CN104849323A - Method for quickly detecting clarifying agent in juice based on electronic nose - Google Patents
Method for quickly detecting clarifying agent in juice based on electronic nose Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 52
- 235000011389 fruit/vegetable juice Nutrition 0.000 title claims abstract description 16
- 239000008395 clarifying agent Substances 0.000 title abstract 2
- 238000007637 random forest analysis Methods 0.000 claims abstract description 40
- 235000015203 fruit juice Nutrition 0.000 claims abstract description 33
- 238000001514 detection method Methods 0.000 claims abstract description 21
- 238000000513 principal component analysis Methods 0.000 claims abstract description 19
- 238000004458 analytical method Methods 0.000 claims abstract description 12
- 235000015205 orange juice Nutrition 0.000 claims abstract description 11
- 108010059820 Polygalacturonase Proteins 0.000 claims abstract description 5
- 108010093305 exopolygalacturonase Proteins 0.000 claims abstract description 5
- 238000003066 decision tree Methods 0.000 claims description 34
- 238000012360 testing method Methods 0.000 claims description 22
- 241000675108 Citrus tangerina Species 0.000 claims description 16
- 238000012952 Resampling Methods 0.000 claims description 9
- 239000003205 fragrance Substances 0.000 claims description 9
- 238000007789 sealing Methods 0.000 claims description 8
- 239000006228 supernatant Substances 0.000 claims description 8
- 239000000706 filtrate Substances 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 5
- 230000002000 scavenging effect Effects 0.000 claims description 5
- 238000004381 surface treatment Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 239000013049 sediment Substances 0.000 claims description 4
- 230000006641 stabilisation Effects 0.000 claims description 4
- 238000011105 stabilization Methods 0.000 claims description 4
- 230000003068 static effect Effects 0.000 claims description 4
- 238000003756 stirring Methods 0.000 claims description 4
- 239000003381 stabilizer Substances 0.000 abstract description 7
- 238000004445 quantitative analysis Methods 0.000 abstract description 2
- 239000000243 solution Substances 0.000 abstract 3
- 238000004140 cleaning Methods 0.000 abstract 1
- 239000011259 mixed solution Substances 0.000 abstract 1
- 238000005070 sampling Methods 0.000 abstract 1
- 239000007789 gas Substances 0.000 description 20
- 230000035945 sensitivity Effects 0.000 description 5
- 229920002101 Chitin Polymers 0.000 description 4
- 239000004615 ingredient Substances 0.000 description 4
- 239000000203 mixture Substances 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 102100028260 Gamma-secretase subunit PEN-2 Human genes 0.000 description 2
- 108010010803 Gelatin Proteins 0.000 description 2
- 101000579663 Homo sapiens Gamma-secretase subunit PEN-2 Proteins 0.000 description 2
- UCKMPCXJQFINFW-UHFFFAOYSA-N Sulphide Chemical compound [S-2] UCKMPCXJQFINFW-UHFFFAOYSA-N 0.000 description 2
- 150000001335 aliphatic alkanes Chemical class 0.000 description 2
- 229910000278 bentonite Inorganic materials 0.000 description 2
- 239000000440 bentonite Substances 0.000 description 2
- SVPXDRXYRYOSEX-UHFFFAOYSA-N bentoquatam Chemical compound O.O=[Si]=O.O=[Al]O[Al]=O SVPXDRXYRYOSEX-UHFFFAOYSA-N 0.000 description 2
- 238000005352 clarification Methods 0.000 description 2
- 235000013399 edible fruits Nutrition 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 229920000159 gelatin Polymers 0.000 description 2
- 239000008273 gelatin Substances 0.000 description 2
- 235000019322 gelatine Nutrition 0.000 description 2
- 235000011852 gelatine desserts Nutrition 0.000 description 2
- 150000004676 glycans Chemical class 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 238000003909 pattern recognition Methods 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 239000003755 preservative agent Substances 0.000 description 2
- 230000002335 preservative effect Effects 0.000 description 2
- 230000001373 regressive effect Effects 0.000 description 2
- 230000008786 sensory perception of smell Effects 0.000 description 2
- RMAQACBXLXPBSY-UHFFFAOYSA-N silicic acid Chemical compound O[Si](O)(O)O RMAQACBXLXPBSY-UHFFFAOYSA-N 0.000 description 2
- 238000002798 spectrophotometry method Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- -1 zeyssatite Substances 0.000 description 2
- 229920001661 Chitosan Polymers 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 239000002671 adjuvant Substances 0.000 description 1
- 238000013019 agitation Methods 0.000 description 1
- 235000011114 ammonium hydroxide Nutrition 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000013361 beverage Nutrition 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 125000004435 hydrogen atom Chemical class [H]* 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 229910044991 metal oxide Inorganic materials 0.000 description 1
- 150000004706 metal oxides Chemical class 0.000 description 1
- 229930003811 natural phenol Natural products 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 230000007170 pathology Effects 0.000 description 1
- 235000010987 pectin Nutrition 0.000 description 1
- 229920001277 pectin Polymers 0.000 description 1
- 239000001814 pectin Substances 0.000 description 1
- 150000008442 polyphenolic compounds Chemical class 0.000 description 1
- 235000013824 polyphenols Nutrition 0.000 description 1
- 239000002244 precipitate Substances 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
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- Investigating Or Analysing Materials By Optical Means (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
The invention discloses a method for quickly detecting clarifying agent in juice based on an electronic nose. Surfaces of oranges are cleaned and dried, and the oranges basically consistent in size and color are selected as detection objects. The oranges are peeled, juiced and filtered for later use. Pectinase is prepared into solutions with different concentrations, and the solutions are mixed with orange juice according to certain ratios. 10 to 30 ml of the mixed solutions are taken and contained into a 500-ml sealed container, and electronic nose detection is performed on the top space gas of each sample, wherein twenty four repeated samples are taken from the solution of each concentration level. The set conditions of an electronic nose sensor are that at certain flow speed, the sampling time is 80s, and the air cleaning time is 60s; steady-state values are taken as characteristic values of the electronic nose. Quantitative analysis is performed on stabilizer in fruit juice by principal component analysis, discriminant analysis and random forests. The method greatly improves the quick detection on the stabilizer in the juice, realizes convenience and objectivity, and has good distinction degree and higher popularization and use values.
Description
Technical field
The invention belongs to food fruit juice clarifier detection technique field, relate to a kind of method detecting clarificant in fruit juice based on Electronic Nose fast.
Background technology
Easily there is turbidity and precipitation in fruit juice, and oxidation deterioration may occur in long-term storage process.Muddy reason is a lot, mainly relevant with the material such as naturally occurring phenols.When the protein in fruit juice and pectin substance and polyphenols coexist for a long time, muddy phaneroplasm will be produced, and even precipitate.So usually various clarificant can be added in the food industry, to remove the material that a part or major part easily cause precipitation, fruit juice is made to form more stable solution.
Beverage industry often can add, as the clarification aids such as bentonite, zeyssatite, gelatin, silicasol, pectase or their conbined usage reach the object of clarification.In current fruit juice, the conventional determining method of stabiliser content has chemical detection, spectrophotometric method, fluorescence spectrophotometry etc., but these methods exist the shortcomings such as complicated operation, analysis time be long.Therefore, it is necessary for exploring a kind of fast and convenient stabilizing agent detection method.
Electronic Nose is also known as smell scanner, the Global Information of sample is supplied with specific sensor and pattern recognition system Quick, the hidden feature of instruction sample, there is high sensitivity, reliability, repeatability, it can quantize sample, fast qualitative can be carried out to some component contents quantitative simultaneously.At present, the correlative study of pectase content in olfactory sensor quantitative determination fruit juice is utilized not yet to report.The object of the invention is stabilizing agent in Quantitative detection fruit juice, fills up both at home and abroad about the blank of the quick detection of stabiliser content in fruit drink simultaneously.
Summary of the invention
For the problems referred to above, the object of this invention is to provide a kind of method detecting clarificant in fruit juice based on Electronic Nose fast.
The object of the invention is to be achieved through the following technical solutions, a kind of method detecting clarificant in fruit juice based on Electronic Nose fast, comprises the steps:
(1) by clean for oranges and tangerines surface treatment, remove the peel, squeeze the juice, filter, abandon filter residue, the oranges and tangerines filtrate of pure concentration is for subsequent use; Take the pectase of 3g, 6g, 9g, 12g respectively, under the water bath condition of 40 DEG C, by pectase redissolve, filter, abandon filter residue; At room temperature, pectinase solution constant volume is put in 100ml volumetric flask, mix with the ratio of pure oranges and tangerines solution 1:11 in mass ratio, stir; Blended fruit juice, after static 1 hour, carries out centrifugal, abandons sediment, get supernatant stand-by under the centrifugal speed being not less than 4000r/min;
(2) get the fruit juice supernatant 10 ~ 30ml after process and put into the airtight container that volume is not less than 250ml, sealing, at room temperature places 30min, and the fragrance level of orange juice is reached capacity in the beaker headspace of sealing; The solution of each concentration scale is no less than 3 repeat samples respectively, for the regression modeling in later stage gathers Electronic Nose data; Suck in the sensor array passage of Electronic Nose by Electronic Nose internal pump by the headspace gas in airtight container, Electronic Nose sensor and sample gas react generation sensor signal; Conductivity G when described sensor signal is sensor contacts sample gas and sensor are at the conductivity G through calibration gas
0ratio, i.e. G/G
0; The detection time of described Electronic Nose sensor is 80s, and scavenging period is 60s, and internal pump gas flow rate is 200ml/min;
(3) the sensor response of detection by electronic nose orange juice is a data matrix, is made up of the response of many sensors, selects each sensor stabilization value as eigenwert, and in SPSS software, employing principal component analysis (PCA), discriminatory analysis are analyzed; In Matlab software, according to eigenwert by bootstrap (boot-strap) resampling technique, constantly generate training sample and test sample book, generate some decision trees by training sample, thus set up Random Forest model; In Random Forest model, be optimized the number of decision tree in the variable number of the tree node of decision tree and random forest, test result is determined by the mean value of decision tree voting results;
(4) sample to be tested of clarificant content the unknown is obtained its Electronic Nose sensor response by step 2, by the Random Forest model that sensor response steps for importing 3 obtains, the clarificant content in final prediction sample to be tested.
Further, in described step 3, by correlation coefficient r, root-mean-square error RMSE value, the Random Forest model after optimization is evaluated, is specially:
Wherein, N represents the number of samples in modeling process;
X
ifor the test value of i-th in modeling process;
for the mean value of the sample responses value in modeling process;
Y
ifor the predicted value of i-th sample in modeling process;
for the mean value of the sample predictions value in modeling process.
The invention has the beneficial effects as follows, by principal component analysis (PCA), discriminatory analysis, random forest scheduling algorithm sets up good qualitative, quantitative forecast model, intelligent sense of smell sensory system is utilized to evaluate the pectase of different content in juice solution and predict, simple to operate, quick, test indirectly having evaluated fast truly, for fruit drink industry adjuvant measures, provide a kind of new method.
Accompanying drawing explanation
Fig. 1 example 1 of the present invention Electronic Nose is sensor response signal when detection contains pectase fruit juice;
The principal component analysis (PCA) result of the pectase samples of juice of Fig. 2 example 1 of the present invention variable concentrations;
The discriminatory analysis result of the pectase samples of juice of Fig. 3 example 1 of the present invention variable concentrations;
The random forest regressive prediction model of the pectase samples of juice of Fig. 4 example 1 of the present invention variable concentrations;
Fig. 5 example 2 of the present invention Electronic Nose is sensor response signal when detection contains chitin fruit juice
The principal component analysis (PCA) result of the chitin samples of juice of Fig. 6 example 2 of the present invention variable concentrations;
The discriminatory analysis result of the chitin samples of juice of Fig. 7 example 2 of the present invention variable concentrations;
The random forest regressive prediction model of the chitin samples of juice of Fig. 8 example 2 of the present invention variable concentrations.
Embodiment
The present invention is applicable to the assay of the various fruit juice stabilizing agents such as pectase, shitosan, bentonite, zeyssatite, gelatin, silicasol.Electronic Nose supplies the Global Information of sample with specific sensor and pattern recognition system Quick, the hidden feature of instruction sample, there is high sensitivity, reliability, repeatability, it can quantize sample, fast qualitative can be carried out to some component contents quantitative simultaneously.Also added in concrete case study on implementation based on sense of smell finger print information to fruit juice in the qualitative and quantitative analysis of shitosan, thus further illustrate popularity of the present invention.
A kind of method detecting clarificant in fruit juice based on Electronic Nose fast of the present invention, utilize olfactory sensor to detect fast the juice solution containing variable concentrations pectase, set up effective Quantitative Prediction Model, concrete steps are as follows:
(1) by clean for oranges and tangerines surface treatment, remove the peel, squeeze the juice, filter, abandon filter residue, the oranges and tangerines filtrate of pure concentration is for subsequent use; Accurately take the pectase of 3g, 6g, 9g, 12g respectively, under the water bath condition of 40 DEG C, by pectase redissolve, filter, abandon filter residue.At room temperature, pectinase solution constant volume is put in 100ml volumetric flask.Mix in the ratio with 1:11 with pure oranges and tangerines solution, stir.Blended fruit juice, after static 1 hour, carries out centrifugal, abandons sediment, get supernatant stand-by under the centrifugal speed being not less than 4000r/min;
(2) get the fruit juice supernatant 10 ~ 30ml after process and put into the airtight container that volume is not less than 250ml, sealing, at room temperature places 30min, and the fragrance level of orange juice is reached capacity in the beaker headspace of sealing.The solution of each concentration scale is no less than 3 repeat samples respectively, for the regression modeling in later stage gathers Electronic Nose data.Suck in the sensor array passage of Electronic Nose by Electronic Nose internal pump by the headspace gas in airtight container, Electronic Nose sensor and sample gas react generation sensor signal; Conductivity G when described sensor signal is sensor contacts sample gas and sensor at the ratio of the conductivity G0 through calibration gas, i.e. G/G0; The detection time of described Electronic Nose sensor is 80s, and scavenging period is 60s, and internal pump gas flow rate is 200ml/min;
(3) the sensor response of detection by electronic nose orange juice is a data matrix, is made up of the response of many sensors, selects each sensor stabilization value as eigenwert, and in SPSS software, employing principal component analysis (PCA), discriminatory analysis are analyzed; In Matlab software, according to eigenwert by bootstrap (boot-strap) resampling technique, constantly generate training sample and test sample book, generate some decision trees by training sample, thus set up Random Forest model; In Random Forest model, be optimized the number of decision tree in the variable number of the tree node of decision tree and random forest, test result is determined by the mean value of decision tree voting results;
(4) sample to be tested of clarificant content the unknown is obtained its Electronic Nose sensor response by step 2, by the Random Forest model that sensor response steps for importing 3 obtains, the clarificant content in final prediction sample to be tested.
In described step 3, by correlation coefficient r, root-mean-square error RMSE value, the Random Forest model after optimization is evaluated, is specially:
Wherein, N represents the number of samples in modeling process;
X
ifor the test value of i-th in modeling process;
for the mean value of the sample responses value in modeling process;
Y
ifor the predicted value of i-th sample in modeling process;
for the mean value of the sample predictions value in modeling process.
Embodiment 1
The present embodiment is using pectase as analytic target.Germany AIRSENSE company PEN2 type Electronic Nose be that detecting instrument elaborates, this Electronic Nose is made up of 10 metal oxide sensors, its model and response characteristic as shown in the table:
| Sequence number | Title | Performance characteristics |
| 1 | S1 | Responsive to fragrance ingredient |
| 2 | S2 | Very sensitive to oxides of nitrogen |
| 3 | S3 | To ammoniacal liquor, fragrance ingredient sensitivity |
| 4 | S4 | Selective to hydrogen |
| 5 | S5 | To alkane, fragrance ingredient sensitivity |
| 6 | S6 | Responsive to methane |
| 7 | S7 | Responsive to sulfide |
| 8 | S8 | To alcohol sensible |
| 9 | S9 | To fragrance ingredient, organic sulfide sensitivity |
| 10 | S10 | Responsive to alkane |
Concrete detecting step is as follows:
1, by clean for oranges and tangerines surface treatment, remove the peel, squeeze the juice, filter, abandon filter residue, the oranges and tangerines filtrate of pure concentration is for subsequent use; Accurately take the pectase of 3g, 6g, 9g, 12g respectively, under the water bath condition of 40 DEG C, by pectase redissolve, filter, abandon filter residue.At room temperature, pectinase solution constant volume is put in 100ml volumetric flask.Mix in the ratio with 1:11 with pure oranges and tangerines solution, stir.Blended fruit juice, after static 1 hour, carries out centrifugal, abandons sediment, get supernatant stand-by under the centrifugal speed being not less than 4000r/min;
2, get the beaker that the fruit juice supernatant 10ml after process puts into volume 500ml, seal with double-deck preservative film, at room temperature place 30min, the fragrance level of orange juice is reached capacity in the beaker headspace of sealing; Fruit juice 24 repeat samples respectively of each concentration scale, for the regression modeling in later stage gathers Electronic Nose data; Suck in the sensor array passage of Electronic Nose by Electronic Nose internal pump by the headspace gas in airtight container, Electronic Nose sensor and sample gas react generation sensor signal; Conductivity G when described sensor signal is sensor contacts sample gas and sensor at the ratio of the conductivity G0 through calibration gas, i.e. G/G0; The detection time of described Electronic Nose sensor is 80s, and scavenging period is 60s, and internal pump gas flow rate is 200ml/min;
3, the sensor response of detection by electronic nose orange juice is a data matrix, is made up of the response of many sensors, selects each sensor in 60s stationary value as eigenwert, and in SPSS software, employing principal component analysis (PCA), discriminatory analysis are analyzed; Fig. 2, Fig. 3 are based on principal component analysis respectively, and linear discriminant analysis is to the preliminary judgement of fruit juice mesochite glycan content; In Matlab software, according to eigenwert by bootstrap (boot-strap) resampling technique, constantly generate training sample and test sample book, generate some decision trees by training sample, thus set up Random Forest model; In Random Forest model, be optimized the number of decision tree in the variable number of the tree node of decision tree and random forest, test result is determined by the mean value of decision tree voting results; The roughly step of random forest is as follows:
(1) bootstrap (boot-strap) resampling technique is utilized, random generation T training set S
1, S
2..., S
t;
Bootstrap (boot-strap) resampling: establish in set and have the individual different sample { x of n
1, x
2..., x
n, from S set, extract a sample if put back at every turn, extract n time altogether, form new S set
*, then S set
*in comprise certain sample x
i(i=1,2 ..., probability n) is
as n → ∞, have
therefore, the total sample number of new set is identical with former set, but contains repeated sample (putting back to extraction), only contains the sample that former S set set is about 1-0.368*100%=63.2% in new set;
(2) utilize each training set, generate corresponding decision tree C
1, C
2... C
t; Each non-leaf nodes is dividing (generally speaking, in the growth course of this random forest, the value of m remains unchanged) this node based on the best divisional mode in the Split Attribute collection m of front nodal point;
(3) the complete growth of every tree, and do not prune;
(4) for test set sample X, utilize each decision tree to test, obtain corresponding classification C
1(X), C
2(X) ..., C
t(X);
(5) adopt the mode of ballot, the net result of Random Forest model is determined by the mean value of T decision tree output valve.
4, in Random Forest model, the number of decision tree in the variable number of the tree node of decision tree and random forest is optimized, test result is determined by the mean value of decision tree voting results, and the variable that the result finally optimized obtains tree node is 3, and the number of decision tree is 40.By correlation coefficient r, root-mean-square error RMSE value, the Random Forest model after optimization is evaluated, is specially:
Wherein, N represents the number of samples in modeling process;
X
ifor the test value of i-th in modeling process;
for the mean value of the sample responses value in modeling process;
Y
ifor the predicted value of i-th sample in modeling process;
for the mean value of the sample predictions value in modeling process.
In Fig. 4 display, black squares point has good correlativity (r=0.9879, RMSE=0.0902) between the content of the pectase of this model prediction of sample of known pectase content and actual pectase content.
5, the sample to be tested of pectase content the unknown is obtained its Electronic Nose sensor response by step 2, by the Random Forest model that sensor response steps for importing 4 obtains, the pectase content in final prediction sample to be tested.As the sample that trigpoint in the white of Fig. 4 is unknown pectase content, Random Forest model has good predictive ability (r=0.9764, RMSE=0.1073) to orange blossom pectase content.
Embodiment 2
The shitosan that present case is commonly used in fruit juice is as analytic target.The PEN2 type Electronic Nose of AIRSENSE company of Germany is that detecting instrument elaborates in case 1.
1, oranges and tangerines surface treatment is clean, reject pathology, get colors, sample that size, shape are consistent.Oranges and tangerines are removed the peel, and squeeze the juice, and filter, abandon filter residue, the oranges and tangerines filtrate of pure concentration is for subsequent use.
2, standard accurately claims 3g, 6g, 9g, 12g shitosan, pours in cold water, soaks, add thermal agitation and fully dissolve, at room temperature, put in 100ml volumetric flask by chitosan solution constant volume 45 DEG C of hot bath methods through 10 minutes.Repeat to mix in 1:11 ratio with the pure oranges and tangerines solution of step (1), treat and detection by electronic nose.
3, get the beaker that the fruit juice 10ml after process puts into volume 500ml, seal with double-deck preservative film, at room temperature place 30min, the fragrance level of orange juice is reached capacity in the beaker headspace of sealing; Fruit juice 24 repeat samples respectively of each concentration scale, for the regression modeling in later stage gathers Electronic Nose data; Suck in the sensor array passage of Electronic Nose by Electronic Nose internal pump by the headspace gas in airtight container, Electronic Nose sensor and sample gas react generation sensor signal; Conductivity G when described sensor signal is sensor contacts sample gas and sensor at the ratio of the conductivity G0 through calibration gas, i.e. G/G0; The detection time of described Electronic Nose sensor is 80s, and scavenging period is 60s, and internal pump gas flow rate is 200ml/min;
4, the sensor response of detection by electronic nose orange juice is a data matrix, is made up of the response of many sensors, selects each sensor stabilization value as eigenwert, and in SPSS software, employing principal component analysis (PCA), discriminatory analysis are analyzed; Fig. 6, Fig. 7 are based on principal component analysis respectively, and linear discriminant analysis is to the preliminary judgement of fruit juice mesochite glycan content; In Matlab software, according to eigenwert by bootstrap (boot-strap) resampling technique, constantly generate training sample and test sample book, generate some decision trees by training sample, thus set up Random Forest model; In Random Forest model, be optimized the number of decision tree in the variable number of the tree node of decision tree and random forest, test result is determined by the mean value of decision tree voting results; The roughly step of random forest is as follows:
(1) bootstrap (boot-strap) resampling technique is utilized, random generation T training set S
1, S
2..., S
t;
Bootstrap (boot-strap) resampling: establish in set and have the individual different sample { x of n
1, x
2..., x
n, from S set, extract a sample if put back at every turn, extract n time altogether, form new S set
*, then S set
*in comprise not individual sample x
i(i=1,2 ..., probability n) is
as n → ∞, have
therefore, the total sample number of new set is identical with former set, but contains repeated sample (putting back to extraction), only contains the sample that former S set set is about 1-0.368*100%=63.2% in new set;
(2) utilize each training set, generate corresponding decision tree C
1, C
2... C
t; Each non-leaf nodes is dividing (generally speaking, in the growth course of this random forest, the value of m remains unchanged) this node based on the best divisional mode in the Split Attribute collection m of front nodal point;
(3) the complete growth of every tree, and do not prune;
(4) for test set sample X, utilize each decision tree to test, obtain corresponding classification C
1(X), C
2(X) ..., C
t(X);
(5) adopt the mode of ballot, the mean value of the head sheet result exported in T decision tree is determined.
4, in Random Forest model, the number of decision tree in the variable number of the tree node of decision tree and random forest is optimized, test result is determined by the mean value of decision tree voting results, and the variable that the result finally optimized obtains tree node is 3, and the number of decision tree is 50.By correlation coefficient r, root-mean-square error RMSE value, the Random Forest model after optimization is evaluated, is specially:
Wherein, N represents the number of samples in modeling process;
X
ifor the test value of i-th in modeling process;
for the mean value of the sample responses value in modeling process;
Y
ifor the predicted value of i-th sample in modeling process;
for the mean value of the sample predictions value in modeling process.
In Fig. 8 display, black squares point has good correlativity (r=0.9950, RMSE=0.0542) between the content of the pectase of this model prediction of sample of known shitosan content and actual shitosan content.
6, the sample to be tested of shitosan content the unknown is obtained its Electronic Nose sensor response by step 3, by the Random Forest model that sensor response steps for importing 5 obtains, the shitosan content in final prediction sample to be tested.As the sample that trigpoint in the white of Fig. 4 is unknown shitosan content, Random Forest model has good predictive ability (r=0.9721, RMSE=0.1449) to orange blossom pectase content.
Claims (2)
1. detect a method for clarificant in fruit juice based on Electronic Nose fast, it is characterized in that, comprise the steps:
(1) by clean for oranges and tangerines surface treatment, remove the peel, squeeze the juice, filter, abandon filter residue, the oranges and tangerines filtrate of pure concentration is for subsequent use; Take the pectase of 3g, 6g, 9g, 12g respectively, under the water bath condition of 40 DEG C, by pectase redissolve, filter, abandon filter residue; At room temperature, pectinase solution constant volume is put in 100ml volumetric flask, mix with the ratio of pure oranges and tangerines solution 1:11 in mass ratio, stir; Blended fruit juice, after static 1 hour, carries out centrifugal, abandons sediment, get supernatant stand-by under the centrifugal speed being not less than 4000r/min;
(2) get the fruit juice supernatant 10 ~ 30ml after process and put into the airtight container that volume is not less than 250ml, sealing, at room temperature places 30min, and the fragrance level of orange juice is reached capacity in the beaker headspace of sealing; The solution of each concentration scale is no less than 3 repeat samples respectively, for the regression modeling in later stage gathers Electronic Nose data; Suck in the sensor array passage of Electronic Nose by Electronic Nose internal pump by the headspace gas in airtight container, Electronic Nose sensor and sample gas react generation sensor signal; Conductivity G when described sensor signal is sensor contacts sample gas and sensor are at the conductivity G through calibration gas
0ratio, i.e. G/G
0; The detection time of described Electronic Nose sensor is 80s, and scavenging period is 60s, and internal pump gas flow rate is 200ml/min;
(3) the sensor response of detection by electronic nose orange juice is a data matrix, is made up of the response of many sensors, selects each sensor stabilization value as eigenwert, and in SPSS software, employing principal component analysis (PCA), discriminatory analysis are analyzed; In Matlab software, according to eigenwert by bootstrap (boot-strap) resampling technique, constantly generate training sample and test sample book, generate some decision trees by training sample, thus set up Random Forest model; In Random Forest model, be optimized the number of decision tree in the variable number of the tree node of decision tree and random forest, test result is determined by the mean value of decision tree voting results;
(4) sample to be tested of clarificant content the unknown is obtained its Electronic Nose sensor response by step 2, by the Random Forest model that sensor response steps for importing 3 obtains, the clarificant content in final prediction sample to be tested.
2. a kind of method detecting clarificant in fruit juice based on Electronic Nose fast according to claim 1, is characterized in that, in described step 3, is evaluated, be specially by correlation coefficient r, root-mean-square error RMSE value to the Random Forest model after optimization:
Wherein, N represents the number of samples in modeling process;
X
ifor the test value of i-th in modeling process;
for the mean value of the sample responses value in modeling process;
Y
ifor the predicted value of i-th sample in modeling process;
for the mean value of the sample predictions value in modeling process.
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