Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
arXiv:2605.13587v3 Announce Type: replace-cross Abstract: Preprocessing screening is often the most expensive part of a near-infrared spectroscopy calibration workflow. It works because smoothing, derivatives, detrending and related filters change the spectral directions seen by partial least squares (PLS) or Ridge regression, but a full external search repeatedly refits nearly the same linear model. This paper studies the case where that search can be collapsed into one calibration step. For a strict linear preprocessing operator A acting on row spectra as XA^T, the transformed PLS cross-cova
The paper provides a method to streamline a laborious and costly part of near-infrared spectroscopy, reducing the need for extensive trial-and-error preprocessing.
Improving the efficiency of data processing in analytical chemistry, particularly in NIR spectroscopy, can accelerate research and development across various industries.
The proposed method allows for integrating preprocessing selection directly into the model calibration, potentially simplifying and speeding up analytical workflows.
- · Analytical chemists
- · Spectroscopy equipment manufacturers
- · AI/ML researchers in chemical analysis
- · Companies relying on manual, resource-intensive spectroscopic analysis methods
More efficient and accurate material analysis in fields like pharmaceuticals, food science, and agriculture.
Reduced operational costs and faster product development cycles for industries heavily reliant on NIR spectroscopy.
Potential for broader application of NIR spectroscopy in real-time quality control due to increased automation and speed.
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Read at arXiv cs.LG