Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate...

Area of Science:

• Analytical Chemistry
• Chemometrics
• Process Analytical Technology (PAT)

Background:

• Developing reliable multivariate calibration models for spectroscopic instruments in process monitoring is challenging, costly, and time-consuming.
• Calibration models can become invalid due to instrumental changes or varying measurement conditions, necessitating frequent replacement.
• Maintaining the long-term predictive ability of these models is crucial for efficient and cost-effective process control.

Purpose of the Study:

• To introduce a novel method, Systematic Prediction Error Correction (SPEC), for preserving the performance of multivariate calibration models.
• To demonstrate SPEC's effectiveness in maintaining model accuracy despite alterations in spectrometers or measurement conditions.
• To compare SPEC's performance against established methods like global PLS, univariate slope and bias correction (SBC), and piecewise direct standardization (PDS).

Main Methods:

• Development of the Systematic Prediction Error Correction (SPEC) algorithm.
• Testing SPEC on two Near-Infrared (NIR) datasets with varying instrumental responses and experimental conditions.
• Comparative analysis of SPEC against global PLS, SBC, and PDS using Root Mean Square Error of Prediction (RMSEP) as a key performance metric.

Main Results:

• SPEC achieved satisfactory analyte predictions with significantly lower RMSEP values compared to global PLS and SBC.
• The method demonstrated effectiveness even with a limited number of standardization samples.
• SPEC proved simpler to implement and required less data than PDS, highlighting its practical advantages.

Conclusions:

• SPEC is a robust and efficient method for maintaining the predictive accuracy of multivariate calibration models in process monitoring.
• The technique offers a cost-effective solution by reducing the need for frequent model recalibration or replacement.
• SPEC presents a valuable alternative for applications where data availability is limited, enhancing the utility of spectroscopic process analytical technology.