Genome-wide association analysis and transgenic characterization for amylose content regulating gene in tuber of Dioscorea zingiberensis.

BACKGROUND: Amylose, a prebiotic found in yams is known to be beneficial for the gut microflora and is particularly advantageous for diabetic patients' diet. However, the genetic machinery underlying amylose production remains elusive. A comprehensive characterization of the genetic basis of amylose content in yam tubers is a prerequisite for accelerating the genetic engineering of yams with respect to amylose content variation. RESULTS: To uncover the genetic variants underlying variation in amylose content, we evaluated amylose content in freshly harvested tubers from 150 accessions of Dioscorea zingibensis. With 30,000 high-quality single nucleotide polymorphisms (SNP), we performed a genome-wide association analysis (GWAS). The population structure analysis classified the D. zingiberensis accessions into three groups. A total of 115 significant loci were detected on four chromosomes. Of these, 112 significant SNPs (log10(p) = 5, q-value < 0.004) were clustered in a narrow window on the chromosome 6 (chr6). The peak SNP at the position 75,609,202 on chr6 could explain 63.15% of amylose variation in the population and fell into the first exon of the ADP-glucose pyrophosphorylase (AGPase) small subunit gene, causing a non-synonymous modification of the resulting protein sequence. Allele segregation analysis showed that accessions with the rare G allele had a higher amylose content than those harboring the common A allele. However, AGPase, a key enzyme precursor of amylose biosynthesis, was not expressed differentially between accessions with A and G alleles. Overexpression of the two variants of AGPase in Arabidopsis thaliana resulted in a significantly higher amylose content in lines transformed with the AGPase-G allele. CONCLUSIONS: Overall, this study showed that a major genetic variant in AGPase probably enhances the enzyme activity leading to high amylose content in D. zingiberensis tuber. The results provide valuable insights for the development of amylose-enriched genotypes.

Ultrasonic processing: effects on the physicochemical and microbiological aspects of dairy products.

Dairy products that are contaminated by pathogenic microorganisms through unhygienic farm practices, improper transportation, and inadequate quality control can cause foodborne illness. Furthermore, inadequate storage conditions can increase the microflora of natural spoilage, leading to rapid deterioration. Ultrasound processing is a popular technology used to improve the quality of milk products using high-frequency sound waves. It can improve food safety and shelf life by modifying milk protein and fats without negatively affecting nutritional profile and sensory properties, such as taste, texture, and flavor. Ultrasound processing is effective in eliminating pathogenic microorganisms, such as Salmonella, Escherichia coli, Staphylococcus aureus, and Listeria monocytogenes. However, the efficiency of processing is determined by the type of microorganism, pH, and temperature of the milk product, the frequency and intensity of the applied waves, as well as the sonication time. Ultrasound processing has been established to be a safe and environmentally friendly alternative to conventional heat-based processing technologies that lead to the degradation of milk quality. There are some disadvantages to using ultrasound processing, such as the initial high cost of setting it up, the production of free radicals, the deterioration of sensory properties, and the development of off-flavors with lengthened processing times. The aim of this review is to summarize current research in the field of ultrasound processing and discuss future directions.

Compatibility of pulse protein in the formulation of plant based yogurt: a review of nutri-functional properties and processing impact.

With the increased environmental concerns and health awareness among consumers, there has been a notable interest in plant-based dairy alternatives. The plant-based yogurt market has experienced rapid expansion in recent years. Due to challenges related to cultivation, higher cost of production and lower protein content researchers have explored the viability of pulse-based yogurt which has arisen as an economically and nutritionally abundant solution. This review aims to examine the feasibility of utilizing pulse protein for yogurt production. The nutritional, antinutritional, and functional characteristics of various pulses were discussed in detail, alongside the modifications in these properties during the various stages of yogurt manufacturing. The review also sheds light on pivotal findings from existing literature and outlines challenges associated with the production of pulse-based yogurt. Pulses have emerged as promising base materials for yogurt manufacturing due to their favorable nutritional and functional characteristics. Further, the fermentation process can effectively reduce antinutritional components and enhance digestibility. Nonetheless, variations in sensorial and rheological properties were noted when different types of pulses were employed. This issue can be addressed by employing suitable combinations to achieve the desired properties in pulse-based yogurt.

Emerging non-invasive microwave and millimeter-wave imaging technologies for food inspection.

The microwave and millimeter-wave (MMW) imaging technology is gaining increasing interest for food inspection. It allows for noninvasive, contactless, and fast scanning capabilities, while being cost-efficient and safe to human. This review paper introduces the fundamentals in the interaction of electromagnetic wave with food materials and the current MMW sensing and imaging systems used for foods. Then we present emerging technologies in MMW imaging for inspecting food quality and safety, aiming to meet the modern food industry's demand. According to the most recent technological advancements, it is expected that high-performance antenna, ultrawide bandwidth signal generation, nano-scale semiconductor technologies, radio frequency identification with inductance-capacitance resonator, and machine learning could significantly enhance the capabilities of MMW imaging systems for food inspection.

Theory of Stoichiometric Intraguild Predation: Algae, Ciliate, and Daphnia.

Consumers respond differently to external nutrient changes than producers, resulting in a mismatch in elemental composition between them and potentially having a significant impact on their interactions. To explore the responses of herbivores and omnivores to changes in elemental composition in producers, we develop a novel stoichiometric model with an intraguild predation structure. The model is validated using experimental data, and the results show that our model can well capture the growth dynamics of these three species. Theoretical and numerical analyses reveal that the model exhibits complex dynamics, including chaotic-like oscillations and multiple types of bifurcations, and undergoes long transients and regime shifts. Under moderate light intensity and phosphate concentration, these three species can coexist. However, when the light intensity is high or the phosphate concentration is low, the energy enrichment paradox occurs, leading to the extinction of ciliate and Daphnia. Furthermore, if phosphate is sufficient, the competitive effect of ciliate and Daphnia on algae will be dominant, leading to competitive exclusion. Notably, when the phosphorus-to-carbon ratio of ciliate is in a suitable range, the energy enrichment paradox can be avoided, thus promoting the coexistence of species. These findings contribute to a deeper understanding of species coexistence and biodiversity.

Application of graphite carbon black assisted-laser desorption ionization-mass spectrometry for soy sauce product discrimination.

In a previous study, we developed a novel analytical method to directly and simultaneously detect taste- and odor-active compounds using graphite carbon black (GCB)-assisted laser desorption ionization mass spectrometry (LDI-MS). In this study, we aimed to evaluate food quality using a variety of soy sauces using the method to discriminate each product. Graphite carbon black-laser desorption ionization-mass spectrometry allowed the provision of hundreds of MS peaks derived from soy sauces in both positive and negative modes without any tedious sample pretreatments. Principal component analysis using the obtained MS peaks clearly distinguished three soy sauce products based on the manufacturing countries (Japan, China, and India). Moreover, this method identified distinct MS peaks for discrimination, which significantly correlated with their quantitative amounts in the products. Thus, GCB-LDI-MS analysis was established as a simple and rapid technique for food analysis, illustrating the chemical patterns of food products.

Cryoprotective role of vacuum infused inulin on the quality of artichoke: Interactive effects of freezing, thawing and storage period.

Freezing of artichoke is a promising alternative to storing it in brine and canning. The perishable vegetable was vacuum infused with inulin to improve freezing tolerance. Artichokes with and without inulin were frozen by static, air blast and individual quick freezing (IQF) methods and thawed by microwave, 25 °C and 4 °C temperature levels at each month of 6-months storage. Process conditions were evaluated by multivariate analysis of variance (MANOVA) and were found significant on the quality parameters. Inulin infusion better conserved the aw, color, texture, ascorbic acid and overall integrity of artichokes during frozen storage. Inulin incorporation and IQF showed mutual positive effect on drip loss. Polyphenol oxidase (PPO) activity values fitted to 2nd order kinetic and the highest residuals were determined in static freezing. PPO showed alleviating effect on total phenolic content. Vacuum impregnation caused a color difference prior to freezing, but was found effective for maintaining color during storage. As a result, the use of quick freezing techniques together with the addition of cryoprotectant was effective in the preservation of artichoke quality attributes during frozen storage.

Recent advances in the degradation efficacy and mechanisms of mycotoxins in food by atmospheric cold plasma.

Food contaminated by mycotoxins has become a worldwide public problem with political and economic implications. Although a variety of traditional methods have been used to eliminate mycotoxins from agri-foods, the results have been somewhat less than satisfactory. As an emerging non-thermal processing technology, atmospheric cold plasma (ACP) has great potential for food decontamination. Herein, this review mainly presents the degradation efficiency of ACP on mycotoxins in vitro and agri-foods as well as its possible degradation mechanisms. Meanwhile, ACP effects on food quality, factors affecting the degradation efficiency and the toxicity of degradation products are also discussed. According to the literatures, ACP could efficiently degrade many mycotoxins (e.g., aflatoxin, deoxynivalenol, zearalenone, ochratoxin A, fumonisin, and T-2 toxin) both in vitro and various foods (e.g., hazelnut, peanut, maize, rice, wheat, barley, oat flour, and date palm fruit) with little effects on the nutritional and sensory properties of food. The degradation efficacy was dependent on many factors including ACP treatment parameter, working gas, mycotoxin property, and food substrate. The mycotoxin degradation by ACP was mainly attributed to the reactive oxygen and nitrogen species in ACP, which can damage the chemical bonds of mycotoxins, consequently reducing the toxicity of mycotoxins.

Combined effects of temperature and diet on the performance of larvae produced by young and old Palaemon serratus females.

Seasonal variations in environmental conditions determine the success of decapod larval development, and females transmit more energy in sub-optimal conditions to maximise the fitness of their offspring. The objective of this study was to focus on the combined effects of temperature (14, 18 and 22 °C) and food quality on the performance of larvae produced by 5 young (0+) and 5 old (I+) Palaemon serratus females. We prepared 3 diets based on Artemia, in decreasing order of total fatty acid content: freshly hatched nauplii (N), unenriched metanauplii (M) and metanauplii enriched with a mixture of microalgae (ME). At hatching, the larvae produced by I+ females had a higher biomass but a similar fatty acid concentration to those produced by 0+ females. Larvae survived better and developed relatively faster as temperature increased, and the longer they waited to metamorphose, the greater their weight at metamorphosis. These performances were diet-dependent, with more survival and more growth in less time with diet N than with the other two. Larvae from I+ females performed better than those from 0+ females, especially under the most stressful conditions. The greater biomass of the larvae of I+ females seems to have enabled them to follow a shorter, and therefore faster, development path than those of 0+ females. The larvae's diet also had an impact on post-metamorphic composition: larvae eating a diet richer in fatty acids produced richer juveniles and those eating a poorer diet produced juveniles with slightly more essential fatty acids. This study supports the high plasticity of caridean shrimp larval development and the importance of maternal effects on the fitness of offspring.

Determination of the Fatty Acid Profile and Lipid Quality Indices in Selected Infant Formulas.

The quality of fat in infant milk is determined by the fatty acid profile and selected indices describing nutritional value. The aim of this study was to analyze the fatty acid profile and lipid quality indices of infant formulas and compare these data with breast milk. The study material included seven types of cow's milk-based follow-on infant formulas and samples of mature breast milk. The determination of fatty acids was performed using the gas chromatography (GC) technique. Lipid quality indices were calculated based on the relevant equations. Infant formulas contained more medium-chain fatty acids (MCFAs) and oleic acid. Moreover, they contained more than 30% more linoleic acid and more than twice as much α-linolenic acid and docosahexaenoic acid. In contrast, significant amounts of trans fatty acids (TFAs) were noted in breast milk, while infant formulas contained trace amounts. Infant formulas were characterized by a lower AI (Index of Atherogenicity) (0.49-0.98) and TI (Index of Thrombogenicity) (0.48-0.60) and a higher H/H (hypocholesterolemic/hypercholesterolemic) ratio (1.93-2.30) compared with breast milk (1.47, 1.60, and 1.21, respectively). The composition of infant formulas depended on the type of fat added at the production stage and differed significantly from breast milk, particularly in terms of polyunsaturated fatty acids and lipid quality indices.

Elemental Fingerprinting of Pecorino Romano and Pecorino Sardo PDO: Characterization, Authentication and Nutritional Value.

Sardinia, located in Italy, is a significant producer of Protected Designation of Origin (PDO) sheep cheeses. In response to the growing demand for high-quality, safe, and traceable food products, the elemental fingerprints of Pecorino Romano PDO and Pecorino Sardo PDO were determined on 200 samples of cheese using validated, inductively coupled plasma methods. The aim of this study was to collect data for food authentication studies, evaluate nutritional and safety aspects, and verify the influence of cheesemaking technology and seasonality on elemental fingerprints. According to European regulations, one 100 g serving of both cheeses provides over 30% of the recommended dietary allowance for calcium, sodium, zinc, selenium, and phosphorus, and over 15% of the recommended dietary intake for copper and magnesium. Toxic elements, such as Cd, As, Hg, and Pb, were frequently not quantified or measured at concentrations of toxicological interest. Linear discriminant analysis was used to discriminate between the two types of pecorino cheese with an accuracy of over 95%. The cheese-making process affects the elemental fingerprint, which can be used for authentication purposes. Seasonal variations in several elements have been observed and discussed.

Causal Artificial Intelligence Models of Food Quality Data.

Research background: The aim of this study is to emphasize the importance of artificial intelligence (AI) and causality modelling of food quality and analysis with 'big data'. AI with structural causal modelling (SCM), based on Bayesian networks and deep learning, enables the integration of theoretical field knowledge in food technology with process production, physicochemical analytics and consumer organoleptic assessments. Food products have complex nature and data are highly dimensional, with intricate interrelations (correlations) that are difficult to relate to consumer sensory perception of food quality. Standard regression modelling techniques such as multiple ordinary least squares (OLS) and partial least squares (PLS) are effectively applied for the prediction by linear interpolations of observed data under cross-sectional stationary conditions. Upgrading linear regression models by machine learning (ML) accounts for nonlinear relations and reveals functional patterns, but is prone to confounding and failed predictions under unobserved nonstationary conditions. Confounding of data variables is the main obstacle to applications of the regression models in food innovations under previously untrained conditions. Hence, this manuscript focuses on applying causal graphical models with Bayesian networks to infer causal relationships and intervention effects between process variables and consumer sensory assessment of food quality. Experimental approach: This study is based on the data available in the literature on the process of wheat bread baking quality, consumer sensory quality assessments of fermented milk products, and professional wine tasting data. The data for wheat baking quality were regularized by the least absolute shrinkage and selection operator (LASSO elastic net). Bayesian statistics was applied for the evaluation of the model joint probability function for inferring the network structure and parameters. The obtained SCMs are presented as directed acyclic graphs (DAG). D-separation criteria were applied to block confounding effects in estimating direct and total causal effects of process variables and consumer perception on food quality. Probability distributions of causal effects of the intervention of individual process variables on quality are presented as partial dependency plots determined by Bayesian neural networks. In the case of wine quality causality, the total causal effects determined by SCMs are positively validated by the double machine learning (DML) algorithm. Results and conclusions: The data set of 45 continuous variables corresponding to different chemical, physical and biochemical variables of wheat properties from seven Croatian cultivars during two years of controlled cultivation were analysed. LASSO regularization of the data set yielded the ten key predictors, accounting for 98 % variance of the baking quality data. Based on the key variables, the quality predictive random forest model with 75 % cross-validation accuracy was derived. Causal analysis between the quality and key predictors was based on the Bayesian model shown as a DAG graph. Protein content shows the most important direct causal effect with the corresponding path coefficient of 0.71, and THMM (total high-molecular-mass glutenin subunits) content was an indirect cause with a path coefficient of 0.42, and protein total average causal effect (ACE) was 0.65. The large data set of the quality of fermented milk products included binary consumer sensory data (taste, odour, turbidity), continuous physical variables (temperature, fat, pH, colour) and three grade classes of products by consumer quality assessment. A random forest model was derived for the prediction of the quality classification with an out-of-bag (OOB) error of 0.28 %. The Bayesian network model predicts that the direct causes of the taste classification are temperature, colour and fat content, while the direct causes of the quality classification are temperature, turbidity, odour and fat content. The key quality grade ACE of temperature -0.04 grade/°C and 0.3 quality grade/fat content were estimated. The temperature ACE dependency shows a nonlinear type as negative saturation with the 'breaking' point at 60 °C, while for fat ACE had a positive linear trend. Causal quality analysis of red and white wine was based on the large data set of eleven continuous variables of physical and chemical properties and quality assessments classified in ten classes, from 1 to 10. Each classification was obtained in triplicate by a panel of professional wine tasters. A non-structural double machine learning (DML) algorithm was applied for total ACE quality assessment. The alcohol content of red and white wine had the key positive ACE relative factor of 0.35 quality/alcohol, while volatile acidity had the key negative ACE of -0.2 quality/acidity. The obtained ACE predictions by the unstructured DML algorithm are in close agreement with the ACE obtained by the structural SCM. Novelty and scientific contribution: Novel methodologies and results for the application of causal artificial intelligence models in the analysis of consumer assessment of the quality of food products are presented. The application of Bayesian network structural causal models (SCM) enables the d-separation of pronounced effects of confounding between parameters in noncausal regression models. Based on the SCM, inference of ACE provides substantiated and validated research hypotheses for new products and support for decisions of potential interventions for improvement in product design, new process introduction, process control, management and marketing.

Synthetic microbial communities: Novel strategies to enhance the quality of traditional fermented foods.

Consumers are attracted to traditional fermented foods due to their unique flavor and nutritional value. However, the traditional fermentation technique can no longer accommodate the requirements of the food industry. Traditional fermented foods produce hazardous compounds, off-odor, and anti-nutritional factors, reducing product stability. The microbial system complexity of traditional fermented foods resulting from the open fermentation process has made it challenging to regulate these problems by modifying microbial behaviors. Synthetic microbial communities (SynComs) have been shown to simplify complex microbial communities and allow for the targeted design of microbial communities, which has been applied in processing traditional fermented foods. Herein, we describe the theoretical information of SynComs, particularly microbial physiological processes and their interactions. This paper discusses current approaches to creating SynComs, including designing, building, testing, and learning, with typical applications and fundamental techniques. Based on various traditional fermented food innovation demands, the potential and application of SynComs in enhancing the quality of traditional fermented foods are highlighted. SynComs showed superior performance in regulating the quality of traditional fermented foods using the interaction of core microorganisms to reduce the hazardous compounds of traditional fermented foods and improve flavor. Additionally, we presented the current status and future perspectives of SynComs for improving the quality of traditional fermented foods.

Recent advances in activated water systems for the postharvest management of quality and safety of fresh fruits and vegetables.

Over the last three decades, decontamination management of fresh fruits and vegetables (FFVs) in the packhouses and along the supply chains has been heavily dependent on chemical-based wash. This has resulted in the emergence of resistant foodborne pathogens and often the deposition of disinfectant byproducts on FFVs, rendering them unacceptable to consumers. The management of foodborne pathogens, microbial contaminants, and quality of FFVs are a major concern for the horticultural industries and public health. Activated water systems (AWS), such as electrolyzed water, plasma-activated water, and micro-nano bubbles, have gained significant attention from researchers over the last decade due to their nonthermal and nontoxic mode of action for microbial inactivation and preservation of FFVs quality. The aim of this review is to provide a comprehensive summary of recent progress on the application of AWS and their effects on quality attributes and microbial safety of FFVs. An overview of the different types of AWS and their properties is provided. Furthermore, the review highlights the chemistry behind generation of reactive species and the impact of AWS on the quality attributes of FFVs and on the inactivation/reduction of spoilage and pathogenic microbes (in vivo or in vitro). The mechanisms of action of microorganism inactivation are discussed. Finally, this work highlights challenges and limitations for commercialization and safety and regulation issues of AWS. The synergistic prospect on combining AWS for maximum microorganism inactivation effectiveness is also considered. AWS offers a potential alternative as nonchemical interventions to maintain quality attributes, inactivate spoilage and pathogenic microorganisms, and extend the shelf-life for FFVs.

Characterizing nutritional phenotypes using experimental nutrigenomics: Is there nutrient-specificity to different types of dietary stress?

The ability to directly measure and monitor poor nutrition in individual animals and ecological communities is hampered by methodological limitations. In this study, we use nutrigenomics to identify nutritional biomarkers in a freshwater zooplankter, Daphnia pulex, a ubiquitous primary consumer in lakes and a sentinel of environmental change. We grew animals in six ecologically relevant nutritional treatments: nutrient replete, low carbon (food), low phosphorus, low nitrogen, low calcium and high Cyanobacteria. We extracted RNA for transcriptome sequencing to identify genes that were nutrient responsive and capable of predicting nutritional status with a high degree of accuracy. We selected a list of 125 candidate genes, which were subsequently pruned to 13 predictive potential biomarkers. Using a nearest-neighbour classification algorithm, we demonstrate that these potential biomarkers are capable of classifying our samples into the correct nutritional group with 100% accuracy. The functional annotation of the selected biomarkers revealed some specific nutritional pathways and supported our hypothesis that animal responses to poor nutrition are nutrient specific and not simply different presentations of slow growth or energy limitation. This is a key step in uncovering the causes and consequences of nutritional limitation in animal consumers and their responses to small- and large-scale changes in biogeochemical cycles.

Plant-Based Dietary Patterns and Parkinson's Disease: A Prospective Analysis of the UK Biobank.

BACKGROUND: Plant-based diets have been associated with a lower risk of several chronic diseases, but the relationship with PD is unknown. OBJECTIVES: We examined the association of three different plant-based diets with PD incidence in the UK Biobank cohort. METHODS: We conducted a prospective study among 126,283 participants from the UK Biobank cohort. Three plant-based diet indices (overall plant-based diet index, PDI; healthful plant-based diet index, hPDI; and unhealthful plant-based diet index, uPDI) were derived from 24-hour dietary recalls based on 17 food groups. Multivariable Cox regression models were used to estimate the risk of PD across quartiles of the PDIs and for each of the food groups that constituted the score. Further analyses were carried out to assess potential heterogeneity in associations between hPDI and PD across strata of some hypothesized effect modifiers. RESULTS: During 11.8 years of follow-up (1,490,139 person-years), 577 cases of PD incidence were reported. After multivariable adjustment, participants in the highest hPDI and overall PDI quartile had lower risk of PD (22% and 18%, respectively), whereas a higher uPDI was associated with a 38% higher PD risk. In food-based analyses, higher intakes of vegetables, nuts, and tea were associated with a lower risk of PD (28%, 31% and 25%, respectively). Stratifying by Polygenic Risk Score (PRS), results were significant only for those with a lower PRS for PD. CONCLUSIONS: Following a healthful plant-based diet and in particular the inclusion of readily achievable intakes of vegetables, nuts and tea in the habitual diet are associated with a lower risk of PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Recent advances in the fabrication of pH-sensitive indicators films and their application for food quality evaluation.

Over a few decades, anthocyanin (ACN)-based colorimetric indicators in intelligent packaging systems have been widely used to monitor the freshness or spoilage of perishable food products. Most of the perishable food products are highly susceptible to enzymatic/microbial spoilage and produce several volatile or nonvolatile organic acid and nitrogenous compounds. As a result, the natural pH of fresh foods significantly changes. Fabrication of CAN-based colorimetric indicators in intelligent packaging systems is an advanced technique that monitors the freshness or spoilage of perishable foods based on the display of color variations at varying pH values. This study focuses on the advancement of pH-sensitive indicators and extraction of colorimetric indicators from commercially available natural sources. Moreover, the fabrication techniques and widespread industrial applications of such indicators have also been discussed. In addition, readers will get information about the color-changing and antioxidant mechanisms of ACN-based indicator films in food packaging.

The impact of nano/micro-plastics toxicity on seafood quality and human health: facts and gaps.

Contamination of the food and especially marine environment with nano/micro-plastic particles has raised serious concern in recent years. Environmental pollution and the resulting seafood contamination with microplastic (MP) pose a potential threat to consumers. The absorption rate of the MP by fish is generally considered low, although the bioavailability depends on the physical and chemical properties of the consumed MP. The available safety studies are inconclusive, although there is an indication that prolonged exposure to high levels of orally administered MP can be hazardous for consumers. This review details novel findings about the occurrence of MP, along with its physical and chemical properties, in the marine environment and seafood. The effect of processing on the content of MP in the final product is also reviewed. Additionally, recent findings regarding the impact of exposure of MP on human health are discussed. Finally, gaps in current knowledge are underlined, and the possibilities for future research are indicated in the review. There is an urgent need for further research on the absorption and bioavailability of consumed MP and in vivo studies on chronic exposure. Policymakers should also consider the implementation of novel legislation related to MP presence in food.

Mathematical modeling of nutritional, color, texture, and microbial activity changes in fruit and vegetables during drying: A critical review.

Retention of quality attributes during drying of fruit and vegetables is a prime concern since the product's acceptability depends on the overall quality; particularly on the nutritional, color, and physical attributes. However, these quality parameters deteriorate during drying. Food quality changes are strongly related to the drying conditions and researchers have attempted to develop mathematical models to understand these relationships. A better insight toward the degradation of quality attributes is crucial for making real predictions and minimizing the quality deterioration. The previous empirical quality models employed kinetic modeling approaches to describe the quality changes and therefore, lack the realistic understanding of fundamental transport mechanisms. In order to develop a physics based mathematical model for the prediction of quality changes during drying, an in-depth understanding of research progress made toward this direction is indispensable. Therefore, the main goal of this paper is to present a critical review of the mathematical models developed and applied to describe the degradation kinetics of nutritional, color, and texture attributes during drying of fruit and vegetables and microbial growth model during storage. This review also presents the advantages and drawbacks of the existing models along with their industrial relevance. Finally, future research propositions toward developing physics-based mathematical model are presented.

Antimicrobial resistomes in food chain microbiomes.

The safety and integrity of the global food system is in a constant state of flux with persistent chemical and microbial risks. While chemical risks are being managed systematically, microbial risks pose extra challenges. Antimicrobial resistant microorganism and persistence of related antibiotic resistance genes (ARGs) in the food chain adds an extra dimension to the management of microbial risks. Because the food chain microbiome is a key interface in the global health system, these microbes can affect health in many ways. In this review, we systematically summarize the distribution of ARGs in foods, describe the potential transmission pathway and transfer mechanism of ARGs from farm to fork, and discuss potential food safety problems and challenges. Modulating antimicrobial resistomes in the food chain facilitates a sustainable global food production system.