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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    Investigation on the protection ability of two commonly packaging methods to apples during express transportation
    (Elsevier, 2025-03-01) Yu, Jincheng; Qiang, Hongli; Shi, Mingwei; Li, Zhiguo; Fadiji, Tobi; Wani, Ali Abas; Burgeon, Clément
    Packaging plays a vital role in the post-harvest sales process of apples. This study conducted express transportation tests to evaluate the protective effectiveness of two commonly used packaging methods for apples. Key parameters assessed included real-time changes in temperature, humidity, vibration load, and CO₂ levels inside the packaging boxes during transit, as well as the storage quality of apples after transportation. Results showed significant variations in load distribution within corrugated partition-based cardboard boxes (CP combination packaging). Conversely, foam holder-based cardboard boxes (FP combination packaging) exhibited CO₂ accumulation. Furthermore, mechanical damage was predominantly localized to the fruit belly. Compared to CP combination packaging box, FP combination packaging box provided more stable shock resistance at lower vibration forces (< 10 N) across transit routes, likely due to its EPS foam design, which restricted fruit movement and absorbed external vibrations. Post-storage analysis showed that damaged apples experienced a 0.16 % increase in mass loss, a 0.83 % rise in soluble solids content (SSC), and a 0.19 MPa reduction in firmness compared to undamaged controls. These findings provide valuable insights into optimizing packaging design to minimize transport-induced damage and enhance apple preservation.
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    Will climate change affect growth and ochratoxin A production of putative biocontrol knockout strains of Aspergillus carbonarius?
    (Elsevier, 2025-08-02) Llobregat, Belén; Cervini, Carla; González-Candelas, Luis; Verheecke-Vaessen, Carol; Ballester, Ana-Rosa; Medina, Angel
    The research explored the effects of abiotic factors associated with climate change (CC) on the growth and metabolite production of wild-type Aspergillus carbonarius ITEM 5010 and three knockout mutants: one knockout in the first gene of the ochratoxin A (OTA) biosynthesis pathway (ΔotaA) and two in the veA and laeA genes (the latter knockout generated in this work) encoding VELVET complex proteins, which regulate metabolism. Variables examined were temperature (30 °C vs 37 °C), water activity (0.98 vs 0.90), and CO₂ levels (400 ppm vs 1000 ppm). Growth, OTA production, and other metabolites were evaluated on grape-based medium. The results showed that abiotic factors significantly influenced fungal growth and mycotoxin production, with aw being the most critical parameter. At aw 0.90, no growth was observed. A temperature of 37 °C combined with 1000 ppm CO₂ resulted in higher OTA production, indicating a greater health risk in predicted CC scenarios. Mutants of global regulatory factors showed altered metabolite production, with elevated OTA levels at 37 °C. The ΔotaA knockout mutant consistently showed no OTA production, suggesting its viability as a biocontrol agent under CC conditions. However, while OTA increased, other secondary metabolites, such as pyranonigrin A and kojic acid, decreased with rising temperatures in all strains. The research highlights the influence of abiotic factors related to CC on A. carbonarius growth and metabolite production, underlining the threat of increased mycotoxin production. This reinforces the need for resilient biocontrol strategies. The ΔotaA mutant has been identified as a potential biocontrol agent, demonstrating resistance to future environmental stresses associated with CC.