Manufacturer's Calibration Research Articles

The Effect of Soil Iron on the Estimation of Soil Water Content Using Dielectric Sensors

Nowadays, the estimation of volumetric soil water content (θ) through apparent dielectric permittivity (εa) is the most widely used method. The purpose of this study is to investigate the effect of the high iron content of two sandy loam soils on estimating their water content using two dielectric sensors. These sensors are the WET sensor operating at 20 MHz and the ML2 sensor operating at 100 MHz. Experiments on specific soil columns, in the laboratory, by mixing different amounts of water in the soils to obtain a range of θ values under constant temperature conditions were conducted. Analysis of the results showed that both sensors, based on manufacturer calibration, led to overestimation of θ. This overestimation is due to the high measured values of εa by both sensors used. The WET sensor, operating at a lower frequency and being strongly affected by soil characteristics, showed the greatest overestimation. The difference of εa values between the two sensors ranged from 14 to 19 units at the maximum actual soil water content (θm). Compared to the Topp equation, the WET sensor measures 2.3 to 2.8 fold higher value of εa. From the results, it was shown that the relationship θm-εa0.5 remained linear even in the case of these soils with high iron content and the multi-point calibration (CALALL) is a good option where individual calibration is needed.

Low-dose-rate iodine-125 seed air kerma strength measurement intercomparison

PurposeThe purpose of this study was to investigate the rate of compliance of air kerma strength (AKS) measurements of iodine-125 (I-125) seeds with international recommendations by departments in Australia and determine the potential impact of noncompliance. Methods and MaterialsTo achieve this aim, we present an intercomparison of AKS measurements for a single I-125 seed performed by 11 radiotherapy departments in Australia. Measurements were performed at two sites, with each participating department traveling to one of the two host sites and measuring the AKS using their own equipment and local protocols. Each of the AKS measurements was compared with each other and the manufacturer-certified AKS. ResultsNine of the 11 participating departments measured AKS fell within ±3% of the manufacturer's calibration certificate value, whereas all participating departments measured AKS within ±5% of the manufacturer's calibration certificate value. The total spread of the measured AKS among the 11 departments was 7.7%. Only two of the 11 participating departments complied with international recommendations and had their well chamber calibrated within the last 2 years. In addition, 2 of the 11 departments used a well chamber calibrated that was calibrated with a different seed model used during the intercomparison, whereas 4 of the 11 departments calibrated their well chamber “in-house” using a factory-calibrated seed provided by the seed manufacturer. ConclusionsA significant variation in the methods used and frequency of calibration of well chambers were observed among the participating departments. The results of this study support the international recommendations on frequency and methodology of well chamber calibration. Failure to follow these recommendations significantly increases the uncertainty in AKS measurement of I-125 seeds.

Chlorophyll fluorescence as measured in situ by animal-borne instruments in the northeastern Pacific Ocean

Abstract Chlorophyll concentration in the ocean is a metric for phytoplankton biomass, which forms the base of most pelagic food webs and is a critical component of the planet's carbon cycle. Phytoplankton biomass has been designated an Essential Ocean Variable (EOV), but in situ chlorophyll measurements are challenging and expensive to obtain, especially in remote regions. We deployed 11 Conductivity-Temperature-Depth-Fluorescence (CTDF) instruments on 15 northern elephant seals (Mirounga angustirostris) during 2014, 2018, and 2019 collecting 2532 temperature, salinity, and chlorophyll fluorescence profiles that exceeded 180 m depth and extended over 2000 km from the nearest landmass. The tags' fluorometers were calibrated in a laboratory and a controlled field setting approximating northeastern Pacific phytoplankton assemblages and conditions, resulting in adjustments to manufacturer calibration curves ranging from −4% to 81%. Subsurface chlorophyll maxima were observed below the first optical depth in 69.2% of the casts, of which 24.9% were deep chlorophyll maxima below the mixed layer. Established fluorescence quenching correction methods were not applicable to this dataset; however, time of day did not introduce a consistent bias to our results. In situ chlorophyll measurements were on average 5.24 times higher than coincident satellite measurements. This in situ chlorophyll dataset measures an EOV in high resolution in remote regions at low cost, a valuable contribution to the Global Ocean Observing System (GOOS). Regional index terms: North Pacific Ocean [35–50°N, 120–180°W].

Field performance assessment and calibration of multi-depth AquaCheck capacitance-based soil moisture probes under permanent pasture for hill country soils

The use of accurate and near real time volumetric soil moisture (θv, m3 m−3) measurements informs not only precise irrigation scheduling but also decisions regarding fertiliser applications, feed supply and stock management in non-irrigated farming systems. To assist θv monitoring, capacitance-based AquaCheck (AquaCheck, South Africa) sensors are being increasingly used in precision agriculture applications. However, the performance of these sensors have not been investigated extensively under field conditions with dynamic soil moisture regimes.For evaluation, 20 probes (4 sensors/unit) were installed in predominantly silt loam soils on a pastoral hill country farm in the southern east coast of the North Island of New Zealand. Raw readings, and θv derived from a manufacturer-recommended calibration, were compared with 400 reference θv values over a one-year timespan (01/11/2016 – 01/12/2017). In addition, three custom calibration methods were developed and assessed, each tailored to suit the availability of soil information.Over all depths (100 to 400 mm), the manufacturer calibration resulted in θv readings with a mean RMSE of 0.106 m3 m−3, a mean MBE of -0.099 m3 m−3 (indicating underestimation), and a coefficient of determination (R2) of 0.58 when correlated to reference θv values. A single custom formula, relevant to the local soils resulted in an improved RMSE of 0.039 m3 m−3 and R2 = 0.58, while probe-specific calibrations achieved an RMSE of 0.029 m3 m−3 and R2 of 0.77. The application of sensor-specific calibration resulted in an RMSE of 0.019 m3 m−3 with R2 = 0.9. Temporal sensor responses to the magnitude of rainfall events closely agreed. Sensor performance and accuracy errors were observed to vary as a function of soil wetness, bulk density (ρb gcm-3) clay and total organic carbon (TOC) content. Increasing clay content and ρb resulted in increased raw output for a given θv whereas TOC demonstrated the opposite trend. These effects were significant (P value < 0.001) but eliminated by the sensor-specific custom calibration.

Simple and efficient thermal calibration for MEMS gyroscopes

Gyroscopes are now becoming one of the most sold MEMS sensors, given that the many applications that require their use are booming. In the medical field, gyroscopes can be found in Inertial Measurement Units used for the development of clinical tools that are dedicated to human-movement monitoring. However, MEMS gyroscopes are known to suffer from a drift phenomenon, which is mainly due to temperature variations. This drift dramatically affects measurement capability, especially that of cheap MEMs gyroscopes. Calibration is therefore a key factor in achieving accurate measurements. However, traditional calibration procedures are often complex and require costly equipment. This paper therefore proposes an easy protocol for performing a thermal gyroscope calibration. In this protocol, accuracy over the angular velocity is evaluated by referring to an optoelectronic measurement, and is compared with the traditional calibration performed by the manufacturer. The RMSE between the reference angular velocity and that obtained with the proposed calibration was of 0.7°/s, which was slightly smaller than the RMSE of 1.1°/s achieved by the manufacturer's calibration. An analysis of uncertainty propagation shows that offset variability is the major source of error over the computed rate of rotation from the tested sensors, since it accounts for 97% of the error. It can be concluded that the proposed simple calibration method leads to a similar degree of accuracy as that achieved by the manufacturer's procedure.

Defining permissible limits for the combined uncertainty budget in the implementation of metrological traceability

In addition to the correct implementation of calibration traceability, the definition and the achievement of an appropriate analytical performance specification for the total uncertainty budget (GU) is essential to ensure that laboratory measurements are clinically usable. To understand if it is possible to fulfil these specifications, limits for combined uncertainty across the entire metrological traceability chain should be defined. We recommended that no more than one third of GU should be consumed by the uncertainty of higher order references and ≤50% of GU by the combined measurement uncertainty at the manufacturer's calibration level. The remaining allowable uncertainty should be available for random sources, i.e. for the imprecision of the commercial measuring system (including the reagent batch-to-batch variation) and the individual laboratory performance, as a safety margin to fulfil GU. Based on this approach, it is of interest to assess for each analyte measured in the clinical laboratory the status of the uncertainty budget of its measurement associated with the selected metrological traceability chain. Accordingly, we report three didactic cases that could occur in the clinical practice. This approach is very helpful to identify those analytes for which further technological improvements are probably needed to reduce uncertainty associated with their measurement.

Evaluating Commercial Moisture Probes in Reference Solutions Covering Mineral to Peat Soil Conditions

Core Ideas Two commercially available moisture probes were tested on reference solutions. Dielectric permittivity was measured across the range from 1 to about 80. Uncertainties increased with increasing dielectric permittivity. Electric conductivity influenced dielectric permittivity measurements. Capability of the probes to measure electric conductivities differed among the probe types. Capacitance and time domain reflectometry (TDR) probes are frequently used for measurements of the volumetric soil water content. The measurement concept is based on the correlation between volumetric water content and dielectric permittivity (ε). While considerable effort has been made to accurately measure ε in the typical range of mineral soils (&lt;40), little attention has been paid to the capability of moisture probes to measure high ε (&gt;40), typical for highly porous media like organic soils. We evaluated the capability of two moisture probe types (TRIME‐PICO 64 and GS3) to measure ε across the range from 1 to 80. In the case of the TRIME probes, different equations to calculate ε from transit times were tested. Measuring in a set of reference solutions, the TRIME probes had an RMSE of 18.73 for ε values derived using the manufacturer's calibration. With a new calibration, the RMSE was decreased to 3.55. The GS3 probes had an RMSE of 3.96. For both probes, uncertainties increased with increasing ε. We also tested the performance for different electrical conductivities of the reference solutions. Accuracy of ε values was unaffected by increasing conductivities for the TRIME probes but decreased for the GS3 probes. The GS3 probes, however, were able to determine electric conductivities accurately, while TRIME probes failed for electrical conductivity although indicated differently by the manufacturer.

Assessing the absolute quantitative accuracy of Positron Emission Tomography for Cu-64 using traceable calibrated phantoms

Using uniform cylindrical phantoms containing calibrated solutions of 18F and 64Cu, we evaluated for the first time the accuracy with which the activity concentration of 64Cu can be quantified on an absolute basis using Positron Emission Tomography (with X-ray Computed Tomography, PET-CT). The scanner was first calibrated for 18F using the manufacturer's calibration protocol and a phantom with an activity concentration value traceable to the U.S. National standard. By using a similarly calibrated 18F solution phantom, we were able to determine a correction factor that can be applied to the 64Cu imaging data that gave a result that is consistent with 100% recovery with a combined standard uncertainty of 2%. We also demonstrate how a calibrated, solid phantom containing 68Ge as a long-lived 18F surrogate can be used to monitor and transfer the correction factor to other studies.

Reduction of Blood Glucose Measurements to Calibrate Subcutaneous Glucose Sensors: A Bayesian Multiday Framework.

In most continuous glucose monitoring (CGM) devices used for diabetes management, the electrical signal measured by the sensor is transformed to glucose concentration by a calibration function whose parameters are estimated using self-monitoring of blood glucose (SMBG) samples. The calibration function is usually a linear model approximating the nonlinear relationship between electrical signal and glucose concentration in certain time intervals. Thus, CGM devices require frequent calibrations, usually twice a day. The aim here is to develop a new method able to reduce the frequency of calibrations. The algorithm is based on a multiple-day model of sensor time-variability with second-order statistical priors on its unknown parameters. In an online setting, these parameters are numerically determined by the Bayesian estimation exploiting SMBG sparsely collected by the patient. The method is assessed retrospectively on 108 CGM signals acquired for 7 days by the Dexcom G4 Platinum sensor, testing progressively less-calibration scenarios. Despite the reduction of calibration frequency (on average from 2/day to 0.25/day), the method shows a statistically significant accuracy improvement compared to manufacturer calibration, e.g., mean absolute relative difference when compared to a laboratory reference decreases from 12.83% to 11.62% (p-value of 0.006). The methodology maintains (sometimes improves) CGM sensor accuracy compared to that of the original manufacturer, while reducing the frequency of calibrations. Reducing the need of calibrations facilitates the adoption of CGM technology both in terms of ease of use and cost, an obvious prerequisite for its use as replacement of traditional SMBG devices.

Intraoral Scanning Systems: Need for Maintenance.

This study aimed to analyze a possible gain in accuracy in intraoral scanning systems by manufacturer calibration. A master model was scanned tenfold with a Lava Chairside Oral Scanner (3m ESPE) and a CEREC Bluecam suspected to be decalibrated. Thereafter the scanners were calibrated and the model was scanned again. An iTero system served as comparison. All scans were compared to the master scan, and the mean deviation (entire arch, single tooth) was calculated. After calibration, there was a significant gain in accuracy for both systems. Intraoral digital scanners may be sensitive to hardware decalibration invisible to the user and therefore need maintenance.

From Two to One Per Day Calibration of Dexcom G4 Platinum by a Time-Varying Day-Specific Bayesian Prior.

In the DexCom G4 Platinum (DG4P) continuous glucose monitoring (CGM) sensor, the raw current signal generated by glucose-oxidase is transformed to glucose concentration by a calibration function whose parameters are periodically updated by matching self-monitoring of blood glucose references, usually twice a day, to compensate for sensor variability in time. The aim of this work is to reduce DG4P calibration frequency to once a day by a recently proposed Bayesian calibration algorithm, which employs a time-varying calibration function and suitable day-specific priors. The database consists of 57 CGM signals that are collected by the DG4P for 7 days. The Bayesian calibration algorithm is used to calibrate the raw current signal following two different schedules, that is, two and one calibration per day. Calibrated glycemic profiles are compared with those originally acquired by the manufacturer, on days 1, 4, and 7, where frequent blood glucose references were available, by using standard metrics, that is, mean absolute relative difference (MARD), percentage of accurate glucose estimates, and percentage of data in the A-zone of Clarke Error Grid. The one per day Bayesian calibration algorithm has accuracy similar to that of two per day (11.8% vs. 11.7% MARD, respectively), and it is statistically better (P-value of 0.0411) than the manufacturer calibration algorithm, which requires two calibrations per day (13.1% MARD). A Bayesian calibration algorithm employing a time-varying calibration function and suitable priors enables a reduction of the calibrations of DG4P sensor from two to one per day.

Novel printed body worn sensor for measuring the human movement orientation

Purpose The purpose of this study is the measuring of the human movement using printed wearable sensor. Human movement measurement is one of the usages for wearable sensors. This technology assists the researchers to collect data from the daily activities of individuals. In other words, the kinematics data of human motion will be extracted from this data and implemented in biomechanical aspects. Design/methodology/approach This study presents an innovative printed wearable sensor which can be used for measuring human movement orientations. In this paper, the manufacturing process, implementation, measurement setup and calibration procedure of this new sensor will be explained, and the results of calibration methods will be presented. The conductive flexible nylon/lycra fabric strain gauge was developed using polypyrrole (PPy)–1, 5-naphthalenedisulfonic acid by using a sophisticated method composed of screen printing followed by chemical vapor deposition at room temperature. Findings The morphological characterization using scanning electron microscopy shows the PPy-coated fabric exhibiting a homogenous and smooth surface. Based on the results, the linearity and hysteresis error are 98 and 8 per cent, respectively. Finally, the behavior of our sensor is evaluated in some cases, and the effects of relaxation and strain rate will be discussed. Practical implications The wearable sensor is one of the most advanced technologies in biomedical engineering. It can be used in several applications for prohibition, diagnosing and treatment of diseases. Originality/value The paper present original data acquired from a technical set-up in biomechanic labs. An innovative method was used for collecting the resistance changing of the sensor. A measurement setup was prepared as a transducer to convert the resistance into voltage.

Soil moisture sensor calibration for organic soil surface layers

Abstract. This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finnish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology (HOBE). For the Decagon 5TE sensor such a function is currently not reported in the literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified. For the Decagon 5TE, apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large specific surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger nonlinearity in the sensor response and signal saturation in the high-level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here-proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and HOBE sites are based on the here-proposed natural log fit. The newly derived ThetaProbe fit functions should be used for hand-held applications only, but prove to be of value for the acquisition of instantaneous large-scale soil moisture estimates.

Inter-comparison of Low-cost Sensors for Measuring the Mass Concentration of Occupational Aerosols.

Low-cost sensors are effective for measuring the mass concentration of ambient aerosols and secondhand smoke in homes, but their use at concentrations relevant to occupational settings has not been demonstrated. We measured the concentrations of four aerosols (salt, Arizona road dust, welding fume, and diesel exhaust) with three types of low-cost sensors (a DC1700 from Dylos and two commodity sensors from Sharp), an aerosol photometer, and reference instruments at concentrations up to 6500 μg/m3. Raw output was used to assess sensor precision and develop equations to compute mass concentrations. EPA and NIOSH protocols were used to assess the mass concentrations estimated with low-cost sensors compared to reference instruments. The detection efficiency of the DC1700 ranged from 0.04% at 0.1 μm to 108% at 5 μm, as expected, although misclassification of fine and coarse particles was observed. The raw output of the DC1700 had higher precision (lower coefficient of variation, CV = 7.4%) than that of the two sharp devices (CV = 25% and 17%), a finding attributed to differences in manufacturer calibration. Aerosol type strongly influenced sensor response, indicating the need for on-site calibration to convert sensor output to mass concentration. Once calibrated, however, the mass concentration estimated with low-cost sensors was highly correlated with that of reference instruments (R2=0.99). These results suggest that the DC1700 and Sharp sensors are useful in estimating aerosol mass concentration for aerosols at concentrations relevant to the workplace.

Field Calibration of FDR Sensors-Limitations and Potentialities

Monitoring soil moisture content is an appropriate method to optimize agricultural irrigation. The use of capacitance sensors has increased over the last years because they have a convenient cost-benefit ratio when compared to neutron moisture gages or time domain reflectometers, which are the most accurate methods to estimate soil moisture content. This project is about the field calibration of a capacitance sensor in clay soils. Two calibration models were applied: a linear model and a quadratic one, which correlate the gravimetric content measured in field soil moisture samples with the excitation received by the sensor (mV) and with the measured dielectric permittivity (?b). The calibration equations were subsequently validated at two different depths at four field sampling sites. Despite the clay soil texture, high organic matter content and compaction of the soils, the equations correctly estimated soil moisture content with an estimation error of ± 0.02 m3m-3, a considerable improvement over the manufacturer's calibration equation, which has an average estimation error of ±0.12 m3m-3. A better understanding of sensor performance in the field should result in more precise water applications. A recommendation is made to perform tests in saline and different textured soils to ascertain the stability and sensitivity of these calibration equations.

An Approach Method to Calibrate the Autocollimator with Small Angle Measurement Range

A new method to calibrate the autocollimator with small angle measurement range has been developed. This method used a system based on rotary encoder for angle measurements with self-calibration function (SelfA47). The SelfA47 has 18,000 graduation lines with angle interval of 0.02°, corresponds to 72 arcsec. Thereby this system cannot be used to calibrate the autocollimator with measurement range <72 arcsec. On the other hand, SelfA47 can be used to calibrate an electronic level. The deviation of electronic level shaped linear gradient. Therefore a small nominal gradient of electronic level can be approximated by interpolation. Since the gradient of a curve corresponds to the angle quantities, it is feasible to calibrate the autocollimator with nominal angle <72 arcsec using SelfA47 and an electronic level. In this proposed method, SelfA47 is placed horizontally on the same plane with electronic level and mirror reflector position. The electronic level value that have been corrected by SelfA47 is used as a reference. This reference value was converted to the angle, thus the angle deviation of autocollimator can be determined. The prominent uncertainty of angle measurement using this new technique derived from the uncertainty of electronic level and rotary encoder. Using this method, the expanded uncertainty of 0.36 arcsec is obtained from calibration of autocollimator with measuring range of ±20 arcsec and it is consistent statistically with the manufacturer calibration result shown by E n value is <1. In conclusion, this approach method can be applied to calibrate the autocollimator with a resolution smaller than SelfA47 as standard, but it is not recommended for the calibration of highly accurate autocollimator.

Density-dependent calibration of multisensor capacitance probes in coarse soil

Zettl, J. D., Huang, M., Barbour, S. L. and Si, B. C. 2015. Density-dependent calibration of multisensor capacitance probes in coarse soil. Can. J. Soil Sci. 95: 331–336. Coarse-textured reconstructed soils are utilized extensively in the reclamation of mining waste. Accurate and continuous sensing of soil water content is required to understand soil water dynamics and evaluate the hydraulic characteristics of these soils. The EnviroSCAN (Sentek Pty. Ltd, Australia) is a semi-permanent multisensor capacitance probe (MCP) capable of continuous measurement of volumetric water content (θv) and has been used to monitor reclamation soil cover performance. Calibration of these probes is required to improve the accuracy of field measurements. In this study, field and laboratory measurements were undertaken over a range of water contents and bulk densities to refine the relationship between θvand scaled frequency (SF) measured by the MCP. The manufacturer's calibration equation tended to underestimate θvunder wet conditions (θv&gt;0.35 cm3cm–3). Our experimental data showed that bulk density (ρb) did affect the MCP calibration and consequently a new calibration equation that includes the effect of ρbis developed using laboratory measurements and validated using field measurements. This equation provided the highest degree of correlation and the smallest standard deviation of prediction to measured values of θvfor laboratory and field measurements, respectively. This calibration improves the application of the EnviroSCAN for coarse-textured soils such as those utilized in this study.

&lt;roman&gt;&lt;italic&gt;In Situ&lt;/italic&gt;&lt;/roman&gt; Calibration of Light Sensors for Long-Term Monitoring of Vegetation

Light sensors are increasingly used to monitor vegetation growing status by measuring reflectance or transmittance in multispectral or photosynthetically active radiation (PAR) bands. The measurements are then used to estimate vegetation indices or the fraction of absorbed PAR (FPAR) in a continuous and long-term manner and to serve as inputs to environmental monitoring and calibration/validation data for satellite remote sensing. However, light-sensor calibration is often overlooked or not properly attended to, which leads to difficulties when comparing the measurement results across sites and through time. In this paper, we investigate a practical and accurate user-level in situ calibration method in daylight. The calibration of a sensor pair is made for measuring either bihemispherical reflectance or hemispherical–conical reflectance, which are the two most common ground-based spectral measurements. Procedures and considerations are suggested for user calibration. We also provide a method for calibrating and measuring a single-sensor reflectance-derived Normalized Difference Vegetation Index (NDVI) from red and near-infrared bands. The calibration error propagation is analyzed, and the induced uncertainties in vegetation reflectance and in the NDVI are evaluated. The analysis and field measurements show that the NDVI estimated from a user calibration factor can be as accurate as, or even more accurate than, the manufacturer's calibration. The in situ calibration described here remedies the situation where reflectance for large field-of-view sensors cannot be always estimated from the manufacturer's calibration. The method developed in this paper may help improve the reliability of long-term field spectral measurements and contributes to the near-surface remote sensing of vegetation.

Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors.

Minimally invasive continuous glucose monitoring (CGM) sensors measure in the subcutis a current signal, which is converted into interstitial glucose (IG) concentration by a calibration process periodically updated using fingerstick blood glucose (BG) references. Though important in diabetes management, CGM sensors still suffer from accuracy problems. Here, we propose a new online calibration method improving accuracy of CGM glucose profiles with respect to manufacturer calibration. The proposed method fits CGM current signal against the BG references collected twice a day for calibration purposes, by a time-varying calibration function whose parameters are identified in a Bayesian framework using a priori second-order statistical knowledge. The distortion introduced by BG-to-IG kinetics is compensated before parameter identification via nonparametric deconvolution. The method was tested on a database where 108 CGM signals were collected for 7days by the Dexcom G4 Platinum sensor. Results show the new method drives to a statistically significant accuracy improvement as measured by three commonly used metrics: mean absolute relative difference reduced from 12.73% to 11.47%; percentage of accurate glucose estimates increased from 82.00% to 89.19%; and percentage of values falling in the "A" zone of the Clark error grid increased from 82.22% to 88.86%. The new calibration method significantly improves CGM glucose profiles accuracy with respect to manufacturer calibration. The proposed algorithm provides a real-time improvement of CGM accuracy, which can be crucial in several CGM-based applications, including the artificial pancreas, thus providing a potential great impact in the diabetes technology research community.

Comparison between light scattering and gravimetric samplers for PM10 mass concentration in poultry and pig houses

The objective of this study was to compare co-located real-time light scattering devices and equivalent gravimetric samplers in poultry and pig houses for PM10 mass concentration, and to develop animal-specific calibration factors for light scattering samplers. These results will contribute to evaluate the comparability of different sampling instruments for PM10 concentrations. Paired DustTrak light scattering device (DustTrak aerosol monitor, TSI, U.S.) and PM10 gravimetric cyclone sampler were used for measuring PM10 mass concentrations during 24 h periods (from noon to noon) inside animal houses. Sampling was conducted in 32 animal houses in the Netherlands, including broilers, broiler breeders, layers in floor and in aviary system, turkeys, piglets, growing-finishing pigs in traditional and low emission housing with dry and liquid feed, and sows in individual and group housing. A total of 119 pairs of 24 h measurements (55 for poultry and 64 for pigs) were recorded and analyzed using linear regression analysis. Deviations between samplers were calculated and discussed. In poultry, cyclone sampler and DustTrak data fitted well to a linear regression, with a regression coefficient equal to 0.41, an intercept of 0.16 mg m−3 and a correlation coefficient of 0.91 (excluding turkeys). Results in turkeys showed a regression coefficient equal to 1.1 (P = 0.49), an intercept of 0.06 mg m−3 (P < 0.0001) and a correlation coefficient of 0.98. In pigs, we found a regression coefficient equal to 0.61, an intercept of 0.05 mg m−3 and a correlation coefficient of 0.84. Measured PM10 concentrations using DustTraks were clearly underestimated (approx. by a factor 2) in both poultry and pig housing systems compared with cyclone pre-separators. Absolute, relative, and random deviations increased with concentration. DustTrak light scattering devices should be self-calibrated to investigate PM10 mass concentrations accurately in animal houses. We recommend linear regression equations as animal-specific calibration factors for DustTraks instead of manufacturer calibration factors, especially in heavily dusty environments such as animal houses.