Infrared Spectrum Baseline Correction Method Based on Improved Iterative Polynomial Fitting

Zhiqiang, 宁志强 Ning; Jiaxiang, 刘家祥 Liu; Yue, 吴越 Wu; Mengqi, 陶孟琪 Tao; Yonghua, 方勇华 Fang

doi:10.3788/lop57.033001

Cited by 5 publications

(1 citation statement)

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“…12 The second type ts the overall spectrum through a mathematical model, for example, using polynomials and exponential polynomials to t background signals. 13,14 The third type uses algorithms to estimate the background and subtracts it from the original spectrum. The rst two methods are too simple and heavily rely on operator experience in the application process.…”

Section: Introductionmentioning

confidence: 99%

A new technique for baseline calibration of soil X-ray fluorescence spectra based on enhanced generative adversarial networks combined with transfer learning

He,

Zhao,

2023

J. Anal. At. Spectrom.

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Section: Introductionmentioning

confidence: 99%

A new technique for baseline calibration of soil X-ray fluorescence spectra based on enhanced generative adversarial networks combined with transfer learning

He,

Zhao,

2023

J. Anal. At. Spectrom.

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Baseline offset correction technique for terahertz signals based on improved wavelet multiresolution analysis

Xiong,

Jiang,

Huang

et al. 2024

Phys. Scr.

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During the terahertz nondestructive testing of bonded structures, the incomplete discharge of the capacitance in the photoconductive antenna within the terahertz time-domain spectroscopy system results in a shift of the terahertz baseline produced by the antenna. This baseline shift causes variations in the amplitude information of the detected signals.Consequently, when feature imaging of the detection waveforms is performed, the baseline shift can lead to erroneous detection results. In this study, an improved wavelet multiresolution analysis method was used to eliminate high-frequency noise and baseline offset in terahertz detection. The method is based on the frequency characteristics of the detection waveforms, setting thresholds and using similarity as a measurement standard to determine the number of decomposition layers. Ultimately, this achieves the correction of the baseline offset in terahertz signals. Compared with other baseline correction methods, the method presented in this paper achieves the lowest root mean square error of 0.57%, the highest signal-to-noise ratio of 12.64%, and a defect identification accuracy of 96.27% in two-dimensional visualization results.

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Study on CO concentration measurement of TDLAS based on baseline nonlinear improvement

Chen

et al. 2020

2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

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Infrared Spectrum Baseline Correction Method Based on Improved Iterative Polynomial Fitting

Cited by 5 publications

References 0 publications

A new technique for baseline calibration of soil X-ray fluorescence spectra based on enhanced generative adversarial networks combined with transfer learning

A new technique for baseline calibration of soil X-ray fluorescence spectra based on enhanced generative adversarial networks combined with transfer learning

Baseline offset correction technique for terahertz signals based on improved wavelet multiresolution analysis

Study on CO concentration measurement of TDLAS based on baseline nonlinear improvement

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