CN103308457A - Establishment method of prediction model for bergamot pear maturity - Google Patents
Establishment method of prediction model for bergamot pear maturity Download PDFInfo
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Abstract
The invention discloses an establishment method of a prediction model for bergamot pear maturity. The establishment method comprises the following steps that: n testing dates are set; p bergamot pears are taken out of any one box of bergamot pears to serve as bergamot pear samples; the samples are respectively detected at the n testing dates; diffuse reflection light of the external surfaces of a plurality of directions of each sample is detected; a diffuse reflection spectrum curve of each direction of each sample is drawn by a spectrograph; a plurality of characteristic values are extracted from the diffuse reflection spectrum curve of each direction by a computer; the characteristic values of the diffuse reflection spectrum curve of each direction are combined into a characteristic value signal by the computer; the characteristic value signal is input into a cross correlation coefficient formula to compute a cross correlation coefficient; and a cross correlation coefficient curve of the sample 1 to the sample p from t1 to tn is defined as the prediction model for the bergamot pear maturity. The establishment method has the characteristics of bergamot pear maturity distinguishing, high detection speed, high detection accuracy, good detection repeatability and low detection cost.
Description
Technical field
The present invention relates to the fruit quality detection range, especially relate to a kind of bergamot pear degree of ripeness forecast model method for building up.
Background technology
Different fruit have different fragrance, this what determines with forming by they self contained aromatic substance kind, although the content of aromatic substance in fruit is very little, it is bigger to fruit quality influence, and because of different its content differences of kind, degree of ripeness and storage time.Vapor-phase chromatography (GC) and gas chromatography-mass spectrography technology (GC-MS) are generally adopted in the detection of fruit aroma and aromatic substance, but these detection method testing cost costlinesses, sense cycle are long.Particularly, the product that the gained odour component all is sample after separating needs just can contrasting after the product reorganization, so test result globality of difficult representative sample all is difficult to do systematization and scientific contrast with people's sense of smell.
In harvesting, storage, logistics link, if can not accurately judge the degree of ripeness of bergamot pear, in above-mentioned link, can cause the rotting rate of bergamot pear to rise, cause storage, logistics cost rising etc., also finally cause the rising of bergamot pear price.
Therefore, press for a kind of objective, method detects the degree of ripeness of bergamot pear fast and effectively.
For example, Chinese patent publication number: CN101893591A in open day on November 24th, 2010, discloses a kind of electric nasus system for the banana quality detection.Comprise sensor array, signal acquisition module, temperature control modules, gas circuit control module, button, display screen, data memory module, communication interface and controller, by the gas circuit control module characteristic gas collection of sample is entered air chamber, be converted into voltage signal through sensor array, convert digital signal to through signal acquisition module again, provide the differentiation result by pattern recognition unit at last.Compare with prior art, this invention has easy to carry; The humiture in working sensor chamber is controlled; The characteristics that system recognition rate is high.This invention weak point is function singleness, can only the degree of ripeness of banana be detected, and can not the degree of ripeness of bergamot pear be detected.
Summary of the invention
This detection invention is can not be in harvesting, storage, logistics link in the prior art in order to overcome, accurately judge the degree of ripeness of bergamot pear, cause the rotting rate of bergamot pear to rise, the deficiency that storage, logistics cost rise provides a kind of bergamot pear degree of ripeness forecast model method for building up.
To achieve these goals, this detection invention is by the following technical solutions:
A kind of bergamot pear degree of ripeness forecast model method for building up, described forecast model method for building up is applicable to a kind of pick-up unit, described pick-up unit comprises spectrometer, fibre-optical probe, Halogen lamp LED and sample cell; Described sample cell comprises that light tight housing, housing are provided with Chi Men, is provided with the sample fixed mount in the sample cell, and the sample fixed mount is provided with several holders that is used for fixing optical fiber probe; Each holder lays respectively on the sample fixed mount of the diverse location of sample outside surface; Fibre-optical probe comprises optical transmitting set and optical receiver, and optical receiver is connected with spectrometer by optical fiber, and optical transmitting set is connected with Halogen lamp LED by optical fiber, and spectrometer is electrically connected with computing machine; Described detection method comprises the steps:
(1-1) holder is m, is respectively first holder, second holder ..., the m holder; In computing machine, set n testDate, be respectively t
1, t
2, t
3..., t
nt
1To t
nNumerical value increase successively; Appoint the p that gets in one case bergamot pear individual as the bergamot pear sample; The initial value 1 of sample sequence number k;
(1-2) at testDate t
1The light probe is contained on each holder successively, pick-up unit detects the outside surface of sample k successively from all directions, spectrometer is drawn out the spectral curve that diffuses of each direction of sample k, computing machine all extracts several eigenwerts to the spectral curve that diffuses of each direction, and each eigenwert is stored in the computing machine;
(1-3) computing machine is combined into eigenwert signal x (t)=(x with the eigenwert of the spectral curve that diffuses of all directions
1, x
2, x
3..., x
m), with x (t)=(x
1, x
2, x
3..., x
m) the cross-correlation coefficient formula of the network system that constituted by a plurality of discrete threshold cell stacks of input
In, calculate cross-correlation coefficient, wherein: η
i(t) and θ
iBe respectively i on the unit noise and the threshold value of corresponding unit;
Computer drawing goes out testDate t
1The cross correlation number curve of sample k, the cross-correlation coefficient profile memory in computing machine, is made testDate sequence number j=2;
(1-4) at testDate t
jFibre-optical probe is contained on each holder successively, pick-up unit detects the outside surface of sample k successively from all directions, spectrometer is drawn out the spectral curve that diffuses of each direction of sample k, computing machine all extracts several eigenwerts to the spectral curve that diffuses of each direction, and each eigenwert is stored in the computing machine;
(1-5) computing machine is combined into eigenwert signal x (t)=(x with the eigenwert of the spectral curve that diffuses of all directions
1, x
2, x
3..., x
m), with x (t)=(x
1, x
2, x
3..., x
m) the cross-correlation coefficient formula of the network system that constituted by a plurality of discrete threshold cell stacks of input
In, calculate cross-correlation coefficient, wherein: η
i(t) and θ
iBe respectively i on the unit noise and the threshold value of corresponding unit;
Computer drawing goes out testDate t
jThe cross correlation number curve of sample k, with the cross-correlation coefficient profile memory in computing machine,
(1-6) as j＜n, make the j value increase by 1, repeat the test process of (1-4) to (1-5), obtain the cross correlation number curve of each testDate of sample k;
(1-7) as k＜p, make the k value increase by 1, the test process of repeating step (1-2) to (1-6); Obtain sample 1 to sample p from t
1To t
nThe cross correlation number curve; With sample 1 to sample p from t
1To t
nThe cross-correlation coefficient curve definitions be bergamot pear degree of ripeness forecast model.
The present invention adopts the visible/near infrared light to the emission of bergamot pear outside surface irradiation Halogen lamp LED, and visible/near infrared light of reception bergamot pear outside surface reflection, the spectral curve that diffuses to visible/near infrared light of receiving extracts eigenwert, the eigenwert of the spectral curve that diffuses of all directions is combined into the eigenwert signal, the eigenwert signal is imported in the cross-correlation coefficient formula of the network system that is constituted by a plurality of discrete threshold cell stacks, computer drawing goes out the cross correlation number curve, thereby sets up bergamot pear degree of ripeness forecast model.
In harvesting, storage, logistics link, adopt the degree of ripeness that bergamot pear degree of ripeness forecast model of the present invention can the accurate detection bergamot pear, have the good reproducibility of detection, characteristics with low cost, can effectively reduce rotting rate, lifting quality, the logistics cost that reduces of fruit, the economic losses that also can in time find and retrieve managerial personnel's improper operation simultaneously and cause etc. increase income the meaning with directiveness for the bergamot pear upgrading.
Utilize the bergamot pear in the harvesting of monitoring device picked at random or storage or the logistics progress to detect, computing machine calculates the cross-correlation coefficient of this bergamot pear sample, compares with the eigenwert of bergamot pear degree of ripeness forecast model, thereby obtains the degree of ripeness of this bergamot pear sample.
Figure 7 shows that the network system of a plurality of discrete threshold cell stack formation that threshold value resonates immediately, wherein x (t)=(x
1, x
2, x
3..., x
m) be input signal, η
i(t) and θ
iBe respectively i on the unit noise and the threshold value of corresponding unit.Y (t) is output signal, and span is [0,1].
The present invention adopts cross-correlation coefficient
Characterize, cross-correlation coefficient has characterized matching degree between the input at random of system and the output signal.
The threshold value accidental resonance more can improve the effect that characteristic information extracts than bistable-state random resonance, because the detection difficulty of bergamot pear sample is bigger, so bistable-state random resonance can not meet the demands, and the threshold value accidental resonance can realize detecting target.
As preferably, step (1-2) comprises the steps:
(2-1) open the Chi Men of sample cell, sample k is put on the sample fixed mount, fibre-optical probe is placed on first holder, close the upper storage reservoir door; The light of Halogen lamp LED penetrates from emitting head, be radiated on the bergamot pear outside surface, optical receiver receives the reflected light of bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value of the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine, sets holder sequence number s=2;
(2-2) open the Chi Men of sample cell, fibre-optical probe is placed on the s holder, close the upper storage reservoir door; The light-illuminating of Halogen lamp LED is on the bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value of the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine;
(2-3) when s＜m, make the s value increase by 1, repeat the test process of (2-2).
As preferably, comprise the steps: in the step (1-4)
(3-1) at testDate tj, fibre-optical probe is placed on first holder, close the upper storage reservoir door; The light of Halogen lamp LED penetrates from emitting head, be radiated on the bergamot pear outside surface, optical receiver receives the reflected light of bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value of the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine, sets direction of illumination sequence number e=2;
(3-2) open the Chi Men of sample cell, fibre-optical probe is placed on the e holder, close the upper storage reservoir door; The light of Halogen lamp LED penetrates from emitting head, be radiated on the bergamot pear outside surface, optical receiver receives the reflected light of bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value of the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine;
(3-3) when e＜m, make the e value increase by 1, repeat the test process of (3-2).
As preferably, comprise the steps: to extract and diffuse the peak value at 439.15nm, 472.67nm, 517.11nm, 563.74nm, 622.03nm, 715.29nm and 743.71nm wavelength place in the spectral curve as eigenwert.
As preferably, described testDate is 5 to 200.
As preferably, described holder is 5, is placed on light probe on each holder and respectively the front, rear, left and right of bergamot pear sample and bottom is shone and received reflected light.
As preferably, the value of p is 5 to 8.
Therefore, this detection invention has following beneficial effect: (1) realizes the degree of ripeness differentiation of bergamot pear; (2) detection speed is fast; (3) detection accuracy height; (4) detect good reproducibility; (5) the detection cost is low.
Description of drawings
Fig. 1 is a kind of process flow diagram of embodiments of the invention;
Fig. 2 is diffuse reflection spectrum curve map of the present invention;
Fig. 3 is the rubble figure of bergamot pear storage time spectroscopic data principal component analysis (PCA) of the present invention;
Fig. 4 is bergamot pear storage time spectroscopic data principal component analysis (PCA) figure of the present invention;
Fig. 5 is bergamot pear degree of ripeness forecast model of the present invention;
Fig. 6 is the principal component analysis (PCA) result of cross-correlation coefficient curvilinear characteristic value of the present invention;
Fig. 7 is the network system that a plurality of discrete threshold cell stacks constitute;
Fig. 8 is the theory diagram of pick-up unit of the present invention.
Among the figure: spectrometer 1, fibre-optical probe 2, Halogen lamp LED 3, optical transmitting set 4, optical receiver 5, optical fiber 6, computing machine 7.
Embodiment
Below in conjunction with the drawings and specific embodiments this detection invention is further described.
Embodiment as shown in Figure 1 is a kind of bergamot pear degree of ripeness forecast model method for building up, and described forecast model method for building up is applicable to a kind of pick-up unit as shown in Figure 8, and described pick-up unit comprises spectrometer 1, fibre-optical probe 2, Halogen lamp LED 3 and sample cell; Described sample cell comprises that light tight housing, housing are provided with Chi Men, is provided with the sample fixed mount in the sample cell, and the sample fixed mount is provided with 5 holders that are used for fixing optical fiber probe; 5 holders lay respectively on the sample fixed mount of the diverse location of sample outside surface; Fibre-optical probe comprises optical transmitting set 4 and optical receiver 5, and optical receiver is connected with spectrometer by optical fiber 6, and optical transmitting set is connected with Halogen lamp LED by optical fiber, and spectrometer is electrically connected with computing machine 7;
Bergamot pear degree of ripeness detection method as shown in Figure 1 comprises the steps:
Step 210 is opened the Chi Men of sample cell, and fibre-optical probe is placed on the s holder, closes the upper storage reservoir door; The light-illuminating of Halogen lamp LED is on the bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value at 439.15nm, 472.67nm, 517.11nm, 563.74nm, 622.03nm, 715.29nm and 743.71nm wavelength place in the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine;
Computer drawing goes out testDate t
1The cross correlation number curve of sample k, the cross-correlation coefficient profile memory in computing machine, is made testDate sequence number j=2;
Computer drawing goes out testDate t
jThe cross correlation number curve of sample k, with the cross-correlation coefficient profile memory in computing machine,
Step 600 when j＜10, makes the j value increase by 1, and repeating step 400 obtains the cross correlation number curve of each testDate of sample k to the test process of step 500;
Step 700 when k＜5, makes the k value increase by 1, and repeating step 200 is to the test process of step 600; Obtain as shown in Figure 5 sample 1 to sample 5 from t
1To t
10The cross correlation number curve; Be bergamot pear degree of ripeness forecast model with cross-correlation coefficient curve definitions as shown in Figure 5.
Each cross correlation number curve in the bergamot pear degree of ripeness forecast model is all extracted peak value as eigenwert, eigenwert is carried out principal component analysis (PCA);
In computer screen, demonstrate principal component analysis (PCA) result as shown in Figure 6.
Fig. 3, Fig. 4 extract each peak value at 439.15nm, 472.67nm, 517.11nm, 563.74nm, 622.03nm, 715.29nm and 743.71nm wavelength place in the spectral curve that diffuses as eigenwert, each eigenwert are carried out the result of principal component analysis (PCA).
As shown in Figure 3, just becoming very mild since the 3rd factor solution curve, that is to say that the variance contribution of the 3rd each main composition that factor solution is later is very little, is negligible, and rubble figure shows that extract three common factors analyzes.
As shown in Figure 4, first three major component PC1 and PC2 contribution rate sum were 97.24%, and the distance between preceding 5 days and the back 5 days samples is bigger, and this explanation is along with the increase in storage time, and comparatively obvious variation has appearred in the spectral detection data of bergamot pear sample at the 5th day.Therefore, the 0th day to the 4th day test sample can be classified as group and be classified as group 2 in 1, the 5 day to the 9th day.Though organize 1 and organize 2 between distinguish apparent in viewly, organize 1 and can not distinguish between the bergamot pear sample in interior 5 days, the samples in the group 2 can not be distinguished equally.Explanation is directly carried out principal component analysis (PCA) with bergamot pear storage time spectroscopic data, can not effectively distinguish the degree of ripeness of bergamot pear.
The contribution rate of PC1 and PC2 is respectively 72.13% and 21.04% among Fig. 6, shows that preceding two major components can be used as the foundation that sample is distinguished.
Distance between the bergamot pear sample number strong point of each resting period is bigger, is distributed in zone separately, has eliminated overlap problem among Fig. 4.As can be seen from the figure, the 0th day, the 5th day and the 9th day
Sample distribution is in the first quartile in major component space, and the 2nd day, the 3rd day, the 4th day sample distribution is in second quadrant, and the 6th day, the 7th day sample is at third quadrant, and sample was in fourth quadrant in the 8th day.And the 1st day sample distribution is near initial point.
Along with the increase of resting period, each sample moves to left along the PC1 direction, moves to right along the PC1 direction after the 7th day.The sample variation degree of identical resting period all increases along the PC2 direction basically.Analysis result shows that bergamot pear degree of ripeness forecast model can effectively be distinguished the Kuerle delicious pear sample of each resting period, predicts that with bergamot pear degree of ripeness forecast model the degree of ripeness of bergamot pear is reliable.
The present invention is converted to the difference of exporting the cross-correlation coefficient characteristic parameter with visible/near infrared spectrum response signal difference, and accidental resonance output cross correlation number curve is only relevant with bergamot pear sample storage time characteristic information, has improved result's stability.
Detection method described in the invention can realize the monitoring of bergamot pear degree of ripeness in harvesting, storage, logistics progress.Along with the increase of resting period, the first principal component of bergamot pear sample moves to left along the PC1 direction, moves to right along the PC1 direction after the 7th day.The sample variation degree of identical resting period all increases along the PC2 direction basically.
Utilize the bergamot pear in the harvesting of monitoring device picked at random or storage or the logistics progress to detect, computing machine calculates the cross-correlation coefficient of this bergamot pear sample, the eigenwert of the bergamot pear degree of ripeness forecast model of Fig. 5 is compared, thereby obtain the degree of ripeness of this bergamot pear sample, can carry out relevant treatment according to degree of ripeness information.
For example: the cross-correlation coefficient of the bergamot pear sample that computing machine calculates is 0.26, and as can be seen from Figure 5, the 8th day peak value (being eigenwert) approaches with 0.26, and then the degree of ripeness of this bergamot pear sample is the 8th day.
Therefore, bergamot pear degree of ripeness detection method of the present invention can effectively reduce rotting rate, lifting quality, the logistics cost that reduces of fruit, the economic losses that also can in time find and retrieve managerial personnel's improper operation simultaneously and cause etc. increase income the meaning with directiveness for the bergamot pear upgrading.
Should be understood that present embodiment only is used for this detections of explanation and invents and be not used in and limit this detection scope of invention.Should be understood that in addition those skilled in the art can make various changes or modifications this detection invention after the content of having read this detection invention instruction, these equivalent form of values fall within the application's appended claims institute restricted portion equally.
Claims (7)
1. a bergamot pear degree of ripeness forecast model method for building up is characterized in that described forecast model method for building up is applicable to a kind of pick-up unit, and described pick-up unit comprises spectrometer (1), fibre-optical probe (2), Halogen lamp LED (3) and sample cell; Described sample cell comprises that light tight housing, housing are provided with Chi Men, is provided with the sample fixed mount in the sample cell, and the sample fixed mount is provided with several holders that is used for fixing optical fiber probe; Each holder lays respectively on the sample fixed mount of the diverse location of sample outside surface; Fibre-optical probe comprises optical transmitting set (4) and optical receiver (5), and optical receiver is connected with spectrometer by optical fiber, and optical transmitting set is connected with Halogen lamp LED by optical fiber (6), and spectrometer is electrically connected with computing machine (7); Described detection method comprises the steps:
(1-1) holder is m, is respectively first holder, second holder ..., the m holder; In computing machine, set n testDate, be respectively t
1, t
2, t
3..., t
nt
1To t
nNumerical value increase successively; Appoint the p that gets in one case bergamot pear individual as the bergamot pear sample; The initial value 1 of sample sequence number k;
(1-2) at testDate t
1The light probe is contained on each holder successively, pick-up unit detects the outside surface of sample k successively from all directions, spectrometer is drawn out the spectral curve that diffuses of each direction of sample k, computing machine all extracts several eigenwerts to the spectral curve that diffuses of each direction, and each eigenwert is stored in the computing machine;
(1-3) computing machine is combined into eigenwert signal x (t)=(x with the eigenwert of the spectral curve that diffuses of all directions
1, x
2, x
3..., x
m), with x (t)=(x
1, x
2, x
3..., x
m) the cross-correlation coefficient formula of the network system that constituted by a plurality of discrete threshold cell stacks of input
In, calculate cross-correlation coefficient, wherein: η
i(t) and θ
iBe respectively i on the unit noise and the threshold value of corresponding unit;
Computer drawing goes out testDate t
1The cross correlation number curve of sample k, the cross-correlation coefficient profile memory in computing machine, is made testDate sequence number j=2;
(1-4) at testDate t
jFibre-optical probe is contained on each holder successively, pick-up unit detects the outside surface of sample k successively from all directions, spectrometer is drawn out the spectral curve that diffuses of each direction of sample k, computing machine all extracts several eigenwerts to the spectral curve that diffuses of each direction, and each eigenwert is stored in the computing machine;
(1-5) computing machine is combined into eigenwert signal x (t)=(x with the eigenwert of the spectral curve that diffuses of all directions
1, x
2, x
3..., x
m), with x (t)=(x
1, x
2, x
3..., x
m) the cross-correlation coefficient formula of the network system that constituted by a plurality of discrete threshold cell stacks of input
In, calculate cross-correlation coefficient, wherein: η
i(t) and θ
iBe respectively i on the unit noise and the threshold value of corresponding unit;
Computer drawing goes out testDate t
jThe cross correlation number curve of sample k, with the cross-correlation coefficient profile memory in computing machine,
(1-6) as j＜n, make the j value increase by 1, repeat the test process of (1-4) to (1-5), obtain the cross correlation number curve of each testDate of sample k;
(1-7) as k＜p, make the k value increase by 1, the test process of repeating step (1-2) to (1-6); Obtain sample 1 to sample p from t
1To t
nThe cross correlation number curve, with sample 1 to sample p from t
1To t
nThe cross-correlation coefficient curve definitions be bergamot pear degree of ripeness forecast model.
2. bergamot pear degree of ripeness forecast model method for building up according to claim 1 is characterized in that step (1-2) comprises the steps:
(2-1) open the Chi Men of sample cell, sample k is put on the sample fixed mount, fibre-optical probe is placed on first holder, close the upper storage reservoir door; The light of Halogen lamp LED penetrates from emitting head, be radiated on the bergamot pear outside surface, optical receiver receives the reflected light of bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value of the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine, sets holder sequence number s=2;
(2-2) open the Chi Men of sample cell, fibre-optical probe is placed on the s holder, close the upper storage reservoir door; The light-illuminating of Halogen lamp LED is on the bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value of the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine;
(2-3) when s＜m, make the s value increase by 1, repeat the test process of (2-2).
3. bergamot pear degree of ripeness forecast model method for building up according to claim 1 is characterized in that, comprises the steps: in the step (1-4)
(3-1) at testDate t
j, fibre-optical probe is placed on first holder, close the upper storage reservoir door; The light of Halogen lamp LED penetrates from emitting head, be radiated on the bergamot pear outside surface, optical receiver receives the reflected light of bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value of the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine, sets direction of illumination sequence number e=2;
(3-2) open the Chi Men of sample cell, fibre-optical probe is placed on the e holder, close the upper storage reservoir door; The light of Halogen lamp LED penetrates from emitting head, be radiated on the bergamot pear outside surface, optical receiver receives the reflected light of bergamot pear outside surface, spectrometer is drawn the spectral curve that diffuses, and with the diffuse reflection spectrum profile memory in computing machine, computing machine extracts each peak value of the spectral curve that diffuses as eigenwert, and each eigenwert is stored in the computing machine;
(3-3) when e＜m, make the e value increase by 1, repeat the test process of (3-2).
4. according to claim 2 or 3 described bergamot pear degree of ripeness forecast model method for building up, it is characterized in that, comprise the steps: to extract and diffuse the peak value at 439.15nm, 472.67nm, 517.11nm, 563.74nm, 622.03nm, 715.29nm and 743.71nm wavelength place in the spectral curve as eigenwert.
5. bergamot pear degree of ripeness forecast model method for building up according to claim 1 is characterized in that, described testDate is 5 to 200.
6. according to claim 1 or 2 or 3 or 5 described bergamot pear degree of ripeness forecast model method for building up, it is characterized in that, described holder is 5, is placed on light probe on each holder and respectively the front, rear, left and right of bergamot pear sample and bottom is shone and received reflected light.
7. according to claim 1 or 2 or 3 or 5 described bergamot pear degree of ripeness forecast model method for building up, it is characterized in that the value of p is 5 to 8.
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| CN104374708A (en) * | 2014-08-18 | 2015-02-25 | 浙江工商大学 | Beef freshness detection device and detection method |
| CN104568776A (en) * | 2015-01-01 | 2015-04-29 | 浙江工商大学 | Beef storage time detection method |
| CN105606543A (en) * | 2016-03-29 | 2016-05-25 | 温州大学 | Portable banana maturity detector |
| CN110118735A (en) * | 2018-02-06 | 2019-08-13 | 中国农业机械化科学研究院 | A kind of high light spectrum image-forming detection method and device detecting bergamot pear male and female |
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| CN104374708A (en) * | 2014-08-18 | 2015-02-25 | 浙江工商大学 | Beef freshness detection device and detection method |
| CN104374708B (en) * | 2014-08-18 | 2017-04-26 | 浙江工商大学 | Beef freshness detection device and detection method |
| CN104568776A (en) * | 2015-01-01 | 2015-04-29 | 浙江工商大学 | Beef storage time detection method |
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| CN105606543A (en) * | 2016-03-29 | 2016-05-25 | 温州大学 | Portable banana maturity detector |
| CN105606543B (en) * | 2016-03-29 | 2019-02-12 | 温州大学 | A kind of portable banana maturity detector |
| CN110118735A (en) * | 2018-02-06 | 2019-08-13 | 中国农业机械化科学研究院 | A kind of high light spectrum image-forming detection method and device detecting bergamot pear male and female |
| CN110118735B (en) * | 2018-02-06 | 2020-08-25 | 中国农业机械化科学研究院 | Hyperspectral imaging detection method and device for detecting male and female bergamot pears |
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