Calibration transfer is a key technique to ensure the consistency of instruments or analytical methods. Near infrared spectra is strongly influenced by the status of instrument or the environment of measurement. Therefore, calibration transfer is essential for practical applications of near infrared spectroscopy. This paper provides an overview of the state-of-the-art pertaining to calibration transfer methods, including those with and without standard samples, and the focus is on the methods based on multivariate calibration, factor analysis, neural network and multi-task learning for algorithms with standard samples and on the methods based on spectral correction, model coefficient correction and robust multivariate calibration for algorithms without standard samples. The formulation and efficiency of different calibration transfer methods are analyzed from the viewpoint of the algorithms. Among the summarized methods, piecewise direct standardization (PDS) and its variants are still the gold standard for calibration transfer, but algorithms based on factor analysis is becoming popular and those based on neural network and multi-task learning has gradually attracted more attention in recent years. In practical applications, however, it is difficult or even impossible to obtain the standard samples for measuring their spectra on both master and slave instruments, calibration transfer without standard samples is more practical. Furthermore, with the development of the instrumentation in miniaturization, imaging and hyper-spectral imaging, calibration transfer will be more and more essential in the future.Contents
1 Introduction
2 Algorithms with standard samples
2.1 Multivariate calibration transfer
2.2 Factor analysis methods
2.3 Neural network
2.4 Multi-task learning
2.5 Others
3 Algorithms without standard samples
3.1 Spectral correction methods
3.2 Model coefficient correction methods
3.3 Robust multivariate calibration
4 Conclusion