CN105092718B - The detection method of Multiple components in food - Google Patents
The detection method of Multiple components in food Download PDFInfo
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Abstract
The present invention provides the application of the detection method and ninhydrin post-column derivation-high performance liquid chromatography of a variety of amino acid in a kind of food in detection drinks in a variety of amino acid.Detection method provided by the invention is characterized in that, detects a variety of amino acid in food under the conditions of specific chromatographic condition and post-column derivation using high performance liquid chromatograph and amino acid post-column derivation device.Method provided by the invention can detect more than 30 kinds of amino acid in food simultaneously, and method is versatile, can be applied to amino acid in different food products and detect.And since derivative reaction occurs after amino acid is separated with other substances, thus the interference of other substances is avoided, be suitble to the analysis of a variety of amino acid in unknown complex sample.
Description
Technical field
The present invention relates to a kind of detection method of Multiple components in food and ninhydrin post-column derivation-high performance liquid chromatography
Application of the method in detection drinks in Multiple components.
Background technique
Safety problem of Chinese food frequently occurred in recent years." artificial honey " event in 2007, some criminals are in vacation
The chemical substances such as thickener, sweetener, preservative, essence and pigment are added in honey, false honey is almost without nutritive value；
The illegal addition event of the raw material of industry in 2008: " tonyred duck's egg ", " melamine milk "." melamine " event is shock
The food safety affair in the world is purged in storm in the industry started immediately, and the public has to receive such reality: milk supply is made
At quality problems be prevalent in the whole industry, the problem of large enterprises, famous brand name, especially concentrates, and China's Dairy Industry is born unprecedentedly
Industry crisis, cause bad influence in world wide；" drop is fragrant " in 2010 is spread unchecked and the foods such as " gutter oil " disturbance
Product security incident.For above situation, national quality superintendent office has descended very great strength to exercise supervision, examine, but many problems
It still fails to solve.
The quality condition of inspection data energy indirect reaction product, is the powerful measure that quality supervision department supervises food enterprise.
Only the basic physical and chemical and sanitary index of food is detected in national standard at present, cannot react the quality of food comprehensively, one
A little illegal retailers catch the loophole of detection, violate the using priciple of food additives, addition may cause damages to human health
The raw material of industry and cover food apoilage the substance that must not be added.The new index and phase of research energy reactor product quality
The detection method answered improves the emphasis that original detection method is foods supervision work from now on.
Protein is the nutriment for maintaining human homergy important, and most of food standards all regulation protein contains
Amount, the detection method of traditional protein is Kjeldahl's method.There are various disadvantages, detection datas cannot represent production comprehensively for this method
The protein content of product cannot distinguish between due protein content in product or the high nitrogen substance artificially added, and such as three
Poly cyanamid.Amino acid is living matter important in organism, is the basic unit of constitutive protein matter.Detect a variety of amino acid
The quality of type and content energy directly reactor product, has great importance to the detection of adulterations of food quality and food.At present
Amino acid detection project can be can increase in the national standard method newly revised, such as new edition national standard method GB/T18187 " brewing
Vinegar " revision exposure draft in increase amino acid (leucine, alanine, valine) content range, difference Solid-state fermentation vinegar with
Liquid fermentation edible vinegar.
Amino acid (aminoacid) is the common name of a kind of organic compound containing amino and carboxyl.Biological function divides greatly
The basic composition unit of sub- protein is the base substance of protein needed for constituting Animal nutrition.It is containing a basic amine group
With the organic compound of an acidic carboxypolymer, amino is connected on α-carbon.What natural amino acid had now been found that has more than 300 kinds,
Wherein the amino acid of needed by human body divides nonessential amino acid and essential amino acid there are about 22 kinds (human body itself can not synthesize).Separately
Have acid, alkalinity, neutral, heterocycle classification, is classified according to its chemical property.Various amino acid have difference in organism
Biological function, if the eubolism of tryptophan and brain in organism has close relationship, L-cysteine can enhance biology
The disease resistance of body, therefore, accurately delicately measure food, in drug and biological sample the content of amino acid have it is particularly significant
Meaning.
The detection means of food quality is not also especially perfect at present, and some illegal retailers drop to reduce food cost
The usage amount of low raw material reaches the content of certain indexs in routine monitoring work, but ammonia in product by adding certain additives
The type and content and normal product of base acid have notable difference, we can be by means of the present invention to amino acid in food
Type and quantity detected, to analyze food quality, achieve the effect that Risk-warning.It is mentioned for supervision department's law-enforcing work
For strong technical support.
At present according to national standard method GB/T5009.124-2003, can only be measured in food using amino-acid analyzer
The content of 16 kinds of amino acid, and automatic amino acid analyzer specificity is strong, and expensive, popularization is not strong, detects amino acid
Type it is few, be far from satisfying the needs of real work, the detection method for developing Multiple components is a problem to be solved.
Summary of the invention
The present invention can only measure the defect of the content of 16 kinds of amino acid in food for existing national standard method, provide
The rapid detection method of Multiple components (up to more than 30 kinds), i.e. ninhydrin post-column derivation-high performance liquid chromatography in a kind of pair of food
Method.
The present invention provides a kind of detection method of Multiple components in food, after high performance liquid chromatograph and amino acid column
Deriving device detects the Multiple components in food, wherein
(1) chromatographic condition:
A, chromatographic column uses:
Pre-column: cationic exchange lithium guard column, internal diameter 2.0 × 20mm, 5 μm；
Analytical column: high-effective cationic exchange lithium column, internal diameter 4.0 × 100mm, 5 μm；
B, mobile phase are as follows: lithium ion eluent Li275 (pH2.75), Li750 (pH7.50)；
Gradient elution program are as follows:
| Time | Li275 (%) | Li750 (%) |
| 0 | 100 | 0 |
| 8 | 100 | 0 |
| 46 | 65 | 35 |
| 86 | 0 | 100 |
| 90 | 0 | 100 |
| 115 | 0 | 94 |
| 122 | 0 | 94 |
| 122.1 | 100 | 0 |
| 140 | 100 | 0 |
C, flow velocity: 0.37mL/min, 20 μ L of sample volume；
D, column temperature program:
(2) post-column derivation condition:
A, post-column derivation reaction temperature: 125 DEG C；
B, post-column derivation reaction capacity: 0.5mL；
C, post-column derivation reagent: ninhydrin reagent；
D, flow velocity: 0.3mL/min.
The present invention also provides a kind of ninhydrin post-column derivation-high performance liquid chromatographies in detection drinks in Multiple components
Using, which is characterized in that the ninhydrin post-column derivation-high performance liquid chromatography is Multiple components in food provided by the invention
Detection method.
Method provided by the invention can detect 30 Multiple components in food simultaneously, and method is versatile, can apply
The detection of Multiple components in different food products.And since derivative reaction occurs after amino acid is separated with other substances,
The interference of other substances is thus avoided, the analysis of Multiple components in unknown complex sample is suitble to.
Research through the invention, it is established that the rapid detection method of Multiple components is the national standard of Multiple components in food
Revision provides suggestion, identifies that adulterated substance provides technological means for quality supervision department, provides technical support for food early warning work.
Detailed description of the invention
Fig. 1 is peak area-concentration standard curve figure of taurine.
Fig. 2 is peak area-concentration standard curve figure of urea.
Fig. 3 is peak area-concentration standard curve figure of aspartic acid.
Fig. 4 is peak area-concentration standard curve figure of hydroxyproline.
Fig. 5 is peak area-concentration standard curve figure of threonine.
Fig. 6 is peak area-concentration standard curve figure of serine.
Fig. 7 is peak area-concentration standard curve figure of asparagine.
Fig. 8 is peak area-concentration standard curve figure of glutamic acid.
Fig. 9 is peak area-concentration standard curve figure of sarcosine.
Figure 10 is peak area-concentration standard curve figure of amion acetic acid.
Figure 11 is peak area-concentration standard curve figure of alanine.
Figure 12 is peak area-concentration standard curve figure of citrulling.
Figure 13 is peak area-concentration standard curve figure of α base aminobutyric acid.
Figure 14 is peak area-concentration standard curve figure of valine.
Figure 15 is peak area-concentration standard curve figure of cystine.
Figure 16 is peak area-concentration standard curve figure of methionine.
Figure 17 is peak area-concentration standard curve figure of isoleucine.
Figure 18 is peak area-concentration standard curve figure of leucine.
Figure 19 is peak area-concentration standard curve figure of tyrosine.
Figure 20 is peak area-concentration standard curve figure of phenylalanine.
Figure 21 is peak area-concentration standard curve figure of β base alanine.
Figure 22 is peak area-concentration standard curve figure of β base aminobutyric acid.
Figure 23 is peak area-concentration standard curve figure of γ-aminobutyric acid.
Figure 24 is peak area-concentration standard curve figure of tryptophan.
Figure 25 is peak area-concentration standard curve figure of ethanol amine.
Figure 26 is peak area-concentration standard curve figure of hydroxylysine.
Figure 27 is peak area-concentration standard curve figure of ammonia.
Figure 28 is peak area-concentration standard curve figure of lysine.
Figure 29 is peak area-concentration standard curve figure of histidine.
Figure 30 is peak area-concentration standard curve figure of carnosine.
Figure 31 is arginic peak area-concentration standard curve figure.
Specific embodiment
High performance liquid chromatograph used in the present invention can be used arbitrary high performance liquid chromatograph, excellent in the present invention
Choosing uses Japanese Shimadzu liquid chromatograph.
Amino acid post-column derivation device used in the present invention spreads out after being preferably purchased from the amino acid column of Pickering company
Generating apparatus.
Pre-column used in the present invention is cationic exchange lithium guard column, and 2.0 × 20mm of internal diameter, is preferably purchased from by 5 μm
The PickeringLaboratoriesCatalogNo.0352020 of Pickering company.
Analytical column used in the present invention be high-effective cationic exchange lithium column, 4.0 × 100mm, 5 μm；Preferably it is purchased from
The PickeringLaboratoriesCatalogNo.0354100T of Pickering company.
Mobile phase used in the present invention are as follows: lithium ion eluent Li275 (pH2.75), Li750 (pH7.50), preferably
Lithium ion eluent purchased from Pickering company, wherein Li275 (pH2.75) are as follows: water 98.4%, lithium citrate 0.7%,
Lithium chloride 0.6%, sulfolane 0.2%, benzoic acid 0.1%；Li750 (pH7.50) are as follows: water 97%, lithium chloride 2%, lithium citrate
0.9%, phenol 0.1% (being above mass percentage).
Post-column derivation reagent used in the present invention is ninhydrin reagent, is preferably purchased from the indenes three of Pickering company
Ketone reagent, are as follows: water 43.5%, sulfolane 39%, lithium acetate 15%, ninhydrin 1.5%, hydrindantin 1% (are above
Mass percentage).
The detection method of Multiple components further includes in food provided by the invention, carries out pre-treatment to food before detection；
The pre-treatment includes: that food samples are diluted 10 times with water, crosses 0.2 μm of filter membrane.Specifically, food samples such as drinks can be taken
Sample 10mL, is placed in 100mL volumetric flask, is settled to scale with water.It is centrifuged 10min under the speed of 8000r/min, takes supernatant
Liquid crosses 0.2 μm of filter membrane.
In the present invention, the food can be any food, preferably drinks, more preferably yellow rice wine.
Below with embodiment, the present invention will be described in more detail.
Embodiment
(1) standard solution is prepared
The mixed standard solution of Multiple components is selected, Multiple components are successively are as follows: taurine, urea, aspartic acid, hydroxyl dried meat ammonia
Acid, threonine, serine, asparagine, glutamic acid, sarcosine, amion acetic acid, alanine, citrulling, butyrine, figured silk fabrics
Propylhomoserin, cystine, methionine, isoleucine, leucine, tyrosine, phenylalanine, Beta-alanine, beta-aminobutyric acid, γ-ammonia
Base butyric acid, tryptophan, ethanol amine, hydroxylysine, ammonia, lysine, histidine, carnosine, arginine；Mixed standard solution concentration
Are as follows: 0.25 μm of ol/mL (Pickering, calibrationstandard)；It is successively diluted with eluent are as follows: 0.005 μm of ol/
ML, 0.0125 μm of ol/mL, 0.025 μm of ol/mL, 0.05 μm of ol/mL, 0.25 μm of ol/mL, five gradients.
(2) determination condition
(1) chromatographic condition:
A, chromatographic column uses:
Pre-column: cationic exchange lithium guard column, internal diameter 2.0 × 20mm, 5 μm of (Pickeri purchased from Pickering company
ngLaboratoriesCatalogNo.0352020)；
Analytical column: high-effective cationic exchange lithium column, internal diameter 4.0 × 100mm, 5 μm of (Pick purchased from Pickering company
eringLaboratoriesCatalogNo.0354100T)；
B, mobile phase are as follows: lithium ion eluent Li275 (pH2.75), Li750 purchased from Pickering company
(pH7.50)；
Gradient elution program are as follows:
| Time | Li275 (%) | Li750 (%) |
| 0 | 100 | 0 |
| 8 | 100 | 0 |
| 46 | 65 | 35 |
| 86 | 0 | 100 |
| 90 | 0 | 100 |
| 115 | 0 | 94 |
| 122 | 0 | 94 |
| 122.1 | 100 | 0 |
| 140 | 100 | 0 |
C, flow velocity: 0.37mL/min, 20 μ L of sample volume；
D, column temperature program:
(2) post-column derivation condition:
A, post-column derivation reaction temperature: 125 DEG C；
B, post-column derivation reaction capacity: 0.5mL；
C, post-column derivation reagent: the ninhydrin reagent purchased from Pickering company；
D, flow velocity: 0.3mL/min.
(3) drafting of standard curve
Use the amino acid post-column derivation device of Japanese Shimadzu liquid chromatograph LC-20AD and Pickering company
After PinnaclePCX detects above-mentioned mixed standard solution, respectively with peak area-concentration mapping, standard curve regression equation is obtained,
As follows.
1, taurine
Its peak area-concentration standard curve figure is as shown in Figure 1, obtain equation of linear regression from Fig. 1 are as follows:
Y=aX+b
A=8.98644e-008
B=6.369296e-004
R^2=0.9999988
R=0.9999994
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:9.365528e-008
RFSD:4.022711e-009
RF%RSD:4.295231
2, urea
Its peak area-concentration standard curve figure is as shown in Fig. 2, obtain equation of linear regression from Fig. 2 are as follows:
Y=aX+b
A=3.391365e-006
B=1.316433e-003
R^2=0.9998763
R=0.9999381
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:3.681337e-006
RFSD:3.875297e-007
RF%RSD:10.52688
3, aspartic acid
Its peak area-concentration standard curve figure is as shown in figure 3, obtain equation of linear regression from Fig. 3 are as follows:
Y=aX+b
A=8.233558e-008
B=1.942854e-004
R^2=0.9999929
R=0.9999964
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.254781e-008
RFSD:1.449103e-009
RF%RSD:1.755471
4, hydroxyproline
Its peak area-concentration standard curve figure is as shown in figure 4, obtain equation of linear regression from Fig. 4 are as follows:
Y=aX+b
A=4.994522e-006
B=3.679774e-004
R^2=0.9995992
R=0.9997996
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:4.802750e-006
RFSD:6.047676e-007
RF%RSD:12.59211
5, threonine
Its peak area-concentration standard curve figure is as shown in figure 5, obtain equation of linear regression from Fig. 5 are as follows:
Y=aX+b
A=7.954622e-008
B=9.531137e-004
R^2=0.9998900
R=0.9999450
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.139004e-008
RFSD:2.894489e-009
RF%RSD:3.556318
6, serine
Its peak area-concentration standard curve figure is as shown in fig. 6, obtain equation of linear regression from Fig. 6 are as follows:
Y=aX+b
A=7.71974e-008
B=1.189979e-003
R^2=0.9999866
R=0.9999933
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.405820e-008
RFSD:8.374029e-009
RF%RSD:9.962180
7, asparagine
Its peak area-concentration standard curve figure is as shown in fig. 7, obtain equation of linear regression from Fig. 7 are as follows:
Y=aX+b
A=1.128944e-007
B=8.154756e-004
R^2=0.9999799
R=0.9999899
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:1.172985e-007
RFSD:4.307811e-009
RF%RSD:3.672520
8, glutamic acid
Its peak area-concentration standard curve figure is as shown in figure 8, obtain equation of linear regression from Fig. 8 are as follows:
Y=aX+b
A=7.783641e-008
B=4.977117e-004
R^2=0.9999766
R=0.9999883
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:7.892650e-008
RFSD:1.830097e-009
RF%RSD:2.318736
9, sarcosine
Its peak area-concentration standard curve figure is as shown in figure 9, obtain equation of linear regression from Fig. 9 are as follows:
Y=aX+b
A=4.89576e-007
B=2.691175e-003
R^2=0.9997014
R=0.9998507
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:5.460605e-007
RFSD:4.705813e-008
RF%RSD:8.617750
10, amion acetic acid
Its peak area-concentration standard curve figure is as shown in Figure 10, obtains equation of linear regression from Figure 10 are as follows:
Y=aX+b
A=8.230658e-008
B=4.62648e-004
R^2=0.9999948
R=0.9999974
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.427597e-008
RFSD:1.697200e-009
RF%RSD:2.013860
11, alanine
Its peak area-concentration standard curve figure is as shown in figure 11, obtains equation of linear regression from Figure 11 are as follows:
Y=aX+b
A=8.163879e-008
B=3.099068e-004
R^2=0.9999964
R=0.9999982
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.295153e-008
RFSD:1.257934e-009
RF%RSD:1.516468
12, citrulling
Its peak area-concentration standard curve figure is as shown in figure 12, obtains equation of linear regression from Figure 12 are as follows:
Y=aX+b
A=7.356079e-008
B=1.386727e-004
R^2=0.9999678
R=0.9999839
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:7.342958e-008
RFSD:2.053270e-009
RF%RSD:2.796244
13, butyrine
Its peak area-concentration standard curve figure is as shown in figure 13, obtains equation of linear regression from Figure 13 are as follows:
Y=aX+b
A=7.806399e-008
B=-2.741552e-004
R^2=0.9999777
R=0.9999889
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:7.646543e-008
RFSD:1.828932e-009
RF%RSD:2.391842
14, valine
Its peak area-concentration standard curve figure is as shown in figure 14, obtains equation of linear regression from Figure 14 are as follows:
Y=aX+b
A=8.191331e-008
B=-7.148848e-004
R^2=0.9999567
R=0.9999783
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:7.738058e-008
RFSD:5.486607e-009
RF%RSD:7.090419
15, cystine
Its peak area-concentration standard curve figure is as shown in figure 15, obtains equation of linear regression from Figure 15 are as follows:
Y=aX+b
A=6.941486e-008
B=5.420029e-005
R^2=0.9999736
R=0.9999868
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:6.911244e-008
RFSD:1.349980e-009
RF%RSD:1.953309
16, methionine
Its peak area-concentration standard curve figure is as shown in figure 16, obtains equation of linear regression from Figure 16 are as follows:
Y=aX+b
A=7.763125e-008
B=9.692988e-004
R^2=0.9999673
R=0.9999836
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.086549e-008
RFSD:2.455915e-009
RF%RSD:3.037037
17, isoleucine
Its peak area-concentration standard curve figure is as shown in figure 17, obtains equation of linear regression from Figure 17 are as follows:
Y=aX+b
A=7.983675e-008
B=8.91366e-004
R^2=0.9999358
R=0.9999679
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.279406e-008
RFSD:3.031037e-009
RF%RSD:3.660935
18, leucine
Its peak area-concentration standard curve figure is as shown in figure 18, obtains equation of linear regression from Figure 18 are as follows:
Y=aX+b
A=7.642577e-008
B=4.769405e-004
R^2=0.9999119
R=0.9999559
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:7.681231e-008
RFSD:3.846369e-009
RF%RSD:5.007491
19, tyrosine
Its peak area-concentration standard curve figure is as shown in figure 19, obtains equation of linear regression from Figure 19 are as follows:
Y=aX+b
A=7.86814e-008
B=4.425558e-004
R^2=0.9999479
R=0.9999740
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.022615e-008
RFSD:1.938928e-009
RF%RSD:2.416827
20, phenylalanine
Its peak area-concentration standard curve figure is as shown in figure 20, obtains equation of linear regression from Figure 20 are as follows:
Y=aX+b
A=7.662237e-008
B=6.405043e-004
R^2=0.9998505
R=0.9999252
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:9.020861e-008
RFSD:2.827356e-008
RF%RSD:31.34242
21, Beta-alanine
Its peak area-concentration standard curve figure is as shown in figure 21, obtains equation of linear regression from Figure 21 are as follows:
Y=aX+b
A=2.830969e-007
B=-2.109264e-003
R^2=0.9996125
R=0.9998062
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:2.728400e-007
RFSD:3.490635e-008
RF%RSD:12.79371
22, beta-aminobutyric acid
Its peak area-concentration standard curve figure is as shown in figure 22, obtains equation of linear regression from Figure 22 are as follows:
Y=aX+b
A=2.276643e-007
B=8.978122e-004
R^2=0.9999911
R=0.9999956
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:2.423748e-007
RFSD:1.903348e-008
RF%RSD:7.852913
23, γ-aminobutyric acid
Its peak area-concentration standard curve figure is as shown in figure 23, obtains equation of linear regression from Figure 23 are as follows:
Y=aX+b
A=9.219998e-008
B=2.380319e-004
R^2=0.9999677
R=0.9999839
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:9.690795e-008
RFSD:1.214699e-008
RF%RSD:12.53456
24, tryptophan
Its peak area-concentration standard curve figure is as shown in figure 24, obtains equation of linear regression from Figure 24 are as follows:
Y=aX+b
A=9.787209e-008
B=2.916982e-003
R^2=0.9998789
R=0.9999395
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:1.197781e-007
RFSD:2.037459e-008
RF%RSD:17.01028
25, ethanol amine
Its peak area-concentration standard curve figure is as shown in figure 25, obtains equation of linear regression from Figure 25 are as follows:
Y=aX+b
A=1.281795e-007
B=7.284879e-004
R^2=0.9997260
R=0.9998630
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:1.264046e-007
RFSD:1.320976e-008
RF%RSD:10.45038
26, hydroxylysine
Its peak area-concentration standard curve figure is as shown in figure 26, obtains equation of linear regression from Figure 26 are as follows:
Y=aX+b
A=1.313387e-007
B=-4.031237e-004
R^2=0.9998336
R=0.9999168
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:1.253601e-007
RFSD:2.090820e-008
RF%RSD:16.67851
27, ammonia
Its peak area-concentration standard curve figure is as shown in figure 27, obtains equation of linear regression from Figure 27 are as follows:
Y=aX+b
A=8.834604e-008
B=-1.724528e-003
R^2=0.9997461
R=0.9998730
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.636639e-008
RFSD:1.446876e-008
RF%RSD:16.75277
28, lysine
Its peak area-concentration standard curve figure is as shown in figure 28, obtains equation of linear regression from Figure 28 are as follows:
Y=aX+b
A=6.925765e-008
B=-1.734958e-003
R^2=0.9998844
R=0.9999422
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:6.372555e-008
RFSD:4.898957e-009
RF%RSD:7.687587
29, histidine
Its peak area-concentration standard curve figure is as shown in figure 29, obtains equation of linear regression from Figure 29 are as follows:
Y=aX+b
A=7.367425e-008
B=-8.074819e-004
R^2=0.9999589
R=0.9999794
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:7.074041e-008
RFSD:3.293665e-009
RF%RSD:4.655988
30, carnosine
Its peak area-concentration standard curve figure is as shown in figure 30, obtains equation of linear regression from Figure 30 are as follows:
Y=aX+b
A=1.334422e-007
B=1.024015e-003
R^2=0.9999749
R=0.9999874
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:1.427308e-007
RFSD:1.013493e-008
RF%RSD:7.100732
31, arginine
Its peak area-concentration standard curve figure is as shown in figure 31, obtains equation of linear regression from Figure 31 are as follows:
Y=aX+b
A=8.562957e-008
B=6.144211e-005
R^2=0.9999693
R=0.9999847
External standard method
Curve fit type: linear
Origin: it does not force
Weighting: nothing
Average RF:8.658582e-008
RFSD:3.452029e-009
RF%RSD:3.986830
The above results show the concentration of Multiple components in 0.25 μm of ol/L~0.0025 μm ol/L, concentration and peak face
Product is in a linear relationship.
By same standard specimen (0.25 μm of ol/L) continuous sample introduction 6 times, the retention time relative standard deviation of each ingredient is obtained
(RSD) < 3%, (RSD) < 5% of peak area.Recovery testu is carried out to certain commercially available yellow rice wine sample, the experimental results showed that, respectively
The rate of recovery of amino acid is between 81% -113%.
It illustrates through the foregoing embodiment, the detection method of Multiple components can detect simultaneously in food provided by the invention
At least 31 kinds of ingredients in food, this illustrates that this method can be used in being especially Multiple components (at least 31 kinds) in drinks in food
Quick detection.And since derivative reaction occurs after amino acid is separated with other substances, thus avoid other objects
The interference of matter is suitble to the analysis of Multiple components in unknown complex sample.
The studies above through the invention, it is established that the rapid detection method of Multiple components substance is a variety of in food
The revision of national standard of ingredient provides suggestion, identifies that adulterated substance provides technological means for quality supervision department, provides for food early warning work
Technical support.
Claims (4)
1. the detection method of Multiple components in a kind of food, which is characterized in that after high performance liquid chromatograph and amino acid column
Deriving device detects the Multiple components in food, and the food is drinks, wherein
(1) chromatographic condition:
A, chromatographic column uses:
Pre-column: cation exchange lithium ion guard column, internal diameter 2.0 × 20mm, 5 μm；
Analytical column: high-effective cationic exchange lithium column, internal diameter 4.0 × 100mm, 5 μm；
B, mobile phase are as follows: lithium ion cleaning solution Li275 (pH2.75), Li750 (pH7.50)；
Gradient wash program are as follows:
C, flow velocity: 0.37mL/min, 20 μ L of sample volume；
D, column temperature program
(2) post-column derivation condition:
A, post-column derivation reaction temperature: 125 DEG C；
B, post-column derivation reaction capacity: 0.5mL；
C, post-column derivation reagent: ninhydrin reagent；
D, flow velocity: 0.3mL/min；
Pre-treatment is carried out to drinks product before detection；The pre-treatment includes that drinks product are diluted 10 times with water, is taken after centrifugation
Clear liquid crosses 0.2 μm of filter membrane；
Ingredient in the drinks of detection includes at least following ingredient:
Taurine, urea, aspartic acid, hydroxyproline, threonine, serine, asparagine, glutamic acid, sarcosine, amino second
Acid, alanine, citrulling, butyrine, valine, cystine, methionine, isoleucine, leucine, tyrosine, phenylpropyl alcohol
Propylhomoserin, Beta-alanine, beta-aminobutyric acid, γ-aminobutyric acid, tryptophan, ethanol amine, hydroxylysine, ammonia, lysine, histidine,
Carnosine, arginine.
2. according to the method described in claim 1, wherein the food is yellow rice wine.
3. a kind of application of ninhydrin post-column derivation-high performance liquid chromatography in detection drinks in Multiple components, feature exist
In the ninhydrin post-column derivation-high performance liquid chromatography is method described in claim 1.
4. application according to claim 3, wherein the drinks is yellow rice wine.
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