CN105092528A - Method for identifying honey quality through physical and chemical indexes and electronic nose technology - Google Patents
Method for identifying honey quality through physical and chemical indexes and electronic nose technology Download PDFInfo
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Abstract
The invention provides a method for identifying the honey quality through physical and chemical indexes and an electronic nose technology, and belongs to the technical field of honey identifying methods. The method comprises the following steps that 1, a sample is processed, 2, the physical and chemical indexes are detected; 3, data are collected with an electronic nose; 4, the honey physical and chemical indexes and the data collected by the electronic nose are analyzed by combining a main component analysis method, and a relatively small square law regression model of the electronic nose to the different bee physical and chemical indexes is built; 5, data of honey to be identified are collected through the electronic nose and analyzed through the model obtained in the step 4, the species of the honey is determined, and the physical and chemical indexes of the honey are speculated and roughly calculated. The method for detecting the bee quality is rapid, effective and comprehensive.
Description
Technical field
The invention belongs to honey discrimination method technical field, be specifically related to a kind of method being differentiated honey quality by physical and chemical index and Electronic Nose Technology.
Background technology
Honey category is various, is divided into different cultivars according to its different nectar sources seeds of flowering plants, is divided into dissimilar according to its different gathering with job operation.Need successively by the mensuration to physical and chemical indexs such as honey moisture, soluble solid content, pH value, acidity, conductivity to the discriminating of honey in prior art, just can reach, discrimination method is complicated, time-consuming, is unfavorable for promoting.
Summary of the invention
For prior art Problems existing, the object of the invention is to design provides a kind of technical scheme being differentiated the method for honey quality by physical and chemical index and Electronic Nose Technology.
A kind of described method being differentiated honey quality by physical and chemical index and Electronic Nose Technology, is characterized in that comprising the following steps:
1) sample preparation
A, all honey samples are stored in-20 DEG C of low temperature refrigerators before analysis;
B, all honey samples before the test, screw bottle cap, and the water-bath being placed in 50 DEG C is warm, after melting completely, are cooled to room temperature rapidly, stir evenly, for subsequent use;
2) physical and chemical index detects
Often kind of honey is carried out successively to the mensuration of following physical and chemical index
The mensuration of a, moisture
Adopt Abbe refractometer to measure, experimental result g/100g represents;
The mensuration of b, soluble solid content
Adopt Abbe refractometer to measure, result g/100g represents;
C, acidity titration
Adopt standard solution of sodium hydroxide to carry out titration, experimental result represents with mL/100g;
The mensuration of d, pH value
In 10g honey, add 75mL ultrapure water, measure with pH meter after stirring;
The mensuration of e, conductivity
Take and be equivalent to the anhydrous honey of 20g, ultrapure water is settled to 100mL volumetric flask after dissolving, and measures with conductivity meter;
3) Electronic Nose data acquisition
Draw 5mL honey sample in 500mL large beaker with syringe, and seal up preservative film; 30 minutes are left standstill to produce enough head space volatilization gas under 25 DEG C of constant temperatures; When test is carried out, ensure room ventilation and there is no obvious peculiar smell; Test parameters: scavenging period is 180 seconds, the test duration is 80 seconds, and sample introduction speed is 400mL/min, and each sample parallel measures 5 times;
4) to the data of honey physical and chemical index obtained above and Electronic Nose collection in conjunction with principal component analysis (PCA) analysis, and set up the less than normal square law regression model of Electronic Nose to different honey physical and chemical index;
5) utilize Electronic Nose to gather the data of honey to be identified, the model that data acquisition step 4) obtains is analyzed, determine the kind of this honey, and infer the physical and chemical index of this honey of budgetary estimate.
A kind of described method being differentiated honey quality by physical and chemical index and Electronic Nose Technology, is characterized in that the regression model equation in described step 4) between Electronic Nose data and moisture is: y=0.9875x; The regression model equation of Electronic Nose data and soluble solid content is: y=1.0020x; The regression model equation of Electronic Nose data and acidity is: y=0.9776x; The regression model equation of Electronic Nose data and pH value is: y=1.0018x; The regression model equation of Electronic Nose data and conductivity is: y=0.8123x.
The present invention, by the mensuration to 5 physical and chemical indexs such as honey moisture, soluble solid content, pH value, acidity, conductivity, carries out Classification and Identification in conjunction with principal component analysis (PCA) to different types of honey sample; Applying electronic nose Fast Detection Technique, carries out modeling analysis to different types of honey sample, thus judges kind and the type of unknown honey sample, and predicts the conventional index of unknown honey sample.The method is a kind of quick, effective, comprehensive honey quality detection method.
Accompanying drawing explanation
Fig. 1 is that Electronic Nose trains original collection of illustrative plates;
Fig. 2 is the PCA qualitative analysis shot chart of variety classes honey;
Fig. 3 is the PCA qualitative analysis load diagram of variety classes honey;
Fig. 4 is the partial least square method forecast of regression model result figure of Electronic Nose to moisture;
Fig. 5 is the partial least square method forecast of regression model result figure of Electronic Nose to soluble solid content;
Fig. 6 is the partial least square method forecast of regression model result figure of Electronic Nose to acidity;
Fig. 7 is the partial least square method forecast of regression model result figure of Electronic Nose to pH;
Fig. 8 is the partial least square method forecast of regression model result figure of Electronic Nose to conductivity.
Embodiment
The present invention is further illustrated below in conjunction with embodiment.
Embodiment 1
1, sample preparation
(1) all honey samples are stored in-20 DEG C of low temperature refrigerators before analysis.
(2) preparation of samples: all honey samples before the test, screw bottle cap, the water-bath being placed in 50 DEG C is warm, after melting completely, is cooled to room temperature rapidly, stirs evenly, for subsequent use.
2, physical and chemical index detects
(1) mensuration of moisture
Adopt Abbe refractometer to measure, experimental result g/100g represents.
The correction of refractometer: before working sample refraction index, first corrects the refraction index of refractometer with fresh distilled water.When adjustment is just 40 DEG C by the water flow temperature of refractometer, separately refractometer two sides prism, distilled water wiped clean (dimethylbenzene can be dipped in if desired or ether is wiped only) is dipped in absorbent cotton, then clean absorbent cotton (or lens wiping paper) is used to wipe dry, after prism bone dry, distilled water 1 ~ 2 is dipped with glass bar, drip on prism below, rapid closing prism, alignment light source, observed by eyepiece, rotation hand wheel, the refraction index of water when making the refraction index on display screen be 40 DEG C just, in observation eyepiece, whether bright-dark cut is in the middle of objective lens cross curve, if there is deviation, then spanner is regulated to rotate adjusting screw with annex square hole, bright-dark cut is made to be transferred to central authorities.After adjustment, when working sample, do not allow to rotate the screw regulated again.
Sample determination: before the assay first by prism wiped clean, in order to avoid leave other materials to affect estimating precision.Dip 1 ~ 2, the sample of mixing with glass bar, drip on prism below, rapid closing prism, leave standstill the several seconds, to treat that sample reaches 40 DEG C.Alignment light source, is observed by eyepiece, rotation compensation device spiral, make bright-dark cut clear and make its bright-dark cut just by the intersection point of cross curve on objective lens, dark space is below cross curve, and area pellucida is above cross curve, refraction index on reading displayed screen, check temperature, be just 40 DEG C simultaneously.
Result calculates:
①X=100－[78+390.7（n－1.4768）]
In formula: moisture in X---sample, %
The refraction index of n---sample 40 DEG C time
The permissible error of parallel experiment is 0.2%;
2. as surveyed refraction index when room temperature, refraction index when can be converted to 20 DEG C by following formula
During N (20 DEG C)=n(room temperature)+0.00023(T-20)
3. reading refraction index as surveyed 20 DEG C time, the percent of moisture can be scaled by table.
(2) mensuration of soluble solid content
Soluble solid content, i.e. brix, can directly read when measuring moisture with Abbe refractometer, result g/100g represents.
(3) acidity titration
According to the method for 3.5 regulations in SN/T0852-2000, adopt standard solution of sodium hydroxide to carry out titration, experimental result represents with (1mol/L NaOH) mL/100g.
The demarcation of 1mol/L standard solution of sodium hydroxide: take 4g NaOH, be dissolved in 1L in the ultrapure water of degassed process, its normal concentration is demarcated by the following method: take Potassium Hydrogen Phthalate (standard reagent 0.8-0.9g dried 125 DEG C time in advance with Potassium Hydrogen Phthalate (standard reagent), be accurate to 0.0002g), be placed in 250mL conical flask, dissolve through the ultrapure water of degassed process with 50mL, add 2-3 and drip phenolphthalein indicator, solution pinkiness is titrated to, with colour-fast for terminal in 10 seconds with sodium hydroxide solution.
Phenolphthalein indicator: 1% ethanolic solution.
Sample determination: take sample 10g(and be accurate to 0.001g), be dissolved in 75mL in the ultrapure water of degassed process, add 2-3 and drip phenolphthalein indicator, be titrated to solution pinkiness with standard solution of sodium hydroxide, with colour-fast for terminal in 10s.
Result calculates:
1. the concentration of standard solution of sodium hydroxide: c=m/ (V*0.2042)
In formula: the concentration of c---standard solution of sodium hydroxide, mol/L
The quality of m---Potassium Hydrogen Phthalate
During V---titration consume the volume of standard solution of sodium hydroxide, mL
The quality of the phthalic acid hydrogen potassium that 0.2042---is suitable with every milliliter of NaOH (c=1.000mol/L) standard solution, g
2. the acidity of sample: X=(V*c/m) * 100
In formula: the acidity of X---sample, %
V---titration consume the volume of standard solution of sodium hydroxide, mL
The volumetric molar concentration of c---standard solution of sodium hydroxide, mol/L
The quality of m---sample, g
The permissible error of parallel experiment result is 0.1;
(4) mensuration of pH value
With reference to the method for the people such as A.BentabolManzanares, in 10g honey, add 75mL ultrapure water, measure with pH meter after stirring.
(5) mensuration of conductivity
According to the method for GB/T18932.15-2003, take and be equivalent to the anhydrous honey of 20g, ultrapure water is settled to 100mL volumetric flask after dissolving.
The calculating of the anhydrous honey sample mass of 20g: according to the method that SN/T0852 specifies, its moisture is measured to sample to be tested, and is equivalent to the sample mass of the anhydrous honey of 20g by formulae discovery: m=20/ (1-c)
In formula: the honey sample mass of m-the be equivalent to anhydrous honey of 20g, unit is gram (g)
C-honey sample moisture, %
Prepared by sample liquid: take the sample being equivalent to the anhydrous honey of 20g, be accurate to 0.01g.Be placed in 100mL beaker, add people 40mL water, stir with glass bar and make it to dissolve completely, be transferred in 100mL volumetric flask, then with after 30mL moisture for several times washing beaker, be transferred in volumetric flask, be finally settled to scale with water, mixing, to be measured.
The correction of conductivity meter: switch on power, opens switch, instrument preheating 30 minutes." range " knob is transferred to inspection, and " constant " knob is transferred to match with the DJS-1C type platinum black electrode selected 1, and " temperature " knob is transferred to 25 DEG C, finally adjusts " calibration " knob and makes screen display be 100.0uS/cm, represents that calibration terminates.
Sample determination: sample liquid is poured in 100mL beaker, conductance cell is inserted in sample liquid, shake gently, drive the bubble in conductance cell out of, adjustment " temperature " knob, to actual sample temperature, is selected suitable " range ", after value stabilization to be determined, write down the conductivity of this sample liquid, its result uS/cm represents.
Above step, carries out parallel experiment mensuration to same sample.
3, Electronic Nose data acquisition
The array apparatus that Electronic Nose used is made up of 10 metal oxide sensors, sensor array senses different detection gas by the resistance value of change, thus memory model.Through training, Electronic Nose can carry out identification computing by chemometrics method to the deviation between reference material and different sample, thus distinguishes given material.Different types of honey is distinguished mutually because it has different smells, adopts Electronic Nose Technology to gather the overall odiferous information of variety classes honey respectively, and carries out identification computing to it, thus reach the object of Classification and Identification.
The collection of Electronic Nose data: for ensuring clean background gas, all honey samples of this test prepare in fuming cupboard.Draw 5mL honey sample in 500mL large beaker with syringe, and seal up preservative film; 30 minutes are left standstill to produce enough head space volatilization gas under 25 DEG C of constant temperatures.When test is carried out, ensure room ventilation and there is no obvious peculiar smell.Best test parameters is determined: scavenging period is 180 seconds, and the test duration is 80 seconds, and sample introduction speed is 400mL/min, and each sample parallel measures 5 times after preliminary test.
4, data process&analysis method
Principal component analysis (PCA) (PrincipalComponentAnalysis, PCA) application in pattern-recognition is that the mode of polytomy variable Projection Character is carried out dimensionality reduction, obtain the characteristic variable that can show in two dimension or three dimensions, then utilize human eye to carry out Classification and Identification, belong to unsupervised mode identification method.
Euclidean distance (Euclid) is the relatively simple ranker of a class, does not have data filtering or conversion, by calculating the distance identification of contiguous model points.Mahalanobis distance (Mahalanobis) is the ranker of a class superior performance, can identify optimum deviation from a given standard value.Correlativity (Correlation) ranker represents the space angle of new measurement data and class models.Difference decision analysis (DiscriminantFunctionsAnalysis, DFA) carries out based on LDA a kind of sorting technique optimizing distinction by reconfiguring sensing data of converting.
It is find certain linear combination in explanatory variable space that partial least square method (Partialleastsquare, PLS) returns, can explain the variation information of response variable better.The linear regression model (LRM) of latent variable about response variable is explained by setting up, relation between indirect reflection explanatory variable and response variable, namely from explanatory variable and response variable, extract 2 groups of latent variable, they are the linear combination of explanatory variable and response variable respectively simultaneously.
Embodiment 2
1, sample preparation
(1) select 14 kinds of honey samples as shown in table 1, be stored in before analysis in-20 DEG C of low temperature refrigerators.
(2) preparation of samples: all honey samples before the test, screw bottle cap, the water-bath being placed in 50 DEG C is warm, after melting completely, is cooled to room temperature rapidly, stirs evenly, for subsequent use.
Table 1 honey sample
Note: RH and RapeHoney, represents rape honey; AH and AcaciaHoney, represents acacia honey; LH and LindenHoney, represents Mel; PH and PaulowniaHoney, represents paulownia honey; WpH and WildpersimmonHoney, represents wild persimmon honey; AsH and AstragalusHoney, represents Radix Astragali honey; BH and BuckwheatHoney, represents buckwheat honey; SH and SunflowerHoney, represents sunflower honey; 1 represents natural material honey; 2 represent the ripe honey of processing; 3 represent Natural ripe honey.
2, physical and chemical index detects
Detect respectively the moisture of each sample, soluble solid content, pH value, acidity, conductivity, etc. 5 physical and chemical indexs.
The physical and chemical index of table 2 honey sample
3, Electronic Nose data acquisition
The collection of Electronic Nose data: for ensuring clean background gas, all honey samples of this test prepare in fuming cupboard.Draw 5mL honey sample in 500mL large beaker with syringe, and seal up preservative film; 30 minutes are left standstill to produce enough head space volatilization gas under 25 DEG C of constant temperatures.When test is carried out, ensure room ventilation and there is no obvious peculiar smell.Best test parameters is determined: scavenging period is 180 seconds, and the test duration is 80 seconds, and sample introduction speed is 400mL/min, and each sample parallel measures 5 times after preliminary test.
After Electronic Nose training terminates, observe original collection of illustrative plates, select the time period relatively stably, as the 68-70 second of Fig. 1, analyze.
4, the PCA qualitative analysis of variety classes honey
Principal component analysis (PCA) is carried out to all kinds of honey samples, as Fig. 2 and 3.Wherein the weight of first principal component accounts for 98.38% of total variable, and the weight of Second principal component, accounts for 1.27%, it can thus be appreciated that first principal component has been extracted the important information of all variablees.Except mature native Mel LH3, mature native sunflower honey SH3, be processed into ripe rape honey RH2 and have overlap, other 11 kinds of honey are distributed in different regions all well.Visible, Electronic Nose can be used for distinguishing different types of honey.There is significant otherness in the overall odiferous information between the honey that same breed is dissimilar.From principal component scores load diagram overlay analysis, No. 2 sensors of Electronic Nose play a leading role to the dissimilar honey of differentiation, are secondly No. 6, No. 8, No. 9 sensors.
5, Electronic Nose cluster and discriminant analysis
Data model is set up to 14 honey samples (each sample adopts 3 groups), other 2 groups as prediction group.The winmuster software carried by PEN3 type Electronic Nose has Euclid, Mahalanobis, Correlation, DFA totally 4 kinds of sorters, carries out forecast analysis, the results are shown in Table 3 to model.Wherein, the accuracy rate that Mahalanobis and DFA differentiates is higher, reaches 75%; And the accuracy rate that Correlation judges is minimum, only have 57.14%.Therefore, classification forecast analysis can be carried out with this sorter of Mahalanobis to unknown sample.
The different ranker of table 3 predicts result of determination to unknown sample
6, the PLS semi-quantitative analysis of variety classes honey
By partial least square method, with the Global Information of Electronic Nose collection, regression model is set up respectively to each conventional index of surveyed variety classes honey, carry out forecast analysis, as Fig. 4,5,6,7 and 8.Regression model equation between Electronic Nose data and moisture is: y=0.9875x; The regression model equation of Electronic Nose data and soluble solid content is: y=1.0020x; The regression model equation of Electronic Nose data and acidity is: y=0.9776x; The regression model equation of Electronic Nose data and pH value is: y=1.0018x; The regression model equation of Electronic Nose data and conductivity is: y=0.8123x.Wherein, the prediction effect of moisture, soluble solid content, pH value is very good; The prediction effect of free acid takes second place.It can be said that bright, Electronic Nose can be used to as a kind of detection means of quick nondestructive the conventional index predicting variety classes honey, and this saves the time and efforts of the many consumption of conventional analysis greatly.
7, utilize Electronic Nose to gather the data of honey to be identified, the model that data acquisition step 6 obtains is analyzed, determine the kind of this honey, and infer the physical and chemical index of this honey of budgetary estimate.
Claims (2)
1. differentiated a method for honey quality by physical and chemical index and Electronic Nose Technology, it is characterized in that comprising the following steps:
1) sample preparation
A, different types of honey sample is stored in-20 DEG C of low temperature refrigerators before analysis;
B, all honey samples before the test, screw bottle cap, and the water-bath being placed in 50 DEG C is warm, after melting completely, are cooled to room temperature rapidly, stir evenly, for subsequent use;
2) physical and chemical index detects
Often kind of honey is carried out successively to the mensuration of following physical and chemical index
The mensuration of a, moisture
Adopt Abbe refractometer to measure, experimental result g/100g represents;
The mensuration of b, soluble solid content
Adopt Abbe refractometer to measure, result g/100g represents;
C, acidity titration
Adopt standard solution of sodium hydroxide to carry out titration, experimental result represents with mL/100g;
The mensuration of d, pH value
In 10g honey, add 75mL ultrapure water, measure with pH meter after stirring;
The mensuration of e, conductivity
Take and be equivalent to the anhydrous honey of 20g, ultrapure water is settled to 100mL volumetric flask after dissolving, and measures with conductivity meter;
3) Electronic Nose data acquisition
Draw 5mL honey sample in 500mL large beaker with syringe, and seal up preservative film; 30 minutes are left standstill to produce enough head space volatilization gas under 25 DEG C of constant temperatures; When test is carried out, ensure room ventilation and there is no obvious peculiar smell; Test parameters: scavenging period is 180 seconds, the test duration is 80 seconds, and sample introduction speed is 400mL/min, and each sample parallel measures 5 times;
4) to the data of honey physical and chemical index obtained above and Electronic Nose collection in conjunction with principal component analysis (PCA) analysis, and set up the less than normal square law regression model of Electronic Nose to different honey physical and chemical index;
5) utilize Electronic Nose to gather the data of honey to be identified, the model that data acquisition step 4) obtains is analyzed, determine the kind of this honey, and infer the physical and chemical index of this honey of budgetary estimate.
2. a kind of method being differentiated honey quality by physical and chemical index and Electronic Nose Technology as claimed in claim 1, is characterized in that the regression model equation in described step 4) between Electronic Nose data and moisture is: y=0.9875x; The regression model equation of Electronic Nose data and soluble solid content is: y=1.0020x; The regression model equation of Electronic Nose data and acidity is: y=0.9776x; The regression model equation of Electronic Nose data and pH value is: y=1.0018x; The regression model equation of Electronic Nose data and conductivity is: y=0.8123x.
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| CN106908435A (en) * | 2017-02-13 | 2017-06-30 | 中国电力科学研究院 | The measuring method and device of snow deposit soluble salt on snowberg insulator |
| CN109839409A (en) * | 2017-11-24 | 2019-06-04 | 江苏省农业科学院 | A method of smell in irradiation Duck Products bag is differentiated using electronic nose |
| CN108088962A (en) * | 2018-02-05 | 2018-05-29 | 中国农业科学院麻类研究所 | A kind of method for differentiating silage fermentation feed quality |
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| CN114586546A (en) * | 2022-03-14 | 2022-06-07 | 西南大学 | Automatic strawberry picking device based on electronic nose and image recognition and control method thereof |
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