Abstract. Exhaustive analysis of chemical measurements requires considerable expenditure of time and personnel. However, many aspects of this can be automated by translating manual work into objective algorithmic routines. To this end, we developed adaptable software for gas chromatography data and
validated analysis steps using whisky samples. We employed an unspecific, larger, in-house volatile organic compound (VOC) database and another specifically curated database of
217 known whisky chemicals, to automate database-matching based on mass spectra and retention indices. We managed to reduce the amount of necessary
interaction, facilitated complex analysis for the less experienced user, and showed that characteristic whisky components constituted the majority
of detected molecules in all 16 analyzed samples. With this approach, we present a decisive contribution towards the automated assessment of aroma
profiles in food.
We derived and implemented a linear classification algorithm for the prediction of a molecule’s odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules as features for algorithmic treatment and uses conditional probabilities combined with tf-idf values. In addition to the prediction of molecular odor, OWSum provides insights into properties of the dataset and allows to understand how algorithmic classifications are reached by quantitatively assigning structural patterns to odors. This provides chemists with an intuitive understanding of underlying interactions. To deal with ambiguities of the natural language used to describe odor, we introduced descriptor overlap as a metric for the quantification of semantic overlap between descriptors. Thus, grouping of descriptors and derivation of higher-level descriptors becomes possible. Our approach poses a large leap forward in our capabilities to understand and predict molecular features.
Abstract. A possible way to reduce the size and complexity of
common gas chromatography (GC) systems is the economization of the column
temperature regulation system. To this end, a temperature compensation
method was developed and validated on a benchtop GC-PDD (pulsed discharge
detector) with ethene. An in-house-developed algorithm correlates the
retention index of a test gas to the retention index of a previously
selected reference gas. To investigate further methods of cost reduction,
commercial gas sensors were tested as cheap, sensitive, and versatile
detectors. Therefore, CO2 was chosen as a naturally occurring reference gas, while ethene was chosen as a maturity marker for climacteric fruits and
hence as a test gas. A demonstrator, consisting of a simple
syringe injection system, a PLOT (porous layer open tubular) column boxed in
a polystyrene-foam housing, a commercial MOS (metal-oxide semiconductor)
sensor for the test gas, and a CO2-specific IR (infrared) sensor, was
used to set up a simple GC system and to apply this method on
test measurements. Sorption parameters for ethene and CO2 were
determined via a van 't Hoff plot, where the entropy S was −11.982 J mol−1 K−1 ΔSEthene0 and 1.351 J mol−1 K−1 ΔSCarbondioxide0, and the enthalpy H was −20.622 kJ mol−1 ΔHEthene0 and −14.792 kJ mol−1 ΔHCarbondioxide0, respectively. Ethene (100 ppm) measurements
revealed a system-specific correction term of 0.652 min. Further
measurements of ethene and interfering gases revealed a mean retention time
for ethene of 3.093 min; the mean predicted retention time is 3.099 min. The
demonstrator was able to identify the test gas, ethene, as a function of
the reference gas, CO2, in a first approach, without a column heating
system and in a gas mixture by applying a temperature compensation algorithm
and a system-specific holdup time correction term.
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