Abstract: The disclosure relates generally to analytical measurement and, more specifically, to the use of ion spectrometry, including mass analysis, for the quantitation and chemical characterization (e.g., identification) of biological or synthetic materials using two different ion spectrometric measurement cycles. The methods may be employed fruitfully in particular with analyte molecules of low molecular weight, such as metabolites.

Abstract: The disclosure relates generally to analytical measurement and, more specifically, to the use of ion spectrometry, including mass analysis, for the quantitation and chemical characterization (e.g., identification) of biological or synthetic materials using two different ion spectrometric measurement cycles. The methods may be employed fruitfully in particular with analyte molecules of low molecular weight, such as metabolites.

Abstract: The present invention relates to a mass spectrometric method for determining (predicting) the presence or absence of a chemical element in an analyte which provides valuable information towards reduction of complexity for annotating a chemical formula to the analyte. The method is based on representing a measured isotopic pattern of an analyte as a feature vector and assigning the feature vector to the presence/absence class using a machine learning algorithm, like a support vector machine (SVM) or an artificial neural network (NN).

Abstract: The present invention relates to a mass spectrometric method for determining (predicting) the presence or absence of a chemical element in an analyte which provides valuable information towards reduction of complexity for annotating a chemical formula to the analyte. The method is based on representing a measured isotopic pattern of an analyte as a feature vector and assigning the feature vector to the presence/absence class using a machine learning algorithm, like a support vector machine (SVM) or an artificial neural network (NN).