Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes analysis systems and methods, comprising impinging radiation from a source on an analyte, detecting energy interactions resulting from the impinging radiation using a detector, adjusting a signal emitted from the radiation detector using a pre-processing method to emphasize specific features of that signal, using a machine learning module to interpret specific parts of the adjusted signal, producing a quantitative and/or qualitative model using the machine learning module, and applying the quantitative and/or qualitative model to a separate energy interaction. The quantitative and qualitative models derived from this training can be applied to new detector inputs from the same or similar instruments. Other embodiments are described.

Abstract: Provided herein are methods and systems for improved material sample analysis and quality control. A computing device may receive sample data associated with a plurality of material samples. The computing device may determine a first subset of the plurality of material samples and a second subset of the plurality of material samples. The computing device may determine the first subset based on a plurality of reference values and a plurality of analysis thresholds. The first subset may include samples associated with acceptable XRF spectra. The second subset may include samples associated with unacceptable XRF spectra. The computing device may generate and manipulate charts, graphs, or other visual displays of the data underlying the first subset and/or the second subset.

Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes an analysis method that comprises impinging radiation from a source on an analyte, detecting the energy interactions resulting from the impinging radiation using a radiation detector, adjusting the signal from the radiation detector using a machine learning module to emphasize specific parts of the detector signal, training the machine learning module in a supervised or unsupervised manner, producing quantitative and qualitative models using the machine leaning module, and then applying the machine learning module to additional energy interactions. The signal received by the detector can be preprocessed to emphasize specific parts of the detector signal, which is then mapped to a machine learning module for training in a supervised or unsupervised manner.

Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes an analysis method that comprises impinging radiation from a source on an analyte, detecting the energy interactions resulting from the impinging radiation using a radiation detector, adjusting the signal from the radiation detector using a machine learning module to emphasize specific parts of the detector signal, training the machine learning module in a supervised or unsupervised manner, producing quantitative and qualitative models using the machine leaning module, and then applying the machine learning module to additional energy interactions. The signal received by the detector can be preprocessed to emphasize specific parts of the detector signal, which is then mapped to a machine learning module for training in a supervised or unsupervised manner.