LU502068B1 - Quantitative detection method and system for cadmium in rice root systems - Google Patents
Quantitative detection method and system for cadmium in rice root systems Download PDFInfo
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- LU502068B1 LU502068B1 LU502068A LU502068A LU502068B1 LU 502068 B1 LU502068 B1 LU 502068B1 LU 502068 A LU502068 A LU 502068A LU 502068 A LU502068 A LU 502068A LU 502068 B1 LU502068 B1 LU 502068B1
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- cadmium
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- signal intensity
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- 229910052793 cadmium Inorganic materials 0.000 title claims abstract description 165
- BDOSMKKIYDKNTQ-UHFFFAOYSA-N cadmium atom Chemical compound [Cd] BDOSMKKIYDKNTQ-UHFFFAOYSA-N 0.000 title claims abstract description 165
- 238000001514 detection method Methods 0.000 title claims abstract description 93
- 235000007164 Oryza sativa Nutrition 0.000 title claims abstract description 44
- 235000009566 rice Nutrition 0.000 title claims abstract description 44
- 240000007594 Oryza sativa Species 0.000 title 1
- 230000003595 spectral effect Effects 0.000 claims abstract description 93
- 241000209094 Oryza Species 0.000 claims abstract description 43
- 238000012360 testing method Methods 0.000 claims abstract description 22
- 238000002536 laser-induced breakdown spectroscopy Methods 0.000 claims abstract description 15
- 238000000034 method Methods 0.000 claims description 7
- 238000012417 linear regression Methods 0.000 description 45
- 239000011159 matrix material Substances 0.000 description 10
- 229910001385 heavy metal Inorganic materials 0.000 description 7
- 210000000056 organ Anatomy 0.000 description 6
- 238000007781 pre-processing Methods 0.000 description 6
- 241000196324 Embryophyta Species 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000001035 drying Methods 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 230000000704 physical effect Effects 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 206010068975 Bone atrophy Diseases 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000001154 acute effect Effects 0.000 description 1
- 231100000570 acute poisoning Toxicity 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 231100000739 chronic poisoning Toxicity 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000003337 fertilizer Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 229940088597 hormone Drugs 0.000 description 1
- 239000005556 hormone Substances 0.000 description 1
- 239000002440 industrial waste Substances 0.000 description 1
- 230000003907 kidney function Effects 0.000 description 1
- 239000003041 laboratory chemical Substances 0.000 description 1
- 239000000575 pesticide Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- 239000002351 wastewater Substances 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
- G01N21/718—Laser microanalysis, i.e. with formation of sample plasma
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/443—Emission spectrometry
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/02—Food
- G01N33/10—Starch-containing substances, e.g. dough
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
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- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Food Science & Technology (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
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Abstract
The invention relates to a quantitative detection method and system for cadmium in rice root systems. In the quantitative detection method and system, spectral data of test rice root systems are acquired by a laser induced breakdown spectroscopy instrument, signal intensity of cadmium emission spectral lines is determined according to the spectral data, and then the cadmium content in the test rice root systems is determined by a cadmium quantitative detection model according to the signal intensity of the cadmium emission spectral lines.
Description
QUANTITATIVE DETECTION METHOD AND SYSTEM FOR CADMIUM IN RICE -V502068
ROOT SYSTEMS
[01] The disclosure relates to the technical field of heavy metal detection, in particular to a quantitative detection method and system for cadmium in rice root systems.
[02] In recent years, soil cadmium pollution has been increasingly serious due to emission of industrial waste gas, wastewater and waste residues and abuse of pesticide and chemical fertilizer. Cadmium cannot be biologically decomposed when entering plants, enters the human body through a food cycle, and is accumulated in biological organisms for a long time, so as to cause acute and chronic poisoning, damage the renal function, cause bone atrophy and greatly harm the human body.
[03] As a main food crop in China, rice mainly absorbs heavy metal elements from soil through roots and then transfers the heavy metal elements to other organs as for heavy metal element accumulation. As front end organs firstly sensing adversity stress, rice root systems are important organs affecting heavy metal accumulation in rice.
[04] Traditionally, heavy metals are detected by sampling, preprocessing and laboratory chemical analysis means, thereby causing complex process, high cost and long consumed time.
Laser induced breakdown spectroscopy (LIBS) performs qualitative and quantitative detection by generating plasma by ablating samples by lasers and analyzing spectral signals emitted by the plasma.
[05] As the plant samples have a complex matrix effect and physical properties, LIBS faces greater challenges in detection application in a plant field. In addition, due to different functional structures, components, matrix effects, physical properties, etc. of different organs in the plants differ greatly. The rice root systems belong to fibrous root systems, and are absorption organs as well as important places for synthesizing certain endogenous hormones, and their growth and development greatly differ from those of aboveground organs. A plasma spectrum generated by LIBS comprises complex variable information, and these information comprises emission spectral lines and continuous background information of target elements as well as matrix information corresponding to matrices. The matrix effect may affect signals of
LIBS and interfere with the applicability of conventional element emission spectral lines for quantitative detection of plant elements. Compared with a traditional spectrometer, a spectrum acquired by a monochromator is high in resolution, matrix information is relatively less, and more target element information is shown.
[06] Thus, providing a method and system for detecting the cadmium content of rice root systems based on LIBS in order to remove matrix effect interference and improve detection precision is a technical problem urgently needed to be solved in the field.
[07] In order to achieve the above objective, the disclosure provides the following solution: 1
[08] a quantitative detection method for cadmium in rice root systems, comprises:
LU502068
[09] acquiring spectral data of test rice root systems by a laser induced breakdown spectroscopy instrument,
[10] determining signal intensity of cadmium emission spectral lines according to the spectral data;
[11] acquiring a cadmium quantitative detection model; wherein the cadmium quantitative detection model is a detection model taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output; and
[12] determining the cadmium content in the test rice root systems by the cadmium quantitative detection model according to the signal intensity of the cadmium emission spectral lines.
[13] Preferably, establishing the cadmium quantitative detection model comprises:
[14] establishing the cadmium quantitative detection model by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through a unary linear regression method and/or a multiple linear regression method.
[15] Preferably, establishing the cadmium quantitative detection model by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through the unary linear regression method and/or the multiple linear regression method, specifically comprises:
[16] establishing a first cadmium quantitative detection model by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through the unary linear regression method,
[17] acquiring a linear regression coefficient, recorded as a first linear regression coefficient, of the first cadmium quantitative detection model;
[18] establishing a second cadmium quantitative detection model by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through the multiple linear regression method,
[19] acquiring a linear regression coefficient, recorded as a second linear regression coefficient, of the second cadmium quantitative detection model;
[20] judging whether the first linear regression coefficient is larger than the second linear regression coefficient, and acquiring a judgment result; if yes, taking the first cadmium quantitative detection model as the cadmium quantitative detection model; and if not, taking the second cadmium quantitative detection model as the cadmium quantitative detection model; wherein
[21] the first linear regression coefficient and the second linear regression coefficient are both the correlation degree between the signal intensity of the cadmium emission spectral lines and the cadmium content. 2
[22] Preferably, before acquiring the cadmium quantitative detection model, the quantitative detection method further comprises: LU502068
[23] acquiring rice root system samples with different cadmium contents, drying, grinding and tabletting the rice root system samples, and then taking the rice root system samples as test samples;
[24] collecting spectral data of the test samples by the laser induced breakdown spectroscopy instrument;
[25] preprocessing the acquired spectral data, and acquiring preprocessed spectral data; wherein preprocessing comprises noise filtering-out and marticulated processing;
[26] determining a plurality of cadmium emission spectral lines according to the preprocessed spectral data, and determining spectral line intensity corresponding to each emission spectral line;
[27] acquiring the real cadmium content of the rice root systems in the test samples; and
[28] training the cadmium quantitative detection model by taking the spectral line intensity as input and the real cadmium content of the rice root systems as output, and acquiring a trained cadmium quantitative detection model.
[29] A quantitative detection system for cadmium in rice root systems, comprises:
[30] a spectral data acquisition model, configured to acquire spectral data of test rice root systems by a laser induced breakdown spectroscopy instrument;
[31] a signal intensity determining module, configured to determine signal intensity of cadmium emission spectral lines according to the spectral data;
[32] a detection model acquisition model, configured to acquire a cadmium quantitative detection model; wherein the cadmium quantitative detection model is a detection model taking the signal intensity of the cadmium emission spectral line as input and the cadmium content as output; and
[33] acadmium content determining module, configured to determine the cadmium content in the test rice root systems according to the signal intensity of the cadmium emission spectral lines by the cadmium quantitative detection model.
[34] Preferably, the quantitative detection system further comprises:
[35] a detection model establishing module, configured to establish the cadmium quantitative detection model by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through a unary linear regression method and/or a multiple linear regression method.
[36] Preferably, the detection model establishing module specifically comprises:
[37] a first cadmium quantitative detection model establishing unit, configured to establish 3 a first cadmium quantitative detection model by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through the unary linear LU502068 regression method;
[38] a first linear coefficient acquisition unit, configured to acquire a linear regression coefficient, recorded as a first linear regression coefficient, of the first cadmium quantitative detection model;
[39] a second cadmium quantitative detection model establishing unit, configured to establish a second cadmium quantitative detection model by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through the multiple linear regression method,
[40] a second linear coefficient acquisition unit, configured to acquire a linear regression coefficient, recorded as a second linear regression coefficient, of the second cadmium quantitative detection model; and
[41] a judgment unit, configured to judge whether the first linear regression coefficient is larger than the second linear regression coefficient, and acquire a judgment result; if yes, the first cadmium quantitative detection model is taken as the cadmium quantitative detection model; and if not, the second cadmium quantitative detection model is taken as the cadmium quantitative detection model; wherein
[42] the first linear regression coefficient and the second linear regression coefficient are both the correlation degree between the signal intensity of the cadmium emission spectral lines and the cadmium content.
[43] Preferably, the quantitative detection system further comprises:
[44] a test sample acquisition module, configured to acquire rice root system samples with different cadmium contents, drying, grinding and tabletting the rice root system samples, and then take the rice root system samples as test samples;
[45] a spectral data collecting module, configured to collect spectral data of the test samples by a laser induced breakdown spectroscopy instrument;
[46] a spectral data preprocessing module, configured to preprocess the acquired spectral data, and acquire preprocessed spectral data; wherein preprocessing comprises noise filtering- out and marticulated processing;
[47] a spectral line intensity determining module, configured to determine a plurality of cadmium emission spectral lines according to the preprocessed spectral data, and determine spectral line intensity corresponding to each emission spectral line;
[48] a real cadmium content acquisition module, configured to acquire the real cadmium content of the rice root systems in the test samples; and
[49] a detection model training module, configured to train the cadmium quantitative detection model by taking the spectral line intensity as input and the real cadmium content of the rice root systems as output, and acquire a trained cadmium quantitative detection model. 4
LU502068
[50] FIG. 1 is a flow diagram of a quantitative detection method for cadmium in rice root systems provided by an embodiment of the disclosure.
[51] FIG. 1 is a flow diagram of a quantitative detection method for cadmium in rice root systems provided by an embodiment of the disclosure, and as shown in FIG. 1, the quantitative detection method for cadmium in rice root systems, comprises: [S2] SI. Spectral data of test rice root systems are acquired by a laser induced breakdown spectroscopy instrument.
[53] S2. Signal intensity of cadmium emission spectral lines is determined according to the spectral data.
[54] S3. A cadmium quantitative detection model is acquired. The cadmium quantitative detection model is a detection model taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output. [S5] S4. The cadmium content in the test rice root system is determined by the cadmium quantitative detection model according to the signal intensity of the cadmium emission spectral lines. [S6] Establishing the cadmium quantitative detection model comprises: [S7] establishing the cadmium quantitative detection model by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through a unary linear regression method and/or a multiple linear regression method. [S8] After establishing cadmium quantitative detection models through different linear regression methods, determining the finally-adopted cadmium quantitative detection model by taking correlation coefficients of the established cadmium quantitative detection models as a judgment basis, specifically comprises: [S9] A first cadmium quantitative detection model is established by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through the unary linear regression method.
[60] A linear regression coefficient, recorded as a first linear regression coefficient, of the first cadmium quantitative detection model is acquired.
[61] A second cadmium quantitative detection model is established by taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output through the multiple linear regression method.
[62] Alinear regression coefficient, recorded as a second linear regression coefficient, of the second cadmium quantitative detection model is acquired.
[63] Whether the first linear regression coefficient is larger than the second linear regression coefficient is judged; if yes, the first cadmium quantitative detection model is taken as the LU502068 cadmium quantitative detection model; and if not, the second cadmium quantitative detection model is taken as the cadmium quantitative detection model.
[64] the first linear regression coefficient and the second linear regression coefficient are both the correlation degree between the signal intensity of the cadmium emission spectral lines and the cadmium content.
[65] Furthermore, before acquiring the cadmium quantitative detection model in the step S3, the method further may comprise:
[66] Rice root system samples with different cadmium contents are acquired, the rice root system samples are dried, ground and tabletted, and then the rice root system samples are taken as test samples.
[67] Spectral data of the test samples are collected by the laser induced breakdown spectroscopy instrument. The spectral data are recorded as X.
[68] The acquired spectral data are preprocessed, and preprocessed spectral data are acquired. Preprocessing comprises noise filtering-out and marticulated processing. A preprocessed spectral matrix is recorded as Xi.
[69] A plurality of cadmium emission spectral lines are determined according to the preprocessed spectral data, and spectral line intensity corresponding to each emission spectral line is determined.
[70] The real cadmium content of the rice root systems in the test samples is acquired. The real cadmium content of the rice root systems is recorded as Y1.
[71] training the cadmium quantitative detection model by taking the spectral line intensity as input and the real cadmium content of the rice root systems as output, and acquiring a trained cadmium quantitative detection model.
[72] As another embodiment of the disclosure, establishing the cadmium quantitative detection model further may comprise:
[73] Nrays of cadmium emission spectral lines are rapidly positioned from spectral data Xi, the spectral lines are recorded as As Ayes dy and corresponding spectral line intensity is
Lodo,
[74] N columns of data, namely data corresponding to the n rays of cadmium emission spectral lines, are extracted from the preprocessed spectral matrix Xi. A new matrix, namely spectral data X2 and corresponding to cadmium, is formed by the acquired n columns of data.
[75] By means of a Kmeans method, a set quantity of samples are selected from the spectral data X2 in a certain proportion to form a modeling set, and the rest is taken as a prediction set.
[76] Linear regression models of the signal intensity of the n rays of cadmium emission 6 spectral lines and the cadmium content are respectively established by taking the signal intensity Lin of the n rays of cadmium emission spectral lines in the modeling set samples as LU502068 input and the real cadmium content Y in the samples as output through the unary linear
Y =al, +b regression method. The linear regression model is recorded as Yu: YF “UA 0,
Yi, =a,l, +b, Y,,=al, +b . . . pe, 7 nn " (n=1, 2, 3, ...). Where, an is a coefficient, and bn is a constant term. Multiple R (linear regression coefficient) of the above models is correlation coefficients R of the signal intensity of the cadmium emission spectral lines and the cadmium content in the corresponding models respectively.
[77] Multiple linear regression models of the signal intensity of the cadmium emission spectral lines and the cadmium content in the modeling set samples are respectively established by taking a signal intensity / combination of different cadmium spectral lines in the modeling set samples as input (the signal intensity “ combination number of different _ 2 3 CL. n cadmium spectral lines is recorded as 7, (=C,+C, + +C) , and C is the combination number) and the real cadmium content Y in the samples as output through the multiple linear regression method. The multiple linear regression model is recorded as YM,
Y, =k +kl, +k, + +k I
M 0 TA 7% 7 (n=1, 2, 3, ..). Where, Yy is a stalk heavy metal content, ko is a constant term, k, is a coefficient, ” is signal intensity of feature wavelength, 4, is the nm spectral line, and n=1,2,3... . The multiple correlation coefficient
Multiple R, also called the correlation coefficient R, of the above models can be used for measuring the correlation degree of the signal intensity of the cadmium emission spectral lines and the cadmium content in the corresponding models.
[78] A maximum Rmax of a correlation coefficient is acquired by comparing the correlation coefficients R in the linear regression models Yu and Ym, and the linear regression model corresponding to Rmax is a final cadmium quantitative detection model Y. 2 I, ; Co 5
[79] The signal intensity ” of the corresponding cadmium emission spectral lines in the prediction set samples is substituted into the established cadmium quantitative detection model
Y, and predicted values of the cadmium content in the samples are acquired. The correlation coefficient of the prediction set of the model is acquired by comparing the prediction result and the real cadmium content in the samples.
[80] The cadmium quantitative detection model Y contains the relation between LIBS spectra and the rice root system Cd content to the maximum degree, the influence of a matrix effect is relieved, and the root system Cd content can be rapidly and quantitatively detected. 7
Claims (2)
1. A quantitative detection method for cadmium in rice root systems, characterized in that the method comprises: acquiring spectral data of test rice root systems by a laser induced breakdown spectroscopy instrument; determining signal intensity of cadmium emission spectral lines according to the spectral data; acquiring a cadmium quantitative detection model; wherein the cadmium quantitative detection model is a detection model taking the signal intensity of the cadmium emission spectral lines as input and the cadmium content as output; and determining the cadmium content in the test rice root systems by the cadmium quantitative detection model according to the signal intensity of the cadmium emission spectral lines.
2. A quantitative detection system for cadmium in rice root systems, characterized in that the system comprises: a spectral data acquisition model, configured to acquire spectral data of test rice root systems by a laser induced breakdown spectroscopy instrument; a signal intensity determining module, configured to determine signal intensity of cadmium emission spectral lines according to the spectral data; a detection model acquisition model, configured to acquire a cadmium quantitative detection model; wherein the cadmium quantitative detection model is a detection model taking the signal intensity of the cadmium emission spectral line as input and the cadmium content as output; and a cadmium content determining module, configured to determine the cadmium content in the test rice root systems according to the signal intensity of the cadmium emission spectral lines by the cadmium quantitative detection model. 8
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| LU502068A LU502068B1 (en) | 2022-05-11 | 2022-05-11 | Quantitative detection method and system for cadmium in rice root systems |
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