Environmental Sustainability

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Browsing Environmental Sustainability by Author "Anastasiadi, Maria"

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    A critical review of conventional and emerging technologies for the detection of contaminants, allergens and adulterants in plant-based milk alternatives
    (Elsevier, 2025) Karimi, Zahra; Campbell, Katrina; Kevei, Zoltan; Patriarca, Andrea; Koidis, Anastasios; Anastasiadi, Maria
    The increasing popularity of plant-based milk alternatives (PBMAs) necessitates effective safety and authentication measures to ensure food product integrity and maintain consumer trust. This review aims to offer a comprehensive overview of potential contaminants, allergens, and adulterants in PBMAs, and the analytical methodologies employed for their detection and quantitation. It details the advantages and limitations of widely employed testing techniques, such as chromatography, spectroscopy, immunoassays and PCR. In addition, it explores recent advancements in portable detection methods based on novel technologies such as CRISPR and biosensor systems that offer new opportunities for rapid and precise analysis. Despite these technological innovations, important challenges remain, particularly in optimizing sample preparation protocols and improving DNA-based methods efficiency. The integration of multiple detection strategies and the development of rapid, cost-effective analytical tools are critical steps towards enhancing both industry compliance and consumer confidence. Furthermore, green analytical methods — such as solvent-free extraction, AI-driven spectroscopy, and sustainable sample preparation techniques — pave the way toward eco-friendly and more efficient PBMA safety testing.
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    Fusion vs. Isolation: evaluating the performance of multi-sensor integration for meat spoilage prediction
    (MDPI, 2025-05-01) Heffer, Samuel; Anastasiadi, Maria; Nychas, George-John; Mohareb, Fady
    High-throughput and portable sensor technologies are increasingly used in food production/distribution tasks as rapid and non-invasive platforms offering real-time or near real-time monitoring of quality and safety. These are often coupled with analytical techniques, including machine learning, for the estimation of sample quality and safety through monitoring of key physical attributes. However, the developed predictive models often show varying degrees of accuracy, depending on food type, storage conditions, sensor platform, and sample sizes. This work explores various fusion approaches for potential predictive enhancement, through the summation of information gathered from different observational spaces: infrared spectroscopy is supplemented with multispectral imaging for the prediction of chicken and beef spoilage through the estimation of bacterial counts in differing environmental conditions. For most circumstances, at least one of the fusion methodologies outperformed single-sensor models in prediction accuracy. Improvement in aerobic, vacuum, and mixed aerobic/vacuum chicken spoilage scenarios was observed, with performance enhanced by up to 15%. The improved cross-batch performance of these models proves an enhanced model robustness using the presented multi-sensor fusion approach. The batch-based results were corroborated with a repeated nested cross-validation approach, to give an out-of-sample generalised view of model performance across the whole dataset. Overall, this work suggests potential avenues for performance improvements in real-world, minimally invasive food monitoring scenarios.
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    Prolonged heat stress in Brassica napus during flowering negatively impacts yield and alters glucosinolate and sugars metabolism
    (Frontiers, 2025-01-01) Kourani, Mariam; Anastasiadi, Maria; Hammond, John P.; Mohareb, Fady
    Oilseed rape (Brassica napus), one of the most important sources of vegetable oil worldwide, is adversely impacted by heatwave-induced temperature stress especially during its yield-determining reproductive stages. However, the underlying molecular and biochemical mechanisms are still poorly understood. In this study, we investigated the transcriptomic and metabolomic responses to heat stress in B. napus plants exposed to a gradual increase in temperature reaching 30°C in the day and 24°C at night for a period of 6 days. High-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC-MS) was used to quantify the content of carbohydrates and glucosinolates, respectively. Results showed that heat stress reduced yield and altered oil composition. Heat stress also increased the content of carbohydrate (glucose, fructose, and sucrose) and aliphatic glucosinolates (gluconapin and progoitrin) in the leaves but decreased the content of the indolic glucosinolate (glucobrassicin). RNA-Seq analysis of flower buds showed a total of 1,892, 3,253, and 4,553 differentially expressed genes at 0, 1, and 2 days after treatment (DAT) and 4,165 and 1,713 at 1 and 7 days of recovery (DOR), respectively. Heat treatment resulted in downregulation of genes involved in respiratory metabolism, namely, glycolysis, pentose phosphate pathway, citrate cycle, and oxidative phosphorylation especially after 48 h of heat stress. Other downregulated genes mapped to sugar transporters, nitrogen transport and storage, cell wall modification, and methylation. In contrast, upregulated genes mapped to small heat shock proteins (sHSP20) and other heat shock factors that play important roles in thermotolerance. Furthermore, two genes were chosen from the pathways involved in the heat stress response to further examine their expression using real-time RT-qPCR. The global transcriptome profiling, integrated with the metabolic analysis in the study, shed the light on key genes and metabolic pathways impacted and responded to abiotic stresses exhibited as a result of exposure to heat waves during flowering. DEGs and metabolites identified through this study could serve as important biomarkers for breeding programs to select cultivars with stronger resistance to heat. In particular, these biomarkers can form targets for various crop breeding and improvement techniques such as marker-assisted selection.