线性B细胞表位的预测及鉴定[J]. 食品科学, 2021, 42(17): 106-112.
[10]	符成刚，刘文玉，陈友志，李玉梅，李欢康，周靖，张孔雀，魏长庆. 加热温度对新疆马脂理化性质、脂肪酸及挥发性风味化合物的影响[J]. 食品科学, 2021, 42(16): 54-60.
[11]	李欢康，杨佳玮，刘文玉，魏长庆. 不同工艺核桃油挥发性物质比对及关键香气成分表征[J]. 食品科学, 2021, 42(16): 185-192.
[12]	王丽艳，荆瑞勇，郭永霞，王鑫淼，赵行健，宋维民，杨佳霓，卫佳琪. 基于氨基酸含量的市售14 种食用蘑菇的综合评价[J]. 食品科学, 2021, 42(16): 203-208.
[13]	王世成，李国琛，王莹，李波，高凝轩，孙辞，宋阳，王颜红. 基于氮稳定同位素比例质谱和液相色谱-高分辨质谱的有机番茄鉴别[J]. 食品科学, 2021, 42(14): 159-164.
[14]	王华杰，滑金杰，余勤艳，江用文，王近近，杨艳芹，邓余良，袁海波. 基于IRAE-HS-SPME/GC-MS分析杀青方式对绿茶栗香形成的影响[J]. 食品科学, 2021, 42(14): 209-217.
[15]	卢洁，田婧，梁振华，王金梅，康文艺，马常阳，李昌勤. 近红外光谱法快速测定香菇总糖含量[J]. 食品科学, 2021, 42(12): 189-194.