The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra

1994 ◽  
Vol 2 (1) ◽  
pp. 43-47 ◽  
Author(s):  
M.S. Dhanoa ◽  
S.J. Lister ◽  
R. Sanderson ◽  
R.J. Barnes
Keyword(s):  
Particle Size ◽  
Standard Deviation ◽  
Correlation Coefficient ◽  
Size Effects ◽  
Scatter Correction ◽  
Standard Normal Variate ◽  
Multiplicative Scatter Correction ◽  
Standard Normal ◽  
Alternative Approaches ◽  
The Mean

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.

Remediation-related monitoring of heavy metal concentration of contaminated Technosol using hyperspectral measurements

2021 ◽  
Author(s):  
Friederike Kaestner ◽  
Magdalena Sut-Lohmann ◽  
Thomas Raab ◽  
Hannes Feilhauer ◽  
Sabine Chabrillat
Keyword(s):  
Heavy Metal ◽  
Random Forest ◽  
Metal Concentration ◽  
Strong Dependence ◽  
Scatter Correction ◽  
Heavy Metal Concentration ◽  
Standard Normal Variate ◽  
Multiplicative Scatter Correction ◽  
Second Derivatives ◽  
Standard Normal

<p>Across Europe there are 2.5 million potentially contaminated sites, approximately one third have already been identified and around 15% have been sanitized. Phytoremediation is a well-established technique to tackle this problem and to rehabilitate soil. However, remediation methods, such as biological treatments with microorganisms or phytoremediation with trees, are still relatively time consuming. A fast monitoring of changes in heavy metal content over time in contaminated soils with hyperspectral spectroscopy is one of the first key factors to improve and control existing bioremediation methods.</p><p>At former sewage farms near Ragow (Brandenburg, Germany), 110 soil samples with different contamination levels were taken at a depth between 15-20 cm. These samples were prepared for hyperspectral measurements using the HySpex system under laboratory conditions, combing a VNIR (400-1000 nm) and a SWIR (1000-2500 nm) line-scan detector. Different spectral pre-processing methods, including continuum removal, first and second derivatives, standard normal variate, normalisation and multiplicative scatter correction, with two established estimation models such as Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR), were applied to predict the heavy metal concentration (Ba, Ni, Cr, Cu) of this specific Technosol. The coefficient of determination (R2) shows for Ba and Ni values between 0.50 (RMSE: 9%) and 0.61 (RMSE: 6%) for the PLSR and between 0.84 (RMSE: 0.03%) and 0.91 (RMSE: 0.02%) for the RFR model. The results for Cu and Cr show values between 0.57 (RMSE: 17.9%) and 0.69 (RMSE: 15%) for the PLSR and 0.86 (0.12%) and 0.93 (0.01%) for the RFR model. The pre-processing method, which improve the robustness and performance of both models best, is multiplicative scatter correction followed by the standard normal variate for the first and second derivatives. Random Forest in a first approach seems to deliver better modeling performances. Still, the pronounced differences between PLSR and RFR fits indicate a strong dependence of the results on the respective modelling technique. This effect is subject to further investigation and will be addressed in the upcoming analysis steps.</p>

scholarly journals PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Yuda Hadiwijaya ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Cucumis Melo ◽  
Partial Least Squares Regression ◽  
Scatter Correction ◽  
Least Squares Regression ◽  
Orthogonal Signal ◽  
Standard Normal Variate ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Standard Normal ◽  
Cucumis Melo L

Penelitian ini bertujuan memprediksi total padatan terlarut buah melon golden menggunakan Vis-SWNIRS dan analisis multivariat. 82 sampel buah melon golden dipanen untuk dianalisis di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Nirvana AG410 spectrometer dengan rentang panjang gelombang 300 sampai 1050 nm digunakan untuk pengambilan data spektra pada sampel buah melon utuh. Metode koreksi spektra yang digunakan yaitu standard normal variate (SNV), multiplicative scatter correction (MSC), dan orthogonal signal correction (OSC). Pemodelan kalibrasi dilakukan menggunakan partial least squares regression (PLSR). Hasil penelitian menunjukkan bahwa penggunaan metode koreksi spektra OSC menampikan model kalibrasi terbaik dibandingkan spektra original dan 2 spektra lainnya yang telah dikoreksi. Koefisien determinasi pada spektra OSC memperlihatkan nilai R2 tertinggi yaitu 0.99, disamping itu nilai ratio performance to deviation (RPD) yang diperoleh sebesar 3.40. Hal ini membuktikan bahwa total padatan terlarut buah melon golden dapat diprediksi dengan akurasi yang tinggi menggunakan Vis-SWNIRS dan analisis multivariat.

On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

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.

scholarly journals Catalogue of Clusters that are Members of Superclusters

1983 ◽  
Vol 104 ◽  
pp. 185-186
Author(s):  
M. Kalinkov ◽  
K. Stavrev ◽  
I. Kuneva
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
The Other ◽  
Clusters Of Galaxies ◽  
Number Of Clusters ◽  
Abell Clusters ◽  
The Mean ◽  
Abell Cluster ◽  
Image Position

An attempt is made to establish the membership of Abell clusters in superclusters of galaxies. The relation is used to calibrate the distances to the clusters of galaxies with two redshift estimates. One is m10, the magnitude of the ten-ranked galaxy, and the other is the “mean population,” P, defined by: where p = 40, 65, 105 … galaxies for richness groups 0, 1, 2 …, and r is the apparent radius in degrees given by: The first iteration for redshift, z1, is obtained from m10 alone: The standard deviation for Eq. (1) is 0.105, the number of clusters with known velocities is 342 and the correlation coefficient between observed and fitted values is 0.921. With zi from Eq. (1), we define Cartesian galactic coordinates Xi = Rih−1 cosBi cosLi, Yi = Rih−1 cosBi sinLi, Zi = Rih−1 sinBi for each Abell cluster, i = 1, …, 2712, where Ri is the distance to the cluster (Mpc), and Ho = 100 h km s−1 Mpc−1.

Loopy MSC: A Simple Way to Improve Multiplicative Scatter Correction

2008 ◽  
Vol 62 (10) ◽  
pp. 1153-1159 ◽  
Author(s):  
Willem Windig ◽  
Jeremy Shaver ◽  
Rasmus Bro
Keyword(s):  
Scatter Correction ◽  
Reference Spectrum ◽  
Data Set ◽  
Multiplicative Scatter Correction ◽  
Additional Correction ◽  
The Mean

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.

scholarly journals Personality and Motivation: A Correlation study among Sports Person

2019 ◽  
Vol 6 (4) ◽  
pp. 5-13
Author(s):  
M Keerthika ◽  
S Punithavathi
Keyword(s):  
Standard Deviation ◽  
Gender Difference ◽  
Correlation Coefficient ◽  
Significant Relationship ◽  
Correlation Study ◽  
Motivation Factors ◽  
The Mean ◽  
The Individual ◽  
Age Range ◽  
The Relationship

In this competitive world, it is essential to grab the sportive nature of sports persons. For different personality type of the individual the motive to engage in sports also varies from person to person. The aim of the present study is to determine the relationship between personality and motivation among sports persons and to identify the gender difference of personality and motivation factors. The sample of this study was 120 sports persons out of which 60 were males and 60 were females belonging to the age range of 18 -30 years. The mean, standard deviation and Pearson’s correlation coefficient were used for analysing the data. Results indicate that there is no significant relationship between Personality and Motivation type of sports persons.

scholarly journals MENENTUKAN PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL CONDITIONAL MEAN VARIANCE

2016 ◽  
Vol 5 (3) ◽  
pp. 82
Author(s):  
I GEDE ERY NISCAHYANA ◽  
KOMANG DHARMAWAN ◽  
I NYOMAN WIDANA
Keyword(s):  
Standard Deviation ◽  
Stock Prices ◽  
Optimal Solution ◽  
Standard Normal Distribution ◽  
Variance Model ◽  
Conditional Mean ◽  
Standard Normal ◽  
The Mean ◽  
T Distribution ◽  
Mean Variance

When the returns of stock prices show the existence of autocorrelation and heteroscedasticity, then conditional mean variance models are suitable method to model the behavior of the stocks. In this thesis, the implementation of the conditional mean variance model to the autocorrelated and heteroscedastic return was discussed. The aim of this thesis was to assess the effect of the autocorrelated and heteroscedastic returns to the optimal solution of a portfolio. The margin of four stocks, Fortune Mate Indonesia Tbk (FMII.JK), Bank Permata Tbk (BNLI.JK), Suryamas Dutamakmur Tbk (SMDM.JK) dan Semen Gresik Indonesia Tbk (SMGR.JK) were estimated by GARCH(1,1) model with standard innovations following the standard normal distribution and the t-distribution.  The estimations were used to construct a portfolio. The portfolio optimal was found when the standard innovation used was t-distribution with the standard deviation of 1.4532 and the mean of 0.8023 consisting of 0.9429 (94%) of FMII stock, 0.0473 (5%) of  BNLI stock, 0% of SMDM stock, 1% of  SMGR stock.

A Comparison of Techniques for Particle Rebound Measurement in Gas Turbine Applications

2015 ◽  
Author(s):  
Jeffrey P. Bons ◽  
Rory Blunt ◽  
Steven Whitaker
Keyword(s):  
Standard Deviation ◽  
Velocity Distribution ◽  
Gas Turbine ◽  
Correlation Coefficient ◽  
Strong Dependence ◽  
Individual Particle ◽  
Aluminum Surface ◽  
Trajectory Data ◽  
Mean Velocity ◽  
The Mean

The rebound characteristics of 100–500μm quartz particles from an aluminum surface were imaged using the particle shadow velocimetry (PSV) technique. Particle trajectory data were acquired over a range of impact velocity (30–90 m/s) and impact angle (20°–90°) typical for gas turbine applications. The data were then analyzed to obtain coefficients of restitution (CoR) using four different techniques: (1) individual particle rebound velocity divided by the same particle’s inbound velocity (2) individual particle rebound velocity divided by inbound velocity taken from the mean of the inbound distribution of velocities from all particles (3) rebound velocity distribution divided by inbound velocity distribution related using distribution statistics and (4) the same process as (3) with additional precision provided by the correlation coefficient between the two distributions. It was found that the mean and standard deviation of the CoR prediction showed strong dependence on the standard deviation of the inbound velocity distribution. The two methods that employed statistical algorithms to account for the distribution shape [methods (3) and (4)] actually overpredicted mean CoR by up to 6% and CoR standard deviation by up to 100% relative to method (1). The error between the methods is shown to be a strong (and linear) function of correlation coefficient, which is typically 0.2–0.6 for experimental CoR data. Non-Gaussianity of the distributions only accounts for up to 1% of the error in mean CoR, and this largely from the non-zero skewness of the inbound velocity distribution. Particle rebound data acquired using field average techniques that do not provide an estimate of correlation coefficient are most accurately evaluated using method (2). Method (3) can be used with confidence if the standard deviation of the inbound velocity distribution is less than 10% of the mean velocity, or if a linear correction based on an assumed correlation coefficient is applied.

Perbandingan Metode Koreksi Pencaran pada Data Hasil Alat Pemantau Kadar Glukosa Darah Non-Invasif

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Siti Raudlah ◽  
Mohammad Masjkur ◽  
Kusman Sadik ◽  
. Erfiani
Keyword(s):  
Scatter Correction ◽  
The Other ◽  
Measuring Instrument ◽  
Simple Linear Regression ◽  
Average Value ◽  
Non Invasive ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
The Time Domain ◽  
Correction Result

Scatter correction is one of the methods in data preprocessing that aim at eliminating the physical properties of the spectrum and reducing the variance between samples. The most commonly methods of scatter correction used are the Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) methods. The MSC method corrects the spectrum by utilizing the results of simple linear regression parameter estimation. The SNV method performs spectral correction with the median and standard deviation. Another alternative method of scatter correction is the Orthogonal Scatter Correction (OSC) applying the principle of orthogonality. The methods  used in this research were MSC, SNV, and OSC methods in order to correct the result data of non-invasive blood glucose measuring instrument. The result of this research showed that the time domain spectrum data and intensity had different amount so that the summarized data was needed. Furthermore, this research found that the OSC method with the five series of statistics gained a good correction result compared to the other methods. The OSC method produced a smaller average value of the variance than the other methods.

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