Relevant bibliographies by topics / Multiplicative scatter correction

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Academic literature on the topic 'Multiplicative scatter correction'

Author: Grafiati
Published: 7 June 2025
Last updated: 9 July 2025

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Journal articles on the topic "Multiplicative scatter correction"

1

Fearn, Tom. "Extended Multiplicative Scatter Correction." NIR news 16, no. 4 (2005): 3–5. http://dx.doi.org/10.1255/nirn.824.

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2

Isaksson, Tomas, and Bruce Kowalski. "Piece-Wise Multiplicative Scatter Correction Applied to Near-Infrared Diffuse Transmittance Data from Meat Products." Applied Spectroscopy 47, no. 6 (1993): 702–9. http://dx.doi.org/10.1366/0003702934066839.

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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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3

Li, Qingbo, Qishuo Gao, and Guangjun Zhang. "Improved Extended Multiplicative Scatter Correction Algorithm Applied in Blood Glucose Noninvasive Measurement with FT-IR Spectroscopy." Journal of Spectroscopy 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/916351.

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In order to improve the predictive accuracy of human blood glucose quantitative analysis model with fourier transform infrared (FT-IR) spectroscopy, this paper uses a method named improved extended multiplicative scatter correction (Im-EMSC), which can effectively eliminate the scattering effects caused by human body strong scattering. The principal components of the differential spectra are used instead of the pure spectra of the analytes in this algorithm. Calibrate the unwanted physical characteristic through the shape of the curve of principal components, and extract the original glucose concentration information. Im-EMSC can efficiently remove most of the pathlength difference and baseline shift influences. Firstly, Im-EMSC is used as a preprocessing method, and then partial least squares (PLS) regression method is adopted to establish a quantitative analysis model. In this paper, the result of Im-EMSC is compared with those popular scattering correction algorithms of multiplicative scatter correction (MSC) and extended multiplicative scatter correction (EMSC) preprocessing methods. Experimental results show that the prediction accuracy has been greatly improved with Im-EMSC method, which is helpful for human noninvasive glucose concentration detection technology.
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4

Dhanoa, M. S., S. J. Lister, R. Sanderson, and R. J. Barnes. "The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra." Journal of Near Infrared Spectroscopy 2, no. 1 (1994): 43–47. http://dx.doi.org/10.1255/jnirs.30.

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We demonstrate that set-dependent multiplicative scatter correction and set-independent standard normal variate transformations of NIR spectra are linearly related as theoretically expected. It is shown that the mean and standard deviation of the set-mean-spectrum together with the correlation coefficient between each individual spectrum and set-mean-spectrum are required to link these two transformations. It is through these three quantities, that set-dependency is incorporated into spectra derived by application of multiplicative scatter correction. MSC and SNV are two alternative approaches to reduce particle size effects and they are interconvertible.
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Pornprasit, Rattapol, Philaiwan Pornprasit, Pruet Boonma, and Juggapong Natwichai. "A study on prediction performance of the mechanical properties of rubber using Fourier-transform near infrared spectroscopy." Journal of Near Infrared Spectroscopy 26, no. 6 (2018): 351–58. http://dx.doi.org/10.1177/0967033518805277.

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Near infrared spectroscopy is a spectroscopic method used for quality and quantity analysis of agriculture products and industry materials. Rubber is a mostly raw material of any products. NIR spectroscopy had been using to analyze the mechanical properties of rubber and polymer materials. Prediction models were built from the correlation between the NIR spectra and mechanical strength values (hardness and tensile strength). Raw data were pretreated to improve the prediction models, where the prediction models were based on partial least squares regression and support vector regression. In the case of hardness prediction, the raw dataset was pretreated with standard normal variate transformation or a combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. For tensile strength prediction, the pretreatments were multiplicative scatter correction or combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. From these processes, the r2 values were greater than 0.9, the bias values were among ±0.5, and the RMSEP values were lower than 5.
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6

Geladi, P., D. MacDougall, and H. Martens. "Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat." Applied Spectroscopy 39, no. 3 (1985): 491–500. http://dx.doi.org/10.1366/0003702854248656.

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This paper is concerned with the quantitative analysis of multicomponent mixtures by diffuse reflectance spectroscopy. Near-infrared reflectance (NIRR) measurements are related to chemical composition but in a nonlinear way, and light scatter distorts the data. Various response linearizations of reflectance (R) are compared ( R with Saunderson correction for internal reflectance, log 1/ R, and Kubelka-Munk transformations and its inverse). A multi-wavelength concept for optical correction (Multiplicative Scatter Correction, MSC) is proposed for separating the chemical light absorption from the physical light scatter. Partial Least Squares (PLS) regression is used as the multivariate linear calibration method for predicting fat in meat from linearized and scatter-corrected NIRR data over a broad concentration range. All the response linearization methods improved fat prediction when used with the MSC; corrected log 1/ R and inverse Kubelka-Munk transformations yielded the best results. The MSC provided simpler calibration models with good correspondence to the expected physical model of meat. The scatter coefficients obtained from the MSC correlated with fat content, indicating that fat affects the NIRR of meat with an additive absorption component and a multiplicative scatter component.
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7

Chen, Jie Yu, Chie Iyo, Fuminori Terada, and Sumio Kawano. "Effect of Multiplicative Scatter Correction on Wavelength Selection for near Infrared Calibration to Determine Fat Content in Raw Milk." Journal of Near Infrared Spectroscopy 10, no. 4 (2002): 301–7. http://dx.doi.org/10.1255/jnirs.346.

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The effect of multiplicative scatter correction (MSC) on wavelength selection for near infrared (NIR) calibration to determine fat content in raw milk was investigated. Short-wave NIR spectra (700–1100 nm) of raw milk samples were measured. The calibration equations for fat content were performed by multiple linear regression (MLR) using original, second derivative and MSC-treated spectra. It was found that first wavelength selection from the fat absorption band for a calibration equation was generally effective in all cases of original, second derivative and MSC-treated spectra. However, correlation plots did not always work well because of the multiplicative scatter effect presented in the samples. Whereas, correlation plots were still useful in the case of MSC-treated spectra and normalised second derivative spectra, even when the original spectra exhibited a multiplicative scatter effect.
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Zhang, Lin, Baohua Zhang, Jun Zhou, Baoxing Gu, and Guangzhao Tian. "Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm." Journal of Analytical Methods in Chemistry 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2525147.

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Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.
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Rosdisham Endut, Mohd Shafiq Amirul Sabri, Syed Alwee Aljunid, Norshamsuri Ali, Abdur Rehman Laili, and Muhammad Hafiz Laili. "Prediction of Potassium (K) Content in Soil Analysis Utilizing Near-Infrared (NIR) Spectroscopy." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 1 (2023): 92–101. http://dx.doi.org/10.37934/araset.33.1.92101.

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The relationship between NIR spectroscopy spectral absorbance and potassium (K) is studied as a representative factor for analysing soil's nutritional content. To determine the potassium content of soil samples without resorting to intrusive and time-consuming chemical analysis techniques, NIR spectroscopy sampling techniques have been tested. Spectrum absorption data from 900 nm to 1600 nm were discovered to correspond with potassium values and were then analysed using a number of pre-processing procedures. Five techniques, Multiplicative Scatter Correction (MSC), Multiplicative Scatter Correction using Common Amplification (MSSCA), Multiplicative Scatter Correction using Common Offset (MSCCO), Detrending (DT), and Mean Normalization (MN), have been identified as the most effective. Using the Partial Least Squares Regression (PLSR) model, both calibration and prediction data are evaluated. In the end study, the MSCCA method was determined to be the most effective pre-processing method for both calibration and prediction outcomes, with R2 values of 0.9998 for calibration and RMSE values of 0.0600 for prediction. Utilising PLSR model and the MSCCA preprocessing method, the relationship between NIRS absorbance data and potassium may be determined. Consequently, we may infer that the NIRS approach can be utilised to detect amount of potassium in soil analysis employing a less time-consuming, non-invasive, and labour-intensive sampling technique.
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Windig, Willem, Jeremy Shaver, and Rasmus Bro. "Loopy MSC: A Simple Way to Improve Multiplicative Scatter Correction." Applied Spectroscopy 62, no. 10 (2008): 1153–59. http://dx.doi.org/10.1366/000370208786049097.

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Multiplicative scatter correction (MSC) is a widely used normalization technique. It aims to correct spectra in such a way that they are as close as possible to a reference spectrum, generally the mean of the data set, by changing the scale and the offset of the spectra. When there are other differences in the spectra than just a scale and an offset, the mean spectrum changes after MSC. As a result, another MSC, with the new mean spectrum as the reference, will result in an additional correction. This paper studies the effect of multiple applications of MSC.
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Book chapters on the topic "Multiplicative scatter correction"

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Vargas-Zapata, Mateo, Marisol Medina-Sierra, Luis Fernando Galeano-Vasco, and Mario Fernando Cerón-Muñoz. "Development of Machine Learning Models for Predicting Soil Texture Variables through Hyperspectral Imaging." In Technologies and Innovations in Agriculture [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1009853.

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Soil texture is a crucial property that can provide insight into its productive capacity. However, determining soil texture can be a complex and time-consuming process. To address this challenge, we aimed to develop machine learning (ML) models that could predict the composition of sand, clay, and silt in soil based on hyperspectral imaging (HSI) data. We collected and analyzed 500 soil samples and processed the HSI data by masking samples with reflectance and transforming the texture variables with Box-Cox. We also employed various techniques, such as moving average, Savitzky–Golay filtering (SG), first and second derivatives (FD and SD), gap-segment (GS) with FD and SD, standard normal variate (SNV), SNV with detrending (DT), SNV-SG, multiplicative scatter correction (MSC), and GS-DT for the covariates. We applied cubist models (CUB), principal component regression (PCR), partial least squares regression (PLSR), and artificial neural networks (ANN). A total of 1240 models were obtained. For clay, the models with the best performance in the TRAIN and TEST sets were a CUB-tuned model, where R2-TEST = 0.93 and the test root-mean-square error (RMSE-TEST) = 3.63%. For silt and sand, CUB models without refinement were selected, with R2-TEST values of 0.63 and 0.61 and RMSE-TEST of 5.55 and 8.65%, respectively. In conclusion, clay prediction with HSI is feasible if purified spectra are used, with outlier detection techniques, evaluation in the overlap zone, and transformations such as GS-FD. The models for silt and sand prediction are not recommended due to their low performance.
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Conference papers on the topic "Multiplicative scatter correction"

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Karabaliev, Miroslav, Boyana Paarvanova, Bilyana Tacheva, Mitko Mitev, Radoslav Ginin, and Stefka Atanassova. "Multiplicative scatter correction and principal component analysis of UV-Vis absorption spectra during acid hemolysis of erythrocyte suspension." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING ICCMSE 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0047856.

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