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The correlation between galaxies' integrated stellar masses and star formation rates (the 'star formation main sequence', SFMS) is a well-established scaling relation. Recently, surveys have found a relationship between the star formation rate (SFR) and stellar mass surface densities on kpc and sub-kpc scales (the 'resolved SFMS', rSFMS). In this work, we demonstrate that the rSFMS emerges naturally in Feedback In Realistic Environments 2 (FIRE-2) zoom-in simulations of Milky Way-mass galaxies. We make SFR and stellar mass maps of the simulated galaxies at a variety of spatial resolutions and star formation averaging time-scales and fit the rSFMS using multiple methods from the literature. While the absolute value of the SFMS slope (αMS) depends on the fitting method, the slope is steeper for longer star formation time-scales and lower spatial resolutions regardless of the fitting method employed. We present a toy model that quantitatively captures the dependence of the simulated galaxies' αMS on spatial resolution and use it to illustrate how this dependence can be used to constrain the characteristic mass of star-forming clumps.

With close pairs of quasars at different redshifts, a background quasar sightline can be used to study a foreground quasar's environment in absorption. We use a sample of 650 projected quasar pairs to study the H I Lyα absorption transverse to luminous, z ∼ 2 quasars at proper separations of 30 kpc < R ⊥ < 1 Mpc. In contrast to measurements along the line-of-sight, regions transverse to quasars exhibit enhanced H I Lyα absorption and a larger variance than the ambient intergalactic medium, with increasing absorption and variance toward smaller scales. Analysis of composite spectra reveals excess absorption characterized by a Lyα equivalent width profile W = 2.3 Å (R ⊥/100 kpc)-0.46. We also observe a high (≃ 60%) covering factor of strong, optically thick H I absorbers (H I column ) at separations R ⊥ < 200 kpc, which decreases to ∼20% at R ⊥ ≃ 1 Mpc, but still represents a significant excess over the cosmic average. This excess of optically thick absorption can be described by a quasar-absorber cross-correlation function ξQA(r) = (r/r 0)γ with a large correlation length (comoving) and . The H I absorption measured around quasars exceeds that of any previously studied population, consistent with quasars being hosted by massive dark matter halos M halo ≈ 1012.5 M⊙ at z ∼ 2.5. The environments of these massive halos are highly biased toward producing optically thick gas, and may even dominate the cosmic abundance of Lyman limit systems and hence the intergalactic opacity to ionizing photons at z ∼ 2.5. The anisotropic absorption around quasars implies the transverse direction is much less likely to be illuminated by ionizing radiation than the line-of-sight. © 2013. The American Astronomical Society. All rights reserved..

Key Message: The principal phenotypic determinants of market class in carrot-the size and shape of the root-are under primarily additive, but also highly polygenic, genetic control. The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided., (© 2021. The Author(s).)

Carrots are among the richest sources of provitamin A carotenes in the human diet, but genetic variation in the carotenoid pathway does not fully explain the high levels of carotenoids in carrot roots. Using a diverse collection of modern and historic domesticated varieties, and wild carrot accessions, an association analysis for orange pigmentation revealed a significant genomic region that contains the Or gene, advancing it as a candidate for carotenoid presence in carrot. Analysis of sequence variation at the Or locus revealed a nonsynonymous mutation cosegregating with carotenoid content. This mutation was absent in all wild carrot samples and nearly fixed in all orange domesticated samples. Or has been found to control carotenoid presence in other crops but has not previously been described in carrot. Our analysis also allowed us to more completely characterize the genetic structure of carrot, showing that the Western domesticated carrot largely forms one genetic group, despite dramatic phenotypic differences among market classes. Eastern domesticated and wild accessions form a second group, which reflects the recent cultivation history of carrots in Central Asia. Other wild accessions form distinct geographic groups, particularly on the Iberian peninsula and in Northern Africa. Using genome-wide F st , nucleotide diversity, and the cross-population composite likelihood ratio, we analyzed the genome for regions putatively under selection during domestication and identified 12 regions that were significant for all three methods of detection, one of which includes the Or gene. The Or domestication allele appears to have been selected after the initial domestication of yellow carrots in the East, near the proposed center of domestication in Central Asia. The rapid fixation of the Or domestication allele in almost all orange and nonorange carrots in the West may explain why it has not been found with less genetically diverse mapping populations., (Copyright © 2018 by the Genetics Society of America.)

Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, petiole number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform also quantified shoot and root shapes in terms of principal components, which do not have traditional, manually measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F 2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a rapid, automated image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.

Background: The majority of the subspecies of Daucus carota have not yet been discriminated clearly by various molecular or morphological methods and hence their phylogeny and classification remains unresolved. Recent studies using 94 nuclear orthologs and morphological characters, and studies employing other molecular approaches were unable to distinguish clearly many of the subspecies. Fertile intercrosses among traditionally recognized subspecies are well documented. We here explore the utility of single nucleotide polymorphisms (SNPs) generated by genotyping-by-sequencing (GBS) to serve as an effective molecular method to discriminate the subspecies of the D. carota complex., Results: We used GBS to obtain SNPs covering all nine Daucus carota chromosomes from 162 accessions of Daucus and two related genera. To study Daucus phylogeny, we scored a total of 10,814 or 38,920 SNPs with a maximum of 10 or 30 % missing data, respectively. To investigate the subspecies of D. carota, we employed two data sets including 150 accessions: (i) rate of missing data 10 % with a total of 18,565 SNPs, and (ii) rate of missing data 30 %, totaling 43,713 SNPs. Consistent with prior results, the topology of both data sets separated species with 2n = 18 chromosome from all other species. Our results place all cultivated carrots (D. carota subsp. sativus) in a single clade. The wild members of D. carota from central Asia were on a clade with eastern members of subsp. sativus. The other subspecies of D. carota were in four clades associated with geographic groups: (1) the Balkan Peninsula and the Middle East, (2) North America and Europe, (3) North Africa exclusive of Morocco, and (4) the Iberian Peninsula and Morocco. Daucus carota subsp. maximus was discriminated, but neither it, nor subsp. gummifer (defined in a broad sense) are monophyletic., Conclusions: Our study suggests that (1) the morphotypes identified as D. carota subspecies gummifer (as currently broadly circumscribed), all confined to areas near the Atlantic Ocean and the western Mediterranean Sea, have separate origins from sympatric members of other subspecies of D. carota, (2) D. carota subsp. maximus, on two clades with some accessions of subsp. carota, can be distinguished from each other but only with poor morphological support, (3) D. carota subsp. capillifolius, well distinguished morphologically, is an apospecies relative to North African populations of D. carota subsp. carota, (4) the eastern cultivated carrots have origins closer to wild carrots from central Asia than to western cultivated carrots, and (5) large SNP data sets are suitable for species-level phylogenetic studies in Daucus.

Background: Digital PCR (dPCR) is a technique for estimating the concentration of a target nucleic acid by loading a sample into a large number of partitions, amplifying the target and using a fluorescent marker to identify which partitions contain the target. The standard analysis uses only the proportion of partitions containing target to estimate the concentration and depends on the assumption that the initial distribution of molecules in partitions is Poisson. In this paper we describe a way to extend such analysis using the quantification cycle (C q ) data that may also be available, but rather than assuming the Poisson distribution the more general Conway-Maxwell-Poisson distribution is used instead., Results: A software package for the open source language R has been created for performing the analysis. This was used to validate the method by analysing C q data from dPCR experiments involving 3 types of DNA (attenuated, virulent and plasmid) at 3 concentrations. Results indicate some deviation from the Poisson distribution, which is strongest for the virulent DNA sample. Theoretical calculations indicate that the deviation from the Poisson distribution results in a bias of around 5 % for the analysed data if the standard analysis is used, but that it could be larger for higher concentrations. Compared to the estimates of subsequent efficiency, the estimates of 1st cycle efficiency are much lower for the virulent DNA, moderately lower for the attenuated DNA and close for the plasmid DNA. Further method validation using simulated data gave results closer to the true values and with lower standard deviations than the standard method, for concentrations up to approximately 2.5 copies/partition., Conclusions: The C q -based method is effective at estimating DNA concentration and is not seriously affected by data issues such as outliers and moderately non-linear trends. The data analysis suggests that the Poisson assumption of the standard approach does lead to a bias that is fairly small, though more research is needed. Estimates of the 1st cycle efficiency being lower than estimates of the subsequent efficiency may indicate samples that are mixtures of single-stranded and double-stranded DNA. The model can reduce or eliminate the resulting bias.

Measurement of RNA can be used to study and monitor a range of infectious and non-communicable diseases, with profiling of multiple gene expression mRNA transcripts being increasingly applied to cancer stratification and prognosis. An international comparison study (Consultative Committee for Amount of Substance (CCQM)-P103.1) was performed in order to evaluate the comparability of measurements of RNA copy number ratio for multiple gene targets between two samples. Six exogenous synthetic targets comprising of External RNA Control Consortium (ERCC) standards were measured alongside transcripts for three endogenous gene targets present in the background of human cell line RNA. The study was carried out under the auspices of the Nucleic Acids (formerly Bioanalysis) Working Group of the CCQM. It was coordinated by LGC (United Kingdom) with the support of National Institute of Standards and Technology (USA) and results were submitted from thirteen National Metrology Institutes and Designated Institutes. The majority of laboratories performed RNA measurements using RT-qPCR, with datasets also being submitted by two laboratories based on reverse transcription digital polymerase chain reaction and one laboratory using a next-generation sequencing method. In RT-qPCR analysis, the RNA copy number ratios between the two samples were quantified using either a standard curve or a relative quantification approach. In general, good agreement was observed between the reported results of ERCC RNA copy number ratio measurements. Measurements of the RNA copy number ratios for endogenous genes between the two samples were also consistent between the majority of laboratories. Some differences in the reported values and confidence intervals ('measurement uncertainties') were noted which may be attributable to choice of measurement method or quantification approach. This highlights the need for standardised practices for the calculation of fold change ratios and uncertainties in the area of gene expression profiling.

Monte Carlo simulation of expert judgments on human errors in a chemical analysis was used for determination of distributions of the error quantification scores (scores of likelihood and severity, and scores of effectiveness of a laboratory quality system in prevention of the errors). The simulation was based on modeling of an expert behavior: confident, reasonably doubting and irresolute expert judgments were taken into account by means of different probability mass functions (pmfs). As a case study, 36 scenarios of human errors which may occur in elemental analysis of geological samples by ICP-MS were examined. Characteristics of the score distributions for three pmfs of an expert behavior were compared. Variability of the scores, as standard deviation of the simulated score values from the distribution mean, was used for assessment of the score robustness. A range of the score values, calculated directly from elicited data and simulated by a Monte Carlo method for different pmfs, was also discussed from the robustness point of view. It was shown that robustness of the scores, obtained in the case study, can be assessed as satisfactory for the quality risk management and improvement of a laboratory quality system against human errors., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Premise of the Study: Analyses of genetic structure and phylogenetic relationships illuminate the origin and domestication of modern crops. Despite being an important worldwide vegetable, the genetic structure and domestication of carrot (Daucus carota) is poorly understood. We provide the first such study using a large data set of molecular markers and accessions that are widely dispersed around the world. •, Methods: Sequencing data from the carrot transcriptome were used to develop 4000 single nucleotide polymorphisms (SNPs). Eighty-four genotypes, including a geographically well-distributed subset of wild and cultivated carrots, were genotyped using the KASPar assay. •, Key Results: Analysis of allelic diversity of SNP data revealed no reduction of genetic diversity in cultivated vs. wild accessions. Structure and phylogenetic analysis indicated a clear separation between wild and cultivated accessions as well as between eastern and western cultivated carrot. Among the wild carrots, those from Central Asia were genetically most similar to cultivated accessions. Furthermore, we found that wild carrots from North America were most closely related to European wild accessions. •, Conclusions: Comparing the genetic diversity of wild and cultivated accessions suggested the absence of a genetic bottleneck during carrot domestication. In conjunction with historical documents, our results suggest an origin of domesticated carrot in Central Asia. Wild carrots from North America were likely introduced as weeds with European colonization. These results provide answers to long-debated questions of carrot evolution and domestication and inform germplasm curators and breeders on genetic substructure of carrot genetic resources.

Repeatability and reproducibility data for microbiological methods in food analysis were collated and assessed with a view to identifying useful or important trends. Generalized additive modeling for location, shape, and scale was used to model the distribution of variances. It was found that mean reproducibility for log10(CFU) data is largely independent of concentration, while repeatability SD of log10(CFU) data shows a strongly significant decrease in repeatability SD with increasing enumeration. The model for reproducibility SD gave a mean of 0.44, with an upper 95th percentile of approximately 0.76. Repeatability variance could be described reasonably well by a simple dichotomous model; at enumerations below 10(5)/g, the model for repeatability SD gave a mean of approximately 0.35 and upper 95th percentile of 0.63. Above 10(5)/g, the model gave a mean of 0.2 and upper 95th percentile of 0.36. A Horwitz-like function showed no appreciable advantage in describing the data set and gave apparently worse fit. The relationship between repeatability and reproducibility of log10(CFU) is not constant across the concentration range studied. Both repeatability and reproducibility were found to depend on matrix class and organism.

A method of calibration for real-time quantitative polymerase chain reaction (qPCR) experiments based on the method of standard additions combined with non-linear curve fitting is described. The method is tested by comparing the results of a traditionally calibrated qPCR experiment with the standard additions experiment in the presence of 2 mM EDTA, a known inhibitor chosen to provide an unambiguous test of the principle by inducing an approximately twofold bias in apparent copy number calculated using traditional calibration. The standard additions method is shown to substantially reduce inhibitor-induced bias in quantitative real-time qPCR.

Background: Microarray data interpretation can be affected by sample RNA integrity. The ScreenTape Degradation Value (SDV) is a novel RNA integrity metric specific to the ScreenTape(R) platform (Lab901). To characterise the performance of the ScreenTape(R) platform for RNA analysis and determine the robustness of the SDV metric, a panel of intentionally degraded RNA samples was prepared. These samples were used to evaluate the ScreenTape(R) platform against an alternative approach for measuring RNA integrity (Agilent Bioanalyzer RIN value). The samples were also subjected to microarray analysis and the resulting data correlated to the RNA integrity metrics., Findings: Measurement of SDV for a panel of intentionally degraded RNA samples ranged from 0 for intact RNA to 37 for degraded RNA, with corresponding RIN values ranging from 10 to 4 for the same set of samples. SDV and RIN scales both demonstrated comparable discrimination between differently treated samples (RIN 10 to 7, SDV 0 to 15), with the SDV exhibiting better discrimination at higher degradation levels. Increasing SDV values correlated with a decrease in microarray sample labelling efficiency and an increase in numbers of differentially expressed genes., Conclusions: The ScreenTape(R) platform is comparable to the Bioanalyzer platform in terms of reproducibility and discrimination between different levels of RNA degradation. The robust nature of the SDV metric qualifies it as an alternative metric for RNA sample quality control, and a useful predictor of downstream microarray performance.

Standard additions is a calibration technique devised to eliminate rotational matrix effects in analytical measurement. Although the technique is presented in almost every textbook of analytical chemistry, its behaviour in practice is not well documented and is prone to attract misleading accounts. The most important limitation is that the method cannot deal with translational matrix effects, which need to be handled separately. In addition, because the method involves extrapolation from known data, the method is often regarded as less precise than external calibration (interpolation) techniques. Here, using a generalised model of an analytical system, we look at the behaviour of the method of standard additions under a range of conditions, and find that, if executed optimally, there is no noteworthy loss of precision.

Consistent treatment of measurement bias, including the question of whether or not to correct for bias, is essential for the comparability of measurement results. The case for correcting for bias is discussed, and it is shown that instances in which bias is known or suspected, but in which a specific correction cannot be justified, are comparatively common. The ISO Guide to the Expression of Uncertainty in Measurement does not provide well for this situation. It is concluded that there is a need for guidance on handling cases of uncorrected bias. Several different published approaches to the treatment of uncorrected bias and its uncertainty are critically reviewed with regard to coverage probability and simplicity of execution. On the basis of current studies, and taking into account testing laboratory needs for a simple and consistent approach with a symmetric uncertainty interval, we conclude that for most cases with large degrees of freedom, linear addition of a bias term adjusted for exact coverage ("U(e)") as described by Synek is to be preferred. This approach does, however, become more complex if degrees of freedom are low. For modest bias and low degrees of freedom, summation of bias, bias uncertainty and observed value uncertainty in quadrature ("RSSu") provides a similar interval and is simpler to adapt to reduced degrees of freedom, at the cost of a more restricted range of application if accurate coverage is desired.

Background: Accurate quantification of DNA using quantitative real-time PCR at low levels is increasingly important for clinical, environmental and forensic applications. At low concentration levels (here referring to under 100 target copies) DNA quantification is sensitive to losses during preparation, and suffers from appreciable valid non-detection rates for sampling reasons. This paper reports studies on a real-time quantitative PCR assay targeting a region of the human SRY gene over a concentration range of 0.5 to 1000 target copies. The effects of different sample preparation and calibration methods on quantitative accuracy were investigated., Results: At very low target concentrations of 0.5-10 genome equivalents (g.e.) eliminating any replicates within each DNA standard concentration with no measurable signal (non-detects) compromised calibration. Improved calibration could be achieved by eliminating all calibration replicates for any calibration standard concentration with non-detects ('elimination by sample'). Test samples also showed positive bias if non-detects were removed prior to averaging; less biased results were obtained by converting to concentration, including non-detects as zero concentration, and averaging all values. Tube plastic proved to have a strongly significant effect on DNA quantitation at low levels (p = 1.8 x 10(-4)). At low concentrations (under 10 g.e.), results for assays prepared in standard plastic were reduced by about 50% compared to the low-retention plastic. Preparation solution (carrier DNA or stabiliser) was not found to have a significant effect in this study.Detection probabilities were calculated using logistic regression. Logistic regression over large concentration ranges proved sensitive to non-detected replicate reactions due to amplification failure at high concentrations; the effect could be reduced by regression against log (concentration) or, better, by eliminating invalid responses., Conclusion: Use of low-retention plastic tubes is advised for quantification of DNA solutions at levels below 100 g.e. For low-level calibration using linear least squares, it is better to eliminate the entire replicate group for any standard that shows non-detects reasonably attributable to sampling effects than to either eliminate non-detects or to assign arbitrary high Ct values. In calculating concentrations for low-level test samples with non-detects, concentrations should be calculated for each replicate, zero concentration assigned to non-detects, and all resulting concentration values averaged. Logistic regression is a useful method of estimating detection probability at low DNA concentrations.

Different methods of treating data which lie close to a natural limit in a feasible range, such as zero or 100% mass or mole fraction, are discussed and recommendations made concerning the most appropriate. The methods considered include discarding observations beyond the limit, shifting observations to the limit, truncation of a classical confidence interval based on Student's t (coupled with shifting the result to the limit if outside the feasible range), truncation and renormalisation of an assumed normal distribution, and the maximum density interval of a Bayesian estimate based on a normal measurement distribution and a uniform prior within the feasible range. Based on consideration of bias and simulation to assess coverage, it is recommended that for most purposes, a confidence interval near a natural limit should be constructed by first calculating the usual confidence interval based on Student's t, then truncating the out-of-range portion to leave an asymmetric interval and adjusting the reported value to within the resulting interval if required. It is suggested that the original standard uncertainty is retained for uncertainty propagation purposes.

The study considers data from 2 UK-based proficiency schemes and includes data from a total of 29 rounds and 43 test materials over a period of 3 years. The results from the 2 schemes are similar and reinforce each other. The amplification process used in quantitative polymerase chain reaction determinations predicts a mixture of normal, binomial, and lognormal distributions dominated by the latter 2. As predicted, the study results consistently follow a positively skewed distribution. Log-transformation prior to calculating z-scores is effective in establishing near-symmetric distributions that are sufficiently close to normal to justify interpretation on the basis of the normal distribution.

This paper reports the use of improved numerical approaches to modelling extraction profiles, and shows that the approach substantially reduces statistical prediction uncertainties compared to those obtained on the basis of a three-point extrapolation from the later part of the extraction curve. Numerical fitting of manually obtained polycyclic aromatic hydrocarbon extraction data to a spherical particle diffusion model showed uncertainties typically reduced by a factor of three (with extremes at 1.02 and 770). Application to pressurised fluid extraction study of pelletised poly(vinylchloride) containing 30 mass% di(2-ethylhexyl)phthalate also showed good improvements. However, this high precision data showed small but significant lack of fit resulting in residual correlation and visibly biased prediction (more so than simple extrapolation). Re-fitting and uncertainty estimation using a first-order autoregression approximation to the covariance matrix produced more realistic uncertainty estimates and closer parameter estimates and is accordingly recommended for treating residual correlation from other causes, but did not entirely alleviate the problem. Different shape models (spherical, plane sheet and cylindrical) were applied without accounting fully for fitting error, and particle size effects were eliminated by modelling a simple size distribution. However, an approximate model based on linearly concentration-dependent diffusion coefficient showed excellent fit, confirming concentration-dependence as the most likely cause. This semiempirical model led to an uncertainty in total extractable material, at 0.2% of the total extractable value (with allowance for correlation). This is potentially good enough for recovery estimation and correction in certification of reference materials for validation purposes.

The choice of an analytical procedure and the determination of an appropriate sampling strategy are here treated as a decision theory problem in which sampling and analytical costs are balanced against possible end-user losses due to measurement error. Measurement error is taken here to include both sampling and analytical variances, but systematic errors are not considered. The theory is developed in detail for the case exemplified by a simple accept or reject decision following an analytical measurement on a batch of material, and useful approximate formulae are given for this case. Two worked examples are given, one involving a batch production process and the other a land reclamation site.

The paper describes experiments for the evaluation of uncertainties associated with a number of chromatographic parameters. Studies of the analysis of vitamins by HPLC illustrate the estimation of the uncertainties associated with experimental "input" parameters such as the detector wavelength, column temperature and mobile phase flow-rate. Experimental design techniques, which allow the efficient study a number of parameters simultaneously, are described. Multiple linear regression was used to fit response surfaces to the data. The resulting equations were used in the estimation of the uncertainties. Three approaches to uncertainty calculation were compared--Kragten's spreadsheet, symmetric spreadsheet and algebraic differentiation. In cases where non-linearity in the model was significant, agreement between the uncertainty estimates was poor as the spreadsheet approaches do not include second-order uncertainty terms.

Response surface modelling is proposed as an approach to the estimation of uncertainties associated with derivatisation, and is compared with a kinetic study. Fatty acid methyl ester formation is used to illustrate the approach, and kinetic data for acid-catalysed methylation and base-catalysed transesterification are presented. Kinetic effects did not lead to significant uncertainty contributions under normal conditions for base-catalysed transesterification of triglycerides. Uncertainties for acid-catalysed methylation with BF3 approach significance, but could be reduced by extending reaction times from 3 to 5 min. Non-linearity is a common feature of response surface models for derivatisation and compromised first-order estimates of uncertainty; it was necessary to include higher order differential terms in the uncertainty estimate. Simulations were used to examine the general applicability of the approach and to study the effects of poor precision and of change of response surface model. It is concluded that reliable uncertainty estimates are available only when the model is statistically significant, robust, representative of the underlying behaviour of the system, and forms a good fit to the data; arbitrary models are not generally suitable for uncertainty estimation. Where statistically insignificant effects were included in models, they gave negligible uncertainty contributions.