CN106770008A - A kind of method of near infrared spectrum quick detection Chinese anise quality - Google Patents
A kind of method of near infrared spectrum quick detection Chinese anise quality Download PDFInfo
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- CN106770008A CN106770008A CN201611142685.9A CN201611142685A CN106770008A CN 106770008 A CN106770008 A CN 106770008A CN 201611142685 A CN201611142685 A CN 201611142685A CN 106770008 A CN106770008 A CN 106770008A
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- chinese anise
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- infrared spectrum
- quick detection
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- 235000012550 Pimpinella anisum Nutrition 0.000 title claims abstract description 80
- 235000007265 Myrrhis odorata Nutrition 0.000 title claims abstract description 76
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 title claims abstract description 25
- 240000009023 Myrrhis odorata Species 0.000 title claims abstract 24
- 238000000605 extraction Methods 0.000 claims abstract description 15
- 238000001228 spectrum Methods 0.000 claims abstract description 14
- 238000012360 testing method Methods 0.000 claims abstract description 6
- 238000011282 treatment Methods 0.000 claims description 15
- 239000002253 acid Substances 0.000 claims description 10
- 230000003595 spectral effect Effects 0.000 claims description 10
- 239000007788 liquid Substances 0.000 claims description 9
- 244000025254 Cannabis sativa Species 0.000 claims description 8
- JXOHGGNKMLTUBP-HSUXUTPPSA-N shikimic acid Chemical compound O[C@@H]1CC(C(O)=O)=C[C@@H](O)[C@H]1O JXOHGGNKMLTUBP-HSUXUTPPSA-N 0.000 claims description 8
- JXOHGGNKMLTUBP-JKUQZMGJSA-N shikimic acid Natural products O[C@@H]1CC(C(O)=O)=C[C@H](O)[C@@H]1O JXOHGGNKMLTUBP-JKUQZMGJSA-N 0.000 claims description 8
- CSCPPACGZOOCGX-UHFFFAOYSA-N Acetone Chemical compound CC(C)=O CSCPPACGZOOCGX-UHFFFAOYSA-N 0.000 claims description 6
- 238000010521 absorption reaction Methods 0.000 claims description 4
- 238000013459 approach Methods 0.000 claims description 4
- 239000004480 active ingredient Substances 0.000 claims description 3
- 238000010992 reflux Methods 0.000 claims description 3
- 238000000556 factor analysis Methods 0.000 claims description 2
- 238000009659 non-destructive testing Methods 0.000 claims description 2
- 230000003287 optical effect Effects 0.000 claims description 2
- 238000003672 processing method Methods 0.000 claims description 2
- 239000002904 solvent Substances 0.000 claims description 2
- 238000004611 spectroscopical analysis Methods 0.000 claims description 2
- 238000002137 ultrasound extraction Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 abstract description 10
- 239000000470 constituent Substances 0.000 abstract description 3
- 240000004760 Pimpinella anisum Species 0.000 description 57
- 239000003921 oil Substances 0.000 description 9
- 239000010617 anise oil Substances 0.000 description 6
- 239000000284 extract Substances 0.000 description 4
- MUBZPKHOEPUJKR-UHFFFAOYSA-N Oxalic acid Chemical compound OC(=O)C(O)=O MUBZPKHOEPUJKR-UHFFFAOYSA-N 0.000 description 3
- HEMHJVSKTPXQMS-UHFFFAOYSA-M Sodium hydroxide Chemical compound [OH-].[Na+] HEMHJVSKTPXQMS-UHFFFAOYSA-M 0.000 description 3
- 238000009499 grossing Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 241001537183 Hypecoum Species 0.000 description 2
- 238000007605 air drying Methods 0.000 description 2
- 239000007864 aqueous solution Substances 0.000 description 2
- 238000013329 compounding Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000001035 drying Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- JQWHASGSAFIOCM-UHFFFAOYSA-M sodium periodate Chemical compound [Na+].[O-]I(=O)(=O)=O JQWHASGSAFIOCM-UHFFFAOYSA-M 0.000 description 2
- GEHJYWRUCIMESM-UHFFFAOYSA-L sodium sulfite Chemical compound [Na+].[Na+].[O-]S([O-])=O GEHJYWRUCIMESM-UHFFFAOYSA-L 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 240000006927 Foeniculum vulgare Species 0.000 description 1
- 235000004204 Foeniculum vulgare Nutrition 0.000 description 1
- 241001529246 Platymiscium Species 0.000 description 1
- 238000000944 Soxhlet extraction Methods 0.000 description 1
- 241000218315 Winteraceae Species 0.000 description 1
- 238000002835 absorbance Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000035622 drinking Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 235000008216 herbs Nutrition 0.000 description 1
- XMBWDFGMSWQBCA-UHFFFAOYSA-N hydrogen iodide Chemical compound I XMBWDFGMSWQBCA-UHFFFAOYSA-N 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011369 optimal treatment Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 235000006408 oxalic acid Nutrition 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 235000010265 sodium sulphite Nutrition 0.000 description 1
- 235000013599 spices Nutrition 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 239000000341 volatile oil Substances 0.000 description 1
- 238000003809 water extraction 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
Abstract
A kind of method of near infrared spectrum quick detection Chinese anise quality, it is related to a kind of method for detecting Chinese anise quality, the invention discloses a kind of near infrared spectrum method for quick of Chinese anise quality, the method includes the structure of sample sets, analysis of Influential Factors, model is set up and performance prediction.This method is set up on the basis of near infrared spectrum, by to sequence of operations such as sample comminution mesh number, test sample amount, scanning times, Pretreated spectra, characteristic spectrum extraction, model foundation and forecast analysis, Chinese anise active constituent content can rapidly and accurately be determined, determine Chinese anise quality, it is that trade supervision management and specification provide technical support, with important actual application value.
Description
Technical field
The present invention relates to a kind of method for detecting Chinese anise quality, more particularly to a kind of near infrared spectrum quick detection
The method of Chinese anise quality.
Background technology
Chinese anise, is Angiospermae Dicotyledoneae anise mesh Winteraceae anise platymiscium also known as aniseed, anise
Fruit, originate in southern area of China and Vietnam, be important " integration of drinking and medicinal herbs " economic tree of China, be China's special product spice
And Chinese medicine.Because the place of production, ecological environment, collecting season, processing method and storage requirement are different, causing the quality of Chinese anise has
Can there is notable difference in difference, particularly its active ingredient, and general organoleptic detection（Color, smell etc.）Cannot accurately judge
The interior quality of aniseed, so the criteria of quality evaluation for setting up aniseed quality is significant.Chinese anise volatile oil is eight
Main Liposoluble in erect hypecoum；Shikimic acid is one kind contained in Chinese anise many through base acid compound, is eight
Content highest water-soluble components in erect hypecoum, therefore can be by measuring oil of badian and thick grass acid content in Chinese anise
To identify aniseed quality, as the leading indicator of Chinese anise evaluation criterion.
The content of the invention
It is an object of the invention to provide a kind of method of near infrared spectrum quick detection Chinese anise quality, the method bag
The structure of sample sets is included, analysis of Influential Factors, model is set up and performance prediction, sets up on the basis of near infrared spectrum, by right
Sample comminution mesh number, test sample amount, scanning times, Pretreated spectra, characteristic spectrum are extracted, model is set up and forecast analysis etc. is
Row operation, can rapidly and accurately determine Chinese anise active constituent content, determine Chinese anise quality, be trade supervision management and
Specification provides technical support.
The purpose of the present invention is achieved through the following technical solutions：
A kind of method of near infrared spectrum quick detection Chinese anise quality, methods described comprises the following steps：
Step（1）：The foundation of sample sets, approach one：20 kinds of Chinese anises of different manufacturers of purchase, approach two：Using the moisture absorption and
Alternate treatment is dried, the different Chinese anise sample of active ingredient is obtained, the Chinese anise after this is processed is incorporated into proportion
In the Chinese anise of purchase, 40 different samples of quality are obtained；
Step（2）：Oil of badian and the extraction of thick grass acid content and the determination of detection method in Chinese anise；
Step（3）：Factor analysis, sample mesh number, scanning times, the test sample amount set up to influence model etc. are analyzed treatment；
Step（4）：Scan 50 different sources and mix the Chinese anise sample matched somebody with somebody, set up optical data storehouse；Using The
The softwares of Unscrambler 6.1 carry out the treatment of spectroscopic data, select least square method（PLS）Set up Chinese anise quality
Near-infrared Non-Destructive Testing calibration model；
Step（5）：10 forecast set samples are predicted by the checking of model prediction performance, determine model prediction performance.
A kind of method of described near infrared spectrum quick detection Chinese anise quality, step（2）In shikimic acid ultrasound
Assisted extraction process is the W of power 120, solid-liquid ratio 1:15,75 DEG C of temperature, the min of extraction time 40.
A kind of method of described near infrared spectrum quick detection Chinese anise quality, step（2）In Extraction solvent be
Acetone, extraction time is 3 h, and liquid ratio is 40: 5（v/w）, liquid reflux temperature is 75 DEG C.
A kind of method of described near infrared spectrum quick detection Chinese anise quality, step（3）In Chinese anise powder
Broken mesh number is 60 mesh.
A kind of method of described near infrared spectrum quick detection Chinese anise quality, step（3）In Chinese anise light
Spectrum scanning times are 3 times.
A kind of method of described near infrared spectrum quick detection Chinese anise quality, step（3）In Chinese anise survey
Sample amount is 50g.
A kind of method of described near infrared spectrum quick detection Chinese anise quality, step（4）In spectrum treatment
Method is the smooth second dervative treatment of Savitzky-Golay.
A kind of method of described near infrared spectrum quick detection Chinese anise quality, step（4）In spectral signature ripple
Section is defined as 1300-1800nm.
The step（3）The optimal treatment condition of middle sample collection is：Crushed the treatment of 60 mesh sieves, the sample-loading amount of sample cell
It is 50g.
The step（4）The condition of middle sample spectra collection is：Carried out entirely with FN602S type near infrared spectrum diffusing reflection instrument
Spectral scan.Instrument scanning is starting point with 1200 nm, and once point is adopted at interval of 1 nm, and scanning wavelength scope is 1200 ~ 2600
Nm, each sample average is scanned 3 times.
The step（5）The preprocess method of middle spectrum determines：It is 5 to use number of smoothing points, second dervative treatment spectrum number
According to.
The step（5）The middle statistical method for setting up calibration model is inclined PLS methods, it is desirable to which coefficient correlation is high, calibration set
Deviation is small.
Advantages of the present invention is with effect：
1. the present invention fully take into account Chinese anise main component（Cover fat-soluble and water miscible composition）, can accurate body
Now its quality, and this method is quick, safety, not damaged, with good actual application value.
2. the present invention includes the structure of sample sets, and analysis of Influential Factors, model is set up and performance prediction.This method is set up
On the basis of near infrared spectrum, extracted by sample comminution mesh number, test sample amount, scanning times, Pretreated spectra, characteristic spectrum,
The sequence of operations such as model foundation and forecast analysis, can rapidly and accurately determine Chinese anise active constituent content, it is determined that anistree
Fennel quality, is that trade supervision management and specification provide technical support.
Brief description of the drawings
Fig. 1 is the near-infrared primary light spectrogram of Chinese anise sample；
Fig. 2 is smoothed and the spectrogram after derivative processing for the near-infrared of Chinese anise sample；
Fig. 3 is the shikimic acid measured value scatter diagram corresponding with predicted value；
Fig. 4 is the oil of badian measured value scatter diagram corresponding with predicted value.
Specific embodiment
With reference to embodiment, the present invention is described in detail.
Embodiment 1
In the embodiment of the present invention, the method for quick of shikimic acid in a kind of Chinese anise based on near-infrared spectrum technique：
1. 20 kinds of Chinese anises of different manufacturers are bought from different supermarkets respectively, as sample sets.
2. 10 kinds of Chinese anises of producer are chosen, using the moisture absorption（It is passed through the smoked 5s of water vapour）And drying（50 DEG C of forced air dryings）
Alternately repeatedly treatment, obtains the Chinese anise sample of quality decline, and the Chinese anise after this is processed is mixed according to certain ratio
Enter in the Chinese anise of purchase, obtain 40 different Chinese anise compounding samples of quality, also act as sample sets.
3. compare the spectral signature of the different samples for crushing the mesh of mesh number 20,40 mesh, 60 mesh and 80 mesh, determine that 60 mesh are optimal
Sample comminution processes mesh number, and sample cell sample-loading amount is 50g, and each sample multiple scanning 3 times takes 3 spectrum mean values as light
Modal data.
4. yield is extracted as index with shikimic acid, water extraction is aided in using orthogonal test method Optimization for Ultrasonic Wave, determined big
Oxalic acid it is optimal
Extraction conditions are：The W of ultrasonic power 120, solid-liquid ratio 1:15,75 DEG C of temperature, the min of extraction time 40.
5. the accurate anistree extract solution for pipetting 0.1 mL is placed in 25 mL volumetric flasks, is separately added into 0.5 %（W/V）Height
The acid iodide aqueous solution and 0.5 %（W/V）Each 1.25 mL of the sodium metaperiodate aqueous solution, shake up, after 37 DEG C of constant temperature place 30 min
Room temperature is cooled to, the mixed liquor of the sodium sulfite of 0.056 mol/L and the NaOH of 1 mol/L is subsequently adding（Volume ratio 2:
3）It is settled to scale.In room temperature environment, absorbance of the solution at 382 nm is read at once.Examination is drawn by regression equation
The content of shikimic acid is tested in the range of 7.2 ~ 10.83 %.
6. the thick grass acid content of acquisition is modeled into required chemical index the most.
7. compare different preprocessing procedures and the optimal spectral band of modeling, finally determine by Savitzky-
After Golay smoothing processings and second dervative treatment, spectral resolution is higher, and information content is more, and profile variations are clear.
8. spectrogram is analyzed, occurs absworption peak higher at 1300 ~ 1490 nm, 1850 ~ 1970 nm, determined optimal
Modeling wave band is 1300-2000nm, using inclined PLS methods, sets up the near-infrared calibration model of shikimic acid, and the parameter of the model is：
Coefficient correlation 0.968, calibration set root-mean-square error 0.413.
9. near-infrared analysis model accuracy is evaluated in the embodiment of the present invention：
10 forecast set samples are chosen, analysis is predicted to it using the thick grass acid profile set up, the results are shown in Table 1.
The estimated performance analytical table of the thick grass acid profile of table 1
| Numbering | Thick grass acid content actual value | Predicted value | Residual error |
| 1 | 78.43 | 77.8 | -0.63 |
| 2 | 87.49 | 87.92 | 0.43 |
| 3 | 80.36 | 80.42 | 0.06 |
| 4 | 73.97 | 73.84 | -0.13 |
| 5 | 85.07 | 84.95 | -0.12 |
| 6 | 86.73 | 87.09 | 0.36 |
| 7 | 88.76 | 88.73 | -0.03 |
| 8 | 91.7 | 91.99 | 0.29 |
| 9 | 92.56 | 91.77 | -0.79 |
| 10 | 91.36 | 91.86 | 0.5 |
| Amount to | -0.06 |
As shown in Table 1, the thick grass acid content model of foundation to the predicted value of forecast set sample with actual value relatively, show residual
Difference sum is -0.06, and residual error sum is close to 0, it was demonstrated that model prediction better performances.
Embodiment 2
In the embodiment of the present invention, the quick detection side of oil of badian in a kind of Chinese anise based on near-infrared spectrum technique
Method：
1. 20 kinds of Chinese anises of different manufacturers are bought from different supermarkets respectively, as sample sets.
2. 10 kinds of Chinese anises of producer are chosen, using the moisture absorption（It is passed through the smoked 5s of water vapour）And drying（50 DEG C of forced air dryings）
Alternately repeatedly treatment, obtains the Chinese anise sample of quality decline, and the Chinese anise after this is processed is mixed according to certain ratio
Enter in the Chinese anise of purchase, obtain 40 different Chinese anise compounding samples of quality, also act as sample sets.
3. compare the spectral signature of the different samples for crushing the mesh of mesh number 20,40 mesh, 60 mesh and 80 mesh, determine that 60 mesh are optimal
Sample comminution processes mesh number, and sample cell sample-loading amount is 50g, and each sample multiple scanning 3 times takes 3 spectrum mean values as light
Modal data.
4. with Chinese anise oil extract yield as index, with acetone as extractant, on the basis of experiment of single factor, knot
Responds Surface Methodology is closed, the extraction process that soxhlet extraction extracts oil of badian in Chinese anise is optimized, determined optimal
Extraction conditions：Extraction time is 3 h, and liquid ratio is 40: 5（v/w）, liquid reflux temperature is 75 DEG C.
5. the calculating of Chinese anise oil content such as formula in Chinese anise：
Chinese anise oil content=extract Chinese anise oil quality/extraction material quality × 100%
By calculating the content of oil of badian in sample in the range of 3.02 ~ 12.08 %.
6. the Chinese anise oil content of acquisition is modeled into required chemical index the most.
7. compare different preprocessing procedures and the optimal spectral band of modeling, finally determine by Savitzky-
After Golay smoothing processings and second dervative treatment, spectral resolution is higher, and information content is more, and profile variations are clear.
8. spectrogram is analyzed, occurs absworption peak higher at 1300 ~ 1490 nm, 1850 ~ 1970 nm, determined optimal
Modeling wave band is 1300-2000nm, using inclined PLS methods, sets up the near-infrared calibration model of oil of badian, the parameter of the model
For：Coefficient correlation 0.974, calibration set root-mean-square error 0.306.
9. near-infrared analysis model accuracy is evaluated in the embodiment of the present invention：
10 forecast set samples are chosen, analysis is predicted to it using the oil of badian model set up, the results are shown in Table 2.
The estimated performance analytical table of the oil of badian model of table 2
As shown in Table 2, the Chinese anise oil content model of foundation to the predicted value of forecast set sample with actual value relatively, show
Show residual error sum for -0.003, residual error sum is close to 0, it was demonstrated that model prediction better performances.
Particular embodiments described above, has been carried out further in detail to the purpose of the present invention, technical scheme and beneficial effect
Describe in detail bright, should be understood that and the foregoing is only specific embodiment of the invention, be not intended to limit the invention, it is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvements done etc., should be included in guarantor of the invention
Within the scope of shield.
Claims (8)
1. a kind of method of near infrared spectrum quick detection Chinese anise quality, it is characterised in that methods described includes following step
Suddenly：
Step（1）：The foundation of sample sets, approach one：20 kinds of Chinese anises of different manufacturers of purchase, approach two：Using the moisture absorption and
Alternate treatment is dried, the different Chinese anise sample of active ingredient is obtained, the Chinese anise after this is processed is incorporated into proportion
In the Chinese anise of purchase, 40 different samples of quality are obtained；
Step（2）：Oil of badian and the extraction of thick grass acid content and the determination of detection method in Chinese anise；
Step（3）：Factor analysis, sample mesh number, scanning times, the test sample amount set up to influence model etc. are analyzed treatment；
Step（4）：Scan 50 different sources and mix the Chinese anise sample matched somebody with somebody, set up optical data storehouse；Using The
The softwares of Unscrambler 6.1 carry out the treatment of spectroscopic data, select least square method（PLS）Set up Chinese anise quality
Near-infrared Non-Destructive Testing calibration model；
Step（5）：10 forecast set samples are predicted by the checking of model prediction performance, determine model prediction performance.
2. the method for a kind of near infrared spectrum quick detection Chinese anise quality according to claim 1, it is characterised in that
Step（2）In shikimic acid ultrasound assisted extraction technique be power 120W, solid-liquid ratio 1:15,75 DEG C of temperature, extraction time
40min。
3. the method for a kind of near infrared spectrum quick detection Chinese anise quality according to claim 1, it is characterised in that
Step（2）In Extraction solvent be acetone, extraction time is 3h, and liquid ratio is 40:5（v/w）, liquid reflux temperature is 75 DEG C.
4. the method for a kind of near infrared spectrum quick detection Chinese anise quality according to claim 1, it is characterised in that
Step（3）In Chinese anise crush mesh number be 60 mesh.
5. the method for a kind of near infrared spectrum quick detection Chinese anise quality according to claim 1, it is characterised in that
Step（3）In Chinese anise spectral scan number of times be 3 times.
6. the method for a kind of near infrared spectrum quick detection Chinese anise quality according to claim 1, it is characterised in that
Step（3）In Chinese anise test sample amount be 50g.
7. the method for a kind of near infrared spectrum quick detection Chinese anise quality according to claim 1, it is characterised in that
Step（4）In the processing method of spectrum be the smooth second dervative treatment of Savitzky-Golay.
8. the method for a kind of near infrared spectrum quick detection Chinese anise quality according to claim 1, it is characterised in that
Step（4）In spectral signature wave band be defined as 1300-1800nm.
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| CN108226084A (en) * | 2018-01-12 | 2018-06-29 | 福州大学 | The method that Radix Notoginseng quality is quickly detected based on CARS-PLS-DA models |
| CN111398212A (en) * | 2020-04-08 | 2020-07-10 | 四川虹微技术有限公司 | Method for establishing pepper detection model based on portable near-infrared spectrometer |
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| CN103091282A (en) * | 2013-02-06 | 2013-05-08 | 吉林烟草工业有限责任公司 | Method for detecting quality of tobacco essence perfume |
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| 王雁飞: "近红外漫反射光谱法快速定量分析八角茴香中八角茴香油", 《中国优秀硕士学位论文全文数据库》 * |
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| CN108226084A (en) * | 2018-01-12 | 2018-06-29 | 福州大学 | The method that Radix Notoginseng quality is quickly detected based on CARS-PLS-DA models |
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Application publication date: 20170531 |