Fast and Quantitative Analysis of Ediphenphos Residue in Rice Using Surface-Enhanced Raman Spectroscopy

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

Nanostructure-Based Surface-Enhanced Raman Spectroscopy Techniques for Pesticide and Veterinary Drug Residues Screening

Pesticide and veterinary drug residues in food and environment pose a threat to human health, and a rapid, super-sensitive, accurate and cost-effective analysis technique is therefore highly required to overcome the disadvantages of conventional techniques based on mass spectrometry. Recently, the surface-enhanced Raman spectroscopy (SERS) technique emerges as a potential promising analytical tool for rapid, sensitive and selective detections of environmental pollutants, mostly owing to its possible simplified sample pretreatment, gigantic detectable signal amplification and quick target analyte identification via finger-printing SERS spectra. So theoretically the SERS detection technology has inherent advantages over other competitors especially in complex environmental matrices. The progress in nanostructure SERS substrates and portable Raman appliances will promote this novel detection technology to play an important role in future rapid on-site assay. This paper reviews the advances in nanostructure-based SERS substrates, sensors and relevant portable integrated systems for environmental analysis, highlights the potential applications in the detections of synthetic chemicals such as pesticide and veterinary drug residues, and also discusses the challenges of SERS detection technique for actual environmental monitoring in the future.

Onsite/on-field analysis of pesticide and veterinary drug residues by a state-of-art technology: A review

Pesticides and veterinary drugs are generally employed to control pests and insects in crop and livestock farming. However, remaining residues are considered potentially hazardous to human health and the environment. Therefore, regular monitoring is required for assessing and legislation of pesticides and veterinary drugs. Various approaches to determining residues in various agricultural and animal food products have been reported. Most analytical methods involve sample extraction, purification (cleanup), and detection. Traditional sample preparation is time-consuming labor-intensive, expensive, and requires a large amount of toxic organic solvent, along with high probability for the decomposition of a compound before the analysis. Thus, modern sample preparation techniques, such as the quick, easy, cheap, effective, rugged, and safe method, have been widely accepted in the scientific community for its versatile application; however, it still requires a laboratory setup for the extraction and purification processes, which also involves the utilization of a toxic solvent. Therefore, it is crucial to elucidate recent technologies that are simple, portable, green, quick, and cost-effective for onsite and infield residue detections. Several technologies, such as surface-enhanced Raman spectroscopy, quantum dots, biosensing, and miniaturized gas chromatography, are now available. Further, several onsite techniques, such as ion mobility-mass spectrometry, are now being upgraded; some of them, although unable to analyze field sample directly, can analyze a large number of compounds within very short time (such as time-of-flight and Orbitrap mass spectrometry). Thus, to stay updated with scientific advances and analyze organic contaminants effectively and safely, it is necessary to study all of the state-of-art technology.

Surface-enhanced Raman spectroscopywith gold nanorods modified by sodium citrate and liquid-liquid interface self-extraction for detection of deoxynivalenol in Fusarium head blight-infected wheat kernels coupled with a fully convolution network

Surface-enhanced Raman spectroscopy (SERS) and deep learning network were adopted to develop a detection method for deoxynivalenol (DON) residues in Fusarium head blight (FHB)-infected wheat kernels. First, the liquid-liquid interface self-extraction was conducted for the rapid separation of DON in samples. Then, the gold nanorods modified with sodium citrate (Cit-AuNRs) were prepared as substrate for a gigantic enhancement of SERS signal. Results showed that the spectral characteristic peaks for DON residues of 99.5-0.5 mg/L were discernible with the relative standard deviation of 4.2%, with the limit of detection of 0.11 mg/L. Meanwhile, the fully convolutional network for the spectra of matrix input form was developed and obtained the optimal quantitative performance, with a root-mean-square error of prediction of 4.41 mg/L and coefficient of determination of prediction of 0.9827. Thus, the proposed method provides a simple, sensitive, and intelligent detection for DON in FHB-infected wheat kernels.

Rapid detection of chlorpyrifos pesticide residue in tea using surface-enhanced Raman spectroscopy combined with chemometrics

Surface enhanced Raman spectroscopy based on rapid pretreatment combined with Chemometrics was used to determine chlorpyrifos residue in tea. Au nanoparticles were used to as enhance substrate. Different dosages of PSA and NBC were investigated to eliminate the tea substrate influence. Competitive adaptive reweighted sampling (CARS) was used to optimize the characteristic peaks, and compared to full spectra variables and the experiment selected variables. The results showed that PSA of 80 mg and NBC of 20 mg was an excellent approach for rapid detecting. CARS - PLS had better accuracy and stability using only 1.7% of full spectra variables. SVM model achieved better performance with R²_p = 0.981, RMSEP = 1.42 and RPD = 6.78. Recoveries for five unknown concentration samples were 98.47 ~ 105.18% with RSD - 1.53% ~ 5.18%. T-test results showed that t value was 0.720, less than t0.05,4 = 2.776, demonstrating that no clear difference between the real value and predicted value. The detection time of a single sample is completed within 15 min. This study demonstrated that SERS coupled with Chemometrics and QuEChERS may be employed to rapidly examine the chlorpyrifos residue in tea towards its quality and safety monitoring.

Optical Screening Methods for Pesticide Residue Detection in Food Matrices: Advances and Emerging Analytical Trends

Pesticides have been extensively used in agriculture to protect crops and enhance their yields, indicating the need to monitor for their toxic residues in foodstuff. To achieve that, chromatographic methods coupled to mass spectrometry is the common analytical approach, combining low limits of detection, wide linear ranges, and high accuracy. However, these methods are also quite expensive, time-consuming, and require highly skilled personnel, indicating the need to seek for alternatives providing simple, low-cost, rapid, and on-site results. In this study, we critically review the available screening methods for pesticide residues on the basis of optical detection during the period 2016-2020. Optical biosensors are commonly miniaturized analytical platforms introducing the point-of-care (POC) era in the field. Various optical detection principles have been utilized, namely, colorimetry, fluorescence (FL), surface plasmon resonance (SPR), and surface enhanced Raman spectroscopy (SERS). Nanomaterials can significantly enhance optical detection performance and handheld platforms, for example, handheld SERS devices can revolutionize testing. The hyphenation of optical assays to smartphones is also underlined as it enables unprecedented features such as one-click results using smartphone apps or online result communication. All in all, despite being in an early stage facing several challenges, i.e., long sample preparation protocols or interphone variation results, such POC diagnostics pave a new road into the food safety field in which analysis cost will be reduced and a more intensive testing will be achieved.

Ultrasensitive and visible light-responsive photoelectrochemical aptasensor for edifenphos based on Zinc phthalocyanine sensitized MoS2 nanosheets

Developing a simple, rapid detection method for the analysis of edifenphos (EDI) is crucial due to its residue is harmful to acetylcholinesterase on the human cellular system, and cause a lot of complications. Herein, we synthesized visible light-responsive MoS₂ nanosheets decorated with Zinc phthalocyanine (ZnPc) nanoparticles (ZnPc/n-MoS₂). Due to the sensitization of ZnPc nanoparticles, the resulting ZnPc/n-MoS₂ exhibited narrower energy bandgap and efficient charge transfer. Especially, the carrier lifetime of ZnPc/n-MoS₂ is 2 more times longer than n-MoS₂, and the photocurrent intensity of ZnPc/n-MoS₂ is 24 times of n-MoS₂ and 22 times of ZnPc nanoparticles under visible light irradiation. Further, a visible light-responsive ultrasensitive photoelectrochemical (PEC) aptasensor for selectivity recognition of EDI was triumphantly established by using EDI aptamer as a biorecognition element, which exhibited a wide linear ranking from 5 ng L^-1 to 10 μg L^-1 (R² = 0.996) and a low detection limit of 1.667 ng L^-1 (S/N = 3). The splendid performance of the ZnPc/n-MoS₂ nanosheet ultrasensitive sensing platform can be applied to detect the concentration of EDI in food, biomedical and environmental analysis.

Detection of Pirimiphos-Methyl in Wheat Using Surface-Enhanced Raman Spectroscopy and Chemometric Methods

Pesticide residue detection is a hot issue in the quality and safety of agricultural grains. A novel method for accurate detection of pirimiphos-methyl residues in wheat was developed using surface-enhanced Raman spectroscopy (SERS) and chemometric methods. A simple pretreatment method was conducted to extract pirimiphos-methyl residue from wheat samples, and highly effective gold nanorods were prepared for SERS measurement. Raman peaks assignment was calculated using density functional theory. The Raman signal of pirimiphos-methyl can be detected when the concentrations of residue in wheat extraction solution and contaminated wheat is as low as 0.2 mg/L and 0.25 mg/L, respectively. Quantification of pirimiphos-methyl was performed by applying regression models developed by partial least squares regression, support vector machine regression and random forest with principal component analysis using different preprocessed methods. As for the contaminated wheat samples, the relative deviation between gas chromatography-mass spectrometry value and predicted value is in the range of 0.10%-6.63%, and predicted recovery is 94.12%-106.63%, ranging from 23.93 mg/L to 0.25 mg/L. Results demonstrated that the proposed SERS method is an effective and efficient analytical tool for detecting pirimiphos-methyl in wheat with high accuracy and excellent sensitivity.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Titanium Dioxide in Food Products: Quantitative Analysis Using ICP-MS and Raman Spectroscopy

Titanium dioxide (TiO₂) is commonly used as a color additive in food products. In this study, a total of 11 food products, such as a coffee cream, yogurt snack, hard candy, and chewy candy, that are widely consumed by adults or children were investigated. For characterization of particle size, size distribution, crystallinity, and concentration of TiO₂, particles were first extracted using an acid digestion method from food, and various analytical techniques were applied. All products investigated in this study contained nanosized TiO₂ particles (21.3-53.7%) in the anatase phase. The particle size of TiO₂ was in the range of 26.9-463.2 nm. The concentration of TiO₂ in the products ranged from 0.015% (150 ppm) to 0.462% (4620 ppm). These values obtained using inductively coupled plasma-mass spectrometry (ICP-MS) were considered as the reference and were compared with Raman results to evaluate the feasibility of using the Raman method to quantitate TiO₂ in food products. The Raman method developed in this study proved to effectively analyze anatase TiO₂ in food products at levels of several hundred parts per million or greater. Limitations of using the Raman method as a quick screening tool for determination of TiO₂ are also discussed.