Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high-fidelity model is challenging due to the system's variability, also known as batch-to-batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar-based fermentation and provides a novel approach for bioprocess dynamic modeling.

中文翻译：

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不确定条件下酵母虾青素生产的动力学和混合模型

虾青素是一种商业化合成的高价值化合物，通过Xanthophyllomyces dendrorhous发酵。使用从生物废物中分解的混合糖进行酵母发酵为提高工艺可持续性提供了一种有希望的选择。然而，几乎没有努力研究多种糖对X. dendrorhous的影响生物质生长和虾青素生产。此外，由于系统的可变性（也称为批次间变化），高保真模型的构建具有挑战性。本研究提出了两项​​创新来应对这些挑战。首先，开发了一个动力学模型来比较基于单糖（葡萄糖）和基于混合糖（葡萄糖和蔗糖）的发酵方法之间的过程动力学。然后，将动力学模型参数本身建模为高斯过程，一种概率机器学习技术，以提高模型预测的准确性和鲁棒性。我们得出结论，虽然蔗糖的存在不会影响生物量生长动力学，它引入了一种竞争性抑制机制，通过诱导不利的环境条件（如渗透梯度）来增强虾青素的积累。此外，混合模型能够大大减少模型模拟误差，并且对不确定性传播特别鲁棒。该研究表明了混合糖基发酵的优势，并为生物过程动态建模提供了一种新方法。