CN103499646A - Honey characteristic fragrance analysis and honey fragrance system simulation method - Google Patents
Honey characteristic fragrance analysis and honey fragrance system simulation method Download PDFInfo
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Abstract
Honey characteristic fragrance analysis and honey fragrance system simulation method is characterized by comprising following steps: extracting materials giving off fragrance from honey by a dynamic headspace circulation enriching technology, carrying out a 1:1 distribution of fragrance content on the end of a chromatographic column, simultaneously measuring the volatile components giving off fragrance and olfaction characteristics of the materials giving off fragrance by the gas chromatography-mass spectrum method and a gas chromatography-olfaction measuring technology, carrying out water-bath heating for crystallized honey, then rapidly cooling to the room temperature, extracting the materials giving off fragrance at a constant room temperature, determining the volatile components in honey by three methods, namely the mass spectrum method, the relative retention index, and the smelling method, and then carrying out a quantifying operation by an internal standard method; wherein the GC-O technology is a combined method of frequency detection and intensity detection, and comprises following steps: establishing a GC-O evaluating group composed of five preferably-selected smelling persons, determining characteristic flavor active fragrances in four volatile phases, namely the primary fragrance, the front fragrance, the body fragrance, and the rear fragrance, proportionately constructing a basic honey fragrance mimetic system A according to the characteristic fragrance species and content proportions in the four volatile phases, then constructing four groups of systems, which are different from the system A, on the basis of the system A, and the system A and each group of system are different in two aspects.
Description
Technical field
The application relates to the method for the analysis of a kind of honey characteristic perfume and the simulation of miel gas system.
Background technology
China's honey output occupies first place in the world, and output keeps the trend of rapid growth always in recent years, and 25.2 ten thousand tons of 40.2 ten thousand tons of being increased to 2009 by 2001, account for Gross World Product and also bring up to more than 30% by nearly 20%.But the driving due to economic interests, honey market is adulterated serious at present, cause adulterated honey to occupy honey market 20%～30%, the bee product of some regional adulterated fraud accounts for 50% left and right, badly damaged consumer's interests, affects the honey industry and develops in a healthy way, hits the export trade and earn foreign exchange.
Due to the impact that lacks detection means, cause adulterated strike difficulties, its basic reason is as follows: (1) is because the main matter of honey itself is relatively simple for structure, comprise water and carbohydrate content, give the adulterated condition of providing convenience, simultaneously, depend merely on detect these several content of material the number can not differentiate at all whether adulterated; (2) be subject to the temperature and humidity of nectariferous plant kind, hive gesture power, sweet time phase length, air due to honey, and the various factors such as processing of honey, storage, crystallization, cause the content range of honey main matter to change greatly, make honey adulterated simple, convenient; (3) the detection of adulterations expense such as C4 high, can't for reality, detect and law enforcement on a large scale.
Fragrance is one of important property of product quality embodiment, and product fragrance characterizes needs outstanding its objectivity, authenticity and comprehensive.Current gas chromatography (GC), gas chromatography-mass spectrography (GC-MS) and gas chromatography-smell methods such as distinguishing (GC-O), limited monomer aroma substance in can only testing product, and there are the phenomenons such as collaborative, modified tone between these fragrance, are difficult to reflect on the whole the flavouring essence quality of sample.And Intelligent Olfaction System (Electronic Nose) can be smelt the news feature by simulating human, the Global Information of comprehensive characterization fragrance, olfactory characteristic and the overall quality of embodiment fragrance, simultaneously more objective, reliable than people's sense of smell.At aspects such as food freshness, the rotten differentiation of edible oil, the detection of fruits and vegetables degree of ripeness, the identification of tea-leaf producing area kind, drinks brand define, carried out correlative study at present.
Contain more than 300 kind of aromatic substance in honey, so it is the important sample that the intelligent sense of smell of research characterizes; Simultaneously different nectar sources, its flavor substance of the different place of production are different, and whether adulterated honey is or quality can embody to some extent on whole fragrance, makes fragrance become honey quality and detect one of important indicator with adulterated discriminating; Absolutely prove that adopting intelligent sense of smell to characterize honey quality has feasibility, also for honey quality, detect and adulterated discriminating provide a kind of fast, economical, accurately and be beneficial to the detection method of real-time application.Therefore select honey to there is Practical significance as research object, its industry healthy development is had more to far-reaching value.
Adopt Electronic Nose to carry out product quality differentiation or adulterated discriminatory analysis, its essence is to utilize the whole fragrance information of intelligent sense of smell collection of illustrative plates, find the otherness of sample room, its core is to find the figure spectrum information of otherness between representative sample, i.e. " differentiation information ", also be " the differentiation figure spectrum information of intelligent sense of smell ".But the sensor array of Electronic Nose has cross-sensitivity, be that every sensor has response in various degree to each fragrance, therefore the aroma-producing substance collection of illustrative plates by the Electronic Nose collection has wide spectrum, the characteristics such as overlapping, be difficult to the naked eye from collection of illustrative plates, distinguish different samples separately, need to carry out " signal excavation ", particularly " excavation of differentiation information between representative sample ", the otherness information of excavation is more, just more contributes to distinguish efficiently product feature and quality.But also very weak aspect the differentiation information excavating at present, be also the bottleneck of restriction Electronic Nose development.
Summary of the invention
A kind of method that honey characteristic perfume is analyzed and miel gas system simulates, it is characterized in that applying dynamic head space and extract the honey aroma-producing substance in conjunction with the circulation beneficiation technologies, after the chromatographic column end carries out the distribution of 1:1 fragrance content, application gas chromatography mass spectrometry and gas chromatography-measurement of olfaction technology are measured its volatility simultaneously and are fragrant composition and sensory characteristic, crystallized honey carries out heating water bath, then be cooled to rapidly room temperature, and in holding chamber, temperature constant state gathers aroma-producing substance, wherein in GC-MS, utilize mass spectrum, Relative Retention Indices hears with smelling the volatile ingredient that three kinds of methods are determined honey, and carry out inner mark method ration, the GC-O technology is the method that proportion detects and detected intensity combines, preferably smell by 5 the GC-O evaluation group that the person of distinguishing forms, determine and represent respectively honey head perfume (or spice), front end fragrance, the active fragrance of the characteristic flavor on basis in four volatilization stages of body note and bottom note, characteristic perfume contamination ratio according to four volatilization stages, proportioning builds basic honey fragrance simulated system A, on the basis of system A, build the four group systems variant with it, the difference of every group of system and primary structure A is embodied in two aspects, in certain volatilization stage or its characteristic perfume content difference, its characteristic perfume component difference, and the aroma component of other three phases and content are all constant.
The accompanying drawing explanation
Fig. 1 abnormity point elimination result: (a) mahalanobis distance is differentiated result; (b) the lever value is differentiated result;
The feature extraction result of Fig. 2 based on variance ratio
The feature point extraction result that Fig. 3 differentiates based on the individual event amount
Fig. 4 ant group algorithm process flow diagram
The feature extraction result of Fig. 5 based on ant group algorithm
The feature point extraction result of Fig. 6 based on core principle component analysis
The feature point extraction result of Fig. 7 based on independent component analysis
Fig. 8 searches plain support vector machine parameter optimization result based on grid
The support vector machine parameter optimization result of Fig. 9 based on genetic algorithm
The support vector machine parameter optimization result of Figure 10 based on particle cluster algorithm
Embodiment
1 about sample collection and preparation
For making studied nectar source difference representative, division according to China geographic area (western part, south China, North China, East China, northeast), select 5 kinds of different nectar sources as the research sample, be respectively: 1) rape honey, pick up from Fuling Chongqing and the Yongchuan District in west area; 2) honey of lychee flowers, pick up from the Nanning of South China; 3) chaste honey, pick up from the ground such as Miyun Region of Beijing of North China; 4) acacia honey, pick up from the Laiyang Shandong Province in East China; 5) Mel, pick up from the ground such as the Jilin Dunhua in northeast and Harbin, Heilungkiang.For guaranteeing authenticity and the accuracy of experiment sample, avoid the interference of market business honey processing technology, sample is directly buied by the beekeeper place by China Agriculture Industitute Bee Research Center.
Be placed in respectively different reagent bottles according to different nectar sources, the different place of production after sample collection.For guaranteeing the not interference of examined condition difference of research, after sample collection, be stored under-18 ℃ of conditions, treat to unify to be tested after all samples collection.Before experiment, after sample takes out from-18 ℃, 5 kinds of nectar source samples are respectively got the 60g left and right, are placed in 40 ℃ of constant water bath box, and heating water bath 15min melts honey sample, and remaining sample continues to be placed under-18 ℃ to be preserved.For guaranteeing that sample melts fully, without crystallization, need every 3min concussion during water-bath once during heating water bath.After the sample water-bath completes, take out and to be placed under room temperature more than cooling 1h, until sample temperature is consistent with room temperature (20 ℃).
2 detection by electronic nose methods
Electronic Nose utilizes gas sensor array from the Adsorption of different volatile ingredients, testing sample honey to be detected.After honey volatile ingredient and sensor characteristics absorption (comprising physisorption and chemisorption), change semiconductor transducer top layer strength of current.By digital conversion, obtain the response curve of each sample, thereby sample is detected to analysis.The present invention adopts Fox 4000 type Electronic Nose (Alpha MOS, France), and this Electronic Nose is comprised of 18 Metal Oxide Semiconductor Gas Sensing sensors (MOS) and HS100 head space automatic sampler.
Instrument concrete operations flow process is as follows:
1) honey sample that is cooled to room temperature after water-bath is added in the head space bottle that volume is 10ml as requested.The head space bottle that installs sample is placed on pallet.The HS100 automatic sampler holds at most 2 pallets, and each pallet can be placed 32 head space bottles.
2) set as requested the instrument testing conditions, comprise head space sampling and detection by electronic nose condition.According to nectar source kind and detection order, each head space bottle on pallet is encoded.
3) the head space bottle is placed into according to the condition arranged that head space is indoor to be heated, and during heating, the head space bottle intermittently shakes, and guarantees head space gas homogeneity.After the head space sample preparation finishes, extract head space gas, in Injection Detector, and by the head space bottle from the indoor taking-up of head space.Fox 4000 is the injection of continuous type air-flow, and gas enters and detects after gas and each sensor generation adsorption and desorption reaction enclosure, and the response curve of each self-generating response.
Simple sample can obtain 18(18 root sensor) * t(detection time) signal matrix.Classic method is analyzed the maximum of each sensor (little) value as the response of this sensor.
3 honey quality modeling methods based on Electronic Nose information
The Electronic Nose characteristic information that utilization extracts is set up the support vector machine discrimination model, and the sample in different nectar sources is classified.Traditional mode identification method is the progressive theory be based upon on the great amount of samples basis, but individual in production application, restriction due to the each side condition, a large amount of sample numbers often is difficult to be guaranteed preferably, under the condition of small sample, according to traditional statistical basis, be difficult to obtain comparatively ideal results of learning and extensive effect.But support vector machine is applicable to the modeling demand under condition of small sample, thus different nectar sources sample is carried out to the pattern-recognition judgement.
Support vector machine (Support Vector Machine, SVM) theory is Vapnik(1995) on traditional statistical learning basis, the integrated structure principle of minimization risk, propose for the characteristics of finite sample.The method can effectively reduce the random row of setting parameter in the traditional mode model of cognition, has overcome the deficiency of empiric risk and expected risk generation bigger difference in the model process of establishing, and concrete SVM is theoretical as follows.
In pattern-recognition, obtain an optimization function f (x, w), make it to unknown sample collection (x
i, y
i) (i=1,2 ..., n; y
for specimen number) while being assessed, expected risk R (W) minimum:
Wherein, F(x, y) be the joint distribution probability, L (y, f (x, w)) predicted y and the loss that causes with f (x, w), be called loss function, for two quasi-mode identification problems, L can be defined as:
In conventional learning algorithms, employing be empiric risk R
emp(W) minimization principle,
But in fact, minimizing of training error is difficult to guarantee the optimum efficiency of predicting, the phenomenon of over-fitting often easily occurs, simultaneously, through further studying and show, experience R
emp(W) there is following relation with practical risk R (W):
Be abbreviated as
The VC dimension that wherein h is function, η is confidence level, n is the training sample number.
As can be seen from the above equation, for making the classification function practical risk minimum of design, not only to make empiric risk reduce as far as possible, also will increase the training set number simultaneously or reduce function VC dimension, could reduce practical risk.This thought is structural risk minimization.
Based on above theory, to sample set (x
i, y
i) (i=1,2 ..., n; The proper vector that x is sample i, y
for specimen number) while being differentiated, find discriminant function
, W and b are carried out after normalization equal proportion regulate, making can meet for all samples
, now the classifying distance of two class samples is spaced apart
.Therefore for obtaining better classification prediction effect, should make two class samples separate as far as possible, ask
minimum value.Meet
point, inner classification plan range minimum, they have determined the optimal classification function, these points are referred to as support vector (Support Vector, SV).
Under this condition, to the problem of optimal classification function, can be converted into optimization problem:
Optimization problem is converted into dual problem and can be expressed as:
α wherein
ifor the Lagrange for constraint condition (7) (Lagrange) factor, i=1,2 ... n, the slope that W is classification function, the intercept that b is classification function.
For the linearly inseparable problem, V.Vapanik introduces the kernel function theory, in lower dimensional space passes through Nonlinear Mapping projection value higher dimensional space by data, can prove, if select suitable kernel function, the inseparable data of lower dimensional space neutral line can be converted into to the data that the higher dimensional space neutral line can divide.After introducing kernel function, full scale equation can be converted into:
Wherein K is selected kernel function.
By solving kernel function, finally can determine corresponding classification function:
The pattern-recognition step of SVM integral body can be summarized as several steps:
(1) select suitable kernel function K;
(2) solve corresponding optimization method, obtain support vector;
(3) obtain optimal classification function f (x)
(4) determine according to the value of sgnf (x) classification of differentiating;
Parameter optimization in 4 detection by electronic nose honey
4.1 determine parameter to be optimized and level
The detection by electronic nose parameter can be divided head space parameter and detected parameters.Wherein detected parameters can be divided into again sample introduction parameter and signals collecting parameter.What consider the detected parameters reaction is the detection characteristics of instrument, and when instrument stabilizer, its impact on testing result is less.The head space parameter affects the generation of sample headspace gas, and head space gas is the direct-detection object of Electronic Nose, directly affects final testing result.Therefore, in the present invention, emphatically the head space parameter is optimized.The head space parameter of Electronic Nose mainly comprises head space temperature and head space time, considers that the difference of different sample sizes in the head space bottle also can affect final testing result simultaneously, and therefore final selection sample size, head space temperature and head space time are optimization object.For selecting optimum combination, utilize orthogonal experiment to be optimized three factors.When the varying level to each factor is selected, consider that head space bottle (10ml) heats indoor need to the concussion of head space, to touch fluid sample and affect instrument performance for preventing sample introduction needle, the sample maximum can not surpass 1/2 of head space bottle.According to the density (being about 1.4g/ml) of honey, determine that three levels of headspace sampling amount are respectively 4g, 5g, 6g.In the level of head space temperature is selected, according to list of references, honey sample character under higher than 68 ℃ of conditions easily changes, and therefore selected three levels are respectively 40,50,60 ℃.In the head space selection of time, consider rate request, the short-term stability of honey sample under hot environment that the large sample amount detects, and the effumability characteristics of honey sample, select the shorter head space time, three levels are respectively 120s, 180s, 240s.The final optimal conditions of determining Three factors-levels, i.e. sample size 4g, 5g, 6g, 40,50,60 ℃ of head space temperature, head space time 120s, 180s, 240s, each factor and level are as shown in table 1.Studying selected honey sample is 5 kinds of different nectar sources honey samples, is respectively rape honey, acacia honey, chaste honey, honey of lychee flowers, Mel, and every class nectar source 6 duplicate samples amount to 30 duplicate samples.
Testing selected nominal price experiment table is L
9(3)
4, the design of experiment table is as shown in table 2
All the other testing conditions are as shown in table 3
4.2 optimize definite evaluation index and method
The present invention turns to guiding with signal difference maximum between different honey samples, selects the best detection by electronic nose condition of distinguishing effect.By the optimum detection condition, expect signal stabilization between similar honey sample, and differ greatly between inhomogeneity honey sample, thus the maximization of Electronic Nose signal difference between the assurance honey sample.
(1) similar Almost Sure Sample Stability evaluation index
In orthogonal experiment, test lower 5 kinds of honey for every group and get 3 duplicate samples, by calculating the standard deviation average of 18 sensors of Electronic Nose to the Different categories of samples signal under this experiment condition, weigh the stability that under this condition, Electronic Nose detects similar sample signal.Computing method are as shown in formula 15,16
Wherein p is nectar source kind, C
kthe stability, the m that are k class sample are the Electronic Nose number of probes, n
kfor the number of sample in k class nectar source,
for the response signal of i sample j root sensor in the sample of k class nectar source,
for the response signal average of k class nectar source sample at j root sensor.(2) otherness of inhomogeneity sample
The otherness of inhomogeneity sample calculates according to the variance of inhomogeneity nectar source sample sample average under identical conditions, and computing method as shown in Equation 17
（17）
Wherein
for the signal average of k class sample,
signal average for all samples
(3) overall assessment index
The optimum optimizing condition that research institute's expectation obtains is signal stabilization between similar sample, and has larger difference between the inhomogeneity sample.Therefore, final definite evaluation index q as shown in Equation 18
4.3 based on the maximized Electronic Nose parameter optimization of signal difference between honey sample result
Using the evaluation index determined as the observed reading of orthogonal experiment, and Orthogonal experiment results is as shown in table 4
As can be seen from Table 4, three kinds of factors all have a certain impact to Electronic Nose differentiation effect.Wherein factor A, factor B, sample size and head space temperature are directly proportional to the signaling zone calibration, and factor C and sample headspace time dead space calibration are inversely proportional to, and the blank group changes less.This is that in the head space bottle, the volatile concentration of testing sample increases gradually due to the increase of sample size and head space temperature, can increase for the special component content of distinguishing, and distinguishes effect better.And, along with the increase of head space time, in temperature, under higher environment, sample volatile ingredient character changes, such variable effect the differentiation of sample class, therefore distinguish effect and descend.Control group is stable shows no significant difference between similar sample repeatedly, and testing result is reliable.For further Orthogonal experiment results being analyzed, experimental result is carried out to variance analysis, variance analysis is as shown in table 5.
In the result of variance analysis, can find out, three factors all have appreciable impact to the discrimination of sample, wherein factor A and factor B conspicuousness large (P<0.01), and the variance contribution ratio of three kinds of factors is respectively 65.34%, 22.16% and 9.66%.It is larger that this result shows that sample size and head space temperature affect the sample area calibration, and the head space time is less on the impact of sample area calibration.
Based on the above results, selecting the optimum optimization combination condition is A3B3C1, i.e. sample size 6g, and the head space temperature 60 C, head space time 120s, the inhomogeneity sample obtained under this condition is distinguished best results.
Exceptional sample point in 5 detection by electronic nose honey is rejected
5.1 exceptional sample point eliminating principle
Before the Electronic Nose signal to honey carries out formal analysis and honey quality modeling, be the stability that guarantees analysis result and model, need to be rejected the exceptional sample in the whole sample of honey, guarantee accuracy and the reliability of picked up signal and sample information.Exceptional sample in detection by electronic nose honey comprises the abnormal of abnormal (as sample number, the classification) of sample information and testing result.Exceptional sample usually easily affects the variation tendency of overall signal, destroys the stable of disaggregated model and thinks.Therefore the rejecting of exceptional sample extremely is necessary.
Cause the factor of Electronic Nose exceptional sample mainly comprise following some: (a) error of sample collection is mainly divided with the mistake of collecting sample, and error coded is main; (b) variation of sample shelf time character, because sample has certain interval by collecting detect to analyze, during this time every under, the variation of chemistry, physical property easily occurs in some unsettled samples; (c) error of operation, comprise the error that claims sample, the cleanliness factor of container etc.; (d) error that instrument detects, due to the variation of testing environment and sensor properties, even by the pre-treatment of signal, still have and be difficult to fully detection signal be proofreaied and correct, and still has part signal and population mean signal that significant difference is arranged.
For obtaining larger sample size, the present invention is directed to three kinds of nectar source honey that the existing market occupation rate is larger and analyzed, rape honey, Mel and acacia honey, research is for analysis and the rejecting of the exceptional sample point in great amount of samples.Wherein 76 rape honeys, 56 Mels, 112 acacia honeys, amount to 244 samples.This is organized in the research of the same application of sample and 7,8.
5.2 exceptional sample point elimination method
(1) mahalanobis distance is differentiated
Mahalanobis distance (Mahalanobis) is the evaluation index of vectorial intensity in hyperspace, is a kind of important method that the multivariate data exceptional value detects.Mahalanobis distance is by the mean vector of calculating sample data and the departure degree between covariance matrix comparative sample signal, and circular is as follows:
Sample average vector wherein, S is sample covariance matrix.
for the mahalanobis distance average of sample,
for the mahalanobis distance standard deviation, λ is the threshold value of accepting scope, x
tbe the proper vector of t sample, T is the proper vector average.Set hard-threshold λ=3 in the present invention.
for acceptable mahalanobis distance scope under this threshold condition.
(2) the lever value is differentiated
The size of sample thick stick embodies the degree of dependence of model to this sample, and the lever value is larger, relies on greatlyr, on model, affects larger.The sample that is usually located at character to be analyzed two ends has larger lever value.Excessive lever value has considerable influence to model, is unfavorable for the stable of model.By the analysis to sample lever value, reject the larger special sample of model impact, thus the stability of increase model.It is as follows that the lever value is differentiated concrete grammar:
1, calculate the score matrix T of sample to be tested by PCA;
2, calculate test matrix H:
;
3, the lever value hi of each sample: hi is i diagoned vector in test matrix H,
;
Similar with the mahalanobis distance differentiation, the lever value is differentiated by setting hard-threshold, and remove and have than the special sample of big lever value point, thus the stability of assurance later stage forecast model.
5.3 the exceptional sample point in the honey detection by electronic nose is rejected validity check
For being rejected to effect, sample verified, select Bayes (Bayes) method of discrimination to be estimated the accuracy rate of discrimination model before and after abnormity point elimination, compare and other mode identification methods, the Bayes method of discrimination is more simple, without relevant parameter is optimized.The basic thought that Bayes differentiates is that supposition has certain understanding before sampling to studied object (totally), and prior probability distribution commonly used is described this understanding.Then the sample based on extracting is revised priori understanding again, obtain so-called posterior probability and distribute, and various statistical inference all distributes to carry out based on posterior probability.Bayesian Decision is different from classical statistical method, and its distinguishing feature is exactly in the situation that the assurance risk of policy making is as far as possible little, applies all possible information as far as possible.
The mahalanobis distance that Fig. 1 is analyzing samples (76 parts of rape honeys, 56 parts of Mels, 112 parts of acacia honeys) differentiates (a) and the lever value is differentiated result (b).Utilize mahalanobis distance to differentiate, reject altogether 36,53,76,79,85,99,117,240,244, amount to 9 abnormity point; And lever is differentiated and to be rejected altogether 36,79,216,240,244 totally 5 abnormity point.
The data that adopt Bayes to differentiate after two kinds of abnormity point methods are processed are carried out the pattern-recognition prediction, predict the outcome as shown in table 6.By table 6, find, compare lever and differentiate, it is more that mahalanobis distance is differentiated the peculiar sample spot of rejecting, but accuracy rate there is no too large difference.This result shows the mahalanobis distance exceptional sample points of rejecting more, although compare and differ larger with the population distribution of sample, but to the accuracy rate as a result of differentiating not being affected greatly, therefore, in order to fully take into account the complete characteristic of sample, select lever to differentiate, reject 5 sample spot, i.e. the 36th, 76,216,240,244 5 exceptional samples, wherein rape honey, each 1 of Mel, 3 of acacia honeys.
6 honey characteristic perfumes are analyzed and honey fragrance simulated system is set up
Apply dynamic head space (Itex) and extract the honey aroma-producing substance in conjunction with the circulation beneficiation technologies, after the chromatographic column end carries out the distribution of 1:1 fragrance content, application gas chromatography mass spectrometry (GC-MS) is measured its volatility with gas chromatography-measurement of olfaction (GC-Olfactometry, GC-O) technology simultaneously and is fragrant composition and sensory characteristic.Crystallized honey carries out heating water bath, then is cooled to rapidly room temperature, and in holding chamber, temperature constant state gathers aroma-producing substance.
Wherein, in GC-MS, utilize mass spectrum (library searching), Relative Retention Indices (RI) and smell three kinds of methods of news and determine the volatile ingredient of honey, and carrying out inner mark method ration.The GC-O technology is the method that proportion detects and detected intensity combines, and by 5, preferably smells the GC-O evaluation group that the person of distinguishing forms, definite honey head perfume, front end fragrance, body note and active fragrance of the characteristic flavor on basis in four volatilization stages of bottom note of representing respectively.
According to the characteristic perfume contamination ratio in four volatilization stages, proportioning builds basic honey fragrance simulated system A.On the basis of system A, build the four group systems variant with it.The difference of every group of system and primary structure A is embodied in two aspects, in certain volatilization stage or its characteristic perfume content difference, or its characteristic perfume component difference, and the aroma component of other three phases and content are all constant.
7 characterize the intelligent sense of smell collection of illustrative plates feature extraction of honey otherness
7.1 the feature extracting method based on variance ratio
Each signaling point to every sensor calculates the middle variance of its sample and plants the internal variance ratio, according to the size of variance ratio, signaling point is selected.The computing method of variance are with the evaluation index q in optimal conditions.With other embedded feature selecting differences, variance ratio is selected directly to pass through relatively under each information point, between kind, variance is recently selected information point with the kind internal variance, need be by the method for other pattern discriminations, so the selection result of the method can not change because selecting the different mode recognition methods.But Variance ratio method belongs to the method for exhaustion, larger to the operand of large sample.
Fig. 2 shows the variance ratio of a signaling point.From figure, can find, the information point that variance ratio differs greatly more concentrates on 900-1200 and 1800-2160, and the 8th to the 10th, 15-the 18th sensor.For same root sensor, variance differs greatly and a little concentrates on detection time is that 20s is in the signaling point of 35s.Be mainly the adsorption time of volatilization gas and sensor in this time period, and the signaling point in the detection later stage of each sensor is that in the desorption time point, difference is less.The experimental selection variance ratio is greater than 1 signaling point as unique point, and its entrained information of signaling point that meets this condition can be reacted the difference of different sample rooms, selects altogether 798 unique points.With this understanding, utilize the SVM discrimination model to integrate than being predicted as the ratio of 2:1 in the checking of modeling collection, finally differentiating result is 89.8734% (71/79, wherein rape honey 21/23, Mel 14/17, acacia honey 36/39).
7.2 the feature extracting method based on the individual event diagnostic method
All unique points are carried out to pattern-recognition one by one, relatively when each signaling point, differentiate the difference of accuracy rate during as single features.The method embeds mode identification method in feature selecting, by conjunction with method of discrimination, can obtain the ability of each signaling point to the sample prediction.Different from front a kind of filtering method, the method relies on selected mode identification method, and selection result can change a lot with the change of method of discrimination.The discriminant criterion of selecting in this research is the Bayes diagnostic method
From Fig. 3, find, different from the variance selection result, unidirectional amount select between different sensors the difference of accuracy rate less, and with differing greatly between the information point under different detection times in the root sensor.But in different sensors, the variation tendency of time point Bayes differentiation accuracy rate is roughly consistent with the variance ratio variation tendency, detect initial stage signal differentiation accuracy rate higher, concentrate on the front 30s of each sensor in detection time, the effect that detects the later stage is poor.Selection differentiation accuracy rate is greater than 60% signaling point as unique point, and totally 598 unique points, utilize svm to be verified.The SVM predictablity rate is 84.8101% (67/79, wherein rape honey 20/23, Mel 13/17, acacia honey 34/39).
7.3 the feature extracting method based on ant group algorithm
The first two algorithm is the exhaustive system of selection, need to calculate one by one each unique point.When unique point is more, calculated amount can be very large, this also fundamentally limit value its feature extraction for a large amount of signaling points.Ant group algorithm belongs to the Heuristic Feature system of selection, utilizes the automatic Iterative of algorithm to evolve, and unique point is selected to carry out automatic optimal, until obtain optimal result.
Ant group algorithm (Ant Colony Optimization, ACO) is applied to travelling salesman's Path Selection at first, shortest path is optimized.In this experiment, ant group algorithm is applied to the selection of unique point.The algorithm simulation genetic algorithm is utilized binary coding to pass proper vector and is encoded each, and this information point is selected in 1 representative, and this information point is given up in 0 representative.Utilize Bayes differentiation accuracy rate and selected feature after each unique point is selected to count as fitness function, seek optimum vector combination.This algorithm main innovate point comprises: (a) unique point is selected number to add in fitness function, and set the cost parameter, regulate by parameter, can count to feature as required and differentiate accuracy rate and accepted or rejected; (b) for to keep away the renewal anisotropy caused due to particular point, optimum collection is set, with optimal set, replace single optimum point to be selected; (c) pheromones renewal degree improves and is directly proportional to fitness function, and algorithm optimization is effective, and renewal amplitude increases; (d) for accelerating computing velocity, the vector poor to effect accelerated evaporation rate, reduces pheromone concentration, reduces it later stage is calculated and disturbs.Algorithm flow as shown in Figure 4.
In ant group algorithm, each parameter is selected as follows: finally select ant group scale (m)=20; Pheromones volatilization concentration (rho)=0.003; Outstanding ant collection (n1)=3; Poor ant collection (n2)=3; Characteristic number punishment ratio (A)=400; The concentration of pheromones when figure below is a final generation.
The 9th, the signaling point of 15-18 root sensor as can be seen from Figure 5, the selected unique point of ant group algorithm focuses mostly near 1000 and 1500 to 2160.Can find out from result, inspire the class algorithm owing to being automatic evolutional algorithm, although algorithm has certain randomness, the overall distribution to feature letter signal be had preferably and selects.With this understanding, finally select feature to count 206, the differentiation accuracy rate is 94.94%(75/79, and wherein rape honey 22/23, Mel 16/17, acacia honey 37/39).The ant group algorithm result is compared the first two exhaust algorithm, differentiates accuracy rate and has a certain upgrade, and selected characteristic signal point still less, has more representativeness simultaneously.
7.4 the feature extracting method based on core principle component analysis
First three is planted extracting method and only signal itself is screened, and the selected unique point of these class methods has certain chemical sense, in conjunction with chemical results, can make an explanation preferably.But in the vector of giving up more after all, entrained some information of having selected vector not possess, be difficult to the accuracy rate that guarantees that the later stage is differentiated.Except direct extraction, utilize dimension reduction method, by matrixing, data message is compressed, effective information is carried out to enrichment, can significantly reduce characteristic signal quantity.In this research, selected two kinds of dimension reduction methods of core principle component analysis and independent component analysis to be extracted.
Core principle component analysis (Kernel Principal Component Analysis, KPCA) is introduced kernel function in principal component analysis (PCA) (Principal Component Analysis, PCA).KPCA utilizes kernel function, by data projection to higher dimensional space.Owing to more disperseing each other after data projection, therefore some inseparable signals in lower dimensional space can be distinguished, and extract and have more representational feature and extracted.In this experiment, KPCA selects the radial basis kernel function.
Under KPCA, the SVM predictablity rate of different number of principal components as shown in Figure 6.Fig. 6 demonstration is down to original signal under different dimensions through KPCA, the accuracy rate of forecast set.As can be seen from the figure, the SVM differentiation accurately takes the lead in increasing with dimension, of short duration plateau occurs afterwards, 25, during to 30 dimension, differentiating accuracy rate increases and significantly increases with dimension, and accuracy rate is relatively stable afterwards, after 100 dimensions, the accuracy rate of sample increases and reduces with dimension.This Changing Pattern shows, in dimension lower-order section, each proper vector all carries the accurate classified information of sample, and between information, existence of redundant is less, increases dimension and contributes to improve and differentiate accuracy rate.After 50 dimensions, redundancy, covering appear in the information between individual characteristic quantity, and the now increase of dimension descends on promoting the impact of differentiating accuracy rate.When the later stage, the redundancy between feature has had influence on the differentiation effect, therefore now differentiates accuracy rate and starts to descend.Finally select when dimension d=81, predictablity rate is the highest, is 93.67(74/79, and wherein rape honey 22/23, Mel 15/17, acacia honey 37/39).
7.5 the feature extracting method based on independent component analysis
Principal component analysis (PCA) (comprising core principle component analysis) is all to maximize and to be classified according to variance between data, i.e. the second moment of data, but ignored the independence of data on High Order Moment.Independent component (Independent components analysis, ICA) utilizes the High Order Moment between computational data to be converted matrix, can further reduce the associated row between proper vector, strengthens the signal compression effect.
The ICA method that this experiment adopts is fastica.Before algorithm carries out, data are carried out to the albefaction processing.Under different independent components, svm differentiates accuracy rate as shown in Figure 7
The overall variation trend of ICA and KPCA type, relax but change relatively, and rate of accuracy reached required intrinsic dimensionality when stablizing is few than KPCA.Result shows, when independent component is 14, the differentiation accuracy rate is 94.94%(75/79, and wherein rape honey 22/23, Mel 16/17, acacia honey 37/39).
The foundation of 8 support vector machine disaggregated model optimization methods
This research and utilization svm classifier device is as the disaggregated model of different nectar sources sample.Wherein, selected kernel function is that radial basis kernel function (RBF) is:
The form parameter that wherein r is the RBF function, x
iwith x
jfor two samples in sample set.
Compare and other kernel functions, select RBF to mainly contain following two kinds of reasons: 1) RBF can complete the linear nonlinear mapping of arriving, by mathematic(al) manipulation, can prove that linear kernel function is only a kind of special case of RBF; 2) than the polynomial kernel function, the RBF parameter is less, and model is relatively simple, and this has also guaranteed the stability of model.
Simultaneously, consider that being difficult to all training points of requirement meets constraint function (7), training points is introduced to slack variable ξ, constraint function (7) can be changed into
Slack variable ξ=(ξ
1, ξ
2, ξ
3... ξ
n) ', embodied all training sets by mistake minute situation.Therefore introduce the weighted value of penalty c as interval between balanced class and wrong minute degree, majorized function (6) can be converted to
Take in the svm classifier device that RBF is kernel function, the classifying quality under different parameters (form parameter r and penalty parameter c) has larger difference, and therefore, this research and utilization Different Optimization method, be optimized the middle r of RBF and the penalty coefficient in the punishment parameter.The data of selecting in this research are the data after the ICA dimensionality reduction in 7.
Concrete Optimizing Flow is as follows:
1, divide in proportion training set and checking collection, ratio is 2:1.By training set according to five folding cross validation methods, be about to training set and be divided into 5 non-cross subsets, select in turn 4 subsets wherein to carry out parameter training, the parameter of selecting with a remaining sub-set pair is verified, calculates the classification accuracy of training set under different parameters.
2,, according to selected kernel function, set kernel functional parameter r and penalty parameter c.
3, with selected parameter according to five folding cross-validation methods in 1 to the model training, and calculate the accuracy rate of different parameters drag.
4, whether the judgment models accuracy rate is up to standard, otherwise the change parameter value.
5, repeat 2,3,4 steps, until obtain best model differentiation rate, or reach stopping criterion for iteration.
Utilize different parameter searching methods in the present invention, form parameter r and penalty parameter c in step 2 are optimized, finally obtain optimization model.
8.1 the SVM parameter based on grid optimization is selected
Grid optimization utilizes the method for exhaustion, in the span of pre-estimating, by certain step-length, the institute in scope is searched for a little one by one, determines the final optimal parameter.Take 2 as the truth of a matter, 2
-4to 2
10between r and c are carried out to exhaustive search.Work as c=5.2780, during r=0.1088, it is the highest that the training set sample is differentiated accuracy rate, is 96.25% as shown in Figure 8.With this understanding, set up model, utilize forecast set to test.Final differentiation accuracy rate is 96.20% (76/79, wherein rape honey 23/23, Mel 16/17, acacia honey 37/39).
8.2 the SVM parameter based on genetic algorithm is selected
Genetic algorithm (Genetic Algorithm, GA) is carried out heuristic search by using for reference biological evolution theory to data, and at first this algorithm is proposed by the J.Holland professor of the U.S. the earliest in 1975.The fundamental operation process of genetic algorithm is as follows:
1) data initialization: maximum evolutionary generation is set, the random number of individuals generated and the colony formed thereof.Select 20 of number of individuals in this research, 100 generations of maximum iteration time.
2) individual evaluation: calculate each individual fitness in colony, the accuracy rate that in this research, fitness is sample classification.
3) Selecting operation: utilize and select operator to select at random each individuality in colony.In this research, utilize roulette method in conjunction with the accuracy rate of individual evaluation, individuality to be selected, thereby higher individual information can be genetic to the next generation by fitness.
4) crossing operation: utilize crossover operator to the restructuring that superposes of the individuality in individuality, thereby produce new individuality, the characteristic information in integrated previous generation's individuality.
5) variation computing: utilize mutation operator to carry out random variation to individuality by probability, guaranteed the generation of new individuality.
Colony is through obtaining colony of future generation after the computing of selecting, intersect, make a variation.
6) stop judgement: if iterations reaches maximum algebraically or fitness, reach necessary requirement and stop iteration.
After optimizing, the accuracy rate of training set is 96.25%, c=3.2277, r=0.1354, as shown in Figure 9.With this understanding, differentiating accuracy rate is 97.4684% (77/79, wherein rape honey 23/23, Mel 16/17, acacia honey 38/39).
8.3 the SVM parameter based on particle cluster algorithm is selected
Particle swarm optimization algorithm (Particle Swarm Optimization, PSO) similar with genetic algorithm, all by the initialization random individual, but in PSO, each initial population was intersected and variation without the later stage, but carried out individuality more by calculating gap between current ideal adaptation degree functional value and colony's optimal-adaptive value.Compare GA, in PSO, on the guiding of optimizing direction, affected by optimum individual, and be not that all individualities carry out cross exchanged.Therefore, the speed of convergence of PSO has larger improvement than GA.Select 200 generations of iteration, population several 20 in this experiment.Figure 10 is the PSO optimum results
Optimum results is: the high-accuracy of training set is 91.25%, and c=32.3362, and r=0.0100.Under this condition, predictablity rate is 88.61%(71/79, and wherein rape honey 21/23, Mel 14/17, acacia honey 36/39).
Than genetic algorithm, the particle cluster algorithm convergence is faster, just reaches optimum point about 6 generations.But its effect of optimization is poor, very large reason is because colony's optimal value representativeness is more unilateral, has been absorbed in local smallest point.From the result of trellis algorithm, can find out, under different parameters, SVM differentiation accuracy rate function is not single convex function.Therefore, with this understanding, although the GA speed of convergence is slower, consider that the overall situation is individual, effect of optimization is better than PSO.
In three kinds of optimized algorithms, trellis algorithm needs certain priori conditions, as roughly determined r and the span of c and the step-length of search.When scope is large or step-length hour, search efficiency descends.Comparatively speaking, GA algorithm optimization effect is better, about 14 generations, can reach optimum solution, and more initial (75/79) of predictablity rate (77/79) after simultaneously optimizing improves.
Research is finally determined and is utilized genetic algorithm combination supporting vector machine mode identification method, and the Electronic Nose sensor signal of obtaining is carried out to discriminant classification, and after optimizing, finally differentiating accuracy rate is 97.46%.
Claims (1)
1. a honey characteristic perfume is analyzed and the method for miel gas system simulation, it is characterized in that applying dynamic head space and extract the honey aroma-producing substance in conjunction with the circulation beneficiation technologies, after the chromatographic column end carries out the distribution of 1:1 fragrance content, application gas chromatography mass spectrometry and gas chromatography-measurement of olfaction technology are measured its volatility simultaneously and are fragrant composition and sensory characteristic, crystallized honey carries out heating water bath, then be cooled to rapidly room temperature, and in holding chamber, temperature constant state gathers aroma-producing substance, wherein in GC-MS, utilize mass spectrum, Relative Retention Indices hears with smelling the volatile ingredient that three kinds of methods are determined honey, and carry out inner mark method ration, the GC-O technology is the method that proportion detects and detected intensity combines, preferably smell by 5 the GC-O evaluation group that the person of distinguishing forms, determine and represent respectively honey head perfume (or spice), front end fragrance, the active fragrance of the characteristic flavor on basis in four volatilization stages of body note and bottom note, characteristic perfume contamination ratio according to four volatilization stages, proportioning builds basic honey fragrance simulated system A, on the basis of system A, build the four group systems variant with it, the difference of every group of system and primary structure A is embodied in two aspects, in certain volatilization stage or its characteristic perfume content difference, its characteristic perfume component difference, and the aroma component of other three phases and content are all constant.
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