Utilizing the full potential of spectroscopic calibrations in changing environments typically requires large amounts of maintenance and/or model updates as the presence of new sources of variation makes the calibration insufficient. In this paper, we propose the use of unlabelled data in order to automize such maintenance. We extend the Linear Joint Trained Framework by Ryan and Culp such that the shifts in mean value and covariance structure are modelled explicitly. The extension yields a more flexible framework, and thus we are able to regularize the final calibration in a more desirable manner. The proposed framework is tested on a simulated dataset where we simulate three different realistic scenarios that are either challenging for classic multivariate calibrations or challenging when adding unlabelled data. Furthermore, we test our framework on two real datasets across multiple data splits. We find that our framework not only achieves the same (and in some instances lower) error level as that of the baseline model (NARE), it also yields better calibration models than the Linear Joint Trained Framework.

中文翻译：

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光谱数据的半监督协变量位移建模

在不断变化的环境中利用光谱校准的全部潜力通常需要大量的维护和/或模型更新，因为新的变异源的存在会使校准不足。在本文中，我们建议使用未标记的数据来自动化此类维护。我们扩展了 Ryan 和 Culp 的线性联合训练框架，以便显式地对平均值和协方差结构的变化进行建模。扩展产生了更灵活的框架，因此我们能够以更理想的方式规范最终校准。所提出的框架在模拟数据集上进行了测试，我们模拟了三种不同的现实场景，这些场景要么对经典的多元校准具有挑战性，要么在添加未标记数据时具有挑战性。此外，我们在跨多个数据拆分的两个真实数据集上测试我们的框架。我们发现我们的框架不仅实现了与基线模型 (NARE) 相同（并且在某些情况下更低）的错误级别，而且还产生了比线性联合训练框架更好的校准模型。