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

Advances in Detection Technologies for Pesticide Residues and Heavy Metals in Rice: A Comprehensive Review of Spectroscopy, Chromatography, and Biosensors

Pesticide residues and heavy metals, originating from diverse sources such as agricultural practices and industrial activities, pose substantial risks to human health and the ecological environment. For instance, residues of organophosphorus pesticides may damage the human nervous system, while heavy metals such as mercury and cadmium accumulate in living organisms, potentially leading to severe organ damage. The contamination of rice with these pollutants has become a critical concern, necessitating the development of innovative detection techniques that are sensitive, accurate, rapid, portable, and intelligent. This review offers an in-depth analysis of the types, sources, health risks, and ecological impacts of pesticide residues and heavy metals in rice, providing a comprehensive understanding of the challenges and solutions associated with these contaminants. It further provides the fundamental principles, comparative advantages, and technical constraints of both conventional and emerging detection methodologies. These encompass traditional analytical techniques such as spectroscopy and chromatography, well-established immunoassay systems, as well as innovative biosensing technologies. This discussion is substantiated with representative case studies demonstrating their practical applications in rice quality assessment and safety testing. In addition, this review envisions future directions for the development of detection technologies, emphasizing the importance of miniaturization, multiplexed detection, integration with nanotechnology, and real-time monitoring systems. By providing a theoretical foundation for advancing food safety innovation, this review aims to contribute to the ongoing efforts to ensure rice quality and safety, protect public health, and preserve ecological balance.

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

Non-linear responses via agglomeration and aggregation of gold nanoparticles for surface-enhanced Raman spectroscopy (SERS) coupled with chemometric analysis for chlorpyrifos detection

This study investigates the behaviour of gold nanoparticles (AuNPs) when exposed to chlorpyrifos, an agricultural pesticide, and its application in detecting the pesticide via surface-enhanced Raman spectroscopy (SERS). Under synergistic addition of NaCl, AuNPs undergo agglomeration at lower chlorpyrifos concentrations but aggregation at higher concentrations, resulting in a distinctive nonlinear SERS response. A linear relationship is obtained between 0.001 and 1 ppm with detection limit (LOD) of 0.009 ppm, while an inverse response is observed at higher concentrations (1-1000 ppm) with a LOD of 1 ppm. Combining the colorimetric response of AuNP solutions, their absorbance spectra, and principal component analysis can improve detection reliability. The assay, coupled with a simple recovery method using acetonitrile swabbing, achieves high reproducibility in detecting chlorpyrifos in cucumber, even at concentrations as low as 0.11 ppm. This approach can be tailored for various chlorpyrifos concentrations not only in cucumbers but also in different food matrices.

A new plant-esterase inhibition based electrochemical sensor with signal amplification by MoS2@N-CDs for chlorpyrifos detection

Chlorpyrifos (CPF) is the most common pesticide entering the food chain and posing a threat to human health. This study presents a new electrochemical biosensor based on molybdenum disulfide nanosheets and nitrogen-doped carbon dot nanocomposite (MoS2@N-CDs) and kidney bean esterase (KdBE), and it is shown to achieve accurate detection of CPF. MoS2@N-CDs were prepared by a facile solvothermal method and characterized by electron microscopy, X-ray diffraction and X-ray photoelectron spectroscopy. Electrochemical characterization confirmed that MoS2@N-CDs facilitated electron transfer and increased the electroactive surface area of the electrode, thereby improved the sensing performance of the electrode. The oxidation peak current of 1-naphthol, which was produced by the hydrolysis of 1-naphthyl acetate catalyzed by KdBE, was adopted as the signal of the sensor. CPF can suppress KdBE activity and consequently cause a decrease in the sensing signal. The experimental results show that the variation of sensing signal is a reliable index to evaluate the CPF level. Under the optimized conditions, the developed enzyme sensor showed superior CPF assay performance with a linear detection range as wide as 0.01-500 μg L-1 and LOD as low as 3.5 × 10-3 μg L-1 (S/N = 3). The inter- and intra-batch RSDs for electrode testing were 4.02% and 2.69%, respectively. Moreover, the developed biosensor also showed good stability and anti-interference. The spiked recoveries of CPF in oilseed rape and cabbage ranged from 98.09% to 106.01% with low relative standard deviation (RSD) (<5.23%), suggesting that the sensor is a promising tool to enable simple, low-cost but highly sensitive large-scale screening of CPF residues in food.

Identification of liquor adulteration by Raman spectroscopy method based on ICNAFS

The health of consumers can be impacted by the additives placed into the liquor. To address the issues of poor accuracy, low reliability, and complex operational procedures in identifying adulteration in existing liquor, an improved convex non-negative matrix factorization (ICNAFS) with an adaptive graph constraint for unsupervised feature extraction is proposed in this paper, with the goal of achieving rapid identification of adulteration in liquor by Raman spectroscopy through dimensionality reduction. For the sake to streamline the calculation process for effective feature extraction and increase the accuracy of the analyzed model, the proposed ICNAFS method incorporates two fundamental models, such as ridge regression and convex non-negative matrix factorization (NMF). In particular, dimensionality reduction of the original spectrum is initially conducted using Principal Component Analysis (PCA), Sequential Projection Algorithm (SPA), Convex Non-Negative Matrix Factorization with an Adaptive Graph Constraint (CNAFS), and ICNAFS respectively. k-means is subsequently employed to merge the four models for clustering analysis. The results suggest that the accuracy of the presented ICNAFS-assisted k-means model is higher than the other techniques, with a clustering accuracy of 98.67%, exhibiting a 4% improvement over the existing CNAFS, through examination of 150 sets of tainted liquor data from five categories of samples. This demonstrates the potency of the proposed ICNAFS-assisted k-means clustering model in conjunction with Raman spectroscopy as a method for detecting tainted liquor.

SERS combined with QuEChERS using NBC and Fe3O4 MNPs as cleanup agents to rapidly and reliably detect chlorpyrifos pesticide in citrus

The surface-enhanced Raman spectroscopy (SERS) technique is being increasingly used for the detection of pesticide residues in agricultural products. However, there are large amounts of fluorescence-producing substances in agricultural products, which seriously affect the Raman signal of the analyte. In this paper, the QuEChERS method was used to remove interfering fluorescent substances in the analyte, and the purification effects of different doses of nano bamboo charcoal (NBC) and Fe3O4 magnetic nanoparticle (Fe3O4 MNP) adsorbents were studied. Meanwhile, the Raman spectral acquisition conditions (AuNPs, test solution, and NaCl) were optimized based on the orthogonal test method. The results showed that 300 µL AuNPs, 40 µL test solution, and 100 µL 1.5% NaCl gave the best SERS response effect. 12.5 mg NBC combined with 10 mg Fe3O4 MNPs could effectively remove the interfering substances from citrus. The Raman spectra of chlorpyrifos molecules were theoretically modeled using density-functional theory (DFT). By comparing the DFT results with the actual tests, five feature peaks, at 338, 522, 558, 672, and 1600 cm-1, were obtained for the detection of chlorpyrifos pesticide residues in citrus. Based on the Raman feature peak intensity at 672 cm-1, the concentration of chlorpyrifos in citrus showed a good linear relationship (R2 = 0.9979) in the concentration range of 3-20 mg kg-1. The recovery rate was 92.12% to 98.38%, and the relative standard deviation (RSD) was 1.77% to 5.29%. The lowest detection concentration was about 3 mg kg-1, and the detection time of a single sample could be completed within 15 min. This study showed that the combination of SERS and QuEChERS preprocessing methods could achieve rapid detection of chlorpyrifos pesticide residues in citrus.