食品科学 ›› 2011, Vol. 32 ›› Issue (22): 171-174.doi: 10.7506/spkx1002-6630-201122034

• 分析检测 • 上一篇    下一篇

三级大豆油酸价的近红外光谱检测

王铭义1,郭建英1,*,张佳宁2,李 越2,于殿宇2   

  1. 1.哈尔滨理工大学测控技术与通信工程学院 2.东北农业大学食品学院
  • 出版日期:2011-11-25 发布日期:2011-11-11
  • 基金资助:
    国家“863”计划项目(2010AA101503);黑龙江省教育厅科学技术研究项目(11551109)

Determination of Acid Value in Third Grade Soybean Oil by Near Infrared Spectroscopy

WANG Ming-yi1,GUO Jian-ying1,*,ZHANG Jia-ning2,LI Yue2,YU Dian-yu2   

  1. (1. School of Measurement-control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150086, China;2. School of Food, Northeast Agricultural University, Harbin 150030, China)
  • Online:2011-11-25 Published:2011-11-11

摘要: 为改进现有的油脂检测方法,以三级大豆油脂酸价检测为研究对象,利用近红外透射光谱技术,结合化学计量学方法,选择4500~6000cm-1为特征波段建立油脂酸价校正模型,首先研究基于小波变换的光谱预处理方法,通过详细比较不同小波分解层数对建模的影响。结果确定db4小波的四层分解去噪效果最佳。结合滤波后重构的光谱信号建立油脂酸价BP神经网络校正模型,利用预测集样本对模型进行验证,决定系数R2和预测均方根误差分别为0.9743和0.1036。证明利用近红外光谱分析技术快速检测油脂酸价是完全可行的。

关键词: 油脂酸价检测, 近红外光谱分析, 小波预处理, 偏最小二乘回归, BP神经网络

Abstract: Near-infrared spectroscopy combined with chemometric methods was employed to determine the acid value of third grade soybean oil as an improvement over the current methods. The characteristic waveband of 4500-6000 cm-1 was selected to establish an acid value calibration model. Spectral pre-processing methods based on wavelet transform were investigated and the effects of different wavelet decomposition levels on prediction results of acid value were compared in detail. The results showed that Daubechies4 (db4) wavelets with four levels of decomposition revealed the best noise removal. A back propagation (BP) neural network calibration model was built based on reconstructed spectral signals with wave filtrating and was validated using the prediction set with a determination coefficient of 0.9743 and a RMSEP of 0.1036. This study supports a high feasibility to apply near-infrared spectroscopy for the rapid determination of acid value of oils.

Key words: acid value detection in oil, near infrared spectroscopy analysis, wavelet pre-processing, partial least square regression, BP neural network

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