Freeze Surface-Enhanced Raman Scattering Coupled with Thin-Layer Chromatography: Pesticide Detection and Quantification Case

Automated identification of pesticide mixtures via machine learning analysis of TLC-SERS spectra

Identification of components in pesticide mixtures has been a major challenge in spectral analysis. In this paper, we assembled monolayer Ag nanoparticles on Thin-layer chromatography (TLC) plates to prepare TLC-Ag substrates with mixture separation and surface-enhanced Raman scattering (SERS) detection. Spectral scans were performed along the longitudinal direction of the TLC-Ag substrate to generate SERS spectra of all target analytes on the TLC plate. Convolutional neural network classification and spectral angle similarity machine learning algorithms were used to identify pesticide information from the TLC-SERS spectra. It was shown that the proposed automated spectral analysis method successfully classified five categories, including four pesticides (thiram, triadimefon, benzimidazole, thiamethoxam) as well as a blank TLC-Ag data control. The location of each pesticide on the TLC plate was determined by the intersection of the information curves of the two algorithms with 100 % accuracy. Therefore, this method is expected to help regulators understand the residues of mixed pesticides in agricultural products and reduce the potential risk of agricultural products to human health and the environment.

Visual whole-process monitoring of pesticide residues: An environmental perspective using surface-enhanced Raman spectroscopy with dynamic borohydride-reduced silver nanoparticles

Environmental monitoring of pesticide residues in crops is essential for both food safety and environmental protection. Traditional methodologies face challenges due to the interference of endogenous compounds in peel and pulp tissues, often being invasive, labor-intensive, and inadequate for real-time observation of hazardous substance distribution. In this study, dynamic borohydride-reduced nanoparticles were employed as enhanced substrates. For the first time, surface-enhanced Raman spectroscopy (SERS) imaging was harnessed to enable whole-process visual detection of pesticide residues. The developed method is both stable and sensitive, boasting a detection lower limit below 1 pg/mL, coupled with robust quantitative analytical capabilities. This technique was successfully employed to detect residue signals across various crops and fruit juices. Furthermore, SERS imaging was utilized to map the distribution of pesticide residues from the exterior to the interior of fruits and vegetables. Vertex component analysis further refined the process by mitigating interference from plant autofluorescence. Collectively, this innovative strategy facilitates comprehensive pesticide residue monitoring, offering a potent tool for controlling hazardous substances in crops. Its potential applications extend beyond food safety, holding significant promise for sustainable agricultural production and enhanced environmental safeguarding.

Paper electrophoretic enrichment-assisted ultrasensitive SERS detection

To achieve sensitive detection of trace substances in fluids by surface-enhanced Raman spectroscopy (SERS), effective enrichment of molecules at subwavelength regions (hot spots) with a large enhancement is adopted. In this work, a glass fibre paper with Ag nanoparticles (AgNPs) is employed for electrodynamic enrichment of analytes in fluids by paper electrophoresis integrated with field amplification sample stacking (FASS) and capillary effects to obtain both Raman and SERS convenient and sensitive detection. With the help of electrophoretic enrichment on the glass fibre paper and surface plasmon enhancement on the AgNPs, this paper electrophoretic enrichment could improve the detection limit of Raman and SERS detection by more than an order of magnitude, even achieving a SERS detection limit of 10-17 M for Nile Blue A. Furthermore, this flexible SERS detection method can also detect trace organic contaminants at the ppt level in aquaculture and food applications.

An electrochemical immunosensor based on a carboxylated multiwalled carbon nanotube-silver nanoparticle-chitosan functional layer for the detection of fipronil

Fipronil (FP) is a very effective phenylpyrazole insecticide and is now widely used in agriculture. At the same time, the water and soil in the environment are polluted by FP. For the rapid detection of FP toxicants in food and the environment, we have designed an entirely novel electrochemical immunosensor that employs the combined functionalities of a cMWCNTs-AgNPs-CS-FAb-BSA layer to modify an SPCE by the freeze-drying technique. The high porosity of chitosan (CS) coupled with an excellent electron transfer enabled by the cMWCNTs and AgNPs increased the surface area for anti-fipronil (FAb) antibody immobilization and enhanced the current signal of the immunosensor. Cyclic voltammetry (CV) was applied for the quantitative determination of FP under optimized conditions (0.1 M PBS, pH 7.5, 35 °C incubation temperature, and 40 min incubation duration). The modified electrochemical immunosensor displayed excellent analytical performance, including a wide linear concentration range from 0.1 to 1000 ng mL-1 with a very low limit of detection of 0.021 ng mL-1 and good reproducibility (RSD = 2.58%, n = 6), stability (80.4% sensitivity after 5 days), and selectivity. Not only could the modified electrochemical immunosensor be applied in the FP residue analysis of agricultural products, but the present immobilization strategy can also potentially be applied to different biomolecules.

Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity

Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed chromatographic techniques. Numerous approaches were reported for retention prediction in different chromatographic techniques, and consistent results demonstrated that the accuracy and effectiveness of deep learning models outclassed the linear machine learning models, mainly in liquid and gas chromatography, as ML algorithms use fewer complex data to train and predict information. Support Vector machine-based neural networks were found to be most utilized for the prediction of retention factors of different compounds in thin-layer chromatography. Cheminformatics, chemometrics, and hybrid approaches were also employed for the modeling and were more reliable in retention prediction over conventional models. Quantitative Structure Retention Relationship (QSRR) was also a potential method for predicting retention in different chromatographic techniques and determining the separation method for analytes. These techniques demonstrated the aids of incorporating QSRR with AI-driven techniques acquiring more precise retention predictions. This review aims at recent exploration of different AI-driven approaches employed for retention prediction in different chromatographic techniques, and due to the lack of summarized literature, it also aims at providing a comprehensive literature that will be highly useful for the society of scientists exploring the field of AI in analytical chemistry.

Integration of Metallic Nanomaterials and Recognition Elements for the Specifically Monitoring of Pesticides in Electrochemical Sensing

Although all countries have been controlling the excessive use of pesticides, incidents of pesticide residues still existed. Electrochemical biosensors are extensively applied detection techniques to monitor pesticides with the help of different types of biorecognition components mainly including, antibodies, aptamers, enzymes (i.e., acetylcholinesterase, organophosphorus hydrolase, etc.), and synthetic molecularly imprinted polymers. Besides, the electrode materials mainly affected the sensitivity of electrochemical biosensors. Metallic nanomaterials with various structures and excellent electrical conductivity were desirable choice to construct electrochemical platforms to achieve the detection with high sensitivity and good specificity toward the target. This work reviewed the developed metallic materials including monometallic nanoparticles, bimetallic nanomaterials, metal atoms, metal oxides, metal molybdates, metal-organic frameworks, MXene, etc. Integration of recognition elements endowed the electrode materials with higher specificity toward the target pesticide. Besides, future challenges of metallic nanomaterials-based electrochemical biosensors for the detection of pesticides are also discussed and described.