This method allows for prediction of subsurface fluid properties (e.g., phase or API gravity) using gas chromatogram data of a small-volume extract. Small volume equates to microliter scale volume (or milligram scale weight) from a subsurface rock sample, where a fluid test may not be available for analysis. The method may also be applied to petroleum liquid samples where drilling fluid or other contaminants preclude accurate direct property measurement. Gas chromatographic data is calibrated to measured petroleum properties; preferably local oils from the same petroleum system, however, a general global calibration can also be used.

Methods for classification of hydrocarbon mixtures that include performing two-dimensional gas chromatography on a hydrocarbon mixture to obtain a chromatogram using a two-dimensional gas chromatograph equipped with a flame ionization detector, a reversed phase column configuration with a primary mid-polar or polar column and a secondary non-polar column, and a standard mixture. Classification is performed in which groups of hydrocarbons are identified and labeled based on peaks associated with the standard mixture, after which a quantification process is performed.

A confirmation ion ratio allowable value calculation unit calculates a confirmation ion ratio allowable value when a target ion and confirmation ions are interchanged based on a preset confirmation ion ratio allowable value, and a peak identification processing unit identifies mass peaks of the target ion and the confirmation ions based on the confirmation ion ratio allowable value. A peak waveform processing unit calculates peak areas of the target ion and the confirmation ions, and a calibration curve creation unit creates calibration curves for quantification based on the target ion and the confirmation ions from a peak area of a target compound included in a standard sample. A quantitative value calculation unit obtains quantitative values while referring to a calibration curve corresponding to a peak area for a target compound included in an unknown sample. A quantitative analysis result display processing unit displays the quantitative values and chromatogram peak waveforms.

When performing an analysis of the difference between a specific sample group and a nonspecific sample group, a principle component analysis processing unit (33) performs principle component analysis on a collection of a plurality of mass spectrums created from data obtained for a single specific sample, and a characteristic spectrum acquisition unit (34) acquires a characteristic spectrum for each of a plurality of principle components using factor loadings. A spectrum similarity calculation unit (35) calculates the similarities between all mass spectrums and the characteristic spectrum for each sample, and obtains a representative value for the same. The similarity representative value for each sample is obtained for all the characteristic spectrums. A difference determination unit (36) checks whether there is a significant difference between the distribution of the similarity representative values of the specific sample group and the distribution of the similarity representative values of the nonspecific sample group and determines that the characteristic spectrum which is the source of the similarities having a significant difference is a difference spectrum. The difference spectrum reflects component information characterizing a sample group difference, so a component identification unit (37) searches for the difference spectrum in a library to identify a component. This makes it possible to perform different analysis without performing spectrum peak detection.

Peaks are detected on a mass chromatogram at multiple m/z ratios characterizing a target component, and the detected peaks are classified into groups according to their occurrence time. The measured mass spectrum is acquired for each group, the measured mass spectrum and standard mass spectrum of the target component are matched for each m/z, and the standard mass spectrum is normalized by multiplying it by the same scale factor for all the m/z ratios such that it does not exceed the peak intensities on the measured mass spectrum. The quantitation ion m/z peak intensity on the normalized standard mass spectrum is then examined, and if this intensity exceeds a preset threshold and the confirmation ion ratio determined based on the measured mass spectrum obtained for the target component is outside a reference range, then that target component is taken as a narrowed result candidate.

Synthetic Cannabinoids are the most complex branch of designer drugs encountered in forensic chemistry. A screening method has been developed that can accurately identify the correct structural category of an unknown Synthetic Cannabinoid. Knowledge of this information is very important when no reference data or standards are available since certain sub-categories contain Schedule I Controlled Dangerous Substances. The Bridge portion of these molecules present a unique carbonyl band cluster within a small 200 wavenumber interval of the mid-infrared region that can only exist in vapor phase through GC/FTIR light-pipe technology or heated static vapor cell FTIR. This special relationship is not applicable to any other forms of solid phase vibrational spectroscopy (FTIR, RAMAN) including GC/FTIR solid-deposit techniques. The carbonyl frequency from the Bridge is used as the first step in the screening process which separates the entire forensically encountered class of Synthetic Cannabinoids into 35 sub-categories. Additional bands within the cluster from secondary functional groups, rotational isomerism, and fermi resonance add further refinement within these categories.

When a user inputs samples per group (S2), a sample tree and a peak matrix are generated. Peak lists per group are shown in the sample tree, and m/z values and signal strength values from the peak lists are coordinates in the peak matrix. Then, a multivariate analysis is applied to the generated peak matrix (S3-S4). The sample tree, peak matrix, score plot, and loading plot are displayed on an analysis main screen. When the user clicks a desired plotted point on the loading plot, a row indicative of a corresponding peak on the peak matrix is discriminated (S5-S7). When the user deletes a checkmark corresponding to the discriminated row, the multivariate analysis is applied to the peak matrix from which the peak has been excluded, and the score plot and other data are updated (S9-S10). When the separation between groups is known from the score plot as failure, the excluded peak may be visually determined as a marker contributing to the group separation. Thus, the marker search can be accurately performed in a simplified manner.

One embodiment of the invention is directed to a method of analyzing liquid chromatography data. The method comprises collecting, by a data processing system, first volume fractions data from a first liquid chromatography column for a first absorbance wavelength of light 1 from a first run of a liquid chromatography process on a mixture, wherein the first liquid chromatography column screens for a first characteristic of the mixture. The method further comprising, normalizing a first relative peak area for a first volume of a component of interest in the mixture for the first absorbance wavelength 1 to obtain a first set of purity quotient values PQ1, collecting second volume fractions data from a second liquid chromatography column for a second absorbance wavelength of light 2 from a second run of a liquid chromatography process on the mixture, wherein the second liquid chromatography column screens for a second characteristic of the mixture, normalizing a second relative peak area for the second volume of the component of interest in the mixture for the second absorbance wavelength 2 to obtain a second set of purity quotient values PQ2, storing the values PQ1 and PQ2 in a memory, calculating a difference between values PQ1 and PQ2 for each volume fraction location of the first and second volumes to obtain a first set of purity quotient difference (PQD) values, displaying in a graphical display the first set of PQD values, and determining which volume fractions to pool together based on the display of the first set of PQD values.

A method for determining a grade of black tea by HPLC detection belongs to the field of tea grade determination. The specific steps are as follows: adding known black tea powder samples of different grades into boiling water of 95-100? ? C. for extraction, and filtering with a filter membrane with a pore size in a range of 0.20-0.25 um to obtain black tea sample liquid; measuring contents of ten components by peak area normalization method; standardizing data of the contents of the ten components in a black tea sample solution; carrying out unsupervised principal component analysis; carrying out supervised partial least squares discriminant analysis; carrying out hierarchical clustering analysis on the basis of partial least squares discriminant analysis, and finally establishing a tea grade discrimination model based on HPLC.

A method for estimating a state of wax in a rubber compound according to a first aspect includes the following: acquiring a chromatogram of an extracted component, by separating components thereof, in which the wax is extracted from the rubber compound including the wax; and deriving a distribution of normal hydrocarbons in the wax based on the chromatogram.