Real-time and in situ monitoring of organosilicon-induced thiram penetration into cabbage leaves by surface-enhanced Raman scattering mapping.

BACKGROUND: Understanding pesticide penetration behavior is important for effective application of pesticides. However, there is a lack of an effective method to monitor pesticide penetration behavior and its changing process. In the present study, a novel surface-enhanced Raman scattering (SERS) mapping method was used for real-time and in situ tracking of the penetration behaviors of thiram and thiram-organosilicon mixture on cabbage leaves. RESULTS: The results suggest that thiram has very weak ability to penetrate into cabbage leaves. However, when the thiram-organosilicon mixture was placed on leaf surfaces, a clear thiram signal was detected inside the leaf after 2 h of exposure, a strong signal was observed after 12 h, and the penetration depth of thiram was approximately 200 µm after 48 h. CONCLUSION: SERS mapping was demonstrated to be a reliable method for in situ monitoring of organosilicon-induced thiram penetration into cabbage leaf over time. The present study provides a new reference for rationally selecting adjuvants, effectively applying pesticides, and reducing pesticides residue in food. © 2022 Society of Chemical Industry.

A Sensitive SERS Method for Determination of Pymetrozine in Apple and Cabbage Based on an Easily Prepared Substrate.

Residual pesticides are one of the major food safety concerns around the world. There is a demand for simple and reliable methods to monitor pesticide residues in foods. In this study, a sensitive method for determination of pymetrozine in apple and cabbage samples using surface-enhanced Raman spectroscopy (SERS) based on decanethiol functionalized silver nanoparticles was established. The proposed method performed satisfactorily with the linear detection range of 0.01-1.00 mg/L and limit of detection (LOD) of 0.01 mg/L in methanol. In addition, it was successfully used to detect pymetrozine in apple and cabbage samples, the LOD was 0.02 and 0.03 mg/L, respectively, and the recoveries of spiked cabbage and apple ranged 70.40-104.00%, with relative standard deviations below 12.18% and 10.33% for intra-day and inter-day tests. Moreover, the results of the correlation test with real cabbage samples of liquid chromatography-tandem mass spectrometry showed that they were highly correlated (slope = 0.9895, R2 = 0.9953). This study provides a sensitive approach for detection of pymetrozine in apple and cabbage, which has great potential for determination of pymetrozine residues in food products.

In situ and rapid determination of acetamiprid residue on cabbage leaf using surface-enhanced Raman scattering.

BACKGROUND: Pesticide residues in agricultural products and foods pose a serious threat to human health, and therefore a simple, rapid and direct method is urgently needed for pesticide residue detection. In addition to realizing the detection of acetamiprid in cabbage extract solution, the main target of this study was to establish an in situ surface-enhanced Raman scattering (SERS) method, which could directly detect acetamiprid residue on cabbage leaf without the need for extraction. Acetamiprid was first used to contaminate the surface of fresh cabbage leaf, and then bimetallic silver-coated gold nanoparticles (Au@AgNPs) were added on the contaminated spots and dried for SERS measurement. RESULTS: Results suggested that acetamiprid can be detected in cabbage extract and on cabbage leaf surface in situ using the SERS method based on the Au@AgNPs substrate. The limit of detection was 0.08 µg mL-1 in cabbage extract and 0.14 mg kg-1 on cabbage leaf, the recovery ranged from 80.5% to 105.5% and the relative standard deviation was in the range 4.37-10.63%. CONCLUSIONS: The proposed SERS method provides an in situ, nondestructive and rapid way to detect acetamiprid residue on the surface of fruits and vegetables, which could serve as an auxiliary approach for early screening of contaminated produce in field or on site in the future. © 2020 Society of Chemical Industry.

New Method for Accurate Determination of Polyphenol Oxidase Activity Based on Reduction in SERS Intensity of Catechol.

Rapid and accurate measurement of polyphenol oxidase (PPO) activity is important in the food industry as PPOs play a vital role in catalyzing enzymatic reactions. The aim of this study was to develop surface-enhanced Raman scattering (SERS) approach for accurate determination of PPO activity in fruit and vegetables using the reduction in SERS intensity of catechol in reaction medium. Within a certain catechol concentration, when a purified PPO solution was analyzed, the reduction in SERS intensity (Δ I) was linear to PPO activity ( Ec) in a wide range of 500-50 000 U/L, and a linear regression equation of log Δ I/Δ t = 0.6223 log Ec + 0.8072, with a correlation coefficient of 0.9689 and a limit of detection of 224.65 U/L, was obtained. The method was used for detecting PPO activity in apple and potato samples, and the results were compared with those obtained from colorimetric assay, which demonstrated that the proposed method could be successfully used for detecting PPO activity in food samples.

Simple Approach for the Rapid Detection of Alternariol in Pear Fruit by Surface-Enhanced Raman Scattering with Pyridine-Modified Silver Nanoparticles.

A simple method based on surface-enhanced Raman scattering (SERS) was developed for the rapid determination of alternariol (AOH) in pear fruits using an easily prepared silver-nanoparticle (AgNP) substrate. The AgNP substrate was modified by pyridine to circumvent the weak affinity of the AOH molecules to the silver surface and to improve the sensitivity of detection. Quantitative analysis was performed in AOH solutions at concentrations ranging from 3.16 to 316.0 µg/L, and the limit of detection was 1.30 µg/L. The novel method was also applied to the detection of AOH residues in pear fruits purchased from the market and in pear fruits that were artificially inoculated with Alternaria alternata. AOH was not found in any of the fresh fruit, whereas it resided in the rotten and inoculated fruits. Finally, the SERS method was cross validated against HPLC. It was revealed that the SERS method has great potential utility in the rapid detection of AOH in pear fruits and other agricultural products.

Regression Algorithms in Hyperspectral Data Analysis for Meat Quality Detection and Evaluation.

Hyperspectral imaging (HSI) system in tandem with chemometric methods is proposed as a rapid, efficient, cost-saving, and nondestructive detection technique, and multivariate data analysis is an indispensable part of this novel detection technique. In recent years, the rapid progress that we have made in using all kinds of chemometric methods to deal with hyperspectral data of meat products, however, cannot meet the practical needs very well. Thus, in order to give some suggestions on how to select an appropriate algorithm for hyperspectral data analysis, this review, first, briefly introduces the principle of the most widely used regression algorithms, and, more importantly, then focuses on the application of different algorithms in modeling the correlation between the quality attributes of the tested sample and their hyperspectral data. The advantages and limitations of these algorithms are compared and discussed. This review article will provide valuable guidelines for data analysis in the future progress of HSI detection technique.