CN111307792A - Color-sensitive bionic sensing detection method for volatile metabolites of pork food-borne pathogenic bacteria - Google Patents
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Abstract
The invention discloses a color-sensitive bionic sensing detection method of volatile metabolites of pork food-borne pathogenic bacteria, which comprises the steps of screening volatile metabolites closely related to the pork food-borne pathogenic bacteria by utilizing a gas chromatography-mass spectrometry technology in combination with a principal component analysis method, selecting a color-sensitive material with an obvious color development response effect on the volatile metabolites to construct a bionic sensor, performing noise filtering, purification and characteristic extraction on data by adopting various information mining algorithms, constructing a quantitative and qualitative evaluation model of the food-borne pathogenic bacteria by utilizing various intelligent learning algorithms, analyzing a color development mechanism of the volatile metabolites and the color-sensitive material, and realizing the color-sensitive bionic sensing detection of the volatile metabolites of the pork food-borne pathogenic bacteria. The invention utilizes the color-sensitive bionic sensor to capture the volatile metabolites of the food-borne pathogenic bacteria, and simultaneously combines the intelligent sensing technology to obtain the characteristic information of the volatile metabolites, thereby providing a new rapid, sensitive and accurate detection scheme.
Description
Technical Field
The invention belongs to the technical field of food safety and environmental monitoring, and particularly relates to a color-sensitive bionic sensing detection method for volatile metabolites of pork food-borne pathogenic bacteria, which integrates a color-sensitive bionic sensing technology and an information intelligent processing technology.
Background
Meat is indispensable food in daily life of people, wherein pork is one of the most important meat foods for residents in China because of delicious taste, rich nutrition and easy digestion and absorption by human bodies, the total consumption amount accounts for 62.5 percent of the total consumption amount of the meat, but the pork is easy to decay and deteriorate due to the pollution of microorganisms because of rich nutrition, the edible value and the commodity value of the pork are reduced, the shelf life of the pork is shortened, and the harm of food-borne diseases is increased. The high-frequency meat product safety emergencies are a remarkable characteristic of meat product safety problems in recent years, such as occurrence of epidemic outbreaks of food-borne pathogenic bacteria such as escherichia coli and listeria monocytogenes, and the safety of pork products becomes a focus of social attention. Food-borne pathogenic bacteria refer to pathogenic bacteria that can cause food poisoning or take food as a transmission medium, the pathogenic bacteria directly or indirectly pollute food and water sources, and human oral infection can cause food-borne diseases. The food-borne pathogenic bacteria detection method mainly comprises the traditional microbial detection technology, molecular biology detection technology, instrument analysis technology and immunology detection technology. Although the existing detection methods have advantages, the existing detection methods have certain limitations, or the pretreatment steps are complex, the time is long, the false positive rate of the detection result is high, or the instruments and equipment are expensive and have requirements on the detection environment. Therefore, the change rule of the volatile smell in the storage process of the pork is discussed, the characteristic volatile substance capable of representing the freshness change of the pork is found, the accurate detection of the volatile substance is realized, and the method has important significance.
At present, a gas chromatography-mass spectrometry technology is combined with a principal component analysis method to screen volatile metabolites closely related to pork food-borne pathogenic bacteria, color sensitive materials with obvious color development response effects with the volatile metabolites are preferably selected, a bionic sensor is constructed, a variety of information mining algorithms such as variable combination cluster analysis and iterative retention of effective variables are combined to perform noise filtering, purification and feature extraction on data, a quantitative evaluation model of the food-borne pathogenic bacteria is constructed by using a variety of intelligent learning algorithms such as a multi-element resolution, a martensite system, a fuzzy support vector machine, a self-adjusting extreme learning machine and a self-adjusting artificial neural network, a color development mechanism of the volatile metabolites and the color sensitive materials is analyzed, and finally, the color sensitive bionic sensing detection of the pork food-borne pathogenic bacteria volatile metabolites is realized by using the method. As a novel color-sensitive bionic sensing detection method, the invention realizes the development of a new way for accurately detecting the pork food-borne pathogenic bacteria to a certain extent.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a color-sensitive bionic sensing detection method for volatile metabolites of pork-meat-borne pathogenic bacteria, on one hand, the color-sensitive bionic sensor is used for capturing the volatile metabolites of the food-borne pathogenic bacteria, and on the other hand, the intelligent sensing technology is combined to obtain the characteristic information of the volatile metabolites, so that the intelligent processing is facilitated. A new color-sensitive bionic sensing detection idea is adopted to establish a rapid, sensitive and accurate detection method for the volatile metabolites of the pork food-borne pathogenic bacteria.
The technical scheme adopted by the invention is as follows:
a bionic color-sensitive sensing detection method for volatile metabolites of pork food-borne pathogenic bacteria, which utilizes a gas chromatography-mass spectrometry technology in combination with a principal component analysis method to screen volatile metabolites closely related to the pork food-borne pathogenic bacteria, preferably selects a color-sensitive material with an obvious color-developing response effect with the volatile metabolites to construct a bionic sensor, and combining with various information mining algorithms such as variable combination cluster analysis, iterative retention of effective variables and the like to filter noise, purify and extract characteristics of data, constructing quantitative and qualitative evaluation models of food-borne pathogenic bacteria by using various intelligent learning algorithms such as multivariate resolution, Matt's system, fuzzy support vector machine, self-adjusting extreme learning machine, self-adjusting artificial neural network and the like, and the color development mechanism of the volatile metabolite and the color sensitive material is analyzed, so that the color sensitive bionic sensing detection of the volatile metabolite of the pork food-borne pathogenic bacteria is realized.
Further, volatile metabolites in different time periods are enriched and cultured by combining a gas chromatography-mass spectrometry technology with an extraction head specifically optimized for food-borne pathogenic bacteria.
Further, the method for screening the volatile metabolites by combining the gas chromatography-mass spectrometry technology with the principal component analysis method comprises the following steps: researching the dynamic change rule of volatile metabolites generated by different food-borne pathogenic bacteria in the culture process, determining the weight of each type of sample metabolite molecules by using the factor contribution rate and the factor load score in the principal component analysis, and screening out the characteristic volatile metabolites representing the change of the food-borne pathogenic bacteria.
Further, the method for detecting volatile metabolites comprises the following steps: and (3) adopting a color-sensitive sensing system self-made in a laboratory to carry out enrichment and detection on the volatile metabolites.
Further, the method for selecting the color sensitive material comprises the following steps: and calculating the adsorption quantity of the color sensitive material to the characteristic volatile metabolites by using a giant regular Monte Carlo method, and preferably selecting the optimal color sensitive material by combining a color sensitive response result.
Further, the method for constructing the quantitative and qualitative evaluation model of the volatile metabolites comprises the following steps: carrying out noise filtering, purification and feature extraction on data by utilizing various information mining algorithms of wavelet analysis and ant colony optimization; then, carrying out noise filtering, purification and feature extraction on the data by utilizing various information mining algorithms of variable combination cluster analysis and iterative retention of effective variables; and (3) constructing a quantitative and qualitative evaluation model of the volatile metabolites by utilizing various intelligent learning algorithms of a multivariate resolution, a Matt system, a fuzzy support vector machine, a self-adjusting extreme learning machine and a self-adjusting artificial neural network.
Further, parameter changes such as atomic charge, molecular system energy, plane included angle and the like before and after the characteristic volatile metabolite and the color sensitive material act are analyzed by using a density functional theory, and the material is subjected to molecular reconstruction according to an analysis result, so that the difference of the efficiencies of different color sensitive materials is determined; by utilizing a DFT method, the geometric structure, bonding characteristics, electronic attributes and vibration frequency of a characteristic volatile metabolite and color sensitive material action system are simulated and calculated, the change rule of the electron cloud density, charge transfer and space structure is revealed, and the influence of site space blocking effect on the color rendering performance is explored.
The invention has the beneficial effects that:
1. the color-sensitive bionic sensing detection method of the volatile metabolites of the pork food-borne pathogenic bacteria, provided by the invention, is used for researching the dynamic change rule of the volatile metabolites generated by different food-borne pathogenic bacteria in the culture process, screening the volatile metabolites by utilizing a gas chromatography-mass spectrometry technology in combination with a principal component analysis method, determining the weight of each type of sample metabolite molecules by utilizing factor contribution rate and factor load score in the principal component analysis, screening out the characteristic volatile metabolites representing the change of the food-borne pathogenic bacteria, and further improving the detection accuracy.
2. Compared with the common food-borne pathogenic bacteria detection method, the detection method disclosed by the invention has the advantages that the constructed rapid detection method is applied to the actual pork sample detection, and the detection speed and accuracy of the traditional method are improved.
3. The detection method of the invention utilizes various information mining algorithms such as wavelet analysis, ant colony optimization and the like to filter noise, purify and extract characteristics of data, utilizes various information mining algorithms such as variable combination cluster analysis, iterative retention of effective variables and the like to filter noise, purify and extract characteristics of data, and utilizes various intelligent learning algorithms such as multivariate resolution, Matta system, fuzzy support vector machine, self-adjusting extreme learning machine, self-adjusting artificial neural network and the like to construct a quantitative and qualitative evaluation model of volatile metabolites. The constructed quantitative and qualitative evaluation model of the volatile metabolites can realize the rapid, sensitive and accurate detection of the volatile metabolites of the pork food-borne pathogenic bacteria.
4. According to the detection method, parameter changes such as atomic charge, molecular system energy, plane included angle and the like before and after the characteristic volatile metabolite and the color sensitive material act are analyzed by using a density functional theory, and the material is subjected to molecular reconstruction according to an analysis result, so that the difference of the efficiencies of different color sensitive materials is determined; by utilizing a DFT method, the geometric structure, bonding characteristics, electronic attributes and vibration frequency of a characteristic volatile metabolite and color sensitive material action system are simulated and calculated, the change rule of the electron cloud density, charge transfer and space structure is revealed, and the influence of site space blocking effect on the color rendering performance is explored.
5. The detection method constructed by the invention is used for detecting the volatile metabolites of the pork food-borne pathogenic bacteria, has high sensitivity, high detection speed and high accuracy, and is widely applied to the technical fields of food safety, environmental monitoring and the like.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In this embodiment, only escherichia coli is taken as an example for illustration, and the food-borne pathogenic bacteria to which the detection method of the present invention is applied are not limited to escherichia coli.
1. Volatile metabolite screening: collecting, separating and identifying volatile metabolites generated by escherichia coli in the culture process at intervals of 3h by utilizing a headspace solid-phase microextraction-gas chromatography-mass spectrometry combined technology and a divinylbenzene/Carboxen/polydimethylsiloxane extraction head, and preferably selecting optimal parameters and test conditions for sample collection; searching unknown components by a computer mass spectrum system NSIT and RTLPEST, and calculating the relative content of each component by adopting an area normalization method; and (3) researching the dynamic change rule of the volatile metabolites generated by the escherichia coli in the culture process, determining the weight of each type of sample metabolite molecules by using the factor contribution rate and the factor load score in the principal component analysis, and screening out the characteristic volatile metabolites representing the change of the escherichia coli.
2. E.coli sample preparation: firstly, respectively inoculating strains of escherichia coli into Luria-Bertani culture media, culturing for 24h at 37 ℃, then centrifuging for 5min at the rotating speed of 5000g, discarding supernatant, cleaning for three times by using ultrapure water, respectively re-dispersing in the ultrapure water, storing obtained bacterial liquid for later use, and simultaneously determining the specific number of bacterial colonies by adopting a colony plate counting method.
3. Optimizing and designing a color-sensitive bionic sensor: depositing the optimized color-sensitive material with obvious color response with the escherichia coli volatile metabolite on different sensor medium substrates by using a micro-sampling device, and determining the dispersion degree of the color-sensitive material on different substrate interfaces and the change rule of the loading capacity; carrying out stability experiments on the sensor under the condition of temperature and humidity change, and ascertaining the correlation mechanism of each environmental factor and the color response of the sensor to determine the optimal environmental factor; on the basis, a color-sensitive bionic sensor with high stability, high specificity and high anti-interference capability is constructed and is arranged in a closed sample reaction chamber.
4. Detection of volatile metabolites: the color-sensitive sensing system is mainly composed of five parts, namely a gas enrichment device, a pump, a gas generation and image acquisition device, a color-sensitive sensor and a computer. An image acquisition system is used for acquiring an original image, namely an image of a color-sensitive sensor array before reaction, then escherichia coli culture solution with different culture time (every 3 hours) is placed in a volatile metabolite enrichment device, a pump is started to enable volatile metabolites to enter a reaction chamber to react with the color-sensitive sensor for 30min, and after the reaction is finished, an image after the color-sensitive sensor reaction is acquired.
5. Model establishment and optimization: researching noise filtering and purifying methods of data, exploring the improving effect of different methods on the detection performance, and exploring various sensor color characteristic information acquisition methods; the method for verifying and optimizing the research model constructs a rapid detection and qualitative identification model of the escherichia coli volatile metabolites under different culture times.
6. Detecting a pork sample: before the pork sample is detected, the sample needs to be pretreated. Soaking 25g of fresh sterile pork in 225ml of alkaline peptone solution containing 3% (w/v) NaCl, homogenizing for 5min, adding food-borne pathogenic bacteria liquid with different concentrations, standing the sample for 30min, and centrifuging to remove large particles and suspended matters; filtering the obtained supernatant through 0.45 mu m filter paper, collecting filtrate, detecting the pork sample containing the escherichia coli by using a designed color-sensitive bionic sensor, and determining the concentration of the escherichia coli by using an established detection model.
7. Analysis of volatile metabolites and sensor response mechanism: analyzing parameter changes such as atomic charge, molecular system energy, plane included angle and the like before and after the characteristic volatile metabolite and the color sensitive material act by using a density functional theory, and performing molecular reconstruction on the material according to an analysis result to preferably select the optimal color sensitive material and determine the difference of the efficiencies of different color sensitive materials; by utilizing a DFT method, the geometric structure, bonding characteristics, electronic attributes and vibration frequency of a characteristic volatile metabolite and color sensitive material action system are simulated and calculated, the change rule of the electron cloud density, charge transfer and space structure is revealed, and the influence of site space blocking effect on the color rendering performance is explored.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (6)
1. A color-sensitive bionic sensing detection method of volatile metabolites of pork food-borne pathogenic bacteria is characterized in that a bionic sensor is constructed by screening volatile metabolites closely related to the pork food-borne pathogenic bacteria by a gas chromatography-mass spectrometry technology in combination with a principal component analysis method, selecting a color-sensitive material with an obvious color development response effect with the volatile metabolites, and combining variable combination cluster analysis and a plurality of information mining algorithms for iteratively retaining effective variables to filter noise, purify and extract characteristics of data, constructing a quantitative and qualitative evaluation model of food-borne pathogenic bacteria by utilizing a plurality of intelligent learning algorithms of multivariate resolution, a Matt system, a fuzzy support vector machine, a self-adjusting extreme learning machine and a self-adjusting artificial neural network, and the color development mechanism of the volatile metabolite and the color sensitive material is analyzed, so that the color sensitive bionic sensing detection of the volatile metabolite of the pork food-borne pathogenic bacteria is realized.
2. The color-sensitive biomimetic sensing detection method of volatile metabolites of pork food-borne pathogenic bacteria according to claim 1, characterized in that volatile metabolites of different time periods are enriched and cultured by combining a gas chromatography-mass spectrometry technology with an extraction head specifically optimized for food-borne pathogenic bacteria.
3. The color-sensitive biomimetic sensing detection method of volatile metabolites of pork food-borne pathogenic bacteria according to claim 2, characterized in that the method for screening the volatile metabolites by combining the gas chromatography-mass spectrometry technology with the principal component analysis method comprises: researching the dynamic change rule of volatile metabolites generated by different food-borne pathogenic bacteria in the culture process, determining the weight of each type of sample metabolite molecules by using the factor contribution rate and the factor load score in the principal component analysis, and screening out the characteristic volatile metabolites representing the change of the food-borne pathogenic bacteria.
4. The color-sensitive biomimetic sensing detection method of volatile metabolites of pork food-borne pathogenic bacteria according to claim 3, characterized in that the method for detecting volatile metabolites is as follows: and (3) adopting a color-sensitive sensing system self-made in a laboratory to carry out enrichment and detection on the volatile metabolites.
5. The color-sensitive biomimetic sensing detection method of volatile metabolites of pork food-borne pathogenic bacteria according to claim 1, characterized in that the method for selecting the color-sensitive material is as follows: and calculating the adsorption quantity of the color sensitive material to the characteristic volatile metabolites by using a giant regular Monte Carlo method, and preferably selecting the optimal color sensitive material by combining a color sensitive response result.
6. The color-sensitive biomimetic sensing detection method for volatile metabolites of pork food-borne pathogenic bacteria according to claim 1, characterized in that the method for constructing a quantitative and qualitative evaluation model of volatile metabolites comprises: carrying out noise filtering, purification and feature extraction on data by utilizing various information mining algorithms of wavelet analysis and ant colony optimization; then, carrying out noise filtering, purification and feature extraction on the data by utilizing various information mining algorithms of variable combination cluster analysis and iterative retention of effective variables; and (3) constructing a quantitative and qualitative evaluation model of the volatile metabolites by utilizing various intelligent learning algorithms of a multivariate resolution, a Matt system, a fuzzy support vector machine, a self-adjusting extreme learning machine and a self-adjusting artificial neural network.
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| CN113358806B (en) * | 2021-06-29 | 2023-10-10 | 江苏大学 | Rapid screening and detecting method and system for meat product characteristic metabolic volatile |
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