Environmental Sustainability

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Browsing Environmental Sustainability by Subject "3006 Food Sciences"

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    Artificial intelligence for prediction of shelf-life of various food products: recent advances and ongoing challenges
    (Elsevier, 2025-05-01) Rashvand, Mahdi; Ren, Yuqiao; Sun, Da-Wen; Senge, Julia; Krupitzer, Christian; Fadiji, Tobi; Miró, Marta Sanzo; Shenfield, Alex; Watson, Nicholas J.; Zhang, Hongwei
    Background: Accurate estimation of shelf-life is essential to maintain food safety, reduce wastage, and improve supply chain efficiency. Traditional methods such as microbial and chemical analysis, and sensory evaluation provide reproducible results but require time and labor and may not be suitable for real-time or high-throughput applications. The integration of artificial intelligence (AI) with advanced analysis techniques offers a suitable alternative for rapid, data-driven estimation of shelf-life in dynamic storage environments. Approach and scope: The current review assesses the application of AI-based techniques such as machine learning (ML), deep learning (DL), and hybrid approaches in food product shelf life prediction. This study highlights how AI can be utilized to examine data from non-destructive testing methods like hyperspectral imaging, spectroscopy, machine vision, and electronic sensors to enhance predictive performance. The review also describes how AI-based techniques contribute to managing food quality, reduce economic losses, and enhance sustainability by ensuring optimized food distribution and reducing waste. Key findings and conclusions: AI techniques overcome conventional techniques by considering intricate, multi-sourced information capturing microbiological, biochemical, and environmental factors influencing food spoilage. Meat, dairy, fruits and vegetables, and beverage case studies illustrate AI techniques' superiority in real-time monitoring and quality assessment. It also identifies limitations such as data availability, model generalizability, and computational cost, constraining extensive applications. Cloud and Internet of Things (IoT) platform integration into future applications has to be considered to enable real-time decision-making and adaptive modeling. AI can be a paradigm-changing tool in food industries with intelligent, scalable, and low-cost interventions in food safety, waste reduction, and sustainability.
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    Artificial intelligence-driven innovation in Ganoderma spp.: potentialities of their bioactive compounds as functional foods
    (Royal Society of Chemistry (RSC), 2025) Khanal, Sonali; Sharma, Aman; Pillai, Manjusha; Thakur, Pratibha; Tapwal, Ashwani; Kumar, Vinod; Verma, Rachna; Kumar, Dinesh
    Ganoderma spp., which are essential decomposers of lignified plant materials, can affect trees in both wild and cultivated settings. These fungi have garnered significant global interest owing to their potential to combat several chronic, complicated, and infectious diseases. As technology progresses, researchers are progressively employing artificial intelligence (AI) for studying various fungal strains. This novel approach has the potential to accelerate the knowledge and application of Ganoderma spp. in the food industry. The development of extensive Ganoderma databases has markedly expedited research on them by enhancing access to information on bioactive components of Ganoderma and promoting collaboration with the food sector. Progress in AI techniques and enhanced database quality have further advanced AI applications in Ganoderma research. Techniques such as machine learning (ML) and deep learning employing various methods, including support vector machines (SVMs), Bayesian networks, artificial neural networks (ANNs), random forests (RFs), and convolutional neural networks (CNNs), are propelling these advancements. Although AI possesses the capacity to transform Ganoderma research by tackling significant difficulties, continuous investment in research, data dissemination, and interdisciplinary collaboration are necessary. AI could facilitate the development of customized functional food products by discerning patterns and correlations in customer data, resulting in more specific and accurate solutions. Thus, the future of AI in Ganoderma research looks auspicious, presenting prospects for ongoing advancement and innovation in this domain.
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    Circular bioeconomy and sustainable food systems: what are the possible mechanisms?
    (Elsevier, 2025-07-01) Nguyen, Thi Hoa; Wang, Xinfang; Utomo, Dhanan; Gage, Ewan; Xu, Bing
    The circular bioeconomy has emerged as a promising pathway for sustainable development, yet its specific role in fostering sustainable food systems remains underexplored. To our best knowledge, this study is the first systematic review to examine how the circular bioeconomy contributes to sustainable food practices. Using content analysis of 111 academic papers from SCOPUS database, we identify key mechanisms through which the circular bioeconomy enhances food safety and security. These include the development of innovative food products manufactured from bio-resources, the extension of product life through utilizing biodegradable films and bio-based compounds, and the improvement of food safety via sustainable packaging. Additionally, circular bioeconomy practices increase agricultural productivity by enhancing crop yields. From a corporate perspective, they optimize resource use, boost profitability, and generate new revenue streams from waste. Socially, these practices improve stakeholder wellbeing and generate employment opportunities. Environmentally, they support natural capital regeneration, reduce ecological footprints, and promote the sustainable use of resources. Despite these benefits, significant research gaps remain, particularly regarding the cross-sectoral relationships and multi-level impacts of circular bioeconomy practices. This study provides actionable implications for policymakers, practitioners, and researchers, emphasizing regulatory development, strategic decision making, and future research on corporate-level impacts.
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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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    Insights into Alternaria in apple fruit causing mouldy core, external infection and mycotoxin production under retail and storage conditions
    (Elsevier, 2025-08-02) Pavicich, María Agustina; Maldonado, María Luisa; Nguyen, Truong Nhat; De Boevre, Marthe; De Saeger, Sarah; Patriarca, Andrea
    Apple fruit is widely consumed worldwide, but fungal contamination in the postharvest stage presents a significant food safety concern. This study evaluates the production and accumulation of Alternaria mycotoxins, including alternariol (AOH), alternariol monomethyl-ether (AME), and the modified forms (AOH-3-S, AME-3-S, AOH-3-G, AME-3-G), altenuene (ALT), tenuazonic acid (TeA), tentoxin (TEN), altertoxin I and II (ATX[sbnd]I, ATX-II), in Red Delicious apples under simulated retail and post-harvest conditions. Three Alternaria tenuissima strains (isolates 02, 31 and 36) were inoculated in apple fruit at two sites separately (core and exterior) and incubated at two temperatures (25 °C and 4 °C) for 1 and 9 months. Mycotoxin production was quantified using LC-MS/MS, revealing significant variability across strains and conditions. Isolates 02 and 36 exhibited significant temperature and site-dependent variability in mycotoxin production. Higher levels of AOH, AME, ALT, and ATX-I were produced at 25 °C and in the core. Long-term cold storage delayed fungal growth but did not prevent mycotoxin accumulation, raising concerns about the safety of processed apple products. These findings highlight the need for stricter monitoring of mycotoxins during post-harvest storage to mitigate health risks. The findings provide insights into their toxigenic capacity in vivo and highlight potential risks for food safety.