scholarly journals BIOMETRIC METHOD OF AGE ESTIMATION: DEVELOPMENT AND EFFICIENCY, IN CASES OF PATHOLOGIES OF TEETH HARD TISSUES

2017 ◽  
Author(s):  
M. Yu. Honcharuk-Khomyn ◽  
Kh. V. Pohoretska ◽  
L. O. Patskan
Keyword(s):  
Root Length ◽  
Age Estimation ◽  
Morphological Changes ◽  
Principal Component Regression ◽  
Principal Component ◽  
Structural Defects ◽  
Estimation Technique ◽  
Radiographic Technique ◽  
Mathematical Analyses ◽  
Different Levels

Background. The physiological changes of tooth are the criteria used in evaluation of regressive formula by Kvaal et al. age estimation technique. But in cases of abnormal occlusion, abnormal chewing habits, bruxism, abrasive factors or structural defects of teeth the intensity of tooth aging accelerates.Objective. The aim of the research was to define the options of age estimation according to dental state of individuals with pathological attrition.Methods. 108 panoramic x-ray photos of patients with pathological attrition of teeth were chosen by a randomized selection (49 males and 59 females). All photos were made by means of Planmeca PROMAX orthopantomograph. Nine measurements were made for each tooth: the tooth length, pulp length, root length, root width and pulp width at three different levels: cement-enamel junction (level A, beginning of root), one-quarter of root length from a cement-enamel junction (level B), and mid-root (level C). Due to these measurements, a number of ratios were calculated in accordance with Kvaal et al. method.Results. The errors that reached 27±8.4 years were found when evaluating the dental age using primary coefficients of equations suggested by the authors of the method used. By means of mathematical analyses, principal component regression method as well, the correlation coefficient of Pearson and method of combining linear regression due to the tooth changes in cases of pathological attrition (lowering level of occlusal surface, dystrophy of pulp structures and deposition of tertiary reparative dentine) by regression analysis, the modified formulas for age estimation using radiographic technique were found. Modified coefficients decreased the error to 13±0.8 years, which was relative to the real age upto nearly 42-48% compared to the primary coefficients of equations for pathological attrition.Conclusions. Age estimation technique can be improved taking into account morphological changes in pathological attrition and the calculated coefficients make it possible to expand the circle of person’s age which needs to be found.

scholarly journals Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4744
Author(s):  
Huawei Cui ◽  
Zhishang Cheng ◽  
Peng Li ◽  
Aimin Miao
Keyword(s):  
Seed Germination ◽  
Correlation Coefficient ◽  
Root Length ◽  
Sweet Corn ◽  
Hyperspectral Image ◽  
Principal Component Regression ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Seed Vigor ◽  
Seedling Length

Vigor identification in sweet corn seeds is important for seed germination, crop yield, and quality. In this study, hyperspectral image (HSI) technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sweet corn seeds. In this study, 89 sweet corn seeds (73 for training and the other 16 for testing) were studied and hyperspectral imaging at the spectral range of 400–1000 nm was applied as a nondestructive and accurate technique to identify seed vigor. The root length and seedling length which represent the seed vigor were measured, and principal component regression (PCR), partial least squares (PLS), and kernel principal component regression (KPCR) were used to establish the regression relationship between the hyperspectral feature of seeds and the germination results. Specifically, the relevant characteristic band associated with seed vigor based on the highest correlation coefficient (HCC) was constructed for optimal wavelength selection. The hyperspectral data features were selected by genetic algorithm (GA), successive projections algorithm (SPA), and HCC. The results indicated that the hyperspectral data features obtained based on the HCC method have better prediction results on the seedling length and root length than SPA and GA. By comparing the regression results of KPCR, PCR, and PLS, it can be concluded that the hyperspectral method can predict the root length with a correlation coefficient of 0.7805. The prediction results of different feature selection and regression algorithms for the seedling length were up to 0.6074. The results indicated that, based on hyperspectral technology, the prediction of seedling root length was better than that of seed length.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

scholarly journals Application of Derivative, Derivative Ratio, and Multivariate Spectral Analysis and Thin-Layer Chomatography-Densitometry for Determination of a Ternary Mixture Containing Drotaverine Hydrochloride, Caffeine, and Paracetamol

2007 ◽  
Vol 90 (2) ◽  
pp. 391-404 ◽  
Author(s):  
Fadia H Metwally ◽  
Yasser S El-Saharty ◽  
Mohamed Refaat ◽  
Sonia Z El-Khateeb
Keyword(s):  
Thin Layer ◽  
Least Squares ◽  
Ternary Mixture ◽  
Principal Component Regression ◽  
Pharmaceutical Preparations ◽  
Principal Component ◽  
Standard Addition ◽  
Drotaverine Hydrochloride ◽  
Assay Precision

Abstract New selective, precise, and accurate methods are described for the determination of a ternary mixture containing drotaverine hydrochloride (I), caffeine (II), and paracetamol (III). The first method uses the first (D1) and third (D3) derivative spectrophotometry at 331 and 315 nm for the determination of (I) and (III), respectively, without interference from (II). The second method depends on the simultaneous use of the first derivative of the ratio spectra (DD1) with measurement at 312.4 nm for determination of (I) using the spectrum of 40 μg/mL (III) as a divisor or measurement at 286.4 and 304 nm after using the spectrum of 4 μg/mL (I) as a divisor for the determination of (II) and (III), respectively. In the third method, the predictive abilities of the classical least-squares, principal component regression, and partial least-squares were examined for the simultaneous determination of the ternary mixture. The last method depends on thin-layer chromatography-densitometry after separation of the mixture on silica gel plates using ethyl acetatechloroformmethanol (16 + 3 + 1, v/v/v) as the mobile phase. The spots were scanned at 281, 272, and 248 nm for the determination of (I), (II), and (III), respectively. Regression analysis showed good correlation in the selected ranges with excellent percentage recoveries. The chemical variables affecting the analytical performance of the methodology were studied and optimized. The methods showed no significant interferences from excipients. Intraday and interday assay precision and accuracy values were within regulatory limits. The suggested procedures were checked using laboratory-prepared mixtures and were successfully applied for the analysis of their pharmaceutical preparations. The validity of the proposed methods was further assessed by applying a standard addition technique. The results obtained by applying the proposed methods were statistically analyzed and compared with those obtained by the manufacturer's method.

scholarly journals Principal component regression in GAMLSS applied to Greek–German government bond yield spreads

2021 ◽  
pp. 1471082X2110229
Author(s):  
D. Stasinopoulos Mikis ◽  
A. Rigby Robert ◽  
Georgikopoulos Nikolaos ◽  
De Bastiani Fernanda
Keyword(s):  
Additive Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Complex Nature ◽  
Government Bond ◽  
German Government ◽  
Yield Spreads ◽  
Explanatory Variables ◽  
Interaction Terms ◽  
Bond Yield

A solution to the problem of having to deal with a large number of interrelated explanatory variables within a generalized additive model for location, scale and shape (GAMLSS) is given here using as an example the Greek–German government bond yield spreads from 25 April 2005 to 31 March 2010. Those were turbulent financial years, and in order to capture the spreads behaviour, a model has to be able to deal with the complex nature of the financial indicators used to predict the spreads. Fitting a model, using principal components regression of both main and first order interaction terms, for all the parameters of the assumed distribution of the response variable seems to produce promising results.

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

scholarly journals Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

2021 ◽  
Vol 11 (13) ◽  
pp. 5895
Author(s):  
Kristina Serec ◽  
Sanja Dolanski Babić
Keyword(s):  
Thin Films ◽  
Learning Algorithm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Conformational Transitions ◽  
Cancer Diagnostics ◽  
Dna Conformation ◽  
Support Vector ◽  
Multivariate Statistical ◽  
The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

scholarly journals Simultaneous determination of various platycosides in Four Platycodon grandiflorum cultivars by UPLC-QTOF/MS

2019 ◽  
Vol 62 (1) ◽  
Author(s):  
Dae Young Lee ◽  
Bo-Ram Choi ◽  
Jae Won Lee ◽  
Yurry Um ◽  
Dahye Yoon ◽  
...  
Keyword(s):  
Principal Component ◽  
Ultra Performance Liquid Chromatography ◽  
Platycodon Grandiflorum ◽  
Pentacyclic Triterpenoid ◽  
Flight Mass Spectrometry ◽  
Platycodi Radix ◽  
Isomeric Pairs ◽  
Different Levels

Abstract In Platycodi Radix (root of Platycodon grandiflorum), there are a number of platycosides that consist of a pentacyclic triterpenoid aglycone and two sugar moieties. Due to the pharmacological activities of platycosides, it is critical to assess their contents in PR, and develop an effective method to profile various platycosides is required. In this study, an analytical method based on ultra performance liquid chromatography coupled with quadrupole time-of-flight/mass spectrometry (UPLC-QTOF/MS) with an in-house library was developed and applied to profile various platycosides from four different Platycodi Radix cultivars. As a result, platycosides, including six isomeric pairs, were successfully analyzed in the PRs. In the principal component analysis, several platycosides were represented as main variables to differentiate the four Platycodi Radix cultivars. Their different levels of platycosides were also represented by relative quantification. Finally, this study indicated the proposed method based on the UPLC-QTOF/MS can be an effective tool for identifying the detail characterization of various platycosides in the Platycodi Radix.

Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil

2019 ◽  
Vol 73 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Yun Zhao ◽  
Mahamed Lamine Guindo ◽  
Xing Xu ◽  
Miao Sun ◽  
Jiyu Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
Principal Component ◽  
Classification Performance ◽  
Soil Samples ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Different Levels

In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.

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