Online Chemical Sensor Signal Processing Using Estimation Theory: Quantification of Binary Mixtures of Organic Compounds in the Presence of Linear Baseline Drift and Outliers

Sothivelr, Karthick; Bender, Florian; Josse, Fabien; Yaz, E.; Ricco, Antonio J.; Mohler, Rachel E.

doi:10.1109/jsen.2015.2485784

Cited by 8 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where Δf(t) is the observed frequency shift at time t, the subscript i = 1, 2, •••, n refers to each analyte in the mixture, a i is a sensitivity parameter for each analyte (which is a function of the sensor platform, the sensor coating, and the analyte 25 ), and C i (t) is the concentration of each analyte in the coating at time t. C i (t) is the solution to the following first-order differential equation…”

Section: Acs Sensorsmentioning

confidence: 99%

“…where C amb,i (t) is the ambient concentration of each analyte at time t (which for these experiments remains constant due to the constant flow of fresh analyte-containing sample solution throughout the measurement), τ i is the response time constant of each analyte for a given coating, and K p−w,i is the polymer/ water partition coefficient of each analyte for a given coating. 25 Equation 1 represents the general analytical model for the sensor response to any number of analytes in the sample, provided that each possible target analyte and all interferents in the sample have been separately characterized for the appropriately selected coating in the relevant concentration range.…”

Section: Acs Sensorsmentioning

confidence: 99%

“…Second, Henry’s law behavior along with comparatively low analyte concentrations dissolved in the polymer film ensure that, at any time t , the change in frequency due to the mixture is the sum of the frequency changes due to each analyte in the mixture. Based on these two assumptions, the sensor response to a mixture of n analytes can be written as where Δ f ( t ) is the observed frequency shift at time t , the subscript i = 1, 2, ···, n refers to each analyte in the mixture, a i is a sensitivity parameter for each analyte (which is a function of the sensor platform, the sensor coating, and the analyte), and C i ( t ) is the concentration of each analyte in the coating at time t . C i ( t ) is the solution to the following first-order differential equation where C amb, i ( t ) is the ambient concentration of each analyte at time t (which for these experiments remains constant due to the constant flow of fresh analyte-containing sample solution throughout the measurement), τ i is the response time constant of each analyte for a given coating, and K p – w ,i is the polymer/water partition coefficient of each analyte for a given coating .…”

Section: Modeling the Sensor Responsementioning

confidence: 99%

“…Based on these two assumptions, the sensor response to a mixture of n analytes can be written as where Δ f ( t ) is the observed frequency shift at time t , the subscript i = 1, 2, ···, n refers to each analyte in the mixture, a i is a sensitivity parameter for each analyte (which is a function of the sensor platform, the sensor coating, and the analyte), and C i ( t ) is the concentration of each analyte in the coating at time t . C i ( t ) is the solution to the following first-order differential equation where C amb, i ( t ) is the ambient concentration of each analyte at time t (which for these experiments remains constant due to the constant flow of fresh analyte-containing sample solution throughout the measurement), τ i is the response time constant of each analyte for a given coating, and K p – w ,i is the polymer/water partition coefficient of each analyte for a given coating . Equation 1 represents the general analytical model for the sensor response to any number of analytes in the sample, provided that each possible target analyte and all interferents in the sample have been separately characterized for the appropriately selected coating in the relevant concentration range.…”

Section: Modeling the Sensor Responsementioning

confidence: 99%

See 3 more Smart Citations

Obtaining Chemical Selectivity from a Single, Nonselective Sensing Film: Two-Stage Adaptive Estimation Scheme with Multiparameter Measurement to Quantify Mixture Components and Interferents

et al. 2018

Self Cite

View full text Add to dashboard Cite

A new approach is reported to detect and quantify the members of a group of small-aromatic-molecule target analytes: benzene, toluene, ethylbenzene, and xylenes (BTEX), dissolved in water, in the presence of interferents, using only the data collected from a single polymer-coated SH-SAW (shear horizontal surface acoustic wave) device and a two-stage adaptive estimation scheme. This technique is composed of exponentially weighted recursive least-squares estimation (EW-RLSE) and a bank of Kalman filters (BKFs) and does not require any prior knowledge of the initial concentration range of the target analytes. The proposed approach utilizes the transient sensor response to sorption and/or desorption of the analytes as well as the error range associated with the response time constants to provide more information about the analyte-specific interactions with the polymer film. The approach assumes that the sensor response to contaminated groundwater is a linear combination of the responses to the single target analytes, the interferents that interact with the selected polymer sensor coatings, and measurement noise. The proposed technique was tested using actual sensor responses to contaminated groundwater samples containing multiple BTEX compounds with concentrations ranging from 10 to 2000 parts per billion, as well as common interferents including ethanol, 1,2,4-trimethylbenzene, naphthalene, n-heptane, and MTBE (methyl tert-butyl ether). Estimated concentration values, accurate to ±10% for benzene/toluene and ±15% for ethylbenzene/xylenes, are obtained in near-real time. The utilization of sorption and/or desorption data enables detection and quantification of BTEX compounds with improved accuracy, high tolerance to measurement noise, and improved chemical selectivity.

show abstract

Section: Acs Sensorsmentioning

confidence: 99%

Section: Acs Sensorsmentioning

confidence: 99%

Section: Modeling the Sensor Responsementioning

confidence: 99%

Section: Modeling the Sensor Responsementioning

confidence: 99%

See 2 more Smart Citations

Obtaining Chemical Selectivity from a Single, Nonselective Sensing Film: Two-Stage Adaptive Estimation Scheme with Multiparameter Measurement to Quantify Mixture Components and Interferents

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Note that the use of EW-RLSE for estimating equilibrium frequency shifts requires an accurate analytical model that describes the response of the SH-SAW sensor to the multi-analyte samples. This model follows directly from the model of single-analyte samples and binary mixtures of analytes, which has been discussed in detail in a previous publication [3]. The sensor response model to a mixture of n analytes can be represented by the following equation:…”

Section: Section II Proposed Signal Processing Proceduresmentioning

confidence: 99%

Detection and Quantification of Multi-Analyte Mixtures Using a Single Sensor and Multi-Stage Data-Weighted RLSE

Sothivelr

Bender

Josse

et al. 2018

2018 IEEE International Frequency Control Symposium (IFCS)

Self Cite

View full text Add to dashboard Cite

show abstract

Solving Sensor Reading Drifting Using Denoising Data Processing Algorithm (DDPA) for Long-Term Continuous and Accurate Monitoring of Ammonium in Wastewater

et al. 2020

View full text Add to dashboard Cite

Sensor reading drifting caused by sensor property deterioration is a major problem of long-term continuous monitoring in wastewater and hinders wide-range application of online wastewater management. This study aims to tackle this problem by developing denoising data processing algorithm (DDPA) for a typical electrochemical sensor, solid-state ion-selective membrane (S-ISM) sensor. Based on data mining and electrochemical principles, DDPA was designed by combining digital filter and outlier analysis to differentiate actual sensor readings from background noise when the S-ISM sensitivity declined over time. The sensor sensitivity was raised from 21 mV/dec to 55 mV/dec after the reading processing, without compromising the detection limit (7 × 10–6 mol/L). Furthermore, long-term accuracy of S-ISM sensors in wastewater was enhanced by adding hydrophobic polytetrafluoroethylene (PTFE) into polymer matrix. The sensitivity (57 mV/dec) of PTFE-loaded S-ISM sensors was the near-theoretical value on the first day and still higher than 35 mV/dec after 24 days in wastewater, providing an excellent stable baseline for DDPA. Combination of sensor material enhancement (adding PTFE) with sensor reading processing (using DDPA) assured the stable and high sensitivity (55 mV/dec after 24 days) and high detection limit (<5 × 10–5 mol/L) for wastewater monitoring. The study demonstrates a new route toward long-term accurate wastewater monitoring and smart wastewater sensor networks by establishing a strong correlation between multiorder derivatives of sensor readings and electrochemical responses with DDPA as an efficient data analysis approach.

show abstract

Online Chemical Sensor Signal Processing Using Estimation Theory: Quantification of Binary Mixtures of Organic Compounds in the Presence of Linear Baseline Drift and Outliers

Cited by 8 publications

References 14 publications

Obtaining Chemical Selectivity from a Single, Nonselective Sensing Film: Two-Stage Adaptive Estimation Scheme with Multiparameter Measurement to Quantify Mixture Components and Interferents

Obtaining Chemical Selectivity from a Single, Nonselective Sensing Film: Two-Stage Adaptive Estimation Scheme with Multiparameter Measurement to Quantify Mixture Components and Interferents

Detection and Quantification of Multi-Analyte Mixtures Using a Single Sensor and Multi-Stage Data-Weighted RLSE

Solving Sensor Reading Drifting Using Denoising Data Processing Algorithm (DDPA) for Long-Term Continuous and Accurate Monitoring of Ammonium in Wastewater

Contact Info

Product

Resources

About