Zhang Jin, Zhou Xu, Li Boyan
Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang 550025, China.
Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang 550025, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Nov 15;301:122978. doi: 10.1016/j.saa.2023.122978. Epub 2023 Jun 4.
Near-infrared (NIR) spectroscopy is a widely used technique for chemical analysis, but it has faced challenges of calibration transfer, maintenance, and enhancement among different instruments and conditions. The parameter-free calibration enhancement (PFCE) framework was developed to address these challenges with non-supervised (NS), semi-supervised (SS), and full-supervised (FS) methods. This study presented PFCE2, an updated version of the PFCE framework that incorporates two new constraints and a new method to improve the robustness and efficiency of calibration enhancement. First, normalized L2 and L1 constraints were introduced to replace the correlation coefficient (Corr) constraint used in the original PFCE. These constraints preserve the parameter-free feature of PFCE and impose smoothness or sparsity on the model coefficients. Second, multitask PFCE (MT-PFCE) was proposed within the framework to address the calibration enhancement among multiple instruments, enabling the framework to be versatile for all possible calibration transfer situations. Demonstrations conducted on three NIR datasets of tablets, plant leaves, and corn showed that the PFCE methods with the new L2 and L1 constraints can result in more accurate and robust predictions than the Corr constraint, especially when the standard sample size is small. Moreover, MT-PFCE could refine all models in the involved scenarios at once, leading to significant enhancement in model performance, compared to the original PFCE method with the same data requirements. Finally, the applicable situations of the PFCE framework and other analogous calibration transfer methods were summarized, facilitating users to choose suitable methods for their application. The source codes written in both MATLAB and Python are available at https://github.com/JinZhangLab/PFCE and https://pypi.org/project/pynir/, respectively.
近红外（NIR）光谱法是一种广泛应用于化学分析的技术，但在不同仪器和条件下，它面临着校准转移、维护和增强等挑战。为应对这些挑战，开发了无参数校准增强（PFCE）框架，该框架采用了无监督（NS）、半监督（SS）和全监督（FS）方法。本研究提出了PFCE2，这是PFCE框架的更新版本，它纳入了两个新的约束条件和一种新方法，以提高校准增强的稳健性和效率。首先，引入了归一化L2和L1约束，以取代原始PFCE中使用的相关系数（Corr）约束。这些约束保留了PFCE的无参数特性，并对模型系数施加了平滑性或稀疏性。其次，在该框架内提出了多任务PFCE（MT-PFCE），以解决多台仪器之间的校准增强问题，使该框架能够适用于所有可能的校准转移情况。在片剂、植物叶片和玉米的三个近红外数据集上进行的演示表明，与Corr约束相比，具有新L2和L1约束的PFCE方法可以产生更准确、更稳健的预测，尤其是在标准样本量较小时。此外，与具有相同数据要求的原始PFCE方法相比，MT-PFCE可以一次性优化所有相关场景中的模型，从而显著提高模型性能。最后，总结了PFCE框架和其他类似校准转移方法的适用情况，方便用户为其应用选择合适的方法。用MATLAB和Python编写的源代码分别可在https://github.com/JinZhangLab/PFCE和https://pypi.org/project/pynir/获取。
