CN111398212A - Method for establishing pepper detection model based on portable near-infrared spectrometer - Google Patents
Method for establishing pepper detection model based on portable near-infrared spectrometer Download PDFInfo
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- CN111398212A CN111398212A CN202010270014.0A CN202010270014A CN111398212A CN 111398212 A CN111398212 A CN 111398212A CN 202010270014 A CN202010270014 A CN 202010270014A CN 111398212 A CN111398212 A CN 111398212A
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- 235000002566 Capsicum Nutrition 0.000 title claims abstract description 45
- 239000006002 Pepper Substances 0.000 title claims abstract description 44
- 235000016761 Piper aduncum Nutrition 0.000 title claims abstract description 44
- 235000017804 Piper guineense Nutrition 0.000 title claims abstract description 44
- 235000008184 Piper nigrum Nutrition 0.000 title claims abstract description 44
- 238000001514 detection method Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 26
- 244000203593 Piper nigrum Species 0.000 title 1
- 241000722363 Piper Species 0.000 claims abstract description 43
- KWGRBVOPPLSCSI-WPRPVWTQSA-N (-)-ephedrine Chemical compound CN[C@@H](C)[C@H](O)C1=CC=CC=C1 KWGRBVOPPLSCSI-WPRPVWTQSA-N 0.000 claims abstract description 30
- 230000003595 spectral effect Effects 0.000 claims abstract description 27
- 238000012549 training Methods 0.000 claims abstract description 23
- 239000000341 volatile oil Substances 0.000 claims abstract description 23
- KWGRBVOPPLSCSI-UHFFFAOYSA-N d-ephedrine Natural products CNC(C)C(O)C1=CC=CC=C1 KWGRBVOPPLSCSI-UHFFFAOYSA-N 0.000 claims abstract description 15
- 229960002179 ephedrine Drugs 0.000 claims abstract description 15
- 238000010238 partial least squares regression Methods 0.000 claims abstract description 9
- 238000012360 testing method Methods 0.000 claims abstract description 9
- 238000002329 infrared spectrum Methods 0.000 claims description 11
- 238000001228 spectrum Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 6
- 238000004128 high performance liquid chromatography Methods 0.000 claims description 6
- 230000009471 action Effects 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 229910052736 halogen Inorganic materials 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 229910052721 tungsten Inorganic materials 0.000 claims description 3
- 239000010937 tungsten Substances 0.000 claims description 3
- 238000010521 absorption reaction Methods 0.000 claims description 2
- 230000010354 integration Effects 0.000 claims description 2
- 239000008159 sesame oil Substances 0.000 claims description 2
- 235000011803 sesame oil Nutrition 0.000 claims description 2
- -1 tungsten halogen Chemical class 0.000 claims description 2
- 238000005259 measurement Methods 0.000 abstract description 4
- 238000012795 verification Methods 0.000 description 6
- 244000025254 Cannabis sativa Species 0.000 description 5
- 235000012766 Cannabis sativa ssp. sativa var. sativa Nutrition 0.000 description 5
- 235000012765 Cannabis sativa ssp. sativa var. spontanea Nutrition 0.000 description 5
- 244000089698 Zanthoxylum simulans Species 0.000 description 5
- 235000009120 camo Nutrition 0.000 description 5
- 235000005607 chanvre indien Nutrition 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 239000011487 hemp Substances 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 235000007650 Aralia spinosa Nutrition 0.000 description 3
- 241000949456 Zanthoxylum Species 0.000 description 3
- 230000000975 bioactive effect Effects 0.000 description 3
- 235000013399 edible fruits Nutrition 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004949 mass spectrometry Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 206010061218 Inflammation Diseases 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 241000758706 Piperaceae Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005536 corrosion prevention Methods 0.000 description 1
- 230000003467 diminishing effect Effects 0.000 description 1
- 150000002367 halogens Chemical class 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
- 238000004476 mid-IR spectroscopy Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- WFKWXMTUELFFGS-UHFFFAOYSA-N tungsten Chemical compound [W] WFKWXMTUELFFGS-UHFFFAOYSA-N 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
- G01N21/17—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
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- 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 method for establishing a pepper detection model based on a portable near-infrared spectrometer, which comprises the following steps: preparing a pepper sample as a training set sample and a testing set sample respectively; detecting the measured values of the ephedrine and the volatile oil in the training set sample; collecting spectral data of a training set sample by adopting a portable near-infrared spectrometer; combining the spectral data of the training set sample with the measured values of the ephedrine and the volatile oil obtained by detection of the spectral data by a partial least squares regression algorithm, and respectively establishing quantitative prediction models of the ephedrine and the volatile oil; and (3) performing combined prediction on the established quantitative prediction model and the test set sample, and optimizing the quantitative prediction model to obtain the pepper detection model. The portable near-infrared spectrometer provided by the invention is low in cost, simple to operate and convenient to carry, and meets the detection requirement of the pepper. The measurement value is obtained by adopting a quantitative detection method only when the model is established, and the rapid measurement can be realized when the pepper component is detected by adopting the model in the later period, so that the product treatment and the complex operation are not needed.
Description
Technical Field
The invention relates to the technical field of near infrared spectrum detection, in particular to a pepper detection model establishing method based on a portable near infrared spectrometer.
Background
The pepper is one of important economic crops in China, and researches show that the bioactive components in the pepper have the functions of oxidation resistance, tumor resistance, inflammation diminishing, bacteriostasis and corrosion prevention. However, due to differences in geographical environment, climate difference, soil, variety and the like, the content and the content of bioactive components in the pepper are different. At present, for the content identification of the pepper, the main detection technologies comprise a gas-mass spectrometry combined method, a high performance liquid chromatography, a mid-infrared spectroscopy and the like, but the methods are mainly applied in laboratories, and the detection cost of the gas-mass spectrometry combined method and the high performance liquid chromatography is expensive, the sample treatment is complicated, the requirement on experimental operation is high, rapid determination cannot be carried out, and great difficulty is brought to pepper detection and classification.
Disclosure of Invention
The invention aims to provide a pepper detection model establishing method based on a portable near-infrared spectrometer, which is used for solving the problems that the method for detecting bioactive components and content in pepper in the prior art is high in cost, complicated in sample treatment, high in operation requirement and incapable of rapid determination.
The invention solves the problems through the following technical scheme:
a pepper detection model establishing method based on a portable near-infrared spectrometer comprises the following steps:
step S100: preparing a pepper sample as a training set sample and a testing set sample respectively;
step S200: detecting to obtain the measured values of the ephedrine and the volatile oil in the training set sample;
step S300: collecting spectral data of a training set sample by adopting a portable near-infrared spectrometer;
step S400: combining the spectral data of the training set sample with the measured values of the ephedrine and the volatile oil obtained by detection of the spectral data by a partial least squares regression algorithm, and respectively establishing quantitative prediction models of the ephedrine and the volatile oil;
step S500: and (3) performing combined prediction on the established quantitative prediction model and the test set sample, and optimizing the quantitative prediction model to obtain the pepper detection model.
Further, in the step S200, the content of ephedrine and volatile oil in the training set sample is quantitatively detected by using high performance liquid chromatography (HP L C).
Further, the step S300 specifically includes:
put portable near-infrared spectrum appearance level and smooth, open and gather the button, the inside tungsten halogen lamp of spectrum appearance sends light outward, light produces the light receiving arrangement that the diffuse reflection effect returned the spectrum appearance again through training set sample surface, receiving arrangement adopts two aspheric lens to change the light path direction, make the reverberation be close to 0 vertical incidence in the Fabry-Perot cavity, the Fabry-Perot cavity carries out the filtering action to the reflection light, and finally, the reflection light after the spectrum appearance absorption filtering obtains corresponding spectral information, portable near-infrared spectrum appearance reads this spectral information, integration and data processing, draw the spectral information picture.
Further, the concrete step S400 is that the hemp content value in the training set sample is selected as a calibration value, a sesame oil quantitative prediction model of the near infrared spectrum data and the hemp calibration value is established by adopting a partial least squares regression method (P L S), and similarly, the volatile oil content value in the training set sample is selected as a calibration value, and a volatile oil quantitative prediction model of the near infrared spectrum data and the volatile oil calibration value is established by adopting a partial least squares regression method.
Compared with the prior art, the invention has the following advantages and beneficial effects:
compared with other chemical analysis technologies, the portable near infrared spectrum technology has the characteristics of rapidness, accuracy, no need of sample pretreatment, no damage to samples, no pollution and the like, and meanwhile, the portable near infrared spectrometer is low in cost, simple to operate and convenient to carry, and can meet the detection requirements of various types of Chinese prickly ash; and the measurement value is obtained by adopting a quantitative detection method only when the model is established, and the rapid measurement can be realized when the pepper component is detected by adopting the model in the later period, so that the operation is simple, and the product treatment is not needed.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples, but the embodiments of the present invention are not limited thereto.
Example (b):
with reference to the attached drawing 1, a method for establishing a pepper detection model based on a portable near-infrared spectrometer comprises the following steps:
step S100: preparing a plurality of pepper samples which are respectively marked as a pepper sample A (training set sample) and a pepper verification sample set B (testing set sample). The method comprises the steps of preparing a plurality of pepper samples by using peppers of the same variety and quality, and respectively establishing a near-infrared prediction model and optimizing the precision of the near-infrared prediction model, wherein a pepper sample used for establishing the near-infrared prediction model is marked as a pepper sample A, and a pepper sample set used for optimizing the precision of the near-infrared prediction model is marked as a pepper verification sample set B.
In this example, the procedure for preparing zanthoxylum bungeanum sample is as follows: slowly pouring the pepper samples into the disc-shaped tooling vessel, slowly shaking the tooling vessel to uniformly distribute the pepper samples in the tooling vessel, and stopping pouring the pepper samples when the thickness of the poured pepper samples reaches 3 cm and the surface of the pepper samples is flat and uniform. The method has the advantages that the pepper sample is kept to have a certain thickness, the test surface is as flat as possible, and errors caused by the environment or the sample to test data are reduced;
the method comprises the step S200 of obtaining the specific content of the hemp fruit and the volatile oil of a pricklyash peel sample A by using a chemical quantitative detection technology, wherein the content of the hemp fruit and the volatile oil in the pricklyash peel is two factors which influence the quality of the pricklyash peel most directly, the current chemical method for measuring the content of the hemp fruit and the volatile oil is high performance liquid chromatography (HP L C), although the method is mainly applied in laboratories at present, the detection cost of the high performance liquid chromatography is expensive, the sample is complex to process, the requirement on experimental operation is high, the detection precision is high, and the precision of a subsequent near infrared prediction model can be effectively improved.
Step S300: the method comprises the following steps of adopting a portable near-infrared spectrometer to collect spectrum data of a training set sample, and specifically comprising the following steps: the portable near-infrared spectrometer is flatly placed on the surface of a pepper sample, a collection button is turned on, a halogen tungsten lamp in the instrument emits light outwards, the light returns to a light receiving device of the instrument again under the action of diffuse reflection generated on the surface of the pepper sample, the receiving device adopts two aspheric lenses to change the direction of a light path, reflected light is close to 0 degree and vertically enters a Fabry-Perot cavity, the Fabry-Perot cavity performs filtering action on the reflected light, finally, the instrument absorbs the reflected light after filtering and reflects corresponding spectral information, the portable near-infrared spectrometer reads the spectral information, integrates and processes data, and a spectral information graph is drawn.
In this embodiment, after the near-infrared sensor in the portable near-infrared spectrometer collects spectral information, an initial spectral signal is transmitted to the operational amplifier, the initial spectral signal is amplified and transmitted to the ADC through the operational amplifier, the spectral signal is transmitted to the ARM chip for processing after analog-to-digital conversion of the ADC, the spectral data is temporarily stored in the built-in F L ASH of the ARM, the ARM chip transmits the spectral data to the cloud through a wired or wireless network, a dedicated database is established, cloud storage is realized, and the spectral data can be conveniently called in the subsequent modeling process.
Step S400: combining the spectral data of the training set sample with the measured values of the ephedrine and the volatile oil obtained by detection of the spectral data by a partial least squares regression algorithm, and respectively establishing quantitative prediction models of the ephedrine and the volatile oil;
and S500, establishing the quantitative prediction of the zanthoxylum bungeanum hemsl by the specific process of selecting the zanthoxylum bungeanum hemsl sample hemsl content value obtained by the chemical quantitative detection technology as a calibration value, and establishing a mathematical model relation between the near infrared spectrum data and the calibration value by adopting a partial least squares regression method (P L S), wherein the mathematical model is a near infrared prediction model of the zanthoxylum bungeanum hemsl volatile oil.
And then, the established prediction model is combined with the pepper verification sample set B for prediction, and the near-infrared prediction model is optimized. And the established prediction model is used for content prediction of the pepper verification sample set B, the model is evaluated through the relative deviation of the verification sample set, the model is optimized according to the deviation value to obtain a near infrared prediction model with high accuracy, and finally the model is further evaluated and optimized in detail through a relative analysis error value (RPD). And RPD is the ratio of standard deviation to standard error of the verification set, when RPD is more than 3, the quantitative effect is good, and the established quantitative model can be used for actual detection.
Although the present invention has been described herein with reference to the illustrated embodiments thereof, which are intended to be preferred embodiments of the present invention, it is to be understood that the invention is not limited thereto, and that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure.
Claims (4)
1. A pepper detection model establishing method based on a portable near-infrared spectrometer is characterized by comprising the following steps:
step S100: preparing a pepper sample as a training set sample and a testing set sample respectively;
step S200: detecting to obtain the measured values of the ephedrine and the volatile oil in the training set sample;
step S300: collecting spectral data of a training set sample by adopting a portable near-infrared spectrometer;
step S400: combining the spectral data of the training set sample with the measured values of the ephedrine and the volatile oil obtained by detection of the spectral data by a partial least squares regression algorithm, and respectively establishing quantitative prediction models of the ephedrine and the volatile oil;
step S500: and (3) performing combined prediction on the established quantitative prediction model and the test set sample, and optimizing the quantitative prediction model to obtain the pepper detection model.
2. The method for establishing the pepper detection model based on the portable near-infrared spectrometer as claimed in claim 1, wherein the content of ephedrine and volatile oil in the training set sample is quantitatively detected by using high performance liquid chromatography (HP L C) in the step S200.
3. The method for establishing the pepper detection model based on the portable near-infrared spectrometer as claimed in claim 1, wherein the step S300 comprises the following steps:
put portable near-infrared spectrum appearance level and smooth, open and gather the button, the inside tungsten halogen lamp of spectrum appearance sends light outward, light produces the light receiving arrangement that the diffuse reflection effect returned the spectrum appearance again through training set sample surface, receiving arrangement adopts two aspheric lens to change the light path direction, make the reverberation be close to 0 vertical incidence in the Fabry-Perot cavity, the Fabry-Perot cavity carries out the filtering action to the reflection light, and finally, the reflection light after the spectrum appearance absorption filtering obtains corresponding spectral information, portable near-infrared spectrum appearance reads this spectral information, integration and data processing, draw the spectral information picture.
4. The method for establishing the pepper detection model based on the portable near-infrared spectrometer as claimed in claim 1, wherein the specific step of the step S400 is to select the ephedrine content value in the training set sample as a calibration value, establish the sesame oil quantitative prediction model of the near-infrared spectrum data and the ephedrine calibration value by adopting a partial least squares regression method (P L S), and similarly, select the volatile oil content value in the training set sample as a calibration value, and establish the volatile oil quantitative prediction model of the near-infrared spectrum data and the volatile oil calibration value by adopting a partial least squares regression method.
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Cited By (1)
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| CN114295580A (en) * | 2021-12-29 | 2022-04-08 | 四川启睿克科技有限公司 | Method for rapidly judging pepper quality based on near infrared spectrum |
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