Evaluation of an Electrochemical Point-of-Care Meter for Measuring Glucose Concentration in Blood from Periparturient Dairy Cattle.

BACKGROUND: The Precision Xtra(®) meter is a promising low cost electrochemical point-of-care unit for measuring blood glucose concentration ([gluc]) in cattle blood. The meter uses an algorithm that assumes the intra-erythrocyte [gluc] equals the plasma [gluc] on a molal basis, and that the hematocrit is similar in humans and cattle. OBJECTIVES: The primary objective was to determine the accuracy of the meter for measuring plasma [gluc] in dairy cattle. Secondary objectives were to characterize the influence of hematocrit and sample temperature on the measured value for [gluc]. ANIMALS: A total of 106 periparturient Holstein-Friesian cattle. METHODS: Blood and plasma samples (1,109) were obtained and Deming regression and Bland-Altman plots were used to determine the accuracy of the meter against the reference method (plasma hexokinase assay). Multivariable regression and linear regression were used to determine the effect of hematocrit and sample temperature on the plasma [gluc] measured by the meter. RESULTS: Intra-erythrocyte [gluc] was 18% of plasma [gluc] on a molar basis. Sample temperature had a significant linear effect on plasma [gluc] as measured by the meter for 3/5 plasma samples when measured [gluc] > 160 mg/dL. CONCLUSIONS AND CLINICAL IMPORTANCE: The meter utilizes an algorithm that is optimized for human blood and is inaccurate when applied to bovine blood. Until a cattle-specific algorithm is developed, we recommend using plasma as the analyte instead of blood and calculating plasma [gluc] using the equation: [gluc] = 0.66 × [gluc]p-meter + 15, where [gluc]p-meter is the value reported by the meter. If blood is measured, then we recommend using the equation: [gluc] = 0.90 × [gluc]b-meter + 15.

Optimal filtering and Bayesian detection for friction-based diagnostics in machines.

Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.