CN100429502C - Non-destructive detection method for quickly detecting inner quality of apple - Google Patents
Non-destructive detection method for quickly detecting inner quality of apple Download PDFInfo
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Abstract
The invention relates to a rapid loss-free method for testing the inner quality of apples, which comprises the following steps: (1) apples to be tested are proceeded of continual spectrum test with a visible-near infrared spectrometer with the range of 638 nm to 1295 nm one by one; (2) the results of the continual spectrum test of the tested apples are put into pre-established discriminant functions of the inner quality of the apples, and the decision of the inner quality of the apples is obtained. When the present invention tests, only a continual spectrum collection to the apples is carried out, and two diseases of watercore and browning are simultaneously tested through the two pre-established discriminant functions respectively. The present invention has the advantages that waste of fruit due to sampling test can be reduced in large quantity, field test can be taken when apples are picked, and apples stored can be tested frequently to reduce storage loss.
Description
Technical field
The present invention relates to a kind of detection apple internal method for quality, particularly detect the apple internal method for quality about a kind of quick nondestructive at apple water core and brown stain.
Background technology
China is the first in the world fruit big producing country, and wherein apple is topmost kind, but annual export volume is less, only accounts for 2.1% of total production.A major reason of restriction China apple outlet is that speed is slow a little less than the domestic sorting detectability to apple, and the experimental enviroment condition is poor, and the sorting technology level does not reach the requirement of international market.The apple water core is that it is sick in growth period and recurrent a kind of internal physiological imbalance of storage period, in each apple producing region of China generation is arranged all, the core of water core fruit is water stain shape, lesion portion is near pericarp when serious, make pericarp the transparent ceraceous of water stain shape also occur, the interior diseased tissues of sick fruit of later stage can be ruined.The apple brown stain is a kind of disease common in storage, except that serious water core can cause the apple brown stain, and high CO
2, low O
2, freezing evil and old and feeble collapse etc. can both cause brown stain.Fruit browning can occur in pulp, core and pericarp, and the brown stain tissue firms, moistening or loose drying, and it is bitter that pulp becomes, the whole brown stains of whole fruit when pathology is serious.Because it is as broad as long in appearance to produce the general and normal fruit of the sick fruit of apple water core, brown stain, so original method of inspection to the apple interior quality, can only be to be undertaken by the section of observing chance sample, but this method belongs to the method for destructive sampling Detection, not only waste is very big, and meaningless to the exported product classification.Therefore must adopt nondestructive nondistructive detecting method that apple internal quality is estimated classification, could adapt to the requirement of international market.
At present the not damaged method that detects the apple water core comprises following several: (1) buoyant density: according to Fidler etc., according to apple water core fruit proportion changed features, can separate the water core fruit by the principle that buoyancy separates, but utilize the water core fruit of alcohol water blend sorting 78%; Reports such as Cavalieri, but according to the serious sick fruit of the density sorting 100% of water core fruit.But apple and serious water core fruit (4 grades) that this Density Detection method is can only disintegrate-quality qualified, and can't separate other apple of adjacent level.(2) printing opacity image density method: report according to Throop, use visual computer to detect the transmittance of apple, and judge the apple water core according to apple image estimation area, bulk density, although this method can correctly be isolated 75% water core fruit, but be not enough to accurately distinguish the order of severity of water core, reason be disease fruit at different levels between density variation very little; And further the discovering of Upchurch and Throop, it is relevant with the camera sensitivity of computing machine visible system to detect effect.(3) X-ray scanning method: Shahin and Tollner report utilizes X-ray scanning apple sample, through can correctly separate 75% slight sick fruit, 40% moderate sick fruit and 60% serious sick fruit to treatment of picture.(4) nuclear magnetic resonance method: Clark and Bieleski report can use Magnetic resonance imaging to follow the tracks of the variation of apple water core in storage, but this technical costs are too high, and detection speed is slow, can't be applied to apple and divide in the route selection.TOE-NIR (time-of-flight Near-infrared) spectral technique is adopted in reports such as Staoru Tsuchikawa, judges water core by detecting light by the time of sample.(5) method of difference: classical optics water core detection method is the method for difference of 760nm and 810nm, and accuracy of detection still is not ideal enough.It is reported that brown stain useful 600nm of fruit and 740nm detect.In addition, Bramlage has inquired into the possibility of utilizing optical means to detect complete apple; And use time-resolved reflectance spectrums such as Paola have been carried out the not damaged detection to the brown heart of pears, but can only detect in the following 2cm scope of pericarp, and the slight brown stain around the core is detected relatively difficulty.
Summary of the invention
At the problems referred to above, the purpose of this invention is to provide a kind of quick nondestructive and detect the apple internal method for quality at apple water core and brown stain.
For achieving the above object, the present invention takes following technical scheme: a kind of quick nondestructive detects the apple internal method for quality, and it may further comprise the steps: (1) one by one carries out continuous spectrum detection at the visible-near-infrared spectrum instrument of 638～1295nm to apple to be measured with spectral range; (2) with the continuous spectrum testing result of tested apple, bring the apple internal quality discrimination function of setting up in advance into, obtain the judgement of apple internal quality.
The described apple internal quality discrimination function of setting up in advance is the discriminant function of apple water core grade, it adopts following method to set up: (1) gathers the apple sample sets, with the visible-near-infrared spectrum instrument sample in the sample sets being carried out wavelength coverage detects at the continuous spectrum of 650nm～950nm, and testing result carried out pre-service, obtain the continuous spectrum that the OD value changes in the continuous spectrum scope; (2) each sample after continuous spectrum detects is cut along equatorial direction, the input computing machine calculated the lesion degree of apple water core, and presses lesion degree to sample classification after serial section was taken pictures; (3), after employing TQ analyst 6.2 analysis software package are carried out computational analysis, determine and the middle corresponding discriminant function of apple water core classification of step (2) to the water core classification results after each sample continuous spectrum in the sample sets and the section; (4) with the wavelength coverage of the tested apple of the unknown after the continuous spectrum testing result of 650nm～950nm is carried out pre-service, the observed reading that will judge is brought in the discriminant function, which discriminant score maximum just belongs to the grade of which kind of apple.
Calculate the degree of apple water core pathology in the described step (2), be meant and calculate the number percent that the pathology area accounts for whole area of section, it comprises Pyatyi: A level=0%, and B level≤1%, C level=1～3%, D level=3～10%, the E level 〉=more than 10%.
The described apple internal quality discrimination function of setting up in advance is the discriminant function of apple brown stain, it adopts following method to set up: (1) gathers the apple sample sets, with the visible-near-infrared spectrum instrument sample in the sample sets being carried out wavelength coverage detects at the continuous spectrum of 650nm～950nm, and testing result carried out pre-service, obtain the continuous spectrum that the OD value changes in the continuous spectrum scope; (2) each sample after continuous spectrum detects is cut along equatorial direction,, be divided into normal fruit and brown stain fruit two-stage according to whether brown stain taking place to the serial section back input computing machine of taking pictures; (3) with the apple brown stain classification results of each sample in the sample sets after the OD value of (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm and section, after adopting SAS 8.0discrim to carry out computational analysis, determine the discriminant function of apple brown stain; The brown stain fruit=-741.58928-71.5161OD (715 ± 5)+1508OD (750 ± 5)-1640OD (810 ± 5); Normal fruit=-830.51874-81.46691OD (715 ± 5)+1600OD (750 ± 5)-1736OD (810 ± 5); (4) with the OD value of the tested apple of the unknown in (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm, bringing discriminant function into calculates, calculate the bigger equation of result value in two equations, be judged as the testing result of the unknown tested apple of representative.
Described pre-service comprises level and smooth, the differentiate of spectroscopic data, polynary scatter correction etc.
Described three characteristic wavelengths are 715nm, 750nm and 810nm.
A kind of quick nondestructive detects the apple internal method for quality, and it may further comprise the steps: (1) uses the optical filter instrument, one by one apple to be measured is detected the OD value of wavelength in (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm; (2) with (715 ± 5) nm, (750 ± 5) nm of tested apple, the OD value of three characteristic wave strong points of (810 ± 5) nm, bring the apple internal quality discrimination function of setting up in advance into, obtain the judgement of apple internal quality.
The described apple internal quality discrimination function of setting up in advance is the discriminant function of apple brown stain, it adopts following method to set up: (1) gathers the apple sample sets, one by one apple to be measured is detected the OD value of wavelength in (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm with the optical filter instrument; (2) sample after each is detected cuts along equatorial direction, to the serial section back input computing machine of taking pictures, is divided into normal fruit and brown stain fruit two-stage according to whether brown stain taking place; (3) to the apple brown stain classification results of each sample in the sample sets after the OD value of (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm and section, after adopting the SAS8.0discrim method to carry out computational analysis, determine the discriminant function of apple brown stain: the brown stain fruit=-741.58928-71.5161OD (715 ± 5)+1508OD (750 ± 5)-1640OD (810 ± 5); Normal fruit=-830.51874-81.46691OD (715 ± 5)+1600OD (750 ± 5)-1736OD (810 ± 5); (4) with the OD value of the tested apple of the unknown in (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm, bringing discriminant function into calculates, calculate the bigger equation of result value in two equations, be judged as the testing result of the unknown tested apple of representative.
Described three characteristic wavelengths are 715nm, 750nm and 810nm.
The present invention is owing to take above technical scheme, it has the following advantages: 1, the present invention is because by carrying out the collection of near infrared continuous spectrum to the apple sample sets in advance, and in conjunction with the sample behind spectra collection section is observed and correlation analysis, set up the discriminant function of apple water core and browning degree classification, therefore no matter when being online detection or when carrying out sampling Detection, can differentiate the grade of each apple one by one, guarantee the sorting quality of each apple.When 2, the present invention detects, only need apple is carried out the collection of one-time continuous spectrum, just can pass through two big class discriminant functions of foundation in advance respectively, detect when carrying out two kinds of illnesss of water core and brown stain.3, the present invention is divided into five grades according to the extent of injury of water core with it, and which rank of the continuous spectrum that detects the tested apple of computer-chronograph computed in software belongs to, and just can obtain water core grade conclusion.4, the present invention considers the inadmissibility of apple brown stain, with its only be divided into brown stain really become reconciled the fruit two grades, and according to experiment and analysis result, the OD value of having determined three characteristic wavelengths (715 ± 5) nm, (750 ± 5) nm and (810 ± 5) nm place is as variable, obtain the discriminant function of brown stain fruit and normal fruit, when therefore detecting, computer software only need be gathered continuous spectrum in the OD of these three characteristic wave strong points value, just can be by discriminant function calculate brown stain really or the conclusion of normal fruit.The present invention adopts method equipment simple, can detect apple water core and apple brown stain simultaneously, a large amount of minimizing because of the fruit waste that sampling Detection caused can carry out the scene to apple when it is not only plucked and detect, and can monitor the apple of storage, reduce storage loss. regularly
Embodiment
Embodiment 1: the quick nondestructive about the apple water core detects
One, at first set up the discriminant function of apple water core:
(1) sample collecting and detecting instrument:
On the different parts on same zone, same harvesting period, different fruit tree strain, in the same fruit tree strain, pluck 200 in representative red fuji apple sample at random, fruit footpath scope 52.4mm～93.3mm.
Adopt CCD grating type visible-near-infrared spectrum instrument, its wavelength coverage is 638nm～1295nm, and TQ analyst 6.2 analysis software package (America NI COLET company) are housed in the computing machine, is used for spectroscopic data is handled.
(2) sample is carried out the continuous spectrum collection:
With CCD grating type visible-near-infrared spectrum instrument each sample in the sample sets is carried out the continuous spectrum collection, wavelength coverage is 638nm～1295nm, every point of 0.35nm irradiation, 2047 points of every spectrum concurrent irradiation, light source is positioned at instrument top, and monitor is positioned at the bottom, and both become 180 °, be 1000ms integral time, and on average shining number of times is 10 times; Can select a plurality of measuring points, be averaged spectrum with performing an analysis.Because before 650nm and the wavelength peak after the 950nm not obvious, so the spectrum of chosen wavelength range between 650nm～950nm only.
(3) with the sample sections observation:
With the sample after each detection, cut along equatorial direction, cross section to section is taken, and photo imported computing machine, carry out the calculating of inner pathology area by Scion Image image processing software comparison film, the number percent that accounts for whole area of section according to the pathology area is determined the lesion degree of each sample water worry, and all samples apple is divided into five ranks artificially by its lesion degree, each rank is as follows: A level=0%, B level≤1%, C level=1～3%, D level=3～10%, the E level=more than 〉=10%.
(4) analyze each sample spectra with the section result correlativity, set up apple water core discriminant function:
According to the accurate record of test process, utilize TQ analyst6.2 analysis software package (America NI COLET company) that the near infrared ray testing result of each sample in the sample sets and the internal soundness classification results after the respective sample section are carried out correlation analysis.At first every spectrum is carried out 3 level and smooth pre-service of Savitzky-Golay25 point, in order to eliminate the influence that apple size and scattering cause, the path change during the spectrum transmitting apple internal utilizes SNV (standard normal variable) to proofread and correct.Program adopts the discriminant analysis (discriminatory analysis) in the sorting technique, selected spectral range is carried out analysis and judgement, computing machine provides the discriminant function of five grade water core pathology correspondences respectively automatically, does not show that its general expression-form is but there is concrete discriminant function:
Group (A, B, C, D, E)=K
0+ K
1* X1+K
2* X2+ ... + K
n* Xn
K
0-constant term, K
n-variation coefficient, the Xn-wavelength
This program is by calculating Mahalanobis (Ma Shi) distance between at different levels, determine with the known materials rank in the most close unknown rank as calculation stage.Be about to new observed reading and be brought in five discriminant functions, which discriminant score maximum, which kind of this observation just belongs to.
Two, quick nondestructive detects the apple water core:
Adopt CCD grating type visible-near-infrared spectrum instrument, apple to be measured is carried out wavelength coverage one by one to be detected at the continuous spectrum of 650nm～950nm, then the continuous spectrum testing result of tested apple is brought into above-mentioned apple internal quality discrimination function, by the result of calculation of computer software, can obtain tested apple more near which rank of.Be that one group of apple water core that application the inventive method is carried out detects differentiation and checking result's (as shown in table 1) below:
Table 1: quick nondestructive detects the apple water core and differentiates the result
Present embodiment is through showing that to tested apple slice check utilize the inventive method quick nondestructive to detect the apple water core, its correct decision rate is as shown in table 2.
Table 2: correct decision rate
| Differentiate classification | The A level | The B level | The C level | The D level | The E level | Total differentiation rate |
| The differentiation rate | 74％ | 89％ | 79％ | 94％ | 100％ | 83％ |
Embodiment 2: the quick nondestructive about the apple brown stain detects
One, at first set up the discriminant function of apple brown stain:
(1) sample collecting and detecting instrument:
On the different parts on same zone, same harvesting period, different fruit tree strain, in the same fruit tree strain, pluck 184 in representative Fuji apple sample at random, fruit footpath scope 7.33cm～9.5cm.Divide four packedly to go in polyvinyl chloride film (thick 0.8mm) sealing bag all samples apple, charge into CO respectively
2Gas also makes its content reach 30%, O
2Content is 5%.Charge into air when sending out flat and make it keep certain air pressure when the polyvinyl chloride film bag leaks gas naturally, and under indoor state of nature, placed one month, take out after impelling apple generation brown stain.
Adopt CCD grating type visible-near-infrared spectrum instrument, its wavelength coverage is 638nm～1295nm, and TQ analyst 6.2 analysis software package (America NI COLET company) are housed in the computing machine, is used for spectroscopic data is handled.
(2) sample is carried out the continuous spectrum irradiation:
With CCD grating type visible-near-infrared spectrum instrument each sample in the sample sets is carried out the continuous spectrum collection, wavelength coverage is 638nm～1295nm; Every point of 0.35nm irradiation, 2047 points of every spectrum concurrent irradiation, light source is positioned at instrument top, and monitor is positioned at the bottom, and both become 180 °, and be 1000ms integral time, on average shining number of times is 10 times.Each apple can shine three times, horizontally rotates 120 degree at every turn, and the averaged spectrum that detects for three times is performed an analysis.Can only upwards do detection towards a direction of light source from carpopodium during detection, also can do detection from both direction, one is along the fruit direction of principal axis, and one is vertical fruit direction of principal axis.
(3) with the sample sections observation:
With the sample after each detection, cut along equatorial direction, cross section to section is taken, and photo imported computing machine, carry out the calculating of apple internal pathology area by Scion Image image processing software comparison film, determine the degree of each apple brown stain according to the long-pending number percent that accounts for whole area of section of sick cross surface, and browning degree is divided into six ranks, each rank is as follows: 0 grade=0%, 1 grade=0～10%, 2 grades=10～25%, 3 grades=25～40%, 4 grades=40～60%, 5 grades＞60%.
(4) analyze spectrum and set up apple brown stain discriminant function with section result's correlativity:
Apple generation brown stain is exactly unacceptable concerning the consumer, only needs two indexs so detect the brown stain apple, and one is what a is bad (brown stain), and we are classified as a class to brown stain in various degree, does discriminatory analysis with good fruit.Select the OD value of three single wavelength 715nm, 750nm, 810nm, as discriminant criterion, 715nm is that brown stain changes the most tangible wavelength, and the 750nm place is an absorption peak, and 810nm absorbs minimum.By the general discriminating program result of calculation of SAS 8.0discrim, obtain the discriminant function of apple brown stain fruit and normal fruit:
The brown stain fruit=-741.58928-71.5161OD715+1508OD750-1640OD810
Normal fruit=-830.51874-81.46691OD715+1600OD750-1736OD810
With the OD value of the tested apple of the unknown at 715nm, 750nm, 810nm place, bring discriminant function into and calculate, calculate the bigger equation of result value in two equations, be judged as the testing result of the unknown tested apple of representative.
Two, quick nondestructive detects the apple brown stain:
Detect the apple brown stain, can adopt two kinds of methods, a kind of is to detect the apple brown stain simultaneously with the apple water core; Another kind is to detect the apple brown stain separately.When detecting simultaneously with water core, adopt same CCD grating type visible-near-infrared spectrum instrument with water core, apple to be measured is carried out wavelength coverage one by one to be detected at the continuous spectrum of 650nm～950nm, then testing result is adopted same computer software to carry out pre-service, bring the continuous spectrum of tested apple into the water core discriminant function again; With the continuous spectrum medium wavelength is that the OD value that 715nm, 750nm, 810nm three select is brought the discriminant function of apple brown stain into, numerical value according to two Equation for Calculating, by the brown stain fruit or the normal fruit of the equation representative that wherein numerical value is bigger, as the result of determination of tested apple quality.
If detect the apple brown stain separately, only need to detect wavelength and be the OD value of 3 of 715nm, 750nm, 810nm, and bring this three OD value of selecting the discriminant function of apple brown stain into, judge the brown stain fruit or normally really get final product with quadrat method according to above-mentioned again.Detect separately the apple brown stain and can adopt other more simple equipment (as optical filter type instrument etc.), obtain wavelength and be the OD value of 3 of 715nm, 750nm, 810nm.
Below be to adopt the inventive method to carry out one group of apple brown stain to detect the result's (as shown in table 3) who judges and verify:
Table 3: quick nondestructive detects the differentiation result of apple browning degree
By table 3 as seen, 184 tested apples, actual brown stain really has 126, and quick nondestructive detects correct decision and goes out 123 of brown stain fruits, accuracy 97.63%; False Rate is 3 of normal fruit, False Rate 2.38%.The actual normal fruit of sample apple has 58, and quick nondestructive detection correct decision goes out 53 of normal fruit, accuracy 91.38%; False Rate is 5 of brown stain fruit, False Rate 8.62%.This result shows that quick nondestructive of the present invention detects the method for apple browning degree, can be used for the quality that quick nondestructive detects apple internal fully.
In the foregoing description, three specific wavelength 715nm, 750nm, the selected of 810nm can change to some extent, get final product but generally change in the scope of ± 5nm.
Because each department; variant trees; and annual climatic condition all can cause the situation of apple internal different; when therefore of the present invention in use; can be according to measurement result and actual observation situation; by method provided by the invention the discrimination model of setting up is carried out some adjustment, these adjustment should not got rid of outside protection scope of the present invention.
Claims (7)
1, a kind of quick nondestructive detects the apple internal method for quality, and it may further comprise the steps:
(1) at the visible-near-infrared spectrum instrument of 638～1295nm apple to be measured is carried out the continuous spectrum detection one by one with spectral range;
(2) with the continuous spectrum testing result of tested apple, bring the apple internal quality discrimination function of setting up in advance into, obtain the judgement of apple internal quality;
The described apple internal quality discrimination function of setting up in advance is the discriminant function of apple water core grade, and it adopts following method to set up:
(A) gather the apple sample sets, with the visible-near-infrared spectrum instrument sample in the sample sets is carried out wavelength coverage and detect, and testing result is carried out pre-service, obtain the continuous spectrum that the OD value changes in the continuous spectrum scope at the continuous spectrum of 650nm～950nm;
(B) each sample after continuous spectrum detects is cut along equatorial direction,, calculate number percent that apple water core pathology area accounts for whole area of section as lesion degree, and press lesion degree sample classification to the serial section back input computing machine of taking pictures;
(C), after employing TQ analyst6.2 analysis software package is carried out computational analysis, determine and the middle corresponding discriminant function of apple water core classification of step (2) to the water core classification results after each sample continuous spectrum in the sample sets and the section;
(D) with the wavelength coverage of the tested apple of the unknown after the continuous spectrum testing result of 650nm～950nm is carried out pre-service, the observed reading that will judge is brought in the discriminant function, which discriminant score maximum just belongs to the grade of which kind of apple.
2, a kind of quick nondestructive as claimed in claim 1 detects the apple internal method for quality, and it is characterized in that: the lesion degree in the described step (B) comprises Pyatyi: A level=0%, and B level≤1%, C level=1～3%, D level=3～10%, the E level 〉=more than 10%.
3, a kind of quick nondestructive as claimed in claim 1 detects the apple internal method for quality, and it is characterized in that: the apple internal quality discrimination function of Jian Liing is the discriminant function of apple brown stain in advance, and it adopts following method to set up:
(1) gathers the apple sample sets, with the visible-near-infrared spectrum instrument sample in the sample sets is carried out wavelength coverage and detect, and testing result is carried out pre-service, obtain the continuous spectrum that the OD value changes in the continuous spectrum scope at the continuous spectrum of 650nm～950nm;
(2) each sample after continuous spectrum detects is cut along equatorial direction,, be divided into normal fruit and brown stain fruit two-stage according to whether brown stain taking place to the serial section back input computing machine of taking pictures;
(3) with the apple brown stain classification results of each sample in the sample sets after the OD value of (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm and section, after adopting SAS 8.0 discrim to carry out computational analysis, determine the discriminant function of apple brown stain;
The brown stain fruit=-741.58928-71.5161OD (715 ± 5)+1508 OD (750 ± 5)-1640 OD (810 ± 5)
Normal fruit=-830.51874-81.46691 OD (715 ± 5)+1600 OD (750 ± 5)-1736 OD (810 ± 5)
(4) with the OD value of the tested apple of the unknown in (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm, bringing discriminant function into calculates, calculate the bigger equation of result value in two equations, be judged as the testing result of the unknown tested apple of representative.
4, detect the apple internal method for quality as claim 1 or 3 described a kind of quick nondestructives, it is characterized in that: described pre-service comprises level and smooth, the differentiate of spectroscopic data, polynary scatter correction.
5, a kind of quick nondestructive as claimed in claim 3 detects the apple internal method for quality, and it is characterized in that: described three characteristic wavelengths are 715nm, 750nm and 810nm.
6, a kind of quick nondestructive detects the apple internal method for quality, and it may further comprise the steps:
(1) uses the optical filter instrument, one by one apple to be measured is detected the OD value of wavelength in (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm;
(2) with (715 ± 5) nm, (750 ± 5) nm of tested apple, the OD value of three characteristic wave strong points of (810 ± 5) nm, bring the apple internal quality discrimination function of setting up in advance into, obtain the judgement of apple internal quality.
The described apple internal quality discrimination function of setting up in advance is the discriminant function of apple brown stain, and it adopts following method to set up:
(A) gather the apple sample sets, one by one apple to be measured is detected the OD value of wavelength in (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm with the optical filter instrument;
(B) sample after each is detected cuts along equatorial direction, to the serial section back input computing machine of taking pictures, is divided into normal fruit and brown stain fruit two-stage according to whether brown stain taking place;
(C) to the apple brown stain classification results of each sample in the sample sets after the OD value of (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm and section, after adopting SAS 8.0discrim method to carry out computational analysis, determine the discriminant function of apple brown stain:
The brown stain fruit=-741.58928-71.5161OD (715 ± 5)+1508 OD (750 ± 5)-1640 OD (810 ± 5)
Normal fruit=-830.51874-81.46691 OD (715 ± 5)+1600 OD (750 ± 5)-1736 OD (810 ± 5)
(D) with the OD value of the tested apple of the unknown in (715 ± 5) nm, (750 ± 5) nm, three characteristic wave strong points of (810 ± 5) nm, bringing discriminant function into calculates, calculate the bigger equation of result value in two equations, be judged as the testing result of the unknown tested apple of representative.
7, a kind of quick nondestructive as claimed in claim 6 detects the apple internal method for quality, and it is characterized in that: described three characteristic wavelengths are 715nm, 750nm and 810nm.
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| CN106000914A (en) * | 2016-05-24 | 2016-10-12 | 西北农林科技大学 | Apple sorting device for on-line nondestructive test on moldy core |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1142205A (en) * | 1994-11-03 | 1997-02-05 | 新奇士种植者公司 | Method and apparatus for detecting surface features of translucent objects |
| US6267222B1 (en) * | 1998-10-14 | 2001-07-31 | Xeda International | Visual inspection station for fruit |
| JP2002098636A (en) * | 2000-09-27 | 2002-04-05 | Kubota Corp | Spectroscopic analyzer |
| CN1394699A (en) * | 2002-08-03 | 2003-02-05 | 浙江大学 | Fruit quality real time detection and grading robot system |
| CN2553907Y (en) * | 2002-08-03 | 2003-06-04 | 浙江大学 | Fruit quality real-time grading control system based on circulation queue control graded |
| JP2003329583A (en) * | 2002-05-15 | 2003-11-19 | Tokan Kogyo Co Ltd | Method and apparatus for inspecting internal quality of fruit and vegetable |
| WO2004059300A1 (en) * | 2002-12-24 | 2004-07-15 | Kubota Corporation | Fruit-vegetable quality evaluation device |
-
2004
- 2004-12-14 CN CNB2004100986214A patent/CN100429502C/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1142205A (en) * | 1994-11-03 | 1997-02-05 | 新奇士种植者公司 | Method and apparatus for detecting surface features of translucent objects |
| US6267222B1 (en) * | 1998-10-14 | 2001-07-31 | Xeda International | Visual inspection station for fruit |
| JP2002098636A (en) * | 2000-09-27 | 2002-04-05 | Kubota Corp | Spectroscopic analyzer |
| JP2003329583A (en) * | 2002-05-15 | 2003-11-19 | Tokan Kogyo Co Ltd | Method and apparatus for inspecting internal quality of fruit and vegetable |
| CN1394699A (en) * | 2002-08-03 | 2003-02-05 | 浙江大学 | Fruit quality real time detection and grading robot system |
| CN2553907Y (en) * | 2002-08-03 | 2003-06-04 | 浙江大学 | Fruit quality real-time grading control system based on circulation queue control graded |
| WO2004059300A1 (en) * | 2002-12-24 | 2004-07-15 | Kubota Corporation | Fruit-vegetable quality evaluation device |
Non-Patent Citations (8)
| Title |
|---|
| 水果内部品质在线近红外分光检测装置及试验. 何东健，前川孝昭，森岛博.农业工程学报，第17卷第1期. 2001 |
| 水果内部品质在线近红外分光检测装置及试验. 何东健，前川孝昭，森岛博. 农业工程学报，第17卷第1期. 2001 * |
| 苹果水心病及褐变光学无损伤检测研究. 刘新鑫.硕士学位论文. 2004 |
| 苹果水心病及褐变光学无损伤检测研究. 刘新鑫. 硕士学位论文. 2004 * |
| 苹果水心病的光学无损检测. 韩东海，刘新鑫，赵丽丽等.农业机械学报，第35卷第5期. 2004 |
| 苹果水心病的光学无损检测. 韩东海，刘新鑫等.农业机械学报，第35卷第5期. 2004 |
| 苹果水心病的光学无损检测. 韩东海，刘新鑫，赵丽丽等. 农业机械学报，第35卷第5期. 2004 * |
| 苹果水心病的光学无损检测. 韩东海，刘新鑫等. 农业机械学报，第35卷第5期. 2004 * |
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