CN106053383A - Near-infrared online detection method for tobacco processing process - Google Patents
Near-infrared online detection method for tobacco processing process Download PDFInfo
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- CN106053383A CN106053383A CN201610488990.7A CN201610488990A CN106053383A CN 106053383 A CN106053383 A CN 106053383A CN 201610488990 A CN201610488990 A CN 201610488990A CN 106053383 A CN106053383 A CN 106053383A
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- near infrared
- analysis
- model
- detection method
- nicotiana tabacum
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- 235000002637 Nicotiana tabacum Nutrition 0.000 title claims abstract description 49
- 238000001514 detection method Methods 0.000 title claims abstract description 31
- 241000208125 Nicotiana Species 0.000 title claims abstract description 22
- 238000012545 processing Methods 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 25
- 238000012544 monitoring process Methods 0.000 claims abstract description 5
- 238000011156 evaluation Methods 0.000 claims abstract description 3
- 238000005457 optimization Methods 0.000 claims abstract description 3
- 244000061176 Nicotiana tabacum Species 0.000 claims description 27
- 239000000203 mixture Substances 0.000 claims description 16
- 229960002715 nicotine Drugs 0.000 claims description 14
- SNICXCGAKADSCV-JTQLQIEISA-N (-)-Nicotine Chemical compound CN1CCC[C@H]1C1=CC=CN=C1 SNICXCGAKADSCV-JTQLQIEISA-N 0.000 claims description 13
- SNICXCGAKADSCV-UHFFFAOYSA-N nicotine Natural products CN1CCCC1C1=CC=CN=C1 SNICXCGAKADSCV-UHFFFAOYSA-N 0.000 claims description 13
- 239000000126 substance Substances 0.000 claims description 12
- 239000000463 material Substances 0.000 claims description 10
- 238000002329 infrared spectrum Methods 0.000 claims description 8
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 6
- 229910052757 nitrogen Inorganic materials 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 4
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 claims description 3
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- 239000000460 chlorine Substances 0.000 claims description 3
- 229910052801 chlorine Inorganic materials 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 239000011591 potassium Substances 0.000 claims description 3
- 229910052700 potassium Inorganic materials 0.000 claims description 3
- 238000004445 quantitative analysis Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 abstract description 3
- 238000012845 near infrared spectroscopy analysis Methods 0.000 abstract 1
- 230000006641 stabilisation Effects 0.000 abstract 1
- 238000011105 stabilization Methods 0.000 abstract 1
- 238000012795 verification Methods 0.000 abstract 1
- 238000000862 absorption spectrum Methods 0.000 description 4
- 235000019504 cigarettes Nutrition 0.000 description 4
- 238000001228 spectrum Methods 0.000 description 4
- 239000000470 constituent Substances 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000004497 NIR spectroscopy Methods 0.000 description 2
- 239000002585 base Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 238000010998 test method Methods 0.000 description 2
- 239000003513 alkali Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 125000004435 hydrogen atom Chemical group [H]* 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 239000000779 smoke Substances 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
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
- 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
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
Abstract
The invention discloses a near-infrared online detection method for a tobacco processing process. The near-infrared online detection method comprises a detection system and an analysis method. The detection system comprises a photoelectric inductor, a mechanical arm, an RFID reader-writer, a Fourier near-infrared instrument and an analysis host. The analysis method adopts a near-infrared spectroscopic analysis model and relates to sample collection, multi-parameter quantitative modeling, evaluation verification, model stabilization, conventional analysis and monitoring and model optimization and transfer. According to the near-infrared online detection method, the detection technique and the homogenizing processing level of the tobacco industry are improved.
Description
Technical field
The invention belongs to Instrumental Analysis field, particularly relate to a kind of near infrared detection method in tobacco processing course.
Background technology
On-line checking is to realize process of manufacture automatization, intelligentized effective means, strictly control finished product sheet cigarette cigarette
Alkali (CV value controls within 5%) and the moisture content of finished products coefficient of variation (CV value controls within 2.5%), it is achieved same module year
Between, between processing batch, stable, physical index relative with batch internal sheet smoke product nicotine composition uniformity, judging parameter is coordinated all
The fluctuation of weighing apparatus, moisture effectively controls, and is that tobacco homogenizes the main contents of quality control and evaluation and analysis.According to " State Bureau
Suggestion about advancing the processing that homogenizes ", advance and improve near-infrared spectrometers modeling technique, being amassed by process data
Tired, improve brand Raw material processing nicotine data model, improve online and laboratory near infrared spectrometer detection accuracy, promote inspection
Survey technology level, carries out production process and homogenizes crudy evaluation and analysis, is the reasonability and accurately ensureing at present the regulation and control that homogenize
The important means of property.
Near infrared spectrum (NIR) is the electromagnetic spectrum between visible ray (VIS) and mid-infrared light (MIR), wavelength model
Enclosing: 800～2500nm, wave number is about: 12500～4000cm-1.Near infrared spectroscopy be utilize containing hydrogen group (X-H, X be:
C, O, N, S etc.) chemical bond (X-H) stretching vibration frequency multiplication and sum of fundamental frequencies, at the absorption spectrum of near infrared region, by selecting suitable change
Learn meterological multivariate calibration methods, the near-infrared absorption spectrum of correcting sample is carried out pass with its constituent concentration or property data
Connection, sets up the relation-calibration model between correcting sample absorption spectrum and its constituent concentration or character, is carrying out sample detection
Time, apply the calibration model and the absorption spectrum of sample built up, so that it may its constituent concentration of detection by quantitative or character.
Summary of the invention
It is an object of the invention to: a kind of near infrared online detection method in tobacco processing course is provided, detect Nicotiana tabacum L.
Nicotine, total sugar, total nitrogen, reducing sugar, potassium, chemical composition and the moisture such as chlorine, promote detection technique and the level of processing that homogenizes.
For achieving the above object, the technical scheme is that the near infrared online detection in a kind of tobacco processing course
Method, including detecting system and analysis method.Detecting system is the reddest by photoelectric sensor, mechanical hand, rfid interrogator, Fourier
Outer instrument, analysis main frame composition, the flow process related to is: the material container that the sensing of a. photoelectric sensor arrives, and is sent out by signal simultaneously
To mechanical hand, the near infrared spectrum of b. Fourier's nir instrument scanning Nicotiana tabacum L., the quantitative model analysis of Nicotiana tabacum L. obtain being swept
Retouch the chemical score composition of Nicotiana tabacum L., including: nicotine, total sugar, total nitrogen, reducing sugar, potassium, chlorine and moisture, c. mechanical hand pushes material and enters
Entering scanning or stop that next material container is moved along, until the Nicotiana tabacum L. in current container is the most scanned, d.RFID reads and writes
Device reads the relevant information of IC-card, the Nicotiana tabacum L. near infrared light simultaneously detected by Fourier transform near infrared instrument on material container
Compose corresponding, and record in data system, e. analyze host record detection and analyze the chemical composition value of Nicotiana tabacum L. obtained and
Corresponding IC card information；Analysis method is to use NIR Spectroscopy Analysis Model, and especially modeling relates to sample collection, wavelength
Scope selection, preprocessing procedures, the multiple parameters quantitative modeling such as determination of PLS main cause subnumber, evaluate checking, stable mode
Type, conventional analysis and monitoring, model optimization and transmission.
The basic step setting up quantitative model is:
(1) collection of sample: select abundant and representational sample composition calibration set；
(2) measure sample composition: by current standard methods or traditional test methods, measure and obtain sample composition chemistry
The information of value.
(3) spectrum is measured: use nir instrument scanning, obtain the near infrared spectrum of sample.
(4) use multiplexed quantitative method to set up initial calibration model, reject out-of-bounds sample, and repeatedly choose different parameters and build
Mould (such as: wave-length coverage, preprocessing procedures, PLS main cause subnumber etc.), stable, outstanding to set up to obtain the parameter of optimum
Calibration model.
(5) it is evaluated verifying to institute's established model with checking collection sample.
(6) with stable, outstanding model, unknown sample is carried out conventional analysis and monitoring.
(7) institute's established model updates further, optimizes and transmission.
Owing to have employed such scheme, the beneficial effects of the present invention is: the present invention can the most accurately detect and record
The value of tobacco leaf chemical composition on automatic production line, for uniformly get the raw materials ready and nicotine homogenize integrated system provide original base number
According to, ensure the uniformity of each unit tobacco leaf cigarette base number that feeds intake, effectively control finished product sheet cigarette nicotine value coefficient of variation CV value 3%
Within.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention is described in further detail.
Embodiment 1: detecting system and on-line checking flow process
Near infrared online detection method in existing a kind of tobacco processing course, its detecting system is by photoelectric sensor, machine
Tool hands, rfid interrogator, Fourier's nir instrument, analysis main frame composition.The Nicotiana tabacum L. of " C3F " amounts to 500 frames (plastic crate), this
The plastic crate filling Nicotiana tabacum L. a bit arrives transfer from horizontal pulling device, and after transfer, plastic crate enters detection belt
On, plastic crate is sent near infrared online detection system by detection belt, and the material first arrived by photoelectric sensor sensing holds
Device, issues mechanical hand simultaneously by signal, the plastic crate of numbering 0001 arrives detecting system position, and photoelectric sensor will sense
Signal issues mechanical hand, currently without the plastic crate being scanned.The plastic crate of numbering 0001 is pushed into the reddest by mechanical hand
The scan position of outer instrument, by the near infrared spectrum of instrument scanning Nicotiana tabacum L., rfid interrogator reads the phase of IC-card on material container
Pass information, the most corresponding with the near infrared spectrum that Fourier transform near infrared instrument detects, analysis main frame calls and is all
The Nicotiana tabacum L. quantitative model of " C3F " kind, obtains the chemical score of the Nicotiana tabacum L. of numbering 0001---nicotine value.Analysis host record detects
The chemical composition value of the Nicotiana tabacum L. arrived and corresponding IC card information, be saved in data system, by that analogy until 500 frames " C3F "
Kind Nicotiana tabacum L. is the most scanned.
Near infrared online detection result is as follows:
First row in form is the nicotine value true value of Nicotiana tabacum L., and the 4th is classified as the nicotine value of on-line checking, and the 5th row are two
Deviation value between person.It is seen that, the data deviation that near infrared online detection result and common detection methods obtain is the least.
Embodiment 2: set up quantitative model
(1) collection of sample: collect each grade, representative strong tobacco sample composition calibration set；
(2) measure sample composition: existing traditional test methods is passed through in laboratory, measure and obtain sample composition chemical score
Information, for the true value of model.
(3) spectrum is measured: use nir instrument scanning 4000～10000cm-1Wave band, obtains the near infrared light of sample
Spectrum.
(4) use multiplexed quantitative method to set up initial calibration model, reject out-of-bounds sample, and repeatedly choose different parameters and build
Mould (such as: wave-length coverage, preprocessing procedures, PLS main cause subnumber etc.), stable, outstanding to set up to obtain the parameter of optimum
Calibration model.
(5) it is evaluated verifying to institute's established model with checking collection sample.
(6) with stable, outstanding model, unknown sample is carried out conventional analysis and monitoring, obtain the chemical score letter of sample
Breath---the predictive value of nicotine value, referred to as model.
(7) later stage, model needs to update further, optimize.
Shown in list, the tobacco sample to 1500 frames " C3F " grade, the near-infrared quantitative model that application this method is set up divides
The result of analysis detection, model components is the nicotine value of Nicotiana tabacum L..
The nicotine value true value (data recorded by general measuring method) of these tobacco samples is shown in 3rd list of form, the
The nicotine Value Data of the Nicotiana tabacum L. using the quantitative model prediction of Nicotiana tabacum L. to obtain is shown in four lists.5th row are between true value and predictive value
Deviation value, represent the deviation size of the data of data and the general measure of model prediction.
After calibration model is built up, verifying calibration model, the cross-validation prediction root-mean-square of computation model is by mistake
Difference RMSECV, coefficient of determination R2, and checked by t and determine whether predictive value has the above deviation of statistics.Result shows,
The R of grade " C3F " Nicotiana tabacum L. near-infrared quantitative model2Being 98.33, RMSECV is 0.087, illustrates that model has stronger prediction energy
Power；In t inspection, the absolute value of t is respectively less than the marginal value that it is relevant, illustrates that model predication value is essentially identical with reference value, correction
Model is the most effective.
The tobacco sample of random choose ad eundem, carries out external certificate to this model.Data in following table represent Nicotiana tabacum L. sample
The predictive value of product.
| Numbering | Filename | True value | Predictive value | Deviation |
| 1 | 5F0648.0 | 2.80 | 2.744 | 0.056 |
| 2 | 5F0691.0 | 2.69 | 2.726 | -0.036 |
| 3 | 5F0692.0 | 3.00 | 2.981 | 0.019 |
| 4 | 5F0702.0 | 2.98 | 2.942 | 0.038 |
| 5 | 5F0703.0 | 3.04 | 2.907 | 0.133 |
| 6 | 5F0707.0 | 2.91 | 2.868 | 0.042 |
| 7 | 5F0708.0 | 2.70 | 2.884 | -0.184 |
| …… | …… | …… | …… | …… |
| 42 | 5F0785.0 | 1.82 | 1.830 | -0.010 |
| 43 | 5F0789.0 | 1.89 | 1.823 | 0.067 |
| 44 | 5F0790.0 | 1.94 | 1.861 | 0.079 |
| 45 | 5F0791.0 | 3.06 | 2.923 | 0.137 |
| 46 | 5F0792.0 | 2.88 | 2.879 | 0.001 |
| 47 | 5F0794.0 | 3.11 | 3.039 | 0.071 |
| 48 | 5F0884.0 | 2.59 | 2.557 | 0.033 |
| 49 | 5F0887.0 | 2.64 | 2.556 | 0.084 |
| 50 | 5F0890.0 | 2.72 | 2.733 | -0.013 |
With this model, tobacco sample being carried out external certificate, obtaining external certificate predicted root mean square error (RMSEP) is
0.092, illustrate that model prediction is the most accurate.Deviation value between true value and the test value of its component is the least, and this model is steady
Fixed, outstanding quantitative model.
Claims (6)
1. the near infrared online detection method in a tobacco processing course, it is characterised in that: include detecting system and analysis side
Method.
Near infrared online detection method in a kind of tobacco processing course the most according to claim 1, it is characterised in that: inspection
Examining system is made up of photoelectric sensor, mechanical hand, rfid interrogator, Fourier's nir instrument, analysis main frame.
Near infrared online detection method in a kind of tobacco processing course the most according to claim 2, the flow process related to is:
A. the material container that photoelectric sensor sensing arrives, issues mechanical hand by signal simultaneously；
B. the near infrared spectrum of Nicotiana tabacum L. in Fourier's nir instrument scanning container, by the quantitative model analysis of Nicotiana tabacum L. obtain by
The chemical composition value of scanning Nicotiana tabacum L., including: nicotine, total sugar, total nitrogen, reducing sugar, potassium, chlorine and moisture；
C. mechanical hand pushes material entrance scanning or stops that next material container is moved along, until the Nicotiana tabacum L. in current container
The most scanned；
D. rfid interrogator reads the relevant information on material container IC-card, is detected by Fourier transform near infrared instrument simultaneously
The Nicotiana tabacum L. near infrared spectrum arrived is corresponding, and records in data system；
E. analyze main frame and call Near-Infrared Quantitative Analysis model, it was predicted that obtain each chemical score information of Nicotiana tabacum L., and record detection
The chemical composition value of the Nicotiana tabacum L. arrived and corresponding IC card information.
Near infrared online detection method in a kind of tobacco processing course the most according to claim 1, it is characterised in that point
Analysis method is to use near infrared spectra quantitative models.
Near infrared online detection method in a kind of tobacco processing course the most according to claim 4, it is characterised in that point
The foundation of analysis model includes: sample collection, multiple parameters quantitative modeling, evaluation checking, stable model, conventional analysis and monitoring,
Model optimization and transmission.
Near infrared online detection method in a kind of tobacco processing course the most according to claim 5, it is characterised in that many
Unit's parameter includes wave-length coverage selection, preprocessing procedures, the determination of PLS main cause subnumber.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201610488990.7A CN106053383A (en) | 2016-06-27 | 2016-06-27 | Near-infrared online detection method for tobacco processing process |
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| CN201610488990.7A CN106053383A (en) | 2016-06-27 | 2016-06-27 | Near-infrared online detection method for tobacco processing process |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110160985A (en) * | 2019-06-19 | 2019-08-23 | 红云红河烟草(集团)有限责任公司 | A kind of method of the online chemical component detector test nicotine content of adjustment |
| CN111665213A (en) * | 2020-08-03 | 2020-09-15 | 湖北中烟工业有限责任公司 | FT-IR spectrum-based tobacco rapid detection and component difference evaluation method |
| CN111879726A (en) * | 2020-08-26 | 2020-11-03 | 中国烟草总公司郑州烟草研究院 | Tobacco hot processing strength and volatility online monitoring method based on synchronous near-infrared analysis before and after processing |
| CN112540971A (en) * | 2020-12-11 | 2021-03-23 | 云南中烟工业有限责任公司 | Full-information online acquisition system and method based on tobacco leaf characteristics |
| CN113804648A (en) * | 2021-09-18 | 2021-12-17 | 上海益实智能科技有限公司 | Tobacco online real-time monitoring device and application thereof in tobacco quality nondestructive rapid quality control |
| CN116223440A (en) * | 2023-05-08 | 2023-06-06 | 四川威斯派克科技有限公司 | Near infrared detection device for tobacco raw material proportioning |
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| CN101995388A (en) * | 2009-08-26 | 2011-03-30 | 北京凯元盛世科技发展有限责任公司 | Near infrared quality control analysis method and system of tobacco |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110160985A (en) * | 2019-06-19 | 2019-08-23 | 红云红河烟草(集团)有限责任公司 | A kind of method of the online chemical component detector test nicotine content of adjustment |
| CN111665213A (en) * | 2020-08-03 | 2020-09-15 | 湖北中烟工业有限责任公司 | FT-IR spectrum-based tobacco rapid detection and component difference evaluation method |
| CN111879726A (en) * | 2020-08-26 | 2020-11-03 | 中国烟草总公司郑州烟草研究院 | Tobacco hot processing strength and volatility online monitoring method based on synchronous near-infrared analysis before and after processing |
| CN112540971A (en) * | 2020-12-11 | 2021-03-23 | 云南中烟工业有限责任公司 | Full-information online acquisition system and method based on tobacco leaf characteristics |
| CN113804648A (en) * | 2021-09-18 | 2021-12-17 | 上海益实智能科技有限公司 | Tobacco online real-time monitoring device and application thereof in tobacco quality nondestructive rapid quality control |
| CN116223440A (en) * | 2023-05-08 | 2023-06-06 | 四川威斯派克科技有限公司 | Near infrared detection device for tobacco raw material proportioning |
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Application publication date: 20161026 |