Statistical and Computational Models for Accurate Calibration in Spectroscopy-Based Environmental Monitoring

Reliable environmental monitoring (e.g. nutrients, turbidity, chemical oxygen demand) at high frequency requires accurate calibration of the spectroscopic sensors. This paper gives an overview of linear regression calibration methods in the field of Spectroscopy for ecological applications, a summary of the recent literature (2023-2025), as well as a validated numerical example with graphical analysis (calibration curve and residual plot). We highlight best practices (design of standards, residual analysis, cross validation, attention to heteroscedasticity) and address limitations and extensions (calibration transfer, matrix effects, multivariate methods). Results demonstrate that for Beer-Lambert behavior and correct measurement noise characterization, ordinary least squares provides accurate and meaningful calibration models, and that the uncertainty is predictable. Recent datasets and algorithmic developments on calibration transfer and chemometric model validation for practitioners who wish to deploy robust field calibrations are presented.