CN114609304A - Grain and oil food flavor substance data analysis method and application thereof - Google Patents
Grain and oil food flavor substance data analysis method and application thereof Download PDFInfo
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- CN114609304A CN114609304A CN202210056031.3A CN202210056031A CN114609304A CN 114609304 A CN114609304 A CN 114609304A CN 202210056031 A CN202210056031 A CN 202210056031A CN 114609304 A CN114609304 A CN 114609304A
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- 239000000126 substance Substances 0.000 title claims abstract description 139
- 239000000796 flavoring agent Substances 0.000 title claims abstract description 123
- 235000019634 flavors Nutrition 0.000 title claims abstract description 122
- 235000013305 food Nutrition 0.000 title claims abstract description 73
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000007405 data analysis Methods 0.000 title claims abstract description 22
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- 238000005516 engineering process Methods 0.000 claims abstract description 16
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- 238000004458 analytical method Methods 0.000 claims description 23
- 238000001228 spectrum Methods 0.000 claims description 23
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Images
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
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
-
- 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
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- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a data analysis method in the process of analyzing grain and oil food flavor substances by a headspace solid phase microextraction-gas chromatography-mass spectrometry technology. The method utilizes Matlab software to develop a food and oil flavor substance data analysis (MSData) program, the program is based on C + + language programming, the retention index of flavor substances in GC-MS experimental data is rapidly calculated, and the retention index is compared with corresponding substances in a NIST spectral library, so that flavor compounds are accurately characterized. The method is beneficial to quick and accurate identification of GC-MS mass spectrum data and quick screening of grain and oil food characteristic flavor substances, shortens the data analysis time from 6 hours to less than 10 minutes, and effectively solves the qualitative and quantitative statistical analysis problem of multi-component flavor substances in a complex system.
Description
Technical Field
The invention relates to the technical field of grain and oil food detection and analysis, in particular to a grain and oil food flavor substance data analysis method and analysis application of the grain and oil food flavor substance data analysis method to typical flavor substances in grains, oil plants and processed foods thereof, processed edible oil and other products.
Background
A grain and oil food flavor substance headspace solid phase microextraction (HS-SPME) method and a gas chromatography-mass spectrometry (GC-MS) analysis technology combine a sample pretreatment technology, a gas chromatography and a mass spectrometry analysis technology mutually to complement each other, and the qualitative and quantitative problems of multiple components in a grain and oil food flavor system are greatly improved. However, the flavor system of grain and oil food is complex, and the components in the food are dozens of and hundreds of. When the number of analysis samples is large, the number of analysis components is large, and the data size is large, a large amount of manual analysis time is required.
At present, after the flavor substances of grain and oil food are analyzed by using a headspace solid phase microextraction (HS-SPME) method and a gas chromatography-mass spectrometry (GC-MS) technical analysis method, experimental data are processed by a GC-MS chemical workstation, unknown compounds are matched with a NIST spectrogram library for qualitative determination, and when the similarity of chemical substance peaks is more than 60 (the maximum value is 100), the identification results are reported. However, this method has two disadvantages: firstly, chemical substances possibly appear to delay the peak when the machine runs, so that the target substances cannot be automatically identified; and secondly, when the same chemical substance peak is identified, the similarity of a plurality of substances is close. Thus, the data analyzed by this method typically requires calculation of an accurate retention index (RI value) of the species from the normal alkane in order to further infer the reliability of the identified compound. The current process of calculating retention indices requires a significant amount of manual intervention, is very time consuming, and often takes more than 60% of the time for the interpretation and analysis of GC-MS data.
The Retention Index (RI) concept was proposed by Kovats in 1958 to calibrate the retention of a component with two n-alkanes respectively adjacent to it one after the other (this is more precise than the relative retention orientation with only one reference substance). The calculation formula is shown as follows:
RI＝100Z+100[tR(x)-tR(z)]/[tR(z+1)-tR(z)]
in the formula: tR (x), tR (z +1) represent retention times of the target component and the n-alkane having a carbon number of Z, Z +1, respectively, and tR (z) < tR (x) < tR (z + 1).
It can be seen that the GC-MS data statistical analysis method greatly influences the timeliness, accuracy and reliability of the analysis of the flavor substances of the grain and oil food. If the chromatographic data is processed before searching and comparing, the method is favorable for reducing excessive manual intervention, and is a new way for quickly, accurately, qualitatively and quantitatively analyzing the HS-SPME/GC-MS data of the flavor substances of the grain and oil foods.
Disclosure of Invention
The invention aims to provide a grain and oil food flavor substance data analysis method and application thereof to guide the rapid analysis of grain and oil food flavor substances. The method of the present invention achieves the above object by including the steps of: analyzing flavor substances of the grain and oil food by adopting a headspace solid-phase microextraction (HS-SPME) method and a gas chromatography-mass spectrometry (GC-MS) analysis method, and obtaining mass spectrum data by GC-MS analysis; deriving flavor compound analysis data by the data comparison of a computer NIST standard spectrum library and the general rule of mass spectrum breakage of the organic compounds; and further calculating the retention index of the compound according to the retention time by a grain and oil food flavor substance analysis program, and comparing each compound with an NIST standard library to perform accurate qualitative analysis on the compound.
Specifically, the invention provides a grain and oil food flavor substance data analysis (MSData) method, which comprises the following steps:
(1) preparation of a detection sample: preparing a sample to be tested of the grain and oil food flavor substances;
(2) enrichment of volatile components: collecting volatile components in the grain and oil food flavor substance to-be-detected sample by adopting a headspace solid phase microextraction (HS-SPME) technology;
(3) detection of volatile components: detecting and analyzing the volatile components and the normal alkane standard by adopting a gas chromatography-mass spectrometry (GC-MS) technology to obtain a flavor substance chromatogram and a normal alkane standard chromatogram;
(4) analysis of chromatographic data: carrying out full integral mode scanning on the flavor substance chromatogram, and retrieving a common ion peak in an NIST 2011 mass spectrum database according to the ion peak matching degree to generate flavor substance mass spectrum data; carrying out full integral mode scanning on the normal paraffin standard chromatogram to obtain normal paraffin standard quality spectrum data;
(5) processing mass spectrum data: introducing the flavor substance mass spectrum data and the normal paraffin standard quality spectrum data into a grain and oil food flavor substance analysis program, and calculating the Retention Index (RI) of the target component according to the following formula (I); comparing the retention index of the obtained target component with the retention index in the NIST 2011 standard spectrum library, wherein the difference between the retention indexes is less than or equal to 50, and judging that the corresponding substance can be a grain and oil flavor substance; if the difference of the retention indexes is more than or equal to 70, judging that the corresponding substances are not grain and oil flavor substances; if the retention index difference is more than 50 and less than 70, the corresponding substances are judged to be probably grain and oil flavor substances,
RI＝100Z+100[tR(x)-tR(z)]/[tR(z+1)-tR(z)](I)
wherein: tR (x), tR (z +1) represent retention times of the target component and a normal alkane having a carbon number of Z, Z +1, respectively, and tR (z) < tR (x) < tR (z + 1).
Advantageous effects
1. The HS-SPME/GC-MS flavor substance data of the grain and oil food can be statistically analyzed through an MS spectrum and a grain and oil food flavor substance data analysis program.
2. The MS spectrum and the normal alkane spectrum are compared according to a grain and oil flavor substance data analysis program, the retention index is calculated, and then the retention index is further compared with an NIST spectrum library, so that the HS-SPME/GC-MS flavor substance data of the grain and oil food can be qualitatively and rapidly analyzed.
3. The method is beneficial to quick and accurate identification of GC-MS mass spectrum data and quick screening of grain and oil food characteristic flavor substances, shortens the data analysis time from 6h to 10min, and effectively solves the qualitative and quantitative statistical analysis problem of multi-component flavor substances in a complex system.
Drawings
FIG. 1 is a flow chart of the specific steps of the grain and oil flavor substance analysis method.
FIG. 2 is a chromatogram for the detection of flavors by the GC-MS chemical workstation.
FIG. 3 is a GC-MS chemical workstation performing a full integral scan of an unknown compound chromatogram.
Figure 4 is a qualitative data of the GC-MS chemical workstation on unknown flavors according to the degree of match of ion fragments.
FIG. 5 is a mass spectrum of n-alkanes.
FIG. 6 shows the result of mass spectrum raw data of flavor substances.
FIG. 7 is a user interface for a data statistical analysis program.
FIG. 8 is a programming interface for a data statistics analysis program.
Fig. 9 is the data of the results of statistical analysis of the flavor data.
Fig. 10 is a summary of the results of statistical analysis of the flavor data.
FIG. 11 shows the main flavors of the special malt obtained by the method of the present invention.
FIG. 12 shows the main flavor substances of rice detected by the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
In one embodiment of the invention, the invention relates to a method of analysis of food flavor data (MSData) comprising the steps of:
(1) preparation of a detection sample: preparing a sample to be tested of the grain and oil food flavor substances;
(2) enrichment of volatile components: collecting volatile components in the grain and oil food flavor substance to-be-detected sample by adopting a headspace solid phase microextraction (HS-SPME) technology;
(3) detection of volatile components: detecting and analyzing the volatile components and the normal alkane standard by adopting a gas chromatography-mass spectrometry (GC-MS) technology to obtain a flavor substance chromatogram and a normal alkane standard chromatogram;
(4) analysis of chromatographic data: carrying out full integral mode scanning on the flavor substance chromatogram, and retrieving a common ion peak in an NIST 2011 mass spectrum database according to the ion peak matching degree to generate flavor substance mass spectrum data; carrying out full integral mode scanning on the normal paraffin standard chromatogram to obtain normal paraffin standard quality spectrum data;
(5) processing mass spectrum data: introducing the flavor substance mass spectrum data and the normal paraffin standard quality spectrum data into a grain and oil food flavor substance analysis program, and calculating the Retention Index (RI) of the target component according to the following formula (I); comparing the retention index of the obtained target component with the retention index in the NIST 2011 standard spectrum library, wherein the difference between the retention indexes is less than or equal to 50, and judging that the corresponding substance can be a grain and oil flavor substance; if the difference of the retention indexes is more than or equal to 70, judging that the corresponding substances are not grain and oil flavor substances; if the retention index difference is more than 50 and less than 70, the corresponding substances are judged to be probably grain and oil flavor substances,
RI＝100Z+100[tR(x)-tR(z)]/[tR(z+1)-tR(z)](I)
wherein: tR (x), tR (z +1) represent retention times of the target component and the normal alkane having a carbon number of Z, Z +1, respectively, and tR (z) < tR (x) < tR (z + 1).
In some embodiments, the grain and oil food product may be in particular a foodstuff and processed products thereof, an oil and processed oil products thereof. Preferably, the grain and its processed product include but are not limited to one or more of wheat and its processed product, barley and its processed product, rice and its processed product, coarse cereals and its processed product, more preferably, the grain and its processed product is one or more of malt and its processed product, rice and its processed product. Preferably, the oil and its processed oil products include, but are not limited to, one or more of peanuts and their processed peanut oil, soybeans and their processed soybean oil, oilseed rape and its processed rapeseed oil.
In a preferred embodiment, the cereal-oil food flavour comprises one or more of the following: isobutyraldehyde, isovaleraldehyde, 2-methylbutyraldehyde, hexanal, heptanal, pentanal, n-hexanal, furfural, 3-furfural, 5-methylfuran aldehyde, benzaldehyde, 2-pyrrole formaldehyde, phenylacetaldehyde, trans-2-octenal, nonanal, cacao aldehyde, trans-2-nonanal, (E) -nonenal, decanal, alpha-ethylene-phenylacetaldehyde, trans-2, 4-decadienal, alpha- (2-methylpropylidene) phenylacetaldehyde, pentadecanal, cocaldehyde, 2-methylpyrazine, 2-ethyl-6-methylpyrazine, 3-ethyl-2, 5-methylpyrazine, 2-ethyl-3, 5-dimethylpyrazine, 2-methyl-3- (2-methylpropyl) pyrazine, 2, 5-dimethyl-3- (3-methylbutyl) pyrazine, 2-methyl-3- (2-methylpropyl) pyrazine, 2-ethyl-3-methylpyrazine, furfuryl alcohol, 1-octen-3-ol, 2-ethylhexanol, 1-nonanol, 2-propyl-1-heptanol, dodecanol, methylmaltol, 4-ethyl-2-methoxyphenol, 2, 4-di-tert-butylphenol, pentadecylmethylether, dimethyldisulfide, n-pentanoic acid, hexanoic acid, styrene, undecane, 2-heptanone, 2-methyl-3-hydroxy-gamma-pyrone, methyl-2, 5-dimethyl-3- (3-methylbutyl) pyrazine, 2-methyl-3- (2-methylpropyl) pyrazine, 2-ethyl-3-methoxy-1-octen-3-ol, 2-ethylhexanol, 1-nonanol, 4-ethyl-2-methoxy-2-methoxy-pentanone, 2-methyl-3-hydroxy-gamma-pentanone, pentanol, hexanol, pentanol, hexanol, pentanol, hexanol, pentanol, hexanol, pentanol, hexanol, pentanol, hexanol, pentanol, and the amino-2, pentanol, hexanol, pentanol, hexanol, and the like, and the amino acid, hexanol, pentanol, hexanol, and the like, 6, 10-dimethyl-5, 9-undecene-2-one, geranylacetone, 2-acetylfuran, 2-n-pentylfuran, 2-acetylpyrrole, 1- (2-furylmethyl) -1H-pyrrole, 2-pentylfuran, gamma-butyrolactone, amyl hexanoate, methyl 10-methylundecanoate, methyl 12-methyltridecanoate, methyl laurate, isobutyl 2,2, 4-trimethylpentanediol, ethylhexyl benzoate, pentamethylfuran bromate, methyl palmitoleate, methyl palmitate, methyl linoleate, methyl oleate, 1-pentanol, n-octanal, methylheptenone, 2-acetyl-1-pyrroline, n-hexanol and decanol.
In preferred embodiments, the grain and its processed products are malt and its processed products, and the grain and oil food flavors include one or more of: isobutyraldehyde, isovaleraldehyde, 2-methylbutyraldehyde, hexanal, heptanal, pentanal, n-hexanal, furfural, 3-furfural, 5-methylfuran aldehyde, benzaldehyde, 2-pyrrole formaldehyde, phenylacetaldehyde, trans-2-octenal, nonanal, cacao aldehyde, trans-2-nonanal, (E) -nonenal, decanal, alpha-ethylene-phenylacetaldehyde, trans-2, 4-decadienal, alpha- (2-methylpropylidene) phenylacetaldehyde, pentadecanal, cocaldehyde, 2-methylpyrazine, 2-ethyl-6-methylpyrazine, 3-ethyl-2, 5-methylpyrazine, 2-ethyl-3, 5-dimethylpyrazine, 2-methyl-3- (2-methylpropyl) pyrazine, 2, 5-dimethyl-3- (3-methylbutyl) pyrazine, 2-methyl-3- (2-methylpropyl) pyrazine, 2-ethyl-3-methylpyrazine, furfuryl alcohol, 1-octen-3-ol, 2-ethylhexanol, 1-nonanol, 2-propyl-1-heptanol, dodecanol, methylmaltol, 4-ethyl-2-methoxyphenol, 2, 4-di-tert-butylphenol, pentadecanoic acid methyl ether, dimethyl disulfide, n-pentanoic acid, hexanoic acid, styrene, undecane, 2-heptanone, 2-methyl-3-hydroxy-gamma-pyrone, 6, 10-dimethyl-5, 9-undecene-2-one, geranylacetone, 2-acetylfuran, 2-n-pentylfuran, 2-acetylpyrrole, 1- (2-furylmethyl) -1H-pyrrole, 2-pentylfuran, gamma-butyrolactone, amyl hexanoate, methyl 10-methylundecanoate, methyl 12-methyltridecanoate, methyl laurate, isobutyl 2,2, 4-trimethylpentanediol, ethylhexyl benzoate, pentamethylfuran bromate, methyl palmitoleate, methyl palmitate, methyl linoleate and methyl oleate.
In preferred embodiments, the food grain and processed products thereof are rice and processed products thereof, and the grain and oil food flavor comprises one or more of the following: valeraldehyde, n-hexanal, heptanal, 2-n-pentylfuran, 1-pentanol, n-octanal, methylheptenone, 2-acetyl-1-pyrroline, n-hexanol, nonanal, trans-2-octenal, 1-octen-3-ol, 2-ethylhexanol, decanol, benzaldehyde, and trans-2-nonanal.
In a preferred embodiment, in the step (1), the crushed grain and oil food is mixed with water in a weight ratio of 1 (5-10) (for example, 1:8), soaked at 60-70 ℃ (for example, 65 ℃) for 12-18 min (for example, 15min), filtered to obtain a filtrate, and then an internal standard solution is added into the filtrate in a volume ratio of 1 (30-40) (for example, 1:33) to obtain the sample to be tested of the flavor substances of the grain and oil food. Preferably, the internal standard solution is 10mg/L of trichloropropane. More preferably, the grain and oil food is malt and its processed products.
In a preferred embodiment, in the step (1), the grain and oil food is mixed with water in a weight ratio of 1 (1-2) (for example, 1:1.4), soaked for 28-32 min (for example, 30min), cooked at 100 ℃ for 18-22 min (for example, 20min) and braised for 5-15 min (for example, 10min), 4-6 g (for example, 5g) of the obtained substance is directly taken without heating, and 2 muL of an internal standard solution is added to obtain the sample to be tested of the grain and oil food flavor substance. Preferably, the internal standard solution is 0.85 μ g/mL of 2-methyl-3-heptanone. More preferably, the grain and oil food is rice and processed products thereof.
In the invention, the headspace solid phase microextraction (HS-SPME) method in the step (2) and the gas chromatography-mass spectrometry (GC-MS) technology in the step (3) are adopted to analyze the flavor substances of the grain and oil food.
In a preferred embodiment, in the step (2), the sample to be tested of the grain and oil food flavor substance is placed in a headspace bottle, pre-balanced at 60-70 ℃ for 20-50 min, and then extracted by a solid phase micro-extraction column for 30-40 min to collect the volatile components.
In a preferred embodiment, in the step (3), the solid phase micro-extraction column is inserted into a sample inlet of a gas chromatography-mass spectrometer for desorption for 3-5 min.
In a preferred embodiment, the chromatographic conditions in step (3) are: chromatographic column WAX capillary column (60 m.times.0.25 mm. times.0.25 μm); the carrier gas is helium, and the flow rate is 1-1.5 mL/min; the temperature of a sample inlet is 240-260 ℃; no-shunt sample introduction; temperature rising procedure: the initial temperature is 40-45 ℃, the temperature is kept for 3-5 min, the temperature is firstly increased to 90-200 ℃ at the speed of 5-8 ℃/min, and then is increased to 98-230 ℃ at the speed of 1-10 ℃/min.
In a preferred embodiment, the mass spectrometry conditions in step (3) are: the temperature of the EI ion source is 230 ℃; the temperature of the quadrupole rods is 150 ℃; electron energy 70 eV; the transmission line temperature is 250 ℃; full scanning; the mass scanning range is m/z 30-500, and the tuning file is standard tuning.
In a preferred embodiment, in the step (3), the normal alkane standard is a C7-C40 normal alkane mixed standard, and the chromatographic and mass spectrum conditions for detecting the normal alkane standard are the same as those of the sample to be detected of the grain and oil food flavor substances.
In a preferred embodiment, in the step (4), the MS spectrum (namely the flavor substance chromatogram) detected by the HS-SPME/GC-MS data is processed by a GC-MS chemical workstation, and the unknown compound is preliminarily matched and qualified with a NIST spectrum library to obtain the detection data (namely the flavor substance mass spectrum data) after preliminary qualification.
In a preferred embodiment, in step (4), the unknown compound is matched with the NIST library and reported as an identification result when the similarity of the chemical peak is greater than 60 (maximum 100).
In the step (5), the detection data after preliminary qualitative determination is introduced into a grain and oil flavor substance data analysis program, and the program rapidly calculates the retention index (RI value) of each compound according to the following formula (I) based on C + + language by comparing the retention time of the compound and the normal alkane according to the retention time, MS spectrum and normal alkane spectrum:
RI＝100Z+100[tR(x)-tR(z)]/[tR(z+1)-tR(z)](I)
wherein: tR (x), tR (z +1) represent retention times of the target component and the normal alkane having a carbon number of Z, Z +1, respectively, and tR (z) < tR (x) < tR (z + 1).
In the invention, the grain and oil flavor substance data analysis program compares the retention index of each compound with the corresponding compound in the NIST spectral library, and the difference of the retention indexes is less than or equal to 50, namely, the corresponding substance is judged to be the grain and oil flavor substance; if the difference of the retention indexes is more than or equal to 70, judging that the corresponding substances are not grain and oil flavor substances; 50 < retention index difference < 70, and judging that the corresponding substances are probably grain and oil flavor substances.
In a preferred embodiment, when the plurality of sets of data are compared, the grain and oil flavor substance data analysis program can also perform de-duplication, summarization and arrangement on a large amount of data to provide help for analysis results.
In a preferred embodiment, the results are calculated from the MS spectrum retention time and the grain and oil flavor data analysis program, and the data analysis time is less than 10 min.
The data file applied to the grain and oil flavor substance data analysis program is used for analyzing grain and oil food flavor substance data and normal paraffin data by a headspace solid phase microextraction (HS-SPME) method and a gas chromatography-mass spectrometry (GC-MS) technology.
The HS-SPME/GC-MS data comprise, but are not limited to, Agilent system analysis data and Shimadzu system analysis data.
The analysis method provided by the invention is beneficial to rapid and accurate identification of GC-MS mass spectrum data and rapid screening of grain and oil food characteristic flavor substances, shortens the data analysis time from 6h to less than 10min, and effectively solves the qualitative and quantitative statistical analysis problem of multi-component flavor substances in a complex system.
Example 1
1. Sample and instrument
1.1 sample: special malt, medium grain malt (Dalian) limited; normal alkanes (HJ894-2017, C7-C40), Shanghai' an spectral laboratory science and technology, Inc.
1.2 Instrument: BSA224S electronic balance, sartorius, germany; DLFU disc mill, Buhler, germany; shimadzu AOC6000 multifunctional sample injector, Shimadzu, Japan; DVB/PDMS/Carbon WR-80 μm extraction head, Shimadzu corporation, Japan); GCMS-2020NX gas chromatography-Mass Spectrometry Combined Instrument, Shimadzu, Japan.
2. Preparation of test samples
2.1 preparation of malt samples: taking a certain amount of malt products, crushing by using a DLFU disc crusher (the disc spacing is 0.2mm), and weighing 80g of malt powder and 640g of purified water at 65 ℃; mixing the weighed malt flour and purified water, pouring the mixture into a thermos cup, and soaking for 15 minutes; and filtering the soaked mixed solution by using filter paper, measuring 5mL of filtrate into a 20mL headspace bottle after filtering for 10 minutes, accurately adding 150 mu L of trichloropropane (10mg/L) internal standard solution to obtain grain and oil food flavor substance extracting solution, and shaking uniformly to be detected.
2.2 preparation of normal alkane samples: 10 mu L of normal paraffin standard substance is measured and put into a 20mL headspace bottle to be tested.
HS-SPME/GC-MS detection method
3.1 enrichment of volatile constituents: collecting special malt volatile components of the grain and oil food flavor substance extracting solution obtained from the 2.1 parts by adopting a headspace solid phase microextraction technology. An automatic sample injector is adopted, the extraction temperature is 60 ℃, the balance time is 40min, the extraction time is 30min, after the adsorption is finished, the solid phase micro-extraction column is inserted into a sample inlet of a gas chromatography-mass spectrometer (GC-MS) for desorption for 3min, the GC-MS detection is carried out under the following conditions, and the detection result is collected.
3.2 GC-MS detection conditions for volatile constituents
Chromatographic conditions are as follows: chromatographic column WAX capillary column (60 m.times.0.25 mm. times.0.25 μm); the carrier gas is helium, and the flow rate is 1.2 mL/min; the temperature of a sample inlet is 250 ℃; no shunt sampling; temperature rising procedure: the initial temperature is 45 ℃, the temperature is kept for 3min, firstly the temperature is increased to 90 ℃ at 5 ℃/min, secondly the temperature is increased to 98 ℃ at 1 ℃/min, then the temperature is increased to 220 ℃ at 5 ℃/min, and the temperature is kept for 1 min.
Mass spectrum conditions: the temperature of the EI ion source is 230 ℃; the temperature of the quadrupole rods is 150 ℃; electron energy 70 eV; the transmission line temperature is 250 ℃; full scanning; the mass scanning range m/z is 30-500, and the tuning file is standard tuning.
3.3GC-MS analysis: the results of all malt samples (fig. 2) were scanned in a full-integration mode by peak area size through a GC-MS chemical workstation (fig. 3) and retrieved in a NIST 2011 mass spectral database. The computer searches in NIST standard spectrum library according to ion peak fragments of unknown compounds broken in mass spectrum, gives corresponding 10 substance lists according to the matching degree of ion peaks, and reports the identification result with the highest matching degree in a qualitative table (figure 4).
Under the same chromatographic conditions, by chromatographic scanning of C7-C40 normal paraffin mixed standard, different carbon numbers and corresponding retention times under the corresponding method can be obtained (figure 5) and used for calculating the Retention Index (RI) value of an unknown compound.
HS-SPME/GC-MS data processing method
4.1 derivation of raw data
Deriving the chromatogram scanned by the GC-MS chemical workstation through a full integral mode (figure 2) and all data information matched with the spectrum library (figure 6), sorting the detection result of the normal alkane mixed standard substance, and recording the retention time of alkane substances containing different carbon atoms (figure 5). The analysis program is opened (fig. 7), and the program is run (fig. 8) to perform data processing.
4.2 obtaining the Retention index by comparison with grain and oil flavor analysis program
The results of the detection of the malt samples processed by the GC-MS chemical workstation were simultaneously introduced into the grain and oil flavor analysis program (fig. 1) along with the results of the normal paraffin mixed standard chromatography, which automatically calculated the retention index of each compound according to the following formula (I):
RI＝100Z+100[tR(x)-tR(z)]/[tR(z+1)-tR(z)](I)
wherein: tR (x), tR (z +1) represent retention times of the target component and the normal alkane having a carbon number of Z, Z +1, respectively, and tR (z) < tR (x) < tR (z + 1).
4.3 data interpretation by grain and oil flavor analysis program
4.3.1 data analysis: the analysis program further compares the Retention indexes of the compounds (i.e., Ri values in FIG. 9) with the Retention indexes of the corresponding compounds in the NIST 2011 spectrogram library (i.e., Retention Index in FIG. 9), and determines that the corresponding substances can be special malt flavor substances (Match) if the difference between the Retention indexes is less than or equal to 50; the retention index is more than or equal to 70, namely the corresponding substance is judged to be not special malt flavor substance (Delete); 50 < Retention index < 70, i.e.the corresponding substances were judged to be possibly specific malt flavors (Pending). A summary of all data processing results is shown in fig. 10.
4.3.2 data Wash: after completion of the data washing by this procedure, the main flavors of the specialty malt were analyzed (results are shown in fig. 11).
Example 2
1. Sample and instrument
1.1 sample: rice, and a middle grain and grain rice management department. Normal alkanes (HJ894-2017, C7-C40), Shanghai' an spectral laboratory science and technology, Inc.
1.2 Instrument: BSA224S electronic balance, sartorius, germany; shimadzu AOC6000 multifunctional sample injector, Shimadzu, Japan; DVB/PDMS/Carbon WR-80 μm extraction head, Shimadzu corporation, Japan); GCMS-2020NX gas chromatography-Mass Spectrometry Combined Instrument, Shimadzu, Japan.
2 preparation of test samples
2.1 preparation of Rice samples: soaking rice and water at a ratio of 1:1.4 for 30 minutes, steaming and boiling with a rice cooker for 30 minutes, stewing for 10 minutes, directly placing 5g of rice into a headspace bottle without cooling, adding 2 muL of 2-methyl-3-heptanone with the content of 0.85 mug/mL, and testing.
2.2 preparation of normal alkane samples: 10 mu L of normal paraffin standard substance is measured and put into a 20mL headspace bottle to be tested.
3. Detection method for HS-SPME/GC-MS
3.1 enrichment of volatile constituents: collecting volatile components of rice by solid phase micro-extraction technology. An automatic sample injector is adopted, the extraction temperature is 60 ℃, the balance time is 20min, the extraction time is 40min, and the solid phase micro-extraction column is inserted into a sample inlet for desorption time of 5min after the adsorption is finished.
3.2 GC-MS detection conditions for volatile constituents
Chromatographic conditions are as follows: chromatographic column WAX capillary column (60m × 0.25mm × 0.25 μm), helium as carrier gas, and flow rate of 1.2 mL/min; the temperature of a sample inlet is 250 ℃; no shunt sampling; temperature rising procedure: the initial temperature is 40 deg.C, the temperature is maintained for 3min, the temperature is first raised to 200 deg.C at 5 deg.C/min, the temperature is maintained for 0min, and then raised to 230 deg.C at 10 deg.C/min, and the temperature is maintained for 3 min.
Mass spectrum conditions: an EI ion source, wherein the electron energy is 70eV, the transmission line temperature is 250 ℃, and the ion source temperature is 230 ℃; the temperature of the quadrupole is 150 ℃, the mass scanning range m/z is 40-500, the scanning mode is full scanning, and the tuning file is standard tuning.
3.3GC-MS analysis: and (3) carrying out full-integration mode scanning on the detection results of all the rice samples according to peak area sizes through a GC-MS chemical workstation, and searching in a NIST 2011 mass spectrum database. The computer searches in NIST standard spectrum library according to ion peak fragments of unknown compounds in mass spectrum, gives corresponding substance lists according to the matching degree of ion peaks, and reports the identification result with the highest matching degree in a qualitative table.
Under the same chromatographic conditions, by chromatographic scanning of C7-C40 normal paraffin mixed standard, different carbon numbers and corresponding retention times under the corresponding methods can be obtained and used for calculating Retention Index (RI) values of unknown compounds.
HS-SPME/GC-MS data processing method
4.1 derivation of raw data
And (3) exporting all data information matched with the chromatogram scanned by the GC-MS chemical workstation through a full integral mode and the comparison spectrum library, sorting the detection result of the normal alkane mixed standard substance, and recording the retention time of alkane substances containing different carbon atoms.
4.2 obtaining the Retention index by comparison with grain and oil flavor analysis program
The results of the detection of the malt samples processed by the GC-MS chemical workstation were simultaneously introduced into the grain and oil flavor analysis program (fig. 1) which automatically calculated the retention index of each compound according to the following formula (I):
RI＝100Z+100[tR(x)-tR(z)]/[tR(z+1)-tR(z)](I)
wherein: tR (x), tR (z +1) represent retention times of the target component and the normal alkane having a carbon number of Z, Z +1, respectively, and tR (z) < tR (x) < tR (z + 1).
4.3 data interpretation by grain and oil flavor analysis program
4.3.1 data analysis: the analysis program further compares the retention index of the compound with the retention index of the corresponding compound in the NIST 2011 spectrogram library, and judges that the corresponding substance can be a rice flavor substance (Match) if the difference between the retention indexes is less than or equal to 50; the retention index is more than or equal to 70, namely the corresponding substance is judged to be not the rice flavor substance (Delete); 50 < Retention index < 70, the corresponding material was judged to be a rice flavor (Pending). All data processing results are summarized at the end of the document.
4.3.2 data Wash: after washing through this program data, the rice was analyzed for major flavor substances (results are shown in fig. 12).
Therefore, the method can realize the rapid identification and screening of the characteristic flavor substances of the grain and oil food, shorten the data analysis time from 6h to less than 10min, effectively solve the qualitative and quantitative statistical analysis problem of the multi-component flavor substances in a complex system, greatly improve the industrial efficiency and have good industrial practicability.
Claims (10)
1. A method of grain food flavour data analysis (MSData), wherein the method comprises the steps of:
(1) preparation of a detection sample: preparing a sample to be tested of the grain and oil food flavor substances;
(2) enrichment of volatile components: collecting volatile components in the grain and oil food flavor substance to-be-detected sample by adopting a headspace solid phase microextraction (HS-SPME) technology;
(3) detection of volatile components: detecting and analyzing the volatile components and the normal alkane standard by adopting a gas chromatography-mass spectrometry (GC-MS) technology to obtain a flavor substance chromatogram and a normal alkane standard chromatogram;
(4) analysis of chromatographic data: carrying out full integral mode scanning on the flavor substance chromatogram, and retrieving a common ion peak in an NIST 2011 mass spectrum database according to the ion peak matching degree to generate flavor substance mass spectrum data; performing full integral mode scanning on the normal alkane standard chromatogram to obtain normal alkane standard quality spectrum data;
(5) processing mass spectrum data: introducing the flavor substance mass spectrum data and the normal paraffin standard quality spectrum data into a grain and oil food flavor substance analysis program, and calculating the Retention Index (RI) of the target component according to the following formula (I); comparing the retention index of the obtained target component with the retention index in the NIST 2011 standard spectrum library, wherein the difference between the retention indexes is less than or equal to 50, and judging that the corresponding substance is a grain and oil flavor substance; if the difference of the retention indexes is more than or equal to 70, judging that the corresponding substances are not grain and oil flavor substances; if the retention index difference is more than 50 and less than 70, the corresponding substances are judged to be probably grain and oil flavor substances,
RI＝100Z+100[tR(x)-tR(z)]/[tR(z+1)-tR(z)] (I)
wherein: tR (x), tR (z +1) represent retention times of the target component and the normal alkane having carbon numbers z, z +1, respectively, and tR (z) < tR (x) < tR (z + 1).
2. The grain-oil food flavor data analysis (MSData) method of claim 1, wherein the grain-oil food comprises one or more of grain and processed products thereof, oil and processed oil products thereof; preferably, the grain and the processed products thereof comprise one or more of wheat and the processed products thereof, barley and the processed products thereof, rice and the processed products thereof, coarse cereals and the processed products thereof; preferably, the oil and its processed oil products comprise one or more of peanuts and their processed peanut oil, soybeans and their processed soybean oil, rape and its processed rapeseed oil;
preferably, the cereal-oil food flavour material comprises one or more of: isobutyraldehyde, isovaleraldehyde, 2-methylbutyraldehyde, hexanal, heptanal, pentanal, n-hexanal, furfural, 3-furfural, 5-methylfuran aldehyde, benzaldehyde, 2-pyrrole formaldehyde, phenylacetaldehyde, trans-2-octenal, nonanal, cacao aldehyde, trans-2-nonanal, (E) -nonenal, decanal, alpha-ethylene-phenylacetaldehyde, trans-2, 4-decadienal, alpha- (2-methylpropylidene) phenylacetaldehyde, pentadecanal, cocaldehyde, 2-methylpyrazine, 2-ethyl-6-methylpyrazine, 3-ethyl-2, 5-methylpyrazine, 2-ethyl-3, 5-dimethylpyrazine, 2-methyl-3- (2-methylpropyl) pyrazine, 2, 5-dimethyl-3- (3-methylbutyl) pyrazine, 2-methyl-3- (2-methylpropyl) pyrazine, 2-ethyl-3-methylpyrazine, furfuryl alcohol, 1-octen-3-ol, 2-ethylhexanol, 1-nonanol, 2-propyl-1-heptanol, dodecanol, methylmaltol, 4-ethyl-2-methoxyphenol, 2, 4-di-tert-butylphenol, pentadecylmethylether, dimethyldisulfide, n-pentanoic acid, hexanoic acid, styrene, undecane, 2-heptanone, 2-methyl-3-hydroxy-gamma-pyrone, methyl-2, 5-dimethyl-3- (3-methylbutyl) pyrazine, 2-methyl-3- (2-methylpropyl) pyrazine, 2-ethyl-3-methoxy-1-octen-3-ol, 2-ethylhexanol, 1-nonanol, 4-ethyl-2-methoxy-2-methoxy-pentanone, 2-methyl-3-hydroxy-gamma-pentanone, pentanol, hexanol, pentanol, hexanol, pentanol, hexanol, pentanol, hexanol, pentanol, hexanol, pentanol, hexanol, pentanol, and the amino-2, pentanol, hexanol, pentanol, hexanol, and the like, and the amino acid, hexanol, pentanol, hexanol, and the like, 6, 10-dimethyl-5, 9-undecene-2-one, geranylacetone, 2-acetylfuran, 2-n-pentylfuran, 2-acetylpyrrole, 1- (2-furylmethyl) -1H-pyrrole, 2-pentylfuran, gamma-butyrolactone, amyl hexanoate, methyl 10-methylundecanoate, methyl 12-methyltridecanoate, methyl laurate, isobutyl 2,2, 4-trimethylpentanediol, ethylhexyl benzoate, pentamethylfuran bromate, methyl palmitoleate, methyl palmitate, methyl linoleate, methyl oleate, 1-pentanol, n-octanal, methylheptenone, 2-acetyl-1-pyrroline, n-hexanol and decanol.
3. The grain and oil food flavor substance data analysis (MSData) method according to claim 1 or 2, wherein in the step (1), the crushed grain and oil food is mixed with water according to the weight ratio of 1 (5-10), soaked for 12-18 min at 60-70 ℃, filtered to obtain a filtrate, and then an internal standard solution is added into the filtrate according to the volume ratio of 1 (30-40) to the filtrate to obtain a sample to be tested of the grain and oil food flavor substance; preferably, the internal standard solution is 10mg/L trichloropropane; more preferably, the grain and oil food is malt and its processed products.
4. The method for analyzing the data (MSData) of the flavor substances of the grain and oil food according to claim 1 or 2, wherein in the step (1), the grain and oil food is mixed with water according to the weight ratio of 1 (1-2), soaked for 28-32 min, cooked for 18-22 min at 100 ℃ and braised for 5-15 min, 4-6 g of the obtained substance is taken, and 2 muL of internal standard solution is added into the substance to obtain the sample to be tested of the flavor substances of the grain and oil food; preferably, the internal standard solution is 0.85 μ g/mL of 2-methyl-3-heptanone; more preferably, the grain and oil food is rice and processed products thereof.
5. The method for analyzing data (MSData) of flavor substances in grain and oil food according to any one of claims 1-4, wherein in the step (2), the sample to be tested for flavor substances in grain and oil food is put into a headspace bottle, pre-equilibrated at 60-70 ℃ for 20-50 min, and extracted by a solid phase micro-extraction column for 30-40 min to collect the volatile components.
6. The method for analyzing data of flavor substances (MSData) in grain and oil food according to any one of claims 1-5, wherein in step (3), the solid phase micro-extraction column is inserted into a sample inlet of a gas chromatography-mass spectrometer for desorption for 3-5 min.
7. The grain and oil food flavor data analysis (MSData) method of any one of claims 1 to 6, wherein the chromatographic conditions in step (3) are: chromatographic column WAX capillary column 60m × 0.25mm × 0.25 μm; the carrier gas is helium, and the flow rate is 1-1.5 mL/min; the temperature of a sample inlet is 240-260 ℃; no shunt sampling; temperature rising procedure: the initial temperature is 40-45 ℃, the temperature is kept for 3-5 min, the temperature is firstly increased to 90-200 ℃ at the rate of 5-8 ℃/min, and then the temperature is increased to 98-230 ℃ at the rate of 1-10 ℃/min.
8. The grain and oil food flavor data analysis (MSData) method of any one of claims 1 to 7, wherein the mass spectrometry conditions in step (3) are: the temperature of the EI ion source is 230 ℃; the temperature of the quadrupole rods is 150 ℃; electron energy 70 eV; the transmission line temperature is 250 ℃; full scanning; the mass scanning range m/z is 30-500, and the tuning file is standard tuning.
9. The method for data analysis (MSData) of flavor substances in grain and oil food according to any one of claims 1 to 8, wherein, in step (3), the normal alkane standard is a mixed standard of C7-C40 normal alkanes, and the chromatographic and mass spectrometric conditions for detecting the normal alkane standard are the same as those of the sample to be tested for the flavor substances in grain and oil food.
10. The method for data analysis of flavor of grain and oil food (MSData) according to any one of claims 1 to 9 wherein in step (4) the unknown compounds in the flavor chromatogram are matched with the library of the NIST 2011 mass spectrometry database and when the similarity of the chemical peak is greater than 60, the common ion peak is identified and reported.
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