Xu Wei, Xu Chenglong, Cui Junqi, Hu Chunhai, Wen Guilin, Zheng Longjiang, Zhang Zhiguo, Sun Zhen, Zhang Yungang
Opt Lett. 2024 Feb 1;49(3):606-609. doi: 10.1364/OL.507901.
Luminescence thermometry is a promising non-contact temperature measurement technique, but improving the precision and reliability of this method remains a challenge. Herein, we propose a thermal sensing strategy based on a machine learning. By using GdGaO: Er-Yb as the sensing medium, a support vector machine (SVM) is preliminarily adopted to establish the relationship between temperature and upconversion emission spectra, and the sensing properties are discussed through the comparison with luminescence intensity ratio (LIR) and multiple linear regression (MLR) methods. Within a wide operating temperature range (303-853 K), the maximum and the mean measurement errors actualized by the SVM are just about 0.38 and 0.12 K, respectively, much better than the other two methods (3.75 and 1.37 K for LIR and 1.82 and 0.43 K for MLR). Besides, the luminescence thermometry driven by the SVM presents a high robustness, although the spectral profiles are distorted by the interferences within the testing environment, where, however, LIR and MLR approaches become ineffective. Results demonstrate that the SVM would be a powerful tool to be applied on the luminescence thermometry for achieving a high sensing performance.
发光测温法是一种很有前景的非接触式温度测量技术，但提高该方法的精度和可靠性仍然是一个挑战。在此，我们提出一种基于机器学习的热传感策略。通过使用GdGaO:Er-Yb作为传感介质，初步采用支持向量机（SVM）建立温度与上转换发射光谱之间的关系，并通过与发光强度比（LIR）和多元线性回归（MLR）方法比较来讨论传感特性。在较宽的工作温度范围（303 - 853 K）内，SVM实现的最大测量误差和平均测量误差分别约为0.38 K和0.12 K，远优于其他两种方法（LIR分别为3.75 K和1.37 K，MLR分别为1.82 K和0.43 K）。此外，由SVM驱动的发光测温法具有很高的稳健性，尽管光谱轮廓在测试环境中受到干扰而失真，但在这种情况下LIR和MLR方法却失效了。结果表明，SVM将是应用于发光测温法以实现高传感性能的有力工具。
