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

Application of nondestructive techniques for peach (Prunus persica) quality inspection: A review

Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the fruit for firmness) are both subjective and destructive, which tend to diminish the integrity of fruit samples, consequently undermining their market value. Compared to traditional detection methods, nondestructive technology offers efficient and noninvasive solutions for rapidly and accurately assessing internal external quality of peaches. This can significantly enhance product classification and quality assurance while reducing the need for extensive human resources and minimizing potential physical damage to peaches. This review provided a comprehensive overview of nondestructive techniques for peach quality evaluation, including visible/near-infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric and optical properties, fluorescence spectroscopy, electronic nose/tongue, and acoustic vibration methods. It also evaluates the effectiveness of each technique in assessing internal quality, maturity, and disease detection of peaches. The advantages and limitations of each method were also summarized. This study focuses specifically on peaches and encompasses all existing nondestructive testing methods, providing valuable insights and references for future studies in the field of peach quality analysis using nondestructive testing methods.

Biochemical and molecular changes in peach fruit exposed to cold stress conditions

Storage or transportation temperature is very important for preserving the quality of fruit. However, low temperature in sensitive fruit such as peach can induce loss of quality. Fruit exposed to a specific range of temperatures and for a longer period can show chilling injury (CI) symptoms. The susceptibility to CI at low temperature varies among cultivars and genetic backgrounds. Along with agronomic management, appropriate postharvest management can limit quality losses. The importance of correct temperature management during postharvest handling has been widely demonstrated. Nowadays, due to long-distance markets and complex logistics that require multiple actors, the management of storage/transportation conditions is crucial for the quality of products reaching the consumer.Peach fruit exposed to low temperatures activate a suite of physiological, metabolomic, and molecular changes that attempt to counteract the negative effects of chilling stress. In this review an overview of the factors involved, and plant responses is presented and critically discussed. Physiological disorders associated with CI generally only appear after the storage/transportation, hence early detection methods are needed to monitor quality and detect internal changes which will lead to CI development. CI detection tools are assessed: they need to be easy to use, and preferably non-destructive to avoid loss of products.

Effect of Fermentation Strategy on the Quality and Aroma Characteristics of Yellow Peach Wines

To obtain high-quality yellow peach wines of varying characteristics, different fermentation strategies, including various pre-fermentative treatments, were applied. This study aimed to determine the effect of different fermentation strategies on the physicochemical properties, monomer phenol content, in vitro antioxidant activity, and volatile compounds of yellow peach wine. The results showed that peach wine P12, fermented with pulp, had higher total phenolic content (TPC), total flavonoid content (TFC), monomer phenol and volatile compound content, and antioxidant activity. Ten monomeric phenols were detected in peach wines, and the content of catechin was the highest. Juice fermentation wine, J12, had a strong floral aroma, and some volatile compounds were released during fermentation when water was added to the pulp, and low alcohol content did not reduce the variety of volatile compounds. The main aroma and common characteristics of the fermented yellow peach wine were dominated by esters, with a relative odor activity value (ROAV) ≥ 1, namely, isoamyl acetate, ethyl hexanoate, and ethyl octanoate. Our results demonstrated that the application of the described fermentation strategies significantly affected the quality and volatile compound content of yellow peach wines. This might assist in the development of a highly diverse yellow peach wine flavor.

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.

Nano-Al2O3 particles affect gut microbiome and resistome in an in vitro simulator of the human colon microbial ecosystem

Nano-Al₂O₃ has been widely used in various consumer products and water treatment processes because of its unique physicochemical properties. The probability of human exposure to nano-Al₂O₃ increases significantly, of which oral ingestion is an important route. However, effects and underlying mechanisms of nano-Al₂O₃ on gut microbiota and resistome are still not well delineated. Here, we systematically investigated the effects of nano-Al₂O₃ on the human gut microbiome by an in vitro simulator of human colon microbial ecosystem. Results indicated that nano-Al₂O₃ interfered with the gut microbiota, and significantly suppressed the short-chain fatty acids metabolism, which might pose adverse effects on the host. More seriously, high level of nano-Al₂O₃ (50 mg/L) was more destructive to the gut flora, though the damage might be temporary. In addition, sub-inhibitory low-dose of nano-Al₂O₃ (0.1 mg/L) significantly enhanced the abundance of antibiotic resistance genes (ARGs) after 7-day exposure. This is attributed to that low concentration of nano-Al₂O₃ can promote horizontal transfer of ARGs by increasing cell membrane permeability and relative abundance of transposase (e.g. tnpA, IS613, and Tp614). Our findings confirmed the adverse effects of nano-Al₂O₃ on the human gut resistome and emphasized the necessity to assess potential risks of nanomaterials on the human gut health.

An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm

To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization–back-propagation neural network (PSO-BPNN), a gray wolf optimization–backward propagation neural network (GWO-BPNN), and a sparrow search algorithm–backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples.

Mechanical damages and packaging methods along the fresh fruit supply chain: A review

Mechanical damage of fresh fruit occurs throughout the postharvest supply chain leading to poor consumer acceptance and marketability. In this review, the mechanisms of damage development are discussed first. Mathematical modeling provides advanced ways to describe and predict the deformation of fruit with arbitrary geometry, which is important to understand their mechanical responses to external forces. Also, the effects of damage at the cellular and molecular levels are discussed as this provides insight into fruit physiological responses to damage. Next, direct measurement methods for damage including manual evaluation, optical detection, magnetic resonance imaging, and X-ray computed tomography are examined, as well as indirect methods based on physiochemical indexes. Also, methods to measure fruit susceptibility to mechanical damage based on the bruise threshold and the amount of damage per unit of impact energy are reviewed. Further, commonly used external and interior packaging and their applications in reducing damage are summarized, and a recent biomimetic approach for designing novel lightweight packaging inspired by the fruit pericarp. Finally, future research directions are provided.HIGHLIGHTSMathematical modeling has been increasingly used to calculate damage to fruit.Cell and molecular mechanisms response to fruit damage is an under-explored area.Susceptibility measurement of different mechanical forces has received attention.Customized design of reusable and biodegradable packaging is a hot topic of research.

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.

Volatile Profile and Biosynthesis of Post-harvest Apples are Affected by the Mechanical Damage

Mechanical damage to fruit causes flavor changes during post-harvest supply chains. It is important to identify the main volatiles and explore their biosynthesis mechanism. In this study, the volatile changes in apples caused by mechanical damage were analyzed by gas chromatography-ion mobility spectrometry. Hexanal and ethyl acetate were accumulated and identified as potential volatile biomarkers to detect damaged apples. The study on the lipoxygenase (LOX) pathway and transcription factors (TFs) shows that mechanical damage up-regulated the expression of MdLOX-like, MdLOX3b, MdLOX7b, MdLOX7c, MdLOX2a, and MdAAT in the LOX pathway and that of one MYB TF (MdMYB-like), five ERF TFs (MdERF073, MdERF003, MdERF114, MdERF15, and MdERF2), and five WRKY TFs (MdWRKY23, MdWRKY17, MdWRKY46, MdWRKY48, and MdWRKY71). Notably, MdAAT was significantly correlated to MdMYB-like, MdWRKY23, MdWRKY71, MdERF15, and MdERF2. Thus, TFs may attribute to the accumulation of hexanal and ethyl acetate by regulating the expression of LOX pathway-related genes.

Toxicity Assessment of Nano-ZnO Exposure on the Human Intestinal Microbiome, Metabolic Functions, and Resistome Using an In Vitro Colon Simulator

Nano-ZnO, as a commonly used nanomaterial, has been found in drinking water, food, and medicine; therefore, it poses potential health risks via the digestion system. However, little is known about the toxicity of nano-ZnO on the human intestinal microbiome, which plays critical roles in human health. This study comprehensively investigated the impact of nano-ZnO on the human gut microbiome, metabolic functions, and resistome using an in vitro colon simulator. Nano-ZnO induced concentration-dependent decreases in the production of short-chain fatty acids (SCFAs). Metagenomic analysis revealed that nano-ZnO not only led to dose-dependent shifts in the composition and diversity of the gut microbiota but also changed the key functional pathways of the gut microbiome. Although the diversity of the gut microbiota basically recovered after stopping exposure to nano-ZnO, SCFAs still showed a concentration-dependent decrease. Furthermore, although a medium concentration of nano-ZnO (2.5 mg/L) reduced the abundance of many antibiotic resistance genes (ARGs) by inhibiting the growth of related host bacteria, a low concentration of nano-ZnO (0.1 mg/L) greatly enriched the abundance of tetracycline resistance genes. Our findings provide evidence that nano-ZnO can impact the diversity, metabolism, and functional pathways of the human gut microbiome, as well as the gut resistome, highlighting the potential health effects of nanoparticles.

Rapid characterization of the volatile profiles in Pu-erh tea by gas phase electronic nose and microchamber/thermal extractor combined with TD-GC-MS

Aroma plays an important role in the quality of Pu-erh tea. However, the quality evaluation of Pu-erh tea aroma is heavily relied on the experience of sensory evaluation, and the theoretical research is relatively scarce. In the present work, the volatile compounds in Pu-erh tea were characterized by using gas phase electronic nose (e-nose) and microchamber/thermal extractor (µ-CTE) combined with thermal desorption coupled to gas chromatography-mass spectrometry (TD-GC-MS). A satisfactory discrimination model (R² Y = 0.95, Q²  = 0.807) was obtained by using orthogonal partial least squares discriminant analysis (OPLS-DA) based on the odor fingerprint of different brands of Pu-erh tea. In addition, based on the double criterion of multivariate analysis with VIP >1.0 and univariate analysis with p ≤ 0.001, 39 volatile components were identified to contribute greatly to the discrimination of five brands of Pu-erh tea. The results suggested that gas phase e-nose and µ-CTE combined with TD-GC/MS were simple, rapid techniques to characterize the volatile compounds in Pu-erh tea and were allowed to effectively distinguish different brands of Pu-erh tea, which would provide an important reference on the quality assessment of Pu-erh tea. PRACTICAL APPLICATION: This work demonstrates that the volatile compounds in Pu-erh tea are simply and rapidly characterized by using µ-CTE/TD-GC/MS and gas phase e-nose, allowing to effectively distinguish different brands of Pu-erh tea, which can provide an important reference for the quality assessment and authentication of Pu-erh tea.

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview

Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

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.