Title: A calibration method for eliminating time error in spectral analysis

Authors: Pingping Fan; Jinxiang Huang; Yong Wang; Huacheng Chi; Yan Liu

Addresses: State Key Laboratory of Physical Oceanography, Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266061, China; Laoshan Laboratory, Qingdao, 266237, China ' State Key Laboratory of Physical Oceanography, Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266061, China ' State Key Laboratory of Physical Oceanography, Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266061, China ' State Key Laboratory of Physical Oceanography, Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266061, China ' State Key Laboratory of Physical Oceanography, Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266061, China; Laoshan Laboratory, Qingdao, 266237, China

Abstract: Calibration transfer between different sampling dates was the primary issue in the practical application of fixed-point monitoring by spectral analysis. Here, we proposed a new method to resolve the calibration transfer across sampling dates. We used a reference sample to calibrate the working state of the spectrometer, thereby eliminating the influence factors that require model transfer. The reference sample were made by the ash mixture of the calibration samples. After calibrating the spectra of calibration samples by the reference sample, a new spectral model is reconstructed, and fast spectral analysis can be performed on new samples collected at different sampling dates without the traditional model transfer. Compared to existing model transfers, our calibration method is a purely computational process, achieving rapid analysis of new sample spectra without the need for model transfer.

Keywords: reflectance spectroscopy; sediments; spectrometer; PLSR; partial least squares regression.

DOI: 10.1504/IJCSM.2026.154316

International Journal of Computing Science and Mathematics, 2026 Vol.23 No.2, pp.165 - 175

Received: 07 Dec 2025
Accepted: 27 Feb 2026
Published online: 19 Jun 2026 *

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