CN114076745A - Saffron identification method based on cloud-interconnection portable near-infrared technology and adulterated product quantitative prediction method thereof - Google Patents
Saffron identification method based on cloud-interconnection portable near-infrared technology and adulterated product quantitative prediction method thereof Download PDFInfo
- Publication number
- CN114076745A CN114076745A CN202010845518.0A CN202010845518A CN114076745A CN 114076745 A CN114076745 A CN 114076745A CN 202010845518 A CN202010845518 A CN 202010845518A CN 114076745 A CN114076745 A CN 114076745A
- Authority
- CN
- China
- Prior art keywords
- saffron
- model
- sample
- authenticity identification
- identification model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 244000124209 Crocus sativus Species 0.000 title claims abstract description 172
- 235000015655 Crocus sativus Nutrition 0.000 title claims abstract description 172
- 235000013974 saffron Nutrition 0.000 title claims abstract description 150
- 239000004248 saffron Substances 0.000 title claims abstract description 150
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000005516 engineering process Methods 0.000 title abstract description 16
- 238000012549 training Methods 0.000 claims abstract description 28
- 238000001228 spectrum Methods 0.000 claims abstract description 25
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 21
- 238000007781 pre-processing Methods 0.000 claims abstract description 16
- 238000010239 partial least squares discriminant analysis Methods 0.000 claims abstract description 9
- 238000010987 Kennard-Stone algorithm Methods 0.000 claims abstract description 8
- 241000723353 Chrysanthemum Species 0.000 claims description 38
- 235000007516 Chrysanthemum Nutrition 0.000 claims description 38
- 240000002853 Nelumbo nucifera Species 0.000 claims description 38
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims description 38
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims description 38
- 235000003255 Carthamus tinctorius Nutrition 0.000 claims description 36
- 244000020518 Carthamus tinctorius Species 0.000 claims description 36
- 240000008042 Zea mays Species 0.000 claims description 29
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 claims description 27
- 235000002017 Zea mays subsp mays Nutrition 0.000 claims description 27
- 235000005822 corn Nutrition 0.000 claims description 27
- 230000003595 spectral effect Effects 0.000 claims description 27
- 229920001131 Pulp (paper) Polymers 0.000 claims description 23
- 238000010238 partial least squares regression Methods 0.000 claims description 15
- 238000012935 Averaging Methods 0.000 claims description 12
- 238000012937 correction Methods 0.000 claims description 6
- 238000000149 argon plasma sintering Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 238000003556 assay Methods 0.000 claims 1
- 238000001514 detection method Methods 0.000 abstract description 12
- 239000000463 material Substances 0.000 abstract description 4
- 238000012360 testing method Methods 0.000 description 12
- 241000628997 Flos Species 0.000 description 4
- 238000002790 cross-validation Methods 0.000 description 4
- 238000009795 derivation Methods 0.000 description 4
- 241001515002 Elaeodendron croceum Species 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 2
- 238000004497 NIR spectroscopy Methods 0.000 description 2
- 235000007244 Zea mays Nutrition 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 229940089639 cornsilk Drugs 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000000144 pharmacologic effect Effects 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- 239000001231 zea mays silk Substances 0.000 description 2
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 241001113425 Iridaceae Species 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000017531 blood circulation Effects 0.000 description 1
- 235000009120 camo Nutrition 0.000 description 1
- 244000213578 camo Species 0.000 description 1
- 238000005266 casting Methods 0.000 description 1
- 208000026106 cerebrovascular disease Diseases 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 239000001209 crocus sativus l. Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 206010012601 diabetes mellitus Diseases 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000002526 effect on cardiovascular system Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 238000004128 high performance liquid chromatography Methods 0.000 description 1
- 238000004949 mass spectrometry Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003960 organic solvent Substances 0.000 description 1
- 238000001558 permutation test Methods 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 208000020016 psychiatric disease Diseases 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000002798 spectrophotometry method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000004809 thin layer chromatography Methods 0.000 description 1
- 231100000167 toxic agent Toxicity 0.000 description 1
- 239000003440 toxic substance Substances 0.000 description 1
- 230000002936 tranquilizing effect Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Images
Classifications
-
- 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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- 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
-
- 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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
- G01N2201/1293—Using chemometrical methods resolving multicomponent spectra
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a saffron authenticity identification model based on a cloud-interconnection portable near-infrared technology, which is constructed by the following steps: (1) collecting known saffron and its counterfeit and/or adulterated sample, collecting near infrared spectrum data, and preprocessing the spectrum data; (2) dividing the sample into a training sample and a prediction sample by using a Kennard-Stone algorithm according to the preprocessing data obtained in the step 1); (3) establishing a saffron authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using a training sample; (4) and (5) verifying the saffron authenticity identification model by using the prediction sample to obtain the saffron authenticity identification model. The saffron authenticity identification model and the counterfeit amount detection model have good accuracy and reliability, can be used for on-site rapid detection of saffron, and provide method reference for rapid detection of other rare traditional Chinese medicinal materials.
Description
Technical Field
The invention relates to the field of geological exploration, in particular to a saffron identification method based on a cloud-interconnection portable near-infrared technology and a adulterated product quantitative prediction method thereof.
Background
Stigma croci Sativi is dry stigma of Crocus sativus L. of Iridaceae, and has effects of promoting blood circulation for removing blood stasis, cooling blood for removing toxic substance, resolving stagnation and tranquilizing. Modern pharmacological research shows that the saffron has various pharmacological effects of treating cardiovascular and cerebrovascular diseases, mental diseases, diabetes, tumor and the like. The yield of saffron is extremely low, and it is reported that 1kg of saffron can be harvested from 10 ten thousand saffron[3]Expensive, also called "plant gold". The phenomenon of false is often caused by mixing saffron on the market, and common saffron false products in China mainly comprise saffron, chrysanthemum, lotus stamens, corn stigma and linear paper pulp. The method for identifying the authenticity mainly comprises a color reaction, a thin layer chromatography, an ultraviolet spectrophotometry, a high performance liquid chromatography, a mass spectrometry and a molecular marking technology. The existing authenticity identification method has the defects of complex pretreatment, use of organic solvent, sample damage, incapability of on-site rapid detection, high detection cost and the like. Therefore, it is urgently needed to develop a simple and nondestructive detection method capable of on-site rapid measurement.
Reports of identifying saffron and its counterfeit products and adulterated products by using near infrared spectroscopy technology are rare, only reports of establishing qualitative and quantitative analysis of saffron and its foreign common counterfeit products by using a desk type near infrared instrument such as Eman Shawky and the like are reported at present, and reports of identifying domestic common saffron counterfeit products such as chrysanthemum, corn silk, lotus silk and linear paper pulp by using near infrared technology are not reported.
Disclosure of Invention
In order to solve the problems, the invention provides a saffron authenticity identification model based on a cloud-interconnection portable near-infrared technology, which is constructed by the following steps:
(1) collecting known saffron and its counterfeit and/or adulterated sample, collecting near infrared spectrum data, and preprocessing the spectrum data;
(2) dividing the sample into a training sample and a prediction sample by using a Kennard-Stone algorithm according to the preprocessing data obtained in the step 1);
(3) establishing a saffron authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using a training sample;
(4) and (5) verifying the saffron authenticity identification model by using the prediction sample to obtain the saffron authenticity identification model.
Further, the instrument for acquiring the near infrared spectrum data in the step (1) is a PV500R-I portable near infrared instrument controlled by a mobile phone.
Further, the wavelength range of the near infrared spectrum is 1350-1850 nm.
Further, the spectrum collection times in the step (1) are 6 times.
Further, the preprocessing method in step (1) is first derivative, second derivative, third derivative, standard normal variable transformation (SNV), light scattering correction (MSC) or raw data averaging spectrum, preferably, raw data averaging spectrum.
Further, the counterfeit product is safflower, corn stigma, lotus stamen, chrysanthemum and/or paper pulp,
furthermore, the number of the saffron authenticity identification models is one or two, and when a saffron counterfeit product is identified, one identification model is preferred; when identifying saffron adulterants, two discriminant models are preferred.
Furthermore, when the saffron true and false identification model is one, a known saffron and a sample of a counterfeit product thereof are taken to establish a model; when the number of the saffron authenticity identification models is two, the first model is established by taking a known saffron and a adulterated sample thereof, the second model is established by taking a adulterated sample, and preferably, the second model is established by taking saffron-doped chrysanthemum, saffron-doped safflower and saffron-doped lotus stamen samples.
The invention also provides a method for distinguishing saffron and a fake product and/or adulterated product thereof, which comprises the following steps:
a. taking a sample to be detected, and obtaining preprocessed spectral data according to the step (1);
b. and (b) inputting the spectral data obtained in the step a into the saffron authenticity identification model, and reading out whether the sample to be detected belongs to a genuine product, a counterfeit product or a adulterated product according to a classification table output by the saffron authenticity identification model.
The invention also provides a measuring model of the adulteration amount of the crocus sativus adulteration product, which is constructed by adopting the following steps:
firstly, collecting known crocus sativus adulterants according to the steps (1) to (2), preprocessing spectral data, and selecting a training sample and a prediction sample;
establishing a stigma croci adulteration amount prediction model based on a Partial Least Squares Regression (PLSR) method by using training samples;
and thirdly, verifying a saffron adulteration amount prediction model by using the prediction sample to obtain the saffron adulteration amount prediction model.
Further, the adulterant is safflower, chrysanthemum, lotus stamen, corn stigma and/or paper pulp.
The invention finally provides a method for measuring the adulteration amount of the crocus sativus adulteration product, which comprises the following steps:
and (2) taking a sample to be detected, obtaining preprocessed spectral data according to the step (1), inputting the data into the saffron adulteration amount prediction model, and outputting data of the saffron adulteration amount prediction model, namely the adulteration amount of the saffron adulterant.
According to the method, a PLS-DA model is established for saffron and its counterfeit products and adulterated products by combining a cloud-interconnection PV500R-I portable near infrared spectrum technology with chemometrics for the first time, the saffron and its counterfeit products can be completely identified by one optimal PLS-DA model, the saffron and its adulterated products can be better identified by two optimal PLS-DA models step by step, the identification accuracy is higher than 93%, and the adulteration identification level is as low as 0.5% -4%. Five PLSR quantitative prediction models are established for the adulteration amounts of the safflower, the corn stigma, the lotus stamen, the chrysanthemum and the paper pulp adulterants, the external prediction correlation coefficient range is 0.920-0.999, the RMSEP range is 0.005-0.044, and when the adulteration amount is more than 8%, the quantitative prediction models can better or well predict the adulteration amount of the adulterants.
The crocus sativus true and false identification method and the counterfeit product adulteration amount detection method based on the cloud-interconnection portable near infrared spectrum technology have better accuracy and reliability. Compared with a conventional desk-top near-infrared instrument, the cloud-interconnection portable near-infrared instrument is simple to operate, can be used for on-site rapid detection of saffron and provides method reference for rapid detection of other rare traditional Chinese medicinal materials.
Obviously, many modifications, substitutions, and variations are possible in light of the above teachings of the invention, without departing from the basic technical spirit of the invention, as defined by the following claims.
The present invention will be described in further detail with reference to the following examples. This should not be understood as limiting the scope of the above-described subject matter of the present invention to the following examples. All the technologies realized based on the above contents of the present invention belong to the scope of the present invention.
Drawings
FIG. 1 photograph of saffron and stained counterfeit
FIG. 2 is a schematic diagram of the main operation of a cloud-interconnection PV500R-I portable near infrared instrument
FIG. 3 shows the original spectra of stigma croci Sativi and its counterfeit (A), stigma croci Sativi and its blended counterfeit (B), a (green), B (dark blue), c (red), d (yellow), e (blue), and f (purple) are stigma croci Sativi, flos Carthami, stigma Maydis, pulp, stamen Nelumbinis, flos Chrysanthemi
FIG. 4 shows the PLS-DA model results of the authenticity identification of saffron and its counterfeit. (A) (B), (C), (D) two-dimensional score maps of principal components 1 and 2, principal components 2 and 4, principal components 1 and 7, and principal components 2 and 6, respectively, (E) the results of the permutation test; a (green), b (dark blue), c (red), d (yellow), e (blue) and f (purple) are respectively saffron, safflower, corn stigma, paper pulp, lotus stamen and chrysanthemum
FIG. 5 shows the PLS-DA model results of the authenticity identification of crocus sativus and adulterants thereof. (A) (B) two-dimensional score maps respectively drawn by main components 1 and 2, main components 1 and 15 and main components 2 and 6, and (D) replacement test results; a (green), b (dark blue), c (red), d (yellow), e (blue) and f (purple) are respectively saffron, safflower, corn stigma, paper pulp, lotus stamen and chrysanthemum
FIG. 6 shows the PLS-DA identification model results of three types of saffron adulterants of chrysanthemum, safflower and lotus stamen. (A) (B) two-dimensional score maps drawn by main components 1 and 2 and main components 1 and 4, respectively, (C) displacement test results a (green), B (dark blue), C (red), d (yellow), e (blue) and f (purple) are saffron, safflower, corn stigma, paper pulp, lotus stamen and chrysanthemum respectively
FIG. 7 shows the external prediction results of the optimal PLSR quantitative prediction model of the adulteration amounts of five saffron adulterants, namely safflower (A), chrysanthemum (B), lotus stamen (C), paper pulp (D) and corn stamens (E).
Detailed Description
Example 1 discrimination of saffron and its counterfeit products in accordance with the present invention
Firstly, establishing an identification model of saffron and its counterfeit products
(1) Respectively taking known saffron, safflower, corn stigma, lotus stamen, chrysanthemum and pulp samples, and collecting near infrared spectrum data in the wavelength range of 1350-;
(2) averaging the spectrum data of each sample in the step (1), and dividing each sample into a training sample and a prediction sample by using a Kennard-Stone algorithm;
(3) establishing a saffron authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using a training sample;
(4) verifying the crocus sativus true and false identification model by using the prediction sample;
secondly, distinguishing the sample to be measured
(5) Collecting a sample to be detected, and collecting near infrared spectrum data in the wavelength range of 1350-1850nm by using a PV500R-I portable near infrared instrument controlled by a mobile phone for 6 times;
(6) averaging the spectrum data of the sample to be measured;
(7) inputting the average spectral data of the step 6) into the saffron authenticity identification model of the step 4), and reading out whether the sample to be detected belongs to a genuine product or a counterfeit product according to a classification table output by the saffron authenticity identification model.
Example 2 discrimination of saffron and its adulterated product
Firstly, establishing an identification model of saffron and adulterants of saffron
(1) Respectively taking known stigma croci, stigma Maydis, stigma croci, stamen Nelumbinis, flos Chrysanthemi and pulp sample, and collecting near infrared spectrum data in wavelength range of 1350-;
(2) averaging the spectrum data of each sample in the step (1), and dividing each sample into a training sample and a prediction sample by using a Kennard-Stone algorithm;
(3) firstly, establishing a first stigma croci authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using training samples of stigma croci, stigma croci doped with safflower, stigma maydis doped with stigma croci, stigma croci doped with stamen, chrysanthemum doped with stigma croci and pulp doped with stigma croci, and establishing a second stigma croci authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using training samples of stigma croci, chrysanthemum doped with stigma croci or stigma croci doped with stamen;
(4) verifying the two saffron authenticity identification models in the step 3) by using the prediction samples;
secondly, distinguishing the sample to be measured
(5) Respectively taking samples to be detected, and collecting near infrared spectrum data in the wavelength range of 1350-1850nm by using a PV500R-I portable near infrared instrument controlled by a mobile phone for 6 times;
(6) averaging the spectrum data of the sample to be measured;
(7) inputting the average spectral data of the step 6) into the first saffron authenticity identification model in the step 4), reading out whether the sample to be detected belongs to a genuine product or a counterfeit product according to a classification table output by the saffron authenticity identification model, if the sample to be detected belongs to a genuine product or a counterfeit product, if the sample to be detected is the saffron mixed with chrysanthemum, the saffron mixed with safflower or the saffron mixed with lotus stamen, inputting the average spectral data of the step 6) into the second saffron authenticity identification model in the step 4), and determining whether the sample to be detected belongs to the saffron mixed with chrysanthemum, the saffron mixed with safflower or the saffron mixed with lotus stamen according to the classification table output by the saffron authenticity identification model.
Example 3 discrimination of crocus sativus, its counterfeit products and adulterated products of the present invention
Firstly, establishing an identification model of saffron and its counterfeit products and adulterated products
(1) Respectively taking samples of known saffron, safflower, corn stigma, lotus stamen, chrysanthemum, paper pulp, saffron-doped safflower, saffron-doped corn stigma, saffron-doped lotus stamen, saffron-doped chrysanthemum and saffron-doped paper pulp, and collecting near infrared spectrum data in the wavelength range of 1350-1850nm by using a PV500R-I portable near infrared instrument controlled by a mobile phone for 6 times;
(2) averaging the spectrum data of each sample in the step (1), and dividing each sample into a training sample and a prediction sample by using a Kennard-Stone algorithm;
(3) establishing a truth identification model of saffron and a counterfeit product:
establishing a saffron authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using training samples of saffron, safflower, corn stigma, lotus stamen, chrysanthemum and paper pulp;
establishing a saffron and adulterant true and false identification model:
firstly, establishing a first stigma croci authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using training samples of stigma croci, stigma croci doped with safflower, stigma maydis doped with stigma croci, stigma croci doped with stamen, chrysanthemum doped with stigma croci and stigma croci doped with paper pulp, and establishing a second stigma croci authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using training samples of stigma croci, chrysanthemum doped with safflower and stigma croci doped with lotus stamen;
(4) and (3) verifying the model:
verifying the authenticity identification model of the saffron and the counterfeit product in the step 3) by using a prediction sample of the saffron, the safflower, the corn stigma, the lotus stamen, the chrysanthemum and the paper pulp;
verifying the authenticity identification model of the saffron and the adulterated product in the step 3) by using the prediction samples of the saffron, the saffron mixed with the safflower, the saffron mixed with the corn stigma, the saffron mixed with the lotus stigma, the saffron mixed with the chrysanthemum and the saffron mixed with the paper pulp;
secondly, distinguishing the sample to be measured
(5) Collecting a sample to be detected, and collecting near infrared spectrum data in the wavelength range of 1350-1850nm by using a PV500R-I portable near infrared instrument controlled by a mobile phone for 6 times;
(6) averaging the spectrum data of the sample to be measured;
(7) inputting the average spectral data of the step 6) into the saffron authenticity identification model in the step 4), reading out whether the sample to be detected belongs to a genuine product, a counterfeit product or a adulterated product according to a classification table output by the saffron authenticity identification model, and determining the specific type of the adulterated product.
EXAMPLE 4 determination of the adulteration amount of crocus sativus adulterant according to the invention
Firstly, establishing an identification model of saffron and its counterfeit products
(1) Respectively taking known saffron-doped saffron samples, saffron-doped corn stigma samples, saffron-doped lotus stamens samples, saffron-doped chrysanthemum samples and saffron-doped paper pulp samples, and collecting near infrared spectrum data in the wavelength range of 1350-1850nm for 6 times by using a PV500R-I portable near infrared instrument controlled by a mobile phone;
(2) averaging the spectral data of each sample in the step (1), and dividing each sample into a training sample and a prediction sample by using a Kennard-Stone algorithm;
(3) establishing a saffron adulteration amount prediction model based on a Partial Least Squares Regression (PLSR) by using training samples;
(4) verifying a saffron adulteration amount prediction model by using a prediction sample;
secondly, measuring the adulteration quantity of the sample to be measured
(5) Collecting a sample to be detected, and collecting near infrared spectrum data by using a PV500R-I portable near infrared instrument controlled by a mobile phone;
(6) averaging the spectrum data of the sample to be measured;
(7) inputting the average spectral data of the step 6) into the saffron adulteration amount prediction model of the step 4), and directly reading the adulteration amount of the sample to be measured according to the data output by the saffron adulteration amount prediction model.
The advantageous effects of the present invention are further illustrated by the following test examples
Test example 1
1 experimental part
1.1 sample
60 portions of saffron sample, 43 portions of safflower sample, 20 portions of chrysanthemum and lotus stamen are randomly purchased from the market of the traditional Chinese medicinal materials in the lotus pool. 20 corn silk samples were randomly purchased from farmer markets. 20 parts of the linear pulp sample was homemade. The crocus sativus is identified as a certified product by professor of the Yangtze casting cloud of Chengdu Chinese medicine university. Stigma croci Sativi and its dye counterfeits are shown in figure 1.
Preparing a adulteration product: fake products are mixed into saffron according to the mass ratio, and the mixing range is as follows: 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 15 parts in total, two parts are prepared in parallel, one part is a training set for constructing the model, and the other part is a testing set for verifying the model.
1.2 cloud-interconnection PV500R-I portable near-infrared technology
The cloud-interconnection PV500R-I portable near-infrared system mainly comprises three parts: wireless PV500R-I portable near infrared instrument (changhong technologies ltd, china), performance parameters: length, width, height 110mm 70mm, weight 400 g; spectral resolution: 20 nm; the wavelength repeatability is +/-2 nm; ultra-large light spots: 70 mm. A mobile phone is provided. And a cloud database. The working schematic diagram of the portable near-infrared spectrometer is shown in figure 2, the portable near-infrared spectrometer is connected with a mobile phone through Bluetooth, scanned sample spectrum data are uploaded to the cloud end through the mobile phone, the type or content of an unknown sample is rapidly calculated through an established prediction model at the cloud end, and the result is rapidly fed back to the mobile phone end.
1.3 near Infrared Spectroscopy
After the portable near-infrared instrument is corrected, the instrument is directly attached to the surface of a sample, and a spectrogram is acquired within the wavelength range of 1350-1850 nm. The measurement was repeated 6 times for each sample, and the spectrum was averaged for modeling.
1.4 spectral data preprocessing and model correction
1.4.1 sample selection and Pre-processing of spectral data
To make the model robust, avoiding overfitting, the Kennard-Stone algorithm was used to select the training set (two thirds of the sample size) and the prediction set (one third of the sample size). Because the sample sizes of various types are different to a certain extent, in order to avoid generating larger unbalanced samples, a training set and a prediction set are selected for each type of sample, and then the training set and the prediction set of each type of sample are added to obtain the final training set and the final prediction set.
SNV and MSC are scattering correction preprocessing technology and are commonly used for eliminating influences caused by uneven particle distribution and particle size scattering, and in addition, SNV and MSC can also eliminate influences of spectral translation and random noise caused by samples in spectral scanning, so that the prediction capability of a model is improved. The first, second and third derivative filters are adopted to improve the spectral resolution and eliminate the baseline drift and background in the original infrared spectrum.
1.4.2 establishment of PLS-DA-based saffron authenticity identification model
Under the condition of the full waveband (1350-. The number of optimum Latent Variables (LVs) was determined using the minimum of the cross-validation Root Mean Square Error (RMSECV) for the 7-fold cross-validation. The optimal model selection principle is as follows: the larger the values of R2X, R2Y, Q2, internal prediction accuracy and external prediction accuracy, the better the model performance. The PLS-DA model was built using Simca (version 13.0, Umetrics, Sweden) software.
1.4.3 establishment of quantitative prediction model of saffron adulteration amount based on PLSR
Under the condition of full wave band, a PLSR quantitative prediction model of five saffron adulterants is established. And according to the regression coefficient size of the variable and the model performance, selecting the optimal regression coefficient and the corresponding important wave band to improve the model performance. The model evaluation indexes include: determining the coefficient (R)2) Root Mean Square Error (RMSE), RMSECV, mean squareRoot error (RMSEP), R2The larger the RMSE, the smaller the RMSECV and RMSEP, and the smaller the difference between RMSECV and RMSEP, indicating better model performance. The PLSR model was established using Unscamblebler (version 7.5, CAMO ASA, Norway) software.
2 results and discussion
2.1 spectral characteristics of saffron and its counterfeit products
FIGS. 3(A) and (B) are the original spectra of saffron and its counterfeit, and its adulterated sample in the range of 1350-1850nm, respectively. The spectral range is a first-order frequency doubling region of stretching vibration of groups such as C-H, O-H, N-H and the like. Intuitively, the spectrums of saffron and the counterfeits thereof have certain cross, but have concentrated distribution ranges respectively, and the spectrums are sequentially the saffron, the lotus stamen, the saffron, the chrysanthemum and the corn stigma from top to bottom, which shows that the spectrums of the saffron and the counterfeits have certain difference, which is mainly caused by the inconsistency of the respective component compositions. The special characteristic is the near infrared spectrum (yellow curve) of the pulp, and the spectrum curve has a sudden drop process in the range of 1560-1660nm, probably because the pulp is an industrial processed product and the components of the pulp are obviously different from other plant source samples. The spectral characteristics of saffron and its counterfeit provide a spectral basis for its identification and quantitative analysis. On the other hand, because saffron accounts for a larger proportion in the adulterated product, spectral curves between the saffron and the adulterated product are overlapped more seriously.
2.2 PLS-DA authentication model
In the modeling process, it is very difficult to find that the saffron, the saffron counterfeit product and the saffron adulterated product are effectively distinguished at the same time. In order to effectively identify saffron and its counterfeit products and adulterants, an optimal identification model is established for the saffron and its counterfeit products, and then 1-2 optimal identification models are established for the saffron and its adulterants.
2.2.1 saffron and its counterfeit identification model
Under the condition of all wave bands, the original data and five kinds of data of crocus sativus and its counterfeit products are usedSix PLS-DA models were constructed from the data processed by the processing methods (first derivative, second derivative, third derivative, MSC, SNV), and the results are shown in Table 1, Table 2, and FIG. 4. As can be seen from table 1, the model built from the raw data has the best performance (R2X ═ 1, R2Y ═ 0.841, Q2 ═ 0.733, and LV ═ 13), the prediction accuracy of the model on the training set sample and the test set sample is 100%, and the model built after the data is preprocessed is not improved, which may be that the noise is reduced and the signal-to-noise ratio is improved during the spectrum preprocessing, but more important information is lost. Table 2 shows the results of the error classification table for the external prediction (test set) of the optimal model, which indicates that all six samples can be distinguished by 100%. Fig. 4(a) is a two-dimensional score chart drawn by the principal components 1 and 2, and it can be seen that the principal component 1 plays a major role in accurately distinguishing saffron, pulp, and safflower from each other, and the pulp can be clearly distinguished from the other five types of samples, while the principal component 2 plays a major role in accurately distinguishing safflower from corn stigma, and safflower from chrysanthemum. Similarly, as shown in FIGS. 4(B), (C) and (D), stamen Nelumbinis can be effectively distinguished from stigma croci, stigma Maydis from flos Chrysanthemi, and stigma croci from stamen Nelumbinis. FIG. 4(E) is the replacement test result (R) of the model2＝0.105，Q2-0.341), all blue Q's are known2All values are in green R2Below the value, the model is shown to be reliable.
TABLE 1 PLS-DA model results for identifying authenticity of crocus sativus and its counterfeit products, crocus sativus and its adulterated products, and 3 kinds of crocus sativus adulterated products, which are established by using original data and data obtained by preprocessing 5 different kinds of data under full-wave band conditions
Note: the bold character represents the optimal model.
2.2.2 saffron and adulterant identification model thereof
Six PLS-DA models of saffron and its adulterants were constructed in the same manner as described above, and the results are shown in Table 1, Table 2, and FIG. 5. Table 1 also shows that the recognition model established from the raw data is optimal (R2X ═ 1, R2Y ═ 0.739, Q2 ═ 0.527, LV ═ 17), and it is used for trainingThe prediction accuracy of the sample set is 91%, and the prediction accuracy of the test set is 89%. Replacement test results (R)2＝0.237，Q2-0.663) (see fig. 5(D)) indicates that the model is reliable. Although the optimal model performs well, some class recognition rates are poor. Analysis table 2 shows that, in addition to the lotus stamen adulterant, the model can completely distinguish other four adulterants from saffron at the same time, fig. 5(a) shows that the major component 1 and the major component 2 can completely distinguish saffron from corn stigma and pulp, and fig. 5(C) shows that the major component 1 and the major component 4 can completely distinguish saffron from chrysanthemum and safflower, which indicates that the adulteration identification level of the four saffron adulterants can be as low as 0.5% and is lower than the lowest adulteration identification level (1% or 5%) of saffron in the past year literature. Four samples with low adulteration of lotus stamen are mistakenly judged to be saffron (see figure (5(C)) with the recognition level of 4%, figure 5(B) and table 2 show that the corn stigma and the pulp can be completely distinguished, the recognition rate of both the corn stigma and the pulp adulterant is higher than 93%, and the other three adulterants are not confused with the samples, which shows that the specificity is high, but the safflower and the lotus stamen adulterants are mistakenly judged to be mutually 80% and 60%, respectively.
TABLE 2 wrong classification table of optimal PLS-DA model of stigma croci Sativi and its counterfeit, stigma croci Sativi and its adulterant, and three categories of stigma croci Sativi adulterants
2.2.3 identification model of three types of adulterants
The same method is used for establishing 6 PLS-DA models for three crocus sativus adulterants of chrysanthemum, safflower and lotus stamen respectively. The results obtained are shown in Table 1 below,table 2 and fig. 6. Similarly, as can be seen from table 1, the model established by the raw data is optimal (R2X ═ 1, R2Y ═ 0.632, Q2 ═ 0.554, and LV ═ 5), the prediction accuracy for the training sample is 98%, the prediction accuracy for the test set is 96%, and the error classification table (see table 2) indicates that the recognition rate of the 3 types of adulterants is above 93%. FIG. 6(A) shows that the chrysanthemum adulterant can be completely distinguished from safflower and lotus stamen, and FIG. 6(B) shows that one lotus stamen adulterant is mistaken for safflower, which is consistent with the results in Table 2. Replacement test results (R)2＝0.116，Q2-0.302) (see fig. 6(C)) indicates that the model is reliable.
2.3 PLSR model of adulteration quantity of five categories of crocus sativus adulterants
Under the condition of full wave band, six PLSR quantitative prediction models are respectively established for original data of adulteration amount of five saffron adulterants including safflower, chrysanthemum, lotus stamen, corn stigma and paper pulp and data processed by five data pretreatment methods, and the results are shown in Table 3 and figure 7. As can be seen from table 3, only the optimal model of safflower is provided for the original spectral data, the correlation coefficients of the correction set, the cross validation set and the prediction set of the optimal model are greater than 0.920, and the error parameter is the minimum of the six models. The optimal quantitative models of the adulteration amounts of other four types of adulterants are provided by first order derivation or second order derivation, the correlation coefficients of the correction set, the cross validation set and the prediction set are all higher than 0.924, and the error parameters of the optimal models of the adulteration amounts of the paper pulp, the lotus stamens, the corn stamens and the chrysanthemum are the minimum values in the corresponding models, so that the optimal models can be obtained more easily after the original spectral data is subjected to first order derivation or second order derivation processing. Meanwhile, the difference between the RMSECV value and the RMSEP value of the five adulterant adulteration amount optimal model is small, which indicates that the five models are reliable and do not have overfitting. FIG. 7 shows that the adulteration reference value and the points corresponding to the predicted value of the external prediction sample of the five categories of crocus sativus adulteration are uniformly distributed on both sides of the external prediction curve, but when the adulteration range is 0.5% -8%, the predicted value and the true value have large relative errors, and the relative errors of the crocus sativus, the paper pulp, the chrysanthemum, the corn stigma and the lotus stamen are respectively as high as 372%, 203%, 61%, 42% and 23%, which indicates that the model is not suitable for predicting samples with low adulteration; when the adulteration amount of the sample is more than 8%, the relative errors of the safflower, the paper pulp, the chrysanthemum, the corn stigma and the lotus stamen are respectively less than 8%, 10%, 8%, 5% and 3%, which shows that the model can better or well predict the adulteration amounts of the five adulterants.
Table 3 PLSR quantitative prediction model results of adulteration amounts of five saffron adulterants including safflower, chrysanthemum, lotus stamen, paper pulp and corn stigma established by using original data and data obtained by preprocessing 5 different data under full-wave band conditions
Note: the bold character represents the optimal model.
3 conclusion
According to the method, a PLS-DA model is established for the saffron and the fake products thereof and adulterated products thereof by combining a cloud-interconnection PV500R-I portable near infrared spectrum technology and chemometrics for the first time, one optimal PLS-DA model can completely distinguish the saffron and the fake products thereof, the two optimal PLS-DA models can enable the prediction accuracy of the saffron and the fake products thereof to reach more than 93%, and the identification level of the adulteration amount is as low as 0.5% -4%. Five PLSR quantitative prediction models are established for the adulteration of the five saffron crocus, the external prediction correlation coefficient range is 0.920-0.999, the RMSEP range is 0.005-0.044, and when the adulteration is more than 8%, the quantitative prediction models can better or well predict the adulteration of the adulteration.
In conclusion, the saffron authenticity identification method and the counterfeit adulteration amount detection method based on the cloud-interconnection portable near infrared spectrum technology have better accuracy and reliability. Compared with a conventional desk-top near-infrared instrument, the cloud-interconnection portable near-infrared instrument is simple to operate, can be used for on-site rapid detection of saffron and provides method reference for rapid detection of other rare traditional Chinese medicinal materials.
Claims (12)
1. The utility model provides a saffron true and false identification model based on high in clouds-portable near-infrared technique of interconnection which characterized in that: the method is constructed by the following steps:
(1) collecting known saffron and its counterfeit and/or adulterated sample, collecting near infrared spectrum data, and preprocessing the spectrum data;
(2) dividing the sample into a training sample and a prediction sample by using a Kennard-Stone algorithm according to the preprocessing data obtained in the step 1);
(3) establishing a saffron authenticity identification model based on a partial least squares discriminant analysis method (PLS-DA) by using a training sample;
(4) and (5) verifying the saffron authenticity identification model by using the prediction sample to obtain the saffron authenticity identification model.
2. The discriminative model of claim 1 wherein: the instrument for acquiring the near infrared spectrum data in the step (1) is a PV500R-I portable near infrared instrument controlled by a mobile phone.
3. The discriminant model of claim 1 or 2, wherein: the wavelength range of the near infrared spectrum is 1350-1850 nm.
4. The discriminative model of claim 1 wherein: the spectrum collection times in the step (1) are 6 times.
5. The discriminative model of claim 1 wherein: the preprocessing method in the step (1) is first derivative, second derivative, third derivative, standard normal variable transformation (SNV), light scattering correction (MSC) or raw data averaging spectrum, preferably, raw data averaging spectrum.
6. The discriminative model of claim 1 wherein: the counterfeit product is safflower, corn stigma, lotus stamen, chrysanthemum and/or paper pulp.
7. The discriminative model of claim 1 wherein: the authenticity identification model of saffron is one or two, and when the counterfeit saffron is identified, one identification model is preferably selected; when identifying saffron adulterants, two discriminant models are preferred.
8. The discriminative model of claim 7 wherein: when the saffron authenticity identification model is one, establishing a model by taking samples of known saffron and counterfeit products thereof; when the number of the saffron authenticity identification models is two, the first model is established by taking a known saffron and a adulterated sample thereof, the second model is established by taking a adulterated sample, and preferably, the second model is established by taking saffron-doped chrysanthemum, saffron-doped safflower and saffron-doped lotus stamen samples.
9. A method for distinguishing saffron and a fake product and/or a fake product mixed with the saffron is characterized by comprising the following steps: it comprises the following steps:
a. taking a sample to be detected, and obtaining preprocessed spectral data according to the step (1);
b. inputting the spectral data obtained in the step a into a saffron authenticity identification model according to claim 1, and reading out whether the sample to be tested belongs to a genuine product, a counterfeit product or a adulterated product according to a classification table output by the saffron authenticity identification model.
10. A measuring model for the adulteration amount of crocus sativus adulterants is characterized by being constructed by the following steps:
firstly, collecting known crocus sativus adulterants according to the steps (1) to (2), preprocessing spectral data, and selecting a training sample and a prediction sample;
establishing a stigma croci adulteration amount prediction model based on a Partial Least Squares Regression (PLSR) method by using training samples;
and thirdly, verifying a saffron adulteration amount prediction model by using the prediction sample to obtain the saffron adulteration amount prediction model.
11. The assay according to claim 10, characterized in that: the adulterant is safflower, chrysanthemum, lotus stamen, corn stigma and/or paper pulp.
12. A method for measuring the adulteration amount of a crocus sativus adulteration product is characterized by comprising the following steps of: it comprises the following steps:
taking a sample to be tested, obtaining preprocessed spectral data according to the step (1), inputting the data into the saffron adulteration amount prediction model according to claim 10, wherein the data output by the saffron adulteration amount prediction model is the adulteration amount of the saffron adulterant.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010845518.0A CN114076745A (en) | 2020-08-20 | 2020-08-20 | Saffron identification method based on cloud-interconnection portable near-infrared technology and adulterated product quantitative prediction method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010845518.0A CN114076745A (en) | 2020-08-20 | 2020-08-20 | Saffron identification method based on cloud-interconnection portable near-infrared technology and adulterated product quantitative prediction method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114076745A true CN114076745A (en) | 2022-02-22 |
Family
ID=80282211
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010845518.0A Pending CN114076745A (en) | 2020-08-20 | 2020-08-20 | Saffron identification method based on cloud-interconnection portable near-infrared technology and adulterated product quantitative prediction method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114076745A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102485249A (en) * | 2010-12-03 | 2012-06-06 | 上海雷允上科技发展有限公司 | Detection method for saffron quality evaluation |
| CN105044025A (en) * | 2015-09-07 | 2015-11-11 | 天津工业大学 | Method for fast recognizing sesame oil and sesame oil doped with soybean oil through near infrared |
| CN105092526A (en) * | 2015-09-11 | 2015-11-25 | 天津工业大学 | Rapid determination method for content of binary adulterated sesame oil based on near-infrared spectroscopy |
| CN107478595A (en) * | 2017-08-14 | 2017-12-15 | 上海海洋大学 | The method that a kind of the quick discriminating pearl powder true and false and quantitative forecast mix pseudo- shell powder content |
| CN108387550A (en) * | 2018-02-10 | 2018-08-10 | 云南小宝科技有限公司 | Portable near infrared spectrum detection method based on MEMS, device and system |
| CN108593592A (en) * | 2018-04-19 | 2018-09-28 | 广东药科大学 | A kind of tuber of pinellia based on near-infrared spectrum technique mixes pseudo- discrimination method |
| WO2019192433A1 (en) * | 2018-04-03 | 2019-10-10 | 深圳市药品检验研究院（深圳市医疗器械检测中心） | Method for chemical pattern recognition of authenticity of traditional chinese medicine chinese honeylocust spine based on near-infrared spectroscopy |
-
2020
- 2020-08-20 CN CN202010845518.0A patent/CN114076745A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102485249A (en) * | 2010-12-03 | 2012-06-06 | 上海雷允上科技发展有限公司 | Detection method for saffron quality evaluation |
| CN105044025A (en) * | 2015-09-07 | 2015-11-11 | 天津工业大学 | Method for fast recognizing sesame oil and sesame oil doped with soybean oil through near infrared |
| CN105092526A (en) * | 2015-09-11 | 2015-11-25 | 天津工业大学 | Rapid determination method for content of binary adulterated sesame oil based on near-infrared spectroscopy |
| CN107478595A (en) * | 2017-08-14 | 2017-12-15 | 上海海洋大学 | The method that a kind of the quick discriminating pearl powder true and false and quantitative forecast mix pseudo- shell powder content |
| CN108387550A (en) * | 2018-02-10 | 2018-08-10 | 云南小宝科技有限公司 | Portable near infrared spectrum detection method based on MEMS, device and system |
| WO2019192433A1 (en) * | 2018-04-03 | 2019-10-10 | 深圳市药品检验研究院（深圳市医疗器械检测中心） | Method for chemical pattern recognition of authenticity of traditional chinese medicine chinese honeylocust spine based on near-infrared spectroscopy |
| CN108593592A (en) * | 2018-04-19 | 2018-09-28 | 广东药科大学 | A kind of tuber of pinellia based on near-infrared spectrum technique mixes pseudo- discrimination method |
Non-Patent Citations (2)
| Title |
|---|
| EMAN SHAWKY 等: ""NIR spectroscopy-multivariate analysis for rapid authentication, detection and quantification of common plant adulterants in saffron (Crocus sativus L.) stigmas"", 《LWT - FOOD SCIENCE AND TECHNOLOGY》, vol. 122, pages 1 - 9 * |
| 杨慧 等: ""基于近红外光谱的大鲵肉粉掺伪鉴别及纯度检测"", 《食品科学》, vol. 40, no. 10, pages 331 - 336 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105044021B (en) | A kind of mid-autumn crisp jujube pol lossless detection method | |
| CN108680515B (en) | Single-grain rice amylose quantitative analysis model construction and detection method thereof | |
| CN107515203A (en) | The research of near infrared technology quantitative analysis rice single grain amylose content | |
| CN105717066B (en) | A kind of near infrared spectrum identification model based on weighted correlation coefficient | |
| CN106841083A (en) | Sesame oil quality detecting method based on near-infrared spectrum technique | |
| CN113008817A (en) | Method for rapidly identifying authenticity and quality of bitter apricot kernels based on hyperspectral imaging technology | |
| CN106092962A (en) | A kind of near infrared spectroscopy quickly detects the method for millet crude protein content | |
| CN103033486A (en) | Method for near infrared spectrum monitoring of quality of pericarpium citri reticulatae and citrus chachiensis hortorum medicinal materials | |
| CN110749565A (en) | Method for rapidly identifying storage years of Pu' er tea | |
| CN107402192A (en) | A kind of method of quick analysis essence and flavoring agent quality stability | |
| CN106226267B (en) | A kind of near-infrared assay method of dry chili color value | |
| CN111795943A (en) | Method for nondestructive detection of exogenous doped sucrose in tea based on near infrared spectrum technology | |
| CN108613943A (en) | A kind of near-infrared single grain crop component detection method based on the transfer of spectrum form | |
| CN110231302A (en) | A kind of method of the odd sub- seed crude fat content of quick measurement | |
| CN104596976A (en) | Method for determining protein of paper-making reconstituted tobacco through ear infrared reflectance spectroscopy technique | |
| CN112485216B (en) | Multi-source information fusion Thailand jasmine rice adulteration identification method | |
| CN109540837A (en) | The method that near-infrared quickly detects Boehmeria nivea leaves wood fibre cellulose content | |
| CN109030410B (en) | Construction method of royal jelly near-infrared quantitative correction model and royal jelly detection method | |
| CN114076745A (en) | Saffron identification method based on cloud-interconnection portable near-infrared technology and adulterated product quantitative prediction method thereof | |
| CN102759515A (en) | Method for rapidly determining oil contents of agricultural products by using mid-infrared spectrometer based on horizontal attenuated total reflection (ATR) | |
| CN110231306A (en) | A kind of method of lossless, the quick odd sub- seed protein content of measurement | |
| CN108318446A (en) | It is a kind of detection the total content of vitamin E of peanut seed near-infrared spectroscopy and application | |
| CN108627498A (en) | A kind of flour doping quantitative detecting method of multispectral data fusion | |
| CN113075197A (en) | Method for rapidly detecting adulteration content of corn flour in white pepper powder | |
| CN102213674A (en) | Method for detecting lime substances in flour by near infrared spectroscopy |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |