摘要： 面粉（小麦粉）是中国北方大部分地区的主食，苯甲酸是重要的酸型食品防腐剂，为了便于食品长期保存，往往会添加苯甲酸以便延长食品保存时间。但食用添加苯甲酸过量的小麦粉会对身体健康产生严重危害。太赫兹技术是一种新兴的检测技术，由于处于特殊的0.1～10 THz的太赫兹频段，在食品安全检测方面体现出了很强的应用潜力。主要致力于探索太赫兹光谱技术检测苯甲酸的合理性、可行性，利用太赫兹时域光谱技术对面粉中的食品添加剂苯甲酸进行实验研究。实验获取了面粉和苯甲酸的太赫兹时域光谱和频域光谱，其吸收系数显示苯甲酸的特征吸收峰在1.94 THz波段，面粉的太赫兹吸收系数几乎以一定的斜率增加，说明可以用THz-TDS（Terahertz time domain spectrum）技术对面粉中的苯甲酸进行特征识别。为建立面粉中添加剂苯甲酸的定量检测模型，实验获取了面粉中掺杂不同百分比（质量分数）苯甲酸的太赫兹时域光谱，计算得到吸收系数谱。实验发现吸收峰幅度的变化是与苯甲酸的含量成正比的，苯甲酸含量增加吸收峰幅度变大。首先探索了不同光谱预处理方法对太赫兹光谱的影响，采用如平滑校正、多元散射校正、基线校正和归一化等方法对原始光谱进行校正处理。校正之后，建立相应的PLS (partial least squares)模型以选择最优预处理方法。然后分别建立苯甲酸浓度和太赫兹吸收系数的MLR (multiple linear regression)、PLS和LS-SVM（partial least squares support vector machines）回归模型，并对比分析不同模型的优劣。将光谱数据归一化后建立的PLS模型更具有优势，预测相关系数Rp为0.979，预测均方根误差RMSEP为1.30%。LS-SVM与PLS和MLR模型相比，LS-SVM模型可以获得更好的建模结果，LS-SVM的预测相关系数Rp为0.987，预测均方根误差RMSEP为1.10%。利用MLR方法仅使用1.946和1.869 THz两个波段点进行建模，建模效果预测相关系数Rp为0.955，预测均方根误差RMSEP为1.90%。通过该研究为面粉中苯甲酸添加剂的无损检测提供了新的解决方案，也为其他类型的添加剂的检测提供了方法指导，对促进面粉行业的健康发展具有重要的意义。
关键词：面粉；太赫兹光谱；偏最小二乘支持向量机；苯甲酸；波段比
Abstract：With the further development of terahertz technology, terahertz has shown its unique advantages in food safety detection. Flour (wheat flour) is the staple food in most areas of northern China. Besides, benzoic acid（BA), as the important preservative of acid food, is often added to extend the preservation time of food. However, the excessive use of food additives would cause serious damage to human health. This paper explores the feasibility of detecting food additives through terahertz technology and conducts empirical study on benzoic acid in flour by terahertz time-domain spectroscopy (THz-TDS) technology. The terahertz time-domain and frequency domain spectrum of the mixed samples (flour and benzoic acid) were obtained. As shown by absorption coefficients, benzoic acid presented obvious absorption peak at 1.94 THz. Meanwhile, the absorption coefficient of flour increased at a certain slope, which indicated that the characteristic identification of benzoic acid in flour could be carried out by terahertz technology. In order to establish the quantitative detection model of benzoic acid additive in flour, terahertz time-domain spectra of benzoic acid doped with different percentages (mass fraction) in flour were collected, and the absorption coefficient spectrum was obtained through calculation. It was found that the absorption peak amplitude enjoys positive correlation with benzoic acid content. As for the detection method, firstly, explore the effects of different spectral pretreatment methods on THz spectroscopy, and then adopt methods like Smoothing, Multiple Scatter Correction (MSC), Baseline and Normalization to carry out correct processing. After correction, PLS model was established to select the optimal pretreatment method. Secondly, establish PLS and LS-SVM regression models for the determination of benzoic acid content in flour. The experimental results verify that PLS model established after normalization was more optimal, with correlation coefficient of prediction (rp) of 0.979 and root mean square error of prediction (RMSEP) of 1.30%. By comparison, it was proved that the most optimal quantitative determination model of benzoic acid content in flour is LS-SVM model with correlation coefficient of prediction (rp) of 0.987 and root mean square error of prediction (RMSEP) of 1.10% after the normalization of terahertz absorption coefficient. MLR model was established by only two bands of 1.946 and 1.869 THz with correlation coefficient of prediction (rp) of 0.955 and root mean square error of prediction (RMSEP) of 1.90%. It is concluded that a new solution for the nondestructive detection of benzoic acid additives in flour was developed, and method guidance was provided for the detection of other types of additives, all of which have an important significance for the healthy development of flour industry.
胡 军，刘燕德，孙旭东，欧阳爱国，蔡会周，刘洪量. 基于LS-SVM和THz光谱技术的面粉中苯甲酸检测研究[J]. 光谱学与光谱分析, 2019, 39(11): 3566-3570.
HU Jun, LIU Yan-de, SUN Xu-dong, OUYANG Ai-guo, CAI Hui-zhou, LIU Hong-liang. Quantitative Determination of Benzoic Acid in Flour Based on Terahertz Time-Domain Spectroscopy and LS-SVM. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3566-3570.
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