CN1719238A - A non-destructive testing method for internal quality of apples - Google Patents
A non-destructive testing method for internal quality of apples Download PDFInfo
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- CN1719238A CN1719238A CNA2005100829006A CN200510082900A CN1719238A CN 1719238 A CN1719238 A CN 1719238A CN A2005100829006 A CNA2005100829006 A CN A2005100829006A CN 200510082900 A CN200510082900 A CN 200510082900A CN 1719238 A CN1719238 A CN 1719238A
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- 238000000034 method Methods 0.000 title abstract description 9
- 235000021016 apples Nutrition 0.000 title description 2
- 238000009659 non-destructive testing Methods 0.000 title description 2
- 244000141359 Malus pumila Species 0.000 title 1
- 235000013399 edible fruits Nutrition 0.000 claims abstract description 41
- 238000001514 detection method Methods 0.000 claims abstract description 16
- 235000012907 honey Nutrition 0.000 claims abstract description 14
- 230000003902 lesion Effects 0.000 claims abstract description 14
- 230000010354 integration Effects 0.000 claims abstract description 3
- 235000017286 Melicoccus bijugatus Nutrition 0.000 claims description 57
- 240000002017 Solanum caripense Species 0.000 claims description 57
- 235000018675 Solanum caripense Nutrition 0.000 claims description 57
- 238000011156 evaluation Methods 0.000 claims description 9
- 235000009508 confectionery Nutrition 0.000 claims description 8
- 230000001953 sensory effect Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000001066 destructive effect Effects 0.000 abstract 1
- 241000220225 Malus Species 0.000 description 51
- 230000008901 benefit Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000007774 longterm Effects 0.000 description 3
- 239000002420 orchard Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 239000000796 flavoring agent Substances 0.000 description 2
- 235000019634 flavors Nutrition 0.000 description 2
- 229910052739 hydrogen Inorganic materials 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 235000010724 Wisteria floribunda Nutrition 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000000306 recurrent effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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Abstract
The present invention relates to a non-destructive detection method of interior quality of apple. Said method includes the following steps: (1) using X-ray to scan the apple to obtain tested apple image, introducing the fruit diameter D' and fruit height H' in the image into the precreated relationship equation between image and tested apple volume so as to obtain complete apple approximate volume Vz; (2)according to the preset honey fruit lesion tissue image gray threshold value removing normal tissue area to obtain lesion region image; according to the linear relationship between natural log of gray and apple thickness calculating volume of every pixel point, then making integration to obtain honey fruit lesion region approximate volume Vs; (3) using ratio value of above-mentioned two approximate volumes Vs and Vz as honey fruit honey index E; (4) utilizing said index E to define honey fruit grade of apple.
Description
Technical field
The present invention relates to a kind of lossless detection method of apple internal quality.
Background technology
The apple water core is that its recurrent a kind of internal physiological imbalance in growth course is sick, in each apple producing region of China generation is arranged all, is commonly called as " mamoncillo ".The core of mamoncillo is water stain shape, and lesion portion makes pericarp water stain shape also occur, transparent ceraceous near pericarp when serious.But both made is that so sick really also influence eaten raw, and the consumer generally believes the mamoncillo unique flavor both at home and abroad, the quality height, such as: in Japan, the price of mamoncillo generally is higher than common apple.The shortcoming of mamoncillo is a storage tolerance not, ruins in storage later stage diseased tissues, can not eat, and causes storage loss.If can utilize the not damaged detection technique that each apple is detected, just mamoncillo can be picked out and classification, sapid mamoncillo sold goods at a high figure as early as possible be used to eat raw, and will fall ill not serious or do not have the morbidity the apple Long-term Storage, thereby the waste in avoiding preserving reduces because sick fruit causes apple by the gross to export or the loss that brings of mark-down sale by the gross.
Simultaneously, 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 sorting technology level does not reach the requirement of international market.
Summary of the invention
At the problems referred to above, the purpose of this invention is to provide a kind of lossless detection method of apple internal quality, use the inventive method to carry out Non-Destructive Testing, and mamoncillo is carried out classification according to testing result to the occurring degree of apple.
For achieving the above object, the present invention takes following technical scheme: a kind of lossless detection method of apple internal quality, it is characterized in that it comprises: (1) is scanned apple with X-ray, obtain carpopodium tested apple image up, bring the fruit in image footpath D ' and the high H ' of fruit into concern between the image of foundation in advance and the tested apple volume equation, obtain complete apple approximate volumes V
z(2) according to predefined mamoncillo lesion tissue image gray threshold, remove normal tissue regions, obtain the diseased region image; According to the natural logarithm of gray scale and the linear relationship between the apple thickness, calculate the volume of each pixel, integration obtains mamoncillo lesion position approximate volumes V then
s(3) with the volume V of tested apple diseased region
sWith complete apple approximate volumes V
zRatio as mamoncillo honey index E; (4) value range of described sweet index is divided, the value range that falls into according to sweet index is determined the rank of tested mamoncillo.
Described complete apple approximate volumes V
zCan be obtained by footpath D ' of the fruit in the image and the high H ' of fruit, its equation is: V
z=f (D, H)=a
1+ b
1F (D ')
3+ c
1F (H ')
2+ d
1F (D ') and f (H '), D ' in the formula-image fruit footpath, H '-image fruit is high, a
1, b
1, c
1, d
1Be constant.
Described mamoncillo lesion position approximate volumes V
sObtain by following steps: (1) according to predefined mamoncillo lesion tissue image gray threshold, removes normal tissue regions on described x-ray image, obtain the diseased region image; (2) according to the linear Lambert law of the natural logarithm of apple normal structure thickness and gray-scale value, so, a pixel is just represented local volume a: V
i=a
2* p * p * f (g
1), in the formula: a
2-being converted into the coefficient of true area by elemental area, p * p is an elemental area, and is relevant with the pixel of the scanning device that uses, g
1Be gray-scale value; (3) each gray values of pixel points on the diseased region x-ray image is added up, can obtain the virtual volume calculated of mamoncillo incidence tissue:
In the formula,
W is all pixel numbers, b
2, c
2Be statistical study gained constant; (4) mamoncillo is divided into n the section of thick 2mm along vertical fruit direction of principal axis; (5) with digital camera each section is taken pictures, photo carries out morbidity volume V on the arbitrary cross-section by image processing software
D1Calculate; (6) edge fruit direction of principal axis adds up and obtains mamoncillo diseased tissues sensory evaluation volume
(7) the volume calculated V virtual according to mamoncillo
s' with sensory evaluation volume V
gCorrelationship V
g=f (V
s'), and according to mamoncillo diseased tissues approximate volumes V
sSensory evaluation volume V with same apple
gEqual in theory, so pass through to V
s' with V
gCarry out regretional analysis, can obtain mamoncillo diseased tissues approximate volumes
Described mamoncillo honey index is:
As follows by the classification of described mamoncillo honey index: E=0%=0 level ,≤5%=1 level, 6～10%=2 level, 11～20%=3 level, 21～30%=4 level, 〉=31%=5 level.
The present invention is owing to take above technical scheme, it has the following advantages: 1, the inventive method can change domestic traditional treatment method to mamoncillo at present, research to mamoncillo is not only rested in the generation that how to prevent to fall ill, but behind apple-picking, with unique flavor, the mamoncillo sorting that quality is high detects, and sells with high added value, and anosis or the slight person of the state of an illness are used for Long-term Storage.2, the inventive method can be carried out the one by one inspection classification to mamoncillo, therefore not only can avoid the orchard worker not have detection means effectively, fruit enterprise rejects mamoncillo, orchard worker's apple by the gross can't sell, the heavy thing of damage takes place, and the serious apple of can avoiding effectively falling ill causes that in storage brown stain can not eat the waste that causes, and has reduced the cost of storage, remarkable in economical benefits.3, the inventive method detects the classification means for orchard worker, fruit enterprise or fruit dealer provide a kind of effective mamoncillo, therefore having great importance aspect the foreign exchange earning of apple, applying of the technology of the present invention can bring huge economic benefit, can fill up the blank of China's not damaged classification mamoncillo simultaneously.
Embodiment
Below in conjunction with embodiment, detection method of the present invention is described in detail.
One, obtains complete apple approximate volumes V
z
1, sets up the really regression equation between footpath, fruit height and complete apple volume in advance
(1) gathers 30～100 in apple sample as sample sets, use the maximum fruit of each apple of kind of calliper footpath D respectively, with the high H of fruit;
(2) measure with drainage, obtain the true volume V of each apple respectively;
(3) according to the regression equation between the foundation of GLM (the General Linear Model) application program in SAS (statistical system) software fruit footpath D, the high H of fruit and apple true volume V:
V
z＝f(D，H)＝a
1+b
1D
3+c
1H
2+d
1DH ①
In the formula: a
1, b
1, c
1, d
1Be constant, batch different and change to some extent according to different apple kinds and apple, adopt 100 of the Fuji apples that produced from Huairou, Beijing such as: present embodiment, after tested and calculate, obtain:
a
1=245.16, b
1=1.256, c
1=17.77, d
1=-27.596, bring constant into equation, obtain:
V
z＝245.16+1.256D
3+17.77H
2-27.596DH
And obtain coefficient of determination R
2, R
2Near 1, illustrate that D, H and the apple volume in the above-mentioned regression equation is approaching more more.The coefficient R that the measurements and calculations of present embodiment obtain
2=0.944, very near 1.
2, any apple that carpopodium is made progress scans with soft X-ray (wavelength is long near ultraviolet light), obtains the x-ray image with fruit footpath D ' and the high H ' of fruit;
3, set up the really corresponding regression equation between the high H ' of apple fruit footpath D and the high H of apple fruit and image fruit footpath D ' and image through regretional analysis:
D＝f(D’)，H＝f(H’) ②
4, with equation 2. the substitution equation 1. obtain the complete apple approximate volumes V that obtains by x-ray image
z. functional equation:
V
z＝f(D，H)＝a
1+b
1f(D’)
3+c
1f(H’)
2+d
1f(D’)f(H’)
Two, obtain mamoncillo diseased tissues approximate volumes V
s
1, on above-mentioned x-ray image, according to predefined mamoncillo lesion tissue image gray threshold, remove normal tissue regions, obtain the diseased region image;
2, according to the linear Lambert law of the natural logarithm of apple normal structure thickness and gray-scale value, so, a pixel is just represented a local volume:
V
i＝a
2×p×p×f(g
i) ③
In the formula: a
2-being converted into the coefficient of true area by elemental area, it can obtain by the statistical study of sample sets.P * p is an elemental area, and its pixel with the scanning device that uses is relevant, g
1Be gray-scale value.
3, each gray values of pixel points on the diseased region x-ray image is added up, can obtain the virtual volume calculated V ' of incidence tissue
s:
In the formula,
W is all pixel numbers, b
2, c
2Be the statistical study constant, it can obtain by the statistical study of sample sets.
But above-mentioned virtual volume calculated V '
sBe the volume that is exaggerated or dwindles, because above-mentioned linear relationship is often by size known apple normal structure thickness and gray-scale value gained, and diseased tissues thickness is difficult to learn; The diseased tissues gray-scale value is bigger or little than normal structure again, so linear by normal structure thickness and gray-scale value natural logarithm, morbidity volume and the true volume obtained are not inconsistent;
4, mamoncillo is divided into n the section of thick 2mm along vertical fruit direction of principal axis;
5, (each pixel 0.14 * 0.14mm) is taken pictures to each section for Olympus C300Z, resolution 600 * 480 pixels, and photo carries out morbidity volume V on the arbitrary cross-section by the MATLAB image processing software with digital camera
DiCalculate;
6, edge fruit direction of principal axis adds up and obtains mamoncillo diseased tissues sensory evaluation volume
7, because the virtual volume calculated V of same mamoncillo
S' with sensory evaluation volume V
gCorrelationship is arranged
Again because mamoncillo diseased tissues approximate volumes V
sSensory evaluation volume V with same apple
gEqual in theory, so pass through to V
S' with V
gCarry out regretional analysis, can obtain mamoncillo diseased tissues approximate volumes
Three, sweet index E is calculated
Mamoncillo diseased tissues approximate volumes V
sWith complete apple approximate volumes V
zPercent value be called sweet index E, as shown in the formula:
Four, sorting classifying
Size according to the E value can be carried out the classification of apple.For example, regulation E=0%=0 level ,≤5%=1 level, 6～10%=2 level, 11～20%=3 level, 21～30%=4 level, 〉=31%=5 level.Progression is high more, and the expression morbidity is serious more, and fresh food quality is good more, but the Long-term Storage variation.
By above embodiment as can be known, if when planning somewhere or certain batch apple to be measured detected, at first to obtain complete apple approximate volumes V by above-mentioned steps to selected sample sets
z, mamoncillo diseased tissues approximate volumes V
s, each funtcional relationship in the sweet index E and concrete constant numerical value, and artificially rule of thumb the corresponding relation between sweet index and the apple is carried out classification, at last the funtcional relationship relevant with x-ray image imported computing machine as software.
When apple one by one being detected with the inventive method, only need each carpopodium apple up to be shone with X-ray, computing machine just can be automatically according to the funtcional relationship of setting up in advance, by the fruit of the image on image footpath D ', the high H ' of image fruit, mamoncillo lesion tissue image gray threshold and gray-scale value etc., obtain and demonstration mamoncillo classification results on display screen.
In the foregoing description, the statistical method of in mathematical model is set up, using, statistical computation software etc. can change, and the instruments such as digital camera that use in carrying out shooting process also can change.
Claims (8)
1, a kind of lossless detection method of apple internal quality is characterized in that it comprises:
(1) with X-ray apple is scanned, obtain carpopodium tested apple image up, bring the fruit in image footpath D ' and the high H ' of fruit into concern between the image of foundation in advance and the tested apple volume equation, obtain complete apple approximate volumes V
z
(2) according to predefined mamoncillo lesion tissue image gray threshold, remove normal tissue regions, obtain the diseased region image; According to the natural logarithm of gray scale and the linear relationship between the apple thickness, calculate the volume of each pixel, integration obtains mamoncillo lesion position approximate volumes V then
s
(3) with the volume V of tested apple diseased region
sWith complete apple approximate volumes V
zRatio as mamoncillo honey index E;
(4) value range of described sweet index is divided, the value range that falls into according to sweet index is determined the rank of tested mamoncillo.
2, the lossless detection method of a kind of apple internal quality as claimed in claim 1 is characterized in that: complete apple approximate volumes V
zCan be obtained by footpath D ' of the fruit in the image and the high H ' of fruit, its equation is:
V
z＝f(D，H)＝a
1+b
1f(D’)
3+c
1f(H’)
2+d
1f(D’)f(H’)
V in the formula
z-complete apple approximate volumes, D '-image fruit footpath, H '-image fruit is high, a
1, b
1, c
1, d
1Be constant.
3, the lossless detection method of a kind of apple internal quality as claimed in claim 1 or 2 is characterized in that described mamoncillo lesion position approximate volumes V
sObtain by following steps:
(1) on described x-ray image, according to predefined mamoncillo lesion tissue image gray threshold, remove normal tissue regions, obtain the diseased region image;
(2) according to the linear Lambert law of the natural logarithm of apple normal structure thickness and gray-scale value, so, a pixel is just represented a local volume:
V
i＝a
2×p×p×f(g
i)
In the formula: a
2-being converted into the coefficient of true area by elemental area, p * p is an elemental area, and is relevant with the pixel of the scanning device that uses, g
iBe gray-scale value;
(3) each gray values of pixel points on the diseased region x-ray image is added up, can obtain the virtual volume calculated of mamoncillo incidence tissue:
In the formula,
W is all pixel numbers, b
2, c
2Be statistical study gained constant;
(4) mamoncillo is divided into n the section of thick 2mm along vertical fruit direction of principal axis;
(5) with digital camera each section is taken pictures, photo carries out morbidity volume V on the arbitrary cross-section by image processing software
DiCalculate;
(6) edge fruit direction of principal axis adds up and obtains mamoncillo diseased tissues sensory evaluation volume
(7) the volume calculated V virtual according to mamoncillo
S' with sensory evaluation volume V
gCorrelationship V
g=f (V
s'), and according to mamoncillo diseased tissues approximate volumes V
sSensory evaluation volume V with same apple
gEqual in theory, so pass through to V
S' with V
gCarry out regretional analysis, can obtain mamoncillo diseased tissues approximate volumes V
S=V
g=f (V
s').
4, the lossless detection method of a kind of apple internal quality as claimed in claim 1 or 2 is characterized in that: described mamoncillo honey index is:
5, the lossless detection method of a kind of apple internal quality as claimed in claim 3 is characterized in that: described mamoncillo honey index is:
6, as the lossless detection method of claim 1 or 2 or 5 described a kind of apple internal qualities, it is characterized in that: as follows by the classification of described mamoncillo honey index: E=0%=0 level ,≤5%=1 level, 6～10%=2 level, 11～20%=3 level, 21～30%=4 level, 〉=31%=5 level.
7, the lossless detection method of a kind of apple internal quality as claimed in claim 3 is characterized in that: as follows by the classification of described mamoncillo honey index: E=0%=0 level ,≤5%=1 level, 6～10%=2 level, 11～20%=3 level, 21～30%=4 level, 〉=31%=5 level.
8, the lossless detection method of a kind of apple internal quality as claimed in claim 4 is characterized in that: as follows by the classification of described mamoncillo honey index: E=0%=0 level ,≤5%=1 level, 6～10%=2 level, 11～20%=3 level, 21～30%=4 level, 〉=31%=5 level.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2005100829006A CN100449309C (en) | 2005-07-11 | 2005-07-11 | A non-destructive testing method for internal quality of apples |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2005100829006A CN100449309C (en) | 2005-07-11 | 2005-07-11 | A non-destructive testing method for internal quality of apples |
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| Publication Number | Publication Date |
|---|---|
| CN1719238A true CN1719238A (en) | 2006-01-11 |
| CN100449309C CN100449309C (en) | 2009-01-07 |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102706908A (en) * | 2012-05-30 | 2012-10-03 | 浙江大学 | Modeling method for quick detecting model of interior quality of fruits |
| CN105806743A (en) * | 2016-04-28 | 2016-07-27 | 西北农林科技大学 | Multi-view apple moldy core detection device and method |
| CN109174685A (en) * | 2018-08-13 | 2019-01-11 | 江西绿萌分选设备有限公司 | A kind of full-automatic navel orange sorting equipment |
| CN113030130A (en) * | 2021-02-24 | 2021-06-25 | 中国水产科学研究院渔业机械仪器研究所 | Shellfish fullness degree judging method and system |
| CN116524494A (en) * | 2023-04-11 | 2023-08-01 | 武汉轻工大学 | Kiwi fruit internal defect nondestructive identification method, device, equipment and storage medium |
| CN116958234A (en) * | 2023-07-28 | 2023-10-27 | 江苏大学 | Apple damage volume prediction method and equipment, storage medium and processor |
| US20240233110A1 (en) * | 2022-04-26 | 2024-07-11 | Zhejiang University | Method for detecting a pomelo flesh mass edible ratio based on x-ray images |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11211677A (en) * | 1998-01-23 | 1999-08-06 | Sumitomo Metal Mining Co Ltd | X-ray transmission type internal judgment device |
| JP2002098654A (en) * | 2000-09-22 | 2002-04-05 | Sumitomo Metal Mining Co Ltd | Method for judging internal quality of fruits and vegetables and method for measuring X-ray optical path length used therefor |
| JP4755752B2 (en) * | 2000-11-28 | 2011-08-24 | 東芝Ｉｔコントロールシステム株式会社 | Fruit and vegetable inspection equipment |
| ATE331947T1 (en) * | 2001-10-22 | 2006-07-15 | Miconos Technology Ltd | X-RAY INSPECTION SYSTEM FOR PRODUCTS, ESPECIALLY FOOD |
-
2005
- 2005-07-11 CN CNB2005100829006A patent/CN100449309C/en not_active Expired - Fee Related
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102706908A (en) * | 2012-05-30 | 2012-10-03 | 浙江大学 | Modeling method for quick detecting model of interior quality of fruits |
| CN105806743A (en) * | 2016-04-28 | 2016-07-27 | 西北农林科技大学 | Multi-view apple moldy core detection device and method |
| CN109174685A (en) * | 2018-08-13 | 2019-01-11 | 江西绿萌分选设备有限公司 | A kind of full-automatic navel orange sorting equipment |
| CN113030130A (en) * | 2021-02-24 | 2021-06-25 | 中国水产科学研究院渔业机械仪器研究所 | Shellfish fullness degree judging method and system |
| US20240233110A1 (en) * | 2022-04-26 | 2024-07-11 | Zhejiang University | Method for detecting a pomelo flesh mass edible ratio based on x-ray images |
| CN116524494A (en) * | 2023-04-11 | 2023-08-01 | 武汉轻工大学 | Kiwi fruit internal defect nondestructive identification method, device, equipment and storage medium |
| CN116958234A (en) * | 2023-07-28 | 2023-10-27 | 江苏大学 | Apple damage volume prediction method and equipment, storage medium and processor |
| WO2025025549A1 (en) * | 2023-07-28 | 2025-02-06 | 江苏大学 | Apple damage volume prediction method, and device, storage medium and processor |
Also Published As
| Publication number | Publication date |
|---|---|
| CN100449309C (en) | 2009-01-07 |
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