Calibration Models Reveal Stability Concerns at Marshall Lake Dam

2018 ◽  
Author(s):  
Ryan Schoolmeesters
Keyword(s):  
Calibration Models

scholarly journals Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4214
Author(s):  
Christopher Zuidema ◽  
Cooper S. Schumacher ◽  
Elena Austin ◽  
Graeme Carvlin ◽  
Timothy V. Larson ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Exposure Assessment ◽  
Epidemiological Study ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Puget Sound ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Calibration Models ◽  
Study Participants

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Home Blood Pressure Monitoring Devices: Device Performance in an Alaska Native and American Indian Population

2021 ◽  
Vol 33 (7-8_suppl) ◽  
pp. 40S-50S
Author(s):  
Krista R. Schaefer ◽  
Amber L. Fyfe-Johnson ◽  
Carolyn J. Noonan ◽  
Michael R. Todd ◽  
Jason G. Umans ◽  
...  
Keyword(s):  
Blood Pressure ◽  
American Indian ◽  
Alaska Native ◽  
Blood Pressure Monitoring ◽  
Intraclass Correlation ◽  
Home Blood Pressure ◽  
Home Blood Pressure Monitoring ◽  
Pressure Monitoring ◽  
Calibration Models ◽  
American Indian People

Objectives: Home blood pressure monitoring (HBPM) is an important component of blood pressure (BP) management. We assessed performance of two HBPM devices among Alaska Native and American Indian people (ANAIs). Methods: We measured BP using Omron BP786 arm cuff, Omron BP654 wrist cuff, and Baum aneroid sphygmomanometer in 100 ANAIs. Performance was assessed with intraclass correlation, paired t-tests, and calibration models. Results: Compared to sphygmomanometer, average BP was higher for wrist cuff (systolic = 4.8 mmHg and diastolic = 3.6 mmHg) and varied for arm cuff (systolic = −1.5 mmHg and diastolic = 2.5 mmHg). Calibration increased performance from grade B to A for arm cuff and from D to B for wrist cuff. Calibration increased false negatives and decreased false positives. Discussion: The arm HBPM device is more accurate than the wrist cuff among ANAIs with hypertension. Most patients are willing to use the arm cuff when accuracy is discussed.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

Strategy for constructing robust multivariate calibration models

1999 ◽  
Vol 49 (1) ◽  
pp. 1-17 ◽  
Author(s):  
H. Swierenga ◽  
A.P. de Weijer ◽  
R.J. van Wijk ◽  
L.M.C. Buydens
Keyword(s):  
Multivariate Calibration ◽  
Calibration Models

scholarly journals Development of Calibration Models to Predict Mean Fibre Diameter in Llama (Lama glama) Fleeces with Near Infrared Spectroscopy

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1998
Author(s):  
José Ignacio Amorena ◽  
Dolores María Eugenia Álvarez ◽  
Elvira Fernández-Ahumada
Keyword(s):  
Textile Industry ◽  
Near Infrared ◽  
Prediction Models ◽  
External Validation ◽  
Fibre Diameter ◽  
Near Infrared Reflectance ◽  
Screening Programs ◽  
Spectral Signatures ◽  
Calibration Models ◽  
Cross Validation Error

Llama fibre has the potential to become the most valuable textile resource in the Puna region of Argentina. In this study near infrared reflectance spectroscopy was evaluated to predict the mean fibre diameter in llama fleeces. Analyses between sets of carded and non-carded samples in combination with spectral preprocessing techniques were carried out and a total of 169 spectral signatures of llama samples in Vis and NIR ranges (400–2500 nm) were obtained. Spectral preprocessing consisted in wavelength selection (Vis–NIR, NIR and discrete ranges) and multiplicative and derivative pretreatments; spectra without pretreatments were also included, while modified partial least squares (M-PLS) regression was used to develop prediction models. Predictability was evaluated through R2: standard cross validation error (SECV), external validation error (SEV) and residual predictive value (RPD). A total of 54 calibration models were developed in which the best model (R2 = 0.67; SECV = 1.965; SEV = 2.235 and RPD = 1.91) was obtained in the Vis–NIR range applying the first derivative pretreatment. ANOVA analysis showed differences between carded and non-carded sets and the models obtained could be used in screening programs and contribute to valorisation of llama fibre and sustainable development of textile industry in the Puna territory of Catamarca. The data presented in this paper are a contribution to enhance the scarce information on this subject.

Evaluation of different validation strategies and long term effects in NIR calibration models

2013 ◽  
Vol 141 (3) ◽  
pp. 2639-2648 ◽  
Author(s):  
Valeria Sileoni ◽  
Ombretta Marconi ◽  
Giuseppe Perretti ◽  
Paolo Fantozzi
Keyword(s):  
Long Term Effects ◽  
Calibration Models
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