首页 > 论文 > 光学学报 > 38卷 > 4期(pp:430003--1)

基于硝酸钠内标物的山梨酸钾拉曼特征峰强校正

Raman Characteristic Peak Intensity Correction of Potassium Sorbate Based on Sodium Nitrate Internal Standard

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

根据硝酸钠自身化学性质较稳定且拉曼特征峰与被测组分山梨酸钾谱峰能完全分离的特点,以硝酸钠为内标物对食品中常用防腐剂山梨酸钾的拉曼光谱进行校正;以质量分数为0.1的硝酸钠在拉曼特征位移1053 cm-1处的特征峰作为内标峰,分别计算其与49个样品中相同浓度硝酸钠特征峰强的相对比值,用相对比值分别校正49个样品的山梨酸钾特征峰强,采用一元线性回归分析对山梨酸钾进行定量建模分析。结果表明:校正后,山梨酸钾预测模型校正集和预测集的相关系数显著增大,山梨酸钾在1399 cm-1处特征峰强的一元线性回归定量预测模型校正集和预测集相关系数的平方分别为0.9885、0.9865,均方根误差分别为3.0384×10-3、3.7643×10-3;基于最佳预测模型对新配制的18个新样品进行预测,预测值和真实值的相关系数的平方为0.9799,均方根误差为4.8702×10-3,说明用硝酸钠内标法可以有效减小检测仪器、检测环境以及人为因素对山梨酸钾拉曼峰强的影响,提高被测物预测模型的精度。

Abstract

The chemical properties of sodium nitrate are stable, and the Raman characteristic peaks of sodium nitrate and potassium sorbate can be separated completely. The Raman spectrum of potassium sorbate, a preservative commonly used in food, is calibrated with Raman spectrum of sodium nitrate, whose characteristic peak at Raman shift 1053 cm-1 of sodium nitrate with mass fraction of 0.1 is used as internal standard peak. We calculate the relative ratios of the intensity of internal standard speak and characteristic peak intensities of the same concentration sodium nitrate in 49 samples respectively, use the relative ratios to correct characteristic peak intensities of potassium sorbate in 49 samples, and establish the quantitative prediction model of potassium sorbate by linear regression. The results show that the correlation coefficients of calibration set and prediction set significantly improve after correction. The squares of correlation coefficients of potassium sorbate at 1399 cm-1 characteristic peak are 0.9885 and 0.9865, the root mean square errors are 3.0384×10-3 and 3.7643×10-3 for calibration set and prediction set, respectively. Based on the optimal prediction model, we predict 18 new samples. The square of correlation coefficient of the predicted and real values is 0.9799 and root mean square error is 4.8702×10-3, which indicate that the internal standard method of sodium nitrate can effectively reduce the influences of detection instrument, detection environment, and human factors on the Raman peak intensity of potassium sorbate, and can improve the accuracy of the predicted model.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:O657.37

DOI:10.3788/aos201838.0430003

所属栏目:光谱学

基金项目:中央高校基本科研业务费专项资金(2017GX001)

收稿日期:2017-10-30

修改稿日期:2017-11-21

网络出版日期:--

作者单位    点击查看

房晓倩:中国农业大学工学院,国家农产品加工技术装备研发分中心, 北京 100083
李永玉:中国农业大学工学院,国家农产品加工技术装备研发分中心, 北京 100083
彭彦昆:中国农业大学工学院,国家农产品加工技术装备研发分中心, 北京 100083
李延:中国农业大学工学院,国家农产品加工技术装备研发分中心, 北京 100083
王凡:中国农业大学工学院,国家农产品加工技术装备研发分中心, 北京 100083
王文秀:中国农业大学工学院,国家农产品加工技术装备研发分中心, 北京 100083

联系人作者:李永玉(yyli@cau.edu.cn)

备注:房晓倩(1992—),女,硕士研究生,主要从事拉曼光谱方面的研究。E-mail: fxiaoqian0213@163.com

【1】Liu Y D, Jin T T. Application of Raman spectroscopy technique to agricultural products quality and safety determination[J]. Spectroscopy and Spectral Analysis, 2015, 35(9): 2567-2572.
刘燕德, 靳昙昙. 拉曼光谱技术在农产品质量安全检测中的应用[J]. 光谱学与光谱分析, 2015, 35(9): 2567-2572.

【2】Xie F, Sun H D, Li Z B, et al. QuEChERS sample preparation method for rapid screening of malathion in legume vegetables by surface-enhanced Raman spectroscopy[J]. Food Science and Technology, 2014, 39(8): 286-290.
谢锋, 孙海达, 李占彬, 等. QuEChERS-表面增强拉曼光谱联用快速测定豆类蔬菜中马拉硫磷残留[J]. 食品科技, 2014, 39(8): 286-290.

【3】Huang Z L, Wang J D, Jiang B B, et al. Quantitative determination of PVC concentration by Raman spectrum[J]. Spectroscopy and Spectral Analysis, 2011, 31(3): 704-708.
黄正梁, 王靖岱, 蒋斌波, 等. 聚氯乙烯溶液浓度的拉曼光谱检测[J]. 光谱学与光谱分析, 2011, 31(3): 704-708.

【4】Liu J, Li X Y, Jin R, et al. Extending hyperspectral detecting model of pH in fresh pork to new breeds[J]. Spectroscopy and Spectral Analysis, 2015, 35(7): 1973-1979.
刘娇, 李小昱, 金瑞, 等. 不同品种冷鲜猪肉pH值高光谱检测模型的传递方法研究[J]. 光谱学与光谱分析, 2015, 35(7): 1973-1979.

【5】Wang J X, Meng F L, Liu L M, et al. Application of sample selection and PDS-PLS algorithms in near infrared spectra analysis model transfer[J]. Acta Armamentarh, 2016, 37(1): 91-96.
王菊香, 孟凡磊, 刘林密, 等. 样品选择结合分段直接校正法和偏最小二乘法用于近红外光谱分析模型传递研究[J]. 兵工学报, 2016, 37(1): 91-96.

【6】Wu X Q, Zheng J Z, Liu W H, et al. Quantitative determination of glucose by internal standard laser Raman spectra[J]. Spectroscopy and Spectral Analysis, 2007, 27(7): 1344-1346.
吴小琼, 郑建珍, 刘文涵, 等. 激光拉曼光谱内标法测定葡萄糖液浓度[J]. 光谱学与光谱分析, 2007, 27(7) : 1344-1346.

【7】Wang W, Xi X X, Wang B, et al. Quantitative analysis of Forsythin in leaves gathered in different periods with laser Raman spectroscopy[J]. The Journal of Light Scattering, 2010, 22(4): 361-366.
王玮, 席欣欣, 王蓓, 等. 激光拉曼光谱法测定不同采收期连翘叶中连翘苷的含量[J]. 光散射学报, 2010, 22(4): 361-366.

【8】Dhakal S, Li Y Y, Peng Y K, et al. Prototype instrument development for non-destructive detection of pesticide residue in apple surface using Raman technology[J]. Journal of Food Engineering, 2014, 123: 94-103.

【9】Zhu Z Y, Gu R A, Lu T H. Application of Raman spectroscopy in chemistry[M]. Shenyang: Northeastern University Press, 1998: 295-301.
朱自莹, 顾仁敖, 陆天虹. 拉曼光谱在化学中的应用[M]. 沈阳: 东北大学出版社, 1998: 295-301.

【10】Xue S X, Wang J P, Hu H, et al. Quantitative determination of acetic acid by handheld Raman spectrometer and internal standard method[J]. Technology & Development of Chemical Industry, 2011, 40(8): 45-47.
薛绍秀, 王江平, 胡宏, 等. 便携式拉曼光谱仪内标法快速测定乙酸浓度[J]. 化工技术与开发, 2011, 40(8): 45-47.

【11】Ding S Y, Wu D Y, Yang Z L, et al. Some progresses in mechanistic studies on surface-enhanced Raman scattering[J]. Chemical Journal of Chinese Universities, 2008, 29(12): 2569-2581.
丁松园, 吴德印, 杨志林, 等. 表面增强拉曼散射增强机理的部分研究进展[J]. 高等学校化学学报, 2008, 29(12): 2569-2581.

引用该论文

Fang Xiaoqian,Li Yongyu,Peng Yankun,Li Yan,Wang Fan,Wang Wenxiu. Raman Characteristic Peak Intensity Correction of Potassium Sorbate Based on Sodium Nitrate Internal Standard[J]. Acta Optica Sinica, 2018, 38(4): 0430003

房晓倩,李永玉,彭彦昆,李延,王凡,王文秀. 基于硝酸钠内标物的山梨酸钾拉曼特征峰强校正[J]. 光学学报, 2018, 38(4): 0430003

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF