应用导数荧光光谱和概率神经网络鉴别合成色素

陈国庆; 吴亚敏; 魏柏林; 刘慧娟; 高淑梅; 孔艳; 朱拓

doi:10.7498/aps.59.5100

摘要

实验测量了食品色素胭脂红、苋菜红、诱惑红和工业色素苏丹红Ⅳ溶液分别在波长为300,400,440和380 nm的光激发下产生的荧光光谱.对这4种红色素的各8个溶液样本选取60个发射波长值所对应的荧光强度作为网络特征参数,训练、建立概率神经网络.据此,对32个色素溶液样本进行种类识别.为解决原始荧光光谱重叠造成识别准确率不高的问题,应用导数荧光光谱,将二阶导数光谱数据作为网络特征参数,建立网络,进行识别,识别准确率达100%.由此,提出了应用二阶导数荧光光谱结合概率神经网络对合成色素方便、快捷、准确地进行种

关键词:

Abstract

Excited respectively by the light with wavelengths of 300, 400, 440 and 380 nm, the fluorescence spectra of synthetic food color ponceau 4R, amaranth, allurea red and industrial dye Sudan Ⅳ have been measured. For each sample, 60 emission wavelength values were selected. The fluorescence intensity corresponding to the selected wavelength was used as the network characteristic parameters, a probabilistic neural network for kind identification was trained and constructed. It was employed to identify the 32 kinds of color solution samples. Because the fluorescence spectra of these colors overlap, the identification rate is low. In order to solve this problem, a derivative fluorescence spectroscopy was introduced. The derivative fluorescence data was used as the network characteristic parameters, a probabilistic neural network was constructed and was employed to identify colors. The identification rate is up to 100%. Based on this, a new method is presented, which combines the derivative fluorescence spectroscopy and probabilistic neural network, and can identify synthetic colors easily, quickly and accurately. This method can provide support for food safety supervision and management.

Keywords:

作者及机构信息

1.
江南大学理学院,无锡 214122

基金项目: 江苏省自然科学基金(批准号:BK2009066)、高等学校博士学科点专项科研基金(批准号:200802950005)和江苏省教育厅基金(批准号:JH08-18,CX08B-088Z) 资助的课题.

Authors and contacts

1.
School of Science, Jiangnan University, Wuxi 214122, China

参考文献

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施引文献

[1]	Pang N N, Bai Y, Liu H W 2009 Univ. Chem. 24 24 (in Chinese) [庞楠楠、白玉、刘虎威 2009 大学化学 24 24]
[2]	Ma W, Fu L, Liu S F, Zeng Q C, Xu S W, Wang H B 2008 Sci. Technol. Food Indust. 29 198 (in Chinese)[马微、付丽、刘世福、曾庆才、许世伟、王海波 2008 食品工业科技 29 198]
[3]	1188 295
[4]	Zhao G H, Zhang P Z 2009 Food Res. Develop. 30 135 (in Chinese)[赵广河、张培正 2009 食品研究与开发 30 135]
[5]	Florian C S, Jan T, Reinhold C 2006 Food Chem. 94 296
[6]	Gergely C K, Esther F, Tibor C, Juan A V 2000 J. Chromatograph. A896 61
[7]	Sacide A, Suna T 2002 J. Food Compos. Analys. 15 667
[8]	Pedro L L A, Leticia L M, Luis M D L R 2002 Electroanalysis 14 197
[9]	Erdal D, Emine B, Murat K, Feyyaz O 2002 Talanta 58 579
[10]	Katerina S M, Christina F S, Nikolaos S T 2007 Analyt. Chim. Acta 583 103
[11]	Joseph S 2000 J. Chromatograph. A880 129
[12]	Panagiotis A, Charlotta T 2008 Talanta 74 1218
[13]	Chailapakul O, Wonsawat W, Siangproh W, Grudpan K, Zhao Y F, Zhu Z W 2008 Food Chem. 109 876
[14]	Florin S, Augustin C M, Costel S 2008 J. Chromatograph. A
[15]	María J C, Agustina V S, Natalia E L, Mariano G, Maria S D N, Beatriz S F B, Héctor C G 2009 J. Chromatograph. A 1216 7063
[16]	Chen G Q, Wu Y M, Wang J, Zhu T, Gao S M 2009 Spectrosc. Spectr. Analys. 29 2518 (in Chinese)[陈国庆、吴亚敏、王俊、朱拓、高淑梅 2009 光谱学与光谱分析 29 2518]
[17]	Liu Y, Song C Y, He W L, Luo X S, Lu J, Ni X W 2007 Acta Phys. Sin. 56 2962 (in Chinese) [刘莹、宋春元、何文亮、骆晓森、陆建、倪晓武 2007 56 2962]
[18]	Liu Y, Song C Y, Luo X S, Lu J, Ni X W 2007 Chin. Phys. 16 1300
[19]	Peng Y, Li S F, Zhang Q Y, Li Y G, Xu L 2007 Acta Phys. Sin. 56 7286 (in Chinese)[彭扬、李善锋、张庆瑜、李毅刚、徐雷 2007 56 7286]
[20]	Zhang Z G 2008 Acta Phys. Sin. 57 5823 (in Chinese)[张治国 2008 57 5823]
[21]	Liu Y, Ni X W 2009 Acta Phys. Sin. 58 3572 (in Chinese)[刘莹、倪晓武 2009 58 3572]
[22]	Specht D F 1990 Neur. Network. 3 109
[23]	Shen M J 2009 J. Math. Med. 22 92 (in Chinese)[申明金 2009 数理医药学杂志 22 92]

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