Abstract: Systems and methods are disclosed for determining if the dynamic range of quantitation in mass spectrometry can be extended. A DIA method is performed on a sample for a compound of interest at each acquisition time of a plurality of acquisition times. A plurality of product ion spectra are produced for each window of two or more precursor ion mass selection windows. A known product ion of the compound of interest is selected. Two or more XICs are calculated from two or more different precursor ion windows for the known product ion. A ratio of one XIC of the two or more XICs to at least one other XIC of the two or more XICs is calculated. If the ratio is above a threshold, the XIC is used in the quantitation. If not, two or more XICs can be combined into a single XIC that is used for the quantitation.

Abstract: Systems and methods are provided for calculating the area of a peak profile using information from one or more correlated peak profiles. One or more compounds are separated from a mixture over time using a separation device. Traces of the one or more compounds are monitored during the separation using a tandem mass spectrometer. A plurality of intensity measurements are received using a processor. A first peak profile for a compound of interest is detected from the plurality of intensity measurements for a first trace and one or more correlated peak profiles for the compound of interest are detected from the plurality of intensity measurements for one or more other traces using the processor. An area of the first peak profile is calculated based on the one or more correlated peak profiles using the processor.

Abstract: A system is disclosed for identifying a precursor ion of a product ion in a scanning DIA experiment. A precursor ion mass selection window is scanned across a precursor ion mass range of interest, producing a series of overlapping windows across the precursor ion mass range. Each overlapping window is fragmented and mass analyzed, producing a plurality of product ion spectra for the mass range. A product ion is selected from the spectra. Intensities for the selected product ion are retrieved for at least one scan across the mass range producing a trace of intensities versus precursor ion m/z. A matrix multiplication equation is created that describes how one or more precursor ions correspond to the trace for the selected product ion. The matrix multiplication equation is solved for one or more precursor ions corresponding to the selected product ion using a numerical method.

Abstract: Systems and methods are disclosed for identifying actual XIC peaks of compounds of interest from samples so that more accurate expected retention times and more accurate expected retention time windows can be calculated. In one system, an actual XIC peak is identified using standard samples. The ratio of the quantity of the compound of interest in any two different samples is known, so this ratios is compared to the intensities of the XIC peak calculated in the two samples to identify an actual XIC peak. In another system, an actual XIC peak is identified using information about other compounds of interest in a plurality of samples. It is known that the XIC peaks of compounds of interest in the same samples have a similar distribution of retention times across those samples, so the distributions of retention times of XIC peaks are compared to identify actual XIC peaks.

Abstract: A system is disclosed for identifying a precursor ion of a product ion in a scanning DIA experiment. A precursor ion mass selection window is scanned across a precursor ion mass range of interest, producing a series of overlapping windows across the precursor ion mass range. Each overlapping window is fragmented and mass analyzed, producing a plurality of product ion spectra for the mass range. A product ion is selected from the spectra. Intensities for the selected product ion are retrieved for at least one scan across the mass range producing a trace of intensities versus precursor ion m/z. A matrix multiplication equation is created that describes how one or more precursor ions correspond to the trace for the selected product ion. The matrix multiplication equation is solved for one or more precursor ions corresponding to the selected product ion using a numerical method.

Abstract: An m/z range of an ion beam is divided into two or more precursor ion mass selection windows. A pattern of two or more different window m/z ranges to be used during two or more successive cycles for at least one precursor ion mass selection window is determined. The pattern includes an initial window m/z range and one or more successively different window m/z ranges. Each of the one or more successively different window m/z ranges includes at least a portion of the initial window m/z range. A tandem mass spectrometer is instructed to select and fragment the two or more precursor ion mass selection windows during each cycle of a plurality of cycles and to repeatedly use the pattern for each group of two or more successive cycles of the plurality of cycles for the selection and fragmentation of the at least one precursor ion mass selection window.

Abstract: One or more known compounds of a sample are ionized. At least one precursor ion corresponding to a compound of the one or more known compounds is selected and fragmented, producing a product ion mass spectrum for the precursor ion. An m/z tolerance probability function that varies from 1 to 0 with increasing values of an m/z difference between two mass peaks and that includes one or more values between 1 and 0 is received. A library product ion mass spectrum for the at least one compound is retrieved from a memory. An m/z difference between at least one experimental product ion mass peak in the product ion mass spectrum and at least one library product ion mass peak in the library product ion mass spectrum is calculated. An m/z tolerance probability that determines if the two peaks are corresponding peaks is calculated from the m/z difference using the probability function.

Abstract: An m/z range of an ion beam is divided into two or more precursor ion mass selection windows. A pattern of two or more different window m/z ranges to be used during two or more successive cycles for at least one precursor ion mass selection window is determined. The pattern includes an initial window m/z range and one or more successively different window m/z ranges. Each of the one or more successively different window m/z ranges includes at least a portion of the initial window m/z range. A tandem mass spectrometer is instructed to select and fragment the two or more precursor ion mass selection windows during each cycle of a plurality of cycles and to repeatedly use the pattern for each group of two or more successive cycles of the plurality of cycles for the selection and fragmentation of the at least one precursor ion mass selection window.

Abstract: One or more known compounds of a sample are ionized. At least one precursor ion corresponding to a compound of the one or more known compounds is selected and fragmented, producing a product ion mass spectrum for the precursor ion. An m/z tolerance probability function that varies from 1 to 0 with increasing values of an m/z difference between two mass peaks and that includes one or more values between 1 and 0 is received. A library product ion mass spectrum for the at least one compound is retrieved from a memory. An m/z difference between at least one experimental product ion mass peak in the product ion mass spectrum and at least one library product ion mass peak in the library product ion mass spectrum is calculated. An m/z tolerance probability that determines if the two peaks are corresponding peaks is calculated from the m/z difference using the probability function.

Abstract: Systems and methods are provided to perform dead time correction. An observed ion count rate is obtained using a non-paralyzable detection system of a mass spectrometer. The detection system includes an ion detector, a comparator/discriminator, a mono-stable circuit and a counter. The non-paralyzable detection system exhibits dead time extension at high count rates. The extension of the dead time occurs because the mono-stable circuit requires a rising edge to trigger and can only be triggered again after the output pulse from the comparator/discriminator has gone low. This allows a second comparator/discriminator pulse arriving just before the end of the dead time started by a first comparator/discriminator pulse to extend the dead time to the trailing edge of the second comparator/discriminator pulse. A true ion count rate is calculated by performing dead time correction of the observed ion count rate.

Abstract: Systems and methods are provided for calculating the area of a peak profile using information from one or more correlated peak profiles. One or more compounds are separated from a mixture over time using a separation device. Traces of the one or more compounds are monitored during the separation using a tandem mass spectrometer. A plurality of intensity measurements are received using a processor. A first peak profile for a compound of interest is detected from the plurality of intensity measurements for a first trace and one or more correlated peak profiles for the compound of interest are detected from the plurality of intensity measurements for one or more other traces using the processor. An area of the first peak profile is calculated based on the one or more correlated peak profiles using the processor.

Abstract: Systems and methods are provided to perform dead time correction. An observed ion count rate is obtained using a non-paralyzable detection system of a mass spectrometer. The detection system includes an ion detector, a comparator/discriminator, a mono-stable circuit and a counter. The non-paralyzable detection system exhibits dead time extension at high count rates. The extension of the dead time occurs because the mono-stable circuit requires a rising edge to trigger and can only be triggered again after the output pulse from the comparator/discriminator has gone low. This allows a second comparator/discriminator pulse arriving just before the end of the dead time started by a first comparator/discriminator pulse to extend the dead time to the trailing edge of the second comparator/discriminator pulse. A true ion count rate is calculated by performing dead time correction of the observed ion count rate.

Abstract: Systems and methods are used to predict intensities for points not measured or not measured with a high degree of confidence of a peak using a peak predictor. A set of data is selected from the plurality of intensity measurements that includes a peak. Confidence values are assigned to each data point in the set of data producing a plurality of confidence value weighted data points. A peak predictor is selected. The peak predictor is applied to the plurality of confidence value weighted data points of the peak that have confidence values greater than a first threshold level using the prediction module, producing predicted intensities for data points of the peak not measured and/or measured data points of the peak that have confidence values less than or equal to a second threshold level. The confidence values can include system confidence values, predictor confidence values, or any combination of the two.

Abstract: A plurality of scans of a sample are performed, producing a plurality of mass spectra. Neighboring mass spectra of the plurality of mass spectra are combined into a collection of mass spectra based on sample location, time, or mass. A background noise estimate is calculated for the collection of mass spectra. The collection of mass spectra is filtered using the background noise estimate, producing a filtered collection of one or more mass spectra. Quantitative or qualitative analysis is performed using the filtered collection of one or more mass spectra. The background noise estimate is calculated by dividing the collection of mass spectra into two or more windows, for example. For each window of the two or more windows, all spectra within each window are combined, producing a combined spectrum for each of the two or more windows. For each combined spectrum, a background noise is estimated.

Abstract: Singular spectrum analysis is used to detect a feature from mass spectrometry data. A plurality of scans of a sample is performed producing mass spectrometry data using a spectrometer. A singular spectrum analysis is performed on the mass spectrometry data using a fixed window width in which one or more components other than the highest ranked component are grouped in a set and the one or more components grouped in the set are summed producing reconstructed data using the processor. A feature of the mass spectrometry data is detected by analyzing an aspect of the reconstructed data using the processor. Analyzing an aspect of the reconstructed data includes using pairs of zero crossings in the reconstructed data to detect bounds on a location of the feature in the mass spectrometry data.

Abstract: Systems and methods are used to predict intensities for points not measured or not measured with a high degree of confidence of a peak using a peak predictor. A set of data is selected from the plurality of intensity measurements that includes a peak. Confidence values are assigned to each data point in the set of data producing a plurality of confidence value weighted data points. A peak predictor is selected. The peak predictor is applied to the plurality of confidence value weighted data points of the peak that have confidence values greater than a first threshold level using the prediction module, producing predicted intensities for data points of the peak not measured and/or measured data points of the peak that have confidence values less than or equal to a second threshold level. The confidence values can include system confidence values, predictor confidence values, or any combination of the two.

Abstract: A plurality of scans of a sample are performed, producing a plurality of mass spectra. Neighboring mass spectra of the plurality of mass spectra are combined into a collection of mass spectra based on sample location, time, or mass. A background noise estimate is calculated for the collection of mass spectra. The collection of mass spectra is filtered using the background noise estimate, producing a filtered collection of one or more mass spectra. Quantitative or qualitative analysis is performed using the filtered collection of one or more mass spectra. The background noise estimate is calculated by dividing the collection of mass spectra into two or more windows, for example. For each window of the two or more windows, all spectra within each window are combined, producing a combined spectrum for each of the two or more windows. For each combined spectrum, a background noise is estimated.

Abstract: Systems and methods are used to predict intensities of a saturated peak using a peak predictor. A set of data is selected from the plurality of intensity measurements that includes a saturated peak. Confidence values are assigned to each data point in the set of data producing a plurality of confidence value weighted data points. A peak predictor is selected. The peak predictor is applied to the plurality of confidence value weighted data points of the saturated peak producing predicted intensities for the saturated peak. The confidence values can include system confidence values, predictor confidence values, or a combination of system confidence values and predictor confidence values. The peak predictor can be a theoretical model, a dynamic model, an artificial neural network, or an analytical function representing a best fit of a plurality of probability density functions to a first set of measured data that includes a representative non-saturated peak.

Abstract: Reference features are updated based on a previous scan during each mass spectrometry scan of a sample. Reference features with reference feature confidence values are generated from a plurality of initial scans. For each subsequent scan of a sample, sample features and sample feature confidence values are calculated. The reference features and sample features are aligned to determine common features. Constants are determined for an equation of mass of the mass spectrometer using confidence weighted regression of the common features. The constants and the equation of mass are used to calculate new mass values for the sample features. The reference features are updated using the sample features and the reference feature confidence values are recalculated in order to maintain the accuracy of reference features for calibration.

Abstract: Groups of correlated representations of variables are identified from a large amount of spectrometry data. A plurality of samples is analyzed and a plurality of measured variables is obtained from a spectrometer. A processor executes a number of steps. The plurality of measured variables is divided into a plurality of measured variable subsets. Principal component analysis followed by variable grouping (PCVG) is performed on each measured variable subset, producing one or more group representations for each measured variable subset and a plurality of group representations for the plurality of measured variable subsets. While the total number of the plurality of group representations is greater than a maximum number, the plurality of group representations is divided into a plurality of representative subsets and PCVG is performed on each subset. PCVG is performed on the remaining the plurality of group representations, producing a plurality of groups of correlated representations of variables.