三张标注日就能把鱼肉鲜度误差压到1.58天，这个少样本高光谱方法比全监督基线都划算。
高光谱成像能非破坏性检测鱼肉储存期间的生化变化，但现有深度学习模型都需要大量逐日标注数据，难以逐产品获取。该论文提出首个用于高光谱食品质量估计的少样本学习框架：每片鱼肉构成一个独立的小样本任务，并用CORAL式序数回归头建模鲜度随时间的等级变化。在16天鲑鱼高光谱数据集上，按严格未见鱼片协议，仅用每个鱼片3个标注日即达到平均绝对误差1.58天、2天准确率72.3%，优于标量回归和标签分布基线。
Few-Shot Ordinal Learning for Day-Wise Freshness Estimation with Hyperspectral Fish Images
Non-destructive food quality assessment has increasingly benefited from hyperspectral imaging (HSI), which captures spectral signatures linked to biochemical changes during storage. Estimating day-wise freshness, however, remains challenging owing to strong inter-fillet variability and scarce labelled data per product. All existing deep learning approaches for HSI-based freshness prediction operate under full supervision, requiring densely annotated training sets that are costly to obtain at the individual-product level. We introduce, to the best of our knowledge, the first few-shot learning framework for HSI-based food quality estimation. Each fillet defines a distinct episodic task, and a CORAL-style ordinal prediction head captures the ranked nature of freshness progression through cumulative threshold modelling. Biologically grounded monotonicity and embedding smoothness constraints further guide predictions toward plausible trajectories. On a 16-day salmon HSI dataset under a strict unseen-fillet protocol, our method achieves a mean absolute error of 1.58 days and 2-day accuracy of 72.3% with only three labelled days per fillet, substantially outperforming scalar regression and label-distribution baselines under an identical unseen-fillet protocol.