Introducing high-performance star-shaped bimetallic nanotags into SERS aptasensor: An ultrasensitive and interference-free method for chlorpyrifos detection.

Implementation of cost-effective, reliable, and efficient technologies for the sensitive, rapid, and accurate detection of pesticide residues in agriproducts presents a promising solution to the escalating food safety concerns. Herein, a high-performance surface-enhanced Raman scattering (SERS) aptasensor based on nanotag (AuNS@4-MBN@Ag-aptamer) was introduced for ultrasensitive, reliable, and interference-free detection of chlorpyrifos (CPF). This aptasensor featured star-shaped bimetallic nanotag as the principal Raman signal enhancement material and 4-mercaptobenzonitrile (4-MBN) as "biological-silent"-window reporter (at 2228 cm-1). Moreover, cDNA-linked Fe3O4@AuNPs (FA-cDNA) served as magnetic substrates to simplify the separation process of FA-cDNA-combined nanotags. In the aptasensor, the formation of FA-cDNA-aptamer-AuNS@4-MBN@Ag hybrids was hindered by CPF, and its Raman intensity decreased with increasing CPF concentration. Under optimal SERS conditions, the aptasensor exhibited a broad linear detection range from 2.5 × 102 to 5.0 × 104 pg⋅mL-1, with an impressively low limit of detection of 220.35 pg⋅mL-1 (signal-to-noise ratio = 3). The selectivity and reproducibility assessments highlighted its exceptional sensitivity and interference-free capabilities. Furthermore, practical applications on wheat and apples demonstrated satisfactory spiked recovery rates, ranging from 89.61% to 107.33% (relative standard deviation ≤ 14.55%). Consequently, the high-performance "biological-silent"-window nanotag-based aptasensor is a promising tool for monitoring trace CPF in complex matrices.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness.

In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.