Environmental Sustainability

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Open Access
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.
Open Access
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.
Open Access
Bioactivity screening of selected Moroccan medicinal and aromatic plants, and the chemical basis of the phytotoxicity of caper, Capparis spinosa L.
(Elsevier, 2025-10-01) Kisiriko, Musa; Bitchagno, Gabin T. M.; Harflett, Claudia; Noleto-Dias, Clarice; Naboulsi, Imane; Anastasiadi, Maria; Terry, Leon A.; Sobeh, Mansour; Beale, Michael H.; Ward, Jane L.
Plant natural products are potential sources of biostimulants that can help plants overcome the effects of stress. The adverse effects of soil salinity on wheat growth necessitate the exploration of alternative sustainable solutions, such as biostimulants from medicinal and aromatic plants, to enhance crop resilience and productivity. This study aimed to screen nine Moroccan medicinal and aromatic plant extracts for their effect on wheat growth under saline and non-saline conditions using a seed soaking treatment, in a completely randomised experiment. Except for Marrubium vulgare leaf and Origanum compactum extracts, which averagely improved root length by 25 % and 14 %, respectively, none of the other extracts had significant positive effects on wheat seedling growth. Capparis spinosa (caper) extracts inhibited wheat emergence and growth, with leaf extracts being more phytotoxic than the stem extracts. The leaf extracts of C. spinosa caused an average reduction of the leaf length, root length, shoot dry weight and root dry weight of the wheat seedlings by 31 %, 21 %, 92 % and 94 %, respectively, compared with the control. Further fractionation of the leaf crude extract and follow-up screening revealed that the phytotoxicity likely resulted from a synergy between compounds in different fractions. Chemical analysis of the most active fraction by UHPLC-MS and NMR revealed loliolide as the major compound, alongside oxylipins and indole alkaloid derivatives. Additionally, a previously undescribed compound, 8-(1H-indol-3-yl-methyl)rutin, was also identified. These compounds potentially contribute to the phytotoxicity. The results of this experiment show that although two extracts enhanced root length, overall biostimulant effects were minimal, with C. spinosa extracts being significantly toxic, indicating the need to prevent their application on wheat.
Open Access
Feasibility study on using combined tomography and spectroscopy techniques to evaluate the physical and chemical characteristics of organo-mineral fertilisers
(Frontiers, 2025-06-04) Sakrabani, Ruben; Mosca, Sara; Liptak, Alexander; Burca, Genoveva
Fertilisers play a key role in agriculture, providing key nutrients needed by crops to ensure a secure food supply. However, with increasing prices and rising environmental concerns, there is a growing need to rely on alternative and sustainable fertiliser sources, introducing the opportunity to use organic amendments to formulate organo-mineral fertilisers (OMF). Despite their environmental advantages, the inherent variability in composition of organic amendments within OMF poses a challenge for their standardization. This study aims to use OMF derived from anaerobic digestate and coupled with carbon capture technologies to analyze for its physical characteristics and chemical composition using neutron computed tomography (NCT), X-ray computed tomography (XCT) and Raman spectroscopy (RS). This is a feasibility study to assess using non-destructive techniques on OMF as previously this has not been explored. This work represents the first attempt to utilize a combination of imaging techniques to investigate on OMF and demonstrates their feasibility for measuring the variability between individual samples. This is a proof-of-concept study which shows that combining NCT and XCT can provide images on how uniformly packed each OMF pellet are. The use of RS is to characterize OMF is more challenging largely due to the high fluorescence background arising from its matrix. This study needs to be further developed to enable image-based analysis using machine learning algorithms to determine characteristics of large batches of OMF. Further development is needed building on this work to quantify OMF pellet characteristics so that it can be confidently used as novel fertilisers in agriculture.
Open Access
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 R.
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.
Open Access
Organic management in coffee: a systematic review of the environmental, economic and social benefits and trade-offs for farmers
(Taylor and Francis, 2025-05-29) Jones, Katharine; Njeru, Ezekiel Mugendi; Garnett, Kenisha; Girkin, Nicholas T.
Global coffee production is expanding, contributing to environmental degradation, notably through extensive use of inorganic fertilizers. Volatile prices, climate change, rising input costs, and pressure to decrease carbon footprints represent key challenges for farmers. Regenerative soil management and the use of organic management as an alternative to conventional mineral fertilizers offer one potential solution to address these challenges. However, information is limited regarding the potential options available for farmers, and their potential environmental, economic, and social impacts. We undertook a systematic review of the literature to assess the benefits and trade-offs from adopting different organic management approaches following PRISMA guidelines. We identified 43 peer-reviewed articles, predominantly focusing on agroforestry, plant-derived additions, soil management or animal manure to improve livelihoods and environment. Research priorities differ by region and there is a skew toward researching the environmental impacts of regenerative techniques. Our synthesis demonstrates multiple potential environmental benefits to organic management, but increasing economic risks and trade-offs for farmers, particularly in transitioning to organic management. We also highlight the social barriers facing farmers, from education to access to knowledge networks to support implementation. These challenges must be addressed to support any future sustainable transitions to organic management in coffee.
Open Access
Peanut value chain development: the case of Lower Lake Victoria Basin of Kenya
(MDPI, 2025-03-25) Odunga, George Okoth; Bidzakin, John K.; Okaka, Philip; Okoth, Sheila; Mutsotso, Beneah; Graves, Anil R.
Peanut is Kenya’s second most important legume after beans, primarily grown in the Nyanza and Western regions. This study maps the peanut value chain in Kenya, aiming to identify key actors, quantify costs and value addition, and outline constraints and opportunities, with a view to upgrading the chain. A cross-sectional survey was conducted among value chain actors in Karachuonyo and Nyakach sub-counties, complemented by secondary data sources. Descriptive statistics were used to analyze socio-economic characteristics, production volumes, pricing, demand trends, and policy-related factors. The findings indicate a predominance of female farmers (68%) in peanut production, though few use improved technologies; only 26% were aware of improved seed varieties, and just 1.5% reported using them. Fertilizer usage was absent, attributed to high costs, soil conditions, and limited knowledge. The wholesale and processing segments are male-dominated, largely due to capital intensity and travel requirements, while female traders dominate the retail sector. Strengths Weaknesses Opportunity and Threats (SWOT) analysis highlighted the significant potential of favorable production ecologies, processing options, and robust demand in local and international markets. Key constraints identified include limited seed availability, high fertilizer costs, pest issues, and declining soil fertility. Policy implications include increasing access to affordable inputs, promoting gender-inclusive programs, investing in agricultural research and infrastructure, supporting sustainable farming practices, and fostering public-private partnerships to expand processing and market access.
Open Access
Unlocking the agro-physiological potential of wheat rhizoplane fungi under low P conditions using a niche-conserved consortium approach
(Oxford University Press (OUP), 2025-05-01) Benbrik, Brahim; Reid, Tessa E.; Nkir, Dounia; Chaouki, Hicham; Aallam, Yassine; Clark, Ian M.; Mauchline, Tim H.; Harris, Jim A.; Pawlett, Mark; Barakat, Abdellatif; Rchiad, Zineb; Bargaz, Adnane
Plant growth-promoting fungi (PGPF) hold promise for enhancing crop yield. This study delves into the fungal diversity of the wheat rhizoplane across seven Moroccan agricultural regions, employing a niche-conserved strategy to construct fungal consortia (FC) exhibiting higher phosphorus (P) acquisition and plant growth promotion. This study combined culture-independent and culture-dependent methods exploring taxonomic and functional diversity in the rhizoplane of wheat plants obtained from 28 zones. Twenty fungal species from eight genera were isolated and confirmed through internal transcribed spacer (ITS) Sanger sequencing. P solubilization (PS) capacity was assessed for individual species, with Talaromyces sp. (F11) and Rhizopus arrhizus CMRC 585 (F12) exhibiting notable PS rates, potentially due to production of organic acids such as gluconic acid. PGPF traits and antagonism activities were considered when constructing 28 niche-conserved FC (using isolates from the same zone), seven intra-region FC (different zones within a region), and one inter-region FC. Under low P conditions, in planta inoculation with niche-conserved FC (notably FC14 and FC17) enhanced growth, physiological parameters, and P uptake of wheat, in both vegetative and reproductive stages. FC14 and FC17, composed of potent fungi such as F11 and F12, demonstrated superior plant growth benefits compared with intra- and inter-region constructed FC. Our study underscores the efficacy of the niche-conserved strategy in designing synthetic fungal community from isolates within the same niche, proving significant agro-physiological potential to enhance P uptake and plant growth of wheat.

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