Zhang Jin, Li Boyan, Hu Yun, Zhou Luoxiong, Wang Guoze, Guo Guo, Zhang Qinghai, Lei Shicheng, Zhang Aihua
Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health/School of Food Science, Guizhou Medical University, Guiyang, 550025, China.
Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health/School of Food Science, Guizhou Medical University, Guiyang, 550025, China.
Anal Chim Acta. 2021 Jan 15;1142:169-178. doi: 10.1016/j.aca.2020.11.006. Epub 2020 Nov 8.
A new parameter-free framework for calibration enhancement (PFCE) was proposed for dealing with the near-infrared (NIR) spectral inconsistency and maintaining the prediction ability of the calibration model under different conditions. The calibration issues encountered in the maintenance with or without using standards, and even the enhancement between instruments have been thoroughly addressed. The general calibration maintenance/enhancement cases were formulated into non-supervised PFCE (NS-PFCE), semi-supervised PFCE (SS-PFCE), and full-supervised PFCE (FS-PFCE). The NS-PFCE made use of both the provided master and slave spectra of standard samples to construct a maintained calibration slave model by implementing a correlation constraint on the regression coefficients. The SS-PFCE and FS-PFCE methods integrated the slave spectra and reference information of standard samples at the same time into the slave spectral calibration, and thus a maintenance or enhancement model could be achieved for the slave spectra, in particular measured on different instruments. The use of dataset1 comprised of 655 pharmaceutical tablets measured on two NIR spectrometers and datset2 containing 117 plant leaf samples in two mesh sizes has demonstrated that the PFCE framework had a significant effect on enhancing the predictions of the slave spectra in the models. The root mean square errors of prediction (RMSEPs) of either active pharmaceutical ingredient (API) amount in tablets or reducing sugar content in plant leaf samples from the slave spectra approached to or were lower than those values predicted from the master spectra in the master models established with the partial least-squares (PLS) regression method. The advantage of PFCE was parameter-free and efficient. First, the method could be flexibly employed in scientific or applicative environment with no regard to the parameter specification. Second, the performance of NS-PFCE was comparable to the classical calibration maintenance methods, yet the SS-PFCE and FS-PFCE could enhance the prediction ability to a level widely considered as the upper boundary of classical calibration maintenance methods reached.The source code of the method is available at https://github.com/JinZhangLab/PFCE.
提出了一种新的无参数校准增强框架（PFCE），用于处理近红外（NIR）光谱不一致问题，并在不同条件下保持校准模型的预测能力。无论是在使用标准品还是不使用标准品进行维护时遇到的校准问题，甚至仪器之间的增强问题都得到了彻底解决。一般的校准维护/增强情况被归纳为无监督PFCE（NS-PFCE）、半监督PFCE（SS-PFCE）和全监督PFCE（FS-PFCE）。NS-PFCE利用标准样品提供的主光谱和从光谱，通过对回归系数实施相关约束来构建维护后的校准从模型。SS-PFCE和FS-PFCE方法将标准样品的从光谱和参考信息同时整合到从光谱校准中，从而可以为从光谱建立维护或增强模型，特别是在不同仪器上测量的从光谱。使用在两台近红外光谱仪上测量的655片药片片剂组成的数据集1和包含两种网格尺寸的117个植物叶片样品的数据集2表明，PFCE框架对增强模型中从光谱的预测有显著效果。片剂中活性药物成分（API）含量或植物叶片样品中还原糖含量的从光谱预测均方根误差（RMSEP）接近或低于使用偏最小二乘（PLS）回归方法建立的主模型中主光谱的预测值。PFCE的优点是无参数且高效。首先，该方法可以灵活应用于科学或应用环境，而无需考虑参数规格。其次，NS-PFCE的性能与经典校准维护方法相当，但SS-PFCE和FS-PFCE可以将预测能力提高到广泛认为是经典校准维护方法所能达到的上限水平。该方法的源代码可在https://github.com/JinZhangLab/PFCE获取。
