We present a calibration procedure for a humidity sensor made of a fiber Bragg grating covered by a polyimide layer. FBGs being intrinsically sensitive to temperature and strain, the calibration should tackle three variables, and, therefore, consists of a three-variable, two-level factorial design tailored to assess the three main sensitivities, as well as the five cross-sensitivities. FBG sensing information is encoded in the reflection spectrum from which the Bragg wavelength should be extracted. We tested six classical peak tracking methods on the results of the factorial design of the experiment applied to a homemade FBG humidity sensor. We used Python programming to compute, from the raw spectral data with six typical peak search algorithms, the temperature, strain and humidity sensitivities, as well as the cross-sensitivities, and showed that results are consistent for all algorithms, provided that the points selected to make the computation are correctly chosen. The best results for this particular sensor are obtained with a 3 $\mathrm{d}$$\mathrm{B}$ threshold, whatever the peak search method used, and allow to compute the effective humidity sensitivity taking into account the combined effect of temperature and strain. The calibration procedure presented here is nevertheless generic and can thus be adapted to other sensors.

中文翻译：

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峰跟踪方法对实验因子设计导出的 FBG 校准的影响

我们介绍了一个湿度传感器的校准程序，该湿度传感器由聚酰亚胺层覆盖的光纤布拉格光栅制成。FBG 本质上对温度和应变很敏感，校准应处理三个变量，因此，包括一个三变量、两级因子设计，用于评估三个主要敏感性以及五个交叉敏感性。FBG 传感信息编码在反射光谱中，应从中提取布拉格波长。我们对应用于自制 FBG 湿度传感器的实验的因子设计结果测试了六种经典的峰值跟踪方法。我们使用 Python 编程从原始光谱数据中使用六种典型的峰值搜索算法计算温度、应变和湿度敏感性以及交叉敏感性，并表明所有算法的结果都是一致的，前提是选择进行计算的点是正确选择的。该特定传感器的最佳结果是通过 3$\mathrm{d}$$\mathrm{乙}$阈值，无论使用何种峰值搜索方法，并允许在考虑温度和应变的综合影响的情况下计算有效湿度灵敏度。然而，此处介绍的校准程序是通用的，因此可以适用于其他传感器。