CN109425588B - Handheld device, method and computer-readable storage medium for quickly distinguishing huanglongbing - Google Patents
Handheld device, method and computer-readable storage medium for quickly distinguishing huanglongbing Download PDFInfo
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- G—PHYSICS
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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- G—PHYSICS
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- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
Abstract
The invention discloses a handheld device for quickly distinguishing yellow dragon disease, which comprises a handle, a gun body and a self-checking standard plate, wherein the handle is connected with the gun body; the gun body is further provided with a light source, a first convergent lens, a collimating lens, a grating, an imaging lens, a DMD chip, a second convergent lens, a single-point detector, a signal processing system and a display screen, wherein the single-point detector is connected with the signal processing system, and the signal processing system is connected with the display. The invention also discloses a method for rapidly distinguishing the huanglongbing and a computer readable storage medium. The invention can quickly and accurately detect the huanglongbing, does not need special staff and is environment-friendly.
Description
Technical Field
The invention relates to a huanglongbing recognition technology, in particular to a handheld device, a method and a computer readable storage medium for fast recognizing huanglongbing.
Background
Currently, huanglongbing is caused by gram-negative bacteria that parasitize the phloem and can rapidly infect other sites. The yellow dragon disease is mainly transmitted in the modes of psylla, semen cuscutae, grafting and the like, the transmission speed is high, one fruit tree is infected, and a large number of orchards can be wasted and the variety can be extinct. The yellow dragon disease brings devastating to the citrus industry, so that the rapid propagation of the yellow dragon disease can be restrained in time, and the rapid cutting of the propagation source is very important. One commonly used diagnostic detection method for huanglongbing is Real-time fluorescent Quantitative PCR (Quantitative Real-time PCR), which is a method for measuring the total amount of products after each Polymerase Chain Reaction (PCR) cycle by using fluorescent chemicals in DNA amplification reaction. And in the real-time fluorescent quantitative PCR, the CT value is used as the basis for judging the plants infected by the Huanglongbing. Wherein the CT value (Cycle threshold) refers to: the number of cycles that the fluorescence signal in each reaction tube undergoes when it reaches a set threshold. The CT value content and the logarithm of the initial copy number are in a linear relation, namely the larger the initial copy number is, the smaller the CT value is. Wherein, most of the Huanglong diseases take the CT value of 32 as the judgment limit, namely, the Huanglong diseases are not Huanglong diseases if the CT value is more than 32, and the Huanglong diseases if the CT value is less than 32. The real-time fluorescent quantitative PCR is a traditional Huanglong disease detection means, and the detection cycle time is long and approximately needs about one week; the detection cost is high, and the detection cost is 100-400 yuan for sending to a third-party detection mechanism once, so that the method is mostly used in a laboratory.
However, the prior art has the following disadvantages:
(1) the folk diagnosis needs experienced experts or technicians, needs abundant diagnosis experience, has large subjective influence factors and poor reproducibility;
(2) the DNA reagent extraction kit has the advantages of non-recyclable reagent, high consumption, poor recyclability and environmental protection, and non-recordable detection result.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a handheld device for rapidly distinguishing huanglongbing, which can rapidly and accurately detect huanglongbing, does not need professional staff and is environment-friendly.
The second purpose of the invention is to provide a method for rapidly distinguishing Huanglong disease, which can rapidly and accurately detect Huanglong disease, does not need professional staff and is environment-friendly.
The invention also aims to provide a computer readable storage medium which can quickly and accurately detect the huanglongbing and is environment-friendly without requiring professional staff.
One of the purposes of the invention is realized by adopting the following technical scheme:
a handheld device for rapidly distinguishing huanglongbing comprising: the self-checking device comprises a handle, a gun body and a self-checking standard plate, wherein the handle is connected with the gun body, the gun body is provided with a trigger, a front cover and a driving rod, the trigger is connected with the front cover through the driving rod, the front cover fixes a sample to be detected, and the self-checking standard plate fixes a standard sample and is used for correcting a detection model; the gun body is also provided with a light source, a first convergent lens, a collimating lens, a grating, an imaging lens, a DMD chip, a second convergent lens, a single-point detector, a signal processing system and a display screen, wherein the single-point detector is connected with the signal processing system, and the signal processing system is connected with the display;
the light of the light source is irradiated to a sample to be detected or a standard sample to be reflected, the light is converged after sequentially passing through the first converging lens, the collimating lens, the grating and the imaging lens, images of slits with different wavelengths are formed on the DMD chip, the images are focused on the single-point detector through the second converging lens after wavelength gating is carried out on the DMD chip, the single-point detector sends received light signals to the signal processing system, and the signal processing system processes the light signals and then displays the processed light signals through the display screen.
Further, the single-point detector is a single-point photodiode.
Further, the rifle body is installed and is detected the button, detect the button with the light source is connected.
Furthermore, a reference plate is installed on the front cover, a reference key corresponding to the reference plate is installed on the gun body, and the reference key is connected with the light source.
The second purpose of the invention is realized by adopting the following technical scheme:
a method for rapidly distinguishing Huanglong disease comprises the following steps:
the determination step comprises: collecting CT value content in CT values contained in a plurality of sampling points in each sample to be detected, and forming a CT value concentration matrix of the sample to be detected as Y, wherein Y is { Y ═ Y }1,y2,y3,...yn}; the sample to be detected comprises the leaves of citrus under yellow shoot and the leaves of citrus under non-yellow shoot; each sample to be tested has only one CT value at different sampling points;
the collection step comprises: collecting the spectrum data of a plurality of sampling points of each sample to be measured, forming a spectrum data matrix to be recorded as X,
modeling: forming a quantitative correction relation between the CT value and the spectral data by a preset quantitative analysis method according to the CT value concentration matrix and the spectral data matrix of the sample to be detected, wherein the quantitative correction relation is B-XTX)-1XTY, wherein B is a quantitative correction relation;
a prediction step: inputting the spectral data of the sample to be detected, and according to the quantitative correction relation and the formula: y isIs unknownObtaining the CT predicted value, Y, of the sample to be tested as XBIs unknownThe CT prediction value is obtained;
a judging step: and judging whether the CT value of the sample to be detected is lower than a preset reference value, if so, judging that the sample to be detected does not suffer from Huanglong disease, and otherwise, judging that the sample to be detected suffers from Huanglong disease.
Further, in the modeling step, the predetermined quantitative analysis method is a partial least squares method.
Further, the preset reference value in the judging step is 32.
The third purpose of the invention is realized by adopting the following technical scheme:
a computer-readable storage medium, on which a computer program is stored which, when being executed by a signal processor, carries out the determining step of the method according to the second object of the invention.
Compared with the prior art, the invention has the beneficial effects that:
(1) through infrared detection, the operation of full-time technicians is not needed, and the method has strong adaptability and wide applicability;
(2) the disposable sanitary towel can be continuously used for many times, is more environment-friendly and is convenient to carry;
(3) can quickly and accurately detect whether the patient suffers from the yellow dragon disease.
Drawings
FIG. 1 is a schematic structural diagram of a handheld device for rapidly identifying Huanglong disease according to the present invention;
FIG. 2 is a cross-sectional view of FIG. 1;
FIG. 3 is a flow chart of a method of rapidly distinguishing Huanglong disease in accordance with the present invention;
FIG. 4 is a spectrum of 268 samples tested;
FIG. 5 is a comparison graph of the measured value of the modeled sample to be measured and the near-infrared predicted value;
fig. 6 is a comparison graph of the measured value and the near-infrared predicted value of the unknown sample to be tested.
In the figure: 10. a handle; 11. a battery; 20. a gun body; 21. a light source; 22. a first converging lens; 23. a collimating lens; 24. a grating; 25. an imaging lens; 26. a DMD chip; 27. a second condenser lens; 28. a single point detector; 30. a trigger; 31. a drive rod; 40. a front cover; 41. a reference plate; 50. a sample to be tested; 60. a signal processing system; 70. a display screen; 80. detecting a key; 90. a reference key; 100. and (5) self-checking the standard plate.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
As shown in fig. 1 and 2, firstly, the invention shows a handheld device for rapidly identifying yellow dragon disease, which comprises a handle 10 and a body 20, wherein the handle 10 is fixedly connected with the body 20, a battery 11 is installed inside the handle 10, the body 20 is provided with a trigger 30, a driving rod 31 and a front cover 40, the trigger 30 is connected with the front cover 40 through the driving rod 31, a reference plate 41 is installed on the front cover 40, the trigger 30 is used for controlling the opening and closing of the front cover 40, the body 20 is also provided with a light source 21, a first convergent lens 22, a collimating lens 23, a grating 24, an imaging lens 25, a DMD chip 26, a second convergent lens 27, a single-point detector 28, a system signal processing 60 and a display screen 70, the single-point detector 28 is connected with the signal processing system 60, the signal processing system 60 is connected with the display screen 70, the light source 21, the DMD chip 26, the single-point detector 28, the signal processing system 60 and the display screen 70 are connected with the battery 11 through wires, the single-point detector 28 is a single-point photodiode, the gun body 20 is further provided with a detection key 80, and the detection key 80 is connected with the light source 21 to control the on and off of the light source 21.
Light emitted by the light source 21 is reflected by a sample 50 to be measured, converged by the first converging lens 22, collimated by the collimating lens 23, irradiated to the grating 24, dispersed into a spectral band, converged and incident on the DMD chip 26 according to a wavelength sequence after passing through the imaging lens 25, focused on the single-point detector 28 by the second converging lens 17 after the DMD chip 26 is subjected to wavelength gating, the received light signal is sent to the signal processing system 60 by the single-point detector 28, and the light signal is displayed by the display screen 70 after being processed by the signal processing system 60.
During the use, open protecgulum 40 through trigger 30, release trigger 30 after placing sample 50 that will await measuring, protecgulum 40 is fixed with sample 50 that awaits measuring this moment, opens light source 21 through detecting button 80 and detects, and the testing result direct display makes things convenient for the measurement personnel to look over on display screen 70, opens protecgulum 40 through trigger 30 after the detection end, takes out sample 50 that awaits measuring, convenient and fast.
As a further embodiment, a reference plate 41 is fixedly mounted on the inside of the front cover 40 for handheld device approval, and when the handheld device is calibrated using the reference plate 41, this is achieved by actuating the reference key 90, which is similar to the operation of the detection key 80. In addition, in order to correct the detection model (steps S1-S4 in the following method), the handheld device further includes a self-checking calibration board 100 for fixing the standard sample, the correction process is also realized by the detection key 80, that is, the standard sample is detected as the sample to be detected, the self-checking calibration board 100 is arranged on the light path instead of the front cover 40, and then the detection result is sent to the detection platform, so that the detection platform can determine the change rate of the handheld device by a certain algorithm and correct the model.
The appearance of the handheld device is pistol type, which is convenient for the detection of operators and the carrying; the cost of the equipment is reduced, and the MEMS technology with minimum improvement is adopted; in order to facilitate the operator to check the result, the device is provided with a display screen, when the detected sample is the Huanglongbing disease, the Huanglongbing disease is displayed, and a prompt sound is dripped; when the sample to be detected is detected to be non-Huanglongbing, displaying the non-Huanglongbing; and when the detected sample to be detected is the non-citrus leaf, displaying that the sample to be detected is abnormal. For epidemic prevention and control of the huanglongbing, a GPS positioning module and a storage module are arranged in the device, and the detection geographical position, the detection result and the detection spectrum of the sample to be detected of each leaf are stored and used for subsequent analysis.
As shown in fig. 3, the present invention also provides a method for rapidly distinguishing huanglongbing, comprising the following steps:
s1: collecting CT value content in CT values contained in a plurality of sampling points in each sample to be detected respectively, wherein each ps sampling point different from the sample to be detected only has one CT value, and forming a CT value concentration matrix of the sample to be detected and recording the CT value concentration matrix as Y, Y ═ Y1,y2,y3,...yn}; the sample to be detected comprises the leaves of citrus under yellow shoot and the leaves of citrus under non-yellow shoot;
in order to establish a good model, a sample to be tested needs to be collected, leaves with various symptoms of huanglongbing, such as yellowing, mottle, green island, small leaves and the like, and samples to be tested without huanglongbing, lack of elements, no obvious symptoms and the like. Because the non-Huanglongbing disease and the Huanglongbing disease have similar symptoms, misjudgment can be easily caused when a near-infrared qualitative model is established by the non-Huanglongbing disease and the Huanglongbing disease, and the quantitative analysis model established by adopting the CT value has the advantages that the content of the CT value of the leaf of the symptoms such as yellowing caused by the Huanglongbing disease is obviously different from the symptoms such as yellowing of the non-Huanglongbing disease to a certain extent, so that the accuracy of the model can be greatly improved during prediction, and the probability of misjudgment is reduced.
In the step, a representative citrus sample to be tested is selected, the near infrared spectrum of the sample to be tested is tested, and a plurality of optimal collection positions, namely the plurality of sampling points, are obtained in the sample to be tested of the leaf according to an experiment. This step may be performed in synchronization with S2 or in reverse order in actual operation.
S2: collecting the spectrum data of a plurality of sampling points of each sample to be measured, forming a spectrum data matrix to be recorded as X,
in the step, a fluorescent quantitative PCR detection instrument and the same primer are adopted to detect the condition of infectious germs in the citrus samples to be detected, so as to obtain CT values corresponding to different sampling points of each sample to be detected, and form a CT value concentration matrix. The sampling points of the spectral data may be set on the front and back surfaces of the sample to be measured, respectively. In addition, due to diseased and non-diseased leaves, the collected spectral data can be divided into spectral data of citrus fruit leaves with yellow shoot and spectral data of citrus fruit leaves without yellow shoot.
S3: forming a quantitative correction relation between the CT value and the spectral data by a preset quantitative analysis method according to the CT value concentration matrix and the spectral data matrix of the sample to be detected, wherein the quantitative correction relation is B-XTX)-1XTY, wherein B is a quantitative correction relation; the preset quantitative analysis method is a least square method;
establishing a quantitative model by a preset quantitative analysis method according to the CT value of the sample to be tested, the front spectrum of the leaves of the citrus under the Huanglongbing effect, the back spectrum of the leaves of the citrus under the Huanglongbing effect, the front spectrum of the leaves of the citrus under the non-Huanglongbing effect and the back spectrum of the leaves of the citrus under the non-Huanglongbing effect; and respectively evaluating the error and the correlation between the near infrared predicted value and the measured value by adopting a predicted Root Mean Square Error (RMSEC), a predicted Root Mean Square Error (RMSEP) of an unknown sample to be detected and a correlation coefficient (RP), wherein the formula is as follows:
wherein the content of the first and second substances,
Cirespectively predicting a predicted value and an actual measurement value of the ith sample to be tested;
CAVErespectively taking the average values of the predicted value and the measured value; m is the number of samples to be predicted; smaller RMSEC and RMSEP values indicate higher accuracy of the model, RPThe larger the value, the higher the correlation between the predicted value and the measured value.
In order to make the model more suitable for the field environment, the spectral data are collected for the experimenters to hold the equipment by hands. The collected spectral data is divided into the leaf base, the middle part and the tail part of the front surface of the leaf, and the leaf base, the middle part and the tail part of the back surface of the leaf. The aim is mainly to distinguish the difference of CT value models of the front side and the back side, namely to establish a model 1: establishing a quantitative model by the front spectral data and the CT value; model 2: and establishing a quantitative model by the spectral data and the CT value of the back. The spectrum of the front side and the back side of the sample has slight difference due to different chlorophyll substance contents, and the purpose of establishing 2 models is to compare the difference of CT values of the front side and the back side of an unknown sample to be detected, so that the prediction of the CT value content and the collection of different parts of the leaf have no influence.
S4: inputting the spectral data of the sample to be detected, and according to the quantitative correction relation and the formula: y isIs unknownObtaining the CT predicted value, Y, of the sample to be tested as XBIs unknownThe CT prediction value is obtained;
s5: and judging whether the CT value of the sample to be detected is lower than a preset reference value, if so, judging that the sample to be detected does not suffer from Huanglong disease, and otherwise, judging that the sample to be detected suffers from Huanglong disease. The predetermined reference value is preferably 32, CT <32, Huanglong disease, CT >32, non-Huanglong disease. Of course, the detection threshold for the unknown sample to be tested is performed exactly according to the value in step S2, since different primers will cause some change in the threshold.
The following is a further description of an embodiment:
taking an NLD-HL10 Huanglongbing rapid detection near-infrared instrument as a detection instrument, testing the citrus varieties: sugar orange, detection index: CT value. The wavelength range of the instrument is 950-1650nm, the interval is 1nm, and the total wavelength is 701. The method comprises the following specific steps:
and S1, testing the 268 sugar orange samples to be tested according to the S1 to obtain a spectral data matrix and CT values, wherein the Table 1 and the figure 4 respectively show the CT value statistical analysis and the spectrogram of the 268 sugar orange samples to be tested.
TABLE 1 CT value statistical analysis of sugar orange samples to be tested
S2, establishing an analysis model of the CT value by adopting a partial least square method, wherein the modeling result is as follows:
TABLE 2 summary of modeling results for partial least squares
And S3, when the factor established by the partial least square method is 18, the RMSEC is minimum. And calculating the CT predicted value of the unknown sample to be tested according to the correction relational expression. And scanning the spectral data of the unknown sample to be detected, and calculating the CT value of the unknown sample to be detected according to the correction relational expression B. The prediction results of the unknown samples to be tested are summarized as follows:
TABLE 3 summary of prediction results for unknown samples to be tested
S4, according to the above CT value and CT value critical value table, and with reference to fig. 5 and 6, it can be seen that the prediction results of 119 unknown samples to be tested are: 42 are to-be-detected yellow dragon disease samples, and 77 are to-be-detected non-yellow dragon disease samples. The results of the measured values are: 30 to-be-detected samples of the huanglongbing and 89 to-be-detected samples of the non-huanglongbing. Comparing the above data shows that: the overall accuracy was 88%. The abscissa of fig. 5 and 6 is an actual measurement value, and the ordinate indicates a predicted value.
The present invention may also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a signal processor, implements step S5.
The above embodiments are only preferred embodiments of the present invention, and the scope of the present invention should not be limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the scope of the present invention as claimed.
Claims (8)
1. A hand-held device for rapidly distinguishing huanglongbing comprising: the self-checking device comprises a handle, a gun body and a self-checking standard plate, wherein the handle is connected with the gun body, the gun body is provided with a trigger, a front cover and a driving rod, the trigger is connected with the front cover through the driving rod, the front cover fixes a sample to be detected, and the self-checking standard plate fixes a standard sample and is used for correcting a detection model; the gun body is also provided with a light source, a first convergent lens, a collimating lens, a grating, an imaging lens, a DMD chip, a second convergent lens, a single-point detector, a signal processing system and a display screen, wherein the single-point detector is connected with the signal processing system, and the signal processing system is connected with the display screen;
the light of the light source is irradiated to a sample to be detected or a standard sample to be reflected, the light is converged after sequentially passing through the first converging lens, the collimating lens, the grating and the imaging lens, images of slits with different wavelengths are formed on the DMD chip, the images are focused on the single-point detector through the second converging lens after wavelength gating is carried out on the DMD chip, the single-point detector sends received light signals to the signal processing system, and the signal processing system processes the light signals and then displays the processed light signals through the display screen.
2. The handheld device of claim 1, wherein: the single-point detector is a single-point photodiode.
3. The handheld device of claim 1, wherein: the rifle body is installed and is detected the button, detect the button with the light source is connected.
4. The handheld device of claim 1, wherein: the gun body is provided with a reference key corresponding to the reference plate, and the reference key is connected with the light source.
5. A method for rapidly distinguishing Huanglong disease applied in the handheld device of any one of claims 1-4, characterized by comprising the steps of:
the determination step comprises: collecting CT value content in CT values contained in a plurality of sampling points in each sample to be detected, and forming a CT value concentration matrix of the sample to be detected as Y, wherein Y is { Y ═ Y }1,y2,y3,...yn}; the sample to be detected comprises the leaves of citrus under yellow shoot and the leaves of citrus under non-yellow shoot; each sample to be tested has only one CT value at different sampling points;
the collection step comprises: collecting the spectrum data of a plurality of sampling points of each sample to be measured, forming a spectrum data matrix to be recorded as X,
modeling: forming a quantitative correction relation between the CT value and the spectral data by a preset quantitative analysis method according to the CT value concentration matrix and the spectral data matrix of the sample to be detected, wherein the quantitative correction relation is B-XTX)-1XTY, wherein B is a quantitative correction relation;
a prediction step: inputting the spectral data of the sample to be detected, and according to the quantitative correction relation and the formula: y isIs unknownObtaining the predicted CT value of the sample to be tested (XB), wherein X is the spectral data (X) of a single sample to be tested1,X2,…Xm)，YIs unknownThe CT prediction value is obtained;
a judging step: and judging whether the CT value of the sample to be detected is lower than a preset reference value, if so, judging that the sample to be detected does not suffer from Huanglong disease, and otherwise, judging that the sample to be detected suffers from Huanglong disease.
6. The method of claim 5, wherein: in the modeling step, a preset quantitative analysis method is a partial least square method.
7. The method of claim 5, wherein: the preset reference value in the judging step is 32.
8. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a signal processor performs the decision steps of the method according to any of claims 5-7.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2927044Y (en) * | 2006-07-24 | 2007-07-25 | 深圳市天瑞仪器有限公司 | X-ray fluorescence energy dispersion spectrograph |
| CN201548539U (en) * | 2009-06-19 | 2010-08-11 | 华中农业大学 | Portable soil water content tacheometer |
| CN102890096A (en) * | 2011-07-19 | 2013-01-23 | 奥林巴斯Ndt公司 | An x-ray analysis apparatus with detector window protection feature |
| CN104502304A (en) * | 2014-12-02 | 2015-04-08 | 天津大学 | Miniature curing near-infrared spectrometer based on virtual slit technology |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4345840A (en) * | 1980-04-08 | 1982-08-24 | California Institute Of Technology | Method and apparatus for instantaneous band ratioing in a reflectance radiometer |
| US5319437A (en) * | 1991-07-26 | 1994-06-07 | Kollmorgen Corporation | Handheld portable spectrophotometer |
| NL1000738C2 (en) * | 1995-07-06 | 1997-01-08 | Dsm Nv | Infrared spectrometer. |
| US6031233A (en) * | 1995-08-31 | 2000-02-29 | Infrared Fiber Systems, Inc. | Handheld infrared spectrometer |
| US6897951B2 (en) * | 2003-02-14 | 2005-05-24 | Raman Systems, Inc. | Probe assemblies for Raman spectroscopy |
| CN201340400Y (en) * | 2009-01-20 | 2009-11-04 | 江苏天瑞仪器股份有限公司 | Hand-held X-ray fluorescence spectrometer with integrated heat sink |
| US10024811B2 (en) * | 2014-04-10 | 2018-07-17 | Olympus Scientific Solutions Americas Inc. | XRF instrument with removably attached window protecting film assembly |
| CN104535526A (en) * | 2014-12-17 | 2015-04-22 | 中国科学院长春光学精密机械与物理研究所 | Linear gradual filter type handheld intelligent spectrometer based on microcomputer Edison |
| JP6800864B2 (en) * | 2015-02-06 | 2020-12-16 | ライフ テクノロジーズ コーポレーション | Systems and methods for biological analysis |
| CN105738323B (en) * | 2016-02-26 | 2018-08-28 | 长春美泰仪器有限公司 | The detection method and field quick detection instrument of the food additives containing aluminium in a kind of Flour product |
| CN106323909A (en) * | 2016-09-14 | 2017-01-11 | 江苏大学 | Handheld near infrared spectrum detection system and detection method for quality of fruits and vegetables |
-
2017
- 2017-08-30 CN CN201710763407.3A patent/CN109425588B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2927044Y (en) * | 2006-07-24 | 2007-07-25 | 深圳市天瑞仪器有限公司 | X-ray fluorescence energy dispersion spectrograph |
| CN201548539U (en) * | 2009-06-19 | 2010-08-11 | 华中农业大学 | Portable soil water content tacheometer |
| CN102890096A (en) * | 2011-07-19 | 2013-01-23 | 奥林巴斯Ndt公司 | An x-ray analysis apparatus with detector window protection feature |
| CN104502304A (en) * | 2014-12-02 | 2015-04-08 | 天津大学 | Miniature curing near-infrared spectrometer based on virtual slit technology |
Non-Patent Citations (2)
| Title |
|---|
| 基于傅里叶变换红外光谱的大白菜根肿病定量测评;王惟萍等;《光谱学与光谱分析》;20150531;第1243-1247页 * |
| 基于可见近红外光谱分析的化肥土壤成分速测模型研究;林志丹;《中国博士学位论文全文数据库 农业科技辑》;20170215;第D043-4页 * |
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