Intelligent analysis of maleic hydrazide using a simple electrochemical sensor coupled with machine learning

Rational design of hybrid sensor arrays combined synergistically with machine learning for rapid response to a hazardous gas leak environment in chemical plants

Combinations of semiconductor metal oxide (SMO) sensors, electrochemical (EC) sensors, and photoionization detection (PID) sensors were used to discriminate chemical hazards on the basis of machine learning. Sensing data inputs were exploited in the form of either numerical or image data formats, and the classification of chemical hazards with high accuracy was achieved in both cases. Even a small amount of gas sensing or purging data (input for ∼30 s) input can be exploited in machine-learning-based gas discrimination. SMO sensors exhibit high performance even in a single-sensor mode, presumably because of the intrinsic cross-sensitivity of metal oxides, which is otherwise considered a major disadvantage of SMO sensors. EC sensors were enhanced through synergistic integration of sensor combinations with machine learning. For precision detection of multiple target analytes, a minimum number of sensors can be proposed for gas detection/discrimination by combining sensors with dissimilar operating principles. The Type I hybrid sensor combines one SMO sensor, one EC sensor, and one PID sensor and is used to identify NH3 gas mixed with sulfur compounds in simulations of NH3 gas leak accidents in chemical plants. The portable remote sensing module made with a Type I hybrid sensor and LTE module can identify mixed NH3 gas with a detection time of 60 s, demonstrating the potential of the proposed system to quickly respond to hazardous gas leak accidents and prevent additional damage to the environment.

Real-Time Monitoring of Translocation, Dissipation, and Cumulative Risk of Maleic Hydrazide in Potato Plants and Tubers by Ion Exclusion Chromatography

In this paper, a high-performance ion exclusion chromatographic (ICE) method was developed and applied for monitoring maleic hydrazide (MH) translocation in complex potato plant tissue and tuber matrices. After middle leaf uptake, most MH was trapped and dissipated in the middle leaf, and the rest was transported to other parts mainly through the phloem. Soil absorption significantly reduced the uptake efficiency of the root system, in which MH was partitioned to dissipate in root protoplasts or transfer through the xylem and persisted in the plant. Tuber uptake enabled MH to remain in the flesh and maintain stable levels under storage conditions, but during germination, MH was translocated from the flesh to the growing buds, where it dissipated through the short-day photoperiodic regime. The results demonstrated successful application of the ICE method and provided necessary insights for real-time monitoring of MH translocation behavior to effectively improve potato edible safety.

Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites. Then, ZSHPC/MWCNTs were used to modify a screen-printed electrode, and a portable electrochemical detection system combined with machine learning methods was used to investigate carbendazim (CBZ) residues in rice and tea. The electrochemical performance of the constructed electrode showed that the electrode had good electrocatalytic ability, large effective surface area, strong stability and anti-interference ability. Support Vector Machine (SVM), Least Square Support Vector Machine (LS-SVM) and Back Propagation-Artificial Neural Network (BP-ANN) were used to establish the prediction model for CBZ residues in rice and tea, and the traditional linear regression was developed. The investigated results showed that the LS-SVM model had the best prediction performance and the lowest prediction error compared with the traditional linear regression, BP-ANN and SVM models. The R², RMSE, and MAE for the training set samples were 0.9969, 0.3605 and 0.2968, respectively. The R², RMSE, MAE and RPD for the prediction set samples were 0.9924, 0.6190, 0.5360 and 10.3097, respectively. The average recovery range of CBZ in tea and rice was 98.77-109.32% and that of RSD was 0.47-2.58%, indicating that the rapid analysis of CBZ pesticide residues in agricultural products based on a portable electrochemical detection system combined with machine learning was feasible.

Exploring Surface-Enhanced Raman Spectroscopy (SERS) Characteristic Peaks Screening Methods for the Rapid Determination of Chlorpyrifos Residues in Rice

Surface-enhanced Raman spectroscopy (SERS), coupled with characteristic peak screening methods, was developed for analyzing chlorpyrifos (CM) pesticide residues in rice. Au nanoparticles (AuNPs) were prepared as Raman signal enhancement. Magnesium sulfate (MgSO₄), primary secondary amine (PSA), and C₁₈ were used to purify the rice extraction. A successive projections algorithm (SPA) was performed to identify the optimal characteristic peaks of CM in rice from full Raman spectroscopy. Support vector machine (SVM) and partial least squares (PLS) were implemented to investigate the quantitative analysis models. The results demonstrated that six Raman peaks such as 671, 834, 1016, 1114, 1436, and 1444 cm^-1 were selected by the SPA and SVM models and had better performance using six peaks (only 0.92% of the full spectra variables) with R²_p = 0.97, RMSEP = 2.89 and RPD = 4.26, and the experiment time for a sample was accomplished within 10 min. Recovery for five unknown concentration samples was 97.45-103.96%, and T-test results also displayed no obvious differences between the measured value and the predicted value. The study stated that SERS, combined with characteristic peak screening methods, can be applied to rapidly monitor the chlorpyrifos residue in rice.