Sources and fate of omega-3 polyunsaturated fatty acids in a highly eutrophic lake.

Omega-3 polyunsaturated fatty acids (ω3-PUFA) are central to the growth and reproduction of aquatic consumers. Dissolved nutrients in aquatic ecosystems strongly affect algal taxonomic composition and thus the production and transfer of specific ω3-PUFA to consumers at higher trophic levels. However, most studies were conducted in nutrient-poor, oligotrophic lakes, leading to an insufficient understanding of how water nutrients affect algal ω3-PUFA and their trophic transfer in consumers in highly eutrophic lakes. We conducted a field investigation in a highly eutrophic lake and collected basal food sources (phytoplankton, periphyton and macrophytes) and aquatic consumers (invertebrates, zooplankton and fish), and measured their fatty acid (FA) composition. Our results showed that periphyton and phytoplankton were both important sources of ω3-PUFA supporting the highly eutrophic lake food web. High water nutrient levels led to low ω3-PUFA levels in phytoplankton and periphyton, resulting in decreased nutritional quality. Consequently, ω3-PUFA of invertebrates and zooplankton reflected variations in ω3-PUFA of phytoplankton and periphyton, respectively. The ω3-PUFA levels of fish decreased as phytoplankton and periphyton ω3-PUFA decreased. Among fish, the Redfin Culter (Cultrichthys erythropterus) and Bar Cheek Goby (Rhinogobius giurinus) exhibited significantly higher levels of EPA and DHA compared to the Pond Loach (Misgurnus anguillicaudatus), which may have been caused by their different feeding modes. Decreases in the ω3-PUFA levels of basal food sources may be one of the causes leading to the reduction of trophic links in aquatic food webs. Our study elucidated the sources and fate of ω3-PUFA in highly eutrophic lakes, complemented previous studies in oligo- and mesotrophic lakes, and emphasized the role of high-quality food sources. Our results offer new perspectives for the conservation and management of highly eutrophic lake ecosystems.

Raman Spectroscopy Applied to Early Detection of Clostridium Infection in Milk.

Detecting Clostridium in milk presents a significant challenge for the dairy industry given that traditional methods are time-consuming and not specific for these bacteria. Microbiological techniques are expensive and require qualified personnel. Clostridium, in the form of spores, can withstand pasteurization and revert to its vegetative form during cheese aging. These gas-producing bacteria are known for their production of carbon dioxide and hydrogen, causing the formation of slits, cracks, and irregular eyes in hard and semi-hard cheeses. However, gas analysis in the vial headspace of appropriate culture can be exploited to specifically detect Clostridium presence, since the closest competing bacterial Bacilli produces only carbon dioxide. The aim of this paper is to present a Raman-spectroscopy-based instrument for a rapid, inexpensive identification of Clostridium in milk with a limit of detection of 29 spores/L. The proposed measurement procedure is analog to that routinely used, based on the most probable number method. The Raman-based instrument speeds up the detection of a vial's positivity. A test conducted with Clostridium spores demonstrated its effectiveness in almost halving the time needed for the measurement campaign compared to the traditional method.

Understanding the Role of Filamentous Actin in Food Quality: From Structure to Application.

Actin, a multifunctional protein highly expressed in eukaryotes, is widely distributed throughout cells and serves as a crucial component of the cytoskeleton. Its presence is integral to maintaining cell morphology and participating in various biological processes. As an irreplaceable component of myofibrillar proteins, actin, including G-actin and F-actin, is highly related to food quality. Up to now, purification of actin at a moderate level remains to be overcome. In this paper, we have reviewed the structures and functions of actin, the methods to obtain actin, and the relationships between actin and food texture, color, and flavor. Moreover, actin finds applications in diverse fields such as food safety, bioengineering, and nanomaterials. Developing an actin preparation method at the industrial level will help promote its further applications in food science, nutrition, and safety.

A comprehensive review on hemocyanin from marine products: Structure, functions, its implications for the food industry and beyond.

Hemocyanin, an oxygen-transport protein, is widely distributed in the hemolymph of marine arthropods and mollusks, playing an important role in their physiological processes. Recently, hemocyanin has been recognized as a multifunctional glycoprotein involved in the immunological responses of aquatic invertebrates. Consequently, the link between hemocyanin functions and their potential applications has garnered increased attention. This review offers an integrated overview of hemocyanin's structure, physicochemical characteristics, and bioactivities to further promote the utilization of hemocyanin derived from marine products. Specifically, we review its implication in two aspects of food and aquaculture industries: quality and health. Hemocyanin's inducible phenoloxidase activity is thought to be an inducer of melanosis in crustaceans. New anti-melanosis agents targeted to hemocyanin need to be explored. The red-color change observed in shrimp shells is related to hemocyanin, affecting consumer preferences. Hemocyanin's adaptive modification in response to the aquatic environment is available as a biomarker. Additionally, hemocyanin is endowed with bioactivities encompassing anti-microbial, antiviral, and therapeutic activities. Hemocyanin is also a novel allergen and its allergenic features remain incompletely characterized.

Plant-based dietary patterns and mortality from all causes, cardiovascular disease, and cancer: The Multiethnic Cohort Study.

BACKGROUND & AIMS: Plant-based dietary patterns have been associated with lower risk of cardiovascular disease (CVD), some cancers, and related mortality in U.S. POPULATIONS: However, the quality of plant foods has rarely been considered in the association between plant-based diets and mortality, especially in a population with various racial and ethnic backgrounds. We investigated whether the adherence to plant-based dietary patterns and the healthiness of plant foods are associated with mortality from all causes, CVD, and cancer and evaluated how the association varies by race and ethnicity. METHODS: A total of 144,729 African American, Japanese American, Latino, Native Hawaiian, and White men and women who participated in the Multiethnic Cohort Study (1993-2019) were included. Cox models were used to estimate HR and 95% CI of mortality from all causes, CVD, and cancer across quintiles of three plant-based diet scores: overall plant-based diet index (PDI), healthful plant-based diet index (hPDI), and unhealthful plant-based diet index (uPDI). RESULTS: Over an average 21 years of follow-up, we identified 65,087 deaths, including 18,663 from CVD and 16,171 from cancer. Comparing the highest versus lowest quintiles, greater scores of PDI and hPDI were associated with a lower risk of all-cause mortality in both men (HR = 0.85, 95% CI: 0.82-0.89 for PDI; HR = 0.88, 95% CI: 0.85-0.91 for hPDI; both P for trend <0.0001) and women (HR = 0.89, 95% CI: 0.86-0.93 for PDI; HR = 0.86, 95% CI: 0.83-0.89 for hPDI; both P for trend <0.0001). An increased risk of all-cause mortality with uPDI was observed only in women (HR = 1.11, 95% CI: 1.07-1.15, P for trend <0.0001; P for heterogeneity by sex = 0.019). A similar trend was shown for CVD mortality with a significant increase in risk with uPDI for both men and women. PDI was associated with a lower risk of cancer mortality in men (HR = 0.86, 95% CI: 0.80-0.92, P for trend <0.0001), while neither hPDI nor uPDI was associated in either sex. Compared with the other racial and ethnic groups within each sex, the association of uPDI with all-cause mortality was stronger in White men (P for heterogeneity by race and ethnicity = 0.009) and weaker in Latino women (P for heterogeneity = 0.002). CONCLUSION: A healthy plant-based dietary pattern emphasizing the quality of plant foods was associated with a lower risk of all-cause and CVD mortality in both men and women, although the magnitude of the associations varied across racial and ethnic groups.

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.

Chitosan Coating with Rosemary Extract Increases Shelf Life and Reduces Water Losses from Beef.

This study aimed to evaluate the influence of films based on chitosan and rosemary extract on the physicochemical, microbiological, and oxidative characteristics of beef. Refrigerated steaks of Longissimus dorsi were distributed in a factorial arrangement (4 × 4) into four treatments consisting of four edible films (control; chitosan; chitosan + 4% rosemary extract; and chitosan + 8% rosemary extract) and four days of aging (0, 2, 4, and 8 days). Incorporating 4% or 8% rosemary extract into the chitosan film improved the characteristics of the films in terms of moisture absorption and elasticity. The edible coatings with chitosan and rosemary extract and the different days of aging increased the tenderness and decreased the lipid oxidation of beef. In addition, the chitosan films containing rosemary extract increased the water-holding capacity and decreased the cooking losses of beef. The films containing 4% and 8% rosemary extract decreased the development of mesophilic and psychrotrophic bacteria and Staphylococcus ssp. in beef. We recommend incorporating 4% rosemary extract into chitosan-based coatings to preserve the quality of refrigerated beef.

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.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys.

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

Fertilizer application alters cadmium and selenium bioavailability in soil-rice system with high geological background levels.

The co-occurrence of cadmium (Cd) pollution and selenium (Se) deficiency commonly exists in global soils, especially in China. As a result, there is great interest in developing practical agronomic strategies to simultaneously achieve Cd remediation and Se mobilization in paddy soils, thereby enhancing food quality/safety. To this end, we conducted a field-plot trial on soils having high geological background levels of Cd (0.67 mg kg-1) and Se (0.50 mg kg-1). We explored 12 contrasting fertilizers (urea, potassium sulfate (K2SO4), calcium-magnesium-phosphate (CMP)), amendments (manure and biochar) and their combinations on Cd/Se bioavailability. Soil pH, total organic carbon (TOC), soil available Cd/Se, Cd/Se fractions and Cd/Se accumulation in different rice components were determined. No significant differences existed in mean grain yield among treatments. Results showed that application of urea and K2SO4 decreased soil pH, whereas the CMP fertilizer and biochar treatments increased soil pH. There were no significant changes in TOC concentrations. Three treatments (CMP, manure, biochar) significantly decreased soil available Cd, whereas no treatment affected soil available Se at the maturity stage. Four treatments (CMP, manure, biochar and manure＋urea＋CMP＋K2SO4) achieved our dual goal of Cd reduction and Se enrichment in rice grain. Structural equation modeling (SEM) demonstrated that soil available Cd and root Cd were negatively affected by pH and organic matter (OM), whereas soil available Se was positively affected by pH. Moreover, redundancy analysis (RDA) showed strong positive correlations between soil available Cd, exchangeable Cd and reducible Cd with grain Cd concentration, as well as between pH and soil available Se with grain Se concentration. Further, there was a strong negative correlation between residual Cd/Se (non-available fraction) and grain Cd/Se concentrations. Overall, this study identified the primary factors affecting Cd/Se bioavailability, thereby providing new guidance for achieving safe production of Se-enriched rice through fertilizer/amendment management of Cd-enriched soils.

Analysis of Erect-Panicle Japonica Rice in Northern China: Yield, Quality Status, and Quality Improvement Directions.

China is the only country that extensively cultivates the indica and japonica rice varieties, with the largest japonica rice production area being in northeast China. A study of the relationship between the yield and quality of japonica rice and the effect of nitrogen fertilizer application on this relationship is important. In this paper, we aimed to assess the current yield and quality of japonica rice in northeast China. We selected erect-panicle varieties as the test materials. Field experiments were conducted using different nitrogen fertilizer levels for two consecutive years to analyze the rice varieties' yield, quality, interrelationship, and nitrogen fertilizer response. The average yield following high- and low-nitrogen treatments exceeded 10,000.00 kg/hm2, with a maximum of 12,285.63 kg/hm2. The high-yield-high-nitrogen treatment group had more panicles, a higher seed-setting rate, and a higher 1000-grain weight than the other groups. The high-yield-low-nitrogen group had a higher number of panicles and seed-setting rate than the other groups. The low-yield-high-nitrogen group had a lower number of whole grains, grain length-to-width ratio, and taste value than the other groups. The low-yield-low-nitrogen group had fewer primary branches than the other groups; excluding the primary branch-setting rate and 1000-grain weight, the values of the other panicle traits of the group were significantly higher than those of the other groups. The high-nitrogen-high-flavor group had lower panicle and spikelet numbers and higher spikelet fertility rates than the other groups. The low-nitrogen-high-flavor group had higher spikelet fertility rates and 1000-grain weight than the other groups. Compared to the other groups, the low-nitrogen-high-flavor group had a higher head rice yield, and the high-nitrogen-high-flavor group had a lower chalkiness rate. The main goal of the breeding and cultivation of high-yield and high-quality erect-panicle japonica rice in northern China is to achieve "dual high, dual low, and one high and one low" conditions, signifying a high yield with high or low nitrogen levels, low protein and amylose contents, high head rice rates, and low chalkiness. This study provides a new technique for enhancing the taste of northern erect-panicle japonica rice to promote the sustainable, high-yield, and high-quality development of japonica rice in northern China.

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.

Predicting the effect of chemicals on fruit using graph neural networks.

The neural network method is a type of machine learning that has made significant advances over the past few years in a variety of fields, particularly text, speech, images, videos, etc. In areas where data is unstructured, traditional machine learning has not been able to surpass the 'glass ceiling'; therefore, researchers have turned to neural networks as auxiliary tools to achieve significant breakthroughs or develop new research methods. An array of computational chemistry challenges can be addressed using neural networks, including virtual screening, quantitative structure-activity relationships, protein structure prediction, materials design, quantum chemistry, and property prediction, among others. This paper proposes a strategy for predicting the chemical properties of fruits by using graph neural networks, and it aims to provide some guidance to researchers and streamline the identification process.

The significance of lead entering the human food chain via livestock ingestion from the agricultural use of biosolids, with special reference to the UK.

Long-term application of biosolids to agricultural soil results in the slow accumulation of potentially toxic elements (PTEs), which are regulated by maximum permitted limit values to protect human health and the environment. Two programmes of UK government-funded (MAFF/DoE) research were commissioned in the 1990s to investigate the safety of the controls on PTEs in relation to the potential transfer to the food chain via the animal ingestion route by sheep grazing biosolids-amended soil. Here, we re-examine this evidence in the light of other recent research and revised food quality standards, to determine the significance of lead (Pb) accumulation in soil from the agricultural use of biosolids. Direct ingestion of biosolids-amended soil is the main transfer pathway of Pb to grazing livestock. The concentrations of Pb in muscle tissue of animals grazing biosolids-amended soil observed in the MAFF/DoE trials, and reported in the scientific literature, were generally small and similar to background, control values. Lambs and ewes ingesting biosolids-amended soil with a total Pb concentration > 200 mg kg-1 dry soil at a rate of 10 % in the diet exceeded the current maximum permitted concentration of Pb in offal (0.5 mg kg-1 fresh weight). However, the decline in PTE concentrations found in biosolids, due to improved industrial practices and stricter controls on the emissions of contaminants to the environment in general and wastewater in particular, has mitigated the risk of Pb accumulation above the food quality standard for this element in offal. Given the significant improvements in biosolids quality, and particularly in the Pb content, regulatory soil and sludge limits for Pb are no longer likely to have a practical or significant impact on the amount of Pb entering the food chain through the animal ingestion route from biosolids-amended agricultural soil.

The Relationship between Healthy Eating Index and Lipid Profile in Healthy Individuals: A Systematic Review.

Background: The Healthy Eating Index (HEI) and Alternate Healthy Eating Index (AHEI) are instruments developed by competing American research teams, aiming to assess the level of adherence to a dietary pattern, claimed to prevent chronic illness conditions such as dyslipidemia. This systematic review evaluated cross-sectional studies examining the association between HEI/AHEI score and the lipid profile in healthy participants. Methods: The systematic review was Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) compliant, and a search process was conducted through Scopus, Web of Knowledge, Google Scholar, Cochrane, PubMed, and ScienceDirect up to November 2022. Studies assessing the relationship between HEI/AHEI and lipid profile (low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG)) were eligible for inclusion. The statistical differences in outcomes, anthropometric indices, and demographic data were extracted from the selected studies. Also, the quality assessment of studies was performed using the Newcastle-Ottawa scale. Results: The systematic search presented 17 cross-sectional studies. Most of the studies revealed a significant correlation between HEI score and lipid profile (LDL-C, HDL-C, TG, and TC) (P < 0.05), while a few of them indicated a significant relationship between AHEI score and these factors. Overall, the elevation of HEI/AHEI score was associated with the improvement in lipid profile (P < 0.05), though this association was more obvious for HEI compared with AHEI. Conclusions: Overall, the results of the study indicated that an improved lipid profile in healthy individuals is associated with a higher score in either HEI or AHEI. Further research in the future is required to confirm the claim.

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.

Pulsed electric field treatment for preservation of Chlorella suspensions and retention of gelling capacity.

Pulsed electric field (PEF) processing has emerged as an alternative to thermal pasteurization for the shelf-life extension of heat-sensitive liquids at industrial scale. It offers the advantage of minimal alteration in physicochemical characteristics and functional properties. In this study, a pilot-scale continuous PEF processing (Toutlet < 55 °C) was applied to microalgae Chlorella vulgaris (Cv) suspensions (pH = 6.5), which was proposed as a functional ingredient for plant-based foods. Cv suspensions were inoculated with three distinct food spoilage microorganisms (Pseudomonas guariconensis, Enterobacter soli and Lactococcus lactis), isolated from the Cv biomass. PEF treatments were applied with varying electric field strength Eel of 16 to 28 kV/cm, pulse repetition rate f of 100 to 140 Hz, with a pulse width τ of 20 µs and an inlet product temperature Tin of 30 °C. The aim was to evaluate the PEF-induced microbial reduction and monitor the microbial outgrowth during a 10-day cold storage period (10 °C). Maximum inactivation of 4.1, 3.7 and 3.6 logs was achieved (28 kV/cm and 120 Hz) for the investigated isolates, respectively. Under these conditions, the critical electric field strengths Ecrit, above which inactivation was observed, ranged from 22.6 to 24.6 kV/cm. Moreover, repeated PEF treatment resulted in similar inactivation efficiency, indicating its potential to enhance shelf-life further.

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. GCB-LDI-MS 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.

Interactions between nitrogen and phosphorus modulate the food quality of the marine crop Pyropia haitanensis (T. J. Chang & B. F. Zheng) N. Kikuchi & M. Miyata (Porphyra haitanensis).

The quality of Pyropia haitanensis (T. J. Chang & B. F. Zheng) N. Kikuchi & M. Miyata (Porphyra haitanensis) is directly affected by nutrient availability. However, the molecular mechanism underlying the synergistic regulatory effects of nitrogen (N) and phosphorus (P) availability on P. haitanensis quality is unknown. Here, we performed physiological and multi-omics analyses to reveal the combined effects of N and P on P. haitanensis quality. The pigments accumulated under high N because of increases in N metabolism and porphyrin metabolism, ultimately resulting in intensely colored thalli. High N also promoted amino acid metabolism and inosine 5'-mononucleotide (IMP) synthesis, but inhibited carbohydrates accumulation. This resulted in increased amino acid, IMP and decreased agaro-carrageenan and cellulose contents, thereby improving the nutritional value and taste. Furthermore, high P promoted carbon metabolism and amino acid metabolism.This study provided the basis for elucidating the mechanism behind N and P regulating the seaweed quality.

Unlocking the future of smart food packaging: biosensors, IoT, and nano materials.

This review examines how biosensors, the Internet of Things (IoT), and nano materials can revolutionize food packaging. It highlights the limitations of traditional packaging, particularly concerning barrier properties and food quality monitoring. The paper aims to provide specific insights into the potential of these technologies. Biosensors enable real-time monitoring and spoilage detection, ensuring safer products, while IoT enhances traceability and transparency in the supply chain, leading to reduced material waste, energy waste, and operational inefficiencies, ultimately improving efficiency. Nano materials offer improved barrier capabilities, strength, and antimicrobial properties, enhancing product quality and sustainability. The review paper also discusses the promising future of smart food packaging, driven by technological advancements and consumer demand for safer and eco-friendly products. However, it acknowledges the challenges related to regulations, sustainability, and consumer acceptance that need to be addressed for widespread adoption. In conclusion, this paper demonstrates how smart food packaging with biosensors, IoT, and nano materials can transform the food industry by overcoming traditional limitations and meeting evolving consumer needs, providing improved food safety, quality, and sustainability.