Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration

Area of Science:

• Chemometrics
• Spectroscopy
• Data Analysis

Background:

• Near-infrared (NIR) spectral data preprocessing is crucial for accurate multivariate calibration.
• Current preprocessing methods often require extensive expertise and can lead to information loss.

Purpose of the Study:

• To develop a novel ensemble preprocessing method for NIR spectral data.
• To reduce reliance on expert knowledge in selecting preprocessing techniques.
• To enhance the accuracy and stability of multivariate calibration models.

Main Methods:

• Proposed an ensemble preprocessing strategy combining multiple differently preprocessed datasets.
• Utilized Monte Carlo cross-validation (MCCV) stacked regression to build partial least squares (PLSs) models.
• Applied the method to two real-world datasets.

Main Results:

• The ensemble preprocessing method requires minimal prior data information and expertise.
• Fusion of complementary information from various preprocessing techniques improved model stability and accuracy.
• Demonstrated advantages over traditional single preprocessing methods on real datasets.

Conclusions:

• The ensemble preprocessing method offers a robust and accessible approach to NIR spectral data analysis.
• This method effectively overcomes limitations associated with traditional, single preprocessing techniques.
• Facilitates wider application of multivariate calibration in fields lacking chemometric expertise.