Estimation of precision of determination of the Yarkovsky effect parameter based on real and model asteroid observations

2021 ◽  
pp. 170-174
Author(s):  
T.Yu. Galushina ◽  
◽  
O.N. Letner ◽  
O.M. Syusina ◽  
◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Observation Interval ◽  
Mean Square ◽  
Yarkovsky Effect ◽  
Effect Parameter ◽  
Large Observation

The paper presents the results of assessment definition precision of the Yarkovsky effect parameter A 2 for asteroids with small perihelion distances, known on epoch January 2021. It is shown that the observation interval has a significant effect on the precision of A 2. As the interval increases, the root mean square error of the parameter decreases. For asteroids (3200) Phaethon and (137924) 2000 BD19 with a large observation interval, an experiment was carried out to reduce the number of real observations. A decrease of the interval and number of observations leads to a loss in the precision of the parameter being determined. Modeling observations based on real ones with an increase in their precision showed that the root mean square error of the A 2 parameter decreases in proportion to the increase in the observation precision. The increase of interval due to model observations confirmed the conclusion about the inverse dependence of the A 2 uncertainty from number and interval of observations.

scholarly journals Rapid determination of tannin in Danshen and Guanxinning injections using UV spectrophotometry for quality control

2018 ◽  
Vol 11 (06) ◽  
pp. 1850034
Author(s):  
Hongxia Huang ◽  
Yuanyuan Lv ◽  
Xiaoyi Sun ◽  
Shuangshuang Fu ◽  
Xuefang Lou ◽  
...  
Keyword(s):  
Quality Control ◽  
Chinese Medicine ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Uv Spectroscopy ◽  
Uv Spectrophotometry ◽  
Mean Square ◽  
Tannin Content

A technique for the determination of tannin content in traditional Chinese medicine injections (TCMI) was developed based on ultraviolet (UV) spectroscopy. Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections, and the model was verified and applied. The results showed that the established UV-based spectral partial least squares regression (PLS) tannin content model performed well with a correlation coefficient ([Formula: see text]) of 0.952, root mean square error of calibration (RMSEC) of 0.476[Formula: see text][Formula: see text]g/ml, root mean square error of validation (RMSEV) of 1.171[Formula: see text][Formula: see text]g/ml, and root mean square error of prediction (RMSEP) of 0.465[Formula: see text][Formula: see text]g/ml. Pattern recognition models using linear discriminant analysis (LDA) and [Formula: see text] nearest neighbor ([Formula: see text]-NN) classifiers based on UV spectrum could successfully classify different types of injections and different manufacturers. The established method to measure tannin content based on UV spectroscopy is simple, rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections.

Determination of Loganin in Qiju Dihuang Pills by Near-Infrared Spectroscopy Technique

2013 ◽  
Vol 807-809 ◽  
pp. 1978-1983 ◽  
Author(s):  
Cai Xia Xie ◽  
Hai Yan Gong ◽  
Jian Ying Liu ◽  
Jing Wei Lei ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square

To establish a rapid analytical method for Loganin in Qiju Dihuang Pills (condensed) by Near-infrared Diffuse Reflectance Technique. Collecting NIR spectra by NIR Diffuse Reflectance Spectroscopy, the partial least square calibration model was built. The correlation coefficients (R2) and the root-mean-square error of cross-validation (RMSECV) were 0.99764 and 0.09340, respectively. In the external validation,coefficients of determination (r2) between NIRS and HPLC values was 0.97348,the root-mean-square error of prediction (RMSEP) was 0.08491. The results showed that the method was rapid, accurate, and could be applied to the fast determination of Loganin in Qiju Dihuang Pills (condensed).

Application of Chemometrics for Spectral Resolving and Determination of Three Analgesics in Water Samples

2020 ◽  
Vol 103 (1) ◽  
pp. 257-264 ◽  
Author(s):  
Ali M Yehia ◽  
Heba T Elbalkiny ◽  
Safa’a M Riad ◽  
Yasser S Elsaharty
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Water Samples ◽  
Principal Component ◽  
Multivariate Curve Resolution ◽  
Mean Square ◽  
Qualitative And Quantitative

Abstract Background: Chemometrics is a discipline that allows the spectral resolution of drugs in a complicated matrix (e.g., environmental water samples) as an alternative to chromatographic methods. Objective: Three analgesics were traced in wastewater samples with simple and cost-effective multivariate approaches using spectrophotometric data. Methods and Results: Four chemometric approaches were applied for the simultaneous determination of diclofenac, paracetamol, and ibuprofen. Partial least squares (PLS), principal component regression (PCR), artificial neural networks (ANN), and multivariate curve resolution (MCR)–alternating least squares (ALS) were selected. The presented methods were compared and validated for their qualitative and quantitative analyses. Moreover, statistical comparison between the results obtained by the proposed methods and the official methods showed no significant differences. Conclusions: The proposed multivariate calibrations were accurate and specific for quantitative analysis of the studied components. MCR-ALS is the only method that has the capacity for both the quantitative and qualitative analysis of the studied drugs. Highlights: Four chemometric approaches were used for analysis of severally overlapped ternary mixture of three analgesics. The analytical performance of PCR, PLS, MCR-ALS, and ANN was compared and validated in terms of root mean square error of calibration (RMSEC), SE of prediction, and recoveries. ANN gave the highest predicted concentrations with the lowest RMSEC and root mean square error of prediction. MCR-ALS has the capacity for both qualitative and quantitative measurement. The methods have been effectively applied for real samples and compared to official methods.

scholarly journals FTIR Spectrometry with PLS Regression for Rapid TBN Determination of Worn Mineral Engine Oils

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6438
Author(s):  
Marie Sejkorová ◽  
Branislav Šarkan ◽  
Petr Veselík ◽  
Ivana Hurtová
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Oil Quality ◽  
Engine Oil ◽  
Base Number ◽  
Pls Regression ◽  
Mean Square ◽  
Ftir Spectrometry

The TBN (Total Base Number) parameter is generally recognized by both engine oil processors and engine manufacturers as a key factor of oil quality. This is especially true for lubricating oils used in diesel and gas engines, which are exposed to relatively high temperatures and, therefore, require more effective protection against degradation. The FTIR spectrometry method together with a multivariate statistical software helped to create a model for the determination of TBN of worn motor oil SAE 15W-40 ACEA: E5/E7, API: CI-4. The best results were provided using a model FTIR with Partial Least Squares (PLS) regression in an overall range of 4000–650 cm−1 without the use of mathematical adjustments of the scanned spectra by derivation. Individual spectral information was condensed into nine principal components with linear combinations of the original absorbances at given wavenumbers that are mutually not correlated. A correlation coefficient (R) between values of TBN predicted by the FTIR-PLS model and values determined using a potentiometric titration in line with the ČSN ISO 3771 standard reached a value of 0.93. The Root Mean Square Error of Calibration (RMSEC) was determined to be 0.171 mg KOH.g−1, and the Root Mean Square Error of Prediction (RMSEP) was determined to be 0.140 mg KOH.g−1. The main advantage of the proposed FTIR-PLS model can be seen in a rapid determination and elimination of the necessity to work with dangerous chemicals. FTIR-PLS is used mainly in areas of oil analysis where the speed of analysis is often more important than high accuracy.

scholarly journals Carotenoid determination in tomato juice using near infrared spectroscopy

2015 ◽  
Vol 29 (3) ◽  
pp. 275-282 ◽  
Author(s):  
Konrád Deák ◽  
Tamás Szigedi ◽  
Zoltán Pék ◽  
Piotr Baranowski ◽  
Lajos Helyes
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Cross Validation ◽  
Diode Array ◽  
Mean Square ◽  
Least Squares Regression ◽  
Carotene Content

AbstractA rapid non-destructive method for profiling tomato carotenoids was developed using NIR spectrometry. One hundred and twenty tomato samples were produced at the Experimental Farm of Szent István University in Gödöllő (Hungary). The sample preparation was based on homogenization. The mixed samples were scanned with a diode array Perten DA7200 NIR Analyzer (950-1650 nm) and analyzed by high performance liquid chromatography. The calibration was based on partial least squares regression with cross-validation. The performance of the final model was evaluated according to root mean square error of cross-validation. The results indicate that the main carotenoid components were accurately predicted. The correlation between the NIR measurement and the β-carotene content of tomatoes was adequately high [R2CV = 0.89; root mean square error of cross-validation = 0.174 μg g−1]. The NIR method was also performed for the determination of the all-trans lycopene content (R2CV = 0.75; root mean square error of cross-validation = 6.88 μg g−1). It can be concluded that the diode array NIR spectrometer has the potential to be used for the determination of the main carotenoids of tomatoes.

scholarly journals Rapid and Simultaneous Measurement of Praeruptorin A, Praeruptorin B, Praeruptorin E, and Moisture Contents in Peucedani Radix Using Near-Infrared Spectroscopy and Chemometrics

2020 ◽  
Vol 103 (2) ◽  
pp. 504-512
Author(s):  
Yijuan Hu ◽  
Hongjian Zhang ◽  
Weiqing Liang ◽  
Pan Xu ◽  
Kelang Lou ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Mean Square ◽  
Determination Coefficient ◽  
Moisture Contents ◽  
Calibration Models

Abstract Background: Peucedani Radix is a popular traditional Chinese medicine herb with a long history in China. Praeruptorin A (PA), praeruptorin B (PB), and praeruptorin E (PE) are usually taken as important quality indexes of Peucedani Radix. Objective: To establish a rapid method for simultaneous determination of PA, PB, PE, and moisture contents in Peucedani Radix using near-infrared (NIR) spectroscopy and chemometrics. Methods: One hundred twenty Peucedani Radix samples were analyzed with HPLC as a reference method. The NIR spectral scanning range was from 12000 cm−1 to 4000 cm−1. Partial least squares (PLS) regression algorithm was used to establish calibration models. Three variable selection methods were investigated, including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and Monte Carlo uninformative variable elimination (MCUVE). The performances of the established models were evaluated by root-mean-square error (RMSEC) and determination coefficient (Rc2) of calibration set, root-mean-square error (RMSEP) and determination coefficient (Rp2) of prediction set, and residual predictive deviation (RPD). Results: A clear ranking of the performance of the calibration models could be as follows: CARS-PLS > MCUVE-PLS > VIP-PLS > Full-PLS. For CARS-PLS, Rp2, RMSEP, and RPD of the prediction set are as follows: 0.9204, 0.0860%, and 3.5850 for PA; 0.8011, 0.0431%, and 2.0868 for PB; 0.8043, 0.0367%, and 2.1569 for PE; and 0.9249, 0.3350%, and 3.6551 for moisture, respectively. Conclusions: The NIR spectroscopy combined with CARS-PLS calibration models could be used for rapid and accurate determination of PA, PB, PE, and moisture contents in Peucedani Radix samples.

scholarly journals Utilization of Picolinaldehyde-4-phenyl-3-thiosemicarbazone in Sodium Dodecylsulfate Micelles for the Spectrophotometric Determination of Iron, Vanadium, and Cobalt Following Partial Least-Squares Regression Analysis

2009 ◽  
Vol 92 (1) ◽  
pp. 248-256
Author(s):  
Aamna Balouch ◽  
Najma Memon ◽  
Muhammad I Bhanger ◽  
Muhammad Y Khuhawar
Keyword(s):  
Root Mean Square Error ◽  
Metal Ions ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sodium Dodecylsulfate ◽  
Mean Square ◽  
Least Squares Regression

Abstract Partial least-squares regression was applied for the simultaneous determination of iron, vanadium, and cobalt after complexation with picolinaldehyde-4-phenyl-3-thiosemicarbazone (PAPT) in the presence of anionic sodium dodecylsulfate (SDS) micelles. These 3 complexed metal ions exhibited overlapping spectra in the 390510 nm region with a maximum absorbance at 415 nm at pH 3.0 and enhanced absorbance in the presence of SDS. The data for the simultaneous determination of these metal ions were analyzed using a simple partial least-squares (SIMPLS) algorithm. Formation constants (log Kf) were found to be 4.65, 3.29, and 4.85 for PAPT complexes of Fe, V, and Co, respectively, and the detection limits for Fe, V, and Co were 0.013, 0.002, and 0.010 g/mL, respectively. Common anions and cations did not interfere with the proposed method. The method was validated by calculating root mean square error of cross-validation, root mean square error of calibration, and root mean square error of prediction and was applied to determine these 3 metal ions in real crude oil samples.

Rapid Determination of Paeoniflorin and Moisture in Xiaoyao Pills (Concentrated) by Near-Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1972-1977
Author(s):  
Yan Bai ◽  
Hai Yan Gong ◽  
Xiao Qing Li ◽  
Cai Xia Xie ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Least Square ◽  
Calibration Model ◽  
Mean Square

The objective of the present research was to establish a rapid analytical method for paeoniflorin and moisture in Xiaoyao Pills (condensed) by near-infrared spectroscopy. The near-infrared spectral data of 97 samples was collected by Nicolet 6700 NIR spectrograph,and the reference value of index component content were obtained by HPLC and oven-drying method. Then the multivariate calibration model of paeoniflorin and moisture were established by patrical least square (PLS) and predicting the content of unknow samples. The results showed that the correlation coefficients (R2) of the quantitative calibration model for paeoniflorin and moisture were 0.99774,0.95352, the root-mean-square error of calibration (RMSEC) were 0.00489,0.132,the root-mean-square error of prediction (RMSEP) were 0.00827,0.177. The results indicated that NIRS can provide a simple and accurate way for the fast determination of index component in large numbers of Xiaoyao Pills (concentrated).

scholarly journals Model Variasi Kalender pada Regresi Runtun Waktu untuk Peramalan Jumlah Pengunjung Grojogan Sewu

2021 ◽  
Vol 4 (2) ◽  
pp. 67
Author(s):  
Etik Zukhronah ◽  
Winita Sulandari ◽  
Isnandar Slamet ◽  
Sugiyanto Sugiyanto ◽  
Irwan Susanto
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Gregorian Calendar ◽  
Time Series Regression ◽  
Sarima Model ◽  
Sarima Models

<p><strong>Abstract.</strong> Grojogan Sewu visitors experience a significant increase during school holidays, year-end holidays, and also Eid al-Fitr holidays. The determination of Eid Al-Fitr uses the Hijriyah calendar so that the occurrence of Eid al-Fitr will progress 10 days when viewed from the Gregorian calendar, this causes calendar variations. The objective of this paper is to apply a calendar variation model based on time series regression and SARIMA models for forecasting the number of visitors in Grojogan Sewu. The data are Grojogan Sewu visitors from January 2009 until December 2019. The results show that time series regression with calendar variation yields a better forecast compared to the SARIMA model. It can be seen from the value of  root mean square error (<em>RMSE</em>) out-sample of time series regression with calendar variation is less than of SARIMA model.</p><p><strong>Keywords: </strong>Calendar variation, time series regression, SARIMA, Grojogan Sewu</p>

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

Sign in / Sign up

Export Citation Format

Share Document