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

A highly sensitive method based on MBs-cDNA@Apt-AuNCs519 was developed for deoxynivalenol (DON) detection in wheat. The MBs-cDNA@Apt-AuNCs519 was established using green emission gold nanoclusters (AuNCs519) 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-AuNCs519 were well connected with MBs-cDNA without DON but dissociated from MBs-cDNA@Apt-AuNCs519 with the addition of DON, leading to a noticeable reduction in the fluorescent intensity of the aptasensor. Moreover, this fluorescence aptasensor showed two linear relationships in the concentration range of 0.1-50 ng/mL and 50-5000 ng/mL with a limit of detection of 3.73 pg/mL with good stability, reproducibility and specificity. The results were consistent with high-performance liquid chromatography and enzyme-linked immunosorbent assay methods, further indicating the potential of this method for accurate trace detection of DON in wheat.

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 AuNCs620 labelled with DON aptamer (Apt-AuNCs620) as an indicator and green fluorescent AuNCs519 modified by complementary DNA (cDNA) and magnetic beads (MBs) as internal reference, namely MBs-cDNA-AuNCs519. Under the optimal conditions, the aptasensor exhibited two good linear ranges of 0.1-50 and 50-5000 pg/mL for DON detection with coefficient of determination (R2) of 0.9937 and 0.9928, respectively, and the low detection limit (LOD) of 4.09 pg/mL was achieved. Furthermore, this aptasensor was feasible to detect DON in positive wheat samples, and the results were in line with those from HPLC and ELISA, thus providing a promising route to detect DON with high sensitivity in cereals, even for other mycotoxins by replacing the suitable aptamer and cDNA.

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. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

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 âˆ¼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 âˆ¼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment.

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. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t-distributed stochastic neighbour embedding (t-SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness.

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. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.