Search Results (103)

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article

Increased consumer awareness on food safety has spurred the development of detection techniques for pesticide residues. In this study, a rapid detection card on the basis of enzyme action was developed for the visual detection of pesticides, in which the thermally crosslinked and surface-decorated polyvinyl alcohol/citric acid nanofiber mat (PCNM) was employed as a novel immobilization matrix for acetylcholinesterase (AChE). The PCNM, crosslinked at 130 °C for 50 min, exhibited appropriate microstructure and water stability, making it suitable for AChE immobilization. The activation of carboxyl groups by surface decoration resulted in a 2.5-fold increase in enzyme loading capacity. Through parameter optimization, the detection limits for phoxim and methomyl were determined to be 0.007 mg/L and 0.10 mg/L, respectively. The detection card exhibited superior sensitivity and a reduced detection time (11 min) when compared to a commercially available pesticide detection card. Furthermore, the detection results of pesticide residues in fruit and vegetable samples confirmed its feasibility and superiority over commercial alternatives, suggesting its great potential for practical application in the on-site detection of pesticide residues. Full article

Over the past few decades, pesticide application has increased significantly, driven by population growth and associated urbanization. To date, pesticide use remains crucial for sustaining global food security by enhancing crop yields and preserving quality. However, extensive pesticide application raises serious environmental and health concerns worldwide due to its chemical persistence and high toxicity to organisms, including humans. Therefore, there is an urgent need to develop rapid and reliable analytical procedures for the quantification of trace pesticide residues to support public health management. Traditional methods, such as chromatography-based detection techniques, cannot simultaneously achieve high sensitivity, selectivity, cost-effectiveness, and portability, which limits their practical application. Nanomaterial-based sensing techniques are increasingly being adopted due to their rapid, efficient, user-friendly, and on-site detection capabilities. In this review, we summarize recent advances and emerging trends in commonly used nanosensing technologies, such as optical and electrochemical sensing, with a focus on recognition elements including enzymes, antibodies, aptamers, and molecularly imprinted polymers (MIPs). We discuss the types of nanomaterials used, preparation methods, performance, characteristics, advantages and limitations, and applications of these nanosensors in detecting pesticide residues in agricultural products. Furthermore, we highlight current challenges, ongoing efforts, and future directions in the development of pesticide detection nanosensors. Full article

Due to the dramatic increase in pesticide usage and improper application, large amounts of unused pesticides enter the environment through paddy water, causing severe pesticide pollution. To find a rapid method for identifying pesticide types and predicting their concentrations, the dielectric properties frequency response of pesticides was analyzed in paddy water. A rapid detection method for typical pesticides such as chlorpyrifos, isoprothiolane, imidacloprid and carbendazim was studied based on their dielectric properties. In this paper, amplitude and phase frequency response data for blank paddy water samples and 15 types of paddy water samples containing pesticides were collected at 10 different temperatures. Principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic frequencies. A species identification model based on support vector machine (SVM) for rapid detection of pesticides in paddy water was established using amplitude and phase frequency response data separately. Frequency response data of 431 sets from nine types of paddy water samples were divided into training and prediction sets in a 3:1 ratio, and a content prediction model based on artificial neural networks (ANN) with multiple inputs and single output was established using amplitude and phase frequency response data after CARS feature extraction. The experimental results show that both PCA-SVM and CARS-SVM species identification models established using amplitude and phase frequency response data have excellent identification effects, reaching over 90%. The PCA-SVM model based on phase frequency response data has the best identification effect for typical pesticides in paddy water with a prediction recognition accuracy range of 97.5–100%. The ANN content prediction model established using phase frequency response data performs well, and the highest R² prediction values of chlorpyrifos, isoprothiolane, imidacloprid and carbendazim in paddy water were 0.8249, 0.8639, 0.9113 and 0.8368 respectively. The research established a dielectric property detection method for the identification and content prediction of typical pesticides in paddy water, providing a theoretical basis for the hardware design of capacitive sensors based on dielectric property and the detection of pesticide residues in paddy water. This provides a new method and approach for pesticide residue detection, which is of great significance for scientific pesticide application and sustainable agricultural development. Full article

Food contaminants, including harmful microbes, pesticide residues, heavy metals and illegal additives, pose significant public health risks. While traditional detection methods are effective, they are often slow and require complex equipment, which limits their application in real-time monitoring and rapid response. Surface-enhanced Raman scattering (SERS) technology has gained widespread use in related research due to its hypersensitivity, non-destructibility and molecular fingerprinting capabilities. In recent years, Au-Ag bimetallic nanoparticles (Au-Ag BNPs) have emerged as novel SERS substrates, accelerating advancements in SERS detection technology. Au-Ag BNPs can be classified into Au-Ag alloys, Au-Ag core–shells and Au-Ag aggregates, among which the Au-Ag core–shell structure is more widely applied. This review discusses the types, synthesis methods and practical applications of Au-Ag BNPs in food contaminants. The study aims to provide valuable insights into the development of new Au-Ag BNPs and their effective use in detecting common food contaminants. Additionally, this paper explores the challenges and future prospects of SERS technology based on Au-Ag BNPs for pollutant detection, including the development of functional integrated substrates, advancements in intelligent algorithms and the creation of portable on-site detection platforms. These innovations are designed to streamline the detection process and offer guidance in selecting optimal sensing methods for the on-site detection of specific pollutants. Full article

Artificial intelligence is comprehensively transforming the food safety governance system by integrating modern technologies and building intelligent control systems that provide rapid solutions for the entire food supply chain from farm to fork. This article systematically reviews the core applications of AI in the orbit of food safety. First, in the production and quality control of primary food sources, the integration of spectral data with AI efficiently identifies pest and disease, food spoilage, and pesticide and veterinary drug residues. Secondly, during food processing, sensors combined with machine learning algorithms are utilized to ensure regulatory compliance and monitor production parameters. AI also works together with blockchain to build an immutable and end-point traceability system. Furthermore, multi-source data fusion can provide personalized nutrition and dietary recommendations. The integration of AI technologies with traditional food detection methods has significantly improved the accuracy and sensitivity of food analytical methods. Finally, in the future, to address the increasing food safety issues, Food Industry 4.0 will expand the application of AI with lightweight edge computing, multi-modal large models, and global data sharing to create a more intelligent, adaptive and flexible food safety system. Full article

This study presents the development of a zinc oxide (ZnO) nanorod-based sensor for the detection and quantification of residual pesticides commonly found in tea plantations, with a focus on quinalphos and thiamethoxam. Exploiting the unique electrical characteristics of ZnO nanorods, the sensor exhibits high sensitivity and selectivity in monitoring trace levels of pesticide residues. The detection mechanism relies on measurable changes in electrical resistance when the ZnO nanorod-coated electrodes interact with varying concentrations of the target pesticides. A performance evaluation was carried out using water samples spiked with different pesticide concentrations. The sensor displayed distinct response profiles for each compound: a linear resistance–concentration relationship for quinalphos and a non-linear, saturating trend for thiamethoxam, reflecting their differential interactions with the ZnO surface. Statistical analysis confirmed the sensor’s reliability, reproducibility, and consistency across repeated measurements. The rapid response time and ease of fabrication underscore its potential for real-time, on-site pesticide monitoring. This method offers a promising alternative to traditional analytical techniques, enhancing food safety assurance and supporting sustainable agricultural practices through effective environmental surveillance. Full article

Food safety is a significant issue of global concern. Consumer safety and government regulations drive the need for the accurate analysis of food contaminants, residues and other chemical constituents of concern. Traditional methods for the detection of food contaminants often present challenges, including lengthy processing times and food matrix interference; they often require expensive equipment, skilled personnel or have limitations in sensitivity or specificity. Developing novel analytical methods that are sensitive, specific, accurate and rapid is therefore crucial for ensuring food safety and the protection of consumers. The immuno-polymerase chain reaction (IPCR) method offers a promising solution in the analysis of food contaminants by combining the specificity of conventional immunological methods with the exponential sensitivity of PCR amplification. This review evaluates the current state of IPCR methods, describes a variety of existing IPCR formats and explores their application in the analysis of food contaminants, including pathogenic bacteria and their toxins, viruses, mycotoxins, allergens, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, phthalic acid esters, pesticides, antibiotics and other food contaminants. Depending on the type of analyte, either sandwich or competitive format IPCR methods are predominantly used. This review also examines limitations of current IPCR methods and explores potential advancements for future implementation in the field of food safety. Full article

Deltamethrin (Del), a widely utilized pyrethroid pesticide, exhibits significant risks to human health due to its persistent environmental residues. This study aims to develop an efficient sensing detector for rapid Del detection through aptamer-based recognition. A modified Capture-SELEX strategy successfully identified Del-1, a high-affinity DNA aptamer demonstrating specific binding to Del with a dissociation constant (Kd) of 82.90 ± 6.272 nM. Molecular docking analysis revealed strong intermolecular interactions between Del-1 and Del, exhibiting a favorable binding energy of −7.35 kcal·mol⁻¹. Leveraging these findings, we constructed a colorimetric detector using gold nanoparticles (AuNPs) and poly dimethyl diallyl ammonium chloride (PDDA)-mediated aggregation modulation. The sensing detector employed dual detection parameters: (1) a characteristic color transition from red to blue and (2) a quantitative ∆A650/A520 ratio measurement. This optimized system achieved a detection limit of 54.57 ng·mL⁻¹ with exceptional specificity against other competitive pesticides. Practical validation using spiked fruit samples (apples and pears) yielded satisfactory recoveries of 74–118%, demonstrating the sensor’s reliability in real-sample analysis. The developed methodology presents a promising approach for the on-site monitoring of pyrethroid contaminants in agricultural products. Full article

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. Full article

With growing consumer concerns about food safety, developing methods for the field-based, non-destructive, and rapid detection of pesticide residues is becoming increasingly critical. This study introduces a field-based, non-destructive, and rapid method for detecting pesticide residues on kumquat surfaces. Initially, spectral data from the visible/near-infrared (VNIR) light bands were collected using a handheld spectrometer from kumquats treated with three pesticides at various gradient concentrations and water. The data were then preprocessed and analyzed using machine learning (SPA-SVM) and deep learning models (1D-CNN, 1D-ResNet) to determine the optimal model. Features from the convolutional layer of the 1D-ResNet model were extracted for visualization and analysis, highlighting significant differences in features between the different pesticides and across varying concentrations. The results indicate that the 1D-ResNet model achieved 97% overall accuracy, with a macro average of 0.96 and a weighted average of 0.97, and that precision, recall, and F1-score approached 1.00 for most pesticide treatment gradients. The results of this research verified the feasibility of the handheld spectrometer combined with 1D-Resnet for the detection of pesticide residues on the surface of kumquat, realized the visualization of pesticide residue characteristics, and also provided a reference for the detection of pesticide residues on the surface of other fruits. Full article

Pesticide residues still pose a risk to human health. With the rapid development of rapid testing technology, the levels of different types of pesticide residues in agricultural products can be identified in a shorter period; thus, the safety of food can be guaranteed. However, the effectiveness of commercially available testing products has yet to be evaluated. In this study, colloidal gold immunochromatographic qualitative testing products manufactured by 34 companies were tested for their assay performance on Emamectin Benzoate, Isocarbophos, and fipronil with standardized cowpea samples. The results indicated that most of the evaluated products were identified as having ‘passed’. Most pesticide residue rapid test immunoassay products can be considered ideal means for testing certain pesticide residues. However, further evaluation of pesticide residue rapid test immunoassay products is needed, as detection technologies are still developing. Full article

Rapid, effective, and cost-effective methods for large-scale screening of pesticide residues in the environment and agricultural products are important for assessing potential environmental risks and safeguarding human health. Here, we constructed a novel aggregation-induced emission (AIE) electrochemical aptamer (Apt) sensor based on red-emissive sulfur quantum dots (SQDs), which aimed at the rapid screening and quantitative detection of malathion. SQDs were prepared using a two-step oxidation method with good electrochemiluminescence (ECL) optical properties. These SQDs were modified onto the electrode surface to serve as ECL luminophores. Subsequently, Apt was introduced and modified to form a double-helix structure with the complementary chain (cDNA). The ECL signal was reduced because the biomolecules had poor electrical conductivity and inefficient electron transfer. When the target malathion was added, the double helix structure was unraveled, the malathion Apt fell off the electrode surface, and the ECL signal was restored. The linear range of detection was 1.0 × 10⁻¹³–1.0 × 10⁻⁸ mol·L⁻¹, and the detection limit was 0.219 fM. The successful preparation of the sensor not only develops the ECL optical properties of SQDs but also expands the application of SQDs in ECL sensing. Full article

Raman spectroscopy is a spectral analysis technique based on molecular vibration. It has gained widespread acceptance as a practical tool for the non-invasive and rapid characterization or identification of multiple analytes and compounds in recent years. In fruit quality detection, Raman spectroscopy is employed to detect organic compounds, such as pigments, phenols, and sugars, as well as to analyze the molecular structures of specific chemical bonds or functional groups, providing valuable insights into fruit disease detection, pesticide residue analysis, and origin identification. Consequently, Raman spectroscopy techniques have demonstrated significant potential in agri-food analysis across various domains. Notably, the frontier of Raman spectroscopy is experiencing a surge in machine learning applications to enhance the resolution and quality of the resulting spectra. This paper reviews the fundamental principles and recent advancements in Raman spectroscopy and explores data processing techniques that use machine learning in Raman spectroscopy, with a focus on its applications in detecting fruit diseases, analyzing pesticide residues, and identifying origins. Finally, it highlights the challenges and future prospects of Raman spectroscopy, offering an effective reference for fruit quality detection. Full article

This review summarizes the latest progress in enzyme preparation, including enzyme design and modification technology, exploration of new enzyme sources, and application of enzyme preparation in food processing, detection, and preservation. The directed evolution technology improved the stability and catalytic efficiency of enzymes, while enzyme immobilization technology enhanced reusability and industrial applicability. Extremozymes and biomimetic enzymes exhibit excellent performance under harsh conditions. In food processing, enzyme preparation can improve food quality and flavor. In food detection, enzymes combined with immune detection and biosensors realize rapid detection of allergens, pollutants, and pesticide residues. In food preservation, enzymes enhance food quality by extending shelf life and inhibiting microbial growth. In the future, enzyme engineering will be combined with computer-aided design, artificial intelligence, and new material technology to promote intelligent enzyme design and multifunctional enzyme preparation development and help the technological upgrading and sustainable development of the food industry and green chemistry. Full article