A two-level strategy for standardization of near infrared spectra by multi-level simultaneous component analysis

Abstract

Standardization of near infrared (NIR) spectra is indispensable in practical applications because the spectra measured on different instruments are commonly used and the difference between the instruments must be corrected. A two-level standardization method is proposed in this study based on multi-level simultaneous component analysis (MSCA) algorithm for correcting the spectral difference between instruments. A two-level MSCA model is used to model the difference between instruments (the first level) and samples (the second level). With the two models, the spectral difference due to instruments and measurement operation can be corrected, respectively. Three NIR spectral datasets of pharmaceutical tablet, corn and plant leaf are used to evaluate the efficiency of the proposed method. The results show that the score of the first level model describes the overall spectral difference between instruments, and the score of the second level model depictures the spectral difference of the same sample between the measurements. The latter difference may include the spectral variations caused by instrument, operation and the measurement conditions. Therefore, both the spectral difference due to the instrument and measurement can be corrected by adjusting the coefficients in the scores of the two level models, respectively. The proposed method provides a good way for standardizing the spectra measured on different instruments when the measurement is not reproducible.