Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Multiple metabolite profiles uncover remarkable bioactive compounds and metabolic characteristics of noni fruit (Morinda citrifolia L.) at various stages of ripeness

This study aimed to investigate the changes in physicochemical properties, bioactive compounds, and metabolic characteristics of noni fruit at different ripeness levels. The results showed that there were significant differences in physicochemical properties. HPLC analysis was conducted, revealing succinic acid, scopoletin, deacetylasperulosidic acid, and asperulosidic acid were key bioactive compounds as the fruit ripened. Additionally, 4 differentbiomarkers (isocitric acid, 4,4-thiodiphenol, lobaric acid, and octocrylene), identified using 1HNMR and LC-IT-TOF-MS, were found to have a VIP value over 1. The results from HS-GC-IMS demonstrated noteworthy that 14 volatile compounds were identified as highly discriminative features during fruit ripening. Furthermore, correlation analysis showed that different ripeness had significant effects on bioactive components and functional activities, e.g., the inhibition rate of enzyme and E. coli of noni fruit with different ripeness exceeded 90% at the last stage. This study contributes new insights into the effective utilization of bioactive ingredients in noni fruit.

Portable and miniature sensors in supply chain for food authentication: a review

Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.

Machine Learning Driven Atom-Thin Materials for Fragrance Sensing

Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.

Advancement of metal oxide nanomaterials on agri-food fronts

The application of metal oxide nanomaterials (MOx NMs) in the agrifood industry offers innovative solutions that can facilitate a paradigm shift in a sector that is currently facing challenges in meeting the growing requirements for food production, while safeguarding the environment from the impacts of current agriculture practices. This review comprehensively illustrates recent advancements and applications of MOx for sustainable practices in the food and agricultural industries and environmental preservation. Relevant published data point out that MOx NMs can be tailored for specific properties, enabling advanced design concepts with improved features for various applications in the agrifood industry. Applications include nano-agrochemical formulation, control of food quality through nanosensors, and smart food packaging. Furthermore, recent research suggests MOx's vital role in addressing environmental challenges by removing toxic elements from contaminated soil and water. This mitigates the environmental effects of widespread agrichemical use and creates a more favorable environment for plant growth. The review also discusses potential barriers, particularly regarding MOx toxicity and risk evaluation. Fundamental concerns about possible adverse effects on human health and the environment must be addressed to establish an appropriate regulatory framework for nano metal oxide-based food and agricultural products.

Evaluation of roasting and storage conditions as a strategy to improve the sensory characteristics and shelf life of coffee

Coffee is one of the most consumed products worldwide. Among the varieties of this product, specialty coffee is a type of coffee that has been growing in the world market. This paper aims to assess the effects that the conditions derived from coffee roasting at different altitude levels have on the quality of the product. It was discovered that processing coffee at a higher altitude level yielded a smaller increase in bitterness. This led to a better Specialty Coffee Association (SCA) score in cupping and, consequently, to better preservation of the coffee quality. The storage time affected the aroma by associating roaster aromas with older coffees. Although the assessed origins had the same NIR spectra, differences in peak intensity lead to variations in the flavor and aroma of the coffee. Furthermore, although green beans prolong quality allowing a SCA score of 84.73 ± 2.81 after 4 months of storage, roasted coffee at higher altitudes could also maintain the quality between production and consumption (SCA score of 80.22 ± 0.91 after 2 months). Finally, this research found that the instrumental equipment helped to find minor changes in the sensorial profile, and with these changes correlated with the sensorial panel, the best conditions to preserve coffee quality were found.

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Intelligent flexible manipulator system based on flexible tactile sensing (IFMSFTS) for kiwifruit ripeness classification

BACKGROUND

Consumers all throughout the world enjoy kiwifruit. After harvest, there are as much as 20-25% of kiwifruit lost along the entire industrial chain. An intelligent flexible manipulator system based on flexible tactile sensing (IFMSFTS) was created to automatically and intelligently classify kiwifruit ripeness in order to minimize loss.

RESULT

The flexible manipulator is coupled with the flexible tactile sensor. When kiwifruits are being gripped by the manipulator, the flexible sensor perceives their firmness, and the mapping relationship between firmness and ripeness allows for the prediction and evaluation of the kiwifruit's ripeness. Principal component analysis (PCA) is employed to minimize the dimension of the sample firmness data set. K-Nearest neighbor (KNN) and support vector machine (SVM) classifiers are utilized to train and test the data. The findings indicate that PCA-KNN's classification accuracy is 97.5% and PCA-SVM's classification accuracy is 96.24%. The first is a better fit.

CONCLUSION

IFMSFTS can precisely classify ripeness, effectively address the issue of fruit loss, and realize the sustainable and clean production of fruit by sensing the firmness of kiwifruit and relying on the mapping link between firmness and ripeness. © 2023 Society of Chemical Industry.

Monitoring of Volatile Compounds of Ready-to-Eat Kiwifruit Using GC-IMS

Ready-to-eat kiwifruit has gained significant market value in recent years due to its convenience and the increasing consumer demand for healthy ready-to-eat snacks. The volatile compound content (VOC) in ready-to-eat kiwifruit is a crucial factor determining its flavor and aroma. VOC is an important characteristic that positively affects the overall evaluation of ready-to-eat kiwifruit. In this study, we utilized gas chromatography-ion mobility spectrometry (GC-IMS) to investigate changes in the composition of VOCs in ready-to-eat kiwifruit during different storage periods (every 12 h). Our results revealed the presence of 55 VOCs in ready-to-eat kiwifruit, with alcohols, esters, and ketones being the dominant compounds responsible for the aromatic flavor. Among these compounds, methyl caproate, ethyl butyrate, and ethyl propionate provided specific fruit flavors to ready-to-eat kiwifruit, whereas esters played a secondary role. Furthermore, varying trends were observed for different compound types as the storage period increased: alcohols exhibited a decreasing trend, whereas ester products and some sulfur-containing compounds showed an increase. Additionally, fingerprint profiles of volatile compounds were established for each storage period, enabling the identification of characteristic substances. This comprehensive analysis of volatile flavor substances during the ripening of ready-to-eat kiwifruit will greatly contribute to enhancing its sensory quality, consumer appeal, and overall marketability.

Functional properties and sensory profile of coffee prepared by different brewing methods

Coffee is one of the main sources of antioxidants in the diet of many countries. The purpose of this research was to assess the effect of different brewing methods, particle size, and coffee quality on the total phenolic content, antioxidant capacity (AC), and sensory profile of the beverage. The brewing methods yielded differences in taste with higher bitterness in immersion methods. However, the main factors that influenced coffee extraction and taste were particle size and coffee type. A finer particle size allowed for greater phenolic and caffeine (CA) extraction (2.82 mg GA/mL; 1.01 mg CA/mL), resulting in higher bitterness and astringency. Additionally, the type of coffee resulted in a higher CA content in commercial coffee (Specialty: 0.72 ± 0.10 mg CA/mL; Commercial: 1.13 ± 0.14 mg CA/mL). The results showed that using a ratio of 1:20 and 1:15 for commercial and specialty coffee, respectively, yielded differences in AC using the DPPH method (Specialty: 11.54 ± 1.12 µmol/mL; Commercial: 10.20 ± 1.88 µmol/mL) but not with the ABTS method (Specialty: 10.38 ± 1.23 µmol/mL; Commercial: 10.37 ± 1.13 µmol/mL). Similarly to the ABTS method, no differences in the total phenol content of the coffee cup were observed (Specialty: 2.52 ± 0.40 mg/mL; Commercial: 2.43 ± 0.28 mg/mL). Thus, the findings suggest that specialty coffee offers consumers a more balanced cup with less CA content. This allows for more coffee consumption without an excessive intake of CA. However, consumers can adjust the functionality, sensory profile, and CA content of a coffee cup by modifying the particle size and the brewing method used.

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R 2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.

Biosynthesis, behavior and fate of volatile organic sulfide in Lentinus edodes (Berk.) upon hot-air drying treatment

This study elucidated the biosynthesis and changing behaviors of organic sulfide in shiitake mushrooms upon hot-air drying treatment. The changes of aw, moisture migration, contours of taste and flavor, organic sulfide, and 4 key enzyme activities were monitored throughout three drying procedures (CT/ST1/ST2). Results showed that drying rate was related to the moisture migration. Key enzymes of γ-GTase, ASFase and CS lyase were heat-resistant proteases, while C-Dase exhibited low thermal stability with the activity decreased during treatment. A total of 17 organic sulfides were identified and PLS analyses suggested 6 cyclic polysulfides were formed by C-Dase desulfurization, while 5 thioethers generation were related to the thermal cleavage of direct precursors (straight-chain di/tris/tetrasulfonyl esters) and Maillard reaction. These results indicated that ST2 drying procedures had a positive effect on the formation of cyclic polysulfides at the end of drying pried and the achievement of premium flavor qualities.

Response Characteristics Study of Ethylene Sensor for Fruit Ripening under Temperature Control

Post-ripening fruits need to be ripened to reach edible conditions, as they are not yet mature enough when picked. Ripening technology is based mainly on temperature control and gas regulation, with the proportion of ethylene being one of the key gas regulation parameters. A sensor's time domain response characteristic curve was obtained through the ethylene monitoring system. The first experiment showed that the sensor has good response speed (maximum of first derivative: 2.01714; minimum of first derivative: -2.01714), stability (xg: 2.42%; trec: 2.05%; Dres: 3.28%), and repeatability (xg: 20.6; trec: 52.4; Dres: 2.31). The second experiment showed that optimal ripening parameters include color, hardness (Change Ⅰ: 88.53%, Change Ⅱ: 75.28%), adhesiveness (Change Ⅰ: 95.29%, Change Ⅱ: 74.72%), and chewiness (Change Ⅰ: 95.18%, Change Ⅱ: 74.25%), verifying the response characteristics of the sensor. This paper proves that the sensor was able to accurately monitor changes in concentration which reflect changes in fruit ripeness, and that the optimal parameters were the ethylene response parameter (Change Ⅰ: 27.78%, Change Ⅱ: 32.53%) and the first derivative parameter (Change Ⅰ: 202.38%, Change Ⅱ: -293.28%). Developing a gas-sensing technology suitable for fruit ripening is of great significance.

Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview

Quality losses in fresh produce throughout the postharvest phase are often due to the inappropriate use of preservation technologies. In the last few decades, besides the traditional approaches, advanced postharvest physical and chemical treatments (active packaging, dipping, vacuum impregnation, conventional heating, pulsed electric field, high hydrostatic pressure, and cold plasma) and biocontrol techniques have been implemented to preserve the nutritional value and safety of fresh produce. The application of these methodologies after harvesting is useful when addressing quality loss due to the long duration when transporting products to distant markets. Among the emerging technologies and contactless and non-destructive techniques for quality monitoring (image analysis, electronic noses, and near-infrared spectroscopy) present numerous advantages over the traditional, destructive methods. The present review paper has grouped original studies within the topic of advanced postharvest technologies, to preserve quality and reduce losses and waste in fresh produce. Moreover, the effectiveness and advantages of some contactless and non-destructive methodologies for monitoring the quality of fruit and vegetables will also be discussed and compared to the traditional methods.

Data fusion of electronic noses and electronic tongues aids in botanical origin identification on imbalanced Codonopsis Radix samples

Codonopsis Radix (CR) is an edible food and traditional Chinese herb medicine in China. Various varieties of Codonopsis Radix have different tastes. To make the flavor of processed food stable, two kinds of electronic sensory devices, electronic nose and electronic tongue, were used to establish a discrimination model to identify the botanical origin of each sample. The optimal model built on the 88 batches of samples was selected from the models trained with all combination of two pretreatment methods and three classification methods. A comparison were performed on the models trained on the data collected by electronic nose and electronic tongue. The results showed that the model trained on the fused dataset outperformed the models trained separately on the electronic nose data and electronic tongue data. The two preprocessing approaches could improve the prediction performance of all classification methods. Classification and Regression Tree approach performed better than Partial Least Square Discriminant Analysis and Linear Discriminant Analysis in terms of accuracy. But Classification and Regression Tree tends to assign the samples of minority class to the majority class. Meanwhile, Partial Least Square Discriminant Analysis keeps a good balance between the identification requirements of all the two groups of samples. Taking all the results above, the model built using the Partial Least Square Discriminant Analysis method on the fused data after z-score was used to identify the botanical origin of Codonopsis Radix.

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.

Prediction of banana maturity based on the sweetness and color values of different segments during ripening

To predict the maturity of bananas, the present study used non-destructive methods to analyze changes in the sweetness and color of the stalks, middles, and tips of bananas during ripening. The results indicated that the respective maturation of these three segments did not occur simultaneously, as indicated by the differential enzyme activity and gene expression levels recorded in these segments. A principal component analysis and cluster plots were used to review the classification of banana maturity, highlighting that banana maturation can be divided into six stages. Two distinct maturity prediction algorithms were established using random forest, artificial neural network, and support vector machines, and they also indicated that dividing the maturity of bananas into six stages was adequate. These findings contribute to the development of quality evaluation and of a rapid grading system for processing, which improves the quality and sale of banana fruits and the related processed products.

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Sweetness and color during ripening were assessed along banana fingers.

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A new maturity prediction model was established for bananas.

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Banana maturity was divided in six stages.

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The theoretical basis for developing a maturity grading detection device was set.

Quality changes and shelf-life prediction model of postharvest apples using partial least squares and artificial neural network analysis

The quality of postharvest apples is greatly affected by storage temperatures. In this paper, the sensory qualities, such as flavor, texture, color, and taste change of apples during storage at 4 °C and 20 °C were investigated. After correlation analysis, the partial least squares (PLS) and artificial neural network (ANN) techniques were used to build a shelf-life prediction model. The results showed that lower temperature storage can better maintain the color, flesh hardness, and release of volatile compounds of apples. The acidity of apples stored at 20 °C decreased much faster than that at 4 °C. The PLS models were successful in predicting the apple shelf life. When modeling using PLS with a single type index, the order of accuracy of the prediction model was texture, color, and flavor. As a nonlinear algorithm, the ANN model was also an effective predictive tool of apple shelf life at both temperatures.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R²), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

Gas Recognition in E-Nose System: A Review

Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as principal component analysis (PCA) have been widely applied in E-nose systems. In recent years, artificial neural network (ANN) has revolutionized the field of E-nose, especially spiking neural network (SNN). In this paper, we investigate recent gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations. We find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise. While ANN-based methods obtain better recognition accuracy with flexible architectures and lots of parameters. However, some ANNs are too complex to be implemented in portable E-nose systems, such as deep convolutional neural networks (CNNs). In contrast, SNN-based gas recognition methods achieve satisfying accuracy and recognize more types of gases, and could be implemented with energy-efficient hardware, which makes them a promising candidate in multi-gas identification.

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 Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Morphological Effects in SnO2 Chemiresistors for Ethanol Detection: A Review in Terms of Central Performances and Outliers

SnO₂ is one of the most studied materials in gas sensing and is often used as a benchmark for other metal oxide-based gas sensors. To optimize its structural and functional features, the fine tuning of the morphology in nanoparticles, nanowires, nanosheets and their eventual hierarchical organization has become an active field of research. In this paper, the different SnO₂ morphologies reported in literature in the last five years are systematically compared in terms of response amplitude through a statistical approach. To have a dataset as homogeneous as possible, which is necessary for a reliable comparison, the analysis is carried out on sensors based on pure SnO₂, focusing on ethanol detection in a dry air background as case study. Concerning the central performances of each morphology, results indicate that none clearly outperform the others, while a few individual materials emerge as remarkable outliers with respect to the whole dataset. The observed central performances and outliers may represent a suitable reference for future research activities in the field.

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer

Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.

Storage effects on the quality quartet of orange juice submitted to moderate thermosonication: Predictive modeling and odor fingerprinting approach

The effects of moderate thermosonication (MTS) on the quality quartet: physico-chemical, microbial, nutritional and sensory qualities of orange juice (OJ) inoculated with Alicyclobacillus acidoterrestris (AAT) were studied during 24 days of storage at ambient and refrigerated temperatures. The bioactive compounds and antioxidant activity of OJ decreased with storage, while the pectin methyl esterase (PME) increased. Nonetheless, noticeable changes were observed from the 12th day of storage. There was no obvious (p > 0.05) variation in pH and total soluble solids. To determine the nutritional and microbial quality characteristics of OJ during storage, non-linear kinetic curves were successfully fitted with least square fitting polynomial and four-parameter log-logistic distribution models. The E-nose sensors succeeded in discriminating between the aroma of non-treated and treated OJ based on linear discriminant analysis (LDA). Furthermore, terpenes, alcohol and partially aromatic compounds were the main spoilage indicators of OJ during storage based on E-nose analysis and confirmed by HS-SPME-GC/MS analysis. Thus, MTS significantly extended the shelf life of the quality quartet of natural OJ at 4 °C. E-nose-GC/MS fusion offered odor fingerprints to AAT microorganisms that can be used as spoilage index without using traditional food analysis techniques. The proposed approach can be used as an alternative tool for rapid detection of spoilage microorganisms in OJ.

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R² ≥ 0.9998).

Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits

This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits’ ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination of color channels allow classifiers to reach similar levels of accuracy; however, combination methods still require higher computational complexity. The highest level of accuracy was obtained using the seven-dimensional principal component analysis feature space.

Tracing internal quality and aroma of a red-fleshed kiwifruit during ripening by means of GC-MS and E-nose

'Hongyang' kiwifruit is a new breed of red-fleshed cultivar that has become broadly popular with consumers in recent years. In this study, the internal quality and aroma of this kiwifruit during ripening were investigated by means of gas chromatography-mass spectrometry (GC-MS) and electronic nose (E-nose). Results showed that the green note aldehydes declined, the main fruity esters increased, and the terpenes had no obvious changes during ripening. Correlations between quality indices, volatile compounds, and E-nose data were analyzed by ANOVA partial least squares regression (APLSR), and the results showed that firmness and titratable acidity (TA) had highly positive correlations with (E)-2-hexenal and hexanal, while soluble solids content (SSC) and SSC/TA ratio had positive correlations with ester compounds. The E-nose sensors of S7, S10, S8, S6, S9, and S2 were positively correlated with ester compounds, S1, S3, and S5 were mainly correlated with hexanal, and S4 was correlated with terpene compounds. Partial least squares regression (PLSR) and support vector machine (SVM) were employed to predict the quality indices by E-nose data, and SVM presented a better performance in predicting firmness, SSC, TA, and SSC/TA ratio (R ² > 0.98 in the training set and R ² > 0.94 in the testing set). This study demonstrated that the E-nose technique could be used as an alternative to trace the flavor quality of kiwifruit during ripening.