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After a brief review of electronic nose technology, the use of an Australian electronic nose to identify an unknown odour out of a set of known odours is described. Multivariate supervised learning is accomplished by applying Bayes' theorem to data from metal oxide semiconductor sensors responding to each of a number of target odours. An odour from an unknown source is then assigned a probability of membership of each of the training sets by applying either a Naïve Bayes algorithm to the deemed independent data from each sensor, or to a multinormal distribution of the data. A flat prior (equal probabilities of each outcome) is usually adopted, but for particular situations where one odour is known to predominate, then suitably weighted priors can be used. A source 'none of the above', which has a small likelihood covering the space of the possible sensor responses, is included for completeness. This also avoids the assignment to a source that has an extremely small probability but which is greater than that of any other source. Examples are given of a single source (detecting diabetes from a patient's breath), and three sources of unpleasant odours in a meat processing plant. The 18th century cleric Thomas Bayes gave his name to an elegant statement of the probability of an event – in this case identification of an odour – given some evidence: output from a number of metal oxide semiconductor sensors. Knowing the distributions of outputs for target odours, we assign the probabilities of an unknown odour. The greatest probability wins! (Image credit: E-nose Pty Ltd and D. B. Hibbert.) This article belongs to the 10th Anniversary Collection of RACI and AAS Award papers. [ABSTRACT FROM AUTHOR]

New and emerging digital technologies are making an impact on how we practice science, and this has implications on how we teach science. We introduce the concept of the Electronic Laboratory Notebook (ELN) as used in the research environment and describe how we have implemented this as a tool for providing undergraduate science students with an authentic research experience. We suggest this tool could be similarly used in schools. The online environment provides the opportunity for science to be conducted in the open. Citizen science and large-scale collaborative open science projects have been developed to enhance the collaborative nature of science practice for groups who can contribute to research projects, large or small, from anywhere in the world and to ensure that the record keeping conventions are adhered to. We know that teaching science by offering students opportunities to participate in research projects is considered best practice, and both the technology and the philosophical shift to practice open and collaborative science has made more accessible opportunities for students to engage in authentic science practice. In this paper, we outline the four methods by which research activities can be integrated into teaching, and describe how each of these can be enhanced by open science and the ELN. We also include examples of how educators, from primary schools to tertiary institutions, have partnered with their students on fascinating projects which inspire.

Gas chromatography using a highly polar column combined with field ionization mass spectrometry (FI-MS) is used as a comprehensive two-dimensional (2D) separation approach to analyze mixtures of fatty acid methyl esters (FAMEs). A unique ordered pattern and classification of FAMEs is obtained in a 2D GC x FI-MS separation plot based on the number of carbons, the degree of unsaturation, and a combination of both by which the geometrical, positional, and structural isomers group together. FAMEs with different chain length but identical geometry, position, and degree of unsaturation follow linear patterns. These subclassificafions (linear functions) can provide information about the geometry, position, and structure of unsaturation of an unknown FAME. Non-FAMEs and FAMEs with different functional groups are identified using the ordered separation pattern of the FAMEs in the GC x FI-MS plot and the exact mass data from the FI-MS mode. Measurement of exact mass also acts as a high-resolution separation technique to separate overlapping peaks. The method is illustrated by application to samples of fish, canola, and biodiesel oils and standard mixtures of 37 FAMEs and of [alpha]-linolenic acid methyl ester geometrical isomers. A great wealth of information is achieved in a single run.

The problem of assigning a probability of matching a number of spectra is addressed. The context is in environmental spills when an EPA needs to show that the material from a polluting spill (e.g., oil) is likely to have originated at a particular site (factory, refinery) or from a vehicle (road tanker or ship). Samples are taken from the spill, and candidate sources and are analyzed by spectroscopy (IR, fluorescence) or chromatography (GC or GC/MS). A matching algorithm is applied to pairs of spectra giving a single statistic (R). This can be a point-to-point match giving a correlation coefficient or a Euclidean distance or a derivative of these parameters. The distributions of R for same and different samples are established from existing data. For matching statistics with values in the range {0, 1} corresponding to no match (0) to a perfect match (1) a [beta] distribution can be fitted to most data. The values of R from the match of the spectrum of a spilled oil and of each of a number of suspects are calculated and Bayes' theorem is applied to give a probability of matches between spill sample and each candidate and the probability of no match at all. The method is most effective when simple inspection of the matching parameters does not lead to an obvious conclusion; i.e., there is overlap of the distributions giving rise to dubiety of an assignment. The probability of finding a matching statistic if there were a match to the probability of finding it if there were no match, expressed as a ratio (called the likelihood ratio), is a sensitive and useful parameter to guide the analyst. It is proposed that this approach may be acceptable to a court of law and avoid challenges of apparently subjective opinion of an analyst. Examples of matching the fluorescence and infrared spectra of diesel oils are given.

A general mass spectrometry technique for the characterization of alkanethiol-modified surfaces is presented. Alkanethiol self-assembled onto a gold surface (in this case, peptides were attached to the gold surface via a thiolate bond) was reductively desorbed in 0.05 M KOH in the presence of octadecyl-derivatized silica gel. The peptide adsorbed onto the silica gel, whereupon it could be filtered, washed to remove any salts, and then eluted using a mixture of 4:1 v/v methanol/water. The eluant containing the peptide was injected into a Fourier transform ion-cyclotron resonance mass spectrometer (FTICR/ MS) via electrospray ionization. The spectrum showed no fragmentation of the peptide, demonstrating the gentleness of the technique. This simple procedure is not limited to FTICK/MS and could be adapted to other mass spectrometers.

A generic fill-and-flow channel biosensor with upstream electrodes to determine the extent of interferences in the sample is described. A pair of upstream electrodes poised at a suitable potential allows both the calculation of the extent of removal of interfering agents and the effect of interfering agents at the detector electrode. A model was developed and tested that predicts the concentrations of all species throughout the channel and, hence, the current at each electrode due to each species. This enables correction of the detector electrode current and a more accurate determination of the analyte concentration. The concept was applied to a biosensor for the determination of glucose in the presence of ascorbic acid, acetamidophenol, and uric acid, as well as glucose in wine samples containing polyphenolic interfering agents.