用多模态数据预测食物干燥时的颜色变化轨迹，效果远超已有方法。
Multi-Modal Zero-Shot Prediction of Color Trajectories in Food Drying
- 融合高维颜色数据与干燥参数，建模颜色动态变化过程。
- 在未知干燥条件下，饼干和苹果的预测误差分别降低90%以上。
- 适合食品质量监测、智能干燥系统研发人员参考。
食物干燥广泛用于降低水分含量、保障安全性和延长保质期。食品样品的颜色演变是干燥过程中产品质量的重要指标。尽管已有研究考察了不同干燥条件下的颜色变化，但现有方法主要依赖低维颜色特征，难以充分捕捉食物样品复杂的动态颜色轨迹。此外，现有建模方法无法泛化到未见过的工艺条件。为解决这些局限，我们提出一种新型多模态颜色轨迹预测方法，整合高维时序颜色信息与干燥过程参数，实现准确且数据高效的色彩轨迹预测。在未见干燥条件下，该模型在饼干干燥中的均方根误差（RMSE）为2.12，在苹果干燥中为1.29，相比基线模型误差降低超过90%。实验结果表明该模型具有优异的准确性、鲁棒性与广泛应用潜力。
原文摘要 · Abstract (English)
Food drying is widely used to reduce moisture content, ensure safety, and extend shelf life. Color evolution of food samples is an important indicator of product quality in food drying. Although existing studies have examined color changes under different drying conditions, current approaches primarily rely on low-dimensional color features and cannot fully capture the complex, dynamic color trajectories of food samples. Moreover, existing modeling approaches lack the ability to generalize to unseen process conditions. To address these limitations, we develop a novel multi-modal color-trajectory prediction method that integrates high-dimensional temporal color information with drying process parameters to enable accurate and data-efficient color trajectory prediction. Under unseen drying conditions, the model attains RMSEs of 2.12 for cookie drying and 1.29 for apple drying, reducing errors by over 90% compared with baseline models. These experimental results demonstrate the model's superior accuracy, robustness, and broad applicability.
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