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.

Abstract: Systems and methods for reducing background noise in a mass spectrum. The method includes the following steps of: (a) obtaining an original mass spectrum; (b) determining a noise mass spectrum corresponding to background noise in the original mass spectrum; and (c) determining a corrected mass spectrum by subtracting the noise mass spectrum from the original mass spectrum. Step (b) of the method may include the steps of: A) effecting a transformation of the original mass spectrum into the frequency domain to obtain an original frequency spectrum; B) identifying at least one dominant frequency in the original frequency spectrum; C) generating a noise frequency spectrum by selectively filtering for said dominant frequencies; and D) determining the noise mass spectrum by effecting a transformation of the noise frequency spectrum into the mass domain.

Abstract: A method for identifying a convolved peak is described. A plurality of spectra is obtained. A multivariate analysis technique is used to assign data points from the plurality of spectra to a plurality of groups. A peak is selected from the plurality of spectra. If the peak includes data points assigned to two or more groups of the plurality of groups, the peak is identified as a convolved peak. Principal component analysis is one multivariate analysis technique that is used to assign data points. A number of principal components are selected. A subset principal component space is created. A data point in the subset principal component space is selected. A vector is extended from the origin of the subset principal component space to the data point. One or more data points within a spatial angle around the vector are assigned to a group.

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: Relative noise is a single scalar value that is used to predict the maximum value of the expected noise at any point and is calculated from the measured signal and a mathematical noise model. The mathematical noise model is selected or estimated from an observation that includes statistical and/or numerical modeling based on a population of measurement points. An absolute noise for a plurality of points of the measured signal is estimated. An array of values is calculated by dividing each of a plurality of points of the absolute noise by a corresponding expected noise value calculated from the mathematical noise model. The relative noise is calculated by taking a standard deviation of a plurality of points of the array. The relative noise can be used to calculate scaled background signal noise, filter regions, denoise data, detect false positives from features, calculate S/N, and determine a stop condition for acquiring data.

Abstract: Systems and methods for reducing background noise in a mass spectrum. The method includes the following steps of: (a) obtaining an original mass spectrum; (b) determining a noise mass spectrum corresponding to background noise in the original mass spectrum; and (c) determining a corrected mass spectrum by subtracting the noise mass spectrum from the original mass spectrum. Step (b) of the method may include the steps of: A) effecting a transformation of the original mass spectrum into the frequency domain to obtain an original frequency spectrum; B) identifying at least one dominant frequency in the original frequency spectrum; C) generating a noise frequency spectrum by selectively filtering for said dominant frequencies; and D) determining the noise mass spectrum by effecting a transformation of the noise frequency spectrum into the mass domain.

Abstract: Systems and methods for reducing background noise in a mass spectrum. The method includes the following steps of: (a) obtaining an original mass spectrum; (b) determining a noise mass spectrum corresponding to background noise in the original mass spectrum; and (c) determining a corrected mass spectrum by subtracting the noise mass spectrum from the original mass spectrum. Step (b) of the method may include the steps of: A) effecting a transformation of the original mass spectrum into the frequency domain to obtain an original frequency spectrum; B) identifying at least one dominant frequency in the original frequency spectrum; C) generating a noise frequency spectrum by selectively filtering for said dominant frequencies; and D) determining the noise mass spectrum by effecting a transformation of the noise frequency spectrum into the mass domain.

Abstract: Relative noise is a single scalar value that is used to predict the maximum value of the expected noise at any point and is calculated from the measured signal and a mathematical noise model. The mathematical noise model is selected or estimated from an observation that includes statistical and/or numerical modeling based on a population of measurement points. An absolute noise for a plurality of points of the measured signal is estimated. An array of values is calculated by dividing each of a plurality of points of the absolute noise by a corresponding expected noise value calculated from the mathematical noise model. The relative noise is calculated by taking a standard deviation of a plurality of points of the array. The relative noise can be used to calculate scaled background signal noise, filter regions, denoise data, detect false positives from features, calculate S/N, and determine a stop condition for acquiring data.

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.

Abstract: According to various embodiments, variables are grouped in an unsupervised manner after principal component analysis of a plurality of variables from a plurality of samples. A number of principal components are selected. A subset principal component space is created for those components. A starting variable is selected. A spatial angle is defined around a vector extending from the origin to the starting variable. A set of one or more variables is selected within the spatial angle. The set is assigned to a group. The set is removed from further analysis. The process is repeated starting with the selection of a new starting variable until all groups are found.

Abstract: A method for identifying a convolved peak is described. A plurality of spectra is obtained. A multivariate analysis technique is used to assign data points from the plurality of spectra to a plurality of groups. A peak is selected from the plurality of spectra. If the peak includes data points assigned to two or more groups of the plurality of groups, the peak is identified as a convolved peak. Principal component analysis is one multivariate analysis technique that is used to assign data points. A number of principal components are selected. A subset principal component space is created. A data point in the subset principal component space is selected. A vector is extended from the origin of the subset principal component space to the data point. One or more data points within a spatial angle around the vector are assigned to a group.

Abstract: According to various embodiments, variables are grouped in an unsupervised manner after principal component analysis of a plurality of variables from a plurality of samples. A number of principal components are selected. A subset principal component space is created for those components. A starting variable is selected. A spatial angle is defined around a vector extending from the origin to the starting variable. A set of one or more variables is selected within the spatial angle. The set is assigned to a group. The set is removed from further analysis. The process is repeated starting with the selection of a new starting variable until all groups are found.

Abstract: Systems and methods for reducing background noise in a mass spectrum. The method includes the following steps of: (a) obtaining an original mass spectrum; (b) determining a noise mass spectrum corresponding to background noise in the original mass spectrum; and (c) determining a corrected mass spectrum by subtracting the noise mass spectrum from the original mass spectrum. Step (b) of the method may include the steps of: A) effecting a transformation of the original mass spectrum into the frequency domain to obtain an original frequency spectrum; B) identifying at least one dominant frequency in the original frequency spectrum; C) generating a noise frequency spectrum by selectively filtering for said dominant frequencies; and D) determining the noise mass spectrum by effecting a transformation of the noise frequency spectrum into the mass domain.