太赫兹光谱技术对红薯淀粉中明矾含量的检测方法研究

Study on the Detection Method of Alum Content in Sweet Potato Starch by Terahertz Spectroscopy

明矾是一种可以改良粉条粉丝易断粗糙特性的违法添加剂,明矾的含量过高进入人体后会直接影响身体健康。结合太赫兹光谱技术探索红薯淀粉中明矾含量快速检测方法。采用太赫兹时域光谱系统(Terahertz time domain spectroscopy, THz-TDS)于常温下获取0.5~7 THz范围内红薯淀粉、明矾及其混合物的光谱数据。因0~0.5 THz测得的频谱均为噪声,高频段区域的吸收系数大、信噪比低,故选取0.5~2 THz波段的吸收系数谱和折射率谱进行分析。发现明矾在该波段存在明显的特征吸收峰,可作为指纹特征用于物质识别。分别采用Savitzky-Golay卷积平滑(SG Smoothing,SG平滑)、基线校正(Baseline)、归一化(Normalization)等方法进行光谱预处理,再结合偏最小二乘(partial least squares, PLS)对红薯淀粉中明矾含量建立预测模型。结果表明,采用原始光谱、 SG平滑、 Baseline、 Normalization等光谱数据建立PLS模型的最佳因子数(principal component factors)分别为3, 3, 3和2;校正集相关系数(r_c)分别为0.982, 0.980, 0.982和0.984;预测集相关系数(r_p)分别为0.982, 0.979, 0.982和0.987;校正集均方根误差(root mean square error of calibration, RMSEC)分别为0.011, 0.012, 0.012和0.011;预测集均方根误差(root mean square error of prediction, RMSEP)分别为0.013, 0.014, 0.013和0.012;可知归一化预处理后建立PLS模型效果最佳。为对比分析线性(PLS)与非线性(LS-SVM)两种定量模型方法的预测精度,采用相同预处理方法后的红薯淀粉中明矾含量光谱数据建立最小二乘支持向量机(least squares support vector machine, LS-SVM)预测模型,选用径向基函数(RBF)作为核函数。结果表明,归一化预处理后建立LS-SVM模型效果最佳,其预测集均方根误差(RMSEP)为0.0047,预测集相关系数(r_p)为0.997 2。发现对红薯淀粉中明矾含量建立LS-SVM预测模型的稳定性更好、精确度更高。采用太赫兹时域光谱结合LS-SVM和PLS对红薯淀粉中明矾含量进行定量分析。结果表明,采用归一化预处理后的LS-SVM比PLS模型的预测效果更优,可能是红薯淀粉与明矾混合物中含有更多的非线性信息。研究表明,太赫兹时域光谱结合化学计量学方法可为红薯淀粉中明矾含量的定量分析提供快速精确的分析方法。

Alum is an illegal additive that can improve the fragile characteristics of vermicelli. If the content of alum is excessive, it will directly affect the health of the body. This paper combines terahertz spectroscopy to explore a rapid detection method for alum in sweet potato starch. The spectral data of sweet potato starch, alum and their mixtures in the range of 0.5~7 THz were obtained by Terahertz Time Domain Spectroscopy(THz-TDS) at room temperature. Since the spectrum measured by 0~0.5 THz is noise, the absorption coefficient of the high-band region is large, and the signal-to-noise ratio is low, the absorption coefficient spectrum and the refractive index spectrum of the 0.5~2 THz band were selected for analysis. It was found that alum has obvious characteristic absorption peaks in terahertz band, which can be used as fingerprint features for material identification. Savitzky-Golay convolution smoothing, Baseline, Normalization were used for spectral pretreatment, and combined with partial least squares(PLS) a prediction model for alum content in sweet potato starch was established. The results showed that the principal component factors of the PLS model were 3, 3, 3, 2 using the original, SG smoothing, Baseline, Normalization spectral data, respectively. The correlation coefficient of calibration(r_c) were 0.982, 0.980, 0.982, 0.984, respectively. The correlation coefficient of prediction(r_p) were 0.982, 0.979, 0.982, and 0.987, respectively. The root mean square error of correction(RMSEC) were 0.011, 0.012, 0.012, and 0.011, respectively. The root mean square error of prediction(RMSEP) were 0.013, 0.014, 0.013, and 0.012, respectively. The PLS model had the best effect after normalization pretreatment. In order to compare and analyze the prediction accuracy of linear(PLS) and nonlinear(LS-SVM) quantitative model methods, the least square support vector machine was established using the spectral data of alum in the sweet potato starch after the same pretreatment method. For the prediction model, the radial basis function was chosen as the kernel function. The results showed that the LS-SVM model is the best after normalization preprocessing. The RMSEP of the prediction set was 0.004 7, and the correlation coefficient of the prediction set was 0.997 2. It was found that the LS-SVM prediction model for the alum content in sweet potato starch was more stable and more accurate. The content of alum in sweet potato starch was quantitatively analyzed by terahertz time domain spectroscopy combined with LS-SVM and PLS. The results showed that the LS-SVM with normalized pretreatment has better prediction effect than the PLS, which may be more nonlinear information in the mixture of sweet potato starch and alum. Studies have shown that terahertz time-domain spectroscopy combined with chemometric methods can provide a fast and accurate analytical method for the quantitative analysis of alum in sweet potato starch.