Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit

Toxic Effect of Methyl-Thiophanate on Bombyx mori Based on Physiological and Transcriptomic Analysis

BACKGROUND/OBJECTIVES

The utilization of methyl-thiophanate (MT) in vegetables and fruits is widespread due to its broad efficiency, yet its potential impact on silkworm growth remains uncertain. This study aims to examine the effects of MT on the growth of silkworms. Specifically, we assessed the weights of fifth-instar larvae that were fed mulberry leaves saturated with three concentrations (2.5, 5, and 10 mg/mL) of MT, as well as the weights of a control group.

METHODS

TEM was used to show the status of the silkworm midgut after MT supplementation. Oxidative stress was evaluated in the presence of MT. Furthermore, a transcriptomic sequencing experiment was conducted to investigate the mechanism through which the development of silkworms is induced by MT.

RESULTS

Our findings indicate that the supplementation of MT hindered larval growth compared to the control group, suggesting a toxic effect of MT on silkworms. The transmission electron microscopy (TEM) results show that MT supplementation induced autophagy in the silkworm midgut. MT was also found to induce oxidative stress in silkworms through the activation of reactive oxygen (ROS), superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) activities. Subsequent transcriptomic analysis revealed 1265 significantly differentially expressed genes (DEGs) in response to MT. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that these DEGs were associated with antioxidant defense, detoxification processes, lysosome biogenesis, and metabolic pathways.

CONCLUSIONS

These findings suggest that MT toxicity in silkworm larvae is mediated through the induction of oxidative stress and alterations in metabolism. This study contributes to our understanding of the impacts of MT exposure on silkworms and provides insights into potential pesticides for use in mulberry gardens.

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.

Specific detection of fungicide thiophanate-methyl: A smartphone colorimetric sensor based on target-regulated oxidase-like activity of copper-doped carbon nanozyme

Nanozyme-based colorimetric assays have shown great potential in the rapid and sensitive determination of pesticide residue in environment. However, the non-specific enzyme inhibition makes the assays generally lack of selectivity. In this study, we proposed a colorimetric sensing platform for the specific detection of the agricultural fungicide thiophanate-methyl (TM) based on its distinctive inhibitory effect on the nanozyme activity. Since TM contains the symmetric ethylenediamine- and bisthiourea-like groups, it displays strong affinity to the metal site, leading to a loss of the catalytic activity. Accordingly, a Cu-doped carbon nanozyme with excellent oxidase-like properties was designed, and the oxidation process of chromogenic substrate is promoted by Cu-induced generation of reactive oxygen species. Interestingly, the nanozyme activity can be directly and strongly restrained by TM, rather than other probably coexistent pesticides. Consequently, the as-proposed analytical method exhibits specific response toward TM and good linear relationship in the range of 0.2-15 μg mL-1 with a low limit of detection of 0.04 μg mL-1 (S/N = 3). Besides, a smartphone-assisted rapid detection was achieved through identifying the RGB value of the chromogenic system. This work provides a new nanozyme inhibition strategy for the specific detection of TM in environmental sample.

Nondestructive testing methods for pesticide residue in food commodities: A review

Pesticides play an important role in increasing the overall yield and productivity of agricultural foods by controlling pests, insects, and numerous plant-related diseases. However, the overuse of pesticides has resulted in pesticide contamination of food products and water bodies, as well as disruption of ecological and environmental systems. Global health authorities have set limits for pesticide residues in individual food products to ensure the availability of safe foods in the supply system and to assist farmers in developing the best agronomic practices for crop production. Therefore, the use of nondestructive testing (NDT) methods for pesticide residue detection is gaining interest in the food supply chain. The NDT techniques have several advantages, such as simultaneous measurement of chemical and physical characteristics of food without destroying the product. Although numerous studies have been conducted on NDT for pesticide residue in agro-food products, there are still challenges in real-time implementation. Further study on NDT methods is needed to establish their potential for supplementing existing methods, identifying mixed pesticides, and performing volumetric quantification (not surface accumulation alone).

Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin

With the events of fake and inferior rice and food products occurring frequently, how to establish a rapid and high accuracy monitoring method for rice food identification becomes an urgent problem. In this work, we investigate using combined laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) with machine learning algorithms to identify the place of origin of rice production. Six geographical origin rice samples grown in different parts of China are selected and pretreated, and measured by the atomic emission spectra of LIBS and the reflection spectra of HSI, respectively. The principal component analysis (PCA) is utilized to realize data dimensionality and extract the data feat of LIBS, HSI and fusion data, and based on this, three models employing the partial least squares discriminant analysis (PLS-DA), the support vector machine (SVM) and the extreme learning machine (ELM) are used to identify the rice geographical origin. The results show that the accuracy of LIBS and HSI analysis with the SVM machine learning algorithm can reach 93.06% and 88.07%, respectively, and the accuracy of combined LIBS and HSI data fusion recognition can reach 99.85%. Besides, the classification accuracy of the three models measured after pretreatment is basically all above 95%, and up to 99.85%. This study proves the effectiveness of using the combined LIBS and HSI with the machine learning algorithm in rice geographical origin identification, which can achieve rapid and accurate rice quality and identity detection.

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.

Visualized detection of quality change of cooked beef with condiments by hyperspectral imaging technique

The heat treatment and seasoning of meat are indispensable before its consumption. In this work, the spectral characteristics of cooked meat and condiments were analysed by hyperspectral imaging (HSI) technology. The spectral reflectance of spices was significantly lower than that of meat protein, and that the spectral reflectance of protein regularly increased upon heating at 800-956 nm range. PCA pre-process and SVM models were used to predict beef moisture (R 2 = 0.912) and tenderness (R 2 = 0.771) based on 100 beef data. Mapping technology clearly showed the dynamic change of meat tenderness during heating, and the performance of 3D mapping was better than that of 2D mapping. Based on 750 nm/900 nm ratio image and machine-vision method, spice uniformity was accurately calculated. Thus, the quality of cooked meat and condiments distribution can be simultaneously evaluated by HSI. This technology can be used in the intelligent production of complex meat products in the future.

Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method

Spray droplet size is the main factor affecting the deposition uniformity on a target crop. Studying the influence of multiple factors on the droplet size distribution as well as the evaluation method is of great significance for improving the utilization of pesticides. In this paper, volume median diameter (VMD) and relative span (RS) were selected to evaluate the droplet size distribution under different hollow cone nozzles, flow rates and spatial positions, and the quantitative models of VMD and RS were established based on machine learning methods. The results showed that support vector regression (SVR) had excellent results for VMD (Rc = 0.9974, Rp = 0.9929), while multi-layer perceptron (MLP) had the best effect for RS (Rc = 0.9504, Rp = 0.9537). The correlation coefficient of the prediction set is higher than 0.95, showing the excellent ability of machine learning on predicting the droplet size distribution. In addition, the visualization images of the droplet size distribution were obtained based on the optimal models, which provided intuitive guidance for realizing the uniform distribution of pesticide deposition. In conclusion, this study provides a novel and feasible method for quantitative evaluation of droplet size distribution and offers a theoretical basis for further determining appropriate operation parameters according to the optimal droplet size.

Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds

Strawberries are susceptible to mechanical damage. The detection of damaged strawberries by their volatile organic compounds (VOCs) can avoid the deficiencies of manual observation and spectral imaging technologies that cannot detect packaged fruits. In the present study, the detection of strawberries with impact damage is investigated using electronic nose (e-nose) technology. The results show that the e-nose technology can be used to detect strawberries that have suffered impact damage. The best model for detecting the extent of impact damage had a residual predictive deviation (RPD) value of 2.730, and the correct rate of the best model for identifying the damaged strawberries was 97.5%. However, the accuracy of the prediction of the occurrence time of impact was poor, and the RPD value of the best model was only 1.969. In addition, the gas chromatography-mass spectrophotometry analysis further shows that the VOCs of the strawberries changed after suffering impact damage, which was the reason why the e-nose technology could detect the damaged fruit. The above results show that the mechanical force of impact caused changes in the VOCs of strawberries and that it is possible to detect strawberries that have suffered impact damage using e-nose technology.

Laser-Induced Breakdown Spectroscopy: An Efficient Tool for Food Science and Technology (from the Analysis of Martian Rocks to the Analysis of Olive Oil, Honey, Milk, and Other Natural Earth Products)

Laser-Induced Breakdown Spectroscopy (LIBS), having reached a level of maturity during the last few years, is generally considered as a very powerful and efficient analytical tool, and it has been proposed for a broad range of applications, extending from space exploration down to terrestrial applications, from cultural heritage to food science and security. Over the last decade, there has been a rapidly growing sub-field concerning the application of LIBS for food analysis, safety, and security, which along with the implementation of machine learning and chemometric algorithms opens new perspectives and possibilities. The present review intends to provide a short overview of the current state-of-the-art research activities concerning the application of LIBS for the analysis of foodstuffs, with the emphasis given to olive oil, honey, and milk.