CN103674864A - Fish water content distribution detection method based on hyperspectral imaging technology - Google Patents
Fish water content distribution detection method based on hyperspectral imaging technology Download PDFInfo
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Abstract
The invention discloses a fish water content distribution detection method based on a hyperspectral imaging technology. The fish water content distribution detection method comprises the following steps: (1) acquiring a single-band spectral image of a fish to be detected on twelve characteristic wavelengths including 956nm, 1047nm, 1100nm, 1134nm, 1167nm, 1205nm, 1244nm, 1325nm, 1354nm, 1373nm, 1575nm and 1637nm; (2) converting the single-band spectral image in the step (1) into a reflectivity image according to a linear relation between a gray value and the reflectivity of the single-band spectral image; and (3) calculating according to a formula to obtain a corresponding fish water content of each pixel point in the reflectivity image of the fish. According to the fish water content distribution detection method based on the hyperspectral imaging technology, the detection precision is high, the detection time is short, the environmental pollution is reduced and the detection cost is reduced to a certain extent.
Description
Technical field
The present invention relates to moisture detection field, be specifically related to a kind of fish and water based on high light spectrum image-forming technology minute content distribution detection method.
Background technology
High light spectrum image-forming technology is a kind of spectrum and image processing techniques to be become one, the feature that simultaneously possesses spectrum and image, and can be accurate to nanoscale by detecting yardstick, owing to possessing these advantages, in prior art, high light spectrum image-forming technology has been widely used in a plurality of industries such as agricultural, food, petrochemical complex, pharmacy, feed, especially in the research of agricultural product life-information fast detecting, has very large application potential.
Moisture is the important component part of fish, by fish moisture is detected to analysis, can realize the detection of the upgrowth situation of fish and monitoring continuously, significant for the output and the quality that improve fish.
Traditional fish and water minute safe standard GB/T 5009.5-2010 of content detection based food carries out, although can obtain reliable measurement result according to this method, but waste time and energy, and can only obtain the long-run average of fish moisture, and can not obtain the distribution situation of fish moisture.
Application publication number be CN 101881729A disclosure of the invention a kind of method of predicting the rotten moisture of wall pollack fish and protein content, comprise the following steps: a-1, utilize near infrared spectrometer to gather respectively the near infrared spectrum reflectivity of the rotten samples of several wall pollack fishes, thereby obtain respectively the near infrared spectrum spectrogram of the rotten sample moisture of several wall pollack fishes and protein content; Measure respectively moisture and the protein content of the rotten sample of several wall pollack fishes, thereby obtain respectively moisture and the protein content determination value of the rotten sample of several wall pollack fishes; By NIRCal5.2 near-infrared analysis software by the rotten moisture of sample of several wall pollack fishes and the measured value of protein content respectively with the rotten moisture of sample of corresponding wall pollack fish and the near infrared spectrum spectrogram matching of protein, obtain the typical curve of the rotten moisture of wall pollack fish and protein content; B, utilize near infrared spectrometer to gather the near infrared spectrum reflectivity of wall pollack fish gruel, thereby obtain the near infrared spectrum spectrogram of the rotten moisture of wall pollack fish and protein content; The typical curve of c, the rotten moisture of the wall pollack fish obtaining by NIRCal5.2 near-infrared analysis software transfer a step and protein content, the rotten moisture of wall pollack fish that b step is obtained and the near infrared spectrum spectrogram of protein content are analyzed, thereby obtain the moisture of wall pollack fish gruel and the predicted value of protein content.
Utilize the method that this invention provides also only can obtain the average content of moisture and protein, and can not obtain distribution, therefore, need to provide a kind of detection method that can fast detecting fish and water minute content distribution.
Summary of the invention
The invention provides a kind of fish and water based on high light spectrum image-forming technology minute content distribution detection method, accuracy of detection is high, and detection time is short, has not only reduced environmental pollution, has also reduced to a certain extent testing cost.
Fish and water based on high light spectrum image-forming technology divides a content distribution detection method, comprises the following steps:
(1) gather fish to be measured at the single band spectrum picture of 12 characteristic wave strong points; Described 12 characteristic wavelengths are respectively 956nm, 1047nm, 1100nm, 1134nm, 1167nm, 1205nm, 1244nm, 1325nm, 1354nm, 1373nm, 1575nm, 1637nm;
(2) according to the linear relationship of gray-scale value and the reflectivity of single band spectrum picture, the single band spectrum picture in step (1) is converted into albedo image;
(3) according to following formula, calculate the corresponding fish moisture of each pixel in the albedo image of fish;
Y=108.61X
1+246.267X
2-206.439X
3+172.62X
4-121.423X
5+214.724X
6-136.875X
7+133.606X
8+141.559X
9-214.251X
10+191.275X
11-149.463X
12+65.536
In formula: X
arepresent in the albedo image of a nm characteristic wave strong point the reflectivity of a certain pixel;
Y represents the fish moisture at respective pixel point place.
As preferably, in described step (2), the obtaining step of the gray-scale value of single band spectrum picture and the linear relationship of reflectivity is as follows:
2-1, gather at least three diffuse reflection on-gauge plates at the benchmark single band spectrum picture of 12 characteristic wave strong points, ask for the gray-scale value of every width benchmark single band image, within the scope of visible and near infrared spectrum, the diffuse reflection on-gauge plate adopting has mutually different reflectivity;
2-2, for each characteristic wavelength, the gray-scale value of corresponding benchmark single band image and reflectivity are carried out to linear fit, obtain the linear relationship of gray-scale value and reflectivity.
As preferably, described diffuse reflection on-gauge plate is three～12.
In each characteristic wave strong point, every diffuse reflection on-gauge plate is a corresponding width single band image separately, the corresponding gray-scale value of every width single band image, the gray-scale value of diffuse reflection on-gauge plate of take is independent variable, the reflectivity of diffuse reflection on-gauge plate of take is dependent variable, and linear fit obtains the relation of gray-scale value and reflectivity.
The number of diffuse reflection on-gauge plate is more, the gray-scale value that linear fit obtains and the relation of reflectivity are more accurate, corresponding consuming time also longer, preferably, described diffuse reflection on-gauge plate is three, is respectively 99% diffuse reflection on-gauge plate, 75% diffuse reflection on-gauge plate and 2% diffuse reflection on-gauge plate.
99% diffuse reflection on-gauge plate refers to: within the scope of whole visible and near infrared spectrum, the reflectivity of diffuse reflection on-gauge plate is 99%.
75% diffuse reflection on-gauge plate refers to: within the scope of whole visible and near infrared spectrum, the reflectivity of diffuse reflection on-gauge plate is 75%.
2% diffuse reflection on-gauge plate refers to: within the scope of whole visible and near infrared spectrum, the reflectivity of diffuse reflection on-gauge plate is 2%.
Adopt 99% diffuse reflection on-gauge plate, 75% diffuse reflection on-gauge plate and 2% diffuse reflection on-gauge plate, at utmost contained the scope of reflectivity, make the linear relationship of the gray-scale value that obtains and reflectivity more accurate.
In order to know intuitively the distributed intelligence of fish moisture, preferably, in described step (3), calculate in the albedo image of fish after the corresponding fish moisture of each pixel, draw fish and water and divide content distribution figure.
Compared with prior art, the present invention has following useful technique effect:
A) at selected a few features wavelength place, gather the high spectrum image of fish, utilize multiple linear regression analysis to obtain the relation of pixel reflectivity in fish moisture and high spectrum image, quick and precisely detect fish and water minute content distribution, save time.
B) do not use any chemical material, without carrying out physico-chemical analysis, reduced testing cost, free from environmental pollution.
C) can analyze the sample of larger-size sample and many kinds, can divide content distribution by real-time online detection fish and water.
D) fish and water minute content distribution spectral information characteristics and light spectrum image-forming information feature are merged on characteristic layer, obtain the visual image of fish and water minute content distribution, be convenient to further analysis.
Accompanying drawing explanation
Fig. 1 is the reflectivity of three diffuse reflection on-gauge plates and the graph of a relation of wavelength;
Fig. 2 is the distribution plan of fish moisture.
Embodiment
Embodiment 1
(1) set up the relation of gray-scale value and the reflectivity of single band spectrum picture
1-a, gather three diffuse reflection on-gauge plates at the benchmark single band spectrum picture (every diffuse reflection on-gauge plate gathers a width benchmark single band spectrum picture in each characteristic wave strong point) of 12 characteristic wave strong points, ask for the gray-scale value of every width benchmark single band image.
Within the scope of whole visible and near infrared spectrum, the diffuse reflection on-gauge plate adopting the respectively reflectivity of correspondence is 99%, 75% and 2%, as shown in Figure 1, the diffuse reflection within the scope of whole visible and near infrared spectrum of three diffuse reflection on-gauge plates is mutually different, for each piece diffuse reflection on-gauge plate, all identical at the reflectivity at all wavelengths place.
1-b, for each characteristic wavelength, the gray-scale value of corresponding benchmark single band image and reflectivity are carried out to linear fit, obtain the linear relationship of gray-scale value and reflectivity.
For each characteristic wavelength, there are corresponding three groups of gray-scale values and reflectance value (each group gray-scale value and reflectivity are obtained by the benchmark single band image of same diffuse reflection on-gauge plate collection), take gray-scale value as independent variable, reflectivity is dependent variable, these three groups of gray-scale values and reflectance value are carried out to linear fit, obtain the linear relationship of gray-scale value and reflectance value.
The acquisition of the linear relationship of gray-scale value and reflectance value in the inventive method, the distribution of the collection of high spectrum image and fish moisture all by ENVI program, automatically complete.
(2) calculate fish and water and divide content distribution
2-a, 150 fishes of collection, first adopt high spectrum image imaging system (ImSpector V10E, Spectral Imaging Ltd., Oulu, Finland) to scan respectively every fish at the single band spectrum picture of 12 characteristic wave strong points; 12 characteristic wavelengths are respectively 956nm, 1047nm, 1100nm, 1134nm, 1167nm, 1205nm, 1244nm, 1325nm, 1354nm, 1373nm, 1575nm, 1637nm; The corresponding width single band spectrum picture in each wavelength place, then adopts GB5009.5-2010 national standard method to measure the moisture of 20 zoness of different of these 150 fishes, is about to every fish and is cut into 20 parts, and all sites of whole piece fish is included in 20 regions.
In 150 fishes, choose at random 100 as modeling collection sample, all the other 50 as forecast set.
2-b, according to the linear relationship of gray-scale value and the reflectivity of single band spectrum picture, every fish is converted into albedo image at the single band spectrum picture of 12 characteristic wave strong points.
The gray-scale value of 12 characteristic wave strong points based in step (1) and the relation of reflectivity, can be the single band spectrum picture of a fish to be measured (site on the corresponding fish of each pixel difference in single band spectrum picture, each pixel has different gray-scale values) be converted to albedo image, reflectivity corresponding to each pixel in albedo image.
100 fishes of concentrating for modeling, high spectrum image for each region can obtain corresponding albedo image, after average, obtain the average reflectance in each region, utilize the fish moisture (GB measure obtain) in each region and relation that on average Reflectance curve fitting obtains fish moisture and average reflectance as shown in the formula shown in (I)
Y’=108.61X’
1+246.267X’
2-206.439X’
3+172.62X’
4-121.423X’
5+
214.724X’
6-136.875X’
7+133.606X’
8+141.559X’
9-214.251X’
10+
191.275X’
11-149.463X’
12+65.536（I）
In formula: X '
arepresent the average reflectance in the albedo image of a nm characteristic wave strong point;
Y ' represents the fish moisture at respective pixel point place.
The formula (I) of utilizing average reflectance and the matching of fish moisture to obtain has been expressed the relation of average reflectance and fish moisture, and formula (I) has also been reacted the relation of each pixel place reflectivity and moisture, obtains formula (II) as follows according to formula (I):
Y=108.61X
1+246.267X
2-206.439X
3+172.62X
4-121.423X
5+214.724X
6-136.875X
7+133.606X
8+141.559X
9-214.251X
10+191.275X
11-149.463X
12+65.536（II）
In formula: X
arepresent in the albedo image of a nm characteristic wave strong point the reflectivity of a certain pixel;
Y represents the fish moisture at respective pixel point place.
The calculating of moisture will be carried out in the corresponding reflectivity substitution of each pixel formula (II) in albedo image, obtain the moisture at each the pixel place in fish diagram picture to be measured, and then draw accordingly the moisture distribution plan of fish, obtain the moisture distributed intelligence at fish each point place, the moisture in a region distributes as shown in Figure 2.
The result of utilizing the inventive method to detect the prediction moisture (by trying to achieve in average reflectance substitution formula (I)) obtaining and the true moisture of utilizing GB to detect to 50 fishes in forecast set (every corresponding 20 regions of fish) compares in Table 1.
Table 1
| Data set | Number of samples | Related coefficient | Root-mean-square |
| Modeling collection | |||
| 100 | 0.9348 | 1.5638 | |
| Forecast set | 50 | 0.9267 | 1.6246 |
As can be seen from Table 1, the predicting the outcome of detection method that the present invention proposes is high correlation with the measured value of national standard method.
Comparative example 1
Choose 12 characteristic wavelengths, be respectively 952nm, 1049nm, 1102nm, 1130nm, 1162nm, 1208nm, 1248nm, 1320nm, 1350nm, 1378nm, 1570nm, 1630nm, and the relation of setting up in the same manner moisture and reflectivity based on these 12 characteristic wavelengths is as shown in the formula (III):
Y=105.025X
1+245.265X
2-204.236X
3+175.145X
4-125.321X
5+216.025X
6-130.126X
7+135.231X
8+145.256X
9-216.124X
10+195.245X
11-145.217X
12+62.327（III）
In formula (III): X
arepresent in the albedo image of a nm characteristic wave strong point the reflectivity of a certain pixel;
Y represents the moisture at respective pixel point place.
12 characteristic wave strong points, obtain the single band spectrum picture of fish, and based on formula (III), calculate the moisture of fish, as shown in table 2 with the result contrast of the true moisture of utilizing national standard method to detect.
Table 2
| Data set | Number of samples | Related coefficient | Root-mean-square |
| Modeling collection | |||
| 100 | 0.7563 | 1.9256 | |
| Forecast set | 50 | 0.7324 | 1.9904 |
Comparative example 2
Choose 12 characteristic wavelengths, be respectively 953nm, 1048nm, 1108nm, 1137nm, 1169nm, 1204nm, 1246nm, 1329nm, 1358nm, 1370nm, 1576nm, 1638nm, and the relation of setting up in the same manner moisture and reflectivity based on these 12 characteristic wavelengths is suc as formula shown in (IV):
Y=107.215X
1+244.265X
2-206.206X
3+178.165X
4-127.458X
5+217.865X
6-138.684X
7+138.478X
8+149.176X
9-211.324X
10+197.354X
11-148.841X
12+60.983（IV）
In formula (IV): X
arepresent in the albedo image of a nm characteristic wave strong point the reflectivity of a certain pixel;
Y represents the moisture at respective pixel point place.
12 characteristic wave strong points, obtain the single band spectrum picture of fish, and based on formula (IV), calculate the moisture of fish, as shown in table 3 with the result contrast of the true moisture of utilizing national standard method to detect.
Table 3
| Data set | Number of samples | Related coefficient | Root-mean-square |
| Modeling collection | |||
| 100 | 0.6824 | 2.0935 | |
| Forecast set | 50 | 0.6597 | 2.2486 |
Result by embodiment 1 and comparative example 1,2, whether selected characteristic wavelength accurately has material impact for detecting fish moisture, the present invention, by choosing suitable characteristic wavelength, has obtained the very high testing result of the coefficient of determination, for carrying out fast the spatial distribution result of fish moisture.
Claims (5)
1. the fish and water based on high light spectrum image-forming technology divides a content distribution detection method, it is characterized in that, comprises the following steps:
(1) gather fish to be measured at the single band spectrum picture of 12 characteristic wave strong points; Described 12 characteristic wavelengths are respectively 956nm, 1047nm, 1100nm, 1134nm, 1167nm, 1205nm, 1244nm, 1325nm, 1354nm, 1373nm, 1575nm, 1637nm;
(2) according to the linear relationship of gray-scale value and the reflectivity of single band spectrum picture, the single band spectrum picture in step (1) is converted into albedo image;
(3) according to following formula, calculate the corresponding fish moisture of each pixel in the albedo image of fish;
Y=108.61X
1+246.267X
2-206.439X
3+172.62X
4-121.423X
5+214.724X
6-136.875X
7+133.606X
8+141.559X
9-214.251X
10+191.275X
11-149.463X
12+65.536
In formula: X
arepresent in the albedo image of a nm characteristic wave strong point the reflectivity of a certain pixel;
Y represents the fish moisture at respective pixel point place.
2. the detection method of the fish moisture based on high light spectrum image-forming technology as claimed in claim 1, is characterized in that, in described step (2), the obtaining step of the gray-scale value of single band spectrum picture and the linear relationship of reflectivity is as follows:
2-1, gather at least three diffuse reflection on-gauge plates at the benchmark single band spectrum picture of 12 characteristic wave strong points, ask for the gray-scale value of every width benchmark single band image, within the scope of visible and near infrared spectrum, the diffuse reflection on-gauge plate adopting has mutually different reflectivity;
2-2, for each characteristic wavelength, the gray-scale value of corresponding benchmark single band image and reflectivity are carried out to linear fit, obtain the linear relationship of gray-scale value and reflectivity.
3. the detection method of the fish moisture based on high light spectrum image-forming technology as claimed in claim 2, is characterized in that, described diffuse reflection on-gauge plate is three～12.
4. the detection method of the fish moisture based on high light spectrum image-forming technology as claimed in claim 3, is characterized in that, described diffuse reflection on-gauge plate is three, is respectively 99% diffuse reflection on-gauge plate, 75% diffuse reflection on-gauge plate and 2% diffuse reflection on-gauge plate.
5. the detection method of the fish moisture based on high light spectrum image-forming technology as claimed in claim 1, it is characterized in that, in described step (3), calculate in the albedo image of fish after the corresponding fish moisture of each pixel, draw fish and water and divide content distribution figure.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6337472B1 (en) * | 1998-10-19 | 2002-01-08 | The University Of Texas System Board Of Regents | Light imaging microscope having spatially resolved images |
| US20070019198A1 (en) * | 2004-06-30 | 2007-01-25 | Chemimage Corporation | Hyperspectral visible absorption imaging of molecular probes and dyes in biomaterials |
| CN101839979A (en) * | 2010-04-22 | 2010-09-22 | 中国农业大学 | Method and device for measuring index number of canopy vegetation of crops |
| CN102033043A (en) * | 2010-10-19 | 2011-04-27 | 浙江大学 | Grain moisture content detecting method based on hyperspectral image technology |
| CN103257118A (en) * | 2013-04-22 | 2013-08-21 | 华南理工大学 | Fish tenderness hyperspectral detection method based on characteristic wave band |
| CN103278464A (en) * | 2013-04-18 | 2013-09-04 | 北京工商大学 | Method and device for fish flesh detection |
-
2013
- 2013-11-12 CN CN201310562403.0A patent/CN103674864B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6337472B1 (en) * | 1998-10-19 | 2002-01-08 | The University Of Texas System Board Of Regents | Light imaging microscope having spatially resolved images |
| US20070019198A1 (en) * | 2004-06-30 | 2007-01-25 | Chemimage Corporation | Hyperspectral visible absorption imaging of molecular probes and dyes in biomaterials |
| CN101839979A (en) * | 2010-04-22 | 2010-09-22 | 中国农业大学 | Method and device for measuring index number of canopy vegetation of crops |
| CN102033043A (en) * | 2010-10-19 | 2011-04-27 | 浙江大学 | Grain moisture content detecting method based on hyperspectral image technology |
| CN103278464A (en) * | 2013-04-18 | 2013-09-04 | 北京工商大学 | Method and device for fish flesh detection |
| CN103257118A (en) * | 2013-04-22 | 2013-08-21 | 华南理工大学 | Fish tenderness hyperspectral detection method based on characteristic wave band |
Non-Patent Citations (2)
| Title |
|---|
| HONG-JU HE等: "Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (salmo salar) fillets using visible and near-infrared hyperspectral imaging", 《INNOVATIVE FOOD SCIENCE AND EMERGING TECHNOLOGIES》 * |
| HONG-JU HE等: "Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (salmo salar) fillets using visible and near-infrared hyperspectral imaging", 《INNOVATIVE FOOD SCIENCE AND EMERGING TECHNOLOGIES》, vol. 18, 30 April 2013 (2013-04-30) * |
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