Critical review on toxic contaminants in surface water ecosystem: sources, monitoring, and its impact on human health.

Surface water pollution is a critical and urgent global issue that demands immediate attention. Surface water plays a crucial role in supporting and sustaining life on the earth, but unfortunately, till now, we have less understanding of its spatial and temporal dynamics of discharge and storage variations at a global level. The contamination of surface water arises from various sources, classified into point and non-point sources. Point sources are specific, identifiable origins of pollution that release pollutants directly into water bodies through pipes or channels, allowing for easier identification and management, e.g., industrial discharges, sewage treatment plants, and landfills. However, non-point sources originate from widespread activities across expansive areas and present challenges due to its diffuse nature and multiple pathways of contamination, e.g., agricultural runoff, urban storm water runoff, and atmospheric deposition. Excessive accumulation of heavy metals, persistent organic pollutants, pesticides, chlorination by-products, pharmaceutical products in surface water through different pathways threatens food quality and safety. As a result, there is an urgent need for developing and designing new tools for identifying and quantifying various environmental contaminants. In this context, chemical and biological sensors emerge as fascinating devices well-suited for various environmental applications. Numerous chemical and biological sensors, encompassing electrochemical, magnetic, microfluidic, and biosensors, have recently been invented by hydrological scientists for the detection of water pollutants. Furthermore, surface water contaminants are monitored through different sensors, proving their harmful effects on human health.

Influence of electrohydrodynamics on the drying characteristics, microstructure and volatile composition of apricot abalone mushroom (Pleurotus eryngii).

The study explored the use of current fluid dynamics drying technology for apricot abalone mushroom, examining how different output voltages (15, 25, and 35 kV) affected drying characteristics, microstructure, and volatile components. Comparisons were made with samples dried using hot air drying (HAD) and natural air drying (AD). Results revealed that HAD had the fastest drying rate at 0.29664(g·h-1). However, apricot abalone mushroom treated with electrohydrodynamic drying (EHD) maintained a color closer to fresh samples, exhibited a 21% increase in the ordered structure of protein secondary structure, a 12.5-fold increase in bound water content, and the most stable cell structure compared to HAD and AD treatments. A total of 83 volatile organic compounds were identified in the apricot abalone mushroom, with alcohols and aldehydes being the most prominent in terms of threshold and relative content, peaking in the 35 kV treatment group. These findings provide both experimental and theoretical insights into applying current fluid dynamics for drying apricot abalone mushroom.

Deep learning domain adaptation to understand physico-chemical processes from fluorescence spectroscopy small datasets and application to the oxidation of olive oil.

Fluorescence spectroscopy is a fundamental tool in life sciences and chemistry, with applications in environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due to the typically small and sparse datasets available. Furthermore, the analysis of EEMs is difficult due to their high dimensionality and overlapping spectral features. This study proposes a new approach that exploits domain adaptation with pretrained vision models, along with a novel interpretability algorithm to address these challenges. Thanks to specialised feature engineering of the neural networks described in this work, we are now able to provide deeper insights into the physico-chemical processes underlying the data. The proposed approach is demonstrated through the analysis of the oxidation process in extra virgin olive oil (EVOO), showing its effectiveness in predicting quality indicators and identifying the spectral bands and thus the molecules involved in the process. This work describes a significantly innovative approach to deep learning for spectroscopy, transforming it from a black box into a tool for understanding complex biological and chemical processes.

Predicting the properties of wheat flour from grains during debranning: A machine learning approach.

In the food industry, meeting food quality demands is challenging. The quality of wheat flour, one of the most commonly used ingredients, depends on the extent of debranning done to remove the aleurone layer before milling. Therefore, the end product management can be simplified by predicting the properties of wheat flour during the debranning stage. Therefore, the chemical and rheological properties of grains were analyzed at different debranning durations (0, 30, 60 s). Then the images of wheat grain were taken to develop a regression model for predicting the chemical quality (i.e., ash, starch, fat, and protein contents) of the wheat flour. The resulting regression model comprises a convolutional neural network and is evaluated using the coefficient of determination (R 2), root-mean-square error, and mean absolute error as metrics. The results demonstrated that wheat flour contained more fat and protein and less ash with increasing debranning time. The model proved reliable in terms of root-mean-square error, mean absolute error, and R 2 for predicting ash content but not starch, fat, or protein contents, which can be attributed to the lack of features in the collected images of wheat kernels during debranning. In addition, the selected method, debranning, was beneficial to the rheological characteristics of wheat flour. The proportion of fine particles increased with the debranning time. The study experimentally revealed that the end product diversity for wheat flour can be controlled to provide selectable ingredients to customers.

Formulation of complementary flours from pretreated pumpkin pulp, soybeans and spinach leaves: Nutritional, functional and sensory characterization.

One of the major causes of the high prevalence of young children suffering from malnutrition in developed countries is inadequate complementary feeding practices, and especially the low quality of homemade complementary foods. The present study aimed to use available local plant foods to formulate a complementary flour Which can be able to meet energy and nutrients requirements of children aged from 6 to 23 months. To achieve this goal, pumpkin was fermented, soybean soaked and roasted, and spinach steamed. The pre-treated ingredients were ground to obtain individual flours, which were blended in various proportions to obtain four complementary flours (PSS1, PSS2, PSS3, PSS4). The proximate and micronutrient composition, and the energy value of the blends were determined, and based on the results, two of them, that is; (PSS1 [Pumpkin 70 %/Soybean 25 %/Spinach 5 %], and PSS2 [Pumpkin 65 %/Soybean 25 %/Spinach 10 %]) were selected to pursue the Study. The functional properties (water absorption capacity, water solubility index, bulk density) and pasting properties of these two flours were then evaluated. Gruels were prepared from the flours and their energy densities, physical as well as sensory properties were evaluated. Moisture, ash, protein, fat, and sugar contents of PSS1 and PSS2 met the FAO/WHO standards. Fiber content in both flours was higher than the recommendation. Vitamin A and iron were sufficient in PSS2, while PSS1 had low iron content. Calcium, phosphorus, and magnesium content of PSS1 and PSS2 were significantly higher than the standards. PSS1 and PSS2 had good water absorption capacity and solubility index, with low viscosity values (213 and 173 cP respectively), interesting functional properties for complementary flours. The gruels prepared with PSS1 and PSS2 flours had good fluidity and energy densities. They were fairly appreciated based on their organoleptic characteristics, with scores of 5.96 and 5.75 for overall acceptability. PSS2 could be recommended as infant flour rich in iron, vitamin A, and protein, with good nutritional values and functional properties.

Blockchain implementation for food safety in supply chain: A review.

Food safety has emerged as the topmost priority in the current fast-paced food industry era. According to the World Health Organization, around 600 million people, approximately 1 in 10 individuals worldwide, experience illness due to contaminated food consumption, resulting in nearly 0.42 million fatalities annually. The recent development in software and hardware sectors has created opportunities to improve the safety concerns in the food supply chain. The objective of this review is to explain the fundamentals of blockchain and its integration into the supply chain of various food commodities to enhance food safety. This paper presents the analysis of 31 conceptual works, 10 implementation works, 39 case studies, and other investigations in blockchain-based food supply chain from a total of 80 published papers. In this paper, the significance of adapting conceptual ideas into practical applications for effectively tracing food commodities throughout the supply chain has been discussed. This paper also describes the transformative role of blockchain platforms in the food industry, providing a decentralized and transparent ledger to access real-time and immutable records of a product's journey. In addition, both the positive impacts and challenges associated with implementing blockchain technology in the food supply chain have been evaluated. In summary, the blockchain-based food supply chains offer greater transparency, traceability, and trust, ultimately resulting in higher standards of food safety and quality.

Artificial intelligence decision making tools in food metabolomics: Data fusion unravels synergies within the hazelnut (Corylus avellana L.) metabolome and improves quality prediction.

This study investigates the metabolome of high-quality hazelnuts (Corylus avellana L.) by applying untargeted and targeted metabolome profiling techniques to predict industrial quality. Utilizing comprehensive two-dimensional gas chromatography and liquid chromatography coupled with high-resolution mass spectrometry, the research characterizes the non-volatile (primary and specialized metabolites) and volatile metabolomes. Data fusion techniques, including low-level (LLDF) and mid-level (MLDF), are applied to enhance classification performance. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) reveal that geographical origin and postharvest practices significantly impact the specialized metabolome, while storage conditions and duration influence the volatilome. The study demonstrates that MLDF approaches, particularly supervised MLDF, outperform single-fraction analyses in predictive accuracy. Key findings include the identification of metabolites patterns causally correlated to hazelnut's quality attributes, of them aldehydes, alcohols, terpenes, and phenolic compounds as most informative. The integration of multiple analytical platforms and data fusion methods shows promise in refining quality assessments and optimizing storage and processing conditions for the food industry.

A Microbial Phenomics Approach to Determine Metabolic Signatures to Enhance Seabream Sparus aurata Traceability, Differentiating between Wild-Caught and Farmed.

BACKGROUND: The need for efficient and simplified techniques for seafood traceability is growing. This study proposes the Biolog EcoPlate assay as an innovative method for assessing wild and farmed Sparus aurata traceability, offering advantages over other molecular techniques in terms of technical simplicity. METHODS: The Biolog EcoPlate assay, known for its high-throughput capabilities in microbial ecology, was utilized to evaluate the functional diversity of microbial communities from various organs of S. aurata (seabream) from the Mediterranean area. Samples were taken from the anterior and posterior gut, cloaca swabs and gills to distinguish between farmed and wild-caught individuals. The analysis focused on color development in OmniLog Units for specific carbon sources at 48 h. RESULTS: Gills provided the most accurate clusterization of sample origin. The assay monitored the development of color for carbon sources such as α-cyclodextrin, D-cellobiose, glycogen, α-D-lactose, L-threonine and L-phenylalanine. A mock experiment using principal component analysis (PCA) successfully identified the origin of a blind sample. Shannon and Simpson indexes were used to statistically assess the diversity, reflecting the clusterization of different organ samples; Conclusions: The Biolog EcoPlate assay proves to be a quick, cost-effective method for discriminate S. aurata traceability (wild vs. farmed), demonstrating reliable reproducibility and effective differentiation between farmed and wild-caught seabream.

Towards Reducing Food Wastage: Analysis of Degradation Products Formed during Meat Spoilage under Different Conditions.

Foodstuffs, particularly perishable ones such as meat, are frequently discarded once the best-before date has been reached, despite the possibility of their continued suitability for human consumption. The implementation of intelligent packaging has the potential to contribute to a reduction in food wastage by enabling the monitoring of meat freshness during storage time independently of the best-before date. The process of meat spoilage is associated with the formation of specific degradation products, some of which can be potentially utilized as spoilage indicators in intelligent packaging. The aim of the review is to identify degradation products whose concentration correlates with meat shelf life and to evaluate their potential use as spoilage indicators in intelligent packaging. To this end, a comprehensive literature research was conducted to identify the factors influencing meat spoilage and the eight key degradation products (carboxylic acids, biogenic amines, total volatile basic nitrogen, aldehydes, alcohols, ketones, sulfur compounds, and esters) associated with this process. These degradation products were analyzed for their correlation with meat shelf life at different temperatures, atmospheres, and meat types and for their applicability in intelligent packaging. The review provides an overview of these degradation products, comparing their potential to indicate spoilage across different meat types and storage conditions. The findings suggest that while no single degradation product universally indicates spoilage across all meat types and conditions, compounds like carboxylic acids, biogenic amines, and volatile basic nitrogen warrant further investigation. The review elucidates the intricacies inherent in identifying a singular spoilage indicator but underscores the potential of combining specific degradation products to expand the scope of applications in intelligent packaging. Further research (e.g., storage tests in which the concentrations of these substances are specifically examined or research on which indicator substance responds to these degradation products) is recommended to explore these combinations with a view to broadening their applicability.

Manothermosonication - A potential alternative to thermal pasteurisation of liquid whole egg: Comparison of physico-chemical attributes.

Liquid egg products are typically exposed to a defined thermal load to achieve the required safety level, under which their functional properties can be adversely affected. In this study, manothermosonication (MTS) (132 µm, 300 kPa) was investigated as alternative preservation for liquid whole egg (LWE) compared to thermal pasteurisation (60 °C, 3.5 min), assessing results against untreated (fresh) LWE in terms of selected physico-chemical properties. Results showed that MTS resulted in improved LWE foaming properties, increasing foam capacity by a 3.2-fold factor compared to thermal treatment. Emulsion stability was also enhanced after MTS, exhibiting smaller droplet size, and a higher elasticity of gels was obtained. Regarding the protein properties, favourable protein changes (protein unfolding) were identified for MTS through direct (asymmetric flow field flow fractionation) and indirect (surface hydrophobicity and sulfhydryl group content) measurements. In addition, an increase in protein solubility of 11.4 % was observed in MTS compared to thermal treatment.