  Raman spectroscopy in combination with machine learning has significant
promise for applications in clinical settings as a rapid, sensitive, and
label-free identification method. These approaches perform well in classifying
data that contains classes that occur during the training phase. However, in
practice, there are always substances whose spectra have not yet been taken or
are not yet known and when the input data are far from the training set and
include new classes that were not seen at the training stage, a significant
number of false positives are recorded which limits the clinical relevance of
these algorithms. Here we show that these obstacles can be overcome by
implementing recently introduced Entropic Open Set and Objectosphere loss
functions. To demonstrate the efficiency of this approach, we compiled a
database of Raman spectra of 40 chemical classes separating them into 20
biologically relevant classes comprised of amino acids, 10 irrelevant classes
comprised of bio-related chemicals, and 10 classes that the Neural Network has
not seen before, comprised of a variety of other chemicals. We show that this
approach enables the network to effectively identify the unknown classes while
preserving high accuracy on the known ones, dramatically reducing the number of
false positives while preserving high accuracy on the known classes, which will
allow this technique to bridge the gap between laboratory experiments and
clinical applications.
