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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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5

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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8

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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9

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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10

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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11

Suryakala S.Vasanthadev and Prince Shanthi. "Influence of data pre-processing techniques for PLSR model to predict blood glucose by NIR spectroscopy." Optics and Spectroscopy 130, no. 5 (2022): 613. http://dx.doi.org/10.21883/eos.2022.05.54448.181-22.

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NIR diffuse reflectance spectroscopic spectra can be mathematically modelled to extract quantitative information by suitable multivariate calibration models. The analysis of spectral data becomes complex as the data is more prone to noise due to light scattering and baseline effects. These errors reduces the robustness and reliability of the developed calibration model. Hence data pre-processing becomes the most important aspect in data analysis. Different mathematical transformations are applied to remove the noise present in the data. This work focuses on the various empirical data pre-processing techniques like baseline correction, multiplicative scatter correction (MSC), robust MSC, extended multiplicative signal correction (EMSC), orthogonal signal correction (OSC) and (-log R) followed by standard normal variate (SNV) techniques for Partial Least Square Regression (PLSR) model in the prediction of blood glucose non-invasively. The performance of the PLSR model for the acquired (raw) spectral data and the same data subjected to different pre-processing techniques is analyzed. The model complexity and robustness is evaluated in terms of the number of latent variables (LVs) required to build the calibration model and obtained mean square prediction error after cross validation. This study utilizes the spectral data collected from 207 subjects from a diabetic center using Diffuse Reflectance Spectrometer (DRS). The analyzed results show that pre-processing based on (-log R) followed by SNV is found to perform well with reduced model complexity and minimum estimated mean square prediction error of 0.23 mg/dl among the other empirical pre-processing techniques. Keywords: multiplicative scatter correction (MSC), orthogonal signal correction (OSC), standard normal variate (SNV), Diffuse Reflectance Spectrometer (DRS).
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12

Maleki, M. R., A. M. Mouazen, H. Ramon, and J. De Baerdemaeker. "Multiplicative Scatter Correction during On-line Measurement with Near Infrared Spectroscopy." Biosystems Engineering 96, no. 3 (2007): 427–33. http://dx.doi.org/10.1016/j.biosystemseng.2006.11.014.

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13

Zhao, Jian, Ji Ming He, Xi Chen, Xiao Qiang Zhu, Jiao Jiao Wu, and Guo Tian He. "A Preprocessing Algorithm of Corn Infrared Spectrum Based on MSC and DOSC." Advanced Materials Research 734-737 (August 2013): 2893–97. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.2893.

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To characteristics the near infrared spectrum of corn, we proposed a preprocessing algorithm combining multiplicative scatter correction (MSC) and direct orthogonal signal correction (DOSC). In this article, we compared the SG first derivative correction, MSC, DOSC and the new algorithm. And using partial least squares analysis (PLS) built the quantitative analysis model. The results show that the preprocessing algorithm combining with the MSC and DOSC can improve the prediction accuracy of all components. This work has some practical and scientific value.
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14

Isaksson, Tomas, and Tormod Næs. "The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy." Applied Spectroscopy 42, no. 7 (1988): 1273–84. http://dx.doi.org/10.1366/0003702884429869.

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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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15

Helland, Inge S., Tormod Næs, and Tomas Isaksson. "Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data." Chemometrics and Intelligent Laboratory Systems 29, no. 2 (1995): 233–41. http://dx.doi.org/10.1016/0169-7439(95)80098-t.

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16

Kaur, Harpreet, Rainer Künnemeyer, and Andrew McGlone. "Investigating aquaphotomics for temperature-independent prediction of soluble solids content of pure apple juice." Journal of Near Infrared Spectroscopy 28, no. 2 (2020): 103–12. http://dx.doi.org/10.1177/0967033519898891.

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The methods of aquaphotomics were explored as an aid to improve near infrared spectroscopic predictive modelling of the soluble solids content of pure apple juice at different temperatures. The study focussed on the first overtone region of the O–H stretching vibration of water (1300–1600 nm). A transmission-based FT-NIR (Fourier transform near infrared) spectrometer was used to acquire 103 spectra of freshly expressed juice samples from individual ‘Braeburn’ apples over the wavelength range of 870–1800 nm with a 1 mm cuvette at three temperatures, 20, 25 and 30°C. The aquagram of the first overtone water region showed a trend of increasing bound water absorption with rising soluble solids content, from 7.3 to 13.7°Brix, and increasing free water absorption with rising temperature from 20 to 30°C. Predictive models for apple juice soluble solids content at 25°C were developed using partial least squares regression with spectral pre-processing by standard normal variate (SNV) followed by second derivative transformation (SNV + 2D) or no pre-processing on absorbance spectra at all. The best result, with lowest standard error of prediction of 0.38°Brix, was obtained using the first overtone water region with partial least squares regression on the SNV + 2D spectra. The method of extended multiplicative scatter correction was used, as an additional pre-processing step, to improve apple juice soluble solids content prediction at different temperatures. The interference component selected for the extended multiplicative scatter correction method was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25 and 30°C). Such extended multiplicative scatter correction pre-processing greatly reduced the soluble solids content prediction bias, when applying the partial least squares regression model developed at 20°C to samples measured at 25 and 30°C, from 0.23 to 0.08 and 0.36 to 0.13°Brix, respectively. Model precision (in terms of standard error of prediction) was also slightly improved by 0.02°Brix in each case, from 0.40 to 0.38 and 0.46 to 0.44°Brix at 25 and 30°C respectively.
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17

Blanco, M., J. Coello, H. Iturriaga, S. Maspoch, and C. de la Pezuela. "Effect of Data Preprocessing Methods in Near-Infrared Diffuse Reflectance Spectroscopy for the Determination of the Active Compound in a Pharmaceutical Preparation." Applied Spectroscopy 51, no. 2 (1997): 240–46. http://dx.doi.org/10.1366/0003702971939947.

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Near-infrared diffuse reflectance spectroscopy (NIRS) with a fiber-optic probe was used for the determination of the active compound in a commercial pharmaceutical preparation. In order to reduce the strong scatter in the spectra and prevent scatter-induced changes in measurements from prevailing over concentration-induced changes, several data preprocessing methods were tested: normalization, derivatives, multiplicative scatter correction, standard normal variate, and detrending. The effectiveness for reducing the scattering of each data preprocessing was assessed, and the best results were obtained with the use of the second derivative. The effect of the treatments on the quantitation of the active compound by partial least-squares regression (PLSR) was studied, similar results being obtained in all cases, with a relative standard error of prediction lower than 1.55%.
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18

Iversen, Arve J., and Torgny Palm. "Multiplicative Scatter Correction of Visible Reflectance Spectra in Color Determination of Meat Surfaces." Applied Spectroscopy 39, no. 4 (1985): 641–46. http://dx.doi.org/10.1366/0003702854250149.

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19

Silalahi, Divo Dharma, Habshah Midi, Jayanthi Arasan, Mohd Shafie Mustafa, and Jean-Pierre Caliman. "Robust generalized multiplicative scatter correction algorithm on pretreatment of near infrared spectral data." Vibrational Spectroscopy 97 (July 2018): 55–65. http://dx.doi.org/10.1016/j.vibspec.2018.05.002.

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20

Thennadil, S. N., H. Martens, and A. Kohler. "Physics-Based Multiplicative Scatter Correction Approaches for Improving the Performance of Calibration Models." Applied Spectroscopy 60, no. 3 (2006): 315–21. http://dx.doi.org/10.1366/000370206776342535.

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21

Konevskikh, Tatiana, Rozalia Lukacs, Reinhold Blümel, Arkadi Ponossov, and Achim Kohler. "Mie scatter corrections in single cell infrared microspectroscopy." Faraday Discussions 187 (2016): 235–57. http://dx.doi.org/10.1039/c5fd00171d.

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Strong Mie scattering signatures hamper the chemical interpretation and multivariate analysis of the infrared microscopy spectra of single cells and tissues. During recent years, several numerical Mie scatter correction algorithms for the infrared spectroscopy of single cells have been published. In the paper at hand, we critically reviewed existing algorithms for the correction of Mie scattering and suggest improvements. We developed an iterative algorithm based on Extended Multiplicative Scatter Correction (EMSC), for the retrieval of pure absorbance spectra from highly distorted infrared spectra of single cells. The new algorithm uses the van de Hulst approximation formula for the extinction efficiency employing a complex refractive index. The iterative algorithm involves the establishment of an EMSC meta-model. While existing iterative algorithms for the correction of resonant Mie scattering employ three independent parameters for establishing a meta-model, we could decrease the number of parameters from three to two independent parameters, which reduced the calculation time for the Mie scattering curves for the iterative EMSC meta-model by a factor of 10. Moreover, by employing the Hilbert transform for evaluating the Kramers–Kronig relations based on a FFT algorithm in Matlab, we further improved the speed of the algorithm by a factor of 100. For testing the algorithm we simulate distorted apparent absorbance spectra by utilizing the exact theory for the scattering of infrared light at absorbing spheres, taking into account the high numerical aperture of infrared microscopes employed for the analysis of single cells and tissues. In addition, the algorithm was applied to measured absorbance spectra of single lung cancer cells.
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22

Sahni, Narinder Singh, Tomas Isaksson, and Tormod Næs. "Comparison of Methods for Transfer of Calibration Models in Near-Infared Spectroscopy: A Case Study Based on Correcting Path Length Differences Using Fiber-Optic Transmittance Probes in In-Line Near-Infrared Spectroscopy." Applied Spectroscopy 59, no. 4 (2005): 487–95. http://dx.doi.org/10.1366/0003702053641522.

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This article addresses problems related to transfer of calibration models due to variations in distance between the transmittance fiber-optic probes. The data have been generated using a mixture design and measured at five different probe distances. A number of techniques reported in the literature have been compared. These include multiplicative scatter correction (MSC), path length correction (PLC), finite impulse response (FIR), orthogonal signal correction (OSC), piecewise direct standardization (PDS), and robust calibration. The quality of the predictions was expressed in terms of root mean square error of prediction (RMSEP). Robust calibration gave good calibration transfer results, while the other methods did not give acceptable results.
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23

Shalini, Wincent Anto Win, Thulasi Rajalakshmi, and Selvanayagam Vasanthadev Suryakala. "Refining thyroid function evaluation: a comparative study of preprocessing methods in diffuse reflectance spectroscopy." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 303–10. https://doi.org/10.11591/ijece.v15i1.pp303-310.

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Thyroid dysfunction, comprising conditions such as hyperthyroidism and hypothyroidism, represents a substantial global health challenge, necessitating timely and precise diagnosis for effective therapeutic intervention and patient welfare. Conventional diagnostic modalities often involve invasive procedures, that could cause discomfort and inconvenience for individuals. The non-invasive techniques like diffuse reflectance spectroscopy (DRS) can offer a promising alternative. This study underscores the critical role of preprocessing methods in enhancing the accuracy of thyroid hormone functionality through a non-invasive approach. In the proposed study the spectral data acquired from the DRS setup are subjected to different preprocessing techniques to improve the efficacy of the prediction model. Thirty individuals with thyroid dysfunction were included in the study, and preprocessing methods such as baseline correction, multiplicative scatter correction (MSC), and standard normal variate (SNV), were systematically evaluated. The study highlights that SNV preprocessing outperformed other methods with a root mean square error (RMSE) of 0.005 and an R² of 0.99. In contrast, MSC resulted in an RMSE of 0.87 and an R² of 0.86, while baseline correction showed a RMSE of 0.84 and an unusual R² of 1.09, indicating potential issues. SNV proved to be the most effective technique.
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24

Kohler, A., C. Kirschner, A. Oust, and H. Martens. "Extended Multiplicative Signal Correction as a Tool for Separation and Characterization of Physical and Chemical Information in Fourier Transform Infrared Microscopy Images of Cryo-Sections of Beef Loin." Applied Spectroscopy 59, no. 6 (2005): 707–16. http://dx.doi.org/10.1366/0003702054280649.

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Extended multiplicative signal correction (EMSC) is used to separate and to characterize physical and chemical information in spectra from Fourier transform infrared (FT-IR) microscopy. This appears especially useful for applications in infrared spectroscopy where the scatter variance in spectra changes with the chemical variance in the sample set. In these cases the chemical information of specific bands that are assigned to functional groups is easier to interpret when the scatter information is removed from the spectra. We show that scatter (physical) information in FT-IR spectra of heat-treated beef loin is related to chemical changes due to heat treatment. This information is caused by textural changes induced by the heat treatment and expressed by physical effects as the optical path length. The chemical absorbance changes introduced in the FT-IR spectra due to heat treatment are shifts in the protein region of the infrared spectrum caused by changes in the secondary structure of the proteins. If the scatter and the chemical information is not separated properly, scatter information may erroneously be interpreted as chemical information.
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Ramadhan, Syahrul, Agus Arip Munawar, and Diswandi Nurba. "Aplikasi NIRS dan Principal Component Analysis (PCA) untuk Mendeteksi Daerah Asal Biji Kopi Arabika (Coffea arabica)." Jurnal Ilmiah Mahasiswa Pertanian 1, no. 1 (2016): 954–60. http://dx.doi.org/10.17969/jimfp.v1i1.1182.

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Abstrak. Kopi merupakan spesies tanaman berbentuk pohon yang termasuk dalam famili Rubiaceae dan genus Coffea, tumbuh tegak, bercabang dan bila dibiarkan dapat tumbuh mencapai tinggi 12 meter. Pendeteksian mutu pangan yang cepat dan efisien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Sebanyak 54 sampel biji kopi diambil dari 6 Provinsi yang berbeda, yaitu: Aceh, Bali, Bengkulu, Nusa Tenggara Barat, Jawa Barat dan Jawa Timur. Pengamatan meliputi Principal Component Analysis (PCA) sebagai metode klasifikasi dan Pretreatment Multiplicative Scatter Correction (MSC) sebagai metode koreksi spektrum. Hasil pengujian menunjukkan bahwa PCA hanya mampu mengklasifikasikan biji kopi dari Provinsi Aceh dan Provinsi Jawa Timur, sedangkan dengan penambahan Pretreatment MSC mampu mengklasifikasikan biji kopi dari Provinsi Aceh dan Provinsi Bali dengan tingkat keberhasilan 100%.Abstract. Coffee is belong to family Rubiaceae and the genus Coffea, grow upright, branched, and can grow up to 12 meters high. The detection of food quality quickly and efficiently can be realized through the development of Near Infrared Reflectance Spectroscopy (NIRS) technology. A total of 54 Coffee bean samples were taken from 6 different province, namely: Aceh, Bali, Bengkulu, West Nusa Tenggara, West Java and East Java. Data analysis included Principal Component Analysis (PCA) were used to classify coffee based on geographic origin. Multiplicative Scatter Correction (MSC) method was used as spectra correction. The results shows that PCA is able to classify coffee beans from the Aceh and East Java province, while the addition of MSC Pretreatment able to classify the coffee beans from the province of Aceh and Bali province with 100% success rate.
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26

Ilari, J. L., H. Martens, and T. Isaksson. "Determination of Particle Size in Powders by Scatter Correction in Diffuse Near-Infrared Reflectance." Applied Spectroscopy 42, no. 5 (1988): 722–28. http://dx.doi.org/10.1366/0003702884429058.

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Diffuse near-infrared reflectance spectroscopy has traditionally been an analytical technique for determining chemical compositions in a sample. We will, in this paper, focus on light scattering effects and their ability to determine the mean particle sizes of powders. The reflectance data of NaCl, broken glass, and sorbitol powders are linearized and submitted to the Multiplicative Scatter Correction (MSC), and the ensuing parameters are used in subsequent multivariate calibrations. The results indicate that particle size can, to a large degree, be determined from NIR reflectance data for a given type of powder. Up to 99% of the partical size variance was explained by the regression.
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27

Pedersen, Dorthe Kjær, Harald Martens, Jesper Pram Nielsen, and Søren Balling Engelsen. "Near-Infrared Absorption and Scattering Separated by Extended Inverted Signal Correction (EISC): Analysis of Near-Infrared Transmittance Spectra of Single Wheat Seeds." Applied Spectroscopy 56, no. 9 (2002): 1206–14. http://dx.doi.org/10.1366/000370202760295467.

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A new extended method for separating, e.g., scattering from absorbance in spectroscopic measurements, extended inverted signal correction (EISC), is presented and compared to multiplicative signal correction (MSC) and existing modifications of this. EISC preprocessing is applied to near-infrared transmittance (NIT) spectra of single wheat kernels with the aim of improving the multivariate calibration for protein content by partial least-squares regression (PLSR). The primary justification of the EISC method is to facilitate removal of spectral artifacts and interferences that are uncorrelated to target analyte concentration. In this study, EISC is applied in a general form, including additive terms, multiplicative terms, wavelength dependency of the light scatter coefficient, and simple polynomial terms. It is compared to conventional MSC and derivative methods for spectral preprocessing. Performance of the EISC was found to be comparable to a more complex dual-transformation model obtained by first calculating the second derivative NIT spectra followed by MSC. The calibration model based on EISC preprocessing performed better than models based on the raw data, second derivatives, MSC, and MSC followed by second derivatives.
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Dhanoa, M. S., S. J. Lister, and R. J. Barnes. "On the Scales Associated with Near-Infrared Reflectance Difference Spectra." Applied Spectroscopy 49, no. 6 (1995): 765–72. http://dx.doi.org/10.1366/0003702953964615.

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Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.
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29

Li, Pai, and Yao Xiang Li. "NIR-Based Wood Water Content Prediction with an Integration of ANN and PCA." Advanced Materials Research 502 (April 2012): 253–57. http://dx.doi.org/10.4028/www.scientific.net/amr.502.253.

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In this paper, an integration of BP neural network and PCA for modeling wood water content of larch combined with NIRS was investigated. The original spectra were collected and pretreated with 9 point smoothing and multiplicative scatter correction (MSC). Five typical principal components were extracted from PCA with the application of establishing prediction model. Full cross-validation approach was applied to achieve desirable modeling performance. The prediction correlation coefficient (R) was 0.952 while the mean square error of prediction (MSEP) was 38.27. This study indicated that NIR is a useful tool for rapid and accurate prediction of wood water content.
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30

Kusumiyati, Kusumiyati, Ine Elisa Putri, and Agus Arip Munawar. "Model Prediksi Kadar Air Buah Cabai Rawit Domba (Capsicum frutescens L.) Menggunakan Spektroskopi Ultraviolet Visible Near Infrared." Agro Bali: Agricultural Journal 4, no. 1 (2021): 15–22. http://dx.doi.org/10.37637/ab.v0i0.615.

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Penelitian ini bertujuan untuk menduga kadar air buah cabai rawit domba (Capsicum frutescens L.) menggunakan spektroskopi UV-Vis-NIR. Total sampel yang digunakan yaitu 45 buah. Analisis dilakukan di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Akuisisi data spektra dengan rentang panjang gelombang 300 – 1050 nm (Nirvana AG410). Spektra diperbaiki dengan metode multiplicative scatter correction (MSC), standard normal variate transformation (SNV), orthogonal signal correction (OSC), first derivative (dg1) dan second derivative (dg2). Analisis data dilakukan dengan menggunakan partial least squares regression (PLSR). Berdasarkan penelitian ini menunjukkan bahwa metode koreksi OSC menghasilkan model kalibrasi tertinggi dengan Rkal, RMSEC, Rval, RMSECV, RPD dan faktornya masing-masing yaitu 0.99, 0.31, 0.98, 0.68, 6.62 dan 4. Hal ini menunjukkan bahwa spektroskopi UV-Vis-NIR dapat digunakan untuk memprediksi kadar air pada buah cabai rawit domba.
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31

Zhang, Lian Shun, Chao Guo, and Bao Quan Wang. "Identification of Liquor Brands Based on near Infrared Spectroscopy." Advanced Materials Research 834-836 (October 2013): 935–38. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.935.

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In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.
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32

Suryakala, S. Vasanthadev, and Shanthi Prince. "Influence of data pre-processing techniques for plsr model to predict blood glucose by nir spectroscopy-=SUP=-*-=/SUP=-." Оптика и спектроскопия 130, no. 5 (2022): 773. http://dx.doi.org/10.21883/os.2022.05.52453.181-22.

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NIR diffuse reflectance spectroscopic spectra can be mathematically modelled to extract quantitative information by suitable multivariate calibration models. The analysis of spectral data becomes complex as the data is more prone to noise due to light scattering and baseline effects. These errors reduces the robustness and reliability of the developed calibration model. Hence data pre-processing becomes the most important aspect in data analysis. Different mathematical transformations are applied to remove the noise present in the data. This work focuses on the various empirical data pre-processing techniques like baseline correction, multiplicative scatter correction (MSC), robust MSC, extended multiplicative signal correction (EMSC), orthogonal signal correction (OSC) and (-log R) followed by standard normal variate (SNV) techniques for Partial Least Square Regression (PLSR) model in the prediction of blood glucose non-invasively. The performance of the PLSR model for the acquired (raw) spectral data and the same data subjected to different pre-processing techniques is analyzed. The model complexity and robustness is evaluated in terms of the number of latent variables (LVs) required to build the calibration model and obtained mean square prediction error after cross validation. This study utilizes the spectral data collected from 207 subjects from a diabetic center using Diffuse Reflectance Spectrometer (DRS). The analyzed results show that pre-processing based on (-log R) followed by SNV is found to perform well with reduced model complexity and minimum estimated mean square prediction error of 0.23 mg/dl among the other empirical pre-processing techniques. Keywords:
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Chen, Huazhou, Qiqing Song, Guoqiang Tang, Quanxi Feng, and Liang Lin. "The Combined Optimization of Savitzky-Golay Smoothing and Multiplicative Scatter Correction for FT-NIR PLS Models." ISRN Spectroscopy 2013 (January 17, 2013): 1–9. http://dx.doi.org/10.1155/2013/642190.

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The combined optimization of Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were discussed based on the partial least squares (PLS) models in Fourier transform near-infrared (FT-NIR) spectroscopy analysis. A total of 5 cases of separately (or combined) using SG smoothing and MSC were designed and compared for optimization. For every case, the SG smoothing parameters were optimized with the number of PLS latent variables (F), with an expanded number of smoothing points. Taking the FT-NIR analysis of soil organic matter (SOM) as an example, the joint optimization of SG smoothing and MSC was achieved based on PLS modeling. The results showed that the optimal pretreatment was successively using SG smoothing and MSC, in which the SG smoothing parameters were 4th degree of polynomial, 2nd-order derivative, and 67 smoothing points, the best corresponding F, RMSEP, and RP were 7, 0.3982 (%), and 0.8862, respectively. This result was far better than those without any pretreatment. The combined optimization of SG smoothing and MSC could obviously improve the modeling result for NIR analysis of SOM. In addition, a new method for the classification of calibration and prediction was proposed by normalization principle. The optimizations were done on this basis of this classification.
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34

Zhang, Lian Shun, and Ai Juan Shi. "Classification of Biological Spectrum Based on Principal Component Cluster Analysis." Advanced Materials Research 605-607 (December 2012): 2245–48. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2245.

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Spectrums of 17 biological tissue phantoms were measured using the fiber-optic spectrometer. Then, the spectrum was preprocessed by multiplicative scatter correction method to devoice the spectrum. Afterwards the features of the spectrum were extracted via principal component analysis. Ultimately, we applied cluster analysis for the spectral features. The results showed that the accumulated credibility of the first 12 spectral principal components was 99.86% for the spectrum after preprocessing; indicating that this spectrum feature extraction might be done in the case of losing no key information. And the results showed that the 17 biological tissue phantoms can be divided into four main categories according their optical features.
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35

Romeo, Melissa J., Michael J. Adams, Andrew R. Hind, Suresh K. Bhargava, and Stephen C. Grocott. "Near Infrared Prediction of Oil Yield from Oil Shale." Journal of Near Infrared Spectroscopy 10, no. 3 (2002): 223–31. http://dx.doi.org/10.1255/jnirs.339.

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Fifty-three oil shale samples from the Stuart Deposit in Central Queensland were analysed spectroscopically for hydrocarbon (kerogen) content. Near infrared (NIR) diffuse reflectance spectra of the shale samples exhibited sloping baselines due to particulate scattering and instrumental drift. Multiplicative scatter correction (MSC) and derivative spectroscopy were investigated as means of removing the effects of scattering and non-linear baselines. Partial least squares (PLS) calibration models have been developed, utilising both the entire spectral region as well as narrower chemometrically determined spectral bands. Compared with conventional chemical analysis techniques, NIR diffuse reflectance spectroscopy can provide an efficient, complementary method for the prediction of oil yield from oil shale.
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36

Xu, Shujun, Yanping Xie, and Chunxiang Xu. "Routine classification of food pathogensStaphylococcusandSalmonellaby ATR–FT-IR spectroscopy." Spectroscopy 26, no. 1 (2011): 53–58. http://dx.doi.org/10.1155/2011/232807.

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Fourier-transform infrared equipped with attenuated total reflection (ATR–FT-IR) was used in combination with multivariate statistical analysis for classification and identification of food pathogensStaphylococcusandSalmonella. The goals of the present study were to validate the feasibility of ATR–FT-IR in collecting information for discriminating different bacteria, and to assess the merits of two routes for effectively identify target foodborne bacteria. The results showed that ATR–FT-IR was able to provide enough chemical information of each species. Cluster-analysis-test was able to identify target bacteria at the genus and species level using Pearson's product-moment correction coefficient and Ward's algorithm. Partial least squares regression discriminant analysis (PLS-DA) coupled with multiplicative scatter correction (MSC), standard normal variate (SNV) and their derivatives demonstrated the probable use of this routine method to differentiate food pathogens at the sub-species level.
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37

Chu, Yan Wu, Shi Song Tang, Shi Xiang Ma, et al. "Accuracy and stability improvement for meat species identification using multiplicative scatter correction and laser-induced breakdown spectroscopy." Optics Express 26, no. 8 (2018): 10119. http://dx.doi.org/10.1364/oe.26.010119.

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38

Sorvaniemi, J., A. Kinnunen, A. Tsados, and Y. Mälkki. "Using Partial Least Squares Regression and Multiplicative Scatter Correction for FT-NIR Data Evaluation of Wheat Flours." LWT - Food Science and Technology 26, no. 3 (1993): 251–58. http://dx.doi.org/10.1006/fstl.1993.1053.

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39

Wang, Song Lei, Gui Shan Liu, Xue Fu Li, and Rui Ming Luo. "Detection of the Protein Content of Ningxia Tan Sheep Using Hyperspectral Reflectance Imaging." Applied Mechanics and Materials 513-517 (February 2014): 4235–38. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4235.

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Near-infrared (NIR) hyperspectral imaging technique (900-1700nm) was evaluated to predict the protein content of Tan sheep. This research adopted NIR hyperspectral imaging to get imaging information of 72 mutton samples, multiplicative scatter correction was used to spectral data preprocessing. The optimal wavelengths were obtained through linear-regression analysis, BP neural network combined with actual measured values were established the prediction model and verified this model. The results showed that the prediction effect of model was very well. Correlation coefficient (Rp) and root mean squared error of prediction (RMSEP) of the protein were 0.87 and 1.19. The results indicated that it is feasible to predict the protein content of Tan sheep for NIR hyperspectral imaging technique.
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40

Apruzzese, Francesca, Ramin Reshadat, and Stephen T. Balke. "In-Line Monitoring of Polymer Processing. II: Spectral Data Analysis." Applied Spectroscopy 56, no. 10 (2002): 1268–74. http://dx.doi.org/10.1366/000370202760354713.

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The objective of this work was to examine the application of various multivariate methods to determine the composition of a flowing, molten, immiscible, polyethylene–polypropylene blend from near-infrared spectra. These spectra were acquired during processing by monitoring the melt with a fiber-optic-assisted in-line spectrometer. Undesired differences in spectra obtained from identical compositions were attributed to additive and multiplicative light scattering effects. Duplicate blend composition data were obtained over a range of 0 to 100% polyethylene. On the basis of previously published approaches, three data preprocessing methods were investigated: second derivative of absorbance with respect to wavelength (d2), multiplicative scatter correction (MSC), and a combination consisting of MSC followed by d2. The latter method was shown to substantially improve superposition of spectra and principal component analysis (PCA) scores. Also, fewer latent variables were required. The continuum regression (CR) approach, a method that encompasses ordinary least squares (OLS), partial least squares (PLS), and principle component regression (PCR) models, was then implemented and provided the best prediction model as one based on characteristics between those of PLS and OLS models.
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41

Wu, Yifan, Silong Peng, Qiong Xie, Quanjie Han, Genwei Zhang, and Haigang Sun. "An improved weighted multiplicative scatter correction algorithm with the use of variable selection: Application to near-infrared spectra." Chemometrics and Intelligent Laboratory Systems 185 (February 2019): 114–21. http://dx.doi.org/10.1016/j.chemolab.2019.01.005.

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42

Shalini, Wincent Anto Win, Thulasi Rajalakshmi, and Selvanayagam Vasanthadev Suryakala. "Refining thyroid function evaluation: a comparative study of preprocessing methods in diffuse reflectance spectroscopy." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 303. http://dx.doi.org/10.11591/ijece.v15i1.pp303-310.

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Thyroid dysfunction, comprising conditions such as hyperthyroidism and hypothyroidism, represents a substantial global health challenge, necessitating timely and precise diagnosis for effective therapeutic intervention and patient welfare. Conventional diagnostic modalities often involve invasive procedures, that could cause discomfort and inconvenience for individuals. The non-invasive techniques like diffuse reflectance spectroscopy (DRS) can offer a promising alternative. This study underscores the critical role of preprocessing methods in enhancing the accuracy of thyroid hormone functionality through a non-invasive approach. In the proposed study the spectral data acquired from the DRS setup are subjected to different preprocessing techniques to improve the efficacy of the prediction model. Thirty individuals with thyroid dysfunction were included in the study, and preprocessing methods such as baseline correction, multiplicative scatter correction (MSC), and standard normal variate (SNV), were systematically evaluated. The study highlights that SNV preprocessing outperformed other methods with a root mean square error (RMSE) of 0.005 and an R² of 0.99. In contrast, MSC resulted in an RMSE of 0.87 and an R² of 0.86, while baseline correction showed a RMSE of 0.84 and an unusual R² of 1.09, indicating potential issues. SNV proved to be the most effective technique.
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43

Li, Gui Feng. "Nondestructive Measurement Model of Apple Internal Browning Based on FT-NIR Spectroscopy." Advanced Materials Research 304 (July 2011): 316–21. http://dx.doi.org/10.4028/www.scientific.net/amr.304.316.

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A rapid and nondestructive method for measuring internal browning of apple was put forward based on FT-NIR spectroscopy and the relationship between NIR spectroscopy nondestructive measurement and internal browning was developed. The NIR spectroscopies were acquired from 512 apples. Cluster analysis algorithm based on Euclidean distance was applied to selection of representative samples. The multivariable analysis concluding partial least squares (PLS) and multiple linear regression (MLR) were applied to build the regression models. The excellent model with high R2 (0.871) was obtained by PLS based on 3 wavelength ranges (950–1440 nm, 1480–1890 nm, 1960–2300 nm) and with multiplicative scatter correction (MSC) pretreatment. These suggest the model of apple internal browning was reliable with good predict ability and can meet the requirement to quick determination of internal browning of apples.
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44

Burger, T., H. J. Ploss, J. Kuhn, S. Ebel, and J. Fricke. "Diffuse Reflectance and Transmittance Spectroscopy for the Quantitative Determination of Scattering and Absorption Coefficients in Quantitative Powder Analysis." Applied Spectroscopy 51, no. 9 (1997): 1323–29. http://dx.doi.org/10.1366/0003702971941999.

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A three-flux approximation of the equation of radiative transfer is used to separately determine the effective specific scattering and absorption coefficients of powder mixtures from hemispherical reflectance and transmittance measurements. For a two-component mixture of lactose and paracetamol, it is demonstrated how the knowledge of the separately known scattering coefficient can be used to improve partial least-squares regression (PLS) calibrations of diffuse reflectance data pretreated by multiplicative scatter correction (MSC). Furthermore it is shown that the measured specific absorption coefficient of the investigated mixtures is not generally a linear function of the constituents concentration, a result which might be caused by the mixing procedure of the samples. With the use of the absorption coefficient, it is demonstrated that artificial neural networks are superior to PLS calibrations when modeling a nonlinear relation.
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45

Isaksson, Tomas, Ziyi Wang, and Bruce Kowalski. "Optimised Scaling (OS-2) Regression Applied to near Infrared Diffuse Spectroscopy Data from Food Products." Journal of Near Infrared Spectroscopy 1, no. 2 (1993): 85–97. http://dx.doi.org/10.1255/jnirs.12.

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A recently presented calibration method, called optimised scaling (OS-2) was tested and compared to multiplicative scatter correction (MSC) and principal component regression (PCR). The predictive ability of these regression methods was tested on eight data sets consisting of diffuse near infrared (NIR) reflectance and transmittance continuous spectra of meat, sausages, soya bean and designed sample sets. Calibration was performed for constituents such as fat, protein, water, carbohydrate, temperature, lactate and glucose. A total of 21 calibration models were validated and compared. OS-2 gave good or promising prediction results for the major constituents with large variation, such as prediction of fat in two of the studied meat sample sets. OS-2 gave poorer prediction results of minor constituents compared to MSC or first derivatives of the data and PCR.
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46

Wardiully, F., N. E. Husna, and AA Munawar. "Application of nirs technology and machine learning for predicting chlorogenic acid in arabica coffee beans (Coffea arabica L.)." IOP Conference Series: Earth and Environmental Science 1476, no. 1 (2025): 012086. https://doi.org/10.1088/1755-1315/1476/1/012086.

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Abstract The Near-Infrared Spectroscopy (NIRS) technology, combined with machine learning, offers a promising approach for non-destructive analysis of agricultural products. This study focuses on predicting chlorogenic acid content in Arabica coffee beans using NIRS and machine learning models. Various preprocessing methods were applied to enhance the accuracy of the model, including peak normalization, Savitzky-Golay smoothing, and extended multiplicative scatter correction (EMSC). The results show that the model can effectively predict chlorogenic acid levels with high accuracy, especially when using Savitzky-Golay smoothing as a preprocessing method. The model achieved a coefficient of determination (R2) of 0.79 and a ratio of prediction to deviation (RPD) of 3.38, indicating a robust and reliable prediction. These findings underscore the potential of integrating NIRS and machine learning for quality control in the coffee industry.
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47

Fernández-Cabanás, V. M., A. Garrido-Varo, M. Delgado-Pertiñez, and A. Gómez-Cabrera. "Nutritive Evaluation of Olive Tree Leaves by Near-Infrared Spectroscopy: Effect of Soil Contamination and Correction with Spectral Pretreatments." Applied Spectroscopy 62, no. 1 (2008): 51–58. http://dx.doi.org/10.1366/000370208783412663.

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Olive leaves obtained as a byproduct in the Mediterranean region could play an important role in the nutrition of extensive ruminant systems. However, the reported variation in their nutritive value, among other reasons due to discrepancies in mineral content, is considered an important obstacle for their common use. Near-infrared spectroscopy (NIRS) could fulfill the requirements of these productive systems, providing analytical information in a rapid and economic way. In this work, the effect of soil contamination on NIR spectra has been studied, as well as its correction with some of the most commonly used spectral pretreatments (derivatives, multiplicative scatter correction, auto scaling, detrending, and a combination of the last two transforms). Effects were evaluated by visual inspection of the transformed spectra and comparison of the calibration statistics obtained to estimate acid insoluble ash and total ash contents and in vitro pepsin cellulase digestibility of organic and dry matter. The incidence of spectral curvature effects caused by soil contamination that can be conveniently corrected with pretreatments such as derivatives was confirmed.
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48

Chen, Yi-Chieh, and Suresh N. Thennadil. "Insights into information contained in multiplicative scatter correction parameters and the potential for estimating particle size from these parameters." Analytica Chimica Acta 746 (October 2012): 37–46. http://dx.doi.org/10.1016/j.aca.2012.08.006.

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49

Kim, Dae-Yong, Byoung-Kwan Cho, and Young-Sik Kim. "Prediction of Internal Quality for Cherry Tomato using Hyperspectral Reflectance Imagery." Food Engineering Progress 15, no. 4 (2011): 324–31. http://dx.doi.org/10.13050/foodengprog.2011.15.4.324.

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Hyperspectral reflectance imaging technology was used to predict internal quality of cherry tomatoes with the spectral range of 400-1000 nm. Partial least square (PLS) regression method was used to predict firmness, sugar content, and acid content. The PLS models were developed with several preprocessing methods, such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC), and derivative of Savitzky Golay. The performance of the prediction models were investigated to find the best combination of the preprocessing and PLS models. The coefficients of determination (Rp2) and standard errors of prediction (SEP) for the prediction of firmness, sugar content, and acid content of cherry tomatoes from green to red ripening stages were 0.876 and 1.875 kgf with mean of normalization, 0.823 and 0.388°Bx with maximum of normalization, and 0.620 and 0.208% with maximum of normalization, respectively.
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50

Kolašinac, Stefan M., Marko Mladenović, Ilinka Pećinar, et al. "Raman and FT-IR Spectroscopy Coupled with Machine Learning for the Discrimination of Different Vegetable Crop Seed Varieties." Plants 14, no. 9 (2025): 1304. https://doi.org/10.3390/plants14091304.

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The aim of this research is to investigate the potential of Raman and FT-IR spectroscopy as well as mathematical linear and non-linear models as a tool for the discrimination of different seed varieties of paprika, tomato, and lettuce species. After visual inspection of spectra, pre-processing was applied in the following combinations: (1) smoothing + linear baseline correction + unit vector normalization; (2) smoothing + linear baseline correction + unit vector normalization + full multiplicative scatter correction; (3) smoothing + baseline correction + unit vector normalization + second-order derivative. Pre-processing was followed by Principal Component Analysis (PCA), and several classification methods were applied after that: the Support Vector Machines (SVM) algorithm, Partial Least Square Discriminant Analysis (PLS-DA), and Principal Component Analysis-Quadratic Discriminant Analysis (PCA-QDA). SVM showed the best classification power in both Raman (100.00, 99.37, and 92.71% for lettuce, paprika, and tomato varieties, respectively) and FT-IR spectroscopy (99.37, 92.50, and 97.50% for lettuce, paprika, and tomato varieties, respectively). Moreover, our novel approach of merging Raman and FT-IR spectra significantly contributed to the accuracy of some models, giving results of 100.00, 100.00, and 95.00% for lettuce, tomato, and paprika varieties, respectively. Our results indicate that Raman and FT-IR spectroscopy coupled with machine learning could be a promising tool for the rapid and rational evaluation and management of genetic resources in ex situ and in situ seed collections.
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