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Odorous clothing can be an annoying and unpleasant problem, particularly when odorants are not effectively removed via laundering. Cotton and polyester knit fabrics were soiled with three selected odorants, representing different polarities and lipophilicities (i.e. octanoic acid, 2-nonenal, dodecane). Fabrics were subjected to 1, 5 and 10 soil/wash cycles using a regular liquid detergent (Tide® Free and Gentle) or a sport liquid detergent (Tide® Plus Febreze Sport). Odorants released into the headspace were collected using solid phase micro-extraction, and odorants retained within the fabric were collected using solvent extraction. Analysis of odorant peaks was carried out using gas chromatography-flame ionization detection. Prior to laundering, higher amounts of all odorants were released into the headspace above polyester fabrics than above cotton fabrics. Cotton fabrics retained more octanoic acid within the fabric and lower amounts of 2-nonenal than polyester. Laundering was more effective at removing odorants from cotton than from polyester, and the polar octanoic acid was more readily removed than the two non-polar odorants from both fabrics. Accumulation of odorants occurred as soil/wash cycles increased from 1 to 5 cycles. However, between 5 and 10 soil/wash cycles the amounts of compounds did not significantly increase, with significantly lower amounts of octanoic acid extracted from cotton at 10 cycles compared to 5 cycles. The results from this study indicate that incomplete removal of odorants during washing, especially from oleophilic polyester fabrics, is a cause for odor build-up in clothing.

Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body&rsquo, s water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC&times, GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine.

Although polyester is well known for smelling strongly after wear, little is known about the propensity of nylon to retain and emit body odor. In this study, we investigated whether odor intensity differed between nylon and polyester fabrics. A secondary aim was to compare odor on fabrics frozen prior to sensory assessment with fabrics stored at room temperature. Eight participants wore T-shirts with fabrics in the underarm. Odor intensity was rated by 13 assessors. Odor reduction rate (ORR) was measured using ISO 17299-3. Overall, no differences were found in odor intensity between nylon and polyester. Any differences found between the two fabrics were likely dependent on the individual who wore the fabric. The ORR was higher for nylon than polyester, indicating that nylon absorbed more odorants. There was some evidence to suggest that odor intensity could increase on nylon fabrics stored at room temperature, but this was less apparent for polyester.

Cluster resolution feature selection (CR-FS) is a hybrid feature selection algorithm which involves the evaluation of ranked variables via sequential backward elimination (SBE) and sequential forward selection (SFS). The implementation of CR-FS requires two main inputs, namely, start and stop number. The start number is the number of the highly ranked variables for the SBE while the stop number is the point at which the search for additional features during the SFS stage is halted. The setting of these critical parameters has always relied on trial and error which introduced subjectivity in the results obtained. The start and stop numbers are known to vary with each dataset. Drawing inspiration from overlapping coefficients, a method for comparing two probability density functions, empirical equations toward the estimation of start and stop number for a dataset were developed. All of the parameters in the empirical equations are obtained from the comparisons of the two probability density functions except the constant termed d. The equations were optimized using three real-world datasets. The optimum range of d was determined to be 0.48 to 0.57. An implementation of CR-FS using two new datasets demonstrated the validity of this approach. Partial least squares discriminant analysis (PLS-DA) model prediction accuracies increased from 90 and 96 to 100% for both datasets using start and stop numbers calculated with this approach. Additionally, there was a twofold increase in the explained variance captured in the first two principal components. Graphical abstract Here, we describe how to determine the start and stop numbers for an automated feature selection routine, ensuring that you get the best model you can for your data with minimal effort.

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a powerful tool for the analysis of complex mixtures, and it is ideally suited to discovery studies where the entire sample is potentially of interest. Unfortunately, when unit mass resolution mass spectrometers are used, many detected compounds have spectra that do not match well with libraries. This could be due to the compound not being in the library, or the compound having a weak/nonexistent molecular ion cluster. While high-speed, high-resolution mass spectrometers, or ion sources with softer ionization than 70 eV electron impact (EI) may help with some of this, many GC×GC systems presently in use employ low-resolution mass spectrometers and 70 eV EI ionization. Scripting tools that apply filters to GC×GC-TOFMS data based on logical operations applied to spectral and/or retention data have been used previously for environmental and petroleum samples. This approach rapidly filters GC×GC-TOFMS peak tables (or raw data) and is available in software from multiple vendors. In this work, we present a series of scripts that have been developed to rapidly classify major groups of compounds that are of relevance to metabolomics studies including: fatty acid methyl esters, free fatty acids, aldehydes, alcohols, ketones, amino acids, and carbohydrates.

Human axillary sweat is a poorly explored biofluid within the context of metabolomics when compared to other fluids such as blood and urine. In this paper, we explore the volatile organic compounds emitted from two different types of fabric samples (cotton and polyester) which had been worn repeatedly during exercise by participants. Headspace solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) were employed to profile the (semi)volatile compounds on the fabric. Principal component analysis models were applied to the data to aid in visualizing differences between types of fabrics, wash treatment, and the gender of the subject who had worn the fabric. Statistical tools included with commercial chromatography software (ChromaTOF) and a simple Fisher ratio threshold-based feature selection for model optimization are compared with a custom-written algorithm that uses cluster resolution as an objective function to maximize in a hybrid backward-elimination forward-selection approach for optimizing the chemometric models in an effort to identify some compounds that correlate to differences between fabric types. The custom algorithm is shown to generate better models than the simple Fisher ratio approach. Graphical Abstract A route from samples and questions to data and then answers.