Rapid and simultaneous determination of mixed pesticide residues in apple using SERS coupled with multivariate analysis.

This study aims to apply multivariate analysis algorithms for modeling the same spectra, for simultaneous determination of pymetrozine and carbendazim residues in apple. To mitigate the impact of competitive adsorption, SERS spectra are obtained from mixed solutions of pymetrozine and carbendazim at varying concentration ratios, which are then utilized for modeling. Results suggest that the PLSR model based on full-band SNV processed spectra shows the best performance for predicting pymetrozine and carbendazim contents, with R2 of 0.

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.

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.

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.

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 ( E) in a wide range of 500-50 000 U/L, and a linear regression equation of log Δ I/Δ t = 0.

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.

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.