Evaluation of trace analyte identification in complex matrices by low-resolution gas chromatography – Mass spectrometry through signal simulation

Statistically sound identification of compounds by low-resolution GC-MS: Identification of tear agents in tear gas sprays

Gas-chromatography hyphenated with low-resolution mass spectrometry is a very flexible tool for the cost-effective identification and quantification of volatile compounds in complex matrices. In some analytical fields, criteria for the agreement between retention time and mass spectra of the analyte in calibrators and samples are defined based on the general understanding of the performance of these parameters. However, since this harmonisation is not based on experimental performance observed for specific GC-MS conditions and analyte it leads to false identifications. This research proposes a novel and robust tool for defining statistically sound criteria for the identification of compounds by GC-MS and LC-MS using experimental data. The Monte Carlo Method (MCM) simulation of the correlated abundance of characteristic ions of analyte mass spectrum allows simulating the abundance ratio difference of the analyte in a calibrator and sample used for statistically sound identifications. The Cholesky decomposition of the covariance matrix of ion abundances for MCM simulations allows the reliable use of many ion abundance ratios in identifications. The developed methodology was implemented in a user-friendly Excel spreadsheet and applied to the identification of tear gas agents in tear gas sprays. Criteria defined by SANTE for identifying pesticide residues in foodstuffs were compared with the developed tool. The cross-validation of computational and SANTE tools allowed concluding that the statistical control of retention time and mass spectra performs according to the defined confidence level. On the other hand, the SANTE criteria can produce up to 92 % false identifications for being too strict considering signal dispersion.

Development and validation of statistically sound criteria for the match of unweathered GC-MS fingerprints in oil spill forensics

The investigation of an oil spill's origin frequently relies on determining the equivalence of oil component patterns in samples from the contaminated environment and suspected oil source. This comparison benefits if based on the ratio of the abundance of unweathered characteristic components of the oil product, Diagnostic Ratios, DR. Replicate determinations of DR from one sample are used to set limits for the second sample's DR. The composition equivalence of oil patterns in both samples is indicated if all compared DR are statistically equivalent with a high confidence level. Some studies define DR limits assuming their normality and using Student's t statistics (S-t). However, since the ratio of correlated abundances can be not normally distributed, this criterion can drive to more false comparisons than predicted by the test confidence level. This work developed a computational tool for the reliable description of the non-normal distribution of the DR based on the Monte Carlo Method (MCM), aiming to allow the accurate control of the confidence of DR comparison. This work concluded that S-t defines 95% or 98% confidence limits with probabilities of falsely rejecting samples equivalence, φ, that can be up to 4.3% higher than predicted by the confidence level of the S-t test (i.e., 5% and 2%). The fragilities of the S-t limits significantly reduce the probability (1-θ) of two samples with the same oil producing equivalent values of all compared DR. For the studied 69 DR from unweathered components, the (1-θ) for 98% confidence level limits, set by the MCM and S-t from triplicate injections of one sample, are 94.8% and 91.7%, respectively. These values are below the confidence level (P) defined for each DR because DR are correlated with a correlation coefficient lower than 1. The (1-θ) can be increased to above P by using MCM limits and accepting composition equivalence if at least one of two sample extract injections produces values within limits set from the other sample's replicate injection. The validated user-friendly MS-Excel file used to set and access comparison criteria is made available as Supplementary Material and was checked experimentally. However, it is not feasible to estimate model confidence exclusively from experimentation because it would require too much independent analysis.

Risk of false pharmaceutical equivalence (non-equivalence) decisions due to measurement uncertainty

The pharmaceutical equivalence between test (generic or similar) and reference medicine is evaluated through in vitro quality tests involving multiple compliance parameters. Despite efforts to ensure the reliability of the analytical results obtained in the pharmaceutical equivalence studies, measurement uncertainties lead to a risk of false decisions. Thus, the aim of this work was to evaluate the measurement uncertainties associated with the analytical results obtained in the pharmaceutical equivalence studies of different pharmaceutical forms and to estimate the risks of false decisions in the evaluation of pharmaceutical equivalence. The measurement uncertainties associated with the test results were evaluated using the bottom-up and top-down approaches. The consumer's or producer's combined particular risks and combined total risks were estimated using the Monte Carlo method implemented in MS-Excel spreadsheet (available as supplemental material). Considering the seven pharmaceutical equivalence studies performed in this work, three studies were not conclusive (risk of false pharmaceutical equivalence decisions higher than 5 %). Moreover, we concluded pharmaceutical equivalence and pharmaceutical non-equivalence in one and three studies, respectively. The particular and total combined risks are useful to make decisions regarding the evaluation of pharmaceutical equivalence between the test (generic or similar) and reference medicines. Regulatory bodies and pharmaceutical equivalence centers are very interested in the estimation of the risks of false decisions, particularly to evaluate the quality of medicines that are not submitted to bioequivalence studies.

Microplastics identification by infrared spectroscopy - Evaluation of identification criteria and uncertainty by the Bootstrap method

The assessment of microplastic contamination in an environmental compartment involves identifying and counting microplastics in a representative fraction of the compartment. Microplastics can be identified by μFTIR spectroscopy where spectra are manually examined for characteristic polymer bands or by an automatic comparison of particle spectrum with reference spectra of polymers. The automatic spectra comparison can involve calculating a correlation coefficient, CC, between particle and reference spectra where a minimum correlation above which identification is adequately reliable should be defined. Correlation can be calculated from original or transformed signals, such as taking the first derivative, and by using unweighted or weighted CC. Weighted CC can highlight the spectral features more relevant to distinguish polymers. This work describes a methodology for setting the minimum CC, P5»P, associated with a true positive result rate, TP, of 95% and for checking if this threshold allows identifications with a false positive result rate, FP, not greater than 5%. This methodology was successfully applied to the use of various CC determined from original or transformed spectra for the identification of polyethylene, PE, and polypropylene, PP, microplastics in river sediments by μFTIR. The analytical portions of sediments were digested with H₂O₂ and microplastics separated from the remaining particles by density using a saturated NaCl solution. Pearson's, Spearman's and Alternative unweighted and weighted correlation coefficients were studied. The P5»P was estimated by the Bootstrap method that resamples spectra CC between a reference material and microparticle of the same polymer collected from the environment. This resampling allows simulating CC distribution required to estimate its 5th percentile (i.e. P5»P). The FP was estimated from the probability of a particle not from the same polymer type of the reference material producing a CC greater than P5»P. Some unweighted and weighted CC determined from original or transformed spectra were successfully used to identify PE or PP particles in river sediments. More particle spectra need to be collected to ensure performance is assessed from a representative diversity of aged polymers with different additives. The spreadsheets used for CC calculations and Bootstrap simulations are made available and can be used for the validation of the identification of other polymer types by μFTIR or ATR-FTIR spectroscopy.

Uncertainty contribution of derivatization in gas chromatography/mass spectrometric analysis

RATIONALE

The purpose of the current work is to realistically assess the uncertainty contribution in gas chromatography/mass spectrometry (GC/MS) analysis originating from less-than-ideal derivatization efficiency.

METHODS

As the exemplary analytical method a two-step derivatization method with KOH and BSTFA (N,O-bis(trimethylsilyl)trifluoroacetamide), applied for the analysis of fatty acid triglycerides (using real measurement data), was selected. The derivatization efficiencies were in the range 0.89-1.04. In this study, two approaches for bottom-up uncertainty evaluation were compared: the traditional GUM approach and the Monte Carlo method (MCM). Both were used with and without taking correlation between input quantities into account.

RESULTS

The most reliable uncertainty estimates were in the range 0.07-0.08 (expanded uncertainties at 95% coverage probability). A strong negative correlation was found between the slope and intercept of the calibration graph (r = -0.71) and it markedly influenced the uncertainty estimate of derivatization efficiency. The MCM was found to give somewhat higher uncertainty estimates, which are considered more realistic.

CONCLUSIONS

Derivatization directly affects the analysis result. Thus, in the case of this exemplary analysis, just derivatization alone (i.e. if all other uncertainty sources are neglected) causes relative expanded uncertainty around 8%, being thus an important and in some cases the dominant uncertainty contributor.

Assessing the measurement uncertainty of qualitative analysis in the clinical laboratory

Measurement uncertainty is a parameter that is associated with the dispersion of measurements. Assessment of the measurement uncertainty is recommended in qualitative analyses in clinical laboratories; however, the measurement uncertainty of qualitative tests has been neglected despite the introduction of many adequate methods. We herein provide an overview of three reasonable statistical methods for quantifying the measurement uncertainties of qualitative assays, namely Bayes’ theorem, the normal distribution method, and the information theoretic approach. Unlike in quantitative analysis, the measurement uncertainty of qualitative analysis is expressed using a conditional probability, likelihood ratio, and entropy. With the necessary theoretical background, the practical applications for clinical laboratories are also provided using statistical calculations. Using statistical approaches, we hope that our review will contribute to the use of measurement uncertainty in qualitative analyses in the clinical laboratory environment.