Shi Guolong, Shen Xinyi, Ren Huan, Rao Yuan, Weng Shizhuang, Tang Xianghu
School of Information and Computer, Anhui Agricultural University, Hefei, China.
School of Electrical Engineering and Automation, Wuhan University, Wuhan, China.
Front Plant Sci. 2022 Jul 19;13:956778. doi: 10.3389/fpls.2022.956778. eCollection 2022.
Surface-enhanced Raman spectroscopy (SERS) has attracted much attention because of its high sensitivity, high speed, and simple sample processing, and has great potential for application in the field of pesticide residue detection. However, SERS is susceptible to the influence of a complex detection environment in the detection of pesticide residues on the surface of fruits, facing problems such as interference from the spectral peaks of detected impurities, unclear dimension of effective correlation data, and poor linearity of sensing signals. In this work, the enhanced raw data of the pesticide thiram residues on the fruit surface using gold nanoparticle (Au-NPs) solution are formed into the raw data set of Raman signal in the IoT environment of Raman spectroscopy principal component detection. Considering the non-linear characteristics of sensing data, this work adopts kernel principal component analysis (KPCA) including radial basis function (RBF) to extract the main features for the spectra in the ranges of 653∼683 cm, 705∼728 cm, and 847∼872 cm, and discusses the effects of different kernel function widths (σ) to construct a qualitative analysis of pesticide residues based on SERS spectral data model, so that the SERS spectral data produce more useful dimensionality reduction with minimal loss, higher mean squared error for cross-validation in non-linear scenarios, and effectively weaken the interference features of detecting impurity spectral peaks, unclear dimensionality of effective correlation data, and poor linearity of sensing signals, reflecting better extraction effects than conventional principal component analysis (PCA) models.
表面增强拉曼光谱（SERS）因其高灵敏度、高速度和简单的样品处理而备受关注，在农药残留检测领域具有巨大的应用潜力。然而，在水果表面农药残留检测中，SERS易受复杂检测环境的影响，面临诸如检测杂质光谱峰的干扰、有效相关数据维度不清晰以及传感信号线性度差等问题。在这项工作中，利用金纳米颗粒（Au-NPs）溶液对水果表面农药福美双残留的增强原始数据，在拉曼光谱主成分检测的物联网环境中形成拉曼信号原始数据集。考虑到传感数据的非线性特征，这项工作采用包括径向基函数（RBF）的核主成分分析（KPCA），对653∼683 cm、705∼728 cm和847∼872 cm范围内的光谱提取主要特征，并讨论不同核函数宽度（σ）的影响，构建基于SERS光谱数据模型的农药残留定性分析，以使SERS光谱数据在损失最小的情况下产生更有用的降维效果，在非线性场景下具有更高的交叉验证均方误差，并有效减弱检测杂质光谱峰的干扰特征、有效相关数据维度不清晰以及传感信号线性度差等问题，比传统主成分分析（PCA）模型具有更好的提取效果。
