Detection of captan residues in apple juice using fluorescence spectroscopy combined with a genetic algorithm and support vector machines

Mixed pesticide recognition based on three-dimensional fluorescence spectroscopy and a convolutional neural network

Kasugamycin, spinosad, and lambda-cyhalothrin are common organic pesticides that are widely used to control and prevent diseases and pests in fruits and vegetables. However, the unreasonable use of pesticides will cause great harm to the natural environment and human health. Pesticides often exist in the form of mixtures in nature. Establishing recognition models for mixed pesticides in large-scale sample testing can provide guidance for further precise analysis and reduce resource waste and time. Therefore, finding a fast and effective identification method for mixed pesticides is of great significance. This paper applies three-dimensional fluorescence spectroscopy to detect mixed pesticides and introduces a convolutional neural network (CNN) model structure based on an improved LeNet-5 to classify mixed pesticides. The input part of the model corresponds to fluorescence spectrum data at excitation wavelengths of 250-306 nm and emission wavelengths of 300-450 nm, and the mixed pesticides are divided into three categories. The research results show that when the learning rate is set to 1 and the number of iterations is 300, the CNN classification model has ideal performance (with a recognition accuracy of 100%) and is superior to the performance of the support vector machine method. This paper provides a certain methodological basis for the rapid identification of mixed pesticides.

Application of ultraviolet-visible spectroscopy coupled with support vector regression for the quantitative detection of thiamethoxam in tea

A model combining UV-visible (UV-Vis) spectroscopy and support vector regression (SVR) for the quantitative detection of thiamethoxam in tea is proposed. First, each original UV-Vis spectrum in the sample set is decomposed into some intrinsic mode functions (IMFs) and a residual via ensemble empirical mode decomposition. Next, the decomposed IMFs are reconstructed into high-frequency and low-frequency matrices, and the residuals are combined into a trend matrix. Then, the SVR is used to build regression sub-models between each matrix and the content of thiamethoxam in tea. Finally, the combination model is established by a weighted average of the sub-models. The prediction results are compared with SVR and SVR coupled with several preprocessing methods, and the results demonstrate the superiority of the proposed approach in the quantitative detection of thiamethoxam in tea.