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Calibration Data Set (calibration + data_set)
Selected AbstractsDetermination of tocopherols and phytosterols in sunflower seeds by NIR spectrometryEUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY, Issue 5 2007Alicia Ayerdi Gotor Abstract The objective of this work was to develop a near-infrared reflectance spectrometry (NIRS) calibration estimating the tocopherol and phytosterol contents in sunflower seeds. Approximately 1000 samples of grinded sunflower kernels were scanned by NIRS at 2-nm intervals from 400 to 2500,nm. For each sample, standard measurements of tocopherol and phytosterol contents were performed. The total tocopherol content was obtained by high-performance liquid chromatography coupled with a fluorescence detector, while the total phytosterol content was assessed by gas chromatography. For tocopherol, the calibration data set ranged from 175 to 1005,mg/kg oil (mean value around 510,±,140,mg/kg oil), whereas for the phytosterol content, the calibration data set ranged from 180 to 470,mg/100,g oil (mean value of 320,±,50,mg/100,g oil). The NIRS calibration showed a relatively good correlation (R2,=,0.64) between predicted by NIRS and real values for the total tocopherol content but a poor correlation for the total phytosterol content (R2,=,0.27). These results indicate that NIRS could be useful to classify samples with high and low tocopherol content. In contrast, the estimation of phytosterol contents by NIRS needs further investigation. Moreover, in this study, calibration was obtained by a modified partial least-squares method; the use of other mathematical treatments can be suitable, particularly for total phytosterol content estimation. [source] An operational model predicting autumn bird migration intensities for flight safetyJOURNAL OF APPLIED ECOLOGY, Issue 4 2007J. VAN BELLE Summary 1Forecasting migration intensity can improve flight safety and reduce the operational costs of collisions between aircraft and migrating birds. This is particularly true for military training flights, which can be rescheduled if necessary and often take place at low altitudes and during the night. Migration intensity depends strongly on weather conditions but reported effects of weather differ among studies. It is therefore unclear to what extent existing predictive models can be extrapolated to new situations. 2We used radar measurements of bird densities in the Netherlands to analyse the relationship between weather and nocturnal migration. Using our data, we tested the performance of three regression models that have been developed for other locations in Europe. We developed and validated new models for different combinations of years to test whether regression models can be used to predict migration intensity in independent years. Model performance was assessed by comparing model predictions against benchmark predictions based on measured migration intensity of the previous night and predictions based on a 6-year average trend. We also investigated the effect of the size of the calibration data set on model robustness. 3All models performed better than the benchmarks, but the mismatch between measurements and predictions was large for existing models. Model performance was best for newly developed regression models. The performance of all models was best at intermediate migration intensities. The performance of our models clearly increased with sample size, up to about 90 nocturnal migration measurements. Significant input variables included seasonal migration trend, wind profit, 24-h trend in barometric pressure and rain. 4Synthesis and applications. Migration intensities can be forecast with a regression model based on meteorological data. This and other existing models are only valid locally and cannot be extrapolated to new locations. Model development for new locations requires data sets with representative inter- and intraseasonal variability so that cross-validation can be applied effectively. The Royal Netherlands Air Force currently uses the regression model developed in this study to predict migration intensities 3 days ahead. This improves the reliability of migration intensity warnings and allows rescheduling of training flights if needed. [source] Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement ErrorBIOMETRICS, Issue 2 2010Samiran Sinha Summary:, We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the relationship between the disease and exposure variable treated semiparametrically, but also the relationship between the surrogate and the true exposure is modeled semiparametrically. The two nonparametric functions are modeled simultaneously via B-splines. In addition, we model the distribution of the exposure variable as a Dirichlet process mixture of normal distributions, thus making its modeling essentially nonparametric and placing this work into the context of functional measurement error modeling. We apply our method to the NIH-AARP Diet and Health Study and examine its performance in a simulation study. [source] Developing a modern pollen,climate calibration data set for NorwayBOREAS, Issue 4 2010ANNE E. BJUNE Bjune, A. E., Birks, H. J. B., Peglar, S. M. & Odland, A. 2010: Developing a modern pollen,climate calibration data set for Norway. Boreas, Vol. 39, pp. 674,688. 10.1111/j.1502-3885.2010.00158.x. ISSN 0300-9483. Modern pollen,climate data sets consisting of modern pollen assemblages and modern climate data (mean July temperature and mean annual precipitation) have been developed for Norway based on 191 lakes and 321 lakes. The original 191-lake data set was designed to optimize the distribution of the lakes sampled along the mean July temperature gradient, thereby fulfilling one of the most critical assumptions of weighted-averaging regression and calibration and its relative, weighted-averaging partial least-squares regression. A further 130 surface samples of comparable taphonomy, taxonomic detail and analyst became available as a result of other projects. These 130 samples, all from new lakes, were added to the 191-lake data set to create the 321-lake data set. The collection and construction of these data sets are outlined. Numerical analyses involving generalized linear modelling, constrained ordination techniques, weighted-averaging partial least-squares regression, and two different cross-validation procedures are used to asses the effects of increasing the size of the calibration data set from 191 to 321 lakes. The two data sets are used to reconstruct mean July temperature and mean annual precipitation for a Holocene site in northwest Norway and a Lateglacial site in west-central Norway. Overall, little is to be gained by increasing the modern data set beyond about 200 lakes in terms of modern model performance statistics, but the down-core reconstructions show less between-sample variability and are thus potentially more plausible and realistic when based on the 321-lake data set. [source] | ||||||||||