Systems and methods for identifying substances contained in a sample

A computer system for identifying one or more substances contained in a sample based on a measured spectrum obtained from the sample via optical spectroscopy, the system comprising:

• an interface configured to obtaining the measured spectrum, and to access at least one library comprising a plurality of reference spectra of known pure substances;
• a pre-processing module configured to pre-process the measured spectrum and the plurality of reference spectra by applying a min-max normalization to each spectrum such that intensity ranges of all spectra are within an interval [0, 1], and to ensure that all spectra have a same spectral range with a same spectral resolution;
• a single substance search module configured to perform a single substance search by:
• m) solving an optimization problem with a first set of hyperparameters for all possible pairs of the measured spectrum with the reference spectra in the library, the optimization problem being formulated as a single optimization problem for regression, substance filtering and spectral corrections, the first set of hyperparameters being configured to optimally adapt the reference spectra to the measured spectrum for a spectral comparison;
• and
• n) determining a similarity score for each pair of measured spectrum and adapted reference spectrum using a similarity metric scoring a spectral match of two spectra, and ranking corresponding reference spectra in accordance with the similarity scores of the respective adapted reference spectra in a HIT list;
• a limited multi-substance search module configured to performing a limited multi-substance search by:
• o) selecting a predefined number M of reference spectra from the HIT list starting with the reference spectrum having a highest similarity score and standardizing the selected reference spectra;
• p) solving the optimization problem with a second set of hyperparameters with the measured spectrum and all standardized reference spectra, wherein the second set of hyperparameters being configured such that substance filtering is emphasized to result in a set of mixture substance candidates;
• q) solving the optimization problem with a third set of hyperparameters with the measured spectrum and N standardized reference spectra associated with the mixture substance candidates, wherein the third set of hyperparameters being configured such that filtering effect is attenuated in relation to the second set of hyperparameters so that substances are discarded until all remaining P substances have a relative weight greater than a predefined threshold of significance, the relative weight being the normalized weight of the result of the optimization problem;
• r) computing a plurality of composition batches from the normalized reference spectra associated with the P remaining substances such that a composition batch is generated for each possible subset of the P remaining substances which includes the remaining substance with the highest relative weight;
• s) solving the optimization problem for each composition batch with the first set of hyperparameters resulting in a composite spectrum per composition batch;
• and
• t) evaluating a quality of each composite spectrum by evaluating the results of the optimization problem, as obtained by using the first set of hyperparameters, with said similarity metric, and providing the one or more substances associated with composite spectra having a quality above a predefined quality threshold as set of substances contained in the sample.

A computer program product for identifying one or more substances contained in a sample based on a measured spectrum obtained from the sample via optical spectroscopy, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:

• obtain the measured spectrum, and accessing at least one library comprising a plurality of reference spectra of known pure substances;
• pre-process the measured spectrum and the plurality of reference spectra by applying a min-max normalization to each spectrum such that intensity ranges of all spectra are within an interval [0, 1], and ensuring that all spectra have a same spectral range with a same spectral resolution;
• perform a single substance search by:
• y) solving an optimization problem with a first set of hyperparameters for all possible pairs of the measured spectrum with the reference spectra in the library, the optimization problem being formulated as a single optimization problem for regression, substance filtering and spectral corrections, the first set of hyperparameters being configured to optimally adapt the reference spectra to the measured spectrum for a spectral comparison;
• and
• z) determining a similarity score for each pair of measured spectrum and adapted reference spectrum using a similarity metric scoring a spectral match of two spectra, and ranking corresponding reference spectra in accordance with the similarity scores of the respective adapted reference spectra in a HIT list;
• perform a limited multi-substance search by:
• aa) selecting a predefined number M of reference spectra from the HIT list starting with the reference spectrum having a highest similarity score, and standardizing the selected reference spectra;
• bb) solving the optimization problem with a second set of hyperparameters with the measured spectrum and all standardized reference spectra, wherein the second set of hyperparameters being configured such that substance filtering is emphasized to result in a set of mixture substance candidates;
• cc) solving the optimization problem with a third set of hyperparameters with the measured spectrum and N standardized reference spectra associated with the mixture substance candidates, wherein the third set of hyperparameters being configured such that a filtering effect is attenuated in relation to the second set of hyperparameters so that substances are discarded until all remaining P substances have a relative weight greater than a predefined threshold of significance, the relative weight being the normalized weight of the result of the optimization problem;
• dd) computing a plurality of composition batches from the normalized reference spectra associated with the P remaining substances such that a composition batch is generated for each possible subset of the P remaining substances which includes the remaining substance with the highest relative weight;
• ee) solving the optimization problem for each composition batch with the first set of hyperparameters resulting in a composite spectrum per composition batch;
• and
• ff) evaluating a quality of each composite spectrum by evaluating the results of the optimization problem, as obtained by using the first set of hyperparameters, with said similarity metric, and providing the one or more substances associated with composite spectra having a quality above a predefined quality threshold as set of substances contained in the sample.