Utilizing a Rapid Multi-Plug Filtration Cleanup Method for 72 Pesticide Residues in Grape Wines Followed by Detection with Gas Chromatography Tandem Mass Spectrometry

Pesticide residues in grapes and wine: An overview on detection, health risks, and regulatory challenges

Grapes are fruits widely grown and known throughout the world, and they have significant economic and nutritional value. However, grapes are highly susceptible to attack by pests and insects, which reduces production and product quality. Pesticides are the most utilized solution for meeting global consumption demands and avoiding losses, and they can be used at any production stage. Although the benefits of using pesticides in grape production are evident, the prescribed limits must be observed to minimize their harmful effects on human health, as the accumulation of these compounds in the body over the long term can decrease life expectancy. This review of the literature intends to give a comprehensive analysis of pesticide residues in grape juice and wine, investigate analytical methodologies for pesticide detection, and set maximum residue limits (MRLs). Furthermore, the impacts of pesticide use and exposure to residues on the gut microbiota and adverse effects on human health were examined. Finally, insights into advances in detection and removal methods to mitigate the impact of pesticide residues in grape wine were presented. This review considers future perspectives in the field, including the development of safe and sustainable pesticides, with the improvement of international regulations to ensure food safety, aiming to expand pre-existing knowledge to provide more sustainable agricultural practices, encouraging global wine production. The target audience for this review includes researchers and academics, professionals in the agricultural and wine industry, regulators and government authorities, health professionals, and health and food safety-conscious consumers.

Research progress of applications for nano-materials in improved QuEChERS method

The quick, easy, cheap, effective, rugged, and safe (QuEChERS) approach is widely used in sample pretreatment in agricultural products, food, environment, etc. And nano-materials are widely used in QuEChERS method due to its small size and large specific surface area. In this review, we examine the typical applications of several commonly used nano-materials in improved QuEChERS method. These materials include multi-walled carbon nanotubes (MWCNTs) and their derivatives, magnetic nanoparticles (MNPs), metal organic frameworks (MOFs), covalent organic frameworks (COFs), graphene oxide (GO), lipid and protein adsorbent (LPAS), cucurbituril (CBs), and carbon nano-cages (CNCs), and so on. The strengths and weaknesses of each nano-material are presented, as well as the challenging aspects that need to be addressed in future research. By comparing the applications and the current technology development, this review suggests utilizing artificial intelligence (AI) to screen suitable combinations of purification agents and performing virtual simulation experiments to verify the reliability of this methodology. By doing so, we aim to accelerate the development of new products and decrease the cost of innovation. It also recommends designing smarter pretreatment instruments to enhance the convenience and automation of the sample pretreatment process and reduce the margin for human error.

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.