Wines really are given a distinct identity by the place where their grapes are grown and the wine is made, according to an analysis of red Bordeaux wines. Alexandre Pouget at the University of Geneva, Switzerland, and his colleagues used machine learning to analyse the chemical composition of 80 red wines from 12 years between 1990 and 2007. All the wines came from seven wine estates in the Bordeaux region of France. "We were interested in finding out whether there is a chemical signature that is specific to each of those chateaux that's independent of vintage," says Pouget, meaning one estate's wines would have a very similar chemical profile, and therefore taste, year after year. To do this, Pouget and his colleagues used a machine to vaporise each wine and separate it into its chemical components.

There are many challenges associated with analysing gas chromatography - mass spectrometry (GC-MS) data. Many of these challenges stem from the fact that electron ionisation can make it difficult to recover molecular information due to the high degree of fragmentation with concomitant loss of molecular ion signal. With GC-MS data there are often many common fragment ions shared among closely-eluting peaks, necessitating sophisticated methods for analysis. Some of these methods are fully automated, but make some assumptions about the data which can introduce artifacts during the analysis. Chemometric methods such as Multivariate Curve Resolution, or Parallel Factor Analysis are particularly attractive, since they are flexible and make relatively few assumptions about the data - ideally resulting in fewer artifacts. These methods do require expert user intervention to determine the most relevant regions of interest and an appropriate number of components, $k$, for each region. Automated region of interest selection is needed to permit automated batch processing of chromatographic data with advanced signal deconvolution. Here, we propose a new method for automated, untargeted region of interest selection that accounts for the multivariate information present in GC-MS data to select regions of interest based on the ratio of the squared first, and second singular values from the Singular Value Decomposition of a window that moves across the chromatogram. Assuming that the first singular value accounts largely for signal, and that the second singular value accounts largely for noise, it is possible to interpret the relationship between these two values as a probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm was tested by investigating the concentration at which the algorithm can no longer pick out chromatographic regions known to contain signal.

GC-MS is regarded as a gold standard in analysis of chemical composition in samples. However, due to the complexity of the instrument, a substance's retention time (RT) may not stay fixed across multiple GC-MS chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules, consequently, they are unable to handle the complexity of GC-MS data from human breath. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and show the model has very good true position rates (up to 99% for easy data sets and up to 92% for very complex data sets). We compared our model with the popular correlation optimized warping (COW) and show our model has much better overall performance. This method can easily be adapted to other similar data such as those from liquid chromatography.