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近红外光谱-偏最小二乘法非破坏分析酱油的主要成分

包春芳1, 刘 彤1,2, 王 彬1, 赵羚志1, 任玉林1   

  1. 1. 吉林大学 化学学院, 长春 130021; 2. 哈尔滨市疾病预防控制中心, 哈尔滨 150056
  • 收稿日期:2008-03-24 修回日期:1900-01-01 出版日期:2009-03-26 发布日期:2009-03-26
  • 通讯作者: 任玉林

Nondestructive Analysis of the Quality Properties of Soy Sauceby Near Infrared Spectroscopy with Partial Least Squares

BAO Chunfang1, LIU Tong1,2, WANG Bin1, ZHAO Lingzhi1, REN Yulin1   

  1. 1. College of Chemistry, Jilin University, Changchun 130021, China;2. Harbin Centre for Disease Control and Prevention, Harbin 150056, China
  • Received:2008-03-24 Revised:1900-01-01 Online:2009-03-26 Published:2009-03-26
  • Contact: REN Yulin

摘要: 将近红外光谱技术与偏最小二乘法(PLS)相结合建立 数学校正模型, 对酱油中的氨基酸态氮、 总酸以及食盐进行快速、 无损定量分析, 并对酱油的色度进行预测, 同时讨论了光谱预处理方法和主成分数对PLS模型预测精度的影响. 结果表明, 采用一阶导数预处理光谱建立的数学校正模型能得到最佳的预测效果, 在对预测集18个样本中的氨基酸态氮、 总酸、 食盐的含量和色度进行预测时, 所得的预测集相对标准 偏差分别为1516%, 1811%, 1798%, 1893%. 实验结果具有较高的预测精度, 可以用于酱油中主要 成分含量的测定.

关键词: 近红外光谱, 偏最小二乘, 酱油, 非破坏分析

Abstract: Nearinfrared spectroscopy combined with partial least squares (PLS) method was applied to establishingan optimal mathematic calibration model, by which the concentrations of amino nitrogen, total acid and salt content in the soy sauce were predicted, and the color ratio of the soy sauce was determined. Furthermore, the influences of spectral preprocessing methods and the numbers of principal components on the prediction ability of the PLS model were discussed. The results show that the calibration model established with firstderivative preprocessing spectra was the best, 18 samples of tested set were predicted with this model, the relative standard errors for the prediction of ami no nitrogen, total acid, salt content and color ratio were 1.516%, 1.811%, 1.798% and 1.893%, respectively. Experimental results demonstrate that this method is fast and convenient, and it has a high ability of prediction, thus it is promising for the nondestructive quality control of soy sauce.

Key words: NIR spectroscopy, partial least squares (PLS), soy sauce, nondestructive analysis

中图分类号: 

  • O657.33