Use of omics methods for the advancement of food quality and food safety

Food circular economy and safety considerations in waste management of urban manufacturing side streams

In food circular economy, the utilization of food manufacturing side streams (FMSS) offers significant potential instead of being discarded. However, reincorporating FMSS into the food value chain raises food safety concerns due to potential food hazards. This perspective explores food safety risks associated with circular management of FMSS by using a 'Quad-Modal hazard dynamic' approach with case studies. Future research and advancements in food safety control strategies are also discussed.

Exploring the Interplay of Food Security, Safety, and Psychological Wellness in the COVID-19 Era: Managing Strategies for Resilience and Adaptation

The global population surge presents a dual challenge and opportunity in the realms of food consumption, safety, and mental well-being. This necessitates a projected 70% increase in food production to meet growing demands. Amid this backdrop, the ongoing COVID-19 pandemic exacerbates these issues, underscoring the need for a deeper understanding of the intricate interplay between food consumption patterns and mental health dynamics during this crisis. Mitigating the spread of COVID-19 hinges upon rigorous adherence to personal hygiene practices and heightened disease awareness. Furthermore, maintaining stringent food quality and safety standards across both public and private sectors is imperative for safeguarding public health and containing viral transmission. Drawing upon existing research, this study delves into the pandemic's impact on mental health, food consumption habits, and food safety protocols. Through a comprehensive analysis, it aims to elucidate the nuanced relationship among food, food safety, and mental well-being amid the COVID-19 pandemic, highlighting synergistic effects and dynamics that underpin holistic human welfare. Our study offers a novel approach by integrating psychological wellness with food security and safety. In conceiving this review, we aimed to comprehensively explore the intricate interplay among food security, safety, and psychological wellness amid the backdrop of the COVID-19 pandemic. Our review is structured to encompass a thorough examination of existing research, synthesizing insights into the multifaceted relationships among food consumption patterns, mental health dynamics, and food safety protocols during the crisis. Our findings provide valuable insights and practical recommendations for enhancing food security and psychological well-being, thus supporting both academic research and real-world applications in crisis management and policy development.

A machine learning approach to identifying Salmonella stress response genes in isolates from poultry processing

We explored the potential of machine learning to identify significant genes associated with Salmonella stress response during poultry processing using whole genome sequencing (WGS) data. The Salmonella isolates (n = 177) used in this study were obtained from various chicken sources (skin before chiller, chicken carcass before chiller, frozen chicken, and post-chill chicken carcass). Six machine learning algorithms (random forest, neural network, cost-sensitive learning, logit boost, and support vector machine linear and radial kernels) were trained on Salmonella WGS data, and model fit was assessed using standard evaluation metrics such as the area under the receiver operating characteristic (AUROC) curve and confusion matrix statistics. All models achieved high performances based on the AUROC metric, with logit boost showing the best performance with an AUROC score of 0.904, sensitivity of 0.889, and specificity of 0.920. The significant genes identified included ybtX, which encodes a Yersiniabactin-associated zinc transporter, and the transferase-encoding genes yccK and thiS. Additionally, genes coding for cold (cspA, cspD, and cspE) and heat shock (rpoH and rpoE) responses were identified. Other significant genes included those involved in lipopolysaccharide biosynthesis (irp1, waaD, rfc, and rfbX), DNA repair and replication (traI), biofilm formation (ccdA and fyuA), and cellular metabolism (irtA).

Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis Using Artificial Neural Networks

Dates are highly perishable fruits, and maintaining their quality during storage is crucial. The current study aims to investigate the impact of storage conditions on the quality of dates (Khalas and Sukary cultivars) at the Tamer stage and predict their quality attributes during storage using artificial neural networks (ANN). The studied storage conditions were the modified atmosphere packing (MAP) gases (CO2, O2, and N), packaging materials, storage temperature, and storage time, and the evaluated quality attributes were moisture content, firmness, color parameters (L*, a*, b*, and ∆E), pH, water activity, total soluble solids, and microbial contamination. The findings demonstrated that the storage conditions significantly impacted (p < 0.05) the quality of the two stored date cultivars. The use of MAP with 20% CO2 + 80% N had a high potential to decrease the rate of color transformation and microbial growth of dates stored at 4 °C for both stored date cultivars. The developed ANN models efficiently predicted the quality changes of stored dates closely aligned with observed values under the different storage conditions, as evidenced by low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. In addition, the reliability of the developed ANN models was further affirmed by the linear regression between predicted and measured values, which closely follow the 1:1 line, with R2 values ranging from 0.766 to 0.980, the ANN models demonstrate accurate estimating of fruit quality attributes. The study's findings contribute to food quality and supply chain management through the identification of optimal storage conditions and predicting the fruit quality during storage under different atmosphere conditions, thereby minimizing food waste and enhancing food safety.

Contribution of omics to biopreservation: Toward food microbiome engineering

Biopreservation is a sustainable approach to improve food safety and maintain or extend food shelf life by using beneficial microorganisms or their metabolites. Over the past 20 years, omics techniques have revolutionised food microbiology including biopreservation. A range of methods including genomics, transcriptomics, proteomics, metabolomics and meta-omics derivatives have highlighted the potential of biopreservation to improve the microbial safety of various foods. This review shows how these approaches have contributed to the selection of biopreservation agents, to a better understanding of the mechanisms of action and of their efficiency and impact within the food ecosystem. It also presents the potential of combining omics with complementary approaches to take into account better the complexity of food microbiomes at multiple scales, from the cell to the community levels, and their spatial, physicochemical and microbiological heterogeneity. The latest advances in biopreservation through omics have emphasised the importance of considering food as a complex and dynamic microbiome that requires integrated engineering strategies to increase the rate of innovation production in order to meet the safety, environmental and economic challenges of the agri-food sector.

Characterization of Ikaria Heather Honey by Untargeted Ultrahigh-Performance Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics and Melissopalynological Analysis

Honey represents a valuable food commodity, known since ancient times for its delicate taste and health benefits due to its specific compositional characteristics, mainly the phenolic compound content. "Anama" honey is a monofloral honey produced from the nectar of Erica manipuliflora plant, a heather bush of the Greek island of Ikaria, one of the Mediterranean's longevity regions. "Anama" is characterized by a unique aroma and taste, with a growing demand for consumption and the potential to be included in the list of products with a protected designation of origin. The aim of this study was to determine the chemical and botanical profile of authentic Anama honey samples and find similarities and differences with honey samples of a different botanical origin from the same geographical area. Untargeted Ultrahigh-Performance Liquid Chromatography-Hybrid Quadrupole-Orbitrap High-Resolution Mass Spectrometry (UHPLC-HRMS) metabolomics study was conducted on authentic heather, pine, and thyme honey samples from Ikaria and neighboring islands. The Principal Component Analysis (PCA), Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA), and differential analysis were performed using the entire metabolic profile of the samples and allowed the identification of chemical markers for sample discrimination. Thirty-two characteristic secondary metabolites (cinnamic acids, phenolic acids, flavonoids, terpenes) and other bioactive phenolic compounds, some of them not previously reported in a heather honey (aucubin, catalpol, domesticoside, leonuriside A, picein among others), emerged as potential chemical indicators of Anama honey. Melissopalynological analysis was also carried out to decipher the botanical and geographical origin of Anama honey. The relative frequency of the pollen of dominant plants of the Ericaceae family and a multitude of nectariferous and nectarless plants contributing to the botanical profile of Anama was evaluated. The identification of the pollen sources enabled a potential correlation of differentially increased secondary metabolites and chemicals with their botanical origin. The physicochemical profile of Anama was also determined, including the parameters of pH, color, electrical conductivity, diastase, moisture, as well as sugars, supporting the high quality of this heather honey.

Metataxonomic sequencing to assess microbial safety of Turkish white cheeses

High-throughput sequencing has provided a way to monitor the large diversity of microorganisms in fermented foods that have complex microbiota. Up to date, many kinds of cheese have been characterized with the metataxonomic approach, but the safety of unpacked Turkish white cheeses, which are widely consumed in Turkey, has not been assessed. In this study, fifteen unpacked white cheeses sold in public bazaars in Ankara province have been collected and subjected to microbial enumeration as well as physicochemical analysis. Five white cheeses, which have relatively the highest foodborne pathogens, out of fifteen white cheeses, have been analyzed by next-generation sequencing and metataxonomic analysis. According to the results, abundant families were Lactobacillaceae, Oceanospirillaceae, Enterococcaceae, Pseudomonadaceae, and Vibrionaceae. Staphylococcus aureus, E. coli, and Salmonella, which are indicators of bad hygiene and sanitation conditions, were found in cheeses. In conclusion, culture-independent methods such as metataxonomic can be important to evaluate the safety of foods.

Discriminant Analysis of the Nutritional Components between Organic Eggs and Conventional Eggs: A 1H NMR-Based Metabolomics Study

The difference of nutrient composition between organic eggs and conventional eggs has always been a concern of people. In this study, ¹H nuclear magnetic resonance (NMR) technique combined with multivariate statistical analyses was conducted to identify the metabolite different in egg yolk and egg white in order to reveal the nutritional components information between organic and conventional eggs. The results showed that the nutrient content and composition characteristics were different between organic and conventional eggs, among which the content of glucose, putrescine, amino acids and their derivatives were found higher in the organic eggs yolk, while phospholipids were demonstrated higher in conventional eggs yolk. Organic acid, alcohol, amine, choline and amino acids were higher in conventional eggs white, but glucose and lactate in organic egg were higher. Our study demonstrated that there are more nutritive components and higher nutritional value in organic eggs than conventional eggs, especially for the growth and development of infants and young children, and conventional eggs have more advantages in promoting lipid metabolism, preventing fatty liver, and reducing serum cholesterol. Eggs have important nutritional value to human body, and these two kinds of eggs can be selected according to the actual nutrient needs.

Metabolomics Approaches for the Comprehensive Evaluation of Fermented Foods: A Review

Fermentation is an important process that can provide new flavors and nutritional and functional foods, to deal with changing consumer preferences. Fermented foods have complex chemical components that can modulate unique qualitative properties. Consequently, monitoring the small molecular metabolites in fermented food is critical to clarify its qualitative properties and help deliver personalized nutrition. In recent years, the application of metabolomics to nutrition research of fermented foods has expanded. In this review, we examine the application of metabolomics technologies in food, with a primary focus on the different analytical approaches suitable for food metabolomics and discuss the advantages and disadvantages of these approaches. In addition, we summarize emerging studies applying metabolomics in the comprehensive analysis of the flavor, nutrition, function, and safety of fermented foods, as well as emphasize the applicability of metabolomics in characterizing the qualitative properties of fermented foods.

Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review

Metabolomics is a high precision analytical approach to obtaining detailed information of varieties of metabolites produced in biological systems, including foods. This study reviews the use of metabolomic approaches such as liquid chromatography mass spectrometry (LCMS), gas chromatography mass spectrometry (GC-MS), matrix assisted laser desorption /ionization tandem time of flight mass spectrometry (MALDI-TOF-MS) and nuclear magnetic resonance (NMR) for investigating the presence of foodborne pathogens and their metabolites. Pathogenic fungi and their notable metabolites (mycotoxins) have been studied more extensively using metabolomics as compared to bacteria, necessitating further studies in this regard. Nevertheless, such identified fungal and bacteria metabolites could be used as biomarkers for a more rapid detection of these pathogens in food. Other important compounds detected through metabolomics could also be correlated to functionality of these pathogenic strains, determined by the composition of the foods in which they exist, thereby providing insights into their metabolism. Considering the prevalence of these food pathogens, metabolomics still has potentials in the determination of food-borne pathogenic microorganisms especially for the determination of pathogenic bacteria toxins and is expected to generate research interests for further studies and applications.

Recent advances in the application of metabolomics for food safety control and food quality analyses

As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.