European Journal of Nutrition & Food Safety

Quality Characteristics and Frozen Storage Stability of Grass Carp (Ctenopharyngodon idella) Fish Fingers: A Comparative Evaluation of Fried and Baked Products

Thu, 22 Oct 2026 00:00:00 +0000

This study investigated the quality characteristics and frozen storage stability of grass carp (Ctenopharyngodon idella) fish fingers, including a comparison of fried and baked products. Fish fingers prepared from minced grass carp meat, with a meat yield of 34.21 ± 2.1%, contained 65.70 ± 1.9% moisture, 17.06 ± 1.1% crude protein, 3.14 ± 0.9% crude fat and 2.20 ± 0.6% ash. During storage at −20°C for 180 days, moisture decreased to 61.59 ± 0.8%, accompanied by a relative increase in the other proximate components. Quality deterioration remained gradual, with TVBN increasing from 4.24 ± 0.4 to 9.91 ± 0.8 mg%, peroxide value from 6.68 ± 0.9 to 10.16 ± 0.8 mmol O₂/kg fat, and FFA from 2.68 ± 0.2 to 8.03 ± 0.6%, indicating limited lipid hydrolysis and oxidation during frozen storage. The total plate count decreased from 2.67 × 10³ to 0.089 × 10³ CFU/g, while sensory evaluation showed good acceptability after 180 days, with an overall acceptability score of 4.0/5.0. Comparison of the cooking methods showed that baked fish fingers had lower crude fat (13.75 ± 1.20%), TVBN (6.37 ± 0.80 mg%), peroxide value (7.54 ± 0.70 mmol O₂/kg fat) and FFA (3.28 ± 0.30%) than fried fingers, which contained 16.02 ± 1.10% fat. Baked fingers also achieved an overall sensory score of 5.0. These findings demonstrate that grass carp is suitable for value-added fish finger production with good frozen storage stability, while baking provides a lower-fat preparation method while retaining high sensory acceptability. The product therefore has potential for convenient ready-to-cook applications and improved utilisation of grass carp through value addition.

Varietal Resistance of Cowpea (Vigna unguiculata (L.) Walp.) to Attack by Callosobruchus maculatus (Fabricius, 1775) (Coleoptera: Chrysomelidae) During Post-Harvest Storage in Mali

Fri, 23 Oct 2026 00:00:00 +0000

Cowpea (Vigna unguiculata (L.) Walp.) is a tropical legume of major economic, nutritional and agronomic value, but its production is severely constrained by post-harvest losses, mainly caused by the cowpea weevil Callosobruchus maculatus. An experimental study was conducted at the Entomology-Parasitology Laboratory of FST/USTTB (November 2023–February 2024) to assess the susceptibility of forty cowpea varieties, grown under irrigated and water-stress conditions, to damage by C. maculatus during three months of storage. Sterilized seeds (30 g per 500-mL jar) were infested with three pairs of newly emerged (0-24 h) C. maculatus adults, using a completely randomised design with three replicates (360 jars in total). Varietal susceptibility was assessed through the median development period (MDP), damage rate, adult mortality, weight loss and susceptibility index (SI). Five varieties (KT2-23-5, CZ16-3-71, IT13K-1424-12, FAKSON, IT15K-2386-1) were resistant under water-stress conditions and five others (SIMBO, KVX78-6PLANT-1, CZ16-1-1-91, TILIGREPLANT-6, KVX61-1) under irrigated conditions. The combined analysis confirmed the resistance of three varieties: KT2-23-5, TILIGREPLANT-6 and KVX61-1. Damage was positively and significantly correlated with MDP, weight loss and SI, whereas adult mortality was not significantly associated with observed losses. These findings suggest that post-harvest control strategies should target reproduction and immature stages rather than adult mortality alone, and identify KT2-23-5, TILIGREPLANT-6 and KVX61-1 as promising parental lines for a cowpea resistance breeding programme against C. maculatus.

Smart Cold Chains for Perishable Foods: Sensor Fusion, Artificial Intelligence and Dynamic Shelf-Life Management

Thu, 22 Oct 2026 00:00:00 +0000

Food cold chains are increasingly instrumented with connected temperature sensors, intelligent packaging, product-quality measurements and predictive algorithms. Yet the transition from monitoring refrigeration conditions to managing actual product deterioration is neither automatic nor equally reliable across commodities. This critical narrative review evaluates how sensing, multimodal data fusion, artificial intelligence and remaining shelf-life models can support decisions throughout refrigerated food supply chains. Peer-reviewed work published from 2000 to 12 August 2026 was considered, alongside selected foundational studies, through accessible scholarly indexes, bibliographic verification and backward citation examination. Evidence was assessed for biological relevance, measurement validity, independent validation, operational deployment and decision-level consequences. Temperature histories provide an essential but incomplete representation of product condition: packaging atmosphere, initial microbiological or physiological state, humidity, mechanical injury and spatial thermal gradients can materially affect deterioration. Laboratory studies demonstrate promising performance for electronic noses, spectroscopy, flexible indicators and machine-learning models, whereas external route-level validation and calibration across product lots remain comparatively limited. Kinetic models offer interpretable relationships between exposure and quality loss but depend on commodity-specific parameters and assumptions; data-driven models can represent complex patterns but are vulnerable to distribution shift and leakage between training and test samples. Digital-twin and remaining-life allocation studies establish plausible pathways towards first-expired-first-out distribution, yet simulated reductions in waste should not be mistaken for demonstrated commercial benefits. A central distinction is made between marketable quality and microbiological safety: freshness estimates cannot independently establish pathogen absence, extend legally defined use-by dates or replace food-safety controls. The review proposes an uncertainty-aware sequence linking measurement, state estimation, validated remaining-life prediction and constrained operational action. Research priorities include prospective multi-site studies, robust ground truth, calibration transfer, human–algorithm decision trials and transparent accounting of energy, waste and equity. Smart cold chains are best understood as governed decision systems rather than collections of sensors or predictive models.