Robust generalized multiplicative scatter correction algorithm on pretreatment of near infrared spectral data

Spectrum signal may contain many peaks or mutations and noise also is not smooth white noise, to this kind of signal analysis, must do signal pretreatment, remove part of signal and extract useful part of signal.Based on the data of blood glucose near-infrared spectrum as the research object to explore the application of wavelet transform in the near infrared spectrum signal denoising, and through the simulation results show that using wavelet analysis of near infrared spectral data pretreatment than the traditional Fourier method can be higher precision of prediction.

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Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango

Data in Brief ◽

10.1016/j.dib.2020.105571 ◽

2020 ◽

Vol 30 ◽

pp. 105571

Author(s):

Rita Hayati ◽

Agus Arip Munawar ◽

F. Fachruddin

Keyword(s):

Spectral Data ◽

Prediction Accuracy ◽

Near Infrared ◽

Quality Parameters ◽

Infrared Spectral

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Kernel partial diagnostic robust potential to handle high-dimensional and irregular data space on near infrared spectral data

Heliyon ◽

10.1016/j.heliyon.2020.e03176 ◽

2020 ◽

Vol 6 (1) ◽

pp. e03176

Author(s):

Divo Dharma Silalahi ◽

Habshah Midi ◽

Jayanthi Arasan ◽

Mohd Shafie Mustafa ◽

Jean-Pierre Caliman

Keyword(s):

Spectral Data ◽

Near Infrared ◽

High Dimensional ◽

Data Space ◽

Infrared Spectral ◽

Irregular Data

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The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

Applied Spectroscopy ◽

10.1366/0003702884429869 ◽

1988 ◽

Vol 42 (7) ◽

pp. 1273-1284 ◽

Cited By ~ 362

Author(s):

Tomas Isaksson ◽

Tormod Næs

Keyword(s):

Near Infrared ◽

Scatter Correction ◽

Principal Component Regression ◽

Nir Spectroscopy ◽

Principal Component ◽

Data Sets ◽

Prediction Errors ◽

Regression Methods ◽

Multiplicative Scatter Correction ◽

Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

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Piece-Wise Multiplicative Scatter Correction Applied to Near-Infrared Diffuse Transmittance Data from Meat Products

Applied Spectroscopy ◽

10.1366/0003702934066839 ◽

1993 ◽

Vol 47 (6) ◽

pp. 702-709 ◽

Cited By ~ 77

Author(s):

Tomas Isaksson ◽

Bruce Kowalski

Keyword(s):

Root Mean Square Error ◽

Near Infrared ◽

Scatter Correction ◽

Meat Product ◽

Meat Products ◽

Correction Method ◽

Prediction Errors ◽

Mean Square ◽

Multiplicative Scatter Correction ◽

Further Development

This paper presents a nonlinear scatter correction method, called piece-wise multiplicative scatter correction (PMSC), that is a further development of the multiplicative scatter correction (MSC) method. Near-infrared diffuse transmittance (NIT) data from meat and meat product samples were used to test the predictive performances of the PMSC and the MSC methods. With the use of PMSC, the prediction errors, expressed as the root mean square error of prediction (RMSEP), were improved by up to 36% for protein, up to 55% for fat, and up to 37% for water, in comparison to uncorrected data. The corresponding improvements by using PMSC compared to MSC were up to 22%, 24%, and 31% for protein, fat, and water, respectively.

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