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High-identification crude oil dactylogram constructing and identifying method

A technology of fingerprint spectrum and crude oil, which is applied in the field of high-discrimination crude oil fingerprint spectrum construction and identification, which can solve the problems of insufficient intuition and obvious differences in fingerprints and insufficient identification power, and achieve simple and convenient fingerprint comparison, improve efficiency, and fingerprint identification strong effect

Active Publication Date: 2017-01-11
DALIAN MARITIME UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when there are many types of suspected oil samples or a high degree of similarity, the identification power of this method is still insufficient, and the fingerprint differences between different oil samples are not intuitive and obvious enough.

Method used

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  • High-identification crude oil dactylogram constructing and identifying method
  • High-identification crude oil dactylogram constructing and identifying method
  • High-identification crude oil dactylogram constructing and identifying method

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Effect test

Embodiment 1

[0056] In this embodiment, 41 crude oil standard samples were used, respectively from Sinopec Beijing Petrochemical Research Institute, Sinopec Fushun Petrochemical Research Institute, and China University of Petroleum (Beijing) Heavy Oil Laboratory. 15 crude oils were randomly selected as blind samples. 20 parallel samples were collected for each standard sample, and 10 parallel samples were collected for each blind sample. The numbers of standard samples and blind samples start with letters P and S respectively, as shown in Table 1.

[0057] Table 1. Origin and number description of different types of crude oil standard samples (41 types) and blind samples (15 types)

[0058]

[0059]

[0060] The spectral fingerprint of each oil sample was collected by a Horiba JY XploRA confocal Raman microscope. The detection range is 50-6000cm -1 , the filter is set to 0.1%, the aperture and slit are 500 μm and 200 μm respectively, the grating is 1200T, the objective lens is 10×...

Embodiment 2

[0087] Using SPSS software, stepwise discriminant method was used to remove intensity variables that were not important for discrimination from the original spectral data. The discriminant method used is Wilk's lambda method, and the discriminant standard is the value of the statistic F. When the F value is greater than F 上限 =1.84, keep this variable; when F value is less than F 下限 =0.71, this variable was eliminated. Through this step, 2558 dimensions of the 2915-dimensional variables of the original spectrum were eliminated, and 357 variables with strong discriminative ability were retained.

[0088] Then, the canonical discriminant function group is established according to the principle of "minimum deviation within the same class and maximum deviation between classes". The corresponding standard sample and blind sample data were respectively substituted into the discriminant function group, so that they were respectively projected into a new low-dimensional (40-dimensio...

Embodiment 3

[0099] Using SPSS software, stepwise discriminant method was used to remove intensity variables that were not important for discrimination from the original spectral data. The discriminant method used is Wilk's lambda method, and the discriminant standard is the value of the statistic F. When the F value is greater than F 上限 =5.84, keep this variable; when F value is less than F 下限 =4.71, this variable was eliminated. Through this step, 2870 dimensions of the 2915-dimensional variables of the original spectrum were eliminated, and 45 variables with strong discriminative ability were retained. These reserved variables correspond to wave groups numbered 1 to 45 in Table 2.

[0100] Then, the canonical discriminant function group is established according to the principle of "minimum deviation within the same class and maximum deviation between classes". The corresponding standard sample and blind sample data were respectively substituted into the discriminant function group, ...

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Abstract

The invention discloses a high-identification crude oil dactylogram constructing and identifying method. The method comprises the specific steps that under the same condition, a spectrum including fluorescence and Raman features is collected on a guide sample and a blind sample; based on sample data, variables not significant to distinguishing are removed, a rule discrimination function set is constructed based on the principle that the same class deviation is the minimum, and the inter-class deviation is the maximum, and intensity data corresponding to reserved significant variable sets is projected to low-dimensional space; barycentric coordinates of a guide sample and a blind sample of the low-dimensional space are subjected to blind sample distinguishing through the system clustering methodology; or columnar stacking diagrams are drawn based on front three-dimensional barycentric coordinates, front four-dimensional barycentric coordinates and front five-dimensional barycentric coordinates respectively, a novel dactylogram of the guide sample and the blind sample is constructed, and blind sample identification is carried out through dactylogram comparison. According to the method, identification efficiency, accuracy and persuasion are remarkably improved. The method has wide application prospects in the fields of lossless identification of artware, cultural objects, jewelry and criminal investigation physical evidence, producing area identification of geo-authentic crude drugs and marine products and medical disease diagnosis.

Description

technical field [0001] The present invention relates to a method for constructing and identifying high-discrimination and low-dimensional fingerprints based on microscopic confocal Raman spectroscopy, in particular to a novel method for constructing crude oil fingerprints, and an oil spill identification method based on the fingerprints . Background technique [0002] In ocean oil transportation and offshore oil and gas development, oil spill accidents occur frequently. Traceability and identification based on the physical and chemical fingerprint information of oil spill samples and suspected oil spill sources can provide reliable scientific basis for accident liability identification and legal rulings, and is also an important prerequisite for timely and effective control measures. [0003] The European Committee for Standardization (CEN), the American Society for Testing and Materials (ASTM), and the International Maritime Organization (IMO) have published oil spill iden...

Claims

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Application Information

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IPC IPC(8): G01N21/65
CPCG01N21/65
Inventor 于迎涛刘云鹏李杰王新年
Owner DALIAN MARITIME UNIVERSITY
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