Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography–mass spectrometry

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.

Review of fruits flavor deterioration in postharvest storage: Odorants, formation mechanism and quality control

Fruits flavor deterioration is extremely likely to occur during post-harvest storage, which not only damages quality but also seriously affects its market value. This work focuses on the study of fruits deterioration odorants during storage by describing their chemical compositions (i.e., alcohols, aldehydes, acids, and sulfur-containing compounds). Besides, the specific flavor deterioration mechanisms (i.e., fermentation metabolism, lipid oxidation, and amino acid degradation) inducing by factors (temperature, oxygen, microorganisms, ethylene) are summarized. Moreover, quality control strategies to mitigate fruits flavor deterioration by physical (temperature control, hypobaric treatment, UV-C, CA) and chemical (1-MCP, MT, NO, MeJA) techniques are also proposed. This review will provide useful references for fruits flavor control technologies development.

Integrated Management of Pathogens and Microbes in Cannabis sativa L. (Cannabis) under Greenhouse Conditions

The increased cultivation of high THC-containing Cannabis sativa L. (cannabis), particularly in greenhouses, has resulted in a greater incidence of diseases and molds that can negatively affect the growth and quality of the crop. Among them, the most important diseases are root rots (Fusarium and Pythium spp.), bud rot (Botrytis cinerea), powdery mildew (Golovinomyces ambrosiae), cannabis stunt disease (caused by hop latent viroid), and a range of microbes that reduce post-harvest quality. An integrated management approach to reduce the impact of these diseases/microbes requires combining different approaches that target the reproduction, spread, and survival of the associated pathogens, many of which can occur on the same plant simultaneously. These approaches will be discussed in the context of developing an integrated plan to manage the important pathogens of greenhouse-grown cannabis at different stages of plant development. These stages include the maintenance of stock plants, propagation through cuttings, vegetative growth of plants, and flowering. The cultivation of cannabis genotypes with tolerance or resistance to various pathogens is a very important approach, as well as the maintenance of pathogen-free stock plants. When combined with cultural approaches (sanitation, management of irrigation, and monitoring for diseases) and environmental approaches (greenhouse climate modification), a significant reduction in pathogen development and spread can be achieved. The use of preventive applications of microbial biological control agents and reduced-risk biorational products can also reduce disease development at all stages of production in jurisdictions where they are registered for use. The combined use of promising strategies for integrated disease management in cannabis plants during greenhouse production will be reviewed. Future areas for research are identified.

Design of a Multisensory Device for Tomato Volatile Compound Detection Based on a Mixed Metal Oxide-Electrochemical Sensor Array and Optical Reader

Insufficient control of tomato ripening before harvesting and infection by fungal pests produce large economic losses in world tomato production. Aroma is an indicative parameter of the state of maturity and quality of the tomato. This study aimed to design an electronic system (TOMATO-NOSE) consisting of an array of 12 electrochemical sensors, commercial metal oxide semiconductor sensors, an optical camera for a lateral flow reader, and a smartphone application for device control and data storage. The system was used with tomatoes in different states of ripeness and health, as well as tomatoes infected with Botrytis cinerea. The results obtained through principal component analysis of the olfactory pattern of tomatoes and the reader images show that TOMATO-NOSE is a good tool for the farmer to control tomato ripeness before harvesting and for the early detection of Botrytis cinerea.

Advances in detecting fruit aroma compounds by combining chromatography and spectrometry

Fruit aroma is produced by volatile compounds, which can significantly enhance fruit flavor. These compounds are highly complex and have remarkable pharmacological effects. The synthesis, concentration, type, and quantity of fruit aroma substances are affected by various factors, both abiotic and biotic. To fully understand the aroma substances of various fruits and their influencing factors, detection technology can be used. Many methods exist for detecting aroma compounds, and approaches combining multiple instruments are widely used. This review describes and compares each detection technology and discusses the potential use of combined technologies to provide a comprehensive understanding of fruit aroma compounds and the factors influencing their synthesis. These results can inform the development and utilization of fruit aroma substances. © 2023 Society of Chemical Industry.

Development of electronic nose for detection of micro-mechanical damages in strawberries

A self-developed portable electronic nose and its classification model were designed to detect and differentiate minor mechanical damage to strawberries. The electronic nose utilises four metal oxide sensors and four electrochemical sensors specifically calibrated for strawberry detection. The selected strawberries were subjected to simulated damage using an H2Q-C air bath oscillator at varying speeds and then stored at 4°C to mimic real-life mechanical damage scenarios. Multiple feature extraction methods have been proposed and combined with Principal Component Analysis (PCA) dimensionality reduction for comparative modelling. Following validation with various models such as SVM, KNN, LDA, naive Bayes, and subspace ensemble, the Grid Search-optimised SVM (GS-SVM) method achieved the highest classification accuracy of 0.84 for assessing the degree of strawberry damage. Additionally, the Feature Extraction ensemble classifier achieved the highest classification accuracy (0.89 in determining the time interval of strawberry damage). This experiment demonstrated the feasibility of the self-developed electronic nose for detecting minor mechanical damage in strawberries.

Detection and classification of volatile compounds emitted by three fungi-infected citrus fruit using gas chromatography-mass spectrometry

Citrus fruit produced some characteristic volatile compounds when infected by fungi compared with the healthy fruit. In the present study, volatile metabolites of postharvest citrus fruit with three different diseases including stem-end rot, blue mold and green mold were detected. Multivariate analysis such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to classify the volatile compounds between the infected and non-infected citrus fruit. The results indicated that volatile compounds of unrotten, unrotten-rotten junction, and rotten tissues were successfully classified. Importantly, eight volatile compounds as biomarkers for stem-end rot and one biomarker for green mold of citrus were screened to discriminate the infected citrus fruit. This study offers the application potential of odor profiling of volatile compounds for detecting the fungi infection in postharvest citrus fruit.

Early detection of Botrytis cinerea in strawberry fruit during quiescent infection using selected ion flow tube mass spectrometry (SIFT-MS)

Botrytis cinerea is a devastating pathogen that can cause huge postharvest losses of strawberry. Although this fungus usually infects strawberries through their flowers, symptoms mainly appear when fruit are fully mature. A fast and sensitive method to detect and quantify the fungal infection, prior to symptom development, is, therefore, needed. In this study, we explore the possibility of using the strawberry volatilome to identify biomarkers for B. cinerea infection. Strawberry flowers were inoculated with B. cinerea to mimic the natural infection. First, quantitative polymerase chain reaction (qPCR) was used to quantify B. cinerea in the strawberry fruit. The detection limit of qPCR for B. cinerea DNA extracted from strawberries was 0.01 ng. Subsequently, changes in the fruit volatilome at different fruit developmental stages were characterized using gas chromatography - mass spectrometry (GC-MS) and selected ion flow tube mass spectrometry (SIFT-MS). Based on GC-MS data, 1-octen-3-ol produced by B. cinerea was confirmed as a potential biomarker of B. cinerea infection. Moreover, the product ion NO⁺ 127, obtained by SIFT-MS measurements, was proposed as a potential biomarker for B. cinerea infection by comparing its relative level with that of 1-octen-3-ol (obtained by GC-MS) and B. cinerea (obtained by qPCR). Separate PLS regressions were carried out for each developmental stages, and 11 product ions were significantly altered at all developmental stages. Finally, PLS regressions using these 11 ions as variables allowed the discrimination between samples containing different amount of B. cinerea. This work showed that profiling the fruit's volatilome using SIFT-MS can be used as a potential alternative to detect B. cinerea during the quiescent stage of B. cinerea infection prior to symptom development. Moreover, the corresponding compounds of potential biomarkers suggest that the volatile changes caused by B. cinerea infection may contribute to strawberry defense.

Portable FBAR based E-nose for cold chain real-time bananas shelf time detection

Being cheap, nondestructive, and easy to use, gas sensors play important roles in the food industry. However, most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing. Also, an ideal electronic nose (E-nose) in a cold chain should be stable to its surroundings and remain highly accurate and portable. In this work, a portable film bulk acoustic resonator (FBAR)-based E-nose was built for real-time measurement of banana shelf time. The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone, and by introducing an air-tight FBAR as a reference, the E-nose can avoid most of the drift caused by surroundings. With the help of porous layer by layer (LBL) coating of the FBAR, the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate, while the detection range is large enough to cover a relative humidity of 0.8. In this regard, the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state, thereby showing the possibility of real-time shelf time detection. This portable FBAR-based E-nose has a large testing scale, high sensitivity, good humidity tolerance, and low frequency drift to its surroundings, thereby meeting the needs of cold-chain usage.

Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.

Emerging Methods of Monitoring Volatile Organic Compounds for Detection of Plant Pests and Disease

Each year, unwanted plant pests and diseases, such as Hendel or potato soft rot, cause damage to crops and ecosystems all over the world. To continue to feed the growing population and protect the global ecosystems, the surveillance and management of the spread of these pests and diseases are crucial. Traditional methods of detection are often expensive, bulky and require expertise and training. Therefore, inexpensive, portable, and user-friendly methods are required. These include the use of different gas-sensing technologies to exploit volatile organic compounds released by plants under stress. These methods often meet these requirements, although they come with their own set of advantages and disadvantages, including the sheer number of variables that affect the profile of volatile organic compounds released, such as sensitivity to environmental factors and availability of soil nutrients or water, and sensor drift. Furthermore, most of these methods lack research on their use under field conditions. More research is needed to overcome these disadvantages and further understand the feasibility of the use of these methods under field conditions. This paper focuses on applications of different gas-sensing technologies from over the past decade to detect plant pests and diseases more efficiently.

The Inhibitory Effect of Chitosan Based Films, Incorporated with Essential Oil of Perilla frutescens Leaves, against Botrytis cinerea during the Storage of Strawberries

Reduction in food waste, as well as non-invasive methods for extending the shelf-life of perishable fruits, are important global challenges. To achieve these objectives, in this paper, the use of natural compounds, chitosan films (CS) incorporated with essential oils from leaves, for postharvest fungal protection of strawberries is proposed. In the present study, the CS films incorporated with the essential oil from Perilla frutescens leaves (PFEO) at different concentrations were prepared and employed for packaging strawberries infected by B. cinerea during refrigerated storage at 4 °C for 10 days. Interestingly, the strawberries coated with CS films containing PFEO at 1.0% during this period possessed an effective antimicrobial effect against B. cinerea infection in potato dextrose agar (PDA). Moreover, the quality properties of the strawberries, (i.e., weight loss, firmness index, decay percentage, yeasts/molds, pH value, total soluble solids, titrable acidity, and maturity index), together with the sensory attributes (i.e., appearance, flavor, taste, and overall acceptability (p < 0.05 or p < 0.01)) were improved. These results demonstrated that (i) PFEO displayed a significant inhibitory effect against B. cinerea infection in strawberries, (ii) CS films containing PFEO at 1.0% could be a sustainable active food packaging for the refrigerated storage of strawberries.

UV excited fluorescence image-based non-destructive method for early detection of strawberry (Fragaria × ananassa) spoilage

The soon spoiled strawberries need to be classified from healthy fruits in an early stage. In this research, a machine vision system is proposed for inspecting the quality of strawberries using ultraviolet (UV) light based on the excitation-emission matrix (EEM) results. Among the 100 fruits which were harvested and stored under 10 °C condition for 7 days, 7 fruits were confirmed to be spoiled by using a firmness meter. The EEM results show the fluorescence compound contributes to a whitish surface on the spoiled fruits. Based on the EEM results, UV fluorescence images from the bottom view of strawberries were used to classify the spoiled fruits and healthy fruits within 1 day after harvest. These results demonstrate the UV fluorescence imaging can be a fast, non-destructive, and low-cost method for inspecting the soon spoiled fruits. The proposed index related to the spoiling time can be a new indicator for qualifying strawberry.

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.

Recent advances in E-monitoring of plant diseases

Infectious plant diseases are caused by pathogenic microorganisms, such as fungi, oomycetes, bacteria, viruses, phytoplasma, and nematodes. Plant diseases have a significant effect on the plant quality and yield and they can destroy the entire plant if they are not controlled in time. To minimize disease-related losses, it is essential to identify and control pathogens in the early stages. Plant disease control is thus a fundamental challenge both for global food security and sustainable agriculture. Conventional methods for plant diseases control have given place to electronic control (E-monitoring) due to their lack of portability, being time consuming, need for a specialized user, etc. E-monitoring using electronic nose (e-nose), biosensors, wearable sensors, and 'electronic eyes' has attracted increasing attention in recent years. Detection, identification, and quantification of pathogens based on electronic sensors (E-sensors) are both convenient and practical and may be used in combination with conventional methods. This paper discusses recent advances made in E-sensors as component parts in combination with wearable sensors, in electronic sensing systems to control and detect viruses, bacteria, pathogens and fungi. In addition, future challenges using sensors to manage plant diseases are investigated.

Strawberry Fungal Leaf Scorch Disease Identification in Real-Time Strawberry Field Using Deep Learning Architectures

Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.

Early identification of Aspergillus spp. contamination in milled rice by E-nose combined with chemometrics

BACKGROUND

Rice grains can be contaminated easily by certain fungi during storage and in the market chain, thus generating a risk for humans. Most classical methods for identifying and rectifying this problem are complex and time-consuming for manufacturers and consumers. However, E-nose technology provides analytical information in a non-destructive and environmentally friendly manner. Two-feature fusion data combined with chemometrics were employed for the determination of Aspergillus spp. contamination in milled rice.

RESULTS

Linear discriminant analysis (LDA) indicated that the efficiency of fusion signals ('80^th s values' and 'area values') outperformed that of independent E-nose signals. Linear discriminant analysis showed clear discrimination of fungal species in stored milled rice for four groups on day 2, and the discrimination accuracy reached 92.86% by using an extreme learning machine (ELM). Gas chromatography-mass spectrometry (GC-MS) analysis showed that the volatile compounds had close relationships with fungal species in rice. The quantification results of colony counts in milled rice showed that the monitoring models based on ELM and the genetic algorithm optimized support vector machine (GA-SVM) (R²  = 0.924-0.983) achieved better performances than those based on partial least squares regression (PLSR) (R²  = 0.877-0.913). The ability of the E-nose to monitor fungal infection at an early stage would help to prevent contaminated rice grains from entering the food chains.

CONCLUSIONS

The results indicated that an E-nose coupled with ELM or GA-SVM algorithm could be a useful tool for the rapid detection of fungal infection in milled rice, to prevent contaminated rice from entering the food chain. © 2021 Society of Chemical Industry.

Data Science in the Food Industry

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Recent progress of nondestructive techniques for fruits damage inspection: a review

In the process of growing, harvesting, and storage, fruits are vulnerable to mechanical damage, microbial infections, and other types of damage, which not only reduce the quality of fruits, increase the risk of fungal infections, in turn greatly affect food safety, but also sharply reduce economic benefits. Hence, it is essential to identify damaged fruits in time. Rapid and nondestructive detection of fruits damage is in great demand. In this paper, the latest research progresses on the detection of fruits damage by nondestructive techniques, including visible/near-infrared spectroscopy, chlorophyll fluorescence techniques, computer vision, multispectral and hyperspectral imaging, structured-illumination reflectance imaging, laser-induced backscattering imaging, optical coherence tomography, nuclear magnetic resonance and imaging, X-ray imaging, electronic nose, thermography, and acoustic methods, are summarized. We briefly introduce the principles of these techniques, summarize their applicability. The challenges and future trends are also proposed to provide beneficial reference for future researches and real-world applications.

Detection of Strawberry Diseases Using a Convolutional Neural Network

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

Detection of Fungi and Oomycetes by Volatiles Using E-Nose and SPME-GC/MS Platforms

Fungi and oomycetes release volatiles into their environment which could be used for olfactory detection and identification of these organisms by electronic-nose (e-nose). The aim of this study was to survey volatile compound emission using an e-nose device and to identify released molecules through solid phase microextraction-gas chromatography/mass spectrometry (SPME-GC/MS) analysis to ultimately develop a detection system for fungi and fungi-like organisms. To this end, cultures of eight fungi (Armillaria gallica, Armillaria ostoyae, Fusarium avenaceum, Fusarium culmorum, Fusarium oxysporum, Fusarium poae, Rhizoctonia solani, Trichoderma asperellum) and four oomycetes (Phytophthora cactorum, P. cinnamomi, P. plurivora, P. ramorum) were tested with the e-nose system and investigated by means of SPME-GC/MS. Strains of F. poae, R. solani and T. asperellum appeared to be the most odoriferous. All investigated fungal species (except R. solani) produced sesquiterpenes in variable amounts, in contrast to the tested oomycetes strains. Other molecules such as aliphatic hydrocarbons, alcohols, aldehydes, esters and benzene derivatives were found in all samples. The results suggested that the major differences between respective VOC emission ranges of the tested species lie in sesquiterpene production, with fungi emitting some while oomycetes released none or smaller amounts of such molecules. Our e-nose system could discriminate between the odors emitted by P. ramorum, F. poae, T. asperellum and R. solani, which accounted for over 88% of the PCA variance. These preliminary results of fungal and oomycete detection make the e-nose device suitable for further sensor design as a potential tool for forest managers, other plant managers, as well as regulatory agencies such as quarantine services.

Targeted versus Nontargeted Green Strategies Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry Combined with Chemometrics for Rapid Detection of Fungal Contamination on Wheat Kernels

Conventional methods for detecting fungal contamination are generally time-consuming and sample-destructive, making them impossible for large-scale nondestructive detection and real-time analysis. Therefore, the potential of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was examined for the rapid determination of fungal infection on wheat samples in a rapid and nondestructive manner. In addition, the validation experiment of detecting the percent A. flavus infection presented in simulated field samples was carried out. Because the dual separation of HS-GC-IMS could generate massive amounts of three-dimensional data, proper chemometric processing was required. In this study, two chemometric strategies including: (i) nontargeted spectral fingerprinting and (ii) targeted specific markers were introduced to evaluate the performances of classification and prediction models. Results showed that satisfying results for the differentiation of fungal species were obtained based on both strategies (>80%) by the genetic algorithm optimized support vector machine (GA-SVM), and better values were obtained based on the first strategy (100%). Likewise, the GA-SVM model based on the first strategy achieved the best prediction performances (R² = 0.979-0.998) of colony counts in fungal infected samples. The results of validation experiment showed that GA-SVM models based on the first strategy could still provide satisfactory classification (86.67%) and prediction (R² = 0.889) performances for percent A. flavus infection presented in simulated field samples at day 4. This study indicated the feasibility of HS-GC-IMS-based approaches for the early detection of fungal contamination in wheat kernels.

New frozen product development from strawberries (Fragaria Ananassa Duch.)

Strawberry fruit has a short shelf life. If stored at ambient temperature only lasts 1 day, so it needs to be dried into a frozen product so that its shelf life is longer. Frozen products are favored by consumers because they still have properties like fresh fruit. This study was aimed at examining the physical and sensory characteristics of new frozen products from strawberries. The research sample was freeze-dried at 3 variations of the heating plate temperature were 40, 50, and 60 °C and 3 variations of the drying time were 24, 36, and 48 h. The research parameters observed were weight loss, water content, texture, color, aroma, and taste. The results showed that the freeze-vacuum drying process has a significant influence on the parameters of weight loss, moisture content, texture, and color of frozen strawberries, but does not influence significantly to aroma and taste. The highest weight loss and evaporation were obtained at 60 °C and 48 h of drying time. Frozen strawberries most preferred by panelists are those that are freeze-dried at 50 °C and a drying time of 36 h because they have aroma and flavor that seem fresh strawberries.

Application of electronic nose and GC–MS for detection of strawberries with vibrational damage

Objectives

This study evaluated the potential of using electronic nose (e-nose) technology to non-destructively detect strawberry fruits with vibrational damage based on their volatile substances (VOCs).

Materials and methods

Four groups of strawberries with different durations of vibrations (0, 0.5, 1, and 2 h) were prepared, and their e-nose signals were collected at 0, 1, 2, and 3 days after vibration treatment.

Results

The results showed that when the samples from all four sampling days during storage were used for modelling, both the levels of vibrational damage and the day after the damage happened were accurately predicted. The best models had residual prediction deviation values of 2.984 and 5.478. The discrimination models for damaged strawberries also obtained good classification results, with an average correct answer rate of calibration and prediction of 99.24%. When the samples from each sampling day or vibration time were used for modelling, better results were obtained, but these models were not suitable for an actual situation. The gas chromatography–mass spectrophotometry results showed that the VOCs of the strawberries varied after experiencing vibrations, which was the basis for e-nose detection.

Limitations

The changes in VOCs released by other forces should be studied in the future.

Conclusions

The above results showed the potential use of e-nose technology to detect strawberries that have suffered vibrational damage.

Aroma dynamic characteristics during the process of variable-temperature final firing of Congou black tea by electronic nose and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry

The drying technology is crucial to the quality of Congou black tea. In this study, the aroma dynamic characteristics during the variable-temperature final firing of Congou black tea was investigated by electronic nose (e-nose) and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). Varying drying temperatures and time obtained distinctly different types of aroma characteristics such as faint scent, floral aroma, and sweet fragrance. GC × GC-TOFMS identified a total of 243 volatile compounds. Clear discrimination among different variable-temperature final firing samples was achieved by using partial least squares discriminant analysis (R2Y = 0.95, Q2 = 0.727). Based on a dual criterion of variable importance in the projection value (VIP > 1.0) and one-way ANOVA (p < 0.05), ninety-one specific volatile biomarkers were identified, including 2,6-dimethyl-2,6-octadiene and 2,5-diethylpyrazine with VIP > 1.5. In addition, for the overall odor perception, e-nose was able to distinguish the subtle difference during the variable-temperature final firing process.

Remote Sensing of Diseases

Detection, identification, and quantification of plant diseases by sensor techniques are expected to enable a more precise disease control, as sensors are sensitive, objective, and highly available for disease assessment. Recent progress in sensor technology and data processing is very promising; nevertheless, technical constraints and issues inherent to variability in host-pathogen interactions currently limit the use of sensors in various fields of application. The information from spectral [e.g., RGB (red, green, blue)], multispectral, and hyperspectral sensors that measure reflectance, fluorescence, and emission of radiation or from electronic noses that detect volatile organic compounds released from plants or pathogens, as well as the potential of sensors to characterize the health status of crops, is evaluated based on the recent literature. Phytopathological aspects of remote sensing of plant diseases across different scales and for various purposes are discussed, including spatial disease patterns, epidemic spread of pathogens, crop characteristics, and links to disease control. Future challenges in sensor use are identified.

Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectra and olfactory sensors

BACKGROUND

Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and an electronic nose (E-nose) were applied to detect and identify freeze-dried Agaricus bisporus infected with the five fungi.

RESULTS

Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (R_c ² ) = 0.786, root mean-square error of calibration (RMSEC) = 0.125 log CFU g^-1 . The A. flavus group had the highest prediction performance: coefficient of prediction (R_p ² ) = 0.821, root mean-square error of prediction (RMSEP) = 0.083 log CFU g^-1 . The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E-nose results for freeze-dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using NIR, MIR, and E-nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E-nose methods.

CONCLUSION

Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E-nose were proven to be effective in monitoring fungal contamination on freeze-dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry.

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.

Rapid and Non-Destructive Detection of Compression Damage of Yellow Peach Using an Electronic Nose and Chemometrics

The rapid and non-destructive detection of mechanical damage to fruit during postharvest supply chains is important for monitoring fruit deterioration in time and optimizing freshness preservation and packaging strategies. As fruit is usually packed during supply chain operations, it is difficult to detect whether it has suffered mechanical damage by visual observation and spectral imaging technologies. In this study, based on the volatile substances (VOCs) in yellow peaches, the electronic nose (e-nose) technology was applied to non-destructively predict the levels of compression damage in yellow peaches, discriminate the damaged fruit and predict the time after the damage. A comparison of the models, established based on the samples at different times after damage, was also carried out. The results show that, at 24 h after damage, the correct answer rate for identifying the damaged fruit was 93.33%, and the residual predictive deviation in predicting the levels of compression damage and the time after the damage, was 2.139 and 2.114, respectively. The results of e-nose and gas chromatography-mass spectrophotometry (GC–MS) showed that the VOCs changed after being compressed—this was the basis of the e-nose detection. Therefore, the e-nose is a promising candidate for the detection of compression damage in yellow peach.

Effect of Co-Fermentation with Lactic Acid Bacteria and K. marxianus on Physicochemical and Sensory Properties of Goat Milk

In this study, a multi-starters fermentation system involved lactic acid bacteria and yeasts was applied to obtain a novel acidified goat milk (AGM). Significant differences were found in the volatile flavor profile among goat milk, goat yogurt, and AGM reflected by principal component analysis of electronic nose (E-nose) data. Gas chromatography–mass spectrometry (GC-MS) results indicated that the relative content of free octanoic acid decreased, and more aromas were formed in AGM, which were considered to mask the goaty smell and give AGM a pleasant flavor. Rheological analysis indicated that AGM had higher apparent viscosity and G’ and G’’ moduli than goat yogurt and goat milk. Therefore, the goat yogurt fermented by lactic acid bacteria and K. marxianus exhibits a new method to alleviate the goaty flavor in goat milk and provides a novel option for those who were allergic to milk protein and dislike goaty flavor in goat milk.

Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat

The study assessed the feasibility of merging data acquired from hyperspectral imaging (HSI) and electronic nose (e-nose) to develop a robust method for the rapid prediction of intramuscular fat (IMF) and peroxide value (PV) of pork meat affected by temperature and NaCl treatments. Multivariate calibration models for prediction of IMF and PV using median spectra features (MSF) and image texture features (ITF) from HSI data and mean signal values (MSV) from e-nose signals were established based on support vector machine regression (SVMR). Optimum wavelengths highly related to IMF and PV were selected from the MSF and ITF. Next, recurring optimum wavelengths from the two feature groups were manually obtained and merged to constitute "combined attribute features" (CAF) which yielded acceptable results with (R_c² = 0.877, 0.891; RMSEC = 2.410, 1.109; R_p² = 0.790, 0.858; RMSEP = 3.611, 2.013) respectively for IMF and PV. MSV yielded relatively low results with (R_c² = 0.783, 0.877; RMSEC = 4.591, 0.653; R_p² = 0.704, 0.797; RMSEP = 3.991, 0.760) respectively for IMF and PV. Finally, data fusion of CAF and MSV was performed which yielded relatively improved prediction results with (R_c² = 0.936, 0.955; RMSEC = 1.209, 0.997; R_p² = 0.895, 0.901; RMSEP = 2.099, 1.008) respectively for IMF and PV. The results obtained demonstrate that it is feasible to mutually integrate spectral and image features with volatile information to quantitatively monitor IMF and PV in processed pork meat. Graphical abstract.

Gas Sensor Array and Classifiers as a Means of Varroosis Detection

The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose

Early detection of Aspergillus spp. contamination in rice was investigated by electronic nose (E-nose) in this study. Sterilized rice artificially inoculated with three Aspergillus strains were subjected to GC-MS and E-nose analyses. Principle Component Analysis (PCA), Partial Least Squares Regression (PLSR), Back-propagation neural network (BPNN), Support Vector Machine (SVM) and Learning Vector Quantization (LVQ) were employed for qualitative classification and quantitative regression. GC-MS analysis revealed a significant correlation between the volatile compounds and total amounts/species of fungi. While X-axis barycenters of PC1 scores were significantly correlated with fungal counts, logistic model could be employed to simulate the growth of individual fungus (R² = 0.978-0.996). Fungal species and counts in rice could be classified and predicted by BPNN (96.4%) and PLSR (R² = 0.886-0.917), respectively. The results demonstrated that E-nose combined with BPNN might offer the feasibility for early detection of Aspergillus spp. contamination in rice.

Volatile compounds associated with growth of Asaia bogorensis and Asaia lannensis-unusual spoilage bacteria of functional beverages

Acetic acid bacteria of the genus Asaia are recognized as common bacterial spoilage in the beverage industry. Their growth in contaminated soft drinks can be visible in the form of flocs, turbidity and flavor changes. Volatile profiles associated with the growth and metabolic activities of Asaia lannensis and As. bogorensis strains were evaluated using comprehensive gas chromatography-time of flight mass spectrometry (GC × GC-ToF MS). Based on obtained results, 33 main compounds were identified. The greatest variety of volatile metabolites was noted for As. lannensis strain W4. 2-Phenylethanol, 3-pentanone, 2-nonanol, 2-hydroxy-3-pentanone, and 2-nitro-1-butanol were detected as dominant volatile compounds. Additionally, As. lannensis strains formed 2-propenoic acid ethyl ester. As. bogorensis ISD1 was distinguished by the higher concentration of 2-hydroxy-3-pentanone and 3-methyl-1-butene but the lowest concentration of 2-phenylethanol. Based on these results, it was found that volatile profiles of Asaia spp. are unique among acetic acid bacteria. Moreover, obtained profiles depended not only on bacterial species and strains but also on the composition of culture media.