CN106053384A - Rapid quantitative detection method for sweet wormwood and honeysuckle alcohol precipitation concentration process - Google Patents
Rapid quantitative detection method for sweet wormwood and honeysuckle alcohol precipitation concentration process Download PDFInfo
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- CN106053384A CN106053384A CN201610563340.4A CN201610563340A CN106053384A CN 106053384 A CN106053384 A CN 106053384A CN 201610563340 A CN201610563340 A CN 201610563340A CN 106053384 A CN106053384 A CN 106053384A
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- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 title claims abstract description 192
- 238000000034 method Methods 0.000 title claims abstract description 171
- 230000008569 process Effects 0.000 title claims abstract description 96
- 238000001514 detection method Methods 0.000 title claims abstract description 34
- 235000001405 Artemisia annua Nutrition 0.000 title abstract 8
- 240000000011 Artemisia annua Species 0.000 title abstract 8
- 241001570521 Lonicera periclymenum Species 0.000 title abstract 8
- 238000001556 precipitation Methods 0.000 title abstract 8
- PZIRUHCJZBGLDY-UHFFFAOYSA-N Caffeoylquinic acid Natural products CC(CCC(=O)C(C)C1C(=O)CC2C3CC(O)C4CC(O)CCC4(C)C3CCC12C)C(=O)O PZIRUHCJZBGLDY-UHFFFAOYSA-N 0.000 claims abstract description 70
- CWVRJTMFETXNAD-FWCWNIRPSA-N 3-O-Caffeoylquinic acid Natural products O[C@H]1[C@@H](O)C[C@@](O)(C(O)=O)C[C@H]1OC(=O)\C=C\C1=CC=C(O)C(O)=C1 CWVRJTMFETXNAD-FWCWNIRPSA-N 0.000 claims abstract description 40
- CWVRJTMFETXNAD-KLZCAUPSSA-N Neochlorogenin-saeure Natural products O[C@H]1C[C@@](O)(C[C@@H](OC(=O)C=Cc2ccc(O)c(O)c2)[C@@H]1O)C(=O)O CWVRJTMFETXNAD-KLZCAUPSSA-N 0.000 claims abstract description 40
- CWVRJTMFETXNAD-JUHZACGLSA-N chlorogenic acid Chemical compound O[C@@H]1[C@H](O)C[C@@](O)(C(O)=O)C[C@H]1OC(=O)\C=C\C1=CC=C(O)C(O)=C1 CWVRJTMFETXNAD-JUHZACGLSA-N 0.000 claims abstract description 40
- 229940074393 chlorogenic acid Drugs 0.000 claims abstract description 40
- FFQSDFBBSXGVKF-KHSQJDLVSA-N chlorogenic acid Natural products O[C@@H]1C[C@](O)(C[C@@H](CC(=O)C=Cc2ccc(O)c(O)c2)[C@@H]1O)C(=O)O FFQSDFBBSXGVKF-KHSQJDLVSA-N 0.000 claims abstract description 40
- 235000001368 chlorogenic acid Nutrition 0.000 claims abstract description 40
- BMRSEYFENKXDIS-KLZCAUPSSA-N cis-3-O-p-coumaroylquinic acid Natural products O[C@H]1C[C@@](O)(C[C@@H](OC(=O)C=Cc2ccc(O)cc2)[C@@H]1O)C(=O)O BMRSEYFENKXDIS-KLZCAUPSSA-N 0.000 claims abstract description 40
- GWTUHAXUUFROTF-UHFFFAOYSA-N pseudochlorogenic acid Natural products C1C(O)C(O)C(O)CC1(C(O)=O)OC(=O)C=CC1=CC=C(O)C(O)=C1 GWTUHAXUUFROTF-UHFFFAOYSA-N 0.000 claims abstract description 30
- CWVRJTMFETXNAD-NXLLHMKUSA-N trans-5-O-caffeoyl-D-quinic acid Chemical compound O[C@H]1[C@H](O)C[C@](O)(C(O)=O)C[C@H]1OC(=O)\C=C\C1=CC=C(O)C(O)=C1 CWVRJTMFETXNAD-NXLLHMKUSA-N 0.000 claims abstract description 30
- 238000001228 spectrum Methods 0.000 claims abstract description 23
- 238000002347 injection Methods 0.000 claims abstract description 17
- 239000007924 injection Substances 0.000 claims abstract description 17
- 238000004519 manufacturing process Methods 0.000 claims abstract description 12
- 238000002790 cross-validation Methods 0.000 claims abstract description 10
- 239000002244 precipitate Substances 0.000 claims description 90
- 241000628997 Flos Species 0.000 claims description 76
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 claims description 54
- 229950009125 cynarine Drugs 0.000 claims description 32
- 238000010828 elution Methods 0.000 claims description 22
- NBIIXXVUZAFLBC-UHFFFAOYSA-N Phosphoric acid Chemical compound OP(O)(O)=O NBIIXXVUZAFLBC-UHFFFAOYSA-N 0.000 claims description 18
- 238000000691 measurement method Methods 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 17
- 239000009839 reduning Substances 0.000 claims description 16
- 239000000243 solution Substances 0.000 claims description 15
- 230000003595 spectral effect Effects 0.000 claims description 15
- 238000003908 quality control method Methods 0.000 claims description 12
- 239000006228 supernatant Substances 0.000 claims description 11
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 11
- 229910000147 aluminium phosphate Inorganic materials 0.000 claims description 9
- 238000004128 high performance liquid chromatography Methods 0.000 claims description 8
- 239000012467 final product Substances 0.000 claims description 7
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 claims description 7
- 239000010931 gold Substances 0.000 claims description 7
- 229910052737 gold Inorganic materials 0.000 claims description 7
- 238000002235 transmission spectroscopy Methods 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 5
- 101150061025 rseP gene Proteins 0.000 claims description 4
- 239000002253 acid Substances 0.000 claims 2
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- 238000007781 pre-processing Methods 0.000 abstract description 3
- GYFFKZTYYAFCTR-JUHZACGLSA-N 4-O-trans-caffeoylquinic acid Chemical compound O[C@@H]1C[C@](O)(C(O)=O)C[C@@H](O)[C@H]1OC(=O)\C=C\C1=CC=C(O)C(O)=C1 GYFFKZTYYAFCTR-JUHZACGLSA-N 0.000 abstract 1
- GYFFKZTYYAFCTR-UHFFFAOYSA-N 5-O-(6'-O-galloyl)-beta-D-glucopyranosylgentisic acid Natural products OC1CC(O)(C(O)=O)CC(O)C1OC(=O)C=CC1=CC=C(O)C(O)=C1 GYFFKZTYYAFCTR-UHFFFAOYSA-N 0.000 abstract 1
- GYFFKZTYYAFCTR-LMRQPLJMSA-N cryptochlorogenic acid Natural products O[C@H]1C[C@@](O)(C[C@H](O)[C@H]1OC(=O)C=Cc2ccc(O)c(O)c2)C(=O)O GYFFKZTYYAFCTR-LMRQPLJMSA-N 0.000 abstract 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
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- 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
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- 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
- G01N2030/022—Column chromatography characterised by the kind of separation mechanism
- G01N2030/027—Liquid chromatography
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- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
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Abstract
The invention discloses a rapid quantitative detection method for a sweet wormwood and honeysuckle alcohol precipitation concentration process. The rapid quantitative detection method comprises the following steps: collecting sweet wormwood and honeysuckle alcohol precipitation concentrated liquid samples of different batches in large-scale production of redlining injection; measuring key indicators of the sweet wormwood and honeysuckle alcohol precipitation concentrated liquid samples; acquiring near infrared spectrum charts of the sweet wormwood and honeysuckle alcohol precipitation concentrated liquid samples; respectively establishing quantitative correction models by selecting a suitable spectrum pre-processing method and a suitable modelling wave band, and inspecting the performance of a model by taking model cross validation correlation coefficients, cross validation mean square root errors, and relative deviation evaluation indexes between predicted values and offline detection values to establish the rapid quantitative detection method for the sweet wormwood and honeysuckle alcohol precipitation concentration process. According to the rapid quantitative detection method, a sample quantitative correction model for the sweet wormwood and honeysuckle alcohol precipitation concentration process is established by introducing a near infrared spectrum technology, so that the changes of the densities of neochlorogenic acid, chlorogenic acid and cryptochlorogenic acid in the samples in the sweet wormwood and honeysuckle alcohol precipitation concentration process are detected rapidly and quantatively; the established analysis method is rapid and green, and can be applied to online detection in a production process.
Description
Technical field
The invention belongs to near-infrared field of fast detection, be specifically related to a kind of 4 kinds of fingers of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process
Mark fast quantitative measurement method for detecting.
Background knowledge
Reduning injection is the Chinese medicine kind that Kangyuan Pharmaceutical Co., Ltd., Jiangsu Prov produces without competition, prescription by
Herba Artemisiae Annuae, Flos Lonicerae, Fructus Gardeniae three taste medical material composition, clinic is mainly used in the caused flu of affection due to external wind and heat, cough, and disease sees high evil hot, micro-
Wind and cold, headache general pain, cough, expectorant Huang；Upper respiratory tract infection, acute bronchitis are shown in above-mentioned patient.From 2005 listing with
Come, owing to it has definite curative effect at the hot aspect of height caused by treatment upper respiratory tract infection, acute bronchitis, obtain vast trouble
Person and the accreditation of Clinical practice unit.
Herba Artemisiae Annuae Flos Lonicerae alcohol deposit fluid enrichment process is CCP in Reduning injection production process, the matter of concentrated solution
Amount is the key affecting subsequent purification effect, if there is larger fluctuation in concentrated solution quality, and the intermediate quality obtained after purification
Just cannot ensure, then have influence on the safety of Reduning injection final finished, quality controllability.Therefore the blue or green gold of strict control
Precipitate with ethanol concentration process key index fluctuation range has important for promoting Reduning injection production process quality control level
Scientific meaning
Near-infrared spectrum technique (NIR) has been widely used in oil, food as the quick process analysis technique of one
The fields such as product.Have also been obtained in tcm manufacturing process on-line monitoring and control in recent years and be widely applied.Near infrared spectrum skill
Art has the advantage that (1) is green, environmental protection, does not destroy sample (2) detection quickly, and abundant information (3) high accuracy (4) can be
Line detects.
Summary of the invention
Present invention aims to the deficiencies in the prior art, it is provided that a kind of green grass or young crops new, that efficiency is high, Detection results is good
Artemisia Flos Lonicerae fast quantitative measurement method for detecting.
The technical problem to be solved is to be realized by following technical scheme.The present invention is a kind of Herba Artemisiae Annuae
Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting, its feature, the method comprises the steps:
(1) the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process no less than 5 different batches in the big production of Reduning injection is gathered
Sample；
(2) 4 crucial quality control indexs of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches are measured: new
Chlorogenic acid, chlorogenic acid, 4-dicaffeoylquinic acid, density；
(3) the NIR transmittance spectroscopy figure of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches is gathered；
(4) choose the 70-90% of total batch and round off to round several batch be calibration set, use offset minimum binary
Method sets up the near-infrared quantitative calibration models of 4 quality control indexs in Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process, and utilizes this model real
Now the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of remaining checking collection batch is carried out Concentration Testing；Realize Herba Artemisiae Annuae Flos Lonicerae alcohol
Heavy concentration process Quantitative detection purpose.
A kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting of the present invention, it is further preferred
Technical scheme is: in step (1): gather the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol of 9-20 different batches in the big production of Reduning injection dense
Compression process sample.
A kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting of the present invention, it is further preferred
Technical scheme is: in step (4): 80% and round off integer the batch of choosing total batch are calibration set.
A kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting of the present invention, it is further preferred
Technical scheme is: in step (1), Reduning injection precipitate with ethanol supernatant concentration process is concentrating under reduced pressure process, and sample sampling uses
Sampling below atmospheric pressure mode, all sample standard deviations gather from concentrating under reduced pressure tank lower floor same position.
A kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting of the present invention, it is further preferred
Technical scheme is: in step (1) Flos Lonicerae Herba Artemisiae Annuae precipitate with ethanol concentration process sampling mode be concentration start after first 1 hour every
20min samples once, samples once every 10min after second hour hour, samples once every 5min after the 3rd hour, sampling
Amount, no less than 10g, terminates until concentrating.
A kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting of the present invention, it is further preferred
Technical scheme is: step (2) described neochlorogenic acid, chlorogenic acid, the sample pretreatment side of high performance liquid chromatography of 4-dicaffeoylquinic acid
Method is: weigh the blue or green precipitate with ethanol concentrated solution 0.45g-0.55g of gold, accurately weighed, is placed in 50mL volumetric flask, 50% methanol constant volume, shakes
Even, 5000rpm-20000rpm is centrifuged 3-10min, takes supernatant, to obtain final product；Chromatographic condition is: phenomenex Luna C18,
250 × 4.6mm, 5 μm；0.1% phosphoric acid water is mobile phase A, and methanol is Mobile phase B, gradient elution, and elution requirement is shown in Table 1；Stream
Speed: 0.8mL/min-1.0mL/min；Column temperature 30 DEG C；Detection wavelength is 324nm, and sample size is 10 μ L.
A kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting of the present invention, it is further preferred
Technical scheme is: step (2) described neochlorogenic acid, chlorogenic acid, the sample pretreatment side of high performance liquid chromatography of 4-dicaffeoylquinic acid
Method is: weigh the blue or green precipitate with ethanol concentrated solution 0.5g of gold, accurately weighed, is placed in 50mL volumetric flask, 50% methanol constant volume, shakes up,
20000rpm is centrifuged 10min, takes supernatant, to obtain final product；Chromatographic condition is: phenomenex Luna C18,250 × 4.6mm, 5 μ
m；0.1% phosphoric acid water is mobile phase A, and methanol is Mobile phase B, gradient elution, and elution requirement is shown in Table 1；Flow velocity: 0.8mL/min；Post
Temperature 30 DEG C；Detection wavelength is 324nm, and sample size is 10 μ L；
Table 1 condition of gradient elution
。
A kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting of the present invention, it is further preferred
Technical scheme is: in step (3): direct-on-line collecting sample near infrared light spectrogram, and acquisition mode is transmission beam method, spectral region
4000～12800cm-1, resolution 2cm-1, employing air is ground control；3 spectrum of each sample collecting, are averaged spectrum
It is worth the near infrared near infrared light spectrogram as sample.
A kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting of the present invention, it is further preferred
Technical scheme is: in step (4): the preprocess method of the quantitative calibration models with neochlorogenic acid concentration as Index Establishment selects one
Order derivative adds at 17 and smooths, and number of principal components selects 9, models waveband selection 7502～5446.2cm-1；With chlorogenic acid concentration as index
The preprocess method selection first derivative of the quantitative calibration models set up adds at 17 and smooths, and number of principal components selects 6, modeling wave band choosing
Select 7502～5446.2cm-1；The preprocess method of the quantitative calibration models with 4-dicaffeoylquinic acid concentration as Index Establishment selects to deduct
Straight line, number of principal components selects 10, models waveband selection 9403.6～5446.2cm-1；Quantitative with density as Index Establishment
The preprocess method of calibration model selects first derivative to add to deduct straight line and add at 17 and smooth, and number of principal components selects 5, models ripple
Section selects 9403.6～5446.2cm-1.The neochlorogenic acid of foundation, chlorogenic acid, 4-dicaffeoylquinic acid, the quantitative correction of 4 indexs of density
Model cross validation coefficient R2It is respectively 95.45%, 97.52%, 96.91%, 97.88%；Cross validation root-mean-square is by mistake
Difference RMSECV is respectively 0.233,0.692,0.258,0.00991.Use the quantitative calibration models set up to precipitate with ethanol concentration process
On-line sample is analyzed, and model predication value is 3.519%, 3.778% with mean relative deviation RSEP of offline inspection value,
3.895%, 0.532%.
In the inventive method, the index determining method of preferred Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample is as follows:
(1) neochlorogenic acid, chlorogenic acid and 4-dicaffeoylquinic acid content assaying method
Chromatographic condition phenomenex Luna C18 (250 × 4.6mm, 5 μm)；0.1% phosphoric acid water is mobile phase A, methanol
For Mobile phase B, gradient elution, elution requirement see table 1；Flow velocity: 0.8ml/min；Column temperature 30 DEG C；Detection wavelength is 324nm, enters
Sample amount is 10 μ L.
Table 1 condition of gradient elution
| Time (minute) | Mobile phase A (%) | Mobile phase B (%) |
| 0 | 85 | 15 |
| 10 | 75 | 25 |
| 30 | 65 | 35 |
| 60 | 50 | 50 |
Specification Curve of Increasing, precision weighs neochlorogenic acid 3.45mg, chlorogenic acid 18.57mg, 4-dicaffeoylquinic acid 8.74mg respectively
Put in 25mL measuring bottle, add 50% methanol and dissolve and be diluted to scale, shake up, obtain mixing reference substance solution.To mixing reference substance
Carrying out 0 times, 2.5 times, 5 times, 10 times, 20 times, 40 times of dilutions, precision draws each 10 μ L of above-mentioned control series product solution, note respectively
Entering high performance liquid chromatograph, measure, with reference substance concentration (μ g/mL) as abscissa (X), peak area is vertical coordinate (Y), draws mark
Directrix curve.Neochlorogenic acid regression equation Y=0.578 0-0.469 7, r=0.999 9, the range of linearity 3.45 μ g/ml～138.0
μ g/ml, chlorogenic acid regression equation Y=0.742 1X-0.857 7, r=0.999 8, the range of linearity 18.57 μ g/ml～742.8 μ
G/ml, 4-dicaffeoylquinic acid regression equation Y=0.615 8X-0.609 4, r=0.999 9, the range of linearity 8.74 μ g/ml～349.6 μ
g/ml。
(2) density measurement: pipette, extract gold green grass or young crops precipitate with ethanol concentrated solution 5mL, precise weighing, record weight also presses formula meter
Calculate medicinal liquid density.ρ=m/v (formula) wherein ρ is the density of medicinal liquid, and m is the quality of medicinal liquid, the volume of V medicinal liquid.
(3) the NIR transmittance spectroscopy figure of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample is gathered；
Use workshop On-line NIR acquisition system, acquisition mode is transmission beam method, spectral region 4000～
12800cm-1, resolution 2cm-1, employing air is ground control；6 spectrum of each sample collecting, are averaged spectral value conduct
The near infrared near infrared light spectrogram of sample.
(4) foundation of quantitative calibration models: use the sample of 8 batches of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration processs by a young waiter in a wineshop or an inn
Multiplication sets up the near-infrared quantitative calibration models of 4 indexs in Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process, and utilizes this model to surplus
1 batch of remaining Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample is predicted and value measured with high performance liquid chromatography in (1) is carried out
Contrast, judges the precision of prediction of the dry model in quantitative school set up by the relative deviation of predictive value and actual value, thus realize right
The quick detection of unknown Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample.
Use the Matrix-F near infrared spectrometer of Brooker spectral instrument company of Germany, be equipped with InGaAs detector and combine
Chemo metric software OPUS7.5, by using partial least square method after near infrared spectrum carries out pretreatment and waveband selection
(PLS) determining of near infrared spectrum data and neochlorogenic acid concentration, chlorogenic acid concentration, 4-dicaffeoylquinic acid concentration and density is set up respectively
Amount calibration model, and investigate model performance by model-evaluation index.
Specifically, in above-mentioned steps (4), neochlorogenic acid concentration is the pretreatment side of the quantitative calibration models of Index Establishment
Method selection first derivative adds at 17 and smooths, and number of principal components selects 9, models waveband selection 7502～5446.2cm-1；Dense with chlorogenic acid
The preprocess method selection first derivative of the quantitative calibration models that degree is Index Establishment adds at 17 and smooths, and number of principal components selects 6, builds
Mould waveband selection 7502～5446.2cm-1；The preprocess method of the quantitative calibration models with 4-dicaffeoylquinic acid concentration as Index Establishment
Selection deducts straight line, and number of principal components selects 10, models waveband selection 9403.6～5446.2cm-1；Build with density for index
The preprocess method of vertical quantitative calibration models selects first derivative to add to deduct straight line and add at 17 and smooth, and number of principal components selects
5, model waveband selection 9403.6～5446.2cm-1.The neochlorogenic acid set up, chlorogenic acid, 4-dicaffeoylquinic acid, density 4 indexs
Quantitative calibration models cross validation correlation coefficient (R2) it is respectively 95.45%, 97.52%, 96.91%, 97.88%；Intersection is tested
Card root-mean-square error (RMSECV) is respectively 0.233,0.692,0.258,0.00991.Use the quantitative calibration models pair set up
Precipitate with ethanol concentration process on-line sample is analyzed, and model predication value with the mean relative deviation (RSEP) of offline inspection value is
3.519%, 3.778%, 3.895%, 0.532%.
It is below the specific formula for calculation of model performance evaluation index:
N is the checking collection sample number for testing model, and m is calibration set sample number, and Ci is the reference of calibration set sample i
Value,Predictive value for unknown sample i.
When coefficient of determination value is close to 1, calibration set error mean square root and stay a cross validation root-mean-square deviation the least, mould is described
The predictive ability of type is higher, good stability.
Compared with prior art, present invention introduces near-infrared spectrum technique as Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample
Detection method, can quickly detect neochlorogenic acid in concentration process, chlorogenic acid, 4-dicaffeoylquinic acid and density data situation of change.This
Invention introduces near-infrared spectrum technique and sets up Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample amounts calibration model, Quantitative detection
Neochlorogenic acid, chlorogenic acid, 4-dicaffeoylquinic acid, the change of density in Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process, the analysis method set up
Green, quickly, production process on-line checking can be applied to, have a extensive future.
Accompanying drawing explanation
Fig. 1 is the original near infrared light spectrogram of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample；
Fig. 2 is the neochlorogenic acid quantitative calibration models set up；
Fig. 3 is the chlorogenic acid quantitative calibration models set up；
Fig. 4 is the 4-dicaffeoylquinic acid quantitative calibration models set up；
Fig. 5 is the density quantitative calibration models set up；
Fig. 6 is that near-infrared quantitative model is to neochlorogenic acid concentration prediction value in unknown sample and practical measurement trendgram；
Fig. 7 is that near-infrared quantitative model is to unknown sample Content of Chlorogenic Acid concentration prediction value and practical measurement trendgram；
Fig. 8 is that near-infrared quantitative model is to 4-dicaffeoylquinic acid concentration prediction value in unknown sample and practical measurement trendgram；
Fig. 9 is that near-infrared quantitative model is to unknown sample density prediction value and practical measurement trendgram.
Detailed description of the invention
The present invention is further described in conjunction with the accompanying drawings and embodiments.
Embodiment 1, a kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting, the method includes walking as follows
Rapid:
(1) the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of 5 different batches in the big production of Reduning injection is gathered；
(2) 4 crucial quality control indexs of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches are measured: new
Chlorogenic acid, chlorogenic acid, 4-dicaffeoylquinic acid, density；
(3) the NIR transmittance spectroscopy figure of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches is gathered；
(4) choosing 4 batches is calibration set, uses partial least square method to set up in Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process 4
The near-infrared quantitative calibration models of individual quality control index, and utilize this model realization Herba Artemisiae Annuae Flos Lonicerae to remaining checking collection batch
Precipitate with ethanol concentration process sample carries out Concentration Testing；Realize Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection purpose.
Embodiment 2, a kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting, the method includes walking as follows
Rapid:
(1) the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of 9 different batches in the big production of Reduning injection is gathered；
(2) 4 crucial quality control indexs of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches are measured: new
Chlorogenic acid, chlorogenic acid, 4-dicaffeoylquinic acid, density；
(3) the NIR transmittance spectroscopy figure of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches is gathered；
(4) choosing 8 batches is calibration set, uses partial least square method to set up in Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process 4
The near-infrared quantitative calibration models of individual quality control index, and utilize this model realization Herba Artemisiae Annuae Flos Lonicerae to remaining checking collection batch
Precipitate with ethanol concentration process sample carries out Concentration Testing；Realize Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection purpose.
Embodiment 3, a kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting, the method includes walking as follows
Rapid:
(1) the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of 20 different batches in the big production of Reduning injection is gathered；
(2) 4 crucial quality control indexs of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches are measured: new
Chlorogenic acid, chlorogenic acid, 4-dicaffeoylquinic acid, density；
(3) the NIR transmittance spectroscopy figure of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches is gathered；
(4) choosing 14 batches is calibration set, uses partial least square method to set up in Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process 4
The near-infrared quantitative calibration models of individual quality control index, and utilize this model realization Herba Artemisiae Annuae gold to remaining 6 checking collection batches
Flos Lonicerae precipitate with ethanol concentration process sample carries out Concentration Testing；Realize Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection purpose.
Embodiment 4, a kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection side described in embodiment 1 or 2 or 3
Method: in step (1), Reduning injection precipitate with ethanol supernatant concentration process is concentrating under reduced pressure process, sample sampling uses sampling below atmospheric pressure
Mode, all sample standard deviations gather from concentrating under reduced pressure tank lower floor same position.
Embodiment 5, the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection side in any of the one of embodiment 1-4
Method: in step (1), Flos Lonicerae Herba Artemisiae Annuae precipitate with ethanol concentration process sampling mode is to sample one every 20min in after concentration starts first 1 hour
Secondary, sample once every 10min after second hour hour, sample once every 5min after the 3rd hour, sampling amount is no less than
10g, terminates until concentrating.
Embodiment 6, the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection side in any of the one of embodiment 1-5
In method: step (2) described neochlorogenic acid, chlorogenic acid, the sample pretreating method of high performance liquid chromatography of 4-dicaffeoylquinic acid be: claim
Depletion green grass or young crops precipitate with ethanol concentrated solution 0.45g, accurately weighed, it is placed in 50mL volumetric flask, 50% methanol constant volume, shakes up, 5000rpm is centrifuged
3min, takes supernatant, to obtain final product；Chromatographic condition is: phenomenex Luna C18,250 × 4.6mm, 5 μm；0.1% phosphoric acid water
For mobile phase A, methanol is Mobile phase B, and gradient elution, elution requirement is shown in Table 1；Flow velocity: 0.8mL/min；Column temperature 30 DEG C；Detection ripple
A length of 324nm, sample size is 10 μ L；
Table 1 condition of gradient elution
Embodiment 7, the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection side in any of the one of embodiment 1-5
In method: step (2) described neochlorogenic acid, chlorogenic acid, the sample pretreating method of high performance liquid chromatography of 4-dicaffeoylquinic acid be: claim
Depletion green grass or young crops precipitate with ethanol concentrated solution 0.55g, accurately weighed, be placed in 50mL volumetric flask, 50% methanol constant volume, shake up, 10000rpm from
Heart 10min, takes supernatant, to obtain final product；Chromatographic condition is: phenomenex Luna C18,250 × 4.6mm, 5 μm；0.1% phosphoric acid
Water is mobile phase A, and methanol is Mobile phase B, gradient elution, and elution requirement is shown in Table 1；Flow velocity: 1.0mL/min；Column temperature 30 DEG C；Detection
Wavelength is 324nm, and sample size is 10 μ L.
Embodiment 8, the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection side in any of the one of embodiment 1-5
In method: step (2) described neochlorogenic acid, chlorogenic acid, the sample pretreating method of high performance liquid chromatography of 4-dicaffeoylquinic acid be: claim
Depletion green grass or young crops precipitate with ethanol concentrated solution 0.5g, accurately weighed, it is placed in 50mL volumetric flask, 50% methanol constant volume, shakes up, 20000rpm is centrifuged
10min, takes supernatant, to obtain final product；Chromatographic condition is: phenomenex Luna C18,250 × 4.6mm, 5 μm；0.1% phosphoric acid water
For mobile phase A, methanol is Mobile phase B, and gradient elution, elution requirement is shown in Table 1；Flow velocity: 0.8mL/min；Column temperature 30 DEG C；Detection ripple
A length of 324nm, sample size is 10 μ L.
Embodiment 9, the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection side in any of the one of embodiment 1-8
In method: in step (3): direct-on-line collecting sample near infrared light spectrogram, acquisition mode is transmission beam method, spectral region 4000～
12800cm-1, resolution 2cm-1, employing air is ground control；3 spectrum of each sample collecting, are averaged spectral value conduct
The near infrared near infrared light spectrogram of sample.
Embodiment 10, the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection in any of the one of embodiment 1-9
In method: in step (4): the preprocess method of the quantitative calibration models with neochlorogenic acid concentration as Index Establishment selects single order to lead
Number adds at 17 and smooths, and number of principal components selects 9, models waveband selection 7502～5446.2cm-1；With chlorogenic acid concentration as Index Establishment
Quantitative calibration models preprocess method select first derivative add 17 smooth, number of principal components select 6, model waveband selection
7502～5446.2cm-1；The preprocess method of the quantitative calibration models with 4-dicaffeoylquinic acid concentration as Index Establishment selects to deduct one
Bar straight line, number of principal components selects 10, models waveband selection 9403.6～5446.2cm-1；Quantitative school with density as Index Establishment
The preprocess method of positive model selects first derivative to add to deduct straight line and add at 17 and smooth, and number of principal components selects 5, models wave band
Select 9403.6～5446.2cm-1.The neochlorogenic acid of foundation, chlorogenic acid, 4-dicaffeoylquinic acid, the quantitative correction mould of 4 indexs of density
Type cross validation coefficient R2It is respectively 95.45%, 97.52%, 96.91%, 97.88%；Cross validation root-mean-square error
RMSECV is respectively 0.233,0.692,0.258,0.00991.Use the quantitative calibration models set up that precipitate with ethanol concentration process is existed
Line sample is analyzed, and model predication value is 3.519%, 3.778% with mean relative deviation RSEP of offline inspection value,
3.895%, 0.532%.
Embodiment 11, a kind of Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting experiment:
Partial least square method is used to set up neochlorogenic acid, chlorogenic acid, 4 index near-infrared quantitative corrections of 4-dicaffeoylquinic acid density
Model
1. Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample collection
Reduning injection precipitate with ethanol supernatant concentration process is concentrating under reduced pressure process, and sample sampling uses negative pressure taking sample prescription
Formula, all sample standard deviations gather from concentrating under reduced pressure tank lower floor same position.Flos Lonicerae Herba Artemisiae Annuae precipitate with ethanol concentration process sampling mode is dense
Within after contracting starts first 1 hour, sample once every 20min, sample once every 10min after second hour hour, every after the 3rd hour
Sampling once every 5min, sampling amount is no less than 10g, terminates until concentrating.
The mensuration of 2.4 indexs
Neochlorogenic acid, chlorogenic acid, 4-dicaffeoylquinic acid assay
Use neochlorogenic acid in high effective liquid chromatography for measuring Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample, chlorogenic acid, hidden
Chlorogenic acid, concentration and the density of each sample.
Neochlorogenic acid, chlorogenic acid and 4-dicaffeoylquinic acid content assaying method
Chromatographic condition phenomenex Luna C18 (250 × 4.6mm, 5 μm)；0.1% phosphoric acid water is mobile phase A, methanol
For Mobile phase B, gradient elution, elution requirement see table 1；Flow velocity: 0.8ml/min；Column temperature 30 DEG C；Detection wavelength is 324nm, enters
Sample amount is 10 μ L.
Table 1 condition of gradient elution
| Time (minute) | Mobile phase A (%) | Mobile phase B (%) |
| 0 | 85 | 15 |
| 10 | 75 | 25 |
| 30 | 65 | 35 |
| 60 | 50 | 50 |
Specification Curve of Increasing, precision weighs neochlorogenic acid 3.45mg, chlorogenic acid 18.57mg, 4-dicaffeoylquinic acid 8.74mg respectively
Put in 25mL measuring bottle, add 50% methanol and dissolve and be diluted to scale, shake up, obtain mixing reference substance solution.To mixing reference substance
Carrying out 0 times, 2.5 times, 5 times, 10 times, 20 times, 40 times of dilutions, precision draws each 10 μ L of above-mentioned control series product solution, note respectively
Entering high performance liquid chromatograph, measure, with reference substance concentration (μ g/mL) as abscissa (X), peak area is vertical coordinate (Y), draws mark
Directrix curve.Neochlorogenic acid regression equation Y=0.578 0-0.469 7, r=0.999 9, the range of linearity 3.45 μ g/ml～138.0
μ g/ml, chlorogenic acid regression equation Y=0.742 1X-0.857 7, r=0.999 8, the range of linearity 18.57 μ g/ml～742.8 μ
G/ml, 4-dicaffeoylquinic acid regression equation Y=0.615 8X-0.609 4, r=0.999 9, the range of linearity 8.74 μ g/ml～349.6 μ
g/ml。
Density measurement: pipette, extract gold green grass or young crops precipitate with ethanol concentrated solution 5mL, precise weighing, record weight also presses formula calculating
Medicinal liquid density.ρ=m/v (formula) wherein ρ is the density of medicinal liquid, and m is the quality of medicinal liquid, the volume of V medicinal liquid.
3. near infrared spectrum data collection
Use workshop On-line NIR acquisition system, acquisition mode is transmission beam method, spectral region 4000～
12800cm-1, resolution 2cm-1, employing air is ground control；6 spectrum of each sample collecting, are averaged spectral value conduct
The near infrared near infrared light spectrogram of sample.
The original near infrared spectrum collected in Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process, is shown in Fig. 1.
4. the foundation of partial least square method quantitative model and prediction to unknown sample
(1) partial least square method modeling optimum factor number determines
Partial least square method is a kind of method using most effect best in current near-infrared spectrum analysis, and it is because of subnumber
Selection be directly connected to the prediction accuracy of set up model, if modeling process use because of subnumber very little, then can not fill
Divide the information utilizing near infrared spectrum；If use because of subnumber too much, in setting up model process, noise can be introduced again, reduces
The predictive ability of model, the most reasonably determines the main cause subnumber participating in modeling, is one of good and bad key factor of institute established model.
Chemo metric software by the ratio curve figure of RMSECV Yu the factor determine best modeled because of subnumber, optimal curve is
RMSECV, along with the increase rapid decrease of the factor, is gradually increased along with the increase of the factor after there is a minima, is generally selected
The dimension of ratio minimum is as modeling optimum factor number.
(2) exceptional sample is rejected
It is typically due to the impact of either objectively or subjectively factor, modeling process often produces exceptional sample, exceptional sample
Exist and have a strong impact on modeling effect.Abnormity point Producing reason has a lot, and such as spectral scan instrument is unstable, and offline inspection sets
Standby is bigger error occur in determined off-line sample index, and change of test operation personnel etc., in order to increase the accurate of model
Property, before modeling, reply sample carries out abnormity point judgement.Mahalanobis distance (Mahalanobis distance) is a kind of effective meter
The method calculating two unknown sample collection similarities, if the mahalanobis distance value of a certain sample is more than given threshold values, then this sample
This is i.e. judged as exceptional sample, should reject during modeling.
(3) selection of near infrared spectrum wave band is modeled
Near infrared spectrum 4000-12000cm-1In wave-length coverage all spectral informations each may participate in PLS modeling, but spectrum
In noise or sample, the superabsorbent of a certain principal component can cause set up model to degenerate, therefore will be to modeling during modeling
Wave band selects.The absorption of infrared spectral region mainly includes the group fundamental vibrations such as C-H, N-H, O-H, S-H, C=O, C=C
With sum of fundamental frequencies and the vibration of frequency multiplication, Reduning injection Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process medicinal liquid system is essentially alcohol-water system, contains
Measuring substantial amounts of O-H base, polarity is very strong, 1440nm (6944cm near infrared spectrum-1) and 1940nm (5155cm-1) place has very
Strong sum of fundamental frequencies and frequency multiplication absorption band, concentrate original near infrared spectrum from Fig. 1 Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol and can be seen that 4500-
5400cm-1、6500-7500cm-1Spectrum range is that " water peak " absorbs spectral coverage.Furthermore, it is generally considered that the ripple that trap is more than 1.5
Long interval belongs to saturated absorption, should not use the spectrum of this wave band during modeling.In conjunction with above two points, first remove before modeling
4000-5400cm-1The spectrum of wave band.Due to 9000-1200cm-1The spectrum of wave band is substantially without absorbing, during in order to save calculating
Between, this wave band is also not involved in modeling ripple, and final selection 5400-9000cm-1 spectral band is that Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentrates quantitatively
Calibration model optimizes wave band.
(4) preprocessing procedures selects
Near infrared spectrum can not only the chemical composition of reacting substance and concentration, the reason such as background noise, the granularity of material, viscosity
Change character spectrum is also had a significant impact.Therefore, when setting up quantitative calibration models, it is intended that extract from spectrum and investigate
The information that index is relevant, eliminates the factor unrelated with inspection target, to setting up reliable and stable model, improves the prediction essence of model
Spend the most crucial.And apply suitable preprocessing procedures can effectively eliminate the garbage in spectrum, improve and investigate
Dependency between index and spectrum.Pretreated spectra common method mainly has smoothing processing (Smoothing), and smoothing processing is
Eliminate the common method of spectrum noise；Derivative method (Derivative), carries out pluriderivative process and can effectively eliminate light spectrum
In spectrum, baseline translates the interference with other background, Resolving Overlapping Peaks Signal, raising resolution and sensitivity；Straight line subtracts poor method, with straight
Line fit-spectra subtractive, it is possible to the spectrum tilted is corrected；Eliminating constant offset, translation Y-axis is by spectrum zero setting.
By (1), (2), (3), (4) step, it is thus achieved that the optimal quantitative calibration models of 4 indexs after optimization is shown in Fig. 2-figure
5；Using above-mentioned 4 index models that unknown sample carries out content prediction, 4 index prediction values of unknown sample become with practical measurement
Gesture figure is shown in Fig. 6-Fig. 9；The relevant parameter of 4 best models is shown in Table 2.
Claims (9)
1. a Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process fast quantitative measurement method for detecting, it is characterised in that the method includes walking as follows
Rapid:
(1) the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample no less than 5 different batches in the big production of Reduning injection is gathered；
(2) 4 crucial quality control indexs of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches are measured: fresh green is former
Acid, chlorogenic acid, 4-dicaffeoylquinic acid, density；
(3) the NIR transmittance spectroscopy figure of the Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of each different batches is gathered；
(4) choose the 70-90% of total batch and round off to round several batch be calibration set, use partial least square method to build
The near-infrared quantitative calibration models of 4 quality control indexs in vertical Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process, and utilize this model to residue
Batch i.e. verifies that in collection sample, 4 quality control indexs are predicted；Reach Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process Quantitative detection
Purpose.
Method the most according to claim 1, it is characterised in that: in step (1): gather in the big production of Reduning injection
The Herba Artemisiae Annuae Flos Lonicerae precipitate with ethanol concentration process sample of 9-20 different batches.
Method the most according to claim 1, it is characterised in that: in step (4): choose the 80% of total batch and by four houses five
Entering integer batch is calibration set.
Method the most according to claim 1, it is characterised in that: Reduning injection precipitate with ethanol supernatant concentration in step (1)
Process is concentrating under reduced pressure process, and sample sampling uses sampling below atmospheric pressure mode, and all sample standard deviations are from the same position of concentrating under reduced pressure tank lower floor
Put collection.
5. according to the method in any of the one of claim 1-4, it is characterised in that: Flos Lonicerae Herba Artemisiae Annuae precipitate with ethanol in step (1)
Concentration process sampling mode is to sample once every 20min for after concentration starts first 1 hour, samples every 10min after second hour
Once, sampling once every 5min after the 3rd hour, sampling amount is no less than 10g, terminates until concentrating.
6. according to the method in any of the one of claim 1-4, it is characterised in that: step (2) described neochlorogenic acid, green
Ortho acid, the sample pretreating method of high performance liquid chromatography of 4-dicaffeoylquinic acid be: weighs blue or green precipitate with ethanol concentrated solution 0.45 g-of gold
0.55g, accurately weighed, it is placed in 50mL volumetric flask, 50% methanol constant volume, shakes up, 5000rpm-20000rpm is centrifuged 3-10
Min, takes supernatant, to obtain final product；Chromatographic condition is: phenomenex Luna C18,250 × 4.6mm, 5 μm；0.1% phosphoric acid water is
Mobile phase A, methanol is Mobile phase B, and gradient elution, elution requirement is shown in Table 1；Flow velocity: 0.8 mL/min-1.0mL/min；Column temperature
30℃；Detection wavelength is 324nm, and sample size is 10 μ L.
Described method the most according to claim 6, it is characterised in that: step (2) described neochlorogenic acid, chlorogenic acid, hidden
The sample pretreating method of the high performance liquid chromatography of chlorogenic acid is: weigh the blue or green precipitate with ethanol concentrated solution 0.5g of gold, accurately weighed, is placed in
In 50mL volumetric flask, 50% methanol constant volume, shake up, 20000 rpm are centrifuged 10 min, take supernatant, to obtain final product；Chromatographic condition is:
Phenomenex Luna C18,250 × 4.6mm, 5 μm；0.1% phosphoric acid water is mobile phase A, and methanol is Mobile phase B, and gradient is washed
De-, elution requirement is shown in Table 1；Flow velocity: 0.8mL/min；Column temperature 30 DEG C；Detection wavelength is 324nm, and sample size is 10 μ L；
Table 1 condition of gradient elution
。
8. according to the method in any of the one of claim 1-4, it is characterised in that: in step (3): direct-on-line collection
Sample near infrared light spectrogram, acquisition mode is transmission beam method, spectral region 4000～12800cm-1, resolution 2 cm-1 , use sky
Gas is ground control；3 spectrum of each sample collecting, are averaged the spectral value near infrared near infrared light spectrogram as sample.
9. according to the method in any of the one of claim 1-4, it is characterised in that: in step (4): dense with neochlorogenic acid
The preprocess method selection first derivative of the quantitative calibration models that degree is Index Establishment adds at 17 and smooths, and number of principal components selects 9, builds
Mould waveband selection 7502 ~ 5446.2 cm-1；The preprocess method choosing of the quantitative calibration models with chlorogenic acid concentration as Index Establishment
Select first derivative add 17 smooth, number of principal components select 6, model waveband selection 7502 ~ 5446.2 cm-1；Dense with 4-dicaffeoylquinic acid
The preprocess method that degree is the quantitative calibration models of Index Establishment selects to deduct straight line, and number of principal components selects 10, models ripple
Section selects 9403.6～5446.2cm-1；The preprocess method of the quantitative calibration models with density as Index Establishment selects single order to lead
Number add deduct straight line add 17 smooth, number of principal components select 5, model waveband selection 9403.6～5446.2cm-1；Set up
Neochlorogenic acid, chlorogenic acid, 4-dicaffeoylquinic acid, the quantitative calibration models cross validation coefficient R of 4 indexs of density2It is respectively
95.45%、97.52%、96.91%、97.88%；Cross validation root-mean-square error RMSECV is respectively 0.233,0.692,0.258,
0.00991；Use the quantitative calibration models set up that precipitate with ethanol concentration process on-line sample is analyzed, model predication value and off-line
Mean relative deviation RSEP of detected value is 3.519%, 3.778%, 3.895%, 0.532%.
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| CN112163181A (en) * | 2020-09-27 | 2021-01-01 | 浙江大学 | Release control method of concentrated solution before alcohol precipitation |
| CN112931915A (en) * | 2021-04-30 | 2021-06-11 | 河南中烟工业有限责任公司 | Blending uniformity detection method for tobacco components in leaf group |
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