Field Nitrogen Dioxide and Ozone Monitoring Using Electrochemical Sensors with Partial Least Squares Regression

2021 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Rachid Laref ◽  
Etienne Losson ◽  
Alexandre Sava ◽  
Maryam Siadat
Keyword(s):  
Nitrogen Dioxide ◽  
Electrochemical Sensors ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Monitoring Networks ◽  
Concentration Estimation ◽  
The Impact

Low-cost gas sensors detect pollutants gas at the parts-per-billion level and may be installed in small devices to densify air quality monitoring networks for the spread analysis of pollutants around an emissive source. However, these sensors suffer from several issues such as the impact of environmental factors and cross-interfering gases. For instance, the ozone (O3) electrochemical sensor senses nitrogen dioxide (NO2) and O3 simultaneously without discrimination. Alphasense proposes the use of a pair of sensors; the first one, NO2-B43F, is equipped with a filter dedicated to measure NO2. The second one, OX-B431, is sensitive to both NO2 and O3. Thus, O3 concentration can be obtained by subtracting the concentration of NO2 from the sum of the two concentrations. This technique is not practical and requires calibrating each sensor individually, leading to biased concentration estimation. In this paper, we propose Partial Least Square regression (PLS) to build a calibration model including both sensors’ responses and also temperature and humidity variations. The results obtained from data collected in the field for two months show that PLS regression provides better gas concentration estimation in terms of accuracy than calibrating each sensor individually.

scholarly journals Spectrophotometric determination of phenol in the presence of congeners by multivariated calibration

2001 ◽  
Vol 73 (4) ◽  
pp. 519-524 ◽  
Author(s):  
KELY VIVIANE DE SOUZA ◽  
PATRICIO PERALTA-ZAMORA
Keyword(s):  
Spectrophotometric Determination ◽  
Multivariate Calibration ◽  
Close Correlation ◽  
Degradation Process ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Calibration Model ◽  
Transient Species

The generation of poly-hydroxilated transient species during the photochemical treatment of phenol usually impedes the spectrophotmetric monitoring of its degradation process. Frequently, the appearance of compounds such as pyrocatechol, hydroquinone and benzoquinone produces serious spectral interference, which hinder the use of the classical univariate calibration process. In this work, the use of multivariate calibration is proposed to permit the spectrophotometric determination of phenol in the presence of these intermediates. Using 20 synthetic mixtures containing phenol and the interferents, a calibration model was developed by using a partial least square regression process (PLSR) and processing the absorbance signal between 180 and 300 nm. The model was validated by using 3 synthetic mixtures. In this operation, typical errors lower than 3% were observed. Close correlation between the results obtained by liquid chromatography and the proposed method was also observed.

scholarly journals Detection of malaria in insectary-reared Anopheles gambiae using near-infrared spectroscopy

2019 ◽  
Author(s):  
Marta F. Maia ◽  
Melissa Kapulu ◽  
Michelle Muthui ◽  
Martin G. Wagah ◽  
Heather M. Ferguson ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Anopheles Gambiae ◽  
Near Infrared Spectroscopy ◽  
Large Scale ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Human Malaria ◽  
The Impact

AbstractLarge-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. This study demonstrates proof-of-concept that near-infrared spectroscopy (NIRS) is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. By using partial least square regression models based on malaria-infected and uninfected Anopheles gambiae mosquitoes, we showed that NIRS can detect oocyst- and sporozoite-stage Plasmodium falciparum infections with 88% and 95% accuracy, respectively. Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

A Local Model and Calibration Set Ensemble Strategy for Open-Path FTIR Gas Measurement with Varying Temperature

2013 ◽  
Vol 20 (3) ◽  
pp. 513-524 ◽  
Author(s):  
Sławomir Cięszczyk
Keyword(s):  
Ensemble Methods ◽  
Partial Least Square ◽  
Least Square ◽  
Atmospheric Pollutants ◽  
Partial Least Square Regression ◽  
Local Model ◽  
Gas Concentration ◽  
Open Path ◽  
Ensemble Strategy ◽  
The Impact

Abstract Open-Path Fourier Transform Infrared OP-FTIR spectrometers are commonly used for the measurement of atmospheric pollutants and of gases in industrial processes. Spectral interpretation for the determination of gas concentrations is based on the HITRAN database line-by-line modeling method. This article describes algorithms used to model gas spectra and to determine gas concentration under variable temperatures. Integration of individual rotational lines has been used to reduce the impact of spectrometer functions on the comparison of both measured and synthetic modeled spectra. Carbon monoxide was used as an example. A new algorithm for gas concentration retrieval consisting of two ensemble methods is proposed. The first method uses an ensemble of local models based on linear and non-linear PLS (partial least square) regression algorithms, while the second is an ensemble of a calibration set built for different temperatures. It is possible to combine these methods to decrease the number of regression models in the first ensemble. These individual models are appropriate for specific measurement conditions specified by the ensemble of the calibration set. Model selection is based on comparison of gas spectra with values determined from each local model

A Consensus Partial Least Squares Regression for Analysis of Near-Infrared Spectroscopy

2013 ◽  
Vol 765-767 ◽  
pp. 528-531
Author(s):  
Dan Peng ◽  
Qing Chen Nie
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Squares Regression ◽  
Prediction Ability ◽  
Successful Model ◽  
Consensus Strategy

To improve the prediction performance of partial least square regression algorithm (PLS), the consensus strategy was applied to develop the multivariate regression model using near-infrared (NIR) spectra and named as C-PLS. Coupled with the consensus strategy, this algorithm can take the advantage of reducing dependence on single model to obtain prediction precision and stability by randomly changing the calibration set. Through an optimization of the parameters involved in the model including criterion threshold and number of sub-models, a successful model was achieved by effectively combining many sub-models with different accuracy and diversity together. To validate the C-PLS algorithm, it was applied to measure the original extract concentration of beer using NIR spectra. The experimental results showed that the prediction ability and robustness of model obtained in subsequent partial least squares calibration using consensus strategy was superior to that obtained using conventional PLS algorithm, and the root mean square error of prediction can improve by up to 45.2%, indicating that it is an efficient tool for NIR spectra regression.

scholarly journals Predicting ambient aerosol thermal–optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

2016 ◽  
Vol 9 (2) ◽  
pp. 441-454 ◽  
Author(s):  
Matteo Reggente ◽  
Ann M. Dillner ◽  
Satoshi Takahama
Keyword(s):  
Mahalanobis Distance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Optical Methods ◽  
Calibration Model ◽  
Optical Reflectance ◽  
Wide Range ◽  
Ft Ir ◽  
Absorbance Spectra

Abstract. Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change, and reduced visibility. Typically OC and EC concentrations are measured using thermal–optical methods such as thermal–optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the functional group composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2 = 0.97 and R2 = 0.95 for OC and EC respectively), and for samples from 9 of the 11 sites not included in the calibration set (R2 = 0.96 and R2 = 0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error; we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the magnitude of mean error when applying a calibration model to a new set of samples.

scholarly journals Managing brand identity: effects on the employees

2017 ◽  
Vol 35 (1) ◽  
pp. 2-23 ◽  
Author(s):  
Rafael Bravo ◽  
Isabel Buil ◽  
Leslie de Chernatony ◽  
Eva Martínez
Keyword(s):  
Financial Services ◽  
Identity Management ◽  
Employee Attitudes ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Brand Identity ◽  
Content Type ◽  
The Uk ◽  
The Impact

Purpose The purpose of this paper is to better understand the brand identity management process from the employees’ perspective. Specifically, it explores how the different dimensions of brand identity management influence employees’ attitudinal and behavioural responses. Design/methodology/approach An empirical study was carried out to test the proposed model. The sample consisted of 297 employees in the UK financial services sector. Hypothesis testing was conducted using partial least square regression. Findings Results indicate that effective brand identity management can increase employees’ identification with their organisations. Specifically, the most influential dimension is the employee-client focus. Results also show that organisational identification is a key variable to explain job satisfaction, word-of-mouth and brand citizenship behaviour. Research limitations/implications This study focusses on the UK financial sector. To explore the generalisability of results, replication studies among other sectors and countries would be useful. The cross-sectional nature of the study also limits its causal inference. Practical implications This study shows the importance of brand identity management to foster positive employee attitudes and actions that go beyond their job responsibilities. The model developed may help organisations analyse the impact of managerial actions, monitoring the potential effects of changes in brand identity management amongst employees. Originality/value Although numerous conceptual frameworks highlight the importance of brand identity management, empirical studies in this area are scarce. The current work extends previous research by empirically analysing the effects of the dimensions of brand identity management from the employees’ perspective.

scholarly journals Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3581
Author(s):  
Rachid Laref ◽  
Etienne Losson ◽  
Alexandre Sava ◽  
Maryam Siadat
Keyword(s):  
Nitrogen Dioxide ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Correction Method ◽  
Climatic Conditions ◽  
Estimation Accuracy ◽  
Calibration Model ◽  
Monitoring Networks ◽  
Correction Model ◽  
Almost All

This paper investigates the long term drift phenomenon affecting electrochemical sensors used in real environmental conditions to monitor the nitrogen dioxide concentration [NO2]. Electrochemical sensors are low-cost gas sensors able to detect pollutant gas at part per billion level and may be employed to enhance the air quality monitoring networks. However, they suffer from many forms of drift caused by climatic parameter variations, interfering gases and aging. Therefore, they require frequent, expensive and time-consuming calibrations, which constitute the main obstacle to the exploitation of these kinds of sensors. This paper proposes an empirical, linear and unsupervised drift correction model, allowing to extend the time between two successive full calibrations. First, a calibration model is established based on multiple linear regression. The influence of the air temperature and humidity is considered. Then, a correction model is proposed to solve the drift related to age issue. The slope and the intercept of the correction model compensate the change over time of the sensors’ sensitivity and baseline, respectively. The parameters of the correction model are identified using particle swarm optimization (PSO). Data considered in this work are continuously collected onsite close to a highway crossing Metz City (France) during a period of 6 months (July to December 2018) covering almost all the climatic conditions in this region. Experimental results show that the suggested correction model allows maintaining an adequate [NO2] estimation accuracy for at least 3 consecutive months without needing any labeled data for the recalibration.

scholarly journals Mass-immigration determines the assembly of activated sludge microbial communities

2021 ◽  
Vol 118 (27) ◽  
pp. e2021589118
Author(s):  
Giulia Dottorini ◽  
Thomas Yssing Michaelsen ◽  
Sergey Kucheryavskiy ◽  
Kasper Skytte Andersen ◽  
Jannie Munk Kristensen ◽  
...  
Keyword(s):  
Activated Sludge ◽  
Microbial Communities ◽  
Abundant Species ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Similar Process ◽  
Microbial Composition ◽  
Species Abundances ◽  
The Impact

The assembly of bacterial communities in wastewater treatment plants (WWTPs) is affected by immigration via wastewater streams, but the impact and extent of bacterial immigrants are still unknown. Here, we quantify the effect of immigration at the species level in 11 Danish full-scale activated sludge (AS) plants. All plants have different source communities but have very similar process design, defining the same overall environmental growth conditions. The AS community composition in each plant was strongly reflected by the corresponding influent wastewater (IWW) microbial composition. Most species in AS across the plants were detected and quantified in the corresponding IWW, allowing us to identify their fate in the AS: growing, disappearing, or surviving. Most of the abundant species in IWW disappeared in AS, so their presence in the AS biomass was only due to continuous mass-immigration. In AS, most of the abundant growing species were present in the IWW at very low abundances. We predicted the AS species abundances from their abundance in IWW by using a partial least square regression model. Some species in AS were predicted by their own abundance in IWW, while others by multiple species abundances. Detailed analyses of functional guilds revealed different prediction patterns for different species. We show, in contrast to the present understanding, that the AS microbial communities were strongly controlled by the IWW source community and could be quantitatively predicted by taking into account immigration. This highlights a need to revise the way we understand, design, and manage the microbial communities in WWTPs.

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