CN108181262A - Method for rapidly determining content of sargassum horneri cellulose by utilizing near infrared spectrum technology - Google Patents
Method for rapidly determining content of sargassum horneri cellulose by utilizing near infrared spectrum technology Download PDFInfo
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- 241001260874 Sargassum horneri Species 0.000 title claims abstract description 94
- 238000000034 method Methods 0.000 title claims abstract description 82
- 229920002678 cellulose Polymers 0.000 title claims abstract description 71
- 239000001913 cellulose Substances 0.000 title claims abstract description 70
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 40
- 238000005516 engineering process Methods 0.000 title abstract description 5
- QAOWNCQODCNURD-UHFFFAOYSA-N Sulfuric acid Chemical compound OS(O)(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-N 0.000 claims abstract description 22
- KMUONIBRACKNSN-UHFFFAOYSA-N potassium dichromate Chemical compound [K+].[K+].[O-][Cr](=O)(=O)O[Cr]([O-])(=O)=O KMUONIBRACKNSN-UHFFFAOYSA-N 0.000 claims abstract description 17
- 238000004445 quantitative analysis Methods 0.000 claims abstract description 10
- 230000003647 oxidation Effects 0.000 claims abstract description 7
- 238000007254 oxidation reaction Methods 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000004458 analytical method Methods 0.000 claims description 21
- 238000001228 spectrum Methods 0.000 claims description 18
- 238000004497 NIR spectroscopy Methods 0.000 claims description 15
- 238000011156 evaluation Methods 0.000 claims description 13
- 238000012937 correction Methods 0.000 claims description 8
- 238000010438 heat treatment Methods 0.000 claims description 8
- 238000000638 solvent extraction Methods 0.000 claims description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 7
- QTBSBXVTEAMEQO-UHFFFAOYSA-N Acetic acid Chemical compound CC(O)=O QTBSBXVTEAMEQO-UHFFFAOYSA-N 0.000 claims description 6
- 239000002244 precipitate Substances 0.000 claims description 5
- 238000009835 boiling Methods 0.000 claims description 4
- 230000008030 elimination Effects 0.000 claims description 4
- 238000003379 elimination reaction Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- AKHNMLFCWUSKQB-UHFFFAOYSA-L sodium thiosulfate Chemical compound [Na+].[Na+].[O-]S([O-])(=O)=S AKHNMLFCWUSKQB-UHFFFAOYSA-L 0.000 claims description 4
- 235000019345 sodium thiosulphate Nutrition 0.000 claims description 4
- 239000004575 stone Substances 0.000 claims description 4
- GRYLNZFGIOXLOG-UHFFFAOYSA-N Nitric acid Chemical compound O[N+]([O-])=O GRYLNZFGIOXLOG-UHFFFAOYSA-N 0.000 claims description 3
- 229920002472 Starch Polymers 0.000 claims description 3
- 229960000583 acetic acid Drugs 0.000 claims description 3
- 239000012362 glacial acetic acid Substances 0.000 claims description 3
- XMBWDFGMSWQBCA-UHFFFAOYSA-N hydrogen iodide Chemical compound I XMBWDFGMSWQBCA-UHFFFAOYSA-N 0.000 claims description 3
- 229910017604 nitric acid Inorganic materials 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 239000008107 starch Substances 0.000 claims description 3
- 235000019698 starch Nutrition 0.000 claims description 3
- 238000005406 washing Methods 0.000 claims description 3
- 238000009833 condensation Methods 0.000 claims description 2
- 230000005494 condensation Effects 0.000 claims description 2
- 238000002474 experimental method Methods 0.000 claims description 2
- 230000002068 genetic effect Effects 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims description 2
- 238000002203 pretreatment Methods 0.000 claims description 2
- 238000003672 processing method Methods 0.000 claims description 2
- 238000007873 sieving Methods 0.000 claims description 2
- 239000007788 liquid Substances 0.000 claims 1
- 241001474374 Blennius Species 0.000 abstract description 5
- 238000003908 quality control method Methods 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 230000007613 environmental effect Effects 0.000 abstract description 2
- 239000000523 sample Substances 0.000 description 59
- 235000010980 cellulose Nutrition 0.000 description 51
- 238000012795 verification Methods 0.000 description 11
- 239000002028 Biomass Substances 0.000 description 10
- 239000000126 substance Substances 0.000 description 9
- 239000000835 fiber Substances 0.000 description 7
- FHVDTGUDJYJELY-UHFFFAOYSA-N 6-{[2-carboxy-4,5-dihydroxy-6-(phosphanyloxy)oxan-3-yl]oxy}-4,5-dihydroxy-3-phosphanyloxane-2-carboxylic acid Chemical compound O1C(C(O)=O)C(P)C(O)C(O)C1OC1C(C(O)=O)OC(OP)C(O)C1O FHVDTGUDJYJELY-UHFFFAOYSA-N 0.000 description 5
- 241000196324 Embryophyta Species 0.000 description 5
- 229940072056 alginate Drugs 0.000 description 5
- 235000010443 alginic acid Nutrition 0.000 description 5
- 229920000615 alginic acid Polymers 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 238000004587 chromatography analysis Methods 0.000 description 4
- 241000195474 Sargassum Species 0.000 description 3
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- 230000008859 change Effects 0.000 description 3
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- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
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- 238000010521 absorption reaction Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 239000012153 distilled water Substances 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000001035 drying Methods 0.000 description 2
- 229910001385 heavy metal Inorganic materials 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 238000004448 titration Methods 0.000 description 2
- 244000003416 Asparagus officinalis Species 0.000 description 1
- 235000005340 Asparagus officinalis Nutrition 0.000 description 1
- 240000008564 Boehmeria nivea Species 0.000 description 1
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 229920002488 Hemicellulose Polymers 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- 235000016639 Syzygium aromaticum Nutrition 0.000 description 1
- 244000223014 Syzygium aromaticum Species 0.000 description 1
- 241000196252 Ulva Species 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 238000003705 background correction Methods 0.000 description 1
- 239000012496 blank sample Substances 0.000 description 1
- 210000002421 cell wall Anatomy 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- CMMUKUYEPRGBFB-UHFFFAOYSA-L dichromic acid Chemical compound O[Cr](=O)(=O)O[Cr](O)(=O)=O CMMUKUYEPRGBFB-UHFFFAOYSA-L 0.000 description 1
- 235000013601 eggs Nutrition 0.000 description 1
- 238000012851 eutrophication Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
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- 239000004459 forage Substances 0.000 description 1
- 239000013505 freshwater Substances 0.000 description 1
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- 239000007789 gas Substances 0.000 description 1
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- 238000004128 high performance liquid chromatography Methods 0.000 description 1
- 229920005610 lignin Polymers 0.000 description 1
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- 238000013178 mathematical model Methods 0.000 description 1
- 238000002156 mixing 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
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- 238000012628 principal component regression Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000005067 remediation Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 241000894007 species Species 0.000 description 1
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- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000003892 spreading Methods 0.000 description 1
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- 238000012360 testing method Methods 0.000 description 1
- 239000004753 textile Substances 0.000 description 1
Classifications
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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)
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- Chemical & Material Sciences (AREA)
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- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
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- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
A method for rapidly determining the content of sargassum horneri cellulose by utilizing a near infrared spectrum technology comprises the following steps: firstly, collecting and preprocessing a Sargassum horneri sample; secondly, determining the cellulose content of the Sargassum horneri sample by adopting an improved sulfuric acid and potassium dichromate oxidation method; thirdly, scanning by a near-infrared spectrometer to obtain a near-infrared spectrum of the Sargassum horneri sample; fourthly, establishing and evaluating a near infrared spectrum quantitative analysis model; and fifthly, applying a near infrared spectrum quantitative analysis model. The method has the advantages of rapidness, accuracy, environmental protection and the like, is beneficial to improving the quality control level of the sargassum horneri cellulose content, and can be popularized and applied to the quality control of other seaweed biomasses.
Description
Technical field
The invention belongs to content of cellulose analysis fields, particularly, are related to a kind of Sargassum horneri based on near-infrared spectrum technique
Content of cellulose rapid assay methods.
Background technology
Marine biomass resource using tangleweed as representative has the advantages that be not take up soil and freshwater resources, develop latent
Power is huge.Sargassum horneri (Sargassum horneri (Turn.) Ag. is commonly called as in " cloves room ", belongs to Sargassum (Sargassum), point
Be distributed in coastal area of china neritic area), the tangleweeds plant such as sargassum fusifome and asparagus it is tall and big, it is with luxuriant foliage and spreading branches in leafy profusion, can be rated as " marine forest ",
It is that marine organisms keep away enemy, forage, the ideal place laid eggs, and is easy to large-scale artificial cultivation, environment remediation (in absorption environment
Nitrogen, phosphorus and heavy metal, discharge oxygen, adjust water pH value, fixed CO2Ability protrudes, and is listed in and rebuilds submarine algae field and reality
Apply one of important species of marine ecology reparation.Sargassum horneri (Sargassum horneri (Turn.) speed of growth is fast, yield is big,
Single property, plant is tall and big, is easy to gather in, and feedstock capture and logistics cost are low, can on a large scale, steadily provide biomass original
Material.But the tangleweed mouthfeel of high-cellulose is poor, the added value for developing consumable products is not high, hinders its cultivation scale
Further expansion；And become feeble and die if being allowed to grow and stay in marine site, not only Environment Management of Eutrophication and heavy metal pollution etc. are asked
Topic can not be resolved, in some instances it may even be possible to Enteromorpha be caused the negative issues such as to be proliferated on a large scale.Therefore, exploitation Sargassum horneri biomass height adds
Value trans-utilization technology has important practical significance.
Cellulose is primarily present in plant cell wall, and the content range in different seaweed plants bodies is 1%~40%,
It is the highest component part of utility value in biomass material.Cellulosic component content in biomass material is to biomass fuel
It is all had a major impact with the production process of Biomass-based chemicals.Therefore, the constituent of quantitative analysis biomass material, especially
It is content of cellulose, is all of great significance to accurately matching raw material, raising product yield and quality.
At present, the analysis Main Basiss textile standard GB/T5889-1986 of plant content of cellulose and papermaking standard GB/
T2677.10-1995.Content of cellulose is measured according to above-mentioned national standard, not only measures and takes up to 2 to 3 days,
And because national standard Law embodiment measure object is ramie and timber, the measure of alginate fibre element is not necessarily suitable, in reality
It has also been found that being missed by a mile using the obtained alginate fibre cellulose content measurement result of this method in continuous mode.Other are adoptable
Method includes Van Soest and its improved method and chromatography, wherein, Van Soest and its improved method can measure fibre simultaneously
Cellulose content, moisture, hemicellulose, lignin and ash content equal size are tieed up, but determination step is cumbersome and time-consuming long；According to gas phase
The chromatographies such as chromatography, high performance liquid chromatography, it is excessively high, time-consuming long there are cost although measurement result is more accurate
It is insufficient.
It can be seen that traditional alginate fibre cellulose content quantitative analysis method generally existing determination step is cumbersome, time-consuming
The shortcomings of, chromatography there are the higher deficiency of testing cost, which has limited the popularization of these methods in the actual production process and
Using.Therefore, there is an urgent need to develop a kind of simple, the time-consuming short and low-cost seaweeds biomass celluloses of analysis program to contain
Measure quantitative analysis method.
Due to the hydric group (C-H, N-H, O-H) in seaweed in different chemical environments to 700~2500nm ranges
The absorbing state of electromagnetic wave (near infrared region) has significant difference, therefore hydrogeneous organic substance is contained near infrared spectrum and is enriched
Structure and composition information.Near infrared spectrum is lacked there are absorption intensity is weak, bands of a spectrum are wide and overlapping serious and characteristic is not strong etc.
Point, but as near infrared spectrum is combined with Chemical Measurement and derives near-infrared spectral analysis technology, foundation can be passed through
Mathematical model quickly and effectively picks out target information contained near infrared spectrum, has nondestructive analysis, analyzes quick, operation letter
Just the advantages that, result is accurate and may be implemented in line analysis.In conclusion near-infrared spectrum technique can be used to implement alginate fibre
The quick measure of cellulose content.
Invention content
In view of the shortcomings that present in traditional alginate fibre cellulose content quantitative analysis method, the purpose of the present invention is to provide one
The method that kind utilizes Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose, to contain to the cellulose in Sargassum horneri biomass
Amount carries out quick, simple, cheap and accurate analysis.
The technical solution adopted by the present invention step is as follows：
A kind of method using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose includes the following steps：
The first step, the acquisition and pretreatment of Sargassum horneri sample；
Second step, using the content of cellulose for improving sulfuric acid and potassium dichromate oxidation measure Sargassum horneri sample；
Third walks, and the near infrared spectrum for obtaining Sargassum horneri sample is scanned by near infrared spectrometer；
4th step, the foundation and evaluation of near infrared spectra quantitative models；
The content of cellulose data that second step obtains and the near infrared spectrum data that third walks are imported into numerical computations
In software matlab 8.3, using including exceptional sample elimination method, sample set partitioning, Pretreated spectra method, characteristic wave bands
Various chemometrics methods including back-and-forth method and Multivariate Correction method establish the Quantitative Analysis Model of Sargassum horneri content of cellulose, and
Using model evaluation parameter Evaluating Model performance.
5th step, the application of near infrared spectra quantitative models；
Using the near infrared spectra quantitative models established, the content of cellulose of unknown Sargassum horneri sample is predicted.
Further, in the first step, Sargassum horneri sample pre-treatment procedure is：Washing, air-dry, drying, pulverizer crush and
It is fitted into sealed transparent bag after the sieving of 60 mesh.
Further, it in the second step, improves sulfuric acid and contains with each Sargassum horneri sample fibres element of potassium dichromate oxidation measure
The step of amount is：
2.1) Sargassum horneri sample is put into the conical flask of the mixed liquor containing glacial acetic acid and nitric acid, boiling water bath heating；
2.2) heating is finished and is cooled to room temperature, be filtered, washed, precipitate after will all precipitate and be placed in containing sulfuric acid and dichromic acid
In the conical flask of potassium mixed liquor, boiling water bath heating；
2.3) heating, which finishes, is cooled to room temperature, and adds in liquor kalii iodide and starch solution, is titrated with sodium thiosulfate, and is same
Stepping line blank check experiment calculates the content of cellulose of Sargassum horneri sample according to the hypo solution volume consumed.
Further, in the third step, near infrared spectra collection condition is：Spectrum, light are acquired under diffusing reflection pattern
Spectrometer scanning wave-number range is 4000cm-1~12000cm-1, resolution ratio 8cm-1。
In 4th step, exceptional sample elimination method include mahalanobis distance method, t methods of inspection, spectrum residual analysis method with
And the combination of the above method.
In 4th step, sample set partitioning includes Kennard-Stone sample sets partitioning, randomly selects sample side
Method, SPXY methods, scalping method and condensation method.
In 4th step, preprocessing procedures include smoothing denoising algorithm, derivative processing method, standard normal variable and become
Change the combination of method, multiplicative scatter correction method, Normalization normalization methods and the above method.
In 4th step, characteristic wave bands back-and-forth method include interval partial least square, moving window Partial Least Squares,
The combination of Monte Carlo Method, correlation coefficient process, successive projection method, genetic algorithm and the above method.
In 4th step, Multivariate Correction method includes principal component regression and Partial Least Squares.
In 4th step, model evaluation parameter stays a validation-cross standard deviation RMSECV, prediction phase including calibration set
Close coefficients R, prediction standard deviation SEP and relation analysis error RPD.
The beneficial effects of the present invention are：The side using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose
Method not only has many advantages, such as quick, accurate, environmental protection, is conducive to improve the quality control level of Sargassum horneri content of cellulose, can also push away
Extensively applied in the quality control of other seaweed biolobic materials.
Description of the drawings
Fig. 1 is the content of cellulose data distribution of 40 Sargassum horneri samples；
Fig. 2 is the original near infrared spectrum of 40 Sargassum horneri samples；
Fig. 3 is the near infrared spectrum residual analysis result of 40 Sargassum horneri samples；
Fig. 4 is Sargassum horneri sample near-infrared spectral characteristic band (the original near infrared spectrum for establishing Quantitative Analysis Model
It is pre-processed through Savitzky-Golay convolution second orders derivative algorithms)；
Fig. 5 is the prediction effect (verification collection) of Sargassum horneri content of cellulose near infrared spectra quantitative models.
Specific implementation method
Below with reference to attached drawing and preferred embodiment, detailed description of embodiments of the present invention.
Reference Fig. 1~Fig. 5, a kind of method using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose, including
Following steps：
The first step, acquires the Sargassum horneri sample of separate sources or different batches, and carries out necessary sample preprocessing.
The Sargassum horneri sample of preferred embodiment of the present invention acquisition comes from Zhe Nan Wenzhou marine site, acquires 40 Sargassum horneri samples altogether.
After sample is by washing the silt and salinity that remove performance attachment, it is positioned under sunlight and simply air-dries, then dried at 105 DEG C
It is dry, the drying Sargassum horneri sample after cleaning with pulverizer is crushed, and pass through multilayer screen cloth, the loading of 60 mesh samples is taken to vacuumize
Sealed transparent bag in.
Second step, using the content of cellulose for improving sulfuric acid and potassium dichromate oxidation measure Sargassum horneri sample.
The detailed implementation steps for improving sulfuric acid and potassium dichromate oxidation measure Sargassum horneri sample fibres cellulose content are as follows：By copper
Algae smash it through 60 mesh sieve, weigh 0.2g (± 0.0001g) Sargassum horneri particle and be placed in 100ml conical flasks, add in 5ml glacial acetic acid and
Mixed liquor (the volume ratio 1 of nitric acid：1), glass stopper is placed in the water-bath boiled and heats 25min, and be stirred continuously beyond the Great Wall；It takes out
It is filtered after being cooled to room temperature, discards filtrate, collected and all precipitate and be washed with distilled water 3 times；Precipitation is placed in 100ml conical flasks
In, 10ml mass fractions are added in into precipitation as 10% sulfuric acid solution and the potassium bichromate solution of 10ml 0.1mol/L, are shaken up
It is placed in the water-bath boiled and heats 10min；10ml distilled water is added in, after solution is cooled to room temperature, adds in 5ml mass fractions
The starch solution that liquor kalii iodide and 1ml mass fractions for 20% are 0.5% uses the sodium thiosulfate of 0.2mol/L after shaking up
Titration, and with 10ml mass fractions for 10% sulfuric acid solution mixing 10ml 0.1mol/L potassium bichromate solution as blank
Sample is titrated.The calculation formula of Sargassum horneri content of cellulose is：
In formula, K represents the concentration of hypo solution, mol/L；A represents the sodium thiosulfate that blank titration is consumed
The volume of solution, ml；B represents the volume of hypo solution that solution is consumed, and ml, n represent the quality of Sargassum horneri particle, g.
The content of cellulose of 40 Sargassum horneri samples is distributed as shown in Figure 1, statistic analysis result such as 1 institute of table of overall data
Show.
Table 1
Third walks, and the near infrared spectrum for obtaining Sargassum horneri sample is scanned by near infrared spectrometer.
The near infrared spectrum data of Sargassum horneri sample (is matched silent in the U.S. by Nicolet iS10 Fourier Transform Near Infrared instruments
Fisher scientific) it collects, which carries integrating sphere accessory, is adopted under diffusing reflection pattern after instrument operates steadily
Collect spectrum.Environment temperature is kept constant (5~25 DEG C) during scanning, and humidity is less than 25%, and Sargassum horneri sample granularity is uniformly and fully dry
Dry, instrument setting automatic collection background spectrum, spectra collection wave-number range is 4000~12000cm-1, often scan 64 automatic guarantors
Deposit averaged spectrum and background correction spectrum, resolution ratio 8cm-1.Suitable Sargassum horneri sample is taken to contain into rotated sample pond, fills and scrapes
It is flat, it is placed on integrating sphere acquisition window and acquires spectrum.Sargassum horneri sample repeats dress sample and scans 3 times, takes the average value of 3 scanning optical spectrums
Original spectrum as Sargassum horneri sample.The scanning result of 40 Sargassum horneri samples is as shown in Figure 2.
4th step, the foundation and evaluation of near infrared spectra quantitative models.
The content of cellulose data that second step measures and the near infrared spectrum data that third walks are imported into numerical computations
In software matlab 8.3, the abnormal data in Sargassum horneri sample is rejected using spectrum residual analysis method, the calculating of spectrum residual error is public
Formula is：
R=YIn advance-YChange
In formula, YIn advanceAnd YChangeThe Sargassum horneri content of cellulose prediction value matrix of forecast set and Sargassum horneri content of cellulose are represented respectively
Wet-chemical analysis data matrix, R represent the Sargassum horneri content of cellulose residual matrix of forecast set, riRepresent the light of i-th of sample in R
Residual values are composed, f is the main cause subnumber of PLS prediction models.The results are shown in Figure 3 for rejecting abnormal data, needs to reject as shown in Figure 3
2 Sargassum horneri sample datas.
It is random to take out the unknown Sargassum horneri sample of 4 conducts, residue from 38 Sargassum horneri sample datas after rejecting abnormalities sample
34 Sargassum horneri samples are divided into calibration set using Kennard-Stone sample set partitionings and collect with verification.
The specific implementation step of Kennard-Stone sample set partitionings is as follows：
(1) the Euclidean distance d of all samples of acquisition between any two is calculatedij, two samples of selection Euclidean distance maximum
(i.e. sample 1 with sample 2) is into calibration set.
(2) calculate Euclidean in remaining 34 samples between each sample and the two samples No. 1 and No. 2 selected away from
From, and respectively it is minimized min (dI, No. 1,dI, No. 2), then choosing wherein has maximum Euclidean distance value max (min (dI, No. 1,
dI, No. 2)) sample 3 enter calibration set.
(3) Euclidean between each sample and these three samples No. 1, No. 2 and No. 3 selected in remaining 33 samples is calculated
Distance, and respectively it is minimized min (dI, No. 1,dI, No. 2,dI, No. 3), then choosing wherein has maximum Euclidean distance value max (min
(dI, No. 1,dI, No. 2,dI, No. 3)) sample 4 enter calibration set.
(4) it repeats the above process, until choosing 22 calibration samples.
The results are shown in Table 2 for the data statistics of calibration set and verification collection.
Table 2
Sargassum horneri sample fibres cellulose content measured value and near infrared spectrum data in calibration set, using Multivariate Correction method
The near infrared spectra quantitative models of Sargassum horneri content of cellulose are established, wherein Multivariate Correction method uses Partial Least Squares, most
Good main cause subnumber is 3.
Collect the external certificate for carrying out near infrared spectra quantitative models using verification, preferred Pretreated spectra method is
Savitzky-Golay convolution second order derivative algorithms, differential width 5, fitting of a polynomial exponent number are 3；Preferred feature wave band selects
It follows the example of as interval partial least square, selected characteristic wave bands are 6883cm-1To 10826cm-1, characteristic wave bands selection result such as Fig. 4
It is shown.
Verification is collected, the prediction effect of Sargassum horneri content of cellulose IR spectrum quantitative analysis model is as shown in Figure 5.
Selection calibration set stay a validation-cross standard deviation RMSECV, prediction related coefficient R, prediction standard deviation SEP and
The model evaluations parameters such as relation analysis error RPD carry out performance evaluation, each model evaluation near infrared spectra quantitative models
The specific formula for calculation of parameter sees below.
Stay a validation-cross standard deviation (RMSECV)：
In formula, yi,actualRepresent the content of cellulose chemical analysis value of i-th of Sargassum horneri sample in calibration set,
yi,predictedRepresent the model predication value of i-th of Sargassum horneri sample fibres cellulose content in calibration set, n represents that the sample of calibration set is total
Number.If standard deviation values are bigger, show there is that abnormal data is bigger in calibration set.
Related coefficient (R)：
In formula, yi,actualRepresent the content of cellulose chemical analysis value of i-th of Sargassum horneri sample,Represent Sargassum horneri fiber
The average value of cellulose content chemical analysis value, yi,predictedRepresent that calibration set or verification concentrate the cellulose of i-th of Sargassum horneri sample to contain
Model predication value is measured, n represents total sample number.
Prediction standard deviation (SEP)：
In formula, yi,actualRepresent the content of cellulose chemical analysis value of i-th of Sargassum horneri sample, yi,predictedRepresent verification
The content of cellulose model predication value of i-th of Sargassum horneri sample is concentrated, m represents the total sample number of calibration set.Prediction standard deviation is got over
Close to zero, then show that the prediction of model is accurately higher.
Relation analysis error (RPD)：
In formula, SDVRepresent that the standard deviation of all Sargassum horneri sample fibres cellulose contents is concentrated in verification.The property of verification collection sample
The wider distribution the more uniform, then SEP is smaller, and RPD values are bigger.
Verification is collected, the model evaluation parametric results such as table of Sargassum horneri content of cellulose near infrared spectra quantitative models
Shown in 3.
Table 3
As shown in Table 3, it is 1.0097 to stay a validation-cross standard deviation (RMSECV), and prediction standard deviation (SEP) is
1.0288, numerical value is relatively small away from zero deviation, illustrates that the model after rejecting abnormalities sample not only has preferable stability, and
And verification is collected, the content of cellulose prediction result and actual value deviation of Sargassum horneri sample are smaller, and related coefficient (R) is
0.9404, it is sufficiently close to 1 and relation analysis error (RPD) is more than 2 for 2.94, show that model entirety prediction effect is preferable.
5th step, the application of near infrared spectra quantitative models.
The Sargassum horneri content of cellulose near infrared spectra quantitative models established using the 4th step, to 4 Sargassum horneri samples
Content of cellulose predicted, and provide model evaluation parametric results.The content of cellulose prediction result of unknown Sargassum horneri sample
As shown in table 4, corresponding model evaluation parametric results are as shown in table 5.By table 4 and table 5 it is found that Sargassum horneri content of cellulose near-infrared
The practical application of quantitative spectrochemical analysis model achieves success.
Table 4
Table 5
It is last it should be noted that preferred embodiment above is only embodiments of the present invention it is more readily appreciated that rather than using
To limit the present invention.Although the present invention has been described in detail by above preferred embodiment, any present invention
It is in technical field it will be appreciated by the skilled person that any modification and change can be made in the formal and details of implementation
Change, without departing from claims of the present invention limited range.
Claims (10)
- A kind of 1. method using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose, which is characterized in that the method Include the following steps：The first step, the acquisition and pretreatment of Sargassum horneri sample；Second step, using the content of cellulose for improving sulfuric acid and potassium dichromate oxidation measure Sargassum horneri sample；Third walks, and the near infrared spectrum for obtaining Sargassum horneri sample is scanned by near infrared spectrometer；4th step, the foundation and evaluation of near infrared spectra quantitative models；The content of cellulose data that second step obtains and the near infrared spectrum data that third walks are imported into numerical computations software In matlab 8.3, the Near-Infrared Spectra for Quantitative Analysis mould of Sargassum horneri content of cellulose is established using various chemometrics methods Type, and suitable model evaluation parameter is selected to evaluate model；5th step, the application of near infrared spectra quantitative models.Using the near infrared spectra quantitative models established, the content of cellulose of unknown Sargassum horneri sample is predicted.
- 2. a kind of method using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 1, It is characterized in that, in the first step, the Sargassum horneri sample pre-treatment procedure is：Washing is air-dried, is dried, pulverizer crushing And 60 mesh sieving after be fitted into sealed transparent bag.
- 3. a kind of side using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 1 or 2 Method, which is characterized in that in the second step, the improvement sulfuric acid measures each Sargassum horneri sample fibres element with potassium dichromate oxidation The step of content is：2.1) Sargassum horneri sample is put into the conical flask of the mixed liquor containing glacial acetic acid and nitric acid, boiling water bath heating；2.2) heating is finished and is cooled to room temperature, be filtered, washed, precipitate after all will precipitate to be placed in and mixed containing sulfuric acid and potassium bichromate In the conical flask for closing liquid, boiling water bath heating；2.3) heating, which finishes, is cooled to room temperature, and adds in liquor kalii iodide and starch solution, is titrated with sodium thiosulfate, and same stepping Line blank check experiment calculates the content of cellulose of Sargassum horneri sample according to the hypo solution volume consumed.
- 4. a kind of side using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 1 or 2 Method, which is characterized in that in the third step, the near infrared spectra collection condition is：Spectrum is acquired under diffusing reflection pattern, Spectrometer scanning wave-number range is 4000cm-1~12000cm-1, resolution ratio 8cm-1。
- 5. a kind of side using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 1 or 2 Method, which is characterized in that in the 4th step, the chemometrics method includes exceptional sample elimination method, sample set is drawn Point-score, Pretreated spectra method, characteristic wave bands back-and-forth method and Multivariate Correction method.
- 6. a kind of side using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 1 or 2 Method, which is characterized in that in the 4th step, the model evaluation parameter stays a validation-cross standard deviation including calibration set RMSECV, prediction related coefficient R, prediction standard deviation SEP and relation analysis error RPD.
- 7. a kind of method using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 5, It is characterized in that, the exceptional sample elimination method includes mahalanobis distance method, t methods of inspection, spectrum residual analysis method and above-mentioned The combination of method.
- 8. a kind of method using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 5, It is characterized in that, the sample set partitioning include Kennard-Stone sample sets partitioning, randomly select Sample Method, SPXY methods, scalping method and condensation method.
- 9. a kind of method using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 5, It is characterized in that, the preprocessing procedures include smoothing denoising algorithm, derivative processing method, standard normal variable converter technique, The combination of multiplicative scatter correction method, Normalization normalization methods and the above method.
- 10. a kind of method using Near Infrared Spectroscopy for Rapid Sargassum horneri content of cellulose according to claim 5, It is characterized in that, the characteristic wave bands back-and-forth method includes interval partial least square, moving window Partial Least Squares, Meng Teka The combination of Luo Fa, correlation coefficient process, successive projection method, genetic algorithm and the above method；The Multivariate Correction method include it is main into Divide the Return Law and Partial Least Squares.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108982409A (en) * | 2018-08-08 | 2018-12-11 | 浙江工业大学 | A method of quickly detecting three constituent content of kelp lignocellulosic based near infrared spectrum |
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| CN117690519A (en) * | 2023-12-13 | 2024-03-12 | 北京理工大学 | Methodfor transferring urotropin-acetic acid solution near-infrared model for improving dung beetle algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105158195A (en) * | 2015-09-29 | 2015-12-16 | 中国林业科学研究院林产化学工业研究所 | Method for rapidly determining holocellulose content of pulping material based on near-infrared spectrum technology |
| CN107314985A (en) * | 2017-06-12 | 2017-11-03 | 华中农业大学 | A kind of method that utilization near infrared spectrum detects rape stem content of cellulose |
-
2017
- 2017-12-18 CN CN201711361539.XA patent/CN108181262A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105158195A (en) * | 2015-09-29 | 2015-12-16 | 中国林业科学研究院林产化学工业研究所 | Method for rapidly determining holocellulose content of pulping material based on near-infrared spectrum technology |
| CN107314985A (en) * | 2017-06-12 | 2017-11-03 | 华中农业大学 | A kind of method that utilization near infrared spectrum detects rape stem content of cellulose |
Non-Patent Citations (2)
| Title |
|---|
| 王会: "《生物质计量测试基础》", 31 March 2017, 浙江大学出版社 * |
| 肖爱平 等: "苎麻纤维素含量近红外预测模型的建立与探讨", 《中国麻业科学》 * |
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