CN107036999A - A kind of five yuan of ready-mixed oil quantitative analysis methods based near infrared spectrum and Chemical Measurement - Google Patents
A kind of five yuan of ready-mixed oil quantitative analysis methods based near infrared spectrum and Chemical Measurement Download PDFInfo
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- CN107036999A CN107036999A CN201611024473.0A CN201611024473A CN107036999A CN 107036999 A CN107036999 A CN 107036999A CN 201611024473 A CN201611024473 A CN 201611024473A CN 107036999 A CN107036999 A CN 107036999A
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- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 18
- 238000004445 quantitative analysis Methods 0.000 title claims abstract description 17
- 239000000126 substance Substances 0.000 title claims abstract description 7
- 238000005259 measurement Methods 0.000 title claims abstract description 6
- 239000003921 oil Substances 0.000 claims abstract description 75
- 238000012937 correction Methods 0.000 claims abstract description 4
- 238000009499 grossing Methods 0.000 claims abstract description 3
- 235000019198 oils Nutrition 0.000 claims description 71
- 235000007164 Oryza sativa Nutrition 0.000 claims description 20
- 235000009566 rice Nutrition 0.000 claims description 20
- 238000001228 spectrum Methods 0.000 claims description 13
- 235000019486 Sunflower oil Nutrition 0.000 claims description 11
- 235000005687 corn oil Nutrition 0.000 claims description 11
- 239000002285 corn oil Substances 0.000 claims description 11
- 239000008159 sesame oil Substances 0.000 claims description 11
- 235000011803 sesame oil Nutrition 0.000 claims description 11
- 239000003549 soybean oil Substances 0.000 claims description 11
- 235000012424 soybean oil Nutrition 0.000 claims description 11
- 239000002600 sunflower oil Substances 0.000 claims description 11
- 238000003672 processing method Methods 0.000 claims description 6
- 240000007594 Oryza sativa Species 0.000 claims 1
- 235000019483 Peanut oil Nutrition 0.000 claims 1
- 239000004006 olive oil Substances 0.000 claims 1
- 235000008390 olive oil Nutrition 0.000 claims 1
- 239000000312 peanut oil Substances 0.000 claims 1
- 241000894007 species Species 0.000 claims 1
- 235000015112 vegetable and seed oil Nutrition 0.000 claims 1
- 239000008158 vegetable oil Substances 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 8
- 238000010238 partial least squares regression Methods 0.000 abstract description 4
- 238000004458 analytical method Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 abstract description 3
- 241000209094 Oryza Species 0.000 description 19
- 230000008859 change Effects 0.000 description 13
- 238000000354 decomposition reaction Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 7
- 239000008157 edible vegetable oil Substances 0.000 description 7
- 238000002203 pretreatment Methods 0.000 description 7
- 238000012360 testing method Methods 0.000 description 5
- 238000009795 derivation Methods 0.000 description 4
- 239000004615 ingredient Substances 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000003595 spectral effect Effects 0.000 description 4
- 235000019774 Rice Bran oil Nutrition 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000002360 preparation method Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 239000008165 rice bran oil Substances 0.000 description 3
- 238000004611 spectroscopical analysis Methods 0.000 description 3
- 238000002790 cross-validation Methods 0.000 description 2
- 238000004186 food analysis Methods 0.000 description 2
- 238000004817 gas chromatography Methods 0.000 description 2
- 238000004128 high performance liquid chromatography Methods 0.000 description 2
- 230000010365 information processing Effects 0.000 description 2
- 238000012628 principal component regression Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000001069 Raman spectroscopy Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000003705 background correction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000003467 diminishing effect Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000004313 glare Effects 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000000870 ultraviolet spectroscopy Methods 0.000 description 1
- 238000002211 ultraviolet spectrum Methods 0.000 description 1
- 239000002699 waste material Substances 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
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/38—Diluting, dispersing or mixing samples
Abstract
The present invention relates to five yuan of ready-mixed oil quantitative analysis methods of a kind of utilization near infrared spectrum and Chemical Measurement.The near infrared spectrum for preparing sample is specially first scanned near infrared spectrometer, near infrared spectrum data is obtained.The pretreating effect of SG exponential smoothings, standard normal variable, multiplicative scatter correction, first derivative, second dervative and continuous wavelet transform and combinations thereof is investigated afterwards.Wavelength selecting method is determined then in conjunction with optimal preprocess method, PLS regression models are finally set up to five yuan of ready-mixed oil quantitative analyses using optimal pretreatment Wavelength selecting method.The present invention is based near infrared spectrum and Chemical Measurement, and efficient lossless, detection is rapid, and the degree of accuracy is high, and the accurate quantitative analysis of each component oil in five yuan of ready-mixed oils is realized well.
Description
Technical field
The invention belongs to the quantitative analysis method of food analysis field ready-mixed oil research, it is related to a kind of based near infrared spectrum
And five yuan of ready-mixed oil quantitative analysis methods of Chemical Measurement.
Background technology
Edible blend oil is by two kinds and two or more refined edible oils are allocated be made in proportion.Due to ready-mixed oil ratio
Single edible oil enriches equilibrium in terms of nutriment, thus is favored by consumers in general.But it is due to preparation ready-mixed oil
There is larger difference in edible oil used, and reconcile the quantitative analysis of oil component and lack national standard in price, therefore be permitted
More illegal businessman, which will increase the ratio of low price edible oil to obtain bigger interests, reduces the cost of ready-mixed oil.In the market
Which composition most of ready-mixed oils have often only been labelled with, and do not mark the ratio shared by every kind of composition, or marked
Ratio does not conform to the actual conditions.Therefore, a kind of method for polynary ready-mixed oil accurate quantitative analysis is studied significant.
The detection method of current ready-mixed oil has gas chromatography, high performance liquid chromatography, fluorescent spectrometry, ultraviolet spectrometry light
Degree method, infra-red sepectrometry and Raman spectroscopy etc..Although these methods can obtain preferable prediction effect, there is also one
A little limitations, such as gas chromatography and high performance liquid chromatography operation waste time and energy and sample pretreatment complexity；Ultraviolet spectra is distinguished
Degree is not high.The advantages of near-infrared spectrum technique is because its is quick, lossless, without sample pretreatment turns into Agricultural Food Analysis and food
The prefered method of product analysis.But near infrared light spectrum signal is weaker, peak overlap is serious, it is necessary to by many in Chemical Measurement
First bearing calibration could carry out quantitative analysis.Multivariate calibration methodses mainly have principal component regression method (PCR), artificial neural network method
(ANN) and the method such as PLS (PLS), wherein PLS because its parameter it is few, simple, it is quick the advantages of turn into application most
For extensive multivariate calibration methodses.Existing many research and inquirement Multivariate Corrections carry out the feasible of quantitative analysis to ready-mixed oil at present
Property, but most of research reconciles oil systems just for binary and ternary, the ready-mixed oil Quantitative Study of more polynary number compared with
It is few.
In the near infrared spectrum of the complex samples such as edible oil in addition to useful information, substantial amounts of redundancy wavelength letter is usually contained
Breath；The spectral information gathered is easily by the influence of test condition such as temperature, noise and veiling glare etc..These factors can cause
The prediction accuracy reduction of multivariate calibration methodses.Accordingly, it would be desirable to using suitable pretreatment and Wavelength selecting method to spectrum
Handled, to eliminate the influence of above-mentioned factor.The former have smoothly, standard normal variable (SNV), multiplicative scatter correction (MSC),
Derivation and continuous wavelet transform (CWT) etc..The latter has genetic algorithm (GA), eliminates (UVE), Monte Carlo-nothing without information variable
Information variable eliminates (MC-UVE) and randomized test (RT) etc..Theoretically, smooth main elimination noise information, SNV, MSC are
Background correction technology, mainly eliminates spuious optical information, and derivation and CWT are background deduction technologies, mainly eliminate background information, wavelength
System of selection mainly selects the wavelength points related to prediction target components.However, for specific system, which kind of method processing method
Effect with reference to treatment effect preferably, it is necessary to choose.
The content of the invention
The purpose of the present invention is to be directed to above-mentioned problem, using near infrared spectrum as means of testing, optimizes spectrum number
According to processing method, resettling multivariate calibration model, there is provided a kind of five yuan of ready-mixed oil quantitative analysis methods.
To realize that technical scheme provided by the present invention comprises the following steps：
1) preparation experiment sample and the near infrared spectrum of sample is gathered
If buying five kinds of single edible oil sample dry doublings by this five kinds of single edible oils from Tianjin large supermarket respectively
Prepare mediation oil samples.Wherein every kind of oily mass percent is 0~40%, and at intervals of 0.8%, 51 concentration are designed altogether.Will
Five kinds of oily 51 concentration are upset and ensure that five kinds of oily mass percent summations are 100% in 1 sample at random respectively.According to
Five kinds of oily concentration prepare mediation oil samples one by one in designed each mediation oil samples.The parameter of nir instrument is set,
Scan the near infrared spectrum of all samples.
2) factor number of deflected secondary air is determined
It is true with the change of factor number (LV) according to the cross validation root-mean-square error (RMSECV) of Monte Carlo Cross-Validation
Determine the factor number of partial least square model, the corresponding factor number of RMSECV minimum values is optimum factor number.
3) spectral signal is handled using different pieces of information processing method and combinations thereof
According to predicted root mean square error (RMSEP) as the change of window determines that SG is smooth and window size of derivation；Root
According to RMSEP as the change of wavelet function and decomposition scale determines CWT wavelet function and decomposition scale.Under optimal parameter,
Investigate SG exponential smoothings, SNV, MSC, first derivative (1stDer), second dervative (2ndDer), the preprocess method such as CWT and its group
Close SNV+1st Der、MSC+1st Der、SNV+2nd Der、MSC+2ndDer, SNV+CWT, MSC+CWT are located in advance to spectrum
The effect of reason.Obtain after optimal preprocess method, then investigate UVE, MCUVE, RT wavelength Selection effect, it is determined that optimal pre- place
Reason-Wavelength selecting method.
4) using deflected secondary air to the data modeling after processing
After optimal pretreatment-Wavelength selecting method processing spectroscopic data, Partial Least-Squares Regression Model is resettled.Will
The spectrum of unknown sample is updated in model, various oil content ratios in five yuan of ready-mixed oils of prediction.
The invention has the advantages that being pre-processed using different pretreatments method and combinations thereof method to data and wavelength choosing
Select, then resettle Partial Least-Squares Regression Model, so as to improve the standard to various oil content ratio detections in five yuan of ready-mixed oils
Exactness.
Brief description of the drawings
Fig. 1 is the atlas of near infrared spectra of 51 five yuan of mediation oil samples
Fig. 2 is the RMSECV of five yuan of ready-mixed oil training samples collection with the variation diagram of factor number
Fig. 3 is five yuan and reconciles the RMSEP that smoothly pre-processes of oil samples with the variation diagram of window
Fig. 4 is the RMSEP of five yuan of mediation oil samples first derivative pretreatments with the variation diagram of window
Fig. 5 is the RMSEP of five yuan of mediation oil samples second dervative pretreatments with the variation diagram of window
Fig. 6 is the RMSEP of five yuan of mediation oil samples semilate rice rice bran oil component continuous wavelet change pretreatments with wavelet function
And the variation diagram of decomposition scale
Fig. 7 is that five yuan of ready-mixed oil sample average spectrum and optimal preprocess method combine three kinds of Wavelength selecting methods to rice
Oil ingredient retains the distribution map of wavelength
Fig. 8 is five yuan and reconciles the optimal preprocess method of oil samples semilate rice rice bran oil component with reference to three kinds of Wavelength selecting methods
RMSEP is with the variation diagram for retaining number of wavelengths
(a) smooth-RT of smooth-MCUVE (c) SG of smooth-UVE (b) SG of SG
Fig. 9 be five yuan mediation oil samples forecast set predicted value and actual value graph of a relation
(a) Rice oil (b) soybean oil (c) corn oil (d) sunflower oil (e) sesame oil
Embodiment
To be best understood from the present invention, the present invention will be described in further detail with reference to the following examples, but of the invention
Claimed scope is not limited to the scope represented by embodiment.
Embodiment：
1) preparation experiment sample and the near infrared spectrum of sample is gathered
Prepared respectively from Tianjin large supermarket purchase Rice oil, soybean oil, corn oil, sunflower oil, sesame oil sample are some
Mediation oil samples.Wherein every kind of oily mass percent is 0~40%, and at intervals of 0.8%, 51 concentration are designed altogether.By five kinds
51 concentration of oil are upset and ensure that five kinds of oily mass percent summations are 100% in 1 sample at random respectively.According to design
Five kinds of oily concentration prepare mediation oil samples one by one in good each mediation oil samples.It is before experiment, the multibands of Vertex 70 is red
Outside/near infrared spectrometer is opened, and sets test parameter, and sampling wave-number range is 12000-4000cm-1, resolution ratio is 8cm-1, the back of the body
Scape and number of sample scan are 32 times, and instrument is preheated into 1 hour.2mm cuvette is chosen, multiband is put into infrared/near
In infrared spectrometer, baseline scan is carried out three times, average as background.It is added dropwise to plastic dropper suction testing sample
About 2/3 position in 2mm cuvettes, will be wiped clean to be put into instrument with lens wiping paper outside cuvette and scanned, each Sample Scan
Three times, the final spectrum averaged as the sample.
Fig. 1 is the atlas of near infrared spectra of 51 five yuan of mediation oil samples, it can be seen that 51 mediation oil samples
Spectrum almost it is completely overlapped together, it is impossible to content information is highly directly obtained according to spectral peak, it is therefore desirable to by stoichiometry
Method carries out quantitative analysis.
2) factor number of deflected secondary air is determined
Data are divided with KS methods, the 2/3 of total number of samples as training set, 1/3 is used as forecast set.By factor number from 1 to
25 are changed, and corresponding factor number is optimum factor number when obtaining RMSECV minimum values.Fig. 2 shows five yuan of ready-mixed oils
Sample RMSECV is with the change of factor number, it can be seen that Rice oil, soybean oil, corn oil, sunflower oil and sesame oil group
The optimum factor number divided is respectively 16,21,16,20,16.
3) spectral signal is handled using different pieces of information processing method and combinations thereof
According to predicted root mean square error (RMSEP) as the change of window determines that SG is smooth and window size of derivation.Fig. 3
Show the lower RMSEP of smooth pretreatment with the change of window.It can be seen that big with the change of window, RMSEP values are first
Tended to be steady after diminishing.Rice oil, soybean oil, corn oil, sunflower oil and the corresponding window difference of sesame oil component RMSEP minimum values
It is the best window that above-mentioned 5 components are smoothly pre-processed for 35,41,51,41 and 9.Fig. 4 and Fig. 5 respectively illustrate first derivative
And the RMSEP under second dervative pretreatment is with the change of window.From two it can be seen from the figure thats, spectroscopic data is led in single order respectively
Number, under the pretreatment of second dervative, Rice oil, soybean oil, corn oil, the best window of sunflower oil and sesame oil are respectively 43,37,
49th, 15,59 and 57,53,59,59,53.
According to RMSEP as the change of wavelet function and decomposition scale determines CWT wavelet function and decomposition scale.Fig. 6
It is five yuan and reconciles RMSEP of the oil samples semilate rice rice bran oil component by CWT pretreatments with the change of wavelet function and decomposition scale
Change figure.Decomposition scale changes to 40 from 1, and wavelet function 1-32 represents Haar, db2-db20, coif1-coif5, sym2- respectively
32 wavelet functions such as sym8.It can be seen that after the RMSEP of Rice oil reduces as the increase of decomposition scale is first rapid
Slow decline finally is tended to be steady, and as the change of wavelet function changes little.The corresponding wavelet function of RMSEP minimum values and
Decomposition scale is respectively db16 and 38.The optimal of soybean oil, corn oil, sunflower oil and sesame oil can be drawn by similar method
Wavelet function and decomposition scale are respectively sym7, Haar, Haar, db10 and 27,39,36,37.
Under optimal parameter, SG smooth, SNV, MSC, 1 are investigatedst Der、2stThe preprocess methods such as Der, CWT and its group
Close, including SNV-1st Der、MSC-1st Der、SNV-2nd Der、MSC-2ndThe methods such as Der, SNV-CWT, MSC-CWT are right
The effect that signal is pre-processed.
Table 1 shows that different pretreatments method predicts the outcome to Rice oil component in five yuan of ready-mixed oils.Can from table
Go out, compared with the PLS methods without pretreatment, in addition to SNV, MSC, other preprocess methods have not to prediction accuracy
With the raising of degree, RMSEP smooth wherein SG is 1.8331, for the minimum value of all preprocess methods, forecast set phase relation
Number R is 0.9812, is the higher value of all preprocess methods, is quantitative so as to draw the smooth pretreating effects of SG preferably
Analyze the optimal preprocess method of rice oil ingredient.It can similarly show that soybean oil, corn oil, sunflower oil and sesame oil are most preferably located in advance
Reason method be respectively MSC-CWT, SG it is smooth, 2stDer and CWT-MSC.
The comparison that the different pretreatments method of table 1 predicts the outcome to Rice oil component in five yuan of ready-mixed oils
On the basis of optimal pretreatment, tested, therefrom selected using UVE, MCUVE, RT equiwavelength's system of selection
Optimal pretreatment-Wavelength selecting method, is used as final modeling result.
Fig. 7 is that 51 five yuan of ready-mixed oil sample average spectrum and optimal preprocess method combine three kinds of Wavelength selecting methods pair
Rice oil ingredient retains the distribution map of wavelength.It can be seen that what different wave length system of selection retained rice oil ingredient
Wavelength is similar, illustrates the reasonability of wavelength selection.Fig. 8 be five yuan reconcile oil samples forecast sets in Rice oil component most preferably locate in advance
Reason method combines the RMSEP of three kinds of Wavelength selecting methods with the variation diagram for retaining number of wavelengths, it can be seen that the smooth-MC- of SG
UVE RMSEP is minimum, therefore the smooth pretreatment-Wavelength selecting methods of-MC-UVE as Rice oil most preferably of selection SG.Pass through
Similar method, the optimum data processing method that can obtain soybean oil, corn oil, sunflower oil and sesame oil is respectively：MSC-
CWT-MCUVE、SG-UVE、2nd Der-CWT-MCUVE、CWT-MSC-UVE。
4) using deflected secondary air to the data modeling after processing
For Rice oil, soybean oil, corn oil, sunflower oil and sesame oil component, SG smooth-MC-UVE, MSC- is respectively adopted
CWT-MCUVE、SG-UVE、2ndDer-CWT-MCUVE and CWT-MSC-UVE is built as optimal pretreatment-Wavelength selecting method
Vertical Partial Least-Squares Regression Model.The spectrum of unknown sample is updated in model, various oil content ratios in five yuan of ready-mixed oils of prediction
Example.
Fig. 9 is shown sets up PLS models after optimal pretreatment-wavelength selection, every kind of oil in five yuan of mediation oil samples
Predicted value and actual value relation, it can be seen that the prediction of Rice oil, soybean oil, corn oil, sunflower oil, sesame oil
Value and actual value all have preferable linear relationship, coefficient R is respectively 0.9980,0.9995,0.9699,0.9936,
0.9845.Before by pretreatment, PLS models are set up to every kind of oil, above-mentioned five kinds of oily coefficient Rs are respectively
0.9356、0.6674、0.8932、0.8494、0.9273.As a result show by optimal pretreatment-Wavelength selecting method processing light
After spectrum, the predictive ability for building partial least square model improves a lot.Therefore, near-infrared spectrum technique combines suitable chemistry
Metrology method can realize the accurate quantitative analysis of each component oil in five yuan of ready-mixed oils well.
Claims (3)
1. five yuan of ready-mixed oil quantitative analysis methods of a kind of utilization near infrared spectrum and Chemical Measurement, it is characterised in that：It is
The near infrared spectrum of sample is first scanned near infrared spectrometer, after handling collection spectrum, optimal pretreatment-wavelength is selected
System of selection, finally sets up optimal PLS models and carries out quantitative analysis to five yuan of ready-mixed oils.
2. five yuan of ready-mixed oil quantitative analysis methods according to claim 1, it is characterised in that：The quantitative analysis method is
First using SG exponential smoothings, standard normal variable, multiplicative scatter correction, first derivative, second dervative and continuous wavelet transform and its
Combination S NV+1st Der、MSC+1st Der、SNV+2nd Der、MSC+2ndDer, SNV+CWT, MSC+CWT are to original spectrum number
According to being pre-processed, Wavelength selecting method is determined with reference to optimal preprocess method, the final optimum data processing method that combines is set up
PLS models, are analyzed optimal PLS models.
3. five yuan of ready-mixed oil quantitative analysis methods according to claim 1, it is characterised in that：To five kinds in five yuan of ready-mixed oils
The species of vegetable oil is not limited, olive oil, sesame oil, peanut oil, Rice oil, corn oil, sunflower oil, soybean oil, etc. it is therein
Any five kinds of progress mediation can.
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Cited By (4)
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| CN107727591A (en) * | 2017-09-27 | 2018-02-23 | 天津工业大学 | A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method |
| CN107727590A (en) * | 2017-09-27 | 2018-02-23 | 天津工业大学 | A kind of quantitative analysis method of polynary ready-mixed oil quick nondestructive |
| CN110095429A (en) * | 2019-04-30 | 2019-08-06 | 济南弗莱德科学仪器有限公司 | A kind of product oil method for quickly detecting quality |
| CN111912815A (en) * | 2019-12-20 | 2020-11-10 | 南开大学 | Near infrared spectrum analysis method for evaluating quality of oil crops |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107727591A (en) * | 2017-09-27 | 2018-02-23 | 天津工业大学 | A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method |
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| CN110095429A (en) * | 2019-04-30 | 2019-08-06 | 济南弗莱德科学仪器有限公司 | A kind of product oil method for quickly detecting quality |
| CN111912815A (en) * | 2019-12-20 | 2020-11-10 | 南开大学 | Near infrared spectrum analysis method for evaluating quality of oil crops |
| CN111912815B (en) * | 2019-12-20 | 2023-03-14 | 南开大学 | Near infrared spectrum analysis method for evaluating quality of oil crops |
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