Detection of Pesticide (Chlorpyrifos) Residues on Fruit Peels Through Spectra of Volatiles by FTIR

Recent developments and applications of surface enhanced Raman scattering spectroscopy in safety detection of fruits and vegetables

This article reviewed the latest research progress of Surface-enhanced Raman Spectroscopy (SERS) in the security detection of fruits and vegetables in recent years, especially in three aspects: pesticide residues, microbial toxin contamination and harmful microorganism infection. The binding mechanism and application potential of SERS detection materials (including universal type and special type) and carrier materials (namely rigid and flexible materials) were discussed. Finally, the application prospect of SERS in fruit and vegetable safety detection was explored, and the problems to be solved and development trends were put forward. The poor stability and reproducibility of SERS substrates make it difficult for practical applications. It is necessary to continuously optimize SERS substrates and develop small and portable Raman spectroscopy analyzers. In the future, SERS technology is expected to play an important role in human health, food safety and economy.

Screening for pesticide residues in cocoa (Theobroma cacao L.) by portable infrared spectroscopy

Rapid assessment of pesticide residues ensures cocoa bean quality and marketability. In this study, a portable FTIR instrument equipped with a triple reflection attenuated total reflectance (ATR) accessory was used to screen cocoa beans for pesticide residues. Cocoa beans (n = 75) were obtained from major cocoa growing regions of Peru and were quantified for pesticides by gas chromatography (GC) or liquid chromatography (LC) coupled with mass spectrometry (MS). The FTIR spectra were used to detect the presence of pesticides in cocoa beans or lipid fraction (butter) by using a pattern recognition (Soft Independent Modeling by Class Analogy, SIMCA) algorithm, which produced a significant discrimination for cocoa nibs (free or with pesticides). The variables related to the class grouping were assigned to the aliphatic (3200-2800 cm^-1) region with an interclass distance (ICD) of 3.3. Subsequently, the concentration of pesticides in cocoa beans was predicted using a partial least squares regression analysis (PLSR), using an internal validation of the PLRS model, the cross-validation correlation coefficient (R_val = 0.954) and the cross-validation standard error (SECV = 14.9 mg/kg) were obtained. Additionally, an external validation was performed, obtaining the prediction correlation coefficient (R_pre = 0.940) and the standard error of prediction (SEP = 16.0 μg/kg) with high statistical performances, which demonstrates the excellent predictability of the PLSR model in a similar real application. The developed FTIR method presented limits of detection and quantification (LOD = 9.8 μg/kg; LOQ = 23.1 μg/kg) with four optimum factors (PC). Mid-infrared spectroscopy (MIR) offered a viable alternative for field screening of cocoa.

Recent advances in assessment methods and mechanism of microbe-mediated chlorpyrifos remediation

Chlorpyrifos (CP) is one of the Organophosphorus pesticides (OPs) primarily used in agriculture to safeguard crops from pests and diseases. The pervasive use of chlorpyrifos is hazardous to humans and the environment as it inhibits the receptor for acetylcholinesterase activity, leading to abnormalities linked to the central nervous system. Hence, there is an ardent need to develop an effective and sustainable approach to the on-site degradation of chlorpyrifos. The role of microbes in the remediation of pesticides is considered the most effective and eco-friendly approach, as they have strong degradative potential due to their gene and enzymes naturally adapted to these sites. Several reports have previously been published on exploring the role of microbes in the degradation of CP. However, detection of CP as an environmental contaminant is an essential prerequisite for developing an efficient microbial-mediated biodegradation method with less harmful intermediates. Most of the articles published to date discuss the fate and impact of CP in the environment along with its degradation mechanism but still fail to discuss the analytical portion. This review is focused on the latest developments in the field of bioremediation of CP along with its physicochemical properties, toxicity, fate, and conventional (UV-Visible spectrophotometer, FTIR, NMR, GC-MS, etc) and advanced detection methods (Biosensors and immunochromatography-based methods) from different environmental samples. Apart from it, this review explores the role of metagenomics, system biology, in-silico tools, and genetic engineering in facilitating the bioremediation of CP. One of the objectives of this review is to educate policymakers with scientific data that will enable the development of appropriate strategies to reduce pesticide exposure and the harmful health impacts on both Human and other environmental components. Moreover, this review provides up-to-date developments related to the sustainable remediation of CP.

Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies

Different types of quantitative technology based on infrared spectroscopy to detect profenofos were compared based on Fourier transform near-infrared (FT-NIR; 12,500-4000 cm^-1) and Fourier transform mid-infrared (FT-MIR; 4000-400 cm^-1) spectroscopies. Standard solutions in the range of 0.1-100 mg/L combined with the dry-extract system for infrared (DESIR) technique were analyzed. Based on partial least-squares regression (PLSR) to develop a calibration equation, FT-NIR-PLSR produced the best prediction of profenofos residues based on the values for R ² (0.87), standard error of prediction or SEP (11.68 mg/L), root-mean-square error of prediction or RMSEP (11.50 mg/L), bias (-0.81 mg/L), and ratio performance to deviation or RPD (2.81). In addition, FT-MIR-PLSR produced the best prediction of profenofos residues based on the values for R ² (0.83), SEP (13.10 mg/L), RMSEP (13.00 mg/L), bias (1.46 mg/L), and RPD (2.49). Based on the ease of use and appropriate sample preparation, FT-NIR-PLSR combined with DESIR was chosen to detect profenofos in Chinese kale, cabbage, and chili spur pepper at concentrations of 0.53-106.28 mg/kg. The quick, easy, cheap, effective, rugged, and safe technique coupled with gas chromatography-mass spectrometry was used to obtain the actual values. The best FT-NIR-PLSR equation provided good profenofos detection in all vegetables based on values for R ² (0.88-0.97), SEP (5.27-11.07 mg/kg), RMSEP (5.25-11.00 mg/kg), bias (-1.39 to 1.30 mg/kg), and RPD (2.91-5.22). These statistics revealed no significant differences between the FT-NIR predicted values and actual values at a confidence interval of 95%, with agreeable results presented at pesticide residue levels over 30 mg/kg. FT-NIR spectroscopy combined with DESIR and PLSR should be considered as a promising screening method for pesticide detection in vegetables.

Dynamic interactions between peroxidase-mimic silver NanoZymes and chlorpyrifos-specific aptamers enable highly-specific pesticide sensing in river water

With growing environmental and health concerns over persistent organic compounds such as organophosphates, regulatory bodies have imposed strict regulations for their use and monitoring in water bodies. Although conventional analytical tools exist for the detection of organophosphorus pesticides, new strategies need to be developed to fulfil the ASSURED (affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable to end users) criteria of the World Health Organisation. One such strategy is to employ the ability of certain nanoparticles to mimic the enzymatic activity of natural enzymes to develop optical sensors. We show that the intrinsic peroxidase-mimic NanoZyme activity of tyrosine-capped silver nanoparticles (Ag-NanoZyme) can be exploited for highly specific and rapid detection of chlorpyrifos, an organophosphorus pesticide. The underlying working principle of the proposed aptasensor is based on the dynamic non-covalent interaction of the chlorpyrifos specific aptamer (Chl) with the NanoZyme (sensor probe) vs. the pesticide target (analyte). The incorporation of the Chl aptamer ensures high specificity leading to a colorimetric response specifically in the presence of chlorpyrifos, while the sensor remains unresponsive to other pesticides from organophosphate and non-organophosphate groups. The robustness of the sensor to work directly in environmental samples was established by evaluating its ability to detect chlorpyrifos in river water samples. The excellent recovery rates demonstrate the sensor robustness, while the simplicity, and rapid sensor response (2 min) to detect the presence of chlorpyrifos highlights the capabilities of the proposed colorimetric sensing system.

Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit

An effective and rapid way to detect thiophanate-methyl residue on mulberry fruit is important for providing consumers with quality and safe of mulberry fruit. Chemical methods are complex, time-consuming, and costly, and can result in sample contamination. Rapid detection of thiophanate-methyl residue on mulberry fruit was studied using laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) techniques. Principal component analysis (PCA) and partial least square regression (PLSR) were used to qualitatively and quantitatively analyze the data obtained by using LIBS and HSI on mulberry fruit samples with different thiophanate-methyl residues. The competitive adaptive reweighted sampling algorithm was used to select optimal variables. The results of model calibration were compared. The best result was given by the PLSR model that used the optimal preprocessed LIBS–HSI variables, with a correlation coefficient of 0.921 for the prediction set. The results of this research confirmed the feasibility of using LIBS and HSI for the rapid detection of thiophanate-methyl residue on mulberry fruit.

Properties comparison between free and immobilized wheat esterase using glass fiber film

In this paper, the catalytic performance of non-purified esterase from wheat bran immobilized on glass fibre membrane carrier is established, the immobilization conditions observed were enzyme 1 mL, phosphate buffer 3 mL (pH 7.0), immobilization time 1 h, immobilization temperature 29 °C. After carrier functionalization some characteristics of immobilized enzyme were studied, the results showed that immobilized enzyme presenting improved characteristic than that of free enzyme. The optimum pH for free and immobilized enzymes were found to be 8 and 7, respectively. As for optimum temperature for free and immobilized enzymes were observed to be 30 °C and 40 °C, respectively. When the enzyme was immobilized on glass fibre membranes, its K_m increased about 7 times. In addition, storage and thermal stability of the free wheat esterase were increased by as a result of membrane immobilization, after 12 days of storage, the immobilized enzyme still retained about 91.10% of its original activity at 4 °C, indicating a great potential in industrial application.

Quantitative Determination of Chlormequat Chloride Residue in Wheat Using Surface-Enhanced Raman Spectroscopy

A simple and sensitive method for detection of chlormequat chloride residue in wheat was developed using surface-enhanced Raman spectroscopy (SERS) coupled with chemometric methods on a portable Raman spectrometer. Pretreatment of wheat samples was performed using a two-step extraction procedure. Effective and uniform active substrate (gold nanorods) was prepared and mixed with the sample extraction solution for SERS measurement. The limit of detection for chlormequat chloride in wheat extracting solutions and wheat samples was 0.25 mg/L and 0.25 μg/g, which was far below the maximum residual value in wheat of China. Then, support vector regression (SVR) and kernel principal component analysis (KPCA), multiple linear regression, and partial least squares regression were employed to develop the regression models for quantitative analysis of chlormequat chloride residue with spectra around the characteristic peaks at 666, 713, and 853 cm^-1. As for the residue in wheat, the predicted recovery of established optimal model was in the range of 94.7% to 104.6%, and the standard deviation was about 0.007 mg/L to 0.066 mg/L. The results demonstrated that SERS, SVR, and KPCA can provide the accurate and quantitative determination for chlormequat chloride residue in wheat.