CN110646375A - Camellia oil adulteration identification method - Google Patents
Camellia oil adulteration identification method Download PDFInfo
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- CN110646375A CN110646375A CN201910842272.9A CN201910842272A CN110646375A CN 110646375 A CN110646375 A CN 110646375A CN 201910842272 A CN201910842272 A CN 201910842272A CN 110646375 A CN110646375 A CN 110646375A
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- 239000010495 camellia oil Substances 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000003595 spectral effect Effects 0.000 claims abstract description 24
- 238000012706 support-vector machine Methods 0.000 claims abstract description 11
- 239000003921 oil Substances 0.000 claims abstract description 9
- 238000001228 spectrum Methods 0.000 claims abstract description 9
- 238000012847 principal component analysis method Methods 0.000 claims abstract description 5
- 235000019486 Sunflower oil Nutrition 0.000 claims description 13
- 239000002600 sunflower oil Substances 0.000 claims description 13
- 238000012549 training Methods 0.000 claims description 11
- 238000012795 verification Methods 0.000 claims description 11
- 235000019198 oils Nutrition 0.000 claims description 8
- 230000010354 integration Effects 0.000 claims description 3
- 238000010200 validation analysis Methods 0.000 claims 1
- 238000001514 detection method Methods 0.000 abstract description 2
- 235000013305 food Nutrition 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 3
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 238000002329 infrared spectrum Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- WRIDQFICGBMAFQ-UHFFFAOYSA-N (E)-8-Octadecenoic acid Natural products CCCCCCCCCC=CCCCCCCC(O)=O WRIDQFICGBMAFQ-UHFFFAOYSA-N 0.000 description 1
- LQJBNNIYVWPHFW-UHFFFAOYSA-N 20:1omega9c fatty acid Natural products CCCCCCCCCCC=CCCCCCCCC(O)=O LQJBNNIYVWPHFW-UHFFFAOYSA-N 0.000 description 1
- QSBYPNXLFMSGKH-UHFFFAOYSA-N 9-Heptadecensaeure Natural products CCCCCCCC=CCCCCCCCC(O)=O QSBYPNXLFMSGKH-UHFFFAOYSA-N 0.000 description 1
- 240000001548 Camellia japonica Species 0.000 description 1
- OYHQOLUKZRVURQ-HZJYTTRNSA-N Linoleic acid Chemical compound CCCCC\C=C/C\C=C/CCCCCCCC(O)=O OYHQOLUKZRVURQ-HZJYTTRNSA-N 0.000 description 1
- ZQPPMHVWECSIRJ-UHFFFAOYSA-N Oleic acid Natural products CCCCCCCCC=CCCCCCCCC(O)=O ZQPPMHVWECSIRJ-UHFFFAOYSA-N 0.000 description 1
- 239000005642 Oleic acid Substances 0.000 description 1
- 235000019484 Rapeseed oil Nutrition 0.000 description 1
- 238000000862 absorption spectrum Methods 0.000 description 1
- DTOSIQBPPRVQHS-PDBXOOCHSA-N alpha-linolenic acid Chemical compound CC\C=C/C\C=C/C\C=C/CCCCCCCC(O)=O DTOSIQBPPRVQHS-PDBXOOCHSA-N 0.000 description 1
- 235000020661 alpha-linolenic acid Nutrition 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 235000018597 common camellia Nutrition 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 239000008157 edible vegetable oil Substances 0.000 description 1
- 238000004817 gas chromatography Methods 0.000 description 1
- 239000004519 grease Substances 0.000 description 1
- QXJSBBXBKPUZAA-UHFFFAOYSA-N isooleic acid Natural products CCCCCCCC=CCCCCCCCCC(O)=O QXJSBBXBKPUZAA-UHFFFAOYSA-N 0.000 description 1
- 235000020778 linoleic acid Nutrition 0.000 description 1
- OYHQOLUKZRVURQ-IXWMQOLASA-N linoleic acid Natural products CCCCC\C=C/C\C=C\CCCCCCCC(O)=O OYHQOLUKZRVURQ-IXWMQOLASA-N 0.000 description 1
- 229960004488 linolenic acid Drugs 0.000 description 1
- KQQKGWQCNNTQJW-UHFFFAOYSA-N linolenic acid Natural products CC=CCCC=CCC=CCCCCCCCC(O)=O KQQKGWQCNNTQJW-UHFFFAOYSA-N 0.000 description 1
- 238000004811 liquid chromatography Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- ZQPPMHVWECSIRJ-KTKRTIGZSA-N oleic acid Chemical compound CCCCCCCC\C=C/CCCCCCCC(O)=O ZQPPMHVWECSIRJ-KTKRTIGZSA-N 0.000 description 1
- 235000021313 oleic acid Nutrition 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011197 physicochemical method Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 239000010453 quartz Substances 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 235000012424 soybean oil Nutrition 0.000 description 1
- 239000003549 soybean oil Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 235000021122 unsaturated fatty acids Nutrition 0.000 description 1
- 150000004670 unsaturated fatty acids Chemical class 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention relates to the technical field of food quality safety, in particular to a camellia oil adulteration identification method, which comprises the following steps of establishing a camellia oil adulteration grade identification model: the method comprises the following steps: collecting spectral data; preparing a plurality of samples doped with other oil products in different proportions, and collecting data of the samples by adopting a near-infrared spectrometer; step two: removing head and tail noises to obtain an original spectrum curve; step three: establishing a principal component-support vector machine discrimination model; (1) selecting a main component; performing dimensionality reduction on the spectral data by adopting a principal component analysis method to obtain key principal components; (2) establishing a adulteration grade discrimination model by taking the main components obtained in the step (1) as a support vector machine; step four: model application, adulteration identification. The method can realize the detection of the adulteration degree of the camellia oil, is simple to operate, and can realize the requirements of rapidness, no damage and the like.
Description
Technical Field
The invention relates to the technical field of food quality safety, in particular to a camellia oil adulteration identification method.
Background
The camellia oil is edible vegetable oil prepared from camellia seeds, is rich in unsaturated fatty acids such as oleic acid, linoleic acid and linolenic acid, is rich in nutrition, has fragrant smell and health-care function, is widely favored by consumers, and has very wide market prospect. However, because the camellia oil has high price and great profit, the phenomenon that illegal merchants blend other grease in the camellia oil to obtain violence is frequently seen in the society. Therefore, the identification of the adulterated camellia oil is significant for guaranteeing the rights and interests of consumers and maintaining the reputation of enterprises.
In the prior art, the quality of camellia oil is often detected by adopting some physicochemical methods. For example, in the chemical aspect, a chemical reagent is added to observe the color change reaction of the adulteration reagent, so that the qualitative detection of the adulteration is realized. The quality of the camellia oil can be quantitatively detected by a gas chromatography instrument, a liquid chromatography instrument and the like. However, the methods all have pollution to samples or are complex to operate, and cannot meet the requirements of rapidness, no damage and the like.
In view of this, an adulteration identification method is established by combining the near infrared spectrum technology with the chemometrics method, and camellia oil doped with other oil products can be distinguished and the adulteration degree can be judged.
Disclosure of Invention
The invention aims to solve the technical problem of providing a camellia oil adulteration identification method for detecting the adulteration degree.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
the key technology of the camellia oil adulteration identification method is that a camellia oil adulteration grade identification model is established, and the camellia oil adulteration grade identification method comprises the following steps:
the method comprises the following steps: collecting spectral data; preparing a plurality of samples doped with other oil products in different proportions, and collecting data of the samples by adopting a near-infrared spectrometer;
step two: removing head and tail noises to obtain an original spectrum curve;
step three: establishing a principal component-support vector machine discrimination model;
(1) selecting a main component; performing dimensionality reduction on the spectral data by adopting a principal component analysis method to obtain key principal components;
(2) establishing a adulteration grade discrimination model by taking the main components obtained in the step (1) as a support vector machine;
step four: model application, adulteration identification.
Furthermore, the other oil products in the step one adopt sunflower oil.
Further, by taking the sunflower oil content of 1% as a gradient, 6 samples with the mass fraction range of 0-5% are prepared and marked as a group A, and 5 samples with the mass fraction range of 6-10% are prepared and marked as a group B; taking the sunflower oil content of 5% as a gradient, preparing 6 samples with the mass fraction range of 15-40%, and marking as a group C; taking the sunflower oil content of 10% as a gradient, preparing 6 samples with the mass fraction range of 50-100%, marking as group D, and dividing each camellia oil sample with different adulteration proportions into 9 parts.
Further, the spectral data is divided into a training set and a verification set; the training set and the verification set are respectively divided randomly according to the ratio of 2:1 by the spectrum data measured by the four groups of samples.
Further, the spectrometer is an AvaSpec-NIR256 near infrared spectrometer.
Furthermore, the spectral range of the spectral data acquisition is 1000-2500nm, the resolution is 4.4-77nm, the spectrometer integration time is 0.0319ms, and the scanning times are 100 times.
Further, the original spectral curve in the second step is the spectral curve after the head and tail noise is removed, and the intercepted wavelength range is 1000-2300 nm.
Further, the identification standard of the fourth step is the judgment accuracy.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
the camellia oil adulteration identification method provided by the invention establishes a principal component-support vector machine discrimination model by utilizing a near infrared spectrum technology in combination with a chemometrics method, can discriminate the camellia oil adulterated with other oil products and judge the adulteration degree of the camellia oil, is simple to operate, and can meet the requirements of rapidness, no damage and the like. The discrimination model is adopted to discriminate the training set and the verification set of the sample, the discrimination accuracy of the training set and the verification set is respectively 96.38 percent and 94.20 percent, and the adulteration grade can be discriminated well.
Detailed Description
The present invention will be described in further detail with reference to the following embodiments. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences of the specification are for clarity only to describe certain embodiments and are not meant to imply a required sequence unless otherwise stated where a certain sequence must be followed.
The method for identifying the adulteration of the camellia oil is characterized in that an identification model of the adulteration grade of the camellia oil is established, and the method comprises the following steps:
the method comprises the following steps: and (6) collecting spectral data. Preparing a plurality of samples doped with other oil products in different proportions, and collecting data of the samples by adopting a near-infrared spectrometer. The other oil product can be sunflower oil, soybean oil, rapeseed oil, etc., and the sunflower oil is adopted in the step.
The prepared samples are characterized in that 1% of sunflower oil content is used as a gradient, 6 samples with the mass fraction range of 0-5% are marked as group A, and 5 samples with the mass fraction range of 6-10% are marked as group B; taking the sunflower oil content of 5% as a gradient, preparing 6 samples with the mass fraction range of 15-40%, and marking as a group C; taking the sunflower oil content of 10% as a gradient, preparing 6 samples with the mass fraction range of 50-100%, marking as group D, dividing each camellia oil sample with different adulteration proportions into 9 parts, and finally obtaining 207 parts of samples. For ease of testing, the samples prepared in different gradients were designated as group a, group B, group C, and group D, respectively.
In order to ensure the accuracy of the test and the subsequent verification, the spectral data are divided into a training set and a verification set. The training set and the verification set are respectively randomly divided according to a ratio of 2:1, 138 samples form the training set, and 69 samples form the verification set.
Step two: and removing head and tail noises to obtain an original spectrum curve. The spectrometer adopted in the invention is an AvaSpec-NIR256 near infrared spectrometer. The spectral range of the spectral data acquisition is 1000-2500nm, and the resolution is 4.4-77 nm. Before spectrum collection, the spectrometer integration time is set to 0.0319ms, the scanning times are 100 times, and special white board information and the dark current of the spectrometer are collected to realize correction. Finally, the sample is placed in a quartz cuvette and placed on a special cuvette support to collect absorption spectra, and spectral data are obtained by matching software Avasoft.
And because the head and tail noises of the spectrometer are relatively large, the original spectrum curve in the second step is the spectrum curve without the head and tail noises, and the intercepted wavelength range is 1000-2300 nm. The 207 original spectral curves of the samples with the head and tail noises removed are very similar, and the adulteration degree of the samples cannot be directly judged from the original spectral curves, so that the samples need to be further analyzed and judged by other methods.
Step three: and establishing a principal component-support vector machine discrimination model. (1) Selecting a main component; and performing dimensionality reduction on the spectral data by adopting a principal component analysis method to obtain key principal components. And 2, the data obtained in the step two is large in size and easy to cause information redundancy, so that the principal component analysis method is adopted to reduce the dimension of the data, and the key principal component score is utilized to represent the internal characteristics and clustering information of the sample. Through analysis, the model established by selecting 4 principal components as input has higher accuracy, so that 4 principal components are finally selected.
(2) And (3) establishing a adulteration grade discrimination model by taking the principal components obtained in the step (1) as a support vector machine. On the basis of principal component analysis, a support vector machine is utilized to establish an adulterated camellia oil identification model. And determining a penalty parameter and a kernel function parameter by using a grid optimization method by using a radial basis kernel function as a support vector machine kernel function.
Step four: model application, adulteration identification. And the identification standard of the step four is the judgment accuracy. The established model is adopted to distinguish the training set and the verification set, the distinguishing accuracy of the training set and the verification set is respectively 96.38% and 94.20%, and adulteration grade can be distinguished well.
Finally, it should be noted that: the present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For those skilled in the art to which the invention pertains, numerous and varied variations and substitutions may be made in accordance with the spirit of the invention, and these simple variations and combinations should also be considered as within the scope of the invention disclosed herein.
Claims (8)
1. The camellia oil adulteration identification method is characterized in that a camellia oil adulteration grade identification model is established, and the method comprises the following steps:
the method comprises the following steps: collecting spectral data; preparing a plurality of samples doped with other oil products in different proportions, and collecting data of the samples by adopting a near-infrared spectrometer;
step two: removing head and tail noises to obtain an original spectrum curve;
step three: establishing a principal component-support vector machine discrimination model;
(1) selecting a main component; performing dimensionality reduction on the spectral data by adopting a principal component analysis method to obtain key principal components;
(2) establishing a adulteration grade discrimination model by taking the main components obtained in the step (1) as a support vector machine;
step four: model application, adulteration identification.
2. The method for identifying the adulteration of camellia oil as claimed in claim 1, wherein the other oil products in the first step are sunflower oil.
3. The method for identifying the adulteration of the camellia oil as claimed in claim 2, wherein 6 samples with the mass fraction range of 0-5% are prepared by taking the content of the sunflower oil as a gradient and are marked as a group A, and 5 samples with the mass fraction range of 6-10% are prepared and are marked as a group B; taking the sunflower oil content of 5% as a gradient, preparing 6 samples with the mass fraction range of 15-40%, and marking as a group C; taking the sunflower oil content of 10% as a gradient, preparing 6 samples with the mass fraction range of 50-100%, marking as group D, and dividing each camellia oil sample with different adulteration proportions into 9 parts.
4. The method for identifying camellia oil adulteration according to claim 3, wherein the spectral data is divided into a training set and a validation set; the training set and the verification set are respectively divided randomly according to the ratio of 2:1 by the spectrum data measured by the four groups of samples.
5. The method for identifying the adulteration of camellia oil as claimed in claim 1, wherein the spectrometer is an AvaPec-NIR 256 near infrared spectrometer.
6. The method as claimed in claim 1, wherein the spectral range of the spectral data acquisition is 1000-2500nm, the resolution is 4.4-77nm, the spectrometer integration time is 0.0319ms, and the scanning times are 100 times.
7. The method as claimed in claim 1, wherein the original spectral curve in the second step is a spectral curve obtained after removing noise from the head and tail, and the cut wavelength range is 1000-2300 nm.
8. The method for identifying the adulteration of camellia oil as claimed in claim 1, wherein the identification criterion of the fourth step is the accuracy of discrimination.
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| CN201910842272.9A CN110646375A (en) | 2019-09-06 | 2019-09-06 | Camellia oil adulteration identification method |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910842272.9A CN110646375A (en) | 2019-09-06 | 2019-09-06 | Camellia oil adulteration identification method |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107121406A (en) * | 2017-05-24 | 2017-09-01 | 福州大学 | A kind of adulterated discrimination method of grape-kernel oil based near infrared spectrum |
| CN107894408A (en) * | 2017-11-24 | 2018-04-10 | 中国农业科学院油料作物研究所 | A kind of edible oil based near infrared spectrometer is polynary to mix pseudo- discrimination method |
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2019
- 2019-09-06 CN CN201910842272.9A patent/CN110646375A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107121406A (en) * | 2017-05-24 | 2017-09-01 | 福州大学 | A kind of adulterated discrimination method of grape-kernel oil based near infrared spectrum |
| CN107894408A (en) * | 2017-11-24 | 2018-04-10 | 中国农业科学院油料作物研究所 | A kind of edible oil based near infrared spectrometer is polynary to mix pseudo- discrimination method |
Non-Patent Citations (1)
| Title |
|---|
| 褚璇 等: "近红外光谱和特征光谱的山茶油掺假鉴别方法研究", 《光谱学与光谱分析》 * |
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Inventor after: Wang Wei Inventor after: Zhong Bin Inventor after: Guo Xiaohuan Inventor after: Lu Yao Inventor after: Song Shuxian Inventor after: Jin Qi Inventor before: Wang Wei Inventor before: Guo Xiaohuan Inventor before: Lu Yao Inventor before: Song Shuxian Inventor before: Jin Qi Inventor before: Zhong Bin |
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Application publication date: 20200103 |