Method for analyzing correlation between odor of polyvinyl chloride resin and volatile organic compound components and application

Abstract

The invention belongs to the field of chemical substance detection, and relates to a polyvinyl chloride resin smell and volatile organic compound component correlation analysis method and application. Comprising the following steps: (1) detecting a plurality of polyvinyl chloride samples by adopting an electronic nose to obtain a plurality of groups of electronic nose detection data; (2) carrying out bag method thermal desorption-GC-MS detection on the plurality of polyvinyl chloride samples to obtain the volatile organic compound composition and content of each polyvinyl chloride sample; (3) classifying the volatile organic compounds of the polyvinyl chloride samples according to functional groups and adding the contents of the same category to obtain the contents of different functional group categories; and (4) performing double-variant correlation analysis on the multiple groups of electronic nose detection data and the contents of different functional group categories to obtain the correlation between the odor of the polyvinyl chloride resin and the volatile organic compound components. According to the invention, the correlation analysis of the PVC odor signal and the VOCs component is realized for the first time, and a scientific and effective reference is provided for the construction of a PVC overall odor evaluation and identification system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of chemical substance detection, and particularly relates to a method for analyzing the correlation between the odor of polyvinyl chloride (PVC) resin and volatile organic compounds (VOCs) components, and more particularly. BACKGROUND

[0002] PVC is a synthetic resin polymer with thermoplastic properties formed by polymerization of vinyl chloride monomer (VCM), which is a non-crystalline white amorphous powder with a particle size of 60-250 μm. Due to its special physicochemical properties, mature synthesis and processing technology, and low production cost, PVC has become one of the most widely used synthetic plastic materials in the world, and is widely used in building materials, packaging materials, daily clothing, etc. At the same time, as a low-cost raw material, the development of high-value-added products in the industry is needed to promote the development of PVC materials in the medical and health fields.

[0003] During the process of making products from PVC resin powder, several stages will affect the human population by the odor. Some VOCs components with odor characteristics that can be smelled by sensory personnel with professional training are called gaseous active substances, which not only affect human emotions, attention, memory and health, but also pollute the air quality. However, not all VOCs will produce odor, and the degree of human perception of odor depends on whether the concentration of the substance is higher than the human sensory threshold. The lower the odor threshold of a substance, the lower the concentration of the substance that can be perceived by humans, i.e. the substance is more easily perceived by people. Therefore, the lower the odor threshold of a substance in VOCs, the greater the contribution of the substance to odor, i.e. even a small amount of product can cause obvious odor problems. The odor problem of PVC greatly limits the daily and high-end application of the material, which is not conducive to the development of China's PVC industry. Although there are methods for analyzing and tracing the odor and VOCs of polypropylene (PP) and polyethylene (PE) at present, due to the differences in processing technology and small molecule components between PP / PE and PVC, the existing methods cannot be used to analyze and detect the odor source of PVC.

[0004] To solve this problem, it is necessary to break through the odor problem of PVC resin powder raw materials, systematically and reasonably use various analysis and detection methods to analyze the VOCs components that contribute more to the odor of PVC resin powder, and find the correlation between odor and VOCs to guide the development of low-odor PVC resin materials. SUMMARY

[0005] The present application aims at the above-mentioned defects of the prior art, and provides a method for analyzing the correlation between PVC odor and volatile organic compound composition based on electronic nose technology and bag method thermal desorption-gas chromatography mass spectrometry, which is simple in operation, avoids the adverse effects and actual damage of human olfactory perception on human body, and has strong intelligence, popularization and practicability.

[0006] The first aspect of the present application provides a method for analyzing the correlation between PVC odor and volatile organic compound composition, which comprises the following steps:

[0007] (1) detecting a plurality of PVC samples by using an electronic nose comprising a plurality of sensors to obtain a plurality of sets of electronic nose detection data;

[0008] (2) detecting the plurality of PVC samples by bag method thermal desorption-GC-MS to obtain the composition and content of volatile organic compounds of each PVC sample;

[0009] (3) classifying the volatile organic compounds of each PVC sample according to functional groups and summing the contents of volatile organic compounds of the same category to obtain the contents of different functional group categories of each PVC sample;

[0010] (4) performing bivariate correlation analysis on the plurality of sets of electronic nose detection data and the contents of different functional group categories to obtain the correlation between PVC odor and volatile organic compound composition.

[0011] The second aspect of the present application provides the application of the above-mentioned method for analyzing the correlation between PVC odor and volatile organic compound composition in rapidly judging the composition and content trend of volatile organic compounds of PVC or odor preliminary judgment.

[0012] The present application uses electronic nose technology to analyze the odor of PVC samples from different sources to obtain the values of each sensor of each PVC sample; under the condition of consistent heating time and temperature, the bag method thermal desorption-GC-MS is used to detect the enrichment of VOCs of the above-mentioned samples, the VOCs data are classified and summed, the bivariate correlation analysis method is used to analyze the correlation between PVC odor and VOCs composition, and the model conclusion is obtained.

[0013] The method of the present application is simple in operation, accurate in correlation and strong in regularity. For new PVC samples, the electronic nose odor signal or the composition and content trend of VOCs of the samples can be directly judged by the model correlation, without the need to simultaneously perform two detection methods, thereby reducing the detection cost. The present application firstly realizes the specific correlation analysis between PVC odor signal and VOCs composition, and provides a scientific and effective reference for constructing a PVC overall odor evaluation and identification system.

[0014] Other features and advantages of the present application will be illustrated in the following detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0015] The exemplary embodiments of the present application will be described in greater detail by referring to the accompanying drawings.

[0016] Figure 1 Fingerprint radar map of different PVC sample odor sensitive to each sensor of electronic nose.

[0017] Figure 2 The GC-MS total ion chromatogram of the sampling bag direct injection method and the thermal desorption injection method is shown. DETAILED DESCRIPTION

[0018] The specific embodiments of the present application will be described in detail below. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0019] The present application provides a method for analyzing the correlation between the odor of polyvinyl chloride resin and the composition of volatile organic compounds, comprising the following steps:

[0020] (1) detecting a plurality of polyvinyl chloride samples using an electronic nose comprising a plurality of sensors to obtain a plurality of sets of electronic nose detection data;

[0021] (2) performing bag method thermal desorption-GC-MS detection on the plurality of polyvinyl chloride samples to obtain the composition and content of volatile organic compounds of each polyvinyl chloride sample;

[0022] (3) classifying the volatile organic compounds of each polyvinyl chloride sample by functional groups and summing the content of volatile organic compounds of the same category to obtain the content of different functional group categories of each polyvinyl chloride sample;

[0023] (4) performing bivariate correlation analysis on the plurality of sets of electronic nose detection data and the content of different functional group categories to obtain the correlation between the odor of polyvinyl chloride resin and the composition of volatile organic compounds.

[0024] Step (1) of the present application is realized on an electronic nose instrument. Commercial electronic nose instruments are usually equipped with a plurality of sensors, and with the advancement of technology, the number of sensors is increasing, and the detection is becoming more and more precise. One of the advantages of the method of the present application is that only six sensors are used to realize the analysis and detection of PVC odor. On this basis, using an electronic nose with more number of sensors can realize higher detection accuracy.

[0025] The method of the present application is to establish a model (i.e. correlation result) through bivariate correlation analysis. In the process of model establishment, data of multiple PVC samples need to be collected. The more the number of PVC samples, the higher the accuracy of the model. The inventors of the present application found in research that the number of PVC samples is at least 5, and the method of the present application can establish a model with high accuracy through 5 PVC samples of different sources and batches.

[0026] According to the method of the present application, in step (1), each group of detection is carried out at least 3 times in parallel, and the data of each group of electronic nose detection is the average value of the maximum response peak of each sensor for the first time.

[0027] According to a specific embodiment of the present application, the electronic nose detection of PVC resin adopts the following steps: a certain amount of PVC resin is placed in a headspace bottle; detection conditions are set in the electronic nose instrument, including but not limited to sample collection amount, collection delay time, heating time, heating temperature, syringe temperature; and the sequence of samples to be detected is set, and the sample bottles are placed in the sample tray in sequence, and then detection is carried out in sequence.

[0028] According to a more specific embodiment of the present application, the electronic nose detection of PVC resin adopts the following steps: 0.1-2g of PVC resin is accurately weighed by a balance in a 1-20mL headspace bottle, and the cap is tightly closed to avoid powder residue outside the cap. Detection conditions are set in the electronic nose instrument, including sample collection amount 500-2500μL, collection delay time 150-300s, heating time 30-240min, heating temperature 60-120℃, syringe temperature 70-130℃; and the sequence of samples to be detected is set, and the sample bottles are placed in the sample tray in sequence, and each sample is detected 3-5 times in parallel.

[0029] According to a preferred embodiment of the present application, step (1) further comprises: performing principal component analysis (PCA) on the multiple groups of electronic nose detection data to obtain the odor discrimination degree between different samples, and arranging the data of each sensor on the circumference according to an angle of 360° / N (wherein N is the number of sensors) to form a fingerprint radar map, so as to reflect the signal strength of each sensor for different samples.

[0030] The bag method in the present application is used to adsorb and enrich volatile organic compounds in PVC samples to improve the detection resolution. The main process of bag method thermal desorption-GC-MS detection is well known to those skilled in the art. First, the volatile organic compounds in the PVC sample are adsorbed and enriched by the bag method, and then the enriched volatile organic compounds are thermally desorbed and detected by GC-MS.

[0031] According to a specific embodiment, the bag method enrichment step comprises: placing the PVC sample in a polytetrafluoroethylene bag, filling with nitrogen, setting the heating temperature, heating time, enriching the gas in the bag with an adsorption tube, and setting the collection flow and collection volume of the adsorption tube. The adsorption tube used is preferably a non-specific selective adsorption tube, more preferably a Tenax tube.

[0032] According to a more specific embodiment of the present application, the bag method enrichment step comprises: weighing 5-30 g of the PVC sample and placing it in a clean 2-10 L polytetrafluoroethylene bag, filling with 1-5 L of nitrogen, setting the heating temperature to 60-120°C and the heating time to 30-240 min, enriching the gas in the bag with an adsorption tube, and setting the collection flow to 0-300 mL / min and the collection volume to 0.2-1 L.

[0033] As described above, both the step (1) electronic nose detection and the step (2) bag method thermal desorption-GC-MS detection include a step of heating the PVC sample; each set of correlation analysis data preferably has the same heating treatment time and heating treatment temperature in the electronic nose detection and the bag method thermal desorption-GC-MS detection, so that the correlation analysis is more accurate. Specifically preferably, the heating treatment time is 30-240 min, and the heating treatment temperature is 60-120°C. Orthogonal experiments show that under the above conditions, the best release effect of VOCs can be achieved. To prevent the possible thermal degradation by-products and the influence on the instrument at high temperature, it is more preferable to select a relatively low temperature and a relatively long treatment time within the above range to ensure the analysis of VOCs. Specifically preferably, the heating treatment time is 100-150 min, and the heating treatment temperature is 70-90°C.

[0034] The thermal desorption method and the subsequent GC-MS can be set according to the process known in the art, and the thermal desorption conditions and the cold trap conditions can be set. The type of chromatographic column is selected, and the column temperature and mass spectrometry conditions are set according to the chromatographic column.

[0035] According to the present application, non-polar, weakly polar or strongly polar chromatographic columns can be used for comprehensive component detection of the plurality of PVC samples, to obtain the VOCs composition, retention time and percentage of each PVC sample.

[0036] The inventors of the present application found in research that the selection of a GC-MS chromatographic column has an important influence on the detection of VOCs components. Different polar columns have different adsorption and desorption degrees for substances with different polarities. The stronger the polarity of the chromatographic column, the stronger the adsorption effect on substances with stronger polarity, and the longer the retention time. PVC contains a large amount of hydrocarbon compounds. In order to avoid that low-abundance polar compounds are covered by high-content hydrocarbon compounds and cannot be effectively separated and detected, the polarity of the chromatographic column can be improved to improve the detection effect of polar compounds.

[0037] According to a preferred embodiment of the present application, at least two of non-polar, weakly polar and strongly polar chromatographic columns are used for comprehensive component detection of the plurality of PVC samples, the contents of different functional groups of each PVC sample under different polarity conditions are obtained, and then the contents of different functional groups of each PVC sample under different polarity conditions are subjected to bivariate correlation analysis with the plurality of electronic nose detection data, respectively, to obtain correlation results under different polarity conditions. The correlation results are collected and summarized to obtain the correlation of the PVC resin odor and volatile organic compound components. It is found that the correlation analysis of the present application using different polar chromatographic columns has no paradox, which shows that the method of the present application is reliable. Since different polar columns have different adsorption and desorption degrees for substances with different polarities, the use of multiple polar chromatographic columns for detection and set collection can improve the accuracy of detection. According to a specific embodiment of the present application, weakly polar and strongly polar chromatographic columns are used for detection and set collection.

[0038] According to the present application, in step (3), the PVC sample is classified according to functional groups, and then classified and added for processing, so as to classify and compare the gas active substances, and the formed categories include but are not limited to two or more of alkanes, esters, acids, ethers, alcohols, benzene, olefins, rings, cyanogen, ketones, sulfur-containing compounds and aldehydes. The group classification can also be determined according to actual needs.

[0039] According to the method of the present application, in step (4), the data analysis software (such as SPSS software) can be used to analyze each group of electronic nose sensor data and VOCs data by bivariate correlation analysis method, to obtain the significant correlation and coefficient of each sensor and VOCs category. The larger the absolute correlation coefficient, the higher the correlation under the same significant condition.

[0040] Specifically, the bivariate correlation analysis can be Person correlation coefficient analysis, or can use two-tailed significance test, or both.

[0041] According to a specific embodiment of the present application, the electronic nose detection adopts six sensors of PA / 2, P30 / 1, P40 / 2, T40 / 2, LY2 / Gh and LY2 / AA, and the volatile organic compounds of the polyvinyl chloride sample are classified into the categories of alkanes, esters, acids, ethers, alcohols, benzene, olefins, rings, cyanogen, ketones, sulfur-containing and aldehydes according to functional groups; under the above conditions, the correlation between the odor of the polyvinyl chloride resin and the volatile organic compound components includes:

[0042] PA / 2 is extremely significantly negatively correlated with alkanes and extremely significantly positively correlated with rings;

[0043] P30 / 1 is extremely significantly negatively correlated with alkanes and extremely significantly positively correlated with rings;

[0044] P40 / 2 is significantly negatively correlated with alcohols;

[0045] T40 / 2 is significantly positively correlated with benzene, extremely significantly positively correlated with rings and significantly positively correlated with ketones;

[0046] LY2 / Gh is significantly positively correlated with sulfur-containing;

[0047] LY2 / AA is significantly positively correlated with cyanogen and significantly positively correlated with sulfur-containing.

[0048] The method for analyzing the correlation between the odor of the polyvinyl chloride resin and the volatile organic compound components of the present application can be applied to quickly determine the composition and content trend of the volatile organic compound components of the polyvinyl chloride or preliminarily distinguish the odor.

[0049] Specifically, the application method comprises:

[0050] The unknown polyvinyl chloride sample is subjected to the electronic nose detection of step (1), and then the composition and content trend of the volatile organic compound components of the polyvinyl chloride sample are determined according to the analysis result of the correlation between the odor of the polyvinyl chloride resin and the volatile organic compound components determined in step (4); and / or,

[0051] The unknown polyvinyl chloride sample is subjected to the bag method thermal desorption-GC-MS detection of steps (2) and (3), and then the odor of the polyvinyl chloride sample is preliminarily distinguished and identified according to the analysis result of the correlation between the odor of the polyvinyl chloride resin and the volatile organic compound components determined in step (4).

[0052] The method has the advantages of simple operation, artificial intelligence of odor information and universality. When a new PVC sample to be detected appears, the composition and content trend of each VOC of the PVC can be obtained based on the data of each sensor of the electronic nose by using the analysis method, or the odor can be distinguished by the VOC composition and content to understand the fingerprint radar chart of the electronic nose. The two kinds of test data can be inferred from each other, and the two kinds of detection do not need to be performed at the same time. The method not only simplifies the detection process, but also correlates the overall odor of the PVC resin with the VOC composition, thereby reducing the detection cost.

[0053] The application will be further described in connection with the following examples, but the scope of the application is not limited to these examples.

[0054] The PVC samples used in the examples are different batches of PVC resins from a petrochemical enterprise and overseas enterprises, in powder form.

[0055] Thermal desorption-gas chromatography: 7890B-5977B, Agilent Technology, USA; thermal desorption device: GERSTEL TD3.5+, GERSTEL, Germany; electronic nose: GERSTEL ODP3, GERSTEL, Germany.

[0056] Example 1

[0057] (1) Electronic nose detection of PVC resins

[0058] 0.5 g of PVC resin powder from different sources was taken into a 20 mL headspace bottle with a disposable sampler, and a magnetic cover with a sealing rubber ring was used to press and seal the cover. The sample was labeled and placed in order. The data acquisition injection amount was set to 1000 μL, the data acquisition delay was set to 210 s, the heating time was set to 120 min, the heating temperature was set to 80℃, the syringe temperature was set to 90℃, and each sample was detected in parallel for 3 times. The electronic nose included six sensors of PA / 2, P30 / 1, P40 / 2, T40 / 2, LY2 / Gh and LY2 / AA. The values of each sensor corresponding to different samples are shown in Table 1.

[0059] Table 1 Values of each sensor corresponding to different samples

[0060]

[0061] (2) Electronic nose data processing of PVC resins

[0062] The data of all sensors of each sample signal reaching the peak value were collected to obtain the odor discrimination degree between different samples by PCA. The data of each sensor were evenly arranged on the circumference according to 60° to form a fingerprint radar chart (as shown in Figure 1 ). The signal strength of each sensor for different samples can be easily reflected.

[0063] (3)PVC resin bag method thermal desorption-GC-MS detection

[0064] 20g of PVC sample was weighed into a clean 10L polytetrafluoroethylene bag, 5L of nitrogen was filled, the heating temperature was 80℃, and the heating time was 120min; then the gas in the bag was enriched with a Tenax tube, the Tenax collection flow rate was 200mL / min, the Tenax collection volume was 1L, the Tenax tube was taken to the machine for testing, and the composition and content of VOCs of each PVC sample were obtained by toluene semi-quantitative method.

[0065] Thermal desorption conditions: initial temperature 40℃, delay time 0.1min, desorption temperature program 60℃ / min to 280℃, desorption temperature 280℃, desorption flow rate 40mL / min, desorption mode no split, gas chromatography running time 33min, helium was introduced. Cold trap conditions: thermal desorption adsorbent is glass wool, cold trap temperature is -150℃, equilibrium time is 0.1min, desorption temperature program is 12℃ / min to 280℃, desorption temperature is 280℃, desorption time is 5min, helium is introduced, no split, transfer line temperature is 280℃.

[0066] The GC-MS chromatographic column is a strong polar column TG-WAXMS, the filler is 100% polyethylene glycol, the size is 60m x 0.25mm x 0.25μm; the column temperature is set as: 50℃ for 3min, then 10℃ / min to 100℃, then 15℃ / min to 250℃, hold for 10min; helium is introduced, the flow rate is 2mL / min; the collection mode is full scan, the scan range is 29-550amu, the ion source temperature is 230℃, the quadrupole temperature is 150℃, the transfer line temperature is 260℃.

[0067] (4)PVC resin bag method thermal desorption-GC-MS data processing

[0068] The VOCs of each sample were screened by category and the content was added and counted, the categories were alkanes, esters, acids, ethers, alcohols, benzene, olefins, rings, cyanogen, ketones, sulfur-containing, aldehydes. The results are shown in Table 2.

[0069] (5)Correlation analysis of electronic nose data and VOCs data

[0070] The data of each sensor of the electronic nose and the classified and added data of VOCs of different PVC samples were introduced into the SPSS software, and the significant correlation and the correlation coefficient between the PVC odor and the VOCs components were obtained by using the bivariate Person correlation analysis method and the double-tailed significance test. The results are shown in Table 3.

[0071] Example 2

[0072] The selection of the GC-MS column has an important influence on the detection of the VOCs components. Different polar columns have different adsorption and desorption degrees for substances with different polarities. The stronger the polarity of the column, the stronger the adsorption effect of the substances with stronger polarity, and the longer the retention time. Based on Example 1, the other conditions were kept unchanged, and a strong polar column was selected, and the parameter setting was changed as follows:

[0073] The weak polar column of the GC-MS column is TG-5SILMS, the filler is 5% diphenyl + 95% dimethyl polysiloxane, and the size is 60 m x 0.25 mm x 0.25 μm; the column temperature setting is: 50℃ for 3 min, then increased to 100℃ at a rate of 10℃ / min, then increased to 280℃ at a rate of 15℃ / min, kept for 4 min, then increased to 320℃ at a rate of 30℃ / min, kept for 8 min; helium was introduced at a flow rate of 2 mL / min; the acquisition mode was full scan, the scan range was 29-550 amu, the ion source temperature was 230℃, the quadrupole temperature was 150℃, and the transfer line temperature was 300℃.

[0074] The classified and added content of VOCs detected by the weak polar column is shown in Table 4, and the correlation coefficient between the PVC odor information and the VOCs components detected by the weak polar column is shown in Table 5.

[0075] The correlation results of the strong polar column and the weak polar column were collected and summarized as follows:

[0076] PA / 2 was extremely significantly negatively correlated with alkanes and extremely significantly positively correlated with rings;

[0077] P30 / 1 was extremely significantly negatively correlated with alkanes and extremely significantly positively correlated with rings;

[0078] P40 / 2 was significantly negatively correlated with alcohols;

[0079] T40 / 2 was significantly positively correlated with benzene, extremely significantly positively correlated with rings, and significantly positively correlated with ketones;

[0080] LY2 / Gh was significantly positively correlated with sulfur-containing compounds;

[0081] LY2 / AA was significantly positively correlated with cyano compounds and significantly positively correlated with sulfur-containing compounds.

[0082]

[0083]

[0084] Example 3

[0085] The heating temperature and time of the sample are two important factors determining the odor and VOCs components and content. Based on Example 1, the incubation time of the electronic nose and GC-MS was optimized orthogonally, and the preferable temperature was determined to be 60-120°C, and the preferable time was determined to be 30-240 min. To prevent the thermal degradation byproducts possibly generated at a higher temperature and the influence on the instrument, the relatively low temperature and relatively long processing time in the above range are more preferable under the condition that the VOCs can be analyzed. For example, the temperature is 70-90°C, and the time is 100-150 min.

[0086] Example 4

[0087] Due to the enrichment effect of the components of VOCs by the bag method thermal desorption pretreatment, the sample amount is too high to affect the resolution of the chromatogram. Based on Example 1, the sample amount of the bag method thermal desorption-GC-MS was determined to be 10 g and 20 g, respectively, while keeping other conditions unchanged. Finally, the sample amount of 20 g is more preferable.

[0088] Comparative Example 1

[0089] The PVC resins were subjected to electronic nose testing: 0.5 g of PVC resin powder of different sources was taken into a 20 mL headspace bottle by using a disposable sampler, and the bottle was tightly sealed by a magnetic lid containing a sealing rubber ring. The bottle was labeled and placed in order. The data acquisition sample amount was set to 1000 μL, the data acquisition delay was 210 s, the heating time was 120 min, the heating temperature was 40°C, the syringe temperature was 50°C, and each sample was detected in parallel for 3 times. The data of all sensors reaching the peak value of each sample signal were collected, as shown in Table 6. The sensor values were not different from the blank control values, indicating that the content of volatile components of the PVC resins at low temperature was small, and detection could not be achieved.

[0090] Table 6. Values of each sensor corresponding to different samples in the comparative example

[0091]

[0092] Comparative Example 2

[0093] Sample 1 in Example 1 was selected and directly sampled by using a sampling bag, and other steps were the same as the GC-MS detection in step (3) of Example 1, Figure 2The GC-MS total ion chromatograms of the direct injection method and the thermal desorption injection method of the sampling bag are shown. It can be seen that under the same heating temperature and time conditions, the types and abundance of substances detected by the thermal desorption method are significantly improved. This shows that the thermal desorption method can better enrich VOCs, effectively improve the detection ability of low-abundance substances, and thus more accurately evaluate the substances contributing to the odor.

[0094] The above has described various embodiments of the present application, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments.

[0095] The endpoints of the ranges and any values disclosed herein are not limited to the precise values recited as the exact dimensions are not critical to the present application. Any numerical range recited herein is intended to include all sub-ranges of the same numbers (i.e., every subset of numbers within the indicated range). As an example, a range of "1 to 10" is intended to include every possible subset between (and including) 1 and 10 (e.g., 1 to 6.1, 2.3 to 9.8, 3.5 to 7.7, 5.5 to 6.8, etc.).

Claims

1. A method for analyzing the correlation between the odor of polyvinyl chloride resin and volatile organic compound components, characterized in that: The method comprises the following steps: (1) Using an electronic nose comprising multiple sensors to detect multiple polyvinyl chloride samples, and obtaining multiple sets of electronic nose detection data; (2) performing bag thermal desorption-GC-MS testing on the plurality of polyvinyl chloride samples to obtain the volatile organic compound composition and content of each polyvinyl chloride sample; (3) categorizing the volatile organic compounds of each polyvinyl chloride sample by functional group and summing the contents of volatile organic compounds of the same category to obtain the contents of different functional group categories of each polyvinyl chloride sample; (4) performing a bivariate correlation analysis on the multiple sets of electronic nose detection data and the contents of the different functional group categories to obtain the correlation between the odor of polyvinyl chloride resin and the volatile organic compound components.

2. The method for analyzing the correlation between the odor of polyvinyl chloride resin and volatile organic compounds according to claim 1, wherein: In step (1), the number of the sensors is at least 6, and the number of the polyvinyl chloride samples is at least 5.

3. The method for analyzing the correlation between the odor of polyvinyl chloride resin and volatile organic compounds according to claim 1, wherein: In step (1), each group of detection is performed at least three times in parallel, and each group of electronic nose detection data is the average value of the maximum response peak value of each sensor that appears for the first time.

4. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: Step (1) also includes: performing principal component analysis on the multiple sets of electronic nose detection data to obtain the odor differentiation between different samples, and evenly arranging the sensor data on the circumference at an angle of 360° / N to form a fingerprint radar map, where N is the number of sensors.

5. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: In step (2), the bag method is used to adsorb and enrich the volatile organic compounds in the polyvinyl chloride sample, and the adsorption tube used is a non-selective adsorption tube, preferably a Tenax tube.

6. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: Both step (1) electronic nose detection and step (2) bag method thermal desorption-GC-MS detection include the step of heating the polyvinyl chloride sample; each set of correlation analysis data has the same heating treatment time and heating treatment temperature in the electronic nose detection and bag method thermal desorption-GC-MS detection.

7. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 6, wherein: The heating treatment time is 30 to 240 minutes, preferably 100 to 150 minutes; the heating treatment temperature is 60° C. to 120° C., preferably 70° C. to 90° C.

8. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: In step (3), the polyvinyl chloride sample is classified according to functional groups to form categories including two or more of alkanes, esters, acids, ethers, alcohols, benzenes, olefins, rings, cyano groups, ketones, sulfur-containing groups and aldehydes.

9. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: In step (4), the bivariate correlation analysis is Person correlation coefficient analysis.

10. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: In step (4), the bivariate correlation analysis is a two-tailed significance test.

11. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: A non-polar, weakly polar or strongly polar chromatographic column is used to perform comprehensive component detection on the multiple polyvinyl chloride samples.

12. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: At least two of non-polar, weakly polar, and strongly polar chromatographic columns are used to perform comprehensive component detection on the multiple polyvinyl chloride samples, and the content of different functional group categories of each polyvinyl chloride sample under different polarity conditions is obtained respectively. Then, bivariate correlation analysis is performed on the content of different functional group categories of each polyvinyl chloride sample under different polarity conditions and the multiple groups of electronic nose detection data to obtain correlation results under different polarity conditions. The correlation results are combined and summarized to obtain the correlation between the polyvinyl chloride resin odor and volatile organic compound components.

13. The method for analyzing the correlation between polyvinyl chloride resin odor and volatile organic compound components according to claim 1, wherein: The electronic nose detection uses six sensors: PA / 2, P30 / 1, P40 / 2, T40 / 2, LY2 / Gh, and LY2 / AA. The volatile organic compounds in the polyvinyl chloride samples are classified into categories according to functional groups: alkanes, esters, acids, ethers, alcohols, benzenes, olefins, rings, cyano groups, ketones, sulfur-containing compounds, and aldehydes. The correlations between the odor of polyvinyl chloride resin and volatile organic compounds were as follows: PA / 2 was extremely significantly negatively correlated with alkanes and extremely significantly positively correlated with rings; P30 / 1 was extremely significantly negatively correlated with alkanes and extremely significantly positively correlated with cyclic hydrocarbons; P40 / 2 was significantly negatively correlated with alcohols; T40 / 2 was significantly positively correlated with benzenes, extremely significantly positively correlated with cyclic hydrocarbons, and significantly positively correlated with ketones; LY2 / Gh was significantly positively correlated with sulfur-containing hydrocarbons; LY2 / AA was significantly positively correlated with cyano hydrocarbons and significantly positively correlated with sulfur-containing hydrocarbons.

14. Use of the method for analyzing the correlation between the odor of polyvinyl chloride resin and volatile organic compounds according to any one of claims 1 to 13 in rapidly determining the composition and content trend of volatile organic compounds in polyvinyl chloride, or in preliminary odor differentiation.

15. The use according to claim 14, wherein: Application methods include: Performing electronic nose detection in step (1) on an unknown polyvinyl chloride sample, and then determining the composition and content trend of the volatile organic compounds of the polyvinyl chloride sample based on the correlation analysis results between the polyvinyl chloride resin odor and the volatile organic compounds determined in step (4); and / or, The unknown polyvinyl chloride sample is subjected to bag method thermal desorption-GC-MS detection in steps (2) and (3), and then the odor of the polyvinyl chloride sample is preliminarily distinguished and identified based on the correlation analysis results of the polyvinyl chloride resin odor and volatile organic compound components determined in step (4).

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