Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

Application, challenges and future prospects of recent nondestructive techniques based on the electromagnetic spectrum in food quality and safety

Safety and quality aspects of food products have always been critical issues for the food production and processing industries. Since conventional quality measurements are laborious, time-consuming, and expensive, it is vital to develop new, fast, non-invasive, cost-effective, and direct techniques to eliminate those challenges. Recently, non-destructive techniques have been applied in the food sector to improve the quality and safety of foodstuffs. The aim of this review is an effort to list non-destructive techniques (X-ray, computer tomography, ultraviolet-visible spectroscopy, hyperspectral imaging, infrared, Raman, terahertz, nuclear magnetic resonance, magnetic resonance imaging, and ultrasound imaging) based on the electromagnetic spectrum and discuss their principle and application in the food sector. This review provides an in-depth assessment of the different non-destructive techniques used for the quality and safety analysis of foodstuffs. We also discussed comprehensively about advantages, disadvantages, challenges, and opportunities for the application of each technique and recommended some solutions and developments for future trends.

Fruit Flies: Challenges and Opportunities to Stem the Tide of Global Invasions

Global trade in fresh fruit and vegetables, intensification of human mobility, and climate change facilitate fruit fly (Diptera: Tephritidae) invasions. Life-history traits, environmental stress response, dispersal stress, and novel genetic admixtures contribute to their establishment and spread. Tephritids are among the most frequently intercepted taxa at ports of entry. In some countries, supported by the rules-based trade framework, a remarkable amount of biosecurity effort is being arrayed against the range expansion of tephritids. Despite this effort, fruit flies continue to arrive in new jurisdictions, sometimes triggering expensive eradication responses. Surprisingly, scant attention has been paid to biosecurity in the recent discourse about new multilateral trade agreements. Much of the available literature on managing tephritid invasions is focused on a limited number of charismatic (historically high-profile) species, and the generality of many patterns remains speculative.

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface-enhanced Raman spectroscopy integrated with deep learning approach

Chlorpyrifos and pyrimethanil are widely used insecticides/fungicides in agriculture. The residual pesticides/fungicides remaining in fruits and vegetables may do harm to human health if they are taken without notice by the customers. Therefore, it is important to develop methods and tools for the rapid detection of pesticides/fungicides in fruits and vegetables, which are highly demanded in the current markets. Surface-enhanced Raman spectroscopy (SERS) can achieve trace chemical detection, while it is still a challenge to apply SERS for the detection and identification of mixed pesticides/fungicides. In this work, we tried to combine SERS technique and deep learning spectral analysis for the determination of mixed chlorpyrifos and pyrimethanil on the surface of fruits including apples and strawberries. Especially, the multi-channel convolutional neural networks-gate recurrent unit (MC-CNN-GRU) classification model was used to extract sequence and spatial information in the spectra, so that the accuracy of the optimized classification model could reach 99% even when the mixture ratio of pesticide/fungicide varied considerably. This work therefore demonstrates an effective application of using SERS combined deep learning approach in the rapid detection and identification of different mixed pesticides in agricultural products.

Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

The mango pulp weevil (MPW) is an aggressive pest that mates seasonally according to the cycle of the mango fruit. After discovering the existence of the mango pulp weevil in Palawan, the island has been under quarantine for exporting mangoes. Detection of the pest proves difficult as the pest does not leave a physical sign that the mango has been damaged. Infested mangoes are wasted as they cannot be sold due to damage. This study serves as a base study for non-invasive mango pulp weevil detection using MATLAB machine learning and audio feature extraction tools. Acoustic sensors were evaluated for best-fit use in the study. The rationale for selecting the acoustic sensors includes local availability and accessibility. Among the three sensors tested, the MEMS sensor had the best result. The data for acoustic frequency are acquired using the selected sensor, which is placed inside a soundproof chamber to minimize the noise and isolate the sound produced by each activity. The identified activity of the adult mango pulp weevil includes walking, resting, and mating. The Mel-frequency cepstral coefficient (MFCC) was used for feature extraction of the recorded audio and training of the SVM classifier. The study achieved 89.81% overall accuracy in characterizing mango pulp weevil activity.

Emerging Insights into the Use of Advanced Nanomaterials for the Electrochemiluminescence Biosensor of Pesticide Residues in Plant-Derived Foodstuff

Pesticides have an important role in rising the overall productivity and yield of agricultural foods by eliminating and controlling insects, pests, fungi, and various plant-related illnesses. However, the overuse of pesticides has caused pesticide pollution of water bodies and food products, along with disruption of environmental and ecological systems. In this regard, developing low-cost, simple, and rapid-detecting approaches for the accurate, rapid, efficient, and on-site screening of pesticide residues is an ongoing challenge. Electrochemiluminescence (ECL) possesses the benefits of great sensitivity, the capability to resolve several analytes using different emission wavelengths or redox potentials, and excellent control over the light radiation in time and space, making it a powerful strategy for sensing various pesticides. Cost-effective and simple ECL systems allow sensitive, selective, and accurate quantification of pesticides in agricultural fields. Particularly, the development and progress of nanomaterials, aptamer/antibody recognition, electric/photo-sensing, and their integration with electrochemiluminescence sensing technology has presented the hopeful potential in reporting the residual amounts of pesticides. Current trends in the application of nanoparticles are debated, with an emphasis on sensor substrates using aptamer, antibodies, enzymes, and molecularly imprinted polymers (MIPs). Different strategies are enclosed in labeled and label-free sensing along with luminescence determination approaches (signal-off, signal-on, and signal-switch modes). Finally, the recent challenges and upcoming prospects in this ground are also put forward.

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

Automated, non-invasive Varroa mite detection by vibrational measurements of gait combined with machine learning

Little is known about mite gait, but it has been suggested that there could be greater variation in locomotory styles for arachnids than insects. The Varroa destructor mite is a devastating ectoparasite of the honeybee. We aim to automatically detect Varroa-specific signals in long-term vibrational recordings of honeybee hives and additionally provide the first quantification and characterisation of Varroa gait through the analysis of its unique vibrational trace. These vibrations are used as part of a novel approach to achieve remote, non-invasive Varroa monitoring in honeybee colonies, requiring discrimination between mite and honeybee signals. We measure the vibrations occurring in samples of freshly collected capped brood-comb, and through combined critical listening and video recordings we build a training database for discrimination and classification purposes. In searching for a suitable vibrational feature, we demonstrate the outstanding value of two-dimensional-Fourier-transforms in invertebrate vibration analysis. Discrimination was less reliable when testing datasets comprising of Varroa within capped brood-cells, where Varroa induced signals are weaker than those produced on the cell surface. We here advance knowledge of Varroa vibration and locomotion, whilst expanding upon the remote detection strategies available for its control.

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).

Early infestation volatile biomarkers of fruit fly Bactrocera dorsalis (Hendel) ovipositional activity in mango (Mangifera indica L.)

Infestation of agricultural commodities by insect pests results in significant economic, import and export, food safety, and invasive insect introduction issues for growers, consumers, and inspectors. The Oriental fruit fly (Bactrocera dorsalis) is considered a highly invasive insect pest with populations reported in more than 60 countries, with prevalent distributions in Asia and Africa. B. dorsalis is phytophagous with a host range encompassing hundreds of fruits and vegetables. Damage to the fruit or vegetable is inflicted through oviposition and subsequent larval feeding resulting in spoilage. Early detection of insect pest infestations is a critical component for ensuring food safety as well as controlling introduction and spread of invasive insects. However, detection of ovipositional activity and early larval development is visually difficult, thus rapid and non-destructive detection often relies on odors associated with infestation. We investigated the odors of mangoes (Mangifera indica L.) infested with B. dorsalis and compared the volatile profiles of infested mangoes to non-infested and mechanically damaged mangoes 24 h post-infestation. GC-MS and multivariate analyses provided the identification of eleven compounds unique to infested mangoes compared to mechanically damaged or non-infested fruit. Results indicated compositional and quantitative differentiation of volatile profiles among treatments for detection of infested fruit at quality checks or points of commerce.

Intelligent detection for fresh-cut fruit and vegetable processing: Imaging technology

Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.

A Deep-Learning Model for Real-Time Red Palm Weevil Detection and Localization

Background and motivation: Over the last two decades, particularly in the Middle East, Red Palm Weevils (RPW, Rhynchophorus ferruginous) have proved to be the most destructive pest of palm trees across the globe. Problem: The RPW has caused considerable damage to various palm species. The early identification of the RPW is a challenging task for good date production since the identification will prevent palm trees from being affected by the RPW. This is one of the reasons why the use of advanced technology will help in the prevention of the spread of the RPW on palm trees. Many researchers have worked on finding an accurate technique for the identification, localization and classification of the RPW pest. This study aimed to develop a model that can use a deep-learning approach to identify and discriminate between the RPW and other insects living in palm tree habitats using a deep-learning technique. Researchers had not applied deep learning to the classification of red palm weevils previously. Methods: In this study, a region-based convolutional neural network (R-CNN) algorithm was used to detect the location of the RPW in an image by building bounding boxes around the image. A CNN algorithm was applied in order to extract the features to enclose with the bounding boxes—the selection target. In addition, these features were passed through the classification and regression layers to determine the presence of the RPW with a high degree of accuracy and to locate its coordinates. Results: As a result of the developed model, the RPW can be quickly detected with a high accuracy of 100% in infested palm trees at an early stage. In the Al-Qassim region, which has thousands of farms, the model sets the path for deploying an efficient, low-cost RPW detection and classification technology for palm trees.

Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy

A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (Rp2 = 0.96, RMSEP = 1.432%). The 1st-PLSR model showed the best prediction performance for L* value (Rp2 = 0.89, RMSEP = 3.25 mg/100 g), but also the lowest accuracy for firmness (Rp2 = 0.60, RMSEP = 2.453). The best classification model was able to predict freshness levels with 88.8% accuracy in mini-Chinese cabbage by supported vector classification (SVC). This study illustrates that the spectral profile obtained by FT-NIR spectroscopy could potentially be implemented for integral assessments of the internal and external quality attributes of mini-Chinese cabbage with nanopacking during storage.

Study of Morphological Features in Pre-Imaginal Honey Bee Impaired by Varroa destructor by Means of Computer Tomography

Simple Summary

The Varroa mite (Varroa destructor) is the most important natural pest of the honey bee, Apis mellifera, worldwide. The extent to which impairments in honey bees occur concomitantly upon infestation by this parasite greatly varies. Inter alia, the Varroa mite causes developmental disorders mediated by deformed wing virus in this host. Although there is a plethora of information regarding the consequences of this parasitism in the fully developed stage, data concerning the pre-imaginal honey bee stage inside the comb are rather scarce. In this study, morphological differences in the main body parts of the honey bee during the development stages of both intact and parasitized larvae were measured inside the comb by means of computed tomography. The images obtained reveal a visualization of the harmful effects of the Varroa mite on the pre-imaginal host. Our results demonstrate that the deformation of certain body parts was due to the presence of the parasite. Deformity, as the most conspicuous sign of infestation, is coupled with a decrease in the total-body size and abdomen size together with a disproportionate ratio of different body parts. In summary, information on the impairment of honey bee development triggered by the Varroa mite gives the opportunity to assess the damage caused by this serious pest, which occurs latently in honey bees.

Abstract

The honey bee (Apis mellifera L. 1778) is an essential element in maintaining the diversity of the biosphere and food production. One of its most important parasites is Varroa destructor, Anderson and Trueman, 2000, which plays a role in the vectoring of deformed wing virus (DWV) in honey bee colonies. Our aim was to measure the potential morphometric changes in the pre-imaginal stage of A. mellifera caused by varroosis by means of computed tomography, hence supplying evidence for the presumable role that V. destructor plays as a virus vector. Based on our results, the developmental disorders in honey bees that ensued during the pre-imaginal stages were evident. The total-body length and abdomen length of parasitized specimens were shorter than those of their intact companions. In addition, the calculated quotients of the total-body/abdomen, head/thorax, and head/abdomen in parasitized samples were significantly altered upon infestation. In our view, these phenotypical disorders can also be traced to viral infection mediated by parasitism, which was confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) analysis. Capitalizing on a non-destructive method, our study reveals the deformation of the honey bee due to mite parasitism and the intermediary role this pest plays in viral infection, inside the brood cell.

A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

A generic intelligent tomato classification system based on DenseNet-201 with transfer learning was proposed and the augmented training sets obtained by data augmentation methods were employed to train the model. The trained model achieved high classification accuracy on the images of different quality, even those containing high levels of noise. Also, the trained model could accurately and efficiently identify and classify a single tomato image with only 29 ms, indicating that the proposed model has great potential value in real-world applications. The feature visualization of the trained models shows their understanding of tomato images, i.e., the learned common and high-level features. The strongest activations of the trained models show that the correct or incorrect target recognition areas by a model during the classification process will affect its final classification accuracy. Based on this, the results obtained in this study could provide guidance and new ideas to improve the development of intelligent agriculture.

Potential of microbial endophytes to enhance the resistance to postharvest diseases of fruit and vegetables

Food loss of fruit and vegetables caused by postharvest diseases is a major issue worldwide. The method used to prevent and control postharvest diseases is usually to use chemical fungicides, but long-term and large-scale use will make the pathogens resistant and potentially have a negative impact on human health and the ecological environment. Therefore, finding a safe and effective biological control method instead of chemical control is a hot research topic in recent years. Endophytes, colonizing plants asymptomatically, can promote the growth of the hosts and enhance their resistance. The use of endophytes as biological control agents for postharvest diseases of fruit and vegetables has attracted increasing attention in the last 20 years. Compared with chemical control, endophytes have the advantages of being more environmentally friendly, sustainable, and safer. However, there are relatively few relevant studies, so herein we summarize the available literature. This review focuses mainly on the recent progress of using endophytes to enhance the resistance of postharvest fruit and vegetables to diseases, with the emphasis on the possible mechanisms and the potential applications. Furthermore, this article suggests future areas for study using antagonistic endophytes to prevent and control fruit and vegetable postharvest diseases: (i) screening more potential broad-spectrum anti-pathogen endophytes and their metabolic active substances by the method of macrogenomics; (ii) elucidating the underlining molecular mechanism among endophytes, harvested vegetables and fruit, pathogens, and microbial communities; (iii) needing more application research to overcome the difficulties of commercialization practice. © 2020 Society of Chemical Industry.