CN1987456A - Predicting method for fruit maturity - Google Patents
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Abstract
The method includes steps: (1) placing fruit sample inside sealed container; after top air portion reaches balance, sampled top air portion is inducted to sensor array reaction chamber; response signal is obtained when reaction takes place between sensor array and top air portion; response signal of sensor is ratio between resistance R after sensor contacts top air and resistance R0 when sensor passes through clean air, i.e. S=R/R0; (2) carrying out detections including compactness, sugar degree, and acidity for detected fruit; (3) using multiple linear regression, major constituent regression, least squares regression, and artificial neural network to build mathematical model of relation between the response signal of sensor and compactness, sugar degree, acidity of fruit sample. The invention extends detection range, lowers interference, increase sensitivity, reliability, and repeatability.
Description
Technical field
The present invention relates to a kind of predicting method for fruit maturity.
Background technology
In recent years, along with the globalization of international market, people require also more and more higher to fruit quality, and the degree of ripeness of fruit is the principal element of decision fruit quality.Degree of ripeness has determined consumer's satisfaction in fruit harvesting, storage and the process of circulation, just seems quite important so fruit maturity detected and control.The research of fruit maturity detection technique has at present obtained continuous development, but their most employings is the detection that damage is arranged.
The smell of fruit is an important means of estimating its quality, also is one of principal element that influences consumer's purchase.Fruit all has different fragrance and special smell separately, and this is determined by they self contained aromatic substance.Aromatic substance is had nothing in common with each other at the content of the different mature periods of fruit.The aroma quality of normal food is to evaluate by people's sense of smell impression, and subjective appreciation mainly relies on what mental condition of physiology of people, and the accuracy of evaluation result often is difficult to guarantee.For this reason, many scholars begin to attempt carrying out the detection of smell with vapor-phase chromatography (GC) and gas chromatography one mass spectrometric hyphenated technique (GC-MS).It is identical that people such as Oshita utilize GC/MS to detect seven odour components of " La France " pears three maturity stages, but the concentration of seven compositions is different.The smell that people such as Chervin utilize GS/MS to detect the fruit under the different storage conditions also is different.But these detection method testing cost costlinesses, sense cycle are long.Fruit consolidation (Firmness) is meant the power of pulp compressive resistance, can be used as an important indicator judging fruit maturity state and quality.The common method of consolidation detection at present is a M-T puncture test method.This method is the steel pressure head with certain diameter, by certain compression speed fruit is carried out compression test, measures force of compression simultaneously, belong to diminish detection, and great amount of samples detects very unrealistic one by one.Other index of fruit quality (pol, acidity etc.) generally also is that fruit is crushed, and extrudes fruit juice and detects also to belong to and diminish detection.
Summary of the invention
The purpose of this invention is to provide a kind of predicting method for fruit maturity.
It comprises the steps:
1) the fruit sample is placed in the closed container, the airtight time is 30-60min, and head space gas reaches after the balance, and the static headspace gas sampling imports in the sensor array reaction chamber, sampling time is 60-90s, sensor array and the head space gas signal that meets with a response that reacts; The sensor response signal is resistance R and the resistance R of sensor through pure air time the after sensor touches static headspace gas
0Ratio, i.e. S=R/R
0
2) then the fruit sample that detected being carried out consolidation, pol and acidity detects;
3) adopt multiple linear regression, principal component regression, least square regression and artificial neural network to set up the mathematical model that concerns between consolidation, pol and the acidity of the sensor response signal and fruit sample;
For consolidation, peach smell multiple linear regression model is:
CF=72.76-26.73 * S1+12.02 * S2-357.37 * S3+7.91 * S4+314.82 * S5-3.92 * S6-45.3 * S7-1.06 * S8 is for pol, and the multiple linear regression model of peach smell is:
SSC=3.81+3.99 * S1-0.87 * S2+4.94 * S3+7.84 * S4-9.65 * S5+7.25 * S6+5.44 * S7-7.64 * S8 is for acidity, and the multiple linear regression model of peach smell is:
PH=4.70+0.11 * S1-0.037 * S2+1.75 * S3+0.9 * S4-2.25 * S5+0.65 * S6-0.52 * S7-0.35 * S8CF represents consolidation, SSC represents pol, pH represents acidity, S1 represents the MQ-3 sensor signal, S2 represents the TGS822 sensor signal, S3 represents the MQ-7 sensor signal, S4 represents the TGS800 sensor signal, S5 represents the TGS824 sensor signal, S6 represents the TGS813 sensor signal, S7 represents the TGS880 sensor signal, S8 represents the TGS825 sensor signal.
The present invention can form the electric nasus system of surveying fruit quality efficiently with the common sensor array of cheapness.Compare with single gas sensor, gas sensor array has enlarged sensing range, has reduced interference, and its sensitivity, reliability and repeatability all are greatly improved.Come the processes sensor array data with qualitative and quantitative model recognition system, set up mathematical model, these models can accurately change into sensor signal and the corresponding to result of conventional sense method testing result.It can arrive different signals according to various odor detection, and with these signal substitution models, judges the maturity state of fruit according to the calculated value of model.
Description of drawings
Fig. 1 be in the example of the present invention 8 sensors to the response curve of ripe peach;
Peach multiple linear regression model the predicting the outcome of three degree of ripeness of Fig. 2 (a) instance graph test of the present invention to consolidation
Peach neural network model the predicting the outcome of three degree of ripeness of Fig. 2 (b) instance graph test of the present invention to consolidation
Peach multiple linear regression model the predicting the outcome of three degree of ripeness of Fig. 3 (a) instance graph test of the present invention to pol
Peach neural network model the predicting the outcome of three degree of ripeness of Fig. 3 (b) instance graph test of the present invention to pol
Peach multiple linear regression model the predicting the outcome of three degree of ripeness of Fig. 4 (a) instance graph test of the present invention to acidity
Peach neural network model the predicting the outcome of three degree of ripeness of Fig. 4 (b) instance graph test of the present invention to acidity
Embodiment
The present invention is according to being that fruit is different at the different smells that mature period distributed, and gas sensor also can be different to its response signal.Utilize this specific character to predict the inside quality of fruit.
Carry out earlier measuring to wanting test sample based on the Electronic Nose of sensor array, sample is placed in the closed container, treat that the top air reaches balance post-sampling pump the head space gas in the container is imported in the sensor array reaction chamber, sensor and gas react and obtain corresponding response signal, and this signal is changed into numeral by capture card and is input to computing machine.
Then consolidation, pol and the acidity of fruit sample are measured.
With computing machine the data of gained are handled, the method that adopts quantitative test is as multiple linear regression, principal component regression, partial least square method and artificial neural network.Set up the mathematical model of the relation between consolidation, pol and the acidity of the sensor response signal of fruit sample and fruit sample by these pattern recognition system, so just can judge consolidation, pol and the acidity of fruit sample by institute's established model.
The present invention has sampling part, part of data acquisition, and signal processing part is grouped into.Described sampling part has the pipeline that has syringe needle to connect the mouth of bleeding of vacuum pump, and outlet nozzle one end that pipeline one end of needle-less connects vacuum pump links to each other with the sensor array chamber.Part of data acquisition is a test casing, and the gas sensor array reaction chamber of a circle is arranged in the casing, and this circle reactor top is respectively equipped with air intake opening and goes out mouth.Described circular reaction chamber inside surface is smooth, does not have the gas dead angle, and a plurality of gas sensors are evenly arranged in the inside, forms sensor array, and each sensor links to each other with a passage of capture card.
Now introduce implementation process of the present invention in detail in conjunction with example.Example is that the peach that utilizes the present invention that difference is plucked period detects its degree of ripeness of evaluation.Test specimen is three Da Bai peaches of plucking period, is respectively prematurity, half ripe and ripe peaches according to expertise.Testing process to peach is as follows:
(1) need preheating can detect more than one hour after the sensor array energising.Place peach sealable tank to produce head space gas down for 25 ℃ in room temperature.
(2) sample after airtight one hour head space gas reach balance, opening sampling pump imports in the sensor array reaction chamber fruit sample headspace gas to be measured, reacting with sensor array S1-S8 obtains corresponding one group of response signal, and this signal is changed into numeral by capture card and is input to computing machine.Computing machine is writing down the signal data of all the sensors always.Stop behind the 90s gathering, extract syringe needle, take out institute's test sample this, and to use pure air cleaning sensor, scavenging period be 60s (sensor release time), so that measure next sample.
Repeating above 1 and 2 steps can take multiple measurements.This example is measured 30 of the peaches of each collecting period, i.e. 90 samples.
Collect through the sample after the smell collection, each fruit is squeezed the juice respectively measure acidity, consolidation and the pol of peach fruit juice by acidometer, universal testing machine and hand-held saccharometer respectively, form database, these data are as the test value of mathematical model.
As shown in Figure 1, in the response curve of the ripe peach of test, horizontal ordinate is the sampling time in the embodiment of the invention, and ordinate is to be that sensor touches the resistance R and the resistance R of sensor when passing through pure air behind the sample volatile matter
0Ratio.
Embodiment
The present invention has set up the multiple linear regression model (MLR) and the anti-phase Propagation Neural Network model (ANN) of peach smell.The sensor response database that obtains with Electronic Nose is as independent variable, the consolidation of peach, pol and acidity are set up the multiple regression pattern as dependent variable respectively, test figure with each 20 sample of the peach of three degree of ripeness is set up model, the predictive ability of each remaining 10 sample data substitution model testing model.8 sensors to the response of peach smell (S1 ..., S8) and the multiple linear regression mould between the degree of ripeness index as follows:
For consolidation, peach smell multiple linear regression model is:
CF=72.76-26.73 * S1+12.02 * S2-357.37 * S3+7.91 * S4+314.82 * S5-3.92 * S6-45.3 * S7-1.06 * S8 is for pol, and the multiple linear regression model of peach smell is:
SSC=3.81+3.99 * S1-0.87 * S2+4.94 * S3+7.84 * S4-9.65 * S5+7.25 * S6+5.44 * S7-7.64 * S8 is for acidity, and the multiple linear regression model of peach smell is;
The sensor response database that pH=4.70+0.11 * S1-0.037 * S2+1.75 * S3+0.9 * S4-2.25 * S5+0.65 * S6-0.52 * S7-0.35 * S8 obtains is as the input of neural network, the consolidation of fruit sample, pol and acidity are respectively as the desired output of network, test figure with each 20 sample of the peach of three degree of ripeness is carried out modeling, and each remaining 10 sample are tested model.To the predictive ability of consolidation, pol and acidity shown in Fig. 2,3 and 4.Multiple linear regression model is to the predictive ability of peach consolidation: the correlativity of predicted value and measured value is 0.92, and predicated error is 2.95N; Prediction to pol: the correlativity of predicted value and measured value is 0.88, and predicated error is 0.38; Prediction to acidity: the correlativity of predicted value and measured value is 0.82, and predicated error is 0.38.
Claims (2)
1. a fruit maturity detects the survey method, and it is characterized in that: it comprises the steps:
1) the fruit sample is placed in the closed container, the airtight time is 30-60min, and head space gas reaches after the balance, and the static headspace gas sampling imports in the sensor array reaction chamber, sampling time is 60-90s, sensor array and the head space gas signal that meets with a response that reacts; The sensor response signal is resistance R and the resistance R of sensor through pure air time the after sensor touches static headspace gas
0Ratio, i.e. S=R/R
0
2) then the fruit sample that detected being carried out consolidation, pol and acidity detects;
3) adopt multiple linear regression, principal component regression, least square regression and artificial neural network to set up the mathematical model that concerns between consolidation, pol and the acidity of the sensor response signal and fruit sample;
For consolidation, peach smell multiple linear regression model is:
CF＝72.76-26.73×S1+12.02×S2-357.37×S3+7.91×S4+314.82×S5-3.92×S6-45.3×S7-1.06×S8
For pol, the multiple linear regression model of peach smell is:
SSC＝3.81+3.99×S1-0.87×S2+4.94×S3+7.84×S4-9.65×S5+7.25×S6+5.44×S7-7.64×S8
For acidity, the multiple linear regression model of peach smell is:
pH＝4.70+0.11×S1-0.037×S2+1.75×S3+0.9×S4-2.25×S5+0.65×S6-0.52×S7-0.35×S8
On behalf of consolidation, SSC, CF represent pol, pH to represent acidity, S1 to represent MQ-3 sensor signal, S2 to represent TGS822 sensor signal, S3 to represent MQ-7 sensor signal, S4 to represent TGS800 sensor signal, S5 to represent TGS824 sensor signal, S6 to represent TGS813 sensor signal, S7 to represent TGS880 sensor signal, S8 to represent the TGS825 sensor signal.
2. a kind of predicting method for fruit maturity according to claim 1 and 2 is characterized in that: described closed container is the twice of fruit sample volume.
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| CN200610155208A CN100575950C (en) | 2006-12-14 | 2006-12-14 | A kind of predicting method for fruit maturity |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102621192A (en) * | 2012-03-17 | 2012-08-01 | 浙江工商大学 | Method for detecting freshness of mangos by aid of electronic nose |
| CN104897738A (en) * | 2015-05-06 | 2015-09-09 | 浙江大学 | Method for rapidly detecting superhigh pressure fruit juice quality based on olfaction fingerprint information |
| CN104914225A (en) * | 2015-05-06 | 2015-09-16 | 浙江大学 | Method for forecasting content of clarifying agent in fruit juice based on smell sense fingerprint information |
| CN105699491A (en) * | 2016-04-13 | 2016-06-22 | 浙江大学 | Online nondestructive detection apparatus and method for fruit firmness |
| CN105738581A (en) * | 2016-02-01 | 2016-07-06 | 浙江大学 | Method for quickly identifying walnuts with different freshnesses based on electronic nose |
| CN106951912A (en) * | 2017-02-15 | 2017-07-14 | 海尔优家智能科技（北京）有限公司 | A kind of method for building up of fruits and vegetables cosmetic variation identification model and recognition methods |
| CN107590799A (en) * | 2017-08-25 | 2018-01-16 | 山东师范大学 | The recognition methods of banana maturity period and device based on depth convolutional neural networks |
| CN110455778A (en) * | 2019-09-12 | 2019-11-15 | 中科院合肥技术创新工程院 | Apple volatilization gas detection method based on hollow-core fiber enhancing Raman spectrum |
| CN112906939A (en) * | 2021-01-18 | 2021-06-04 | 齐鲁工业大学 | Method for predicting fig harvesting time point |
| CN113267466A (en) * | 2021-04-02 | 2021-08-17 | 中国科学院合肥物质科学研究院 | Fruit sugar degree and acidity nondestructive testing method based on spectral wavelength optimization |
| CN117265251A (en) * | 2023-09-20 | 2023-12-22 | 索罗曼(广州)新材料有限公司 | Titanium flat bar oxygen content online monitoring system and method thereof |
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2006
- 2006-12-14 CN CN200610155208A patent/CN100575950C/en not_active Expired - Fee Related
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102621192A (en) * | 2012-03-17 | 2012-08-01 | 浙江工商大学 | Method for detecting freshness of mangos by aid of electronic nose |
| CN104897738B (en) * | 2015-05-06 | 2017-12-12 | 浙江大学 | A kind of method based on smell finger print information quick detection super-pressure fruit juice quality |
| CN104897738A (en) * | 2015-05-06 | 2015-09-09 | 浙江大学 | Method for rapidly detecting superhigh pressure fruit juice quality based on olfaction fingerprint information |
| CN104914225A (en) * | 2015-05-06 | 2015-09-16 | 浙江大学 | Method for forecasting content of clarifying agent in fruit juice based on smell sense fingerprint information |
| CN105738581A (en) * | 2016-02-01 | 2016-07-06 | 浙江大学 | Method for quickly identifying walnuts with different freshnesses based on electronic nose |
| CN105699491A (en) * | 2016-04-13 | 2016-06-22 | 浙江大学 | Online nondestructive detection apparatus and method for fruit firmness |
| CN106951912B (en) * | 2017-02-15 | 2019-11-08 | 海尔优家智能科技（北京）有限公司 | A kind of method for building up of fruits and vegetables cosmetic variation identification model and recognition methods |
| CN106951912A (en) * | 2017-02-15 | 2017-07-14 | 海尔优家智能科技（北京）有限公司 | A kind of method for building up of fruits and vegetables cosmetic variation identification model and recognition methods |
| CN107590799A (en) * | 2017-08-25 | 2018-01-16 | 山东师范大学 | The recognition methods of banana maturity period and device based on depth convolutional neural networks |
| CN110455778A (en) * | 2019-09-12 | 2019-11-15 | 中科院合肥技术创新工程院 | Apple volatilization gas detection method based on hollow-core fiber enhancing Raman spectrum |
| CN112906939A (en) * | 2021-01-18 | 2021-06-04 | 齐鲁工业大学 | Method for predicting fig harvesting time point |
| CN112906939B (en) * | 2021-01-18 | 2022-08-09 | 齐鲁工业大学 | Method for predicting fig harvesting time point |
| CN113267466A (en) * | 2021-04-02 | 2021-08-17 | 中国科学院合肥物质科学研究院 | Fruit sugar degree and acidity nondestructive testing method based on spectral wavelength optimization |
| CN113267466B (en) * | 2021-04-02 | 2023-02-03 | 中国科学院合肥物质科学研究院 | Fruit sugar degree and acidity nondestructive testing method based on spectral wavelength optimization |
| CN117265251A (en) * | 2023-09-20 | 2023-12-22 | 索罗曼(广州)新材料有限公司 | Titanium flat bar oxygen content online monitoring system and method thereof |
| CN117265251B (en) * | 2023-09-20 | 2024-04-09 | 索罗曼(广州)新材料有限公司 | Titanium flat bar oxygen content online monitoring system and method thereof |
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