Use of multivariate calibration models based on UV-Vis spectra for seawater quality monitoring in Tianjin Bohai Bay, China

2015 ◽  
Vol 71 (10) ◽  
pp. 1444-1450 ◽  
Author(s):  
Xianhua Liu ◽  
Lili Wang
Keyword(s):  
Latent Variables ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Oxygen Demand ◽  
Large Data ◽  
Quality Monitoring ◽  
Coefficient Of Determination ◽  
Bohai Bay ◽  
Calibration Models ◽  
Seawater Quality

A series of ultraviolet-visible (UV-Vis) spectra from seawater samples collected from sites along the coastline of Tianjin Bohai Bay in China were subjected to multivariate partial least squares (PLS) regression analysis. Calibration models were developed for monitoring chemical oxygen demand (COD) and concentrations of total organic carbon (TOC). Three different PLS models were developed using the spectra from raw samples (Model-1), diluted samples (Model-2), and diluted and raw samples combined (Model-3). Experimental results showed that: (i) possible nonlinearities in the signal concentration relationships were well accounted for by the multivariate PLS model; (ii) the predicted values of COD and TOC fit the analytical values well; the high correlation coefficients and small root mean squared error of cross-validation (RMSECV) showed that this method can be used for seawater quality monitoring; and (iii) compared with Model-1 and Model-2, Model-3 had the highest coefficient of determination (R2) and the lowest number of latent variables. This latter finding suggests that only large data sets that include data representing different combinations of conditions (i.e., various seawater matrices) will produce stable site-specific regressions. The results of this study illustrate the effectiveness of the proposed method and its potential for use as a seawater quality monitoring technique.

scholarly journals Fuel park wastewater monitoring with UV-Vis spectra and partial least squares models

2008 ◽  
Vol 27 (1) ◽  
pp. 19
Author(s):  
Nídia D. Lourenço ◽  
José C. Menezes ◽  
Helena M. Pinheiro ◽  
David Diniz
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Mean Squared Error ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Original Dataset ◽  
Calibration Models ◽  
Wastewater Quality ◽  
On Line ◽  
Determination Of Parameters

In the context of the high application potentials for on-line and in-line measurements in wastewater quality monitoring, UV-Vis spectrophotometry has received recent attention. In the present work the development of Partial Least Squares (PLS) calibration models for the fast determination of parameters legally required at the discharge of a fuel park wastewater treatment plant was attempted using UV-Vis spectra and the corresponding standard analytical values. A PLS model was developed and validated for Chemical Oxygen Demand (COD) determination with 3.8 mg O2/l RMSEP (root mean squared error of prediction). The models developed for 5-day Biochemical Oxygen Demand (BOD5) and Total Suspended Solids (TSS) were validated (3.5 mg O2/l and 2.9 mg/l RMSEP, respectively), but presented some predictive limitations mainly attributed to the narrow interval of concentration values present in the original dataset.

scholarly journals Raman Calibration Models for Chemical Species Determination in CO2-Loaded Aqueous MEA Solutions Using PLS and ANN Techniques

2021 ◽  
Vol 5 (4) ◽  
pp. 87
Author(s):  
Ahmad Syukri Hanafiah ◽  
Abdulhalim Shah Maulud ◽  
Muhammad Zubair Shahid ◽  
Humbul Suleman ◽  
Azizul Buang
Keyword(s):  
Carbon Capture ◽  
Mean Squared Error ◽  
Chemical Species ◽  
Operating Conditions ◽  
In Situ Monitoring ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Species Determination ◽  
Species Concentration ◽  
Calibration Models

The improvement in energy efficiency is recognized as one of the significant parameters for achieving our net-zero emissions target by 2050. One exciting area for development is conventional carbon capture technologies. Current amine absorption-based systems for carbon capture operate at suboptimal conditions resulting in an efficiency loss, causing a high operational expenditure. Knowledge of qualitative and quantitative speciation of CO2-loaded alkanolamine systems and their interactions can improve the equipment design and define optimal operating conditions. This work investigates the potential of Raman spectroscopy as an in situ monitoring tool for determining chemical species concentration in the CO2-loaded aqueous monoethanolamine (MEA) solutions. Experimental information on chemical speciation and vapour-liquid equilibrium was collected at a range of process parameters. Then, partial least squares (PLS) regression and an artificial neural network (ANN) were applied separately to develop two Raman species calibration models where the Kent–Eisenberg model correlated the species concentrations. The data were paired and randomly distributed into calibration and test datasets. A quantitative analysis based on the coefficient of determination (R2) and root mean squared error (RMSE) was performed to select the optimal model parameters for the PLS and ANN approach. The R2 values of above 0.90 are observed for both cases indicating that both regression techniques can satisfactorily predict species concentration. ANN models are slightly more accurate than PLS. However, PLS (being a white box model) allows the analysis of spectral variables using a weight plot.

Smart Multivariate Spectrophotometric Assisted Techniques for Simultaneous Determination of Ephedrine Hydrochloride and Naphazoline Nitrate in Presence of Interfering Parabens

2020 ◽  
Vol 16 ◽  
Author(s):  
Basma M. Eltanany ◽  
Aya A. Mouhamed ◽  
Nesrine T. Lamie ◽  
Nadia M. Mostafa
Keyword(s):  
Simultaneous Determination ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Principal Component Regression ◽  
Principal Component ◽  
Large Data ◽  
Resolving Power ◽  
Data Set ◽  
Ephedrine Hydrochloride

Background: Partial least squares (PLS) and principal component regression (PCR) are two well-known chemometric methods based on dimension reduction techniques. They can be very practical analyzing a large data set of multiple correlated predictor variables. Objective: In the presented work, the resolving power of spectrophotometric assisted mathematical techniques was implemented for the simultaneous determination of two active ingredients; ephedrine hydrochloride (EPH) and naphazoline nitrate (NAPH), in a matrix of excipients. Methods: To build the PLS and PCR models, a calibration set was prepared where the two drugs, in combination with the interfering parabens, were modeled by multilevel multifactor design. The proposed models successfully predicted the concentrations of both drugs in validation samples with low root mean squared error of prediction (RMSEP). Results: The results revealed the ability of the mentioned multivariate calibration models to analyze EPH and NAPH in presence of the interfering parabens with high selectivity in the concentration ranges of 4.00-20.00 µg mL-1 and 1.00-9.00 µg mL-1, respectively. Conclusion: A commercially available nasal spray was successfully analyzed using the developed methods without interfering with other dosage form additives.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals A Raman algorithm to estimate human age from protein structural variations in autopsy skin samples: a protein biological clock

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daisuke Miyamori ◽  
Takeshi Uemura ◽  
Wenliang Zhu ◽  
Kei Fujikawa ◽  
Takaaki Nakaya ◽  
...  
Keyword(s):  
Protein Folding ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
Biological Clock ◽  
Estimation Method ◽  
Forensic Identification ◽  
Random Coil ◽  
Coefficient Of Determination ◽  
Skin Samples

AbstractThe recent increase of the number of unidentified cadavers has become a serious problem throughout the world. As a simple and objective method for age estimation, we attempted to utilize Raman spectrometry for forensic identification. Raman spectroscopy is an optical-based vibrational spectroscopic technique that provides detailed information regarding a sample’s molecular composition and structures. Building upon our previous proof-of-concept study, we measured the Raman spectra of abdominal skin samples from 132 autopsy cases and the protein-folding intensity ratio, RPF, defined as the ratio between the Raman signals from a random coil an α-helix. There was a strong negative correlation between age and RPF with a Pearson correlation coefficient of r = 0.878. Four models, based on linear (RPF), squared (RPF2), sex, and RPF by sex interaction terms, were examined. The results of cross validation suggested that the second model including linear and squared terms was the best model with the lowest root mean squared error (11.3 years of age) and the highest coefficient of determination (0.743). Our results indicate that the there was a high correlation between the age and RPF and the Raman biological clock of protein folding can be used as a simple and objective forensic age estimation method for unidentified cadavers.

scholarly journals Adsorption and Photocatalytic Mineralization of Bromophenol Blue Dye with TiO2 Modified with Clinoptilolite/Activated Carbon

Catalysts ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Emmanuel Kweinor Tetteh ◽  
Sudesh Rathilal
Keyword(s):  
Activated Carbon ◽  
Photocatalytic Degradation ◽  
Oxygen Demand ◽  
Freundlich Isotherm ◽  
Reaction Rate Constant ◽  
Coefficient Of Determination ◽  
Bromophenol Blue ◽  
Blue Dye ◽  
Order Kinetic ◽  
Co Precipitation

This study presents a hybridized photocatalyst with adsorbate as a promising nanocomposite for photoremediation of wastewater. Photocatalytic degradation of bromophenol blue (BPB) in aqueous solution under UV-irradiation of wavelength 400 nm was carried out with TiO2 doped with activated carbon (A) and clinoptilolite (Z) via the co-precipitation technique. The physiochemical properties of the nanocomposite (A–TiO2 and Z–TiO2) and TiO2 were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier-transform infrared (FTIR) spectroscopy. Results of the nanocomposite (A–TiO2 and Z–TiO2) efficiency was compared to that with the TiO2, which demonstrated their adsorption and synergistic effect for the removal of chemical oxygen demand (COD) and color from the wastewater. At an optimal load of 4 g, the photocatalytic degradation activity (Z–TiO2 > A–TiO2 > TiO2) was found favorably by the second-order kinetic model. Consequently, the Langmuir adsorption isotherms favored the nanocomposites (Z–TiO2 > A–TiO2), whereas that of the TiO2 fitted very well on the Freundlich isotherm approach. Z–TiO2 evidently exhibited a high photocatalytic efficacy of decomposition over 80% of BPB (COD) at reaction rate constant (k) and coefficient of determination (R2) values of 5.63 × 10−4 min−1 and 0.989, respectively.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

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