Calibration transfer is an essential activity in analytical chemistry in order to avoid a complete recalibration. Currently, the most popular calibration transfer methods, such as piecewise direct standardization and dynamic orthogonal projection, require a certain amount of standard or reference samples to guarantee their effectiveness. To achieve higher efficiency, it is desirable to perform the transfer with as few reference samples as possible. To this end, we propose a new calibration transfer method by using a calibration database from a master instrument (source domain) and only one spectrum with known properties from a slave instrument (target domain). We first generate a counterpart of this spectrum in the source domain by a multivariate Gaussian kernel. Then, we train a filter to make the response function of the slave instrument equivalent to that of the master instrument. To avoid the need for labels from the target domain, we also propose an unsupervised way to implement our method. Compared with several state-of-the-art methods, the results on one simulated dataset and two real-world datasets demonstrate the effectiveness of our method. Traditionally, the demand for certain amounts of reference samples during calibration transfer is cumbersome. Our approach, which requires only one reference sample, makes the transfer process simple and fast. In addition, we provide an alternative for performing unsupervised calibration transfer. As such, the proposed method is a promising tool for calibration transfer.

中文翻译：

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通过滤波器学习进行校准传输

校准转移是分析化学中的一项重要活动，以避免完全重新校准。目前最流行的校准传递方法，例如分段直接标准化和动态正交投影，都需要一定量的标准或参考样本来保证其有效性。为了实现更高的效率，需要使用尽可能少的参考样本进行转移。为此，我们提出了一种新的校准传输方法，通过使用主仪器（源域）的校准数据库和从属仪器（目标域）仅具有已知属性的一个光谱。我们首先通过多元高斯核在源域中生成该频谱的对应部分。然后，我们训练一个滤波器，使从仪器的响应函数与主仪器的响应函数相当。为了避免需要目标域的标签，我们还提出了一种无监督的方式来实现我们的方法。与几种最先进的方法相比，一个模拟数据集和两个真实世界数据集的结果证明了我们方法的有效性。传统上，在校准传输期间需要一定量的参考样品是很麻烦的。我们的方法只需要一个参考样本，使转移过程变得简单而快速。此外，我们还提供了执行无监督校准传输的替代方案。因此，所提出的方法是校准转移的有前途的工具。