Multiple kinds of pesticide residue detection using fluorescence spectroscopy combined with partial least-squares models

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.

All-fiber surface-enhanced Raman scattering detection system combining an integrated microfluidic chip and micro-lensed fiber

It is a challenge to perform simple and rapid detection of substances due to their complex structure. Biochemical molecules play a vital role in human health and environmental testing. Surface-enhanced Raman scattering (SERS) detection has the characteristics of strong specificity and real-time performance. At present, most SERS systems are expensive and not portable. Here, we demonstrate a SERS detection system with all-fiber connection, combined with a microfluidic chip and micro-lenses. Compared with the conventional SERS system that uses the spatial optical path, the devices in our system are connected by optical fibers, making the system more stable and operable. Besides, the microfluidic chips are introduced to further improve the system integration and stability. Owing to the micro-lensed fiber probe, the detected Raman signal intensity is increased by 2-3 times. We anticipate that the presented work will lead toward a rapid and portable SERS system and corresponding detection system. It also lays the foundation for real-time recognition in various complex environments in the design of a future optical fiber system.

Non-contact fluorescent detection of pesticide residues based on segment prediction using PLS and a curve fitting algorithm

Fluorescence spectral analysis is an important method to detect the pesticide residues, which is vital for food safety issues. It has been demonstrated that the traditional curve fitting (CF) method can predict the concentration of pesticide with a high accuracy. However, low absorption of the samples at low concentration of pesticide is required; moreover, the pre-process of fruit juice is time-consuming and destructive to the samples. To overcome these disadvantages while maintaining the high accuracy in the high concentration range, the segment detection method is proposed in this paper. Two models were employed to predict the concentration according to the fluorescence intensity. The partial least squares (PLS) model was used to predict the concentration of the samples when the fluorescence intensity at 356 nm was smaller than 1, while the CF method was used to predict the concentration of samples when the fluorescence intensity at 356 nm was larger than 1 in our system. In total, 101 samples with concentration ranging from 0 to 0.0714 mg/mL were used to validate this method. The results indicated that the PLS method exhibited a high sensitivity in the low concentration range, while the CF method exhibited high accuracy in the high concentration range.

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

The captan residues in apple juice were detected by fluorescence spectrometry, and the content level of captan was predicted based on a genetic algorithm and support vector machines (GA-SVMs). According to the captan concentration in apple juice, the experimental samples were divided into four levels, including no excess, slight excess, moderate excess, and severe excess. A GA was used to select the characteristic wavelength and optimize SVM parameters, and SVM was applied to train the classification model. 50 characteristic wavelength points were selected from the original fluorescence spectra, which contained 401 wavelength points, and the classification accuracy of the training set and test set is 99.02% and 100%, respectively, which is higher than the traditional PLS method. The results show that a GA can effectively select the feature wavelengths, and an SVM model can accurately predict the content level of captan residues. A fast and non-destructive analysis method, combined with a GA and SVM based on fluorescence spectroscopy, was realized.