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.

GPU-accelerated image registration algorithm in ophthalmic optical coherence tomography.

Limited to the power of the light source in ophthalmic optical coherence tomography (OCT), the signal-to-noise ratio (SNR) of the reconstructed images is usually lower than OCT used in other fields. As a result, improvement of the SNR is required. The traditional method is averaging several images at the same lateral position. However, the image registration average costs too much time, which limits its real-time imaging application. In response to this problem, graphics processing unit (GPU)-side kernel functions are applied to accelerate the reconstruction of the OCT signals in this paper. The SNR of the images reconstructed from different numbers of A-scans and B-scans were compared. The results demonstrated that: 1) There is no need to realize the axial registration with every A-scan. The number of the A-scans used to realize axial registration is suitable to set as ∼25, when the A-line speed was set as ∼12.5kHz. 2) On the basis of ensuring the quality of the reconstructed images, the GPU can achieve 43× speedup compared with CPU.

Detection of Ningnanmycin Using Fluorescence Spectroscopy Combined with BP Neural Network.

BACKGROUND: Ningnanmycin is a new antibiotic pesticide with good bactericidal and antiviral efficacy, which is widely used in the control of fruit and vegetable diseases, and the excessive pesticide residues pose a serious threat to the environment and human health. METHODS: In this study, we used fluorescence spectrometer to scan the three-dimensional spectrum of ningnanmycin samples. We used a BP neural network to complete the regression analysis of content prediction based on the fluorescence spectra. After that, the prediction performance of the BP neural network was compared with the exponential fitting method. RESULTS: The results of the BP neural network modeling based on the obtained samples showed that the mean square error of the prediction results of the test set is less than 10-4, the R-square is greater than 0.99, the average recovery is 99.11%, and the model performance of the BP neural network is better than exponential fitting. CONCLUSION: Studies have shown that fluorescence spectroscopy combined with BP neural network can effectively predict the concentration of ningnanmycin.

Detection of Dihydrocoumarin in Coconut Juice via Photoelectric Detection System Based on Ultraviolet Absorption Spectrometry.

Based on ultraviolet absorption spectroscopy technology combined with stoichiometry, a portable photoelectric detection system with wireless transmission was designed with the advantages of simple operation, low cost, and quick response to realize the non-destructive detection of dihydrocoumarin content in coconut juice. Through the detection of a sample solution, the light intensity through the solution is measured and converted into absorbance. Particle swarm optimization (PSO) is applied to optimize support vector regression (SVR) to establish a corresponding concentration prediction model. At the same time, in order to solve the shortcomings of the conventional portable photoelectric detection equipment in data storage, data transmission, and other aspects, based on the optimal PSO-SVR model, we used Python language to develop a friendly graphical user interface (GUI), integrating data collection, storage, analysis, and prediction modeling in one, greatly simplifying the operation process. The experimental results show that, compared with the traditional methods, the system achieves the purpose of rapid and non-destructive detection and has a small gap compared with the detection results of the ultraviolet spectrophotometer. It provides a good method for the determination of dihydrocoumarin in coconut juice.

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.

Analysis of pesticide residues by a support vector machine combined with fluorescence spectroscopy.

Pesticide residues enter a lake through the water cycle, causing harm to the water environment and human health. It is necessary to select highly sensitive fluorescence spectroscopy to detect pesticides (bifenthrin, prochloraz, and cyromazine), and a support vector machine (SVM) is used to analyze the concentration of pesticides. In addition, this paper adopts K-fold cross validation and a grid search to optimize the SVM algorithm. The performance evaluation index and running time prove the reliability of the results of this experiment. They show that fluorescence spectroscopy combined with SVM is efficient in predicting pesticide residue content.

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

Compared with high-performance liquid chromatography and mass spectroscopy, fluorescence spectroscopy has attracted considerable attention in the field of pesticide residue detection due to its practical advantages of providing rapid, simultaneous analysis and non-destructive detection. However, given that the concentration of pesticide residue detected via fluorescence spectroscopy is calculated in accordance with the Beer-Lambert law, this method can only detect samples containing a single kind of pesticide or several kinds of pesticides with completely different fluorescences. Multiple partial least-squares (PLS) models are introduced in this work to overcome this disadvantage and achieve the concentration of zhongshengmycin, paclobutrazol, boscalid, and pyridaben, whose fluorescences are overlapping. The R squares of the models for zhongshengmycin, paclobutrazol, boscalid, and pyridaben were 0.9942, 0.9912, 0.9913, and 0.9847, respectively. Results indicated that fluorescence spectroscopy combined with multiple PLS models can be used to detect multiple kinds of pesticides in the water.

Determination and Modeling on Ultraviolet Light Degradation of Pyridaben Based on Fluorescence Spectrum.

AIMS AND OBJECTIVE: Pesticide residues seriously affect human health, so it is very important to study the degradation of pesticide residues for food safety. The degradation of pyridaben by ultraviolet (UV) irradiation was studied, the degradation characteristics and modeling were analyzed in this paper. This study was undertaken to fully reveal the degradation mechanism of UV irradiation for pyridaben residue and provided the evaluation method of degradation effect. MATERIALS AND METHODS: Firstly, the fluorescence spectra of pyridaben samples were measured by LS55 fluorescence photometer, and the relationship between pyridaben concentration and the fluorescence intensity of characteristic peak was established. Then, using UV irradiation approach, the pyridaben was degraded to different degrees by controlling the irradiation time. The degradation process was characterized according to the change of fluorescence characteristic peak intensity before and after degradation. The relationship between degradation time and fluorescence intensity was established at last. RESULTS: The results showed that the fluorescence characteristic peak of pyridaben was located at 356 nm. The pyridaben content prediction model function was obtained with the correlation coefficient of 0.9989 and the average recovery of 99.70%. The relative standard deviation (RSD%), the limit of detection (LOD) and the limit of quantity (LOQ) was 1.71%, 0.0058 ug/ml and 0.0193 ug/ml, respectively. The exponential function model between UV degradation time and fluorescence intensity was obtained, the corresponding correlation coefficient was 0.9991, and the average recovery was 100.49%. CONCLUSION: UV light irradiation can effectively degrade pyridaben, degradation process can be characterized by the change of fluorescence intensity, and the degradation model was tested to be accurate.

Study on fluorescence spectra of chlorothalonil residues and the interaction between chlorothalonil and Chinese herbal medicines.

The fluorescence spectrum was studied for the chlorothalonil (0.2928 mg x mL(-1)) using spectrofluorophotometer. The experiment results showed that the characteristic peaks (352 and 366 nm) are found in the spectrum of chlorothalonil standard solution when the excitation wavelength is 320 nm. And it was found that the shoulder peak gradually disappeared at 366 nm, while the fluorescence peak is stable at 352 nm with the decline of the solution concentration The exponential functional relationship between the concentration of chlorothalonil and fluorescence intensity at 352 nm was obtained, and its correlation coefficient is 0.999. The experimental results are consistent with the theoretical formula about fluorescence intensity and concentration The prediction model functions were also obtained through the liner fitting to the chlorothalonil solution of low concentration, and the correlation coefficient is 0. 995. The limit of detection (LOD) is 0.0188 microg x mL(-1), the limit of quantification (LOQ) is 0.0627 microg x mL(-1), and the linear range is 0.0627-28.45 microg x mL(-1). And fluorescence spectra were studied for the mixed system of astragalus, medlar and chlorothalonil. It was found that the fluorescence intensity of chlorothalonil solution is all declined with the addition of two kinds of Chinese Herbal Medicines, which indicates that there is an interaction between them. The decay rate of fluorescence intensity was obtained which is 88.5% and 99.7%, respectively. Then the model functions were established between fluorescence intensity and the volume of addition, and the correlation coefficient is 0.994 and 0.997, respectively. This study provides the experimental foundation for the detection of chlorothalonil residues using fluorescence spectrum. It is shown that it is possible to detect pesticide residues of chlorothalonil using fluorescence spectra directly, and the relevant parameter value satisfied the requirement of testing standard. Therefore there is an important value for further detecting the pesticide residues in fruit juice using fluorescence spectrum. It was also found that the fluorescence intensity of chlorothalonil is decreased with the addition of astragalus or medlar, which provides the new research approach to studying the pesticide degradation using medicinal and edible Chinese Herbal Medicines.

[Study on experiment of absorption spectroscopy detection of pesticide residues of carbendazim in orange juice].

Absorption spectra were studied for the carbendazim, in the mixed solution of orange juice and carbendazim using spectrophotometer. The most intensive characteristic peak (285 nm) was found in the spectrum of carbendazim standard solution. Compared with the carbendazim drug solution, the peak position of absorption spectrum has the blue shift (285-280 nm) when carbendazim (0.28 mg x mL(-1))was added in the orange juice. So that we can conclude that interaction happened between the orange juice and carbendazim. Through the method of least squares fitting, the prediction models between the absorbance of orange juice and carbendazim content was obtained with a good linear relationship. The linear function model was: I = 2.41 + 9.26x, the correlation coefficient was 0.996, and the recovery was: 81%-102%. According to the regression model, we can obtain the amount of carbendazim pesticide residues in orange juice. It was verified that the method of using ultraviolet-visible absorption spectra was feasible to detect the carbendazim residues in orange juice. The result proved that it is possible to detect pesticide residues of carbendazim in orange juice, and it can meet the needs of rapid analysis. This study provides a new way for the detection of pesticide residues.

[Study on experiment of fluorescence spectra detection of pesticide residue of imidacloprid in apple juices].

The fluorescence properties of imidacloprid was studied based on the basic theory that organic molecules can emit fluorescence as they are excited by rays. The fluorescence spectra were obtained under the condition of different content of imidacloprid in apple juices and pure apple juices respectively through fluorescence spectrometer, and the relation between their fluorescence intensity and content of imidacloprid was analyzed. The experiment results show that the most intensive fluorescence (373 nm) was found in the spectrum of imidacloprid, while the fluorescence was not found in the pure apple juices with 234 nm as the excitation wavelength. Then the imidacloprid solution was added to the fruit juices increasingly. The best prediction model was obtained for the contend of imidacloprid in the apple juices, the coefficient of determination is 0.99674, and the accuracy is higher than 90%. As a result, it is fast and feasible to carry out the detection and analysis of the pesticide residue of imidacloprid in the apple juices.