A parameter-free framework for calibration enhancement of near-infrared spectroscopy based on correlation constraint.

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.