scholarly journals Application of FTIR-ATR Spectrometry in Conjunction with Multivariate Regression Methods for Viscosity Prediction of Worn-Out Motor Oils

2021 ◽  
Vol 11 (9) ◽  
pp. 3842
Author(s):  
Marie Sejkorová ◽  
Marián Kučera ◽  
Ivana Hurtová ◽  
Ondřej Voltr
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Kinematic Viscosity ◽  
Principal Component ◽  
Laboratory Method ◽  
Infrared Spectrometry ◽  
Capillary Viscometer ◽  
Mean Square ◽  
Motor Oils

Viscosity is considered to be a key factor in the quality of lubrication by oil and engine manufacturers and is therefore one of the most monitored parameters of lubricants. FTIR (Fourier-transform infrared) spectrometry in combination with Partial Least Squares (PLS) and Principal Component Regression (PCR) was therefore proposed and tested as an alternative to the standardized method for determining the kinematic viscosity at 100 °C with an Ubbelohde capillary viscometer (CSN EN ISO 3104) of worn-out motor oil grade SAE 15W-40. The FTIR-PLS model in the spectral region of 1750–650 cm−1 with modification of the spectra by the second derivative proved to be the most suitable. A significant dependence of R = 0.95 was achieved between the viscosity values of 190 samples of worn-out motor oils, which were determined by a standardized laboratory method, and the values predicted by the FTIR-PLS model. The Root Mean Square Error of Calibration (RMSEC) parameter reached 0.148 mm2s−1 and the Root Mean Square Error of Prediction (RMSEP) parameter reached 0.190 mm2s−1. The proposed method for determining the kinematic viscosity at 100 °C by the FTIR-PLS model is faster compared to the determination according to the CSN EN ISO 3104 standard, requires a smaller amount of oil sample for analysis and produces less waste chemicals.

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.

Assessment of Pareto Calibration, Stability, and Wavelength Selection

2003 ◽  
Vol 57 (3) ◽  
pp. 309-316 ◽  
Author(s):  
Kelly J. Anderson ◽  
John H. Kalivas
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Principal Component Regression ◽  
Principal Component ◽  
Data Sets ◽  
Mean Square ◽  
Wavelength Selection ◽  
Pareto Model ◽  
Improve Analysis

Recent work has shown that ridge regression (RR) is Pareto to partial least squares (PLS) and principal component regression (PCR) when the variance indicator Euclidian norm of the regression coefficients, ‖p̂‖, is plotted against the bias indicator root mean square error of calibration (RMSEC). Simplex optimization demonstrates that RR is Pareto for several other spectral data sets when ‖p̂‖ is used with RMSEC and the root mean square error of evaluation (RMSEE) as optimization criteria. From this investigation, it was observed that while RR is Pareto optimal, PLS and PCR harmonious models are near equivalent to harmonious RR models. Additionally, it was found that RR is Pareto robust, i.e., models formed at one temperature were then used to predict samples at another temperature. Wavelength selection is commonly performed to improve analysis results such that bias indicators RMSEC, RMSEE, root mean square error of validation, or root mean square error of cross-validation decrease using a subset of wavelengths. Just as critical to an analysis of selected wavelengths is an assessment of variance. Using wavelengths deemed optimal in a previous study, this paper reports on the variance/bias tradeoff. An approach that forms the Pareto model with a Pareto wavelength subset is suggested.

scholarly journals Weather-Based Neural Network, Stepwise Linear and Sparse Regression Approach for Rabi Sorghum Yield Forecasting of Karnataka, India

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1645
Author(s):  
Shankarappa Sridhara ◽  
Nandini Ramesh ◽  
Pradeep Gopakkali ◽  
Bappa Das ◽  
Soumya Venkatappa ◽  
...  
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Ridge Regression ◽  
Principal Component ◽  
Growth Period ◽  
Weather Conditions ◽  
Mean Square ◽  
Yield Forecasting

Sorghum is an important dual-purpose crop of India grown for food and fodder. Prevailing weather conditions during the crop growth period determine the yield of sorghum. Hence, the crop yield forecasting models based on weather parameters will be an appropriate option for policymakers and researchers to develop sustainable cropping strategies. In the present study, six multivariate weather-based models viz., least absolute shrinkage and selection operator (LASSO), elastic net (ENET), principal component analysis (PCA) in combination with stepwise multiple linear regression (SMLR), artificial neural network (ANN) alone and in combination with PCA and ridge regression model are examined by fixing 90% of the data for calibration and remaining dataset for validation to forecast rabi sorghum yield for different districts of Karnataka. The R2 and root mean square error (RMSE) during calibration ranged between 0.42 to 0.98 and 30.48 to 304.17 kg ha−1, respectively, without actual evapotranspiration (AET) whereas, these evaluation parameters varied from 0.38 to 0.99 and 19.84 to 308.79 kg ha−1, respectively with AET inclusion. During validation, the RMSE and nRMSE (normalized root mean square error) varied between 88.99 to 1265.03 kg ha−1 and 4.49 to 96.84%, respectively without AET and including AET as one of the weather variable RMSE and nRMSE were 63.48 to 1172.01 kg ha−1 and 4.16 to 92.56%, respectively. The performance of six multivariate models revealed that LASSO was the best model followed by ENET compared to PCA_SMLR, ANN, PCA_ANN and ridge regression models because of reduced overfitting through penalisation of regression coefficient. Thus, it can be concluded that LASSO and ENET weather-based models can be effectively utilized for the district level forecast of sorghum yield.

scholarly journals Quantitative Analysis ofPanax ginsengby FT-NIR Spectroscopy

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xin-fang Xu ◽  
Li-xing Nie ◽  
Li-li Pan ◽  
Bian Hao ◽  
Shao-xiong Yuan ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Mean Square ◽  
Least Squares Regression ◽  
Regression Methods

Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides inPanax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R2) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R2) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control ofGinseng Radix et Rhizoma.

scholarly journals The classification of arabica gayo wine coffee using UV-visible spectroscopy and PCA-DA method

2018 ◽  
Vol 197 ◽  
pp. 09002 ◽  
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Visible Region ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Visible Spectroscopy ◽  
Uv Visible

The unique processing of Arabica Gayo Wine coffee produces special attributes to the beverage and could increase its value. However, it is important to prove the authenticity of Arabica Gayo Wine coffee using reliable methods. The objective of this study was to evaluate the potential of UV-visible spectroscopy and principal component analysis-discriminant analysis (PCA-DA) method for classification of ground roasted Arabica Gayo Wine coffee. A number of 200 samples of Arabica Gayo Wine coffee and 200 samples of Arabica Gayo normal (not Wine) coffee was used. The spectral data obtained in the UV-visible region were analyzed using PCA-DA with standard normal variate (SNV) and followed by Savitzky-Golay smoothing with different number of smoothing point (NSP). The results showed that the best PCA-DA model was obtained with NSP = 23 with coefficient of determination for calibration (R2) = 0.99, root mean square error of calibration (RMSEC) = 0.005692 and root mean square error of validation (RMSEV) = 0.006112. Using this model, a good classification between Gayo Wine and Gayo normal in prediction step was achieved with 100% accuracy, sensitivity and specificity. Thus, the proposed method can be used for the evaluation of authenticity of ground roasted Arabica Gayo Wine coffee.

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.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

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