High sensitive Ratiometric fluorescent Aptasensor with AIE properties for Deoxynivalenol (DON) detection.

An emerging fluorescent ratiometric aptasensor based on gold nanoclusters (AuNCs) with aggregation-induced emission (AIE) properties was prepared and studied for deoxynivalenol (DON) detection. The ratiometric aptasensor used red fluorescent AuNCs labelled with DON aptamer (Apt-AuNCs) as an indicator and green fluorescent AuNCs modified by complementary DNA (cDNA) and magnetic beads (MBs) as internal reference, namely MBs-cDNA-AuNCs. Under the optimal conditions, the aptasensor exhibited two good linear ranges of 0.

A fluorescent Aptasensor based on magnetic-separation strategy with gold nanoclusters for Deoxynivalenol (DON) detection.

A highly sensitive method based on MBs-cDNA@Apt-AuNCs was developed for deoxynivalenol (DON) detection in wheat. The MBs-cDNA@Apt-AuNCs was established using green emission gold nanoclusters (AuNCs) with aggregation-induced emission properties as signal probes and combining amino-modified DON-aptamer (Apt), biotin-modified DNA strand (the partially complementary to Apt (cDNA)), and streptavidin-modified magnetic beads (MBs). The Apt-AuNCs were well connected with MBs-cDNA without DON but dissociated from MBs-cDNA@Apt-AuNCs with the addition of DON, leading to a noticeable reduction in the fluorescent intensity of the aptasensor.

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety.

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described.

Fast real-time monitoring of meat freshness based on fluorescent sensing array and deep learning: From development to deployment.

A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 ∼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C.

Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms.

This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection.

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions.