2026-01-29 北海道大学,北海道立工業技術センター
魚類の鮮度と食べ頃の可視化ソフト「MIRASAL®」
＜関連情報＞
- https://www.hokudai.ac.jp/news/2026/01/k.html
- https://www.hokudai.ac.jp/news/pdf/260129_pr2.pdf
- https://www.sciencedirect.com/science/article/abs/pii/S0260877426000257
海水魚中のアデノシン三リン酸の分解挙動に基づく鮮度予測モデルの構築：ホッケへの適用 Predictive model for estimating fish freshness based on adenosine triphosphate degradation in marine fish: Application to Atka mackerel (Pleurogrammus azonus)
Yuji Shinohara, Takeya Yoshioka, Naoto Tsubouchi
Journal of Food Engineering Available online: 20 January 2026
DOI:https://doi.org/10.1016/j.jfoodeng.2026.112987
Highlights
- Sequential kinetic model predicted fish K-value from ATP degradation.
- Chub mackerel model successfully predicted horse mackerel K-values (R2 ≥ 0.96).
- Predicted K-values in Atka mackerel were within ±30 % of measured values.
- Framework supports real-time, non-destructive freshness sensing in cold chains.
Abstract
A kinetic model for predicting the K-value, a widely used indicator of fish freshness, was developed based on the degradation of adenosine triphosphate (ATP)-related compounds. Parameters were estimated using time-series K-value data for chub mackerel (Scomber japonicus) stored at 0 °C, reconstructed from published literature, and validated against three independent datasets, showing excellent agreement between calculated and measured values (R2 ≥ 0.96). Generalizability was evaluated by applying the model to horse mackerel (Trachurus japonicus) using published K-value data. The predicted values showed strong agreement with the experimental results, with correlations of R2 ≥ 0.96. Further validation was conducted using Atka mackerel (Pleurogrammus azonus), with high-performance liquid chromatography-determined ATP degradation data at 0 °C. Predicted K-values closely matched observed values (R2 = 0.98), with most within ±30 % error. This transferable model provides a robust framework for freshness evaluation and supports potential applications in cold-chain monitoring. In addition, its simplicity and species-independent structure suggest potential integration with emerging IoT-based sensing platforms for real-time freshness prediction.
