CN110879258A - Method for predicting relation between food material processing and flavor quality of food material based on electronic nose and application of method - Google Patents
Method for predicting relation between food material processing and flavor quality of food material based on electronic nose and application of method Download PDFInfo
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Abstract
The invention discloses a method for predicting relation between food material processing and flavor quality based on an electronic nose, which comprises the following steps: based on food flavor substances obtained by different treatments of food materials, quantitative treatment is carried out on the food flavor substances by using sensor response signals of an electronic nose, the flavor components of the food materials subjected to different treatments are analyzed by adopting principal component analysis with the signal response values of different modules as identification bases, and a prediction model between food material treatment and flavor quality is established. The method is accurate and objective, can quickly establish, identify and regulate the association between the processing of the food materials and the flavor quality of the food materials, and has important significance for scientifically guiding the food processing process and improving the food quality and the nutritional value.
Description
Technical Field
The invention belongs to the technical field of food detection by applying electronic sense, and particularly relates to a method for predicting the relation between food material processing and flavor quality of food material based on an electronic nose and application of the method.
Background
The diet culture of all countries in the world is profound, and the treatment of food materials is long. The flavor quality, i.e. the organoleptic properties, which the food material exhibits upon processing, in particular heat treatment, is undoubtedly an important attribute of the food material. The food material treatment mode can change the color, the aroma and the taste of the food, determine the composition, the composition and the properties of the food and directly influence the flavor and the quality of the food. The unique flavor quality can form a memory point of an eater, and the popularity of the product and the propagation degree of culture are improved. Similarly, improper or excessive treatment can destroy the nutritional ingredients of the food materials, reduce the flavor and quality of the food materials, not only affect the nutritional value of the food materials, but also possibly damage the health of eaters. At present, unified opinions and suggestions on food material treatment and treatment degree are not formed in various countries in the world, and an evaluation judgment method is not formed in the aspects of treatment process and effect after corresponding treatment. In order to solve the problem, scientific technology needs to be applied to identify and regulate the processing of the food materials in a more accurate and rational manner so as to establish the correlation between the food materials and the flavor quality of the food materials.
The electronic nose is an instrument simulating human olfactory function, and integrates a gas sampling system, a gas sensor array, signal preprocessing, a mode recognition system and five parts of data display. Different types of flavor compounds can be identified and detected by the sensor, in combination with a pattern recognition system, to provide flavor information for the sample being tested. The method has the advantages of being more visual, objective and rapid, and can accurately identify and distinguish the complex volatile gas. The electronic nose is used in the field of food detection to identify food materials from different sources in different production places and detect and identify the quality of different products.
Through searching, no patent publication related to the present patent application has been found.
Disclosure of Invention
The method is accurate and objective, can quickly establish, identify and regulate and control the association between the processing of the food materials and the flavor quality of the food materials, and has important significance for scientifically guiding the food processing process and improving the food quality and the nutritional value.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for predicting the relation between food material processing and flavor quality based on an electronic nose comprises the following steps:
based on food flavor substances obtained by different treatments of food materials, quantitative treatment is carried out on the food flavor substances by using sensor response signals of an electronic nose, the flavor components of the food materials subjected to different treatments are analyzed by adopting principal component analysis with the signal response values of different modules as identification bases, and a prediction model between food material treatment and flavor quality is established.
The method comprises the following specific steps:
selecting 5 representative food materials, namely shallot, ginger, garlic, pepper and aniseed, processing the food materials in 5 representative traditional dish processing modes, namely stewing, steaming, frying and burning, and monitoring and recording flavor quality detection data of 25 samples in total; the method comprises the steps of establishing a change relation of flavor quality of food materials before and after different treatments by using a signal sensor of an electronic nose and using signal response value variables obtained by different treatments of different food materials as a basis, analyzing the flavor components of the food materials after different treatments by using a principal component analysis method, and establishing a prediction model between food material treatment and the flavor quality.
Moreover, the electronic nose is a PEN3 model electronic nose.
Furthermore, the PEN3 model electronic nose includes 10 sensors, which are respectively: W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S.
Moreover, the data acquisition conditions of the electronic nose are as follows: the cleaning time of the electronic nose sensor is 100s, the sample preparation time is 10s, the sample introduction time is 300s, the air inlet speed is 500mL/min, and the detection time is 200 s.
Moreover, the food flavor substance is analyzed by collecting characteristic flavor data by using an electronic nose, and the specific steps are as follows:
converting and reducing dimensions of multi-index information acquired by an electronic nose sensor to obtain the maximum and most main factors of the contribution rate; analyzing the similarity and difference between the collected data by taking the principal component analysis space distribution map as a carrier; and performing cluster analysis according to the principal component analysis data, fitting a regression curve, and establishing a prediction model.
Moreover, the prediction model is further processed as follows:
and analyzing the volatile flavor substances of the processed food materials by adopting a solid-phase microextraction-gas chromatography-mass spectrometry technology, and verifying the reliability of the prediction model from the perspective of structural changes of the flavor substances before and after processing.
The method for predicting the relation between the food material processing and the flavor quality of the food material based on the electronic nose is applied to quality monitoring in food processing.
The invention has the advantages and positive effects that:
1. the processing mode of the food material can change the composition, structure and properties of the food, and directly influences the flavor and quality of the food. The method comprises the steps of carrying out quantitative processing on response signals of an electronic nose sensor on the basis of food flavor substances obtained by carrying out different processing on food materials, analyzing the flavor components of the food materials processed differently by adopting a principal component analysis method by taking signal response values of different modules as identification bases, establishing a prediction model between food material processing and flavor quality, and verifying a prediction result by a solid phase microextraction-gas chromatography-mass spectrometry technology. The method is accurate and objective, can quickly establish, identify and regulate the association between the processing of the food materials and the flavor quality of the food materials, and has important significance for scientifically guiding the food processing process and improving the food quality and the nutritional value.
2. The method of the invention utilizes the electronic nose technology to convert the artificial sense of the food flavor quality into the signal of the sensor for visual processing, and visually and objectively evaluates the correlation connection so as to efficiently control the food quality.
Drawings
FIG. 1 is a diagram of a simulation apparatus for testing different treatments of food materials according to the present invention; wherein: FIG. 1(a) shows a stewing process simulation apparatus; (b) a steaming processing simulation device; (c) a frying process simulation device; (d) a frying process simulation device; (e) a burning treatment simulation device;
FIG. 2 is a radar chart of the response values of the electronic nose sensor of the food material (ginger) of the present invention after different treatments;
FIG. 3 is a diagram showing the analysis of the main components of the electronic nose of the food material (ginger) according to the present invention after different treatments.
Detailed Description
The following detailed description of the embodiments of the present invention is provided for the purpose of illustration and not limitation, and should not be construed as limiting the scope of the invention.
The raw materials used in the invention are conventional commercial products unless otherwise specified; the methods used in the present invention are conventional in the art unless otherwise specified.
A method for predicting the relation between food material processing and flavor quality based on an electronic nose comprises the following steps:
based on food flavor substances obtained by different treatments of food materials, quantitative treatment is carried out on the food flavor substances by using sensor response signals of an electronic nose, the flavor components of the food materials subjected to different treatments are analyzed by adopting principal component analysis with the signal response values of different modules as identification bases, and a prediction model between food material treatment and flavor quality is established.
Preferably, the electronic nose is a PEN3 model electronic nose.
Preferably, the PEN3 model electronic nose includes 10 sensors, which are respectively: W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S.
Preferably, the data acquisition conditions of the electronic nose are as follows: the cleaning time of the electronic nose sensor is 100s, the sample preparation time is 10s, the sample introduction time is 300s, the air inlet speed is 500mL/min, and the detection time is 200 s.
Preferably, the food flavor substance is analyzed by collecting characteristic flavor data by using an electronic nose, and the specific steps are as follows:
converting and reducing dimensions of multi-index information acquired by an electronic nose sensor to obtain the maximum and most main factors of the contribution rate; analyzing the similarity and difference between the collected data by taking the principal component analysis space distribution map as a carrier; and performing cluster analysis according to the principal component analysis data, fitting a regression curve, and establishing a prediction model.
Preferably, the prediction model is further processed as follows:
and analyzing the volatile flavor substances of the processed food materials by adopting a solid-phase microextraction-gas chromatography-mass spectrometry technology, and verifying the reliability of the prediction model from the perspective of structural changes of the flavor substances before and after processing.
The method for predicting the relation between the food material processing and the flavor quality of the food material based on the electronic nose is applied to quality monitoring in food processing.
Specifically, the method comprises the following steps:
a method for predicting relation between food material processing and flavor quality based on an electronic nose. Selecting 5 representative food materials, namely shallot, ginger, garlic, pepper and aniseed, processing the food materials in 5 representative traditional dish processing modes, namely stewing, steaming, frying and burning, and monitoring and recording flavor quality detection data of 25 samples in total. The method comprises the steps of establishing a change relation of flavor quality of food materials before and after different treatments on the basis of signal response value variables obtained by adopting 10 signal sensors of an electronic nose and corresponding to different treatments for different food materials, analyzing the flavor components of the food materials subjected to different treatments by adopting a principal component analysis method, and establishing a prediction model between food material treatment and the flavor quality of the food materials. And (3) analyzing the attribution of the volatile flavor substances of the processed food materials by adopting a solid-phase microextraction-gas chromatography-mass spectrometry technology, and verifying the reliability of the prediction model from the perspective of the change of the flavor substance structure before and after processing.
The specific pretreatment method of blank contrast detection before treatment of different food materials comprises the following steps: and (3) taking 5g of food material sample, putting the food material sample into a 45mL headspace extraction bottle, inserting an extraction needle, timing, balancing and testing.
The specific pretreatment method for detecting samples after different food materials are treated comprises the following steps: according to the characteristic requirements of different treatment modes, designing a corresponding flavor substance collection simulation device, timing, balancing and testing.
The verification method for establishing the model comprises the following steps: detecting semi-volatile flavor substances of the food materials in different treatment modes by adopting a solid phase microextraction-gas chromatography-mass spectrometry technology, and analyzing and comparing differences of types and contents of flavor compounds before and after treatment.
The solid phase microextraction (solid phase microextraction needle model 50/30 μm DVB/CAR/PDMS, Supelco corporation, USA) -gas chromatography-mass spectrometry (triple quadrupole gas chromatography-mass spectrometer, model 7890B-7000C, Agilent) analysis detection conditions: chromatographic conditions (Agilent 7890B): a chromatographic column: HP-5MS (30mm × 0.25mm × 0.25 μ L); sample inlet temperature: 250 ℃; carrier gas: helium gas; constant pressure: 91.65 kPa; average linear velocity: 22.693 cm/s; split-flow sample introduction split ratio: 10: 1; temperature programming of a chromatographic column: maintaining at 40 deg.C for 3min, increasing the temperature to 240 deg.C at 5 deg.C/min, and maintaining for 0 min; mass spectrometry conditions (Agilent 7000C): EI source, electron energy: 70 ev; ion source temperature: 230 ℃; the scanning type is as follows: MSI full scanning; the scanning range is 40-500 m/z. And (5) performing similarity comparison by adopting a standard library NIST14, confirming flavor substances, and calculating the content of each component according to a peak area normalization method.
Based on different prediction models of the relation between the electronic nose food material processing and the flavor quality thereof, the goodness of fit (R) is obtained by regression analysis2) The content of the volatile flavor substances collected after the food materials are processed is between 0.92 and 0.98, and the types and the contents of the volatile flavor substances have difference, so that different processing modes can be obviously distinguished. The solid phase microextraction-gas chromatography-mass spectrometry technology verifies that the types and the contents of the volatile substance components obtained by analysis are consistent with the analysis result of the electronic nose, and the model established by the method has reasonable reliability and can reflect the representativeness of different treatments of different food materials.
Specifically, more specific examples are as follows:
example 1 method for predicting relationship between ginger steaming treatment and flavor quality thereof based on electronic nose
Cleaning fresh Laiwu rhizoma Zingiberis recens with thick root tuber and no pathological changes and mildew, peeling, and cutting into strips of 3mm × 3mm × 20 mm. Accurately weighing 20g of fresh ginger strips, putting the fresh ginger strips into a 500mL dropping partial pressure funnel, measuring 300mL of distilled water again as shown in the figure 1, putting the fresh ginger strips into a 1L flask, immersing the bottom of the flask into an oil bath kettle, gradually raising the temperature until the distilled water in the flask is completely boiled, recording the temperature of the oil bath kettle to be 110 ℃, starting timing when water vapor is diffused into the funnel, collecting the generated gas by using a gas collecting bag, timing for 30min, and setting three groups of parallels for each test. The electronic nose data acquisition conditions are as follows: the cleaning time of the electronic nose sensor is 100s, the sample preparation time is 10s, the sample introduction time is 300s, the air inlet speed is 500mL/min, and the detection time is 200 s. The 10 sensors of PEN3 model electronic nose include: W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S. The solid phase microextraction-gas chromatography-mass spectrometry detection method is characterized in that a solid phase microextraction device is introduced into a gas collection position, sample collection and analysis are carried out after the same time, and five groups of parallel tests are set in each time. The GC conditions (Agilent7890B) were: a chromatographic column: HP-5MS (30mm × 0.25mm × 0.25 μ L); sample inlet temperature: 250 ℃; carrier gas: helium gas; constant pressure: 91.65 kPa; average linear velocity: 22.693 cm/s; split-flow sample introduction split ratio: 10: 1; temperature programming of a chromatographic column: maintaining at 40 deg.C for 3min, increasing the temperature to 240 deg.C at 5 deg.C/min, and maintaining for 0 min; mass spectrometry conditions (Agilent 7000C): EI source, electron energy: 70 ev; ion source temperature: 230 ℃; the scanning type is as follows: MSI full scanning; the scanning range is 40-500 m/z. And (5) performing similarity comparison by adopting a standard library NIST14, confirming flavor substances, and calculating the content of each component according to a peak area normalization method.
The flavor substance collected by steaming ginger is taken as a sample, and the response of 10 sensors of the electronic nose to the flavor substance generated by steaming ginger is quantified. The radar chart of the response value of the electronic nose sensor of ginger after steaming is shown in fig. 2, the main component analysis is carried out by taking 10 quantitative response values as variables, and the analysis result is shown in fig. 3. Performing cluster analysis according to the principal component analysis data, fitting a regression curve, and establishing a prediction model to obtain y-0.0539 x2+0.2842x+0.5065，R20.96. Verified by a solid phase microextraction-gas chromatography-mass spectrometry technology,the types and the contents of the volatile substance components obtained by analysis are consistent with the analysis result of the electronic nose, which shows that the model established by the method has reasonable reliability and can embody the characteristics of the flavor substances of the ginger after steaming treatment.
Example 2 method for predicting the relationship between ginger stir-frying and flavor quality thereof based on electronic nose
Cleaning fresh Laiwu rhizoma Zingiberis recens with thick root tuber and no pathological changes and mildew, peeling, and cutting into strips of 3mm × 3mm × 20 mm. Accurately weighing 5g of fresh ginger strips for later use, accurately weighing 25g of soybean oil, pouring into a 1L three-neck flask, setting the oil bath pan as shown in figure 1, starting to put ginger when the temperature is stable, heating for 1min after timing, lifting the flask after 1min, starting timing, collecting generated gas with a gas collecting bag, timing for 30min, and setting three groups of parallels for each test. The electronic nose data acquisition conditions are as follows: the cleaning time of the electronic nose sensor is 100s, the sample preparation time is 10s, the sample introduction time is 300s, the air inlet speed is 500mL/min, and the detection time is 200 s. The 10 sensors of PEN3 model electronic nose include: W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S. The solid phase microextraction-gas chromatography-mass spectrometry detection method is characterized in that a solid phase microextraction device is introduced into a gas collection position, sample collection and analysis are carried out after the same time, and five groups of parallel tests are set in each time. The GC conditions (Agilent7890B) were: a chromatographic column: HP-5MS (30mm × 0.25mm × 0.25 μ L); sample inlet temperature: 250 ℃; carrier gas: helium gas; constant pressure: 91.65 kPa; average linear velocity: 22.693 cm/s; split-flow sample introduction split ratio: 10: 1; temperature programming of a chromatographic column: maintaining at 40 deg.C for 3min, increasing the temperature to 240 deg.C at 5 deg.C/min, and maintaining for 0 min; mass spectrometry conditions (Agilent 7000C): EI source, electron energy: 70 ev; ion source temperature: 230 ℃; the scanning type is as follows: MSI full scanning; the scanning range is 40-500 m/z. And (5) performing similarity comparison by using a standard library NIST14, confirming flavor substances, and calculating the content of each component according to a peak area normalization method.
The flavor substance collected by stir-frying the ginger is taken as a sample, and the response of 10 sensors of the electronic nose to the flavor substance generated by stir-frying the ginger is quantified. The radar chart of the response value of the electronic nose sensor of ginger after stir-frying is shown in figure 2,the principal component analysis was performed using the 10 response values after quantification as variables, and the analysis results are shown in fig. 3. Performing cluster analysis according to the principal component analysis data, fitting a regression curve, and establishing a prediction model to obtain y-0.2116 x2+1.3357x+1.3401，R20.93. The type and content of the volatile substance components obtained by analysis are consistent with the analysis result of the electronic nose through the verification of a solid phase microextraction-gas chromatography-mass spectrometry technology, which shows that the model established by the method has reasonable reliability and can embody the characteristic of the flavor substances of the fried ginger.
Although the embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the scope of the invention is not limited to the disclosure of the embodiments and the accompanying drawings.
Claims (8)
1. A method for predicting relation between food material processing and flavor quality based on an electronic nose is characterized by comprising the following steps: the method comprises the following steps:
based on food flavor substances obtained by different treatments of food materials, quantitative treatment is carried out on the food flavor substances by using sensor response signals of an electronic nose, the flavor components of the food materials subjected to different treatments are analyzed by adopting principal component analysis with the signal response values of different modules as identification bases, and a prediction model between food material treatment and flavor quality is established.
2. The method for predicting relationship between food material processing and flavor quality based on electronic nose as claimed in claim 1, wherein: the method comprises the following specific steps:
selecting 5 representative food materials, namely shallot, ginger, garlic, pepper and aniseed, processing the food materials in 5 representative traditional dish processing modes, namely stewing, steaming, frying and burning, and monitoring and recording flavor quality detection data of 25 samples in total; the method comprises the steps of establishing a change relation of flavor quality of food materials before and after different treatments by using a signal sensor of an electronic nose and using signal response value variables obtained by different treatments of different food materials as a basis, analyzing the flavor components of the food materials after different treatments by using a principal component analysis method, and establishing a prediction model between food material treatment and the flavor quality.
3. The method for predicting food material processing and flavor quality relationship based on electronic nose according to claim 1 or 2, wherein: the electronic nose is PEN3 type electronic nose.
4. The method for predicting relationship between food material processing and flavor quality based on electronic nose as claimed in claim 3, wherein: the PEN3 model electronic nose comprises 10 sensors, which are respectively: W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S.
5. The method for predicting food material processing and flavor quality relationship based on electronic nose according to claim 1 or 2, wherein: the data acquisition conditions of the electronic nose are as follows: the cleaning time of the electronic nose sensor is 100s, the sample preparation time is 10s, the sample introduction time is 300s, the air inlet speed is 500mL/min, and the detection time is 200 s.
6. The method for predicting food material processing and flavor quality relationship based on electronic nose according to claim 1 or 2, wherein: the food flavor substance is analyzed by collecting characteristic flavor data by using an electronic nose, and the method comprises the following specific steps:
converting and reducing dimensions of multi-index information acquired by an electronic nose sensor to obtain the maximum and most main factors of the contribution rate; analyzing the similarity and difference between the collected data by taking the principal component analysis space distribution map as a carrier; and performing cluster analysis according to the principal component analysis data, fitting a regression curve, and establishing a prediction model.
7. The method for predicting food material processing and flavor quality relationship based on electronic nose according to claim 1 or 2, wherein: the prediction model is further processed as follows:
and analyzing the volatile flavor substances of the processed food materials by adopting a solid-phase microextraction-gas chromatography-mass spectrometry technology, and verifying the reliability of the prediction model from the perspective of structural changes of the flavor substances before and after processing.
8. Use of the method for predicting food material processing and flavor quality relationship thereof based on electronic nose according to any one of claims 1 to 7 in quality monitoring in food processing.
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| CN112505185A (en) * | 2020-12-04 | 2021-03-16 | 天津科技大学 | Method for establishing flavor quality prediction model for regulating and controlling spicy essential oil by different vegetable oils based on partial least square method |
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