CN113218932B - Discrimination method for detecting content of titanium dioxide in persimmon frost based on Raman spectrum - Google Patents
Discrimination method for detecting content of titanium dioxide in persimmon frost based on Raman spectrum Download PDFInfo
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- CN113218932B CN113218932B CN202110484449.XA CN202110484449A CN113218932B CN 113218932 B CN113218932 B CN 113218932B CN 202110484449 A CN202110484449 A CN 202110484449A CN 113218932 B CN113218932 B CN 113218932B
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G—PHYSICS
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- 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
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Abstract
The invention is suitable for the technical field of analysis and detection of food additives, and provides a method for judging the content of titanium dioxide in persimmon frost based on Raman spectrum.
Description
Technical Field
The invention belongs to the technical field of analysis and detection of food additives, and particularly relates to a distinguishing method for detecting the content of titanium dioxide in persimmon frost based on Raman spectrum.
Background
Along with the evaporation of water in the production process of dried persimmon, white dry crystals are gradually formed on the surface of the persimmon pulp, and the white dry crystals are called persimmon frost. Persimmon frost is a traditional Chinese medicine used for treating cough, hemoptysis and diarrhea. The variation of temperature and other parameters in the production reduces the frosting quantity of the persimmon. In order to achieve the purposes of getting improper economic benefits, partial illegal vendors add excessive food additive titanium dioxide to beautify dried persimmon. However, excessive titanium dioxide particles may cause adverse health effects such as inflammation, tissue necrosis and immune response in the human body. Illegal actions of excessive addition of titanium dioxide raise public health concerns. Therefore, the development of a rapid nondestructive testing method for the additive titanium dioxide in the dried persimmon has important significance for the adulteration identification of the dried persimmon.
Many advanced analytical methods have been applied to the field of food additive detection, including Thin Layer Chromatography (TLC), enzyme-linked immunosorbent assay (ELISA), high Performance Liquid Chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), and the like. Although the methods have the characteristics of high sensitivity and high accuracy, the methods have the defects of long time consumption, high cost, complex sample pretreatment and the like, and are unfavorable for realizing quick online detection and large-scale sample screening.
Compared with the technology, the spectrum technology has multi-component detection capability and has wide application in food safety detection. Raman spectrum is generated by inelastic scattering of incident light to the surface of a substance, and the substance is detected by the vibration frequency of a molecular functional group, so that the raman spectrum of each substance has uniqueness. The Raman spectrum has the advantages of no damage, no pretreatment of samples, high response speed and the like, and can be used for qualitative or quantitative analysis and detection in various fields such as food safety, biochemistry and the like. Especially, the portable Raman spectrometer has the characteristics of convenient carrying, simple operation, short detection time and the like, and is widely applied to food safety on-line detection and large-scale sample screening.
Disclosure of Invention
The invention provides a method for judging the content of titanium dioxide in persimmon frost based on Raman spectrum detection, and aims at the current situation that excessive titanium dioxide is difficult to detect in the persimmon frost.
The invention discloses a distinguishing method for detecting the content of titanium dioxide in persimmon frost based on Raman spectrum, which comprises the following steps:
s1, sample Raman spectrum acquisition: collecting a pure persimmon frost Raman spectrum, a pure titanium dioxide Raman spectrum and original Raman spectra of two pure substances in different mixing ratios by a portable Raman instrument;
s2, correcting a Raman spectrum baseline: preprocessing Raman spectrum data, wherein the preprocessing operation mainly comprises the step of removing fluorescent background interference in Raman spectrum by using a self-adaptive iteration weighted partial least square method; and preliminarily identifying a Raman characteristic peak of the titanium dioxide from the spectrum;
s3, cluster analysis: carrying out cluster analysis on the preprocessed Raman characteristic peak data according to the Raman characteristic peak of the titanium dioxide pointed out in the step S2, and verifying the validity of the Raman characteristic peak of the titanium dioxide pointed out in the step S2;
s4, data division: randomly dividing all kinds of Raman spectrum data into a modeling set and a prediction set according to the proportion of 2:1;
s5, establishing a discrimination model of doping titanium dioxide with different concentrations in the persimmon frost: after the modeling set and the prediction set are divided, a discrimination model of titanium dioxide in the persimmon frost is established by utilizing Raman characteristic peak data in the modeling set and classification labels of two pure substances with different mixing ratios;
s6, verifying a model: and (5) verifying the discrimination model established in the step (S5) by utilizing the Raman spectrum characteristic peak data in the prediction set and the classification labels of the two pure substances with different blending ratios.
Preferably, the portable Raman instrument is provided with a 785nm laser, and the collected spectrum range is 2000-200cm -1 Spectral resolution of 4cm -1 The laser power was 0.25w and the exposure time was 0.851s.
Compared with the prior art, the invention has the beneficial effects that: the invention discloses a distinguishing method for detecting the content of titanium dioxide in persimmon frost based on Raman spectrum.
The invention can be used for detecting the titanium dioxide in the persimmon frost, can detect whether the persimmon frost contains the titanium dioxide or not, and can detect the content of the titanium dioxide in the persimmon frost containing the titanium dioxide.
The invention has the beneficial effects that: the method is simple to operate, does not need to pretreat the sample, does not damage the sample, and does not need other chemical reagents; the specific chemical bonds in the titanium dioxide are detected by utilizing the Raman spectrum, and a discrimination model is built by utilizing the three pointed characteristic Raman peaks, so that the discrimination accuracy is high and the discrimination speed is high.
Drawings
FIG. 1 is a flow chart of a distinguishing method for detecting the content of titanium dioxide in persimmon frost based on Raman spectrum.
FIG. 2 is a Raman graph of pure persimmon frost doped with titanium dioxide with different concentrations.
FIG. 3 is a graph showing the principal component clustering of the Raman spectrum of pure persimmon frost and a titanium dioxide sample doped with different concentrations.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, the present invention provides a technical solution: a discrimination method for detecting the content of titanium dioxide in persimmon frost based on Raman spectrum, in this example, the edible titanium dioxide is produced by Henan Wanban company and purchased from a market. The dried persimmon is produced by farmers at home, and no additive is added. Scraping the dried persimmon from the dried persimmon skin by using a clean knife, and collecting 10 g of dried persimmon. According to national Standard for food additive use sanitary Standard of the people's republic of China GB2760-2007, the highest limit of titanium dioxide in the preserved fruit is 2g/kg. The titanium dioxide is mixed into the persimmon frost according to the proportion of 2g/kg, 4g/kg and 6g/kg, and the mixture is fully ground, so that three persimmon frost samples mixed with the titanium dioxide with different proportions are obtained. And obtaining two samples of pure titanium dioxide and pure persimmon frost. Five samples were obtained in total.
The following are five sample spectrum collection and model establishment methods, including the following steps:
s1, sample Raman spectrum collection. Sample data acquisition was performed using a portable Raman spectrometer, ACCUMANSR-510Pro (OceanInsight, FL, USA), equipped with a 785nm laser. At 2000-200cm -1 Is subjected to spectrum acquisition within the spectrum range of 4cm in spectrum resolution -1 . The laser power and exposure time were 0.25w and 0.851s, respectively. 50 scans were performed on each sample to obtain 50 spectra. As shown in fig. 2, an average spectrum was obtained. The same procedure was used for all sample spectral acquisitions. After the data acquisition of the five samples is completed, the data are exported and stored in a PC.
S2, correcting a Raman spectrum baseline. An adaptive iterative weighted partial least squares algorithm (air-PLS) is used for each spectrum, automatically subtracting the light environment and subtracting the fluorescent background in the raman spectrum by iterative regression. At 398, 515 and 640cm -1 There are three distinct raman featuresPeak symptoms. As the concentration of the titanium dioxide increases, the Raman spectrum of the sample decreases, so that three Raman characteristic peaks can be used for detecting the addition amount of the titanium dioxide in the persimmon frost.
S3, cluster analysis. Use 398, 515 and 640cm -1 And carrying out cluster analysis on the persimmon frost sample doped with titanium dioxide according to the Raman spectrum data corresponding to the 3 characteristic peaks, as shown in figure 3. The other samples, except for the 4 abnormal points, were very aggregated. The cluster analysis verifies the effectiveness of the Raman characteristic peak of the titanium dioxide pointed out in the step S2.
S4, establishing a discrimination model of doping titanium dioxide with different concentrations in the persimmon frost. The raman spectrum data of all categories are randomly divided into a modeling set and a prediction set according to a ratio of 2:1. And establishing a discrimination model of titanium dioxide in the persimmon frost by utilizing the Raman characteristic peak data in the modeling set and the classification labels of the three blending ratios. The established mathematical model is as follows:
(1) model 1: y=1.046×10e -4 x+0.439. Wherein x is 398cm -1 Raman spectral values at the wavelength band. If the result y is calculated<0.5, the detected substance is pure persimmon frost. If the result y is calculated>And 0.5, the detected substance is the persimmon frost doped with titanium dioxide, and the model 2 is required to be further used.
(2) Model 2: y= -x (: 1) 0.002-x (: 2) 0.001+0.0018 x (: 3) +1.0673. Wherein x (: 1) is 398cm -1 Raman spectral values of the wavebands; x (: 2) is 515cm -1 Raman spectral values of the wavebands; x (: 3) is 640cm -1 Raman spectral values of the wavelength bands.
S5, verifying a model: and (3) verifying the discrimination model established in the fourth step by utilizing the Raman spectrum characteristic peak data in the prediction set and the classification labels of the three blending ratios.
The invention discloses a distinguishing method for detecting the content of titanium dioxide in persimmon frost based on Raman spectrum, which is mainly characterized by identifying a Raman characteristic peak of titanium dioxide and establishing a distinguishing model of additive titanium dioxide in the persimmon frost according to the Raman characteristic peak.
The invention can be used for detecting the titanium dioxide in the persimmon frost, can detect whether the persimmon frost contains the titanium dioxide or not, and can detect the content of the titanium dioxide in the persimmon frost containing the titanium dioxide.
The invention has the beneficial effects that: the method is simple to operate, does not need to pretreat the sample, does not damage the sample, and does not need other chemical reagents; the specific chemical bonds in the titanium dioxide are detected by utilizing the Raman spectrum, and a discrimination model is built by utilizing the three pointed characteristic Raman peaks, so that the discrimination accuracy is high and the discrimination speed is high.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (1)
1. A discrimination method for detecting the content of titanium dioxide in persimmon frost based on Raman spectrum is characterized by comprising the following steps: the method comprises the following steps:
s1, sample Raman spectrum acquisition: collecting a pure persimmon frost Raman spectrum, a pure titanium dioxide Raman spectrum and original Raman spectra of two pure substances in different mixing ratios by a portable Raman instrument;
s2, correcting a Raman spectrum baseline: preprocessing Raman spectrum data, wherein the preprocessing operation mainly comprises the step of removing fluorescent background interference in Raman spectrum by using a self-adaptive iteration weighted partial least square method; and preliminarily identifying a Raman characteristic peak of the titanium dioxide from the spectrum;
s3, cluster analysis: carrying out cluster analysis on the preprocessed Raman characteristic peak data according to the Raman characteristic peak of the titanium dioxide pointed out in the step S2, and verifying the validity of the Raman characteristic peak of the titanium dioxide pointed out in the step S2;
s4, data division: randomly dividing all kinds of Raman spectrum data into a modeling set and a prediction set according to the proportion of 2:1;
s5, establishing a discrimination model of doping titanium dioxide with different concentrations in the persimmon frost: after the modeling set and the prediction set are divided, a discrimination model of titanium dioxide in the persimmon frost is established by utilizing Raman characteristic peak data in the modeling set and classification labels of two pure substances with different mixing ratios, and the established mathematical model is as follows:
(1) model 1:y=1.046×10e -4 x+0.439 of whichx398cm -1 Raman spectrum value at wave band, if the calculation resulty<0.5, if the detected substance is pure persimmon frost, calculating the resulty>0.5, the detected substance is the persimmon frost doped with titanium dioxide, and model 2 is needed to be further used;
(2) model 2:y=-x(:,1)*0.002-x(:,2)*0.001+0.0018*x(: 3) +1.0673, wherex(: 1) is 398cm -1 Raman spectral values of the wavebands;x(: 2) 515cm -1 Raman spectral values of the wavebands;x(: 3) 640cm -1 Raman spectral values of the wavebands;
s6, verifying a model: verifying the discrimination model established in the step S5 by utilizing the Raman spectrum characteristic peak data in the prediction set and the classification labels of the two pure substances with different mixing ratios;
the portable Raman instrument is provided with a 785nm laser, and the collected spectrum range is 2000-200cm -1 Spectral resolution of 4cm -1 The laser power was 0.25w and the exposure time was 0.851s.
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