Building a multivariate calibration model is typically accomplished using partial least
squares, principal component regression, or ridge regression, also derived as the standard …

In recent decades, with the rapid development of artificial intelligence, data mining, and
cloud computing, new chemometric methods have sprung up and become one of the fastest …

Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on
building and using large soil spectral libraries with the purpose of supporting many activities …

Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy
because the measurements of the spectra may be performed on different instruments and …

Multivariate calibration methods such as partial least-squares build calibration models that
are not parsimonious: all variables (either wavelengths or samples) are used to define a …

Calibration model transfer is essential for practical applications of near infrared (NIR)
spectroscopy because the measurements of the spectra may be performed on different …

Updating a calibration model formed in original (primary) sample and spectral measurement
conditions to predict analyte values in novel (secondary) conditions is an essential activity in …

A new calibration transfer method that applies canonical correlation analysis (CCA) to
transfer the informative components extracted from a spectral dataset is proposed to reduce …

Model transfer is essential for practical applications of near infrared (NIR) spectroscopy
because differences may exist between the spectra measured using different instruments …

Calibration transfer is of great necessity for practical applications of near infrared (NIR)
spectroscopy, since the original calibration model would become invalid when spectra are …