WO2013098169A1 - Procédé d'analyse de données issues d'une analyse chimique - Google Patents

Procédé d'analyse de données issues d'une analyse chimique Download PDF

Info

Publication number
WO2013098169A1
WO2013098169A1 PCT/EP2012/076244 EP2012076244W WO2013098169A1 WO 2013098169 A1 WO2013098169 A1 WO 2013098169A1 EP 2012076244 W EP2012076244 W EP 2012076244W WO 2013098169 A1 WO2013098169 A1 WO 2013098169A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
peaks
chromatography
sample
mass spectrometry
Prior art date
Application number
PCT/EP2012/076244
Other languages
English (en)
Inventor
Arno KNORR
Original Assignee
Philip Morris Products S.A
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Philip Morris Products S.A filed Critical Philip Morris Products S.A
Publication of WO2013098169A1 publication Critical patent/WO2013098169A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211—Selection of the most significant subset of features
    • G06F18/2113—Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8886—Analysis of industrial production processes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
    • G01N30/8686—Fingerprinting, e.g. without prior knowledge of the sample components

Definitions

  • the present invention relates to a method for comparing and analysing the results of a chemical analysis of two samples.
  • a number of different chemical analysis techniques are known for analysing chemical or biological samples.
  • spectroscopy the interaction of a sample with electromagnetic radiation of various wavelengths is measured.
  • chromatography a sample is separated into its component chemicals by their different mobility in a medium, such as gas chromatography, wherein the sample is vaporised and the components in gas phase travel at different speeds within the chromatographic matrix and are separated in a chromatography column.
  • mass spectrometry a sample is treated to create ionised fragments of the component molecules, which are then separated by their mass-to-charge ratios and detected.
  • the results of such forms of chemical analysis are often displayed graphically in the form of a series of peaks.
  • the peaks will show absorption by the sample of radiation at particular wavelengths.
  • the peaks will show concentrations or amounts of sample having particular retention times in the column.
  • mass spectrometry the peaks will represent the number of fragments detected at specific mass-charge ratios.
  • Chemical analysis can be used in a targeted manner, to identify particular chemical compounds in a single sample.
  • analysis of the data may require identification of peaks or troughs at particular points in the data spectrum.
  • a chemical analysis technique may be used to compare two samples. These may be different samples of a product in which a change has been made in preparation, storage, treatment or a combination of the foregoing, but the affect of that change is previously unknown or uncharacterised.
  • the aim is to focus on differences between the results represented in data sets obtained from products with a change and products without the change.
  • the first stage might be to identify the most significant differences between the two data sets, with a second stage being to identify the particular chemical compound that are responsible for those differences.
  • Such comparative analysis is often carried out to determine the effect of a change in the product, such as use of a different manufacturing technique, different tobacco curing methods, storing the products at different environments for varying periods of time, use of a different blend of tobacco, the inclusion of an additive, a change in a cigarette component including material or its construction (e.g., a filter), heating the product at a different temperature over varying periods of time, or a combination of the foregoing, on the chemical composition of the smoke.
  • a change in the product such as use of a different manufacturing technique, different tobacco curing methods, storing the products at different environments for varying periods of time, use of a different blend of tobacco, the inclusion of an additive, a change in a cigarette component including material or its construction (e.g., a filter), heating the product at a different temperature over varying periods of time, or a combination of the foregoing, on the chemical composition of the smoke.
  • a particular type of chemical analysis commonly used is a combination of a chromatography stage and a mass spectrometry stage.
  • Two particular examples are two dimensional gas chromatography- time of flight mass spectrometry (GCXGC-TOF) and liquid chromatography- high resolution mass spectrometry.
  • GCXGC-TOF two dimensional gas chromatography- time of flight mass spectrometry
  • a sample is first separated into its constituent chemicals in a chromatography stage, and each fraction is then subjected to a mass spectrometry analysis.
  • a second chromatography stage uses another column with a stationary phase of a different selectivity to further separate chemicals that elute from the first column at the same retention time.
  • the present invention provides a method of analysing data obtained from a first sample and a second sample using the same chemical analysis technique, the data comprising a first data set from the first sample and a second data set from the second sample, comprising the steps of:
  • Rn k[En 3 /1000] * [L1 n + L2n]/2 wherein k is a constant
  • the Rank formula provides a reliable method of selecting the variables that have the most significant difference between the two data sets, and thus allows further analysis to concentrate on those particular variables.
  • the formula takes into account the relative difference between the observed values of a variable as reflected by the Effect score and the abundance of the variable in question in the two samples expressed as the "average value" or [L1 n + L2n]/2.
  • the variable may be any property that is being measured by the chemical analysis in both data sets.
  • the variable may be the magnitude of a peak at a specific retention time, and a plurality of variables can be the magnitudes of peaks at various selected retention times.
  • the variable may be the magnitude or intensity of an absorption peak at a particular wavelength or frequency, and the plurality of variables are the magnitudes or intensities of peaks at different selected wavelength or frequency.
  • the data set may comprise a series of peaks at n positions along an axis
  • the values L1 n and L2n are measures of the magnitudes Ln of first and second peaks having the nth position on the axis in the first set and second data set respectively.
  • the Rank formula allows the selection of peaks showing the most relevant difference(s) between the two data sets.
  • the analysis can therefore identify the result of changes between the two samples.
  • the values are the integrated areas under the peaks. In most forms of chemical analysis, particularly chromatography, the area under a peak most accurately reflects the concentration of the chemical entity responsible for the peak.
  • the actual values from the data may be converted to concentrations by reference to a reference peak in the data, resulting from inclusion of a reference compound in the sample.
  • the variables Ln to be analysed are first selected by comparing L1 n and L2n, and calculating the effect score for those variables where
  • the selection is made by applying a t-test for each pairs of variables in the two data sets and excluding variables which result in a t score greater than a threshold value given a p value.
  • This initial statistical filtering step removes variables that are not significantly different between the two samples.
  • the peaks represent concentrations of chemical compounds in the samples, and L1 n and L2n are concentrations of a particular n th chemical compound in the first and second samples respectively.
  • the chemical analysis is a chromatography stage in a combined chromatography- mass spectrometry analysis, and the method further comprises selecting the chromatography peaks having the highest rank scores, and analysing the mass spectrometry data for the selected chromatography peaks. Further analysis can then focus on those peaks that constitute the most relevant and significant differences between the data sets.
  • the Rank formula can be used to select the peaks showing most difference between the chromatography data sets.
  • Each peak corresponds to a chemical compound, which can be identified by reference to the mass spectrometry data. Therefore, the present invention reduces the amount of data that must be processed, and therefore increases speed of processing by focussing on differences in the chromatography spectra.
  • compounds from the two samples are ranked individually according to its relevance considering the relative differences in abundance of each of the compounds as well as the quantitatively or semi-quantitatively determined abundance of each in the respective samples.
  • Figure 1 is a flow diagram of the method
  • FIG. 2 illustrates graphically the allocation of HIT values
  • Figure 3 show the correlation of HIT values in GCXGC-TOF data by using the invented Rank procedure compared to the results from a common approach using PLS-DA.
  • FIG. 1 is a flow diagram illustrating the method of the present invention used to analyse data obtained from a chemical analysis technique, particularly with reference to a combined chromatography-mass spectrometry analysis technique such as two dimensional gas chromatography- time of flight mass spectrometry (GCXGC-TOF) or liquid chromatography-high resolution mass spectrometry (LC-HR-MS).
  • a combined chromatography-mass spectrometry analysis technique such as two dimensional gas chromatography- time of flight mass spectrometry (GCXGC-TOF) or liquid chromatography-high resolution mass spectrometry (LC-HR-MS).
  • a first sample and a second sample are subjected to the same chemical analysis technique to obtain a first data set and a second data set.
  • each data set is represented as a series of chromatography peaks, representing amounts of chemical entities that are eluted at certain retention times.
  • the data set further includes mass spectrometry data for the fractions which have been separated in the chromatography stage.
  • the chromatography data may itself include two dimensions, in the case where the chromatography stage is two dimensional gas chromatography.
  • the data from the two samples is compared and corresponding peaks aligned to produce a consistent data matrix.
  • the chromatography data is compared and corresponding peaks in the two data sets aligned based on mass-spectral similarity and chromatographic property, such as retention time or retention index.
  • mass-spectral similarity and chromatographic property such as retention time or retention index.
  • Various existing software packages can be used for this, such as ChromaTOF for GCXGC-TOF and MZmine for LC-HR-MS.
  • peak integration step 300 the aligned peaks of each data set are integrated to calculate the area under each peak, which is a measure of the concentration of the chemical entity in the sample that contributes to the peak.
  • a chromatography peak represents the totality of any substances having the same retention time, and can thus represent a mixture of chemical compounds.
  • a peak will represent a single chemical compound in the samples.
  • the peaks are normalised.
  • chromatography to determine concentrations of chemical compounds in a sample, the peaks corresponding to the chemical compounds may be compared to a peak from an internal standard compound which is included at a known concentration with both the samples.
  • An example of such an internal standard is ds-isophorone.
  • the data is filtered statistically to remove corresponding peaks from both data sets that are not visibly or meaningfully different.
  • This step comprises applying a statistical technique, for example, the t-test to compare the determined values of each of the same variable (or same peak representing a chemical entity) in the two samples.
  • a statistical technique for example, the t-test to compare the determined values of each of the same variable (or same peak representing a chemical entity) in the two samples.
  • n- ⁇ and n 2 multiple determinations of the value of each variable (or peak) are made.
  • each sample is subjected to the same chemical analysis technique multiple times, i.e., n- ⁇ and n 2 , to obtain replicated data sets for the same sample.
  • the number of replication for each sample can be the same or different. A low number of repetitions n-i and n 2 such as 3 to 5, is contemplated.
  • the t-test is a statistical technique that will be well known to the person skilled in the art, but is outlined below.
  • replicated data sets for each sample are generated experimentally.
  • t-test (dataset L1 , dataset L2, tails, type) where :
  • Si and s 2 are the standard deviation of the first data set and the second data set and n-i are the number of observations for each data set.
  • the test statistic was approximately t- distributed with the degrees of freedom (D.F.) calculated using.
  • a table of Student's t-distribution confidence intervals can be used to determine the significance level at which two distributions differ.
  • a value of p at 0.05 or at 0.1 can be used to determine whether statistically the peaks from the two samples are not significantly different, and are thus excluded from further analysis.
  • the remaining pairs of peaks may be numbered from 1 to N.
  • the calculated concentration corresponding to the area of the nth peak can be represented as L1 n in the first data set and L2n for the same peak in the corresponding nth position in the second data set.
  • the effect score En is calculated based on the formula:
  • the RANK formula considers both the relative difference of the abundance of the same compound in both samples as well as the estimated absolute abundance of the compound in both samples, (ie the greater the difference and absolute abundance, the greater the relevance).
  • the constant k is simply a scaling factor which may vary depending on the units used for L1 n and L2n.
  • the aim is to compare Rank values to each other, so the values can be scaled in a linear way by any constant without affecting the resulting comparison. For example, k can be 1.
  • Rank values Rn have been calculated for the various peaks, these can be ranked in order of the Rn values, wherein the higher the absolute value of Rn, the more significant the difference in the corresponding peaks between the two samples.
  • the Rank value may be positive or negative, depending on the arbitrary choice of which data set is taken as the "first data set" and which is the "second data set". For example, if L1 n is higher than L2n, Rn will be positive, but if L1 n and L2n are reversed, Rn will have the same absolute value, but will be negative.
  • a peak in the total ion current for GCxGC or in a well defined mass- trace in the case of high resolution LC-MS
  • the RANK formula allows the recognition of chromatography peaks with the most relevant differences between two samples, wherein each of these peaks represent a chemical compound in the samples. Therefore, in selection step 700, a set of peaks are selected based on the Rank values, for further analysis. These might be, for example, the top N Rank values (absolute values), or they may be all peaks having an absolute Rank value higher than a threshold.
  • the pairs of peaks may be allocated a HIT value, which is simply a ranking allocated by placing the Rank values in order. Therefore, the highest Rank value is allocated a HIT value of 1 , the second highest is allocated a HIT value of 2, and so on. For negative Rank the negative value having the highest absolute value is allocated a HIT value of -1 , the second highest absolute value is allocated a HIT value of -2, and so on.
  • Figure 2 illustrates graphically the allocation of HIT values to pairs of peaks.
  • the peaks selected in the selection step 700 are chemically identified. This can be performed by reference to the results of a further chemical analysis step.
  • the mass spectrometry data for the selected peaks can be analysed to identify the chemical compounds responsible for the chromatography peaks. This analysis of the mass spectrometry data may be performed manually, but preferably the analysis involves a computer program which matches the mass spectrometry data with reference data in a mass spectra library.
  • the method of the present invention is preferably implemented in the form of a computer program which may be run on a computer system.
  • the method of the invention is verified by analysis of smoke samples from a combusted reference cigarette (2R4F) and fortifying the same reference cigarettes smoke samples with known amounts of 10 selected compounds.
  • This fortified smoke samples results in samples containing different known absolute concentrations and different known relative differences in concentration for the selected compounds among thousands of compounds that are present in the reference cigarette that remain unchanged.
  • the non-targeted differential screenjng assay using GCxGC-TOF consists of 2 analytical methods, 1 for nonpolar compounds and 1 for polar compounds. The precision and accuracy of data acquisition, data processing, and data evaluation were determined by comparing the theoretical ranking, calculated from the fortified concentrations of the selected compounds and the experimentally determined ranking.
  • the Reference Cigarette 2R4F was purchased from the University of Kentucky, Kentucky Tobacco Research and Development Center. The cigarettes were conditioned following ISO standard 3402 (1999). The samples were generated on a 20-port Borgwaldt smoking machine RM20H according to ISO standard 3308 (2000). Total particulate matter (TPM) of a 2R4F sample was fortified with standard solutions containing 10 compounds each. The standard solutions for fortification were prepared in 4 different compositions (4 fortification mixtures) resulting in 4 different fortification levels on TPM for each of the 2 analytical methods. The 4 fortification levels covered a concentration range of 1 ⁇ g/cig. to 30 ⁇ g/cig. with a maximum difference in concentration of 30-fold.
  • TPM level 1 Fortification level 1 (TPM level 1 ) was compared against fortification level 2 (TPM level 2), and fortification level 3 (TPM level 3) was compared against fortification level 4 (TPM level 4) (Table 1 ).
  • the smoke samples were generated by trapping the TPM on a glass fiber filter, followed by an extraction with a dichloromethane:acetone mixture (80 : 20, v/v). Aliquots of this TPM extracts were used for the analyses. Every sample was analyzed in triplicate.
  • classification regions exclude inclusion of, e.g., bleed, high abundant compounds triacetine, nicotine, tailing of high abundant fatty acids
  • Each processing block contains:
  • Lx Measured values of level or group to be compared with Ly Ly: Measured values of level or group to be compared with Lx
  • the data set was divided for positive (Lx > Ly) and negative (Lx ⁇ Ly) rank values and sorted by increasing absolute rank values for the positive as well as the negative effect.
  • the theoretical rank values were calculated for the fortification matrix. Then, the compounds were sorted by relevance according to their theoretical rank values.
  • Table 2 shows the theoretical Rank values and theoretical HIT numbers for the GCxGC-TOF nonpolar method as the chemical analysis technique.
  • the HIT values are simply placing the ten Rank values in order of magnitude, from -5 to +5.
  • the result table gave the experimentally found HIT numbers for the fortified compounds.
  • the method's ability to perform an appropriate ranking of compounds in fortified matrix samples differing in fortified concentrations was verified. This was done by correlating the theoretical reciprocal HIT numbers against the experimentally found reciprocal HIT numbers. The correlation was done by using the Pearson correlation coefficient.
  • Table 3 shows the results for the GCxGC-TOF nonpolar method as the chemical analysis technique.
  • correlation coefficient for the reciprocal theoretical HIT numbers and the experimentally found reciprocal HIT numbers must be >0.98.
  • Other thresholds may be applicable depending on the application.
  • the specifity and selectivity of the assay was shown by comparing the experimentally found HIT numbers of the relevant chemical differences to the theoretical HIT numbers.
  • the specificity and selectivity of the assay was sufficient to extract the fortified compounds and correlate the theoretical reciprocal HIT numbers and the experimentally found reciprocal HIT numbers with a predefined correlation coefficient, >0.98 for the nonpolar method.
  • the polar method showed less specifity/selectivity resulting in less significant results than the nonpolar method.
  • the p-value of the t-test was adapted to p ⁇ 0.1 for the polar method to enable the extraction of the fortified compounds and the correlation of the theoretical reciprocal HIT numbers and the experimentally found reciprocal HIT numbers with a correlation coefficient of >0.98.
  • Figure 3 shows a correlation in HIT values in GCXGC-TOF data by using the invented Rank procedure compared to the results obtained from partial least square-discriminant analysis (PLS-DA), a commonly applied method for this type of analysis.
  • PLS-DA partial least square-discriminant analysis
  • the RANK formula provides a method by which differences in two corresponding spectra from a chemical analysis can be compared, and the differences ranked by mathematical modelling.
  • the model has been generated based on expert chemical knowledge and generated a numerical reflection of the relevance of a found difference within a comparative chemical assay.

Landscapes

  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Chemical & Material Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Library & Information Science (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)

Abstract

La présente invention concerne un procédé d'analyse de données obtenues à partir d'un premier échantillon et d'un second échantillon en utilisant la même technique d'analyse chimique, les données comprenant un premier ensemble de données du premier échantillon et un second ensemble de données du second échantillon. Le procédé prend en compte à la fois la différence relative entre les quantités du composé dans les deux échantillons et l'abondance absolue estimée du même composé dans les deux échantillons. Le procédé est particulièrement applicable aux données issues d'une analyse combinée de chromatographie-spectrométrie de masse, le procédé étant appliqué aux données de l'étape de chromatographie pour sélectionner les pics de chromatographie ayant les scores de rangs les plus élevés, puis les données de spectrométrie de masse pour les pics de chromatographie sélectionnés étant analysées plus en détail.
PCT/EP2012/076244 2011-12-30 2012-12-19 Procédé d'analyse de données issues d'une analyse chimique WO2013098169A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP11196234.6 2011-12-30
EP11196234 2011-12-30

Publications (1)

Publication Number Publication Date
WO2013098169A1 true WO2013098169A1 (fr) 2013-07-04

Family

ID=47458961

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2012/076244 WO2013098169A1 (fr) 2011-12-30 2012-12-19 Procédé d'analyse de données issues d'une analyse chimique

Country Status (1)

Country Link
WO (1) WO2013098169A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109640708A (zh) * 2016-07-04 2019-04-16 英美烟草(投资)有限公司 用于将烟草样本分类为一组预定口味类别中的一个的设备和方法
CN109965332A (zh) * 2017-12-28 2019-07-05 贵州中烟工业有限责任公司 一种卷烟叶组化学成分品质评价方法及装置
CN114577966A (zh) * 2020-11-18 2022-06-03 湘潭大学 一种mscc结合调制峰归类的gc×gc指纹快速比较方法
CN114931230A (zh) * 2022-05-13 2022-08-23 中国烟草总公司郑州烟草研究院 一种烟叶烘烤过程工艺执行指标分析表征方法
EP4006537A4 (fr) * 2019-07-25 2023-08-09 Hitachi High-Tech Corporation Appareil d'analyse de spécimen

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007012643A1 (fr) * 2005-07-25 2007-02-01 Metanomics Gmbh Moyens et procedes d'analyse d'un echantillon par spectrometrie de masse/chromatographie

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007012643A1 (fr) * 2005-07-25 2007-02-01 Metanomics Gmbh Moyens et procedes d'analyse d'un echantillon par spectrometrie de masse/chromatographie

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
CHEN P X ET AL: "Mainstream Smoke Chemical Analyses for 2R4F Kentucky Reference Cigarette", BEITRAEGE ZUR TABAKFORSCHUNG INTERNATIONAL, HAMBURG, DE, vol. 20, no. 7, 1 November 2003 (2003-11-01), pages 448 - 458, XP003024851, ISSN: 0173-783X *
DALLUGE J ET AL: "Unravelling the composition of very complex samples by comprehensive gas chromatography coupled to time-of-flight mass spectrometry - Cigarette smoke", JOURNAL OF CHROMATOGRAPHY, ELSEVIER SCIENCE PUBLISHERS B.V, NL, vol. 974, no. 1-2, 18 October 2002 (2002-10-18), pages 169 - 184, XP004387548, ISSN: 0021-9673, DOI: 10.1016/S0021-9673(02)01384-5 *
PÄR JONSSON ET AL: "High-Throughput Data Analysis for Detecting and Identifying Differences between Samples in GC/MS-Based Metabolomic Analyses", ANALYTICAL CHEMISTRY, vol. 77, no. 17, 1 September 2005 (2005-09-01), pages 5635 - 5642, XP055026079, ISSN: 0003-2700, DOI: 10.1021/ac050601e *
PIERCE ET AL: "Recent advancements in comprehensive two-dimensional separations with chemometrics", JOURNAL OF CHROMATOGRAPHY, ELSEVIER SCIENCE PUBLISHERS B.V, NL, vol. 1184, no. 1-2, 28 February 2008 (2008-02-28), pages 341 - 352, XP022503348, ISSN: 0021-9673, DOI: 10.1016/J.CHROMA.2007.07.059 *
V. V. MIHALEVA ET AL: "Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index", BIOINFORMATICS, vol. 25, no. 6, 28 January 2009 (2009-01-28), pages 787 - 794, XP055020002, ISSN: 1367-4803, DOI: 10.1093/bioinformatics/btp056 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109640708A (zh) * 2016-07-04 2019-04-16 英美烟草(投资)有限公司 用于将烟草样本分类为一组预定口味类别中的一个的设备和方法
CN109965332A (zh) * 2017-12-28 2019-07-05 贵州中烟工业有限责任公司 一种卷烟叶组化学成分品质评价方法及装置
EP4006537A4 (fr) * 2019-07-25 2023-08-09 Hitachi High-Tech Corporation Appareil d'analyse de spécimen
CN114577966A (zh) * 2020-11-18 2022-06-03 湘潭大学 一种mscc结合调制峰归类的gc×gc指纹快速比较方法
CN114577966B (zh) * 2020-11-18 2023-08-08 湘潭大学 一种mscc结合调制峰归类的gc×gc指纹快速比较方法
CN114931230A (zh) * 2022-05-13 2022-08-23 中国烟草总公司郑州烟草研究院 一种烟叶烘烤过程工艺执行指标分析表征方法
CN114931230B (zh) * 2022-05-13 2023-10-27 中国烟草总公司郑州烟草研究院 一种烟叶烘烤过程工艺执行指标分析表征方法

Similar Documents

Publication Publication Date Title
Kalogiouri et al. Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies-A review
Xu et al. Pretreatments of chromatographic fingerprints for quality control of herbal medicines
EP2322922B1 (fr) Procédé d'amélioration de la résolution des composants élués d'un dispositif de chromatographie
Bansal et al. Chemometrics: A new scenario in herbal drug standardization
Kovačevič et al. Determination and verification of hop varieties by analysis of essential oils
Lafhal et al. Chemometric analysis of French lavender and lavandin essential oils by near infrared spectroscopy
DE112004000746B4 (de) Verfahren und Vorrichtung zum Verarbeiten von LC-MS- oder LC-MS-/MS-Daten bei Stoffwechseluntersuchungen
Ruiz-Samblás et al. Multivariate analysis of HT/GC-(IT) MS chromatographic profiles of triacylglycerol for classification of olive oil varieties
CN108181263B (zh) 基于近红外光谱的烟叶部位特征提取及判别方法
WO2013098169A1 (fr) Procédé d'analyse de données issues d'une analyse chimique
Cordero et al. Comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry featuring tandem ionization: Challenges and opportunities for accurate fingerprinting studies
Aliakbarzadeh et al. Classification of gas chromatographic fingerprints of saffron using partial least squares discriminant analysis together with different variable selection methods
JP4704034B2 (ja) クロマトグラフィ／分光測定データの解析でデータビンニングを用いる方法
CN109324015B (zh) 基于光谱相似的烟叶替代方法
EP1846757A2 (fr) Procede et systeme d'analyse spectrale de masse
Masson et al. Oxygenated heterocyclic compounds to differentiate Citrus spp. essential oils through metabolomic strategies
US20140088884A1 (en) Methods of source attribution for chemical compounds
CN103344713A (zh) 一种基于随机森林法分析致香物在烤烟香型分类中的重要性及预测香型的方法
Sun et al. Differentiation of flue-cured tobacco leaves in different positions based on neutral volatiles with principal component analysis (PCA)
Manfredi et al. Direct analysis in real time mass spectrometry for the nondestructive investigation of conservation treatments of cultural heritage
Hou et al. Development of the mass spectral fingerprint by headspace-solid-phase microextraction-mass spectrometry and chemometric methods for rapid quality control of flavoring essence
Kalogiouri et al. Headspace solid-phase microextraction followed by gas chromatography-mass spectrometry as a powerful analytical tool for the discrimination of truffle species according to their volatiles
Stilo et al. Delineating unique and discriminant chemical traits in Brazilian and Italian extra-virgin olive oils by quantitative 2D-fingerprinting and pattern recognition algorithms
Yin et al. Identification of aroma types and their characteristic volatile compounds of Chinese faint-scent cigarettes based on descriptive sensory analysis and GC–MS and partial least squares regression
Lehallier et al. Systematic ratio normalization of gas chromatography signals for biological sample discrimination and biomarker discovery

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 12808375

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 12808375

Country of ref document: EP

Kind code of ref document: A1