CA3126171A1 - Low-field nuclear magnetic resonance-based device and method for intelligent detection of microwave-dried spicy vegetable flavor - Google Patents
Low-field nuclear magnetic resonance-based device and method for intelligent detection of microwave-dried spicy vegetable flavor Download PDFInfo
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Classifications
-
- A—HUMAN NECESSITIES
- A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
- A23B—PRESERVING, e.g. BY CANNING, MEAT, FISH, EGGS, FRUIT, VEGETABLES, EDIBLE SEEDS; CHEMICAL RIPENING OF FRUIT OR VEGETABLES; THE PRESERVED, RIPENED, OR CANNED PRODUCTS
- A23B7/00—Preservation or chemical ripening of fruit or vegetables
- A23B7/02—Dehydrating; Subsequent reconstitution
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
- G01N24/08—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance
Abstract
A low-field nuclear magnetism-based device and method for the intelligent detection of a microwave-dried spicy vegetable flavor, which relate to the technical field of the intelligent identification of fruit and vegetable drying quality. Microwave vacuum drying is performed on a sample of a spicy vegetable until the drying is complete. During the microwave vacuum drying process, staged sampling is performed for low-field nuclear magnetic resonance analysis. An electronic nose is used to measure changes in the flavor of the material, and the relationships between a low-field nuclear magnetic relaxation time signal and a peak area signal of the spicy vegetable in the drying process and a feature sensor of the electronic nose are established. An artificial neural network intelligent analysis system is used to analyze and predict changes in the flavor quality of the spicy vegetable in the microwave vacuum drying process. The use of low-field nuclear magnetic resonance detection technology solves the technical problems of flavor change detection during the original drying process on the basis of ensuring the shape of the spicy vegetable to the greatest extent, achieves non-destructive, fast and intelligent detection, improves detection work efficiency and product integrity, and effectively monitors changes in flavor during the drying process.
Description
LOW-FIELD NUCLEAR MAGNETIC RESONANCE-BASED DEVICE AND
METHOD FOR INTELLIGENT DETECTION OF MICROWAVE-DRIED SPICY
VEGETABLE FLAVOR
TECHNICAL FIELD
The present invention belongs to the technical field of the intelligent identification of spicy vegetable drying quality, and relates to a low-field nuclear magnetic resonance-based device and method for intelligent detection of a microwave-dried spicy vegetable flavor.
BACKGROUND
Spicy vegetables can give off an aromatic smell or have a pungent taste, and are generally eaten raw, or used as soups, seasonings or dish decorations. Such spicy vegetables can increase appetite and also have many medicinal values. Commonly cultivated varieties of spicy vegetables in China are coriander, green onions, garlic, ginger, leaf fennel, etc. Spicy vegetables contain abundant nutrients. For example, the fresh ginger is rich in nutrients and contain ginger oil, gingerol, fatty oil, resin, starch, pentosan, cellulose, protein, pigment, wax and trace mineral elements, etc. There are more than 100 species complex chemical have been discovered, which can be divided into three categories: gingerols, terpenoid volatile oils and diphenylheptanes.
Among them, the aroma and flavor of ginger are related to the volatile oil of ginger essential oil contained therein, and the spicy flavor of ginger mainly depends on the gingerols in the non-volatile oil of ginger oleoresin. In addition, it also contains a variety of trace elements such as multiple amino acids, vitamins, copper, iron, manganese, zinc, chromium, nickel, cobalt, and multiple functional ingredients. It has the functions of expelling wind and cold, anti-oxidation, anti-tumor, lowering cholesterol, lowering blood sugar, detoxification and sterilization, etc., so that it has attracted widespread attention from consumers and scholars at home and abroad.
Fresh garlic contains carbohydrates, proteins, dietary fibers, fats, vitamins, minerals, and abundant amino acids. Among them, the amino acid in vegetables is one of the important nutrients in vegetables, and its composition and content directly affect the nutritional value of vegetables such as white garlic, and are closely related to human taste.
Dehydration drying is an important technology for long-term storage of agricultural products.
Usually, the drying method of spicy vegetables is mainly hot air drying, which is simple in operation and low in Date Recue/Date Received 2021-07-08 investment, but which also has disadvantages such as long drying time, low efficiency and poor quality. Microwave vacuum drying is an energy-saving, environment-friendly, modem high-tech drying technology. The water content of spicy vegetables is high. Due to its unique advantages, the new drying technology of microwave vacuum drying has attracted widespread attention from scholars at home and abroad in the field of fruit and vegetable dehydration in recent years. Microwave vacuum drying can better retain the original color, fragrance and taste, vitamins and other heat-sensitive nutrients or biologically active ingredients of the material being dried. Spicy vegetables are processed into powder, which can be directly consumed as seasoning or solid drink, and can also be used as raw materials of medicinal materials and health foods, and as raw materials for bread, candy, biscuits and other foods.
Therefore, the processing of spicy vegetable powder is of important application value and broad development prospects.
During the dehydration process, the most important thing for spicy vegetables is the change of a flavor. There are many kinds of flavor substances in spicy vegetables, mainly including volatile flavor substances such as alcohols, aldehydes, esters, acids, alkanes, acids, and sulfur-containing compounds, and non-volatile flavor substances such as soluble sugars, organic acids, free amino acids, etc. The former determines the characteristic taste of food and provides precursors for the synthesis of the latter, while the latter is macroscopically manifested as the food smell. These substances are very small in content and different in odor, and together form the flavor system of food. The contribution of the unique aroma of spicy vegetables to the flavor is related to its content and threshold. The unique flavor substances can not only work up people's appetite, but also promote the secretion of digestive juice, so that the human body can digest and absorb nutrients quickly. An electronic nose is used to evaluate the flavor quality of spicy vegetables during the drying process, which has the advantages of being less affected by subjective factors, high reliability and high repeatability. The electronic nose can realize the qualitative and quantitative analysis of different sensitive types of substances by means of a metal sensor, and it has been widely used in the fields of food quality detection, flavor evaluation, authenticity discrimination and characteristic aroma recognition.
Low-field nuclear magnetic resonance (LF-NMR) uses the spin relaxation characteristics of hydrogen nuclei in a magnetic field to explain the changes in distribution and the migration of water in the sample from a microscopic point of view through the change of relaxation time, and it has the advantages of being fast, accurate, non-destructive, non-invasive, etc., and has been widely used in the field of food science in recent years.
Sun Qun et al. (Patent Application Number: CN201711307888.3) disclosed a low-field nuclear magnetic resonance non-destructive testing line applicable to dried shell fruits. Low-
METHOD FOR INTELLIGENT DETECTION OF MICROWAVE-DRIED SPICY
VEGETABLE FLAVOR
TECHNICAL FIELD
The present invention belongs to the technical field of the intelligent identification of spicy vegetable drying quality, and relates to a low-field nuclear magnetic resonance-based device and method for intelligent detection of a microwave-dried spicy vegetable flavor.
BACKGROUND
Spicy vegetables can give off an aromatic smell or have a pungent taste, and are generally eaten raw, or used as soups, seasonings or dish decorations. Such spicy vegetables can increase appetite and also have many medicinal values. Commonly cultivated varieties of spicy vegetables in China are coriander, green onions, garlic, ginger, leaf fennel, etc. Spicy vegetables contain abundant nutrients. For example, the fresh ginger is rich in nutrients and contain ginger oil, gingerol, fatty oil, resin, starch, pentosan, cellulose, protein, pigment, wax and trace mineral elements, etc. There are more than 100 species complex chemical have been discovered, which can be divided into three categories: gingerols, terpenoid volatile oils and diphenylheptanes.
Among them, the aroma and flavor of ginger are related to the volatile oil of ginger essential oil contained therein, and the spicy flavor of ginger mainly depends on the gingerols in the non-volatile oil of ginger oleoresin. In addition, it also contains a variety of trace elements such as multiple amino acids, vitamins, copper, iron, manganese, zinc, chromium, nickel, cobalt, and multiple functional ingredients. It has the functions of expelling wind and cold, anti-oxidation, anti-tumor, lowering cholesterol, lowering blood sugar, detoxification and sterilization, etc., so that it has attracted widespread attention from consumers and scholars at home and abroad.
Fresh garlic contains carbohydrates, proteins, dietary fibers, fats, vitamins, minerals, and abundant amino acids. Among them, the amino acid in vegetables is one of the important nutrients in vegetables, and its composition and content directly affect the nutritional value of vegetables such as white garlic, and are closely related to human taste.
Dehydration drying is an important technology for long-term storage of agricultural products.
Usually, the drying method of spicy vegetables is mainly hot air drying, which is simple in operation and low in Date Recue/Date Received 2021-07-08 investment, but which also has disadvantages such as long drying time, low efficiency and poor quality. Microwave vacuum drying is an energy-saving, environment-friendly, modem high-tech drying technology. The water content of spicy vegetables is high. Due to its unique advantages, the new drying technology of microwave vacuum drying has attracted widespread attention from scholars at home and abroad in the field of fruit and vegetable dehydration in recent years. Microwave vacuum drying can better retain the original color, fragrance and taste, vitamins and other heat-sensitive nutrients or biologically active ingredients of the material being dried. Spicy vegetables are processed into powder, which can be directly consumed as seasoning or solid drink, and can also be used as raw materials of medicinal materials and health foods, and as raw materials for bread, candy, biscuits and other foods.
Therefore, the processing of spicy vegetable powder is of important application value and broad development prospects.
During the dehydration process, the most important thing for spicy vegetables is the change of a flavor. There are many kinds of flavor substances in spicy vegetables, mainly including volatile flavor substances such as alcohols, aldehydes, esters, acids, alkanes, acids, and sulfur-containing compounds, and non-volatile flavor substances such as soluble sugars, organic acids, free amino acids, etc. The former determines the characteristic taste of food and provides precursors for the synthesis of the latter, while the latter is macroscopically manifested as the food smell. These substances are very small in content and different in odor, and together form the flavor system of food. The contribution of the unique aroma of spicy vegetables to the flavor is related to its content and threshold. The unique flavor substances can not only work up people's appetite, but also promote the secretion of digestive juice, so that the human body can digest and absorb nutrients quickly. An electronic nose is used to evaluate the flavor quality of spicy vegetables during the drying process, which has the advantages of being less affected by subjective factors, high reliability and high repeatability. The electronic nose can realize the qualitative and quantitative analysis of different sensitive types of substances by means of a metal sensor, and it has been widely used in the fields of food quality detection, flavor evaluation, authenticity discrimination and characteristic aroma recognition.
Low-field nuclear magnetic resonance (LF-NMR) uses the spin relaxation characteristics of hydrogen nuclei in a magnetic field to explain the changes in distribution and the migration of water in the sample from a microscopic point of view through the change of relaxation time, and it has the advantages of being fast, accurate, non-destructive, non-invasive, etc., and has been widely used in the field of food science in recent years.
Sun Qun et al. (Patent Application Number: CN201711307888.3) disclosed a low-field nuclear magnetic resonance non-destructive testing line applicable to dried shell fruits. Low-
2 Date Recue/Date Received 2021-07-08 field nuclear magnetic resonance equipment is integrated with a conveying device of a dried shell fruit. Principal component analysis is performed on the existing sample data, and good and bad seed area division and boundary equation calculation are performed to obtain a suitable mathematical model. In the detection process, low-field NMR technology is used to detect the transverse magnetization signal quantity of a dried shell fruit, and then through comparison with the constructed mathematical model, the quality of good seeds, mildewed seeds and moth-eaten seeds of the dried shell fruit is obtained quickly, and the accuracy rate can reach 85% or above.
Guo Tao et al. (Patent Application Number: CN201710984507.9) disclosed a fast identification method for grape seed oil adulteration based on low-field nuclear magnetism, which is applicable to the identification of grape seed oil and adulterated grape seed oil. A low-field nuclear magnetic resonance analyzer is used as a main measuring tool, the difference between relaxation map data of grape seed oil and relaxation map data of adulterated grape seed oil is taken as a main identification basis, a nuclear magnetic resonance signal is taken as a main research object, and a mathematical model analysis method is used to realize quick and accurate identification of grape seed oil and adulterated grape seed oil.
Li Dajing et al. (Patent Application Number: CN201510967968.6) disclosed a method for characterizing a drying end point of far infrared dried Agaricus bisporus based on water distribution. In the method, fresh agaricus bisporus slices are selected as raw materials, far-infrared drying is performed, and the agaricus bisporus slices during the drying process are scanned using low-field nuclear magnetic resonance technology to obtain an inversion map of water distribution, and the drying end point is determined according to the size of the free water relaxation area.
Tan Mingqian et al. (Patent Application Number: CN201610279790.0) disclosed a method for measuring oil content and water content of soybeans using low-field nuclear magnetic resonance technology. Soybean samples are measured by using a CPMG sequence of low-field nuclear magnetic resonance technology, to obtain relaxation spectrum data of each soybean sample, the actual oil content and water content of each soybean sample is corresponding to the relaxation spectrum data, and fitting is performed with a chemometric method to obtain a prediction model of oil content and water content of the soybeans.
Wang Xin et al. (Patent Application No.: CN201210435185.X) disclosed a low-field nuclear magnetic resonance detection method for a frying use limit of soybean oil. A low-field nuclear magnetic resonance analyzer is used as a main measuring tool, a mathematical model
Guo Tao et al. (Patent Application Number: CN201710984507.9) disclosed a fast identification method for grape seed oil adulteration based on low-field nuclear magnetism, which is applicable to the identification of grape seed oil and adulterated grape seed oil. A low-field nuclear magnetic resonance analyzer is used as a main measuring tool, the difference between relaxation map data of grape seed oil and relaxation map data of adulterated grape seed oil is taken as a main identification basis, a nuclear magnetic resonance signal is taken as a main research object, and a mathematical model analysis method is used to realize quick and accurate identification of grape seed oil and adulterated grape seed oil.
Li Dajing et al. (Patent Application Number: CN201510967968.6) disclosed a method for characterizing a drying end point of far infrared dried Agaricus bisporus based on water distribution. In the method, fresh agaricus bisporus slices are selected as raw materials, far-infrared drying is performed, and the agaricus bisporus slices during the drying process are scanned using low-field nuclear magnetic resonance technology to obtain an inversion map of water distribution, and the drying end point is determined according to the size of the free water relaxation area.
Tan Mingqian et al. (Patent Application Number: CN201610279790.0) disclosed a method for measuring oil content and water content of soybeans using low-field nuclear magnetic resonance technology. Soybean samples are measured by using a CPMG sequence of low-field nuclear magnetic resonance technology, to obtain relaxation spectrum data of each soybean sample, the actual oil content and water content of each soybean sample is corresponding to the relaxation spectrum data, and fitting is performed with a chemometric method to obtain a prediction model of oil content and water content of the soybeans.
Wang Xin et al. (Patent Application No.: CN201210435185.X) disclosed a low-field nuclear magnetic resonance detection method for a frying use limit of soybean oil. A low-field nuclear magnetic resonance analyzer is used as a main measuring tool, a mathematical model
3 Date Recue/Date Received 2021-07-08 between multi-component relaxation spectrum data and total polar compound (TPC) data of soybean oil is established as a basis, a nuclear magnetic resonance signal during the frying process of soybean oil is used as a main observation object, and the frying use limit of soybean oil is judged by analyzing multi-component transverse relaxation spectrum data of soybean oil in the frying process.
Tan Mingqian et al. (Patent Application Number: CN201610285372.2) disclosed a method for rapid and non-destructive detection of the water content in abalone during the drying and rehydration process. echo attenuation curve data of fresh and dried abalone samples are collected respectively, and the CPMG signals of the samples are collected. The dried abalone samples are rehydrated, the CPMG signals of the samples are collected during the rehydration process, and the true water content value of each sample is measured. In correspondence to the true water content value, a water content prediction model during the drying and rehydration process is established. These inventions take advantage of the fact that the contents of hydrogen proton-containing ingredients (for example, water in fruits and vegetables, water in soybean oil, and water in abalone) are different in sample matrices in different states, and accordingly, relaxation profile information in a low-field nuclear magnetic field are different, so as to make quick and effective identification and prediction.
Cheng Xinfeng et al. studied the water diffusion characteristics of taro chips during the microwave vacuum drying process. A drying test of taro chips is performed using a microwave vacuum drying oven at three microwave intensities of 1.5, 2.0 and 2.5 W/g, and water migration and distribution in the taro chips during the microwave vacuum drying are measured using low-field nuclear magnetic resonance technology. MRI detection shows that MVD taro loses water internally and externally at the same time, and the higher the microwave intensity, the faster the relaxation signal disappears. This study has revealed the law of water diffusion in taro during microwave vacuum drying, that is, the higher the microwave intensity, the faster the water diffusion rate and the conversion between different components of water in the sample.
The change of flavor of spicy vegetables during the drying process is an important indicator to measure the drying quality. At present, there is still no device to quickly detect the flavor change of spicy vegetables during the drying process. Low-field nuclear magnetic resonance technology is widely used in intelligent detection of water content in fruits and vegetables in the drying process. In the present invention, the water content and flavor substance content of a material in the drying process are in a constantly changing process. With the detection principle of nuclear magnetic resonance technology, when hydrogen protons are excited by a pulse in a magnetic field to obtain a transverse relaxation time signal, the intensity of the
Tan Mingqian et al. (Patent Application Number: CN201610285372.2) disclosed a method for rapid and non-destructive detection of the water content in abalone during the drying and rehydration process. echo attenuation curve data of fresh and dried abalone samples are collected respectively, and the CPMG signals of the samples are collected. The dried abalone samples are rehydrated, the CPMG signals of the samples are collected during the rehydration process, and the true water content value of each sample is measured. In correspondence to the true water content value, a water content prediction model during the drying and rehydration process is established. These inventions take advantage of the fact that the contents of hydrogen proton-containing ingredients (for example, water in fruits and vegetables, water in soybean oil, and water in abalone) are different in sample matrices in different states, and accordingly, relaxation profile information in a low-field nuclear magnetic field are different, so as to make quick and effective identification and prediction.
Cheng Xinfeng et al. studied the water diffusion characteristics of taro chips during the microwave vacuum drying process. A drying test of taro chips is performed using a microwave vacuum drying oven at three microwave intensities of 1.5, 2.0 and 2.5 W/g, and water migration and distribution in the taro chips during the microwave vacuum drying are measured using low-field nuclear magnetic resonance technology. MRI detection shows that MVD taro loses water internally and externally at the same time, and the higher the microwave intensity, the faster the relaxation signal disappears. This study has revealed the law of water diffusion in taro during microwave vacuum drying, that is, the higher the microwave intensity, the faster the water diffusion rate and the conversion between different components of water in the sample.
The change of flavor of spicy vegetables during the drying process is an important indicator to measure the drying quality. At present, there is still no device to quickly detect the flavor change of spicy vegetables during the drying process. Low-field nuclear magnetic resonance technology is widely used in intelligent detection of water content in fruits and vegetables in the drying process. In the present invention, the water content and flavor substance content of a material in the drying process are in a constantly changing process. With the detection principle of nuclear magnetic resonance technology, when hydrogen protons are excited by a pulse in a magnetic field to obtain a transverse relaxation time signal, the intensity of the
4 Date Recue/Date Received 2021-07-08 relaxation signal is proportional to the number of nuclei with a fixed magnetic moment contained in a tested sample, and the signal attenuation process is closely related to the composition and structure of the tested substance, which can provide valuable information such as the physical and chemical environment inside the nucleus. A correlation analysis between .. flavor characteristics and nuclear magnetic response parameters is carried out, and an artificial neural network (BP-ANN) intelligent analysis system is performed, so that the flavor changes of microwave vacuum dried fruits and vegetables can be reflected by the nuclear magnetic relaxation spectrum information, achieving non-destructive, fast and intelligent detection.
SUMMARY
The objective of the present invention is to provide a method for intelligently detecting the flavor changes of microwave-vacuum-dried spicy vegetables. The use of low-field nuclear magnetic resonance detection technology solves the problem of high complexity of the original flavor detection technology for spicy vegetables on the basis of ensuring the shape and nutrients of the spicy vegetables to the greatest extent, and achieves non-destructive, convenient and .. intelligent detection.
A low-field nuclear magnetism-based device for the intelligent detection of a microwave-dried spicy vegetable flavor, the device comprising a microwave dryer, a computer 1, a temperature sensor 2, a moving slide bar 5, a vacuum chamber 6, a raw material 7, a moving plate 8, an NMR coil 9, a vacuum controller 10, a microwave controller 11, a temperature .. controller 12, a magnetron 13, an NMR box 14, a vacuum tube 15, and a vacuum pump 16;
where the vacuum chamber 6 is disposed in the microwave dryer, the bottom of the vacuum chamber 6 is used to place the raw material 7, the moving slide bar 5 and the temperature sensor 2 are disposed in the vacuum chamber 6, the moving slide bar 5 is used to move a drying chamber, and the temperature sensor 2 is used to measure the temperature in the microwave dryer in real-time; the vacuum chamber 6 is connected to the vacuum pump 16 outside the microwave dryer through the vacuum tube 15; the vacuum controller 10, the microwave controller 11, the temperature controller 12 and the magnetron 13 are disposed on the microwave dryer; the vacuum controller 10 is used to control the vacuum pump 16 to adjust the degree of vacuum in the vacuum chamber 6; the microwave controller 11 is used to control the .. microwave parameters of the microwave dryer; the temperature controller 12 is used to adjust the temperature in the microwave dryer; the magnetron 13 is used to convert energy obtained from a constant electric field into microwave energy;
SUMMARY
The objective of the present invention is to provide a method for intelligently detecting the flavor changes of microwave-vacuum-dried spicy vegetables. The use of low-field nuclear magnetic resonance detection technology solves the problem of high complexity of the original flavor detection technology for spicy vegetables on the basis of ensuring the shape and nutrients of the spicy vegetables to the greatest extent, and achieves non-destructive, convenient and .. intelligent detection.
A low-field nuclear magnetism-based device for the intelligent detection of a microwave-dried spicy vegetable flavor, the device comprising a microwave dryer, a computer 1, a temperature sensor 2, a moving slide bar 5, a vacuum chamber 6, a raw material 7, a moving plate 8, an NMR coil 9, a vacuum controller 10, a microwave controller 11, a temperature .. controller 12, a magnetron 13, an NMR box 14, a vacuum tube 15, and a vacuum pump 16;
where the vacuum chamber 6 is disposed in the microwave dryer, the bottom of the vacuum chamber 6 is used to place the raw material 7, the moving slide bar 5 and the temperature sensor 2 are disposed in the vacuum chamber 6, the moving slide bar 5 is used to move a drying chamber, and the temperature sensor 2 is used to measure the temperature in the microwave dryer in real-time; the vacuum chamber 6 is connected to the vacuum pump 16 outside the microwave dryer through the vacuum tube 15; the vacuum controller 10, the microwave controller 11, the temperature controller 12 and the magnetron 13 are disposed on the microwave dryer; the vacuum controller 10 is used to control the vacuum pump 16 to adjust the degree of vacuum in the vacuum chamber 6; the microwave controller 11 is used to control the .. microwave parameters of the microwave dryer; the temperature controller 12 is used to adjust the temperature in the microwave dryer; the magnetron 13 is used to convert energy obtained from a constant electric field into microwave energy;
5 Date Recue/Date Received 2021-07-08 the NMR box 14 is disposed below the microwave dryer by means of the moving plate 8, and the vacuum chamber 6 is movable up and down within the microwave dryer and the NMR
box 14 to ensure real-time sampling; the NMR coil 9 is disposed in the NMR box 14 and is used to monitor in real-time the NMR parameters during the drying process;
the computer 1 is connected to the temperature sensor 2, the microwave dryer, and the NMR box 14, respectively, and is used to transfer a detected data parameter to the computer 1;
a neural network model is in-built within the computer 1, and the detected data parameter is input to the neural network model for real-time analysis of data.
The microwave dryer and the NMR box 14 are connected to the computer through a microwave dryer data cable 3 and an NMR data cable 4, respectively.
Description of device operation:
First, the material 7 is placed in the vacuum chamber 6 of the microwave dryer, and the respective physical parameters of the vacuum controller 10, the microwave controller 11, the temperature controller 12, the moving plate 8, and the NMR coil 9 are set up, the computer 1 and its analysis software are turned on, the vacuum pump 16 is turned on, and the magnetron 13 (microwave generator) is turn on when the corresponding vacuum degree is reached, and the drying begins.
Secondly, the moving plate 8 is opened after the drying reaches the set time, and the moving slide bar 5 is operated to send the vacuum chamber 6 and the material 7 into the NMR box 14.
Sampling is performed by means of the NMR coil 9 for collection of nuclear magnetic parameters. After the collection is complete, the slide bar 5 is moved to pull the drying chamber up, the moving plate 8 is closed, and the drying continues.
Finally, a neural network model is in-built within the computer 1, and a detected data parameter is input to the neural network model for real-time analysis of data.
A low-field nuclear magnetism-based method for intelligent detection of microwave-dried spicy vegetable flavor, comprising the following steps:
(1) Pretreatment of spicy vegetables before drying: the raw material of spicy vegetables is cleaned up and cut into 1 X 1 x 1 cm cubes, which are placed on a dry tray.
(2) Microwave vacuum drying process: the spicy vegetable raw material is put into the vacuum chamber 6 of a microwave vacuum machine, and the vacuum pump 16 is turned on.
When the vacuum degree reaches 10 MPa, the microwave controller is adjusted and controlled, entering the drying stage. During the microwave vacuum drying process, staged sampling is
box 14 to ensure real-time sampling; the NMR coil 9 is disposed in the NMR box 14 and is used to monitor in real-time the NMR parameters during the drying process;
the computer 1 is connected to the temperature sensor 2, the microwave dryer, and the NMR box 14, respectively, and is used to transfer a detected data parameter to the computer 1;
a neural network model is in-built within the computer 1, and the detected data parameter is input to the neural network model for real-time analysis of data.
The microwave dryer and the NMR box 14 are connected to the computer through a microwave dryer data cable 3 and an NMR data cable 4, respectively.
Description of device operation:
First, the material 7 is placed in the vacuum chamber 6 of the microwave dryer, and the respective physical parameters of the vacuum controller 10, the microwave controller 11, the temperature controller 12, the moving plate 8, and the NMR coil 9 are set up, the computer 1 and its analysis software are turned on, the vacuum pump 16 is turned on, and the magnetron 13 (microwave generator) is turn on when the corresponding vacuum degree is reached, and the drying begins.
Secondly, the moving plate 8 is opened after the drying reaches the set time, and the moving slide bar 5 is operated to send the vacuum chamber 6 and the material 7 into the NMR box 14.
Sampling is performed by means of the NMR coil 9 for collection of nuclear magnetic parameters. After the collection is complete, the slide bar 5 is moved to pull the drying chamber up, the moving plate 8 is closed, and the drying continues.
Finally, a neural network model is in-built within the computer 1, and a detected data parameter is input to the neural network model for real-time analysis of data.
A low-field nuclear magnetism-based method for intelligent detection of microwave-dried spicy vegetable flavor, comprising the following steps:
(1) Pretreatment of spicy vegetables before drying: the raw material of spicy vegetables is cleaned up and cut into 1 X 1 x 1 cm cubes, which are placed on a dry tray.
(2) Microwave vacuum drying process: the spicy vegetable raw material is put into the vacuum chamber 6 of a microwave vacuum machine, and the vacuum pump 16 is turned on.
When the vacuum degree reaches 10 MPa, the microwave controller is adjusted and controlled, entering the drying stage. During the microwave vacuum drying process, staged sampling is
6 Date Recue/Date Received 2021-07-08 carried out.
(3) Low-field nuclear magnetic resonance analysis of the dried material: low-field nuclear magnetic resonance analysis is carried out to obtain various nuclear magnetism response signal parameters of samples; where the nuclear magnetic response signal parameters include transverse relaxation time and peak area; the transverse relaxation time includes three types in total: bound water relaxation time T21, immobile water relaxation time T22, and free water relaxation time T23; the peak area includes four types in total: bound water peak area A21, immobile water peak area A22, free water peak area A23 and total water peak area Atotal.
(4) Flavor detection of the dried material: an electronic nose is used to measure the changes of different types of flavor substances in the same species of spicy vegetable, and response values of a flavor feature sensor of the electronic nose are obtained.
(5) Establishment of a microwave-dried spicy vegetable flavor prediction model based on low-field nuclear magnetism: a database for flavor feature sensor response values of the electronic nose and corresponding nuclear magnetic response signal parameters of various samples are obtained through staged sampling in a single drying experiment and repeated drying experiments, and a relationship is established by BP-ANN, to obtain a microwave-dried spicy vegetable flavor prediction model.
(6) Intelligent detection of the flavor changes of spicy vegetables during the microwave drying process: the spicy vegetable samples during the drying are sampled for low-field NMR
analysis, and the current changes of flavor substances are predicted by the microwave-dried spicy vegetable flavor prediction model obtained in step (5).
Further, in the step (2), the microwave power is 150 W, and sampling is performed every 10 min until the water content of the spicy vegetable raw material is less than 10% on the dry basis.
Further, in the step (3), a CPMG (can-purcell-meiboom-gill) pulse sequence is used in the low-field nuclear magnetic resonance analysis for signal collection. The parameters used in the CPMG sequence are: number of sampling points TD = 784794, spectral width 100 kHz, number of echoes 18000, number of repetitive scans NS=4, and sampling repetition time TW = 4000 ms. The collected signal is processed by nuclear magnetic resonance T2 inversion software to obtain a T2 inversion spectrum and corresponding NMR parameters.
Further, in the step (4), the changes of different types of flavor substances in the same species of spiced vegetable are measured by using the electronic nose, and the sample (2.0 g on
(3) Low-field nuclear magnetic resonance analysis of the dried material: low-field nuclear magnetic resonance analysis is carried out to obtain various nuclear magnetism response signal parameters of samples; where the nuclear magnetic response signal parameters include transverse relaxation time and peak area; the transverse relaxation time includes three types in total: bound water relaxation time T21, immobile water relaxation time T22, and free water relaxation time T23; the peak area includes four types in total: bound water peak area A21, immobile water peak area A22, free water peak area A23 and total water peak area Atotal.
(4) Flavor detection of the dried material: an electronic nose is used to measure the changes of different types of flavor substances in the same species of spicy vegetable, and response values of a flavor feature sensor of the electronic nose are obtained.
(5) Establishment of a microwave-dried spicy vegetable flavor prediction model based on low-field nuclear magnetism: a database for flavor feature sensor response values of the electronic nose and corresponding nuclear magnetic response signal parameters of various samples are obtained through staged sampling in a single drying experiment and repeated drying experiments, and a relationship is established by BP-ANN, to obtain a microwave-dried spicy vegetable flavor prediction model.
(6) Intelligent detection of the flavor changes of spicy vegetables during the microwave drying process: the spicy vegetable samples during the drying are sampled for low-field NMR
analysis, and the current changes of flavor substances are predicted by the microwave-dried spicy vegetable flavor prediction model obtained in step (5).
Further, in the step (2), the microwave power is 150 W, and sampling is performed every 10 min until the water content of the spicy vegetable raw material is less than 10% on the dry basis.
Further, in the step (3), a CPMG (can-purcell-meiboom-gill) pulse sequence is used in the low-field nuclear magnetic resonance analysis for signal collection. The parameters used in the CPMG sequence are: number of sampling points TD = 784794, spectral width 100 kHz, number of echoes 18000, number of repetitive scans NS=4, and sampling repetition time TW = 4000 ms. The collected signal is processed by nuclear magnetic resonance T2 inversion software to obtain a T2 inversion spectrum and corresponding NMR parameters.
Further, in the step (4), the changes of different types of flavor substances in the same species of spiced vegetable are measured by using the electronic nose, and the sample (2.0 g on
7 Date Recue/Date Received 2021-07-08 the dry basis) is placed in a sealed vial (20 mL) and is allowed to stand for 60 min. The collection time is 150 s.
Further, in the step (5), when a relationship equation between the peak area of different components of water in nuclear magnetic response signal parameters and the sensor response value of the electronic nose for the sample is established, the peak area needs to be normalized by mass.
The spicy vegetables include, but not limited to, ginger, garlic, green onion, and pepper.
1. The present invention uses low-field nuclear magnetic resonance to solve the technical problems of flavor change detection during the original spicy vegetable drying process on the basis of ensuring the shape to the greatest extent, achieves non-destructive, convenient and intelligent detection, improves detection work efficiency and product integrity, and effectively monitors changes in flavor during the drying process.
2. The present invention has convenient operation, simple process, high accuracy of detection results, short time-consuming, and no damage to samples, and can effectively monitor in real-time the changes in flavor during the drying process.
3. The method proposed by the present invention can accurately and effectively judge the changes of different types of flavor substances in spicy vegetables during the drying process, which is very helpful in adjustment and control of the drying process.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a BP-ANN prediction model of ginger flavor during microwave vacuum drying.
In the figure, (a) is a training set, (b) is a verification set, (c) is a testing set, and (d) is a comprehensive set.
FIG. 2 is a BP-ANN prediction model of garlic flavor during microwave vacuum drying.
In the figure, (a) is a training set, (b) is a verification set, (c) is a testing set, and (d) is a comprehensive set.
FIG. 3 is a simplified integration diagram of the device of the present invention.
FIG. 4 is a diagram of states of sample detection, in which (a) is a state when microwave drying is on, (b) is a state when a moving plate is removed, (c) is a state when a low-field NMR
measurement is performed, and (d) is a state when returning to the microwave drying.
In the figures: 1 computer; 2 temperature sensor; 3 microwave dryer data cable; 4 NMR
data cable; 5 moving slide bar; 6 vacuum chamber; 7 raw material; 8 moving plate; 9 NMR
Further, in the step (5), when a relationship equation between the peak area of different components of water in nuclear magnetic response signal parameters and the sensor response value of the electronic nose for the sample is established, the peak area needs to be normalized by mass.
The spicy vegetables include, but not limited to, ginger, garlic, green onion, and pepper.
1. The present invention uses low-field nuclear magnetic resonance to solve the technical problems of flavor change detection during the original spicy vegetable drying process on the basis of ensuring the shape to the greatest extent, achieves non-destructive, convenient and intelligent detection, improves detection work efficiency and product integrity, and effectively monitors changes in flavor during the drying process.
2. The present invention has convenient operation, simple process, high accuracy of detection results, short time-consuming, and no damage to samples, and can effectively monitor in real-time the changes in flavor during the drying process.
3. The method proposed by the present invention can accurately and effectively judge the changes of different types of flavor substances in spicy vegetables during the drying process, which is very helpful in adjustment and control of the drying process.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a BP-ANN prediction model of ginger flavor during microwave vacuum drying.
In the figure, (a) is a training set, (b) is a verification set, (c) is a testing set, and (d) is a comprehensive set.
FIG. 2 is a BP-ANN prediction model of garlic flavor during microwave vacuum drying.
In the figure, (a) is a training set, (b) is a verification set, (c) is a testing set, and (d) is a comprehensive set.
FIG. 3 is a simplified integration diagram of the device of the present invention.
FIG. 4 is a diagram of states of sample detection, in which (a) is a state when microwave drying is on, (b) is a state when a moving plate is removed, (c) is a state when a low-field NMR
measurement is performed, and (d) is a state when returning to the microwave drying.
In the figures: 1 computer; 2 temperature sensor; 3 microwave dryer data cable; 4 NMR
data cable; 5 moving slide bar; 6 vacuum chamber; 7 raw material; 8 moving plate; 9 NMR
8 Date Recue/Date Received 2021-07-08 coil; 10 vacuum controller; 11 microwave controller; 12 temperature controller; 13 megnetron;
14 NMR box; 15 vacuum tube; 16 vacuum pump.
DETAILED DESCRIPTION OF THE EMBODIMENTS
The technical solution of the present invention is further illustrated below with reference to specific examples and the accompanying drawings.
Example 1: low-field nuclear magnetism-based method and device for the intelligent detection of a microwave-vacuum-dried ginger flavor 1. Ginger was cleaned up with water, peeled and cut into lx1x1 cm cubes, which were placed on a tray for microwave vacuum drying. The vacuum pump was turned on, the microwave heating system was activated when the vacuum degree reached 10 kPa, and the microwave power was set to 150 W. Staged sampling was performed for low-field nuclear magnetic resonance analysis, to obtain a transverse relaxation time T2 curve and various response signal parameters of a sample. And an electronic nose was used to measure changes in the flavor of the sample. A feature sensor of the electronic nose was determined.
2. Model establishment and intelligent adjustment and control: repeated experiments were performed many times, to obtain a database for flavor feature sensor response values and corresponding nuclear magnetic response signal parameters of a large number of samples. The transverse relaxation time and peak area data of ginger during the drying process were fitted with the feature sensor through metrology software, and the nuclear magnetic signal (T21, T22, T23, A21, A22, A23, and Atotal) was used as input parameters of BP-ANN, the feature sensor of the electronic nose was used as as an output parameter, and 70% of the sample size was randomly selected as a training set to establish a flavor-related prediction model (as shown in Fig. 1). It can be seen from the figure that there is a good correlation between the predicted values of the flavor of the ginger sample obtained by the BP-ANN method and the chemical values, and the R of the training set is greater than 0.9 in all cases. In order to verify the accuracy and stability of the prediction model, 15% of the samples were used as a verification set and 15% of the samples were used as a test set. The results show that the R of the verification set is greater than 0.9 in all cases, indicating that the predictive ability of the model is very good, and the comprehensive R is 0.97798, which indicates that the stability of the prediction model is good. Twenty groups of samples during the drying process were randomly selected for low-field nuclear magnetic resonance analysis by means of an automatic sampling system. The established ginger flavor BP-ANN analysis model was used to predict the current flavor
14 NMR box; 15 vacuum tube; 16 vacuum pump.
DETAILED DESCRIPTION OF THE EMBODIMENTS
The technical solution of the present invention is further illustrated below with reference to specific examples and the accompanying drawings.
Example 1: low-field nuclear magnetism-based method and device for the intelligent detection of a microwave-vacuum-dried ginger flavor 1. Ginger was cleaned up with water, peeled and cut into lx1x1 cm cubes, which were placed on a tray for microwave vacuum drying. The vacuum pump was turned on, the microwave heating system was activated when the vacuum degree reached 10 kPa, and the microwave power was set to 150 W. Staged sampling was performed for low-field nuclear magnetic resonance analysis, to obtain a transverse relaxation time T2 curve and various response signal parameters of a sample. And an electronic nose was used to measure changes in the flavor of the sample. A feature sensor of the electronic nose was determined.
2. Model establishment and intelligent adjustment and control: repeated experiments were performed many times, to obtain a database for flavor feature sensor response values and corresponding nuclear magnetic response signal parameters of a large number of samples. The transverse relaxation time and peak area data of ginger during the drying process were fitted with the feature sensor through metrology software, and the nuclear magnetic signal (T21, T22, T23, A21, A22, A23, and Atotal) was used as input parameters of BP-ANN, the feature sensor of the electronic nose was used as as an output parameter, and 70% of the sample size was randomly selected as a training set to establish a flavor-related prediction model (as shown in Fig. 1). It can be seen from the figure that there is a good correlation between the predicted values of the flavor of the ginger sample obtained by the BP-ANN method and the chemical values, and the R of the training set is greater than 0.9 in all cases. In order to verify the accuracy and stability of the prediction model, 15% of the samples were used as a verification set and 15% of the samples were used as a test set. The results show that the R of the verification set is greater than 0.9 in all cases, indicating that the predictive ability of the model is very good, and the comprehensive R is 0.97798, which indicates that the stability of the prediction model is good. Twenty groups of samples during the drying process were randomly selected for low-field nuclear magnetic resonance analysis by means of an automatic sampling system. The established ginger flavor BP-ANN analysis model was used to predict the current flavor
9 Date Recue/Date Received 2021-07-08 conditions. The correlation coefficient R of the model validation set is 0.9688, indicating that the low-field nuclear magnetic resonance combined with the BP-ANN model can accurately predict the flavor changes of ginger during the drying process.
Example 2: low-field nuclear magnetism-based method and device for the intelligent detection of a microwave-vacuum-dried garlic flavor 1. Garlic was peeled, cleaned up, and cut into 0.4 cm slices, which were placed on a tray for microwave vacuum drying. The vacuum pump was turned on, the microwave heating system was activated when the vacuum degree reached 10 kPa, and the microwave power was set to 150 W. Staged sampling was performed for low-field nuclear magnetic resonance analysis, to obtain a transverse relaxation time T2 curve of a sample and various response signal parameters. And an electronic nose was used to measure changes in the flavor of the sample. A
characteristic sensor of the electronic nose was determined as S4.
2. Model establishment and intelligent adjustment and control: repeated experiments were performed many times, to obtain a database for flavor feature sensor response values and corresponding nuclear magnetic response signal parameters of a large number of samples. The transverse relaxation time and peak area data of garlic during the drying process were fitted with the feature sensor through metrology software, and the nuclear magnetic signal (T21, T22, T23, A21, A22, A23, and Atotal) was used as input parameters of BP-ANN, the feature sensor of the electronic nose was used as as an output parameter, and 70% of the sample size was randomly selected as a training set to establish a flavor-related prediction model (as shown in Fig. 2). It can be seen from the figure that there is a good correlation between the predicted values of the flavor of the different samples obtained by the BP-ANN method and the chemical values, and the R of the training set is greater than 0.9 in all cases. In order to verify the accuracy and stability of the prediction model, 15% of the samples were used as a verification set and 15% of the samples were used as a test set. The results show that the R of the verification set is greater than 0.9 in all cases, indicating that the predictive ability of the model is very good, and the comprehensive R is 0.97581, which indicates that the stability of the prediction model is good. Twenty groups of samples during the drying process were randomly selected for low-field nuclear magnetic resonance analysis by means of an automatic sampling system. The established garlic flavor BP-ANN analysis model was used to predict the current flavor conditions. The correlation coefficient R of the model validation set is 0.9589, indicating that the low-field nuclear magnetic resonance combined with the BP-ANN model can accurately predict the flavor changes of garlic during the drying process.
Date Recue/Date Received 2021-07-08
Example 2: low-field nuclear magnetism-based method and device for the intelligent detection of a microwave-vacuum-dried garlic flavor 1. Garlic was peeled, cleaned up, and cut into 0.4 cm slices, which were placed on a tray for microwave vacuum drying. The vacuum pump was turned on, the microwave heating system was activated when the vacuum degree reached 10 kPa, and the microwave power was set to 150 W. Staged sampling was performed for low-field nuclear magnetic resonance analysis, to obtain a transverse relaxation time T2 curve of a sample and various response signal parameters. And an electronic nose was used to measure changes in the flavor of the sample. A
characteristic sensor of the electronic nose was determined as S4.
2. Model establishment and intelligent adjustment and control: repeated experiments were performed many times, to obtain a database for flavor feature sensor response values and corresponding nuclear magnetic response signal parameters of a large number of samples. The transverse relaxation time and peak area data of garlic during the drying process were fitted with the feature sensor through metrology software, and the nuclear magnetic signal (T21, T22, T23, A21, A22, A23, and Atotal) was used as input parameters of BP-ANN, the feature sensor of the electronic nose was used as as an output parameter, and 70% of the sample size was randomly selected as a training set to establish a flavor-related prediction model (as shown in Fig. 2). It can be seen from the figure that there is a good correlation between the predicted values of the flavor of the different samples obtained by the BP-ANN method and the chemical values, and the R of the training set is greater than 0.9 in all cases. In order to verify the accuracy and stability of the prediction model, 15% of the samples were used as a verification set and 15% of the samples were used as a test set. The results show that the R of the verification set is greater than 0.9 in all cases, indicating that the predictive ability of the model is very good, and the comprehensive R is 0.97581, which indicates that the stability of the prediction model is good. Twenty groups of samples during the drying process were randomly selected for low-field nuclear magnetic resonance analysis by means of an automatic sampling system. The established garlic flavor BP-ANN analysis model was used to predict the current flavor conditions. The correlation coefficient R of the model validation set is 0.9589, indicating that the low-field nuclear magnetic resonance combined with the BP-ANN model can accurately predict the flavor changes of garlic during the drying process.
Date Recue/Date Received 2021-07-08
Claims (10)
1. A low-field nuclear magnetism-based device for the intelligent detection of a microwave-dried spicy vegetable flavor, the device comprising a microwave dryer, a computer (1), a temperature sensor (2), a moving slide bar (5), a vacuum chamber (6), a raw material (7), a moving plate (8), an NMR coil (9), a vacuum controller (10), a microwave controller (11), a temperature controller (12), a magnetron (13), an NMR box (14), a vacuum tube (15), and a vacuum pump (16);
wherein the vacuum chamber (6) is disposed in the microwave dryer, the bottom of the vacuum chamber (6) is used to place the raw material (7), the moving slide bar (5) and the temperature sensor (2) are disposed in the vacuum chamber (6), the moving slide bar (5) is used to move a drying chamber, and the temperature sensor (2) is used to measure the temperature in the microwave dryer in real-time; the vacuum chamber (6) is connected to the vacuum pump (16) outside the microwave dryer through the vacuum tube (15); the vacuum controller (10), the microwave controller (11), the temperature controller (12) and the magnetron (13) are disposed on the microwave dryer; the vacuum controller (10) is used to control the vacuum pump (16) to adjust the degree of vacuum in the vacuum chamber (6); the microwave controller (11) is used to control the microwave parameters of the microwave dryer; the temperature controller (12) is used to adjust the temperature in the microwave dryer; the magnetron (13) is used to convert energy obtained from a constant electric field into microwave energy;
the NMR box (14) is disposed below the microwave dryer by means of the moving plate (8), and the vacuum chamber (6) is movable up and down within the microwave dryer and the NMR
box (14) to ensure real-time sampling; the NMR coil (9) is disposed in the NMR
box (14) and is used to monitor in real-time the NMR parameters of a material during the drying process;
the computer (1) is connected to the temperature sensor (2), the microwave dryer, and the NMR
box (14), respectively, and is used to transfer a detected data parameter to the computer (1); a neural network model is in-built within the computer (1), and the detected data parameter is input to the neural network model for real-time analysis of data.
wherein the vacuum chamber (6) is disposed in the microwave dryer, the bottom of the vacuum chamber (6) is used to place the raw material (7), the moving slide bar (5) and the temperature sensor (2) are disposed in the vacuum chamber (6), the moving slide bar (5) is used to move a drying chamber, and the temperature sensor (2) is used to measure the temperature in the microwave dryer in real-time; the vacuum chamber (6) is connected to the vacuum pump (16) outside the microwave dryer through the vacuum tube (15); the vacuum controller (10), the microwave controller (11), the temperature controller (12) and the magnetron (13) are disposed on the microwave dryer; the vacuum controller (10) is used to control the vacuum pump (16) to adjust the degree of vacuum in the vacuum chamber (6); the microwave controller (11) is used to control the microwave parameters of the microwave dryer; the temperature controller (12) is used to adjust the temperature in the microwave dryer; the magnetron (13) is used to convert energy obtained from a constant electric field into microwave energy;
the NMR box (14) is disposed below the microwave dryer by means of the moving plate (8), and the vacuum chamber (6) is movable up and down within the microwave dryer and the NMR
box (14) to ensure real-time sampling; the NMR coil (9) is disposed in the NMR
box (14) and is used to monitor in real-time the NMR parameters of a material during the drying process;
the computer (1) is connected to the temperature sensor (2), the microwave dryer, and the NMR
box (14), respectively, and is used to transfer a detected data parameter to the computer (1); a neural network model is in-built within the computer (1), and the detected data parameter is input to the neural network model for real-time analysis of data.
2. The device according to claim 1, wherein the microwave dryer and the NMR
box (14) are connected to the computer through a microwave dryer data cable (3) and an NMR
data cable (4), respectively.
Date Recue/Date Received 2021-07-08
box (14) are connected to the computer through a microwave dryer data cable (3) and an NMR
data cable (4), respectively.
Date Recue/Date Received 2021-07-08
3. A low-field nuclear magnetism-based method for the intelligent detection of a microwave-dried spicy vegetable flavor using the device of any one of claim 1 or 2, comprising the following steps:
(1) pretreatment of spicy vegetables before drying: the raw material of spicy vegetables is .. cleaned up and cut into lx1x1 cm cubes, which are placed on a dry tray;
(2) microwave vacuum drying process: the spicy vegetable raw material is put into the vacuum chamber (6) of a microwave vacuum machine, and the vacuum pump (16) is turned on; when the vacuum degree reaches 10 MPa, the microwave controller (11) is adjusted and controlled, entering the drying stage; during the microwave vacuum drying process, staged sampling is carried out;
(3) low-field nuclear magnetic resonance analysis of the dried material: low-field nuclear magnetic resonance analysis is carried out to obtain various nuclear magnetism response signal parameters of samples; wherein the nuclear magnetic response signal parameters include transverse relaxation time and peak area; the transverse relaxation time includes three types in total: bound water relaxation time T21, immobile water relaxation time T22, and free water relaxation time T23; the peak area includes four types in total: bound water peak area A21, immobile water peak area A22, free water peak area A23 and total water peak area A =
total, (4) flavor detection of the dried material: an electronic nose is used to measure the changes of different types of flavor substances in the same species of spicy vegetable, and response values of a flavor feature sensor of the electronic nose are obtained;
(5) establishment of a microwave-dried spicy vegetable flavor prediction model based on low-field nuclear magnetism: a database for flavor feature sensor response values of the electronic nose and corresponding nuclear magnetic response signal parameters of various samples are obtained through staged sampling in a single drying experiment and repeated drying .. experiments, and a relationship is established by means of BP-ANN, to obtain a microwave-dried spicy vegetable flavor prediction model;
(6) intelligent detection of the flavor changes of spicy vegetables during the microwave drying process: the spicy vegetable samples during the drying are sampled for low-field NMR analysis, and the current changes of flavor substances are predicted by the microwave-dried spicy vegetable flavor prediction model obtained in step (5).
(1) pretreatment of spicy vegetables before drying: the raw material of spicy vegetables is .. cleaned up and cut into lx1x1 cm cubes, which are placed on a dry tray;
(2) microwave vacuum drying process: the spicy vegetable raw material is put into the vacuum chamber (6) of a microwave vacuum machine, and the vacuum pump (16) is turned on; when the vacuum degree reaches 10 MPa, the microwave controller (11) is adjusted and controlled, entering the drying stage; during the microwave vacuum drying process, staged sampling is carried out;
(3) low-field nuclear magnetic resonance analysis of the dried material: low-field nuclear magnetic resonance analysis is carried out to obtain various nuclear magnetism response signal parameters of samples; wherein the nuclear magnetic response signal parameters include transverse relaxation time and peak area; the transverse relaxation time includes three types in total: bound water relaxation time T21, immobile water relaxation time T22, and free water relaxation time T23; the peak area includes four types in total: bound water peak area A21, immobile water peak area A22, free water peak area A23 and total water peak area A =
total, (4) flavor detection of the dried material: an electronic nose is used to measure the changes of different types of flavor substances in the same species of spicy vegetable, and response values of a flavor feature sensor of the electronic nose are obtained;
(5) establishment of a microwave-dried spicy vegetable flavor prediction model based on low-field nuclear magnetism: a database for flavor feature sensor response values of the electronic nose and corresponding nuclear magnetic response signal parameters of various samples are obtained through staged sampling in a single drying experiment and repeated drying .. experiments, and a relationship is established by means of BP-ANN, to obtain a microwave-dried spicy vegetable flavor prediction model;
(6) intelligent detection of the flavor changes of spicy vegetables during the microwave drying process: the spicy vegetable samples during the drying are sampled for low-field NMR analysis, and the current changes of flavor substances are predicted by the microwave-dried spicy vegetable flavor prediction model obtained in step (5).
4. The method according to claim 3, wherein in the step (2), the microwave power is 150 W, and sampling is performed every 10 min until the water content of the spicy vegetable raw material is less than 10% on the dry basis.
Date Recue/Date Received 2021-07-08
Date Recue/Date Received 2021-07-08
5. The method according to claim 3, wherein in the step (3), a CPMG pulse sequence is used in low-field nuclear magnetic resonance analysis for signal collection; the parameters used in the CPMG sequence are: number of sampling points TD = 784794, spectral width 100 kHz, number of echoes 18000, number of repetitive scans NS=4, and sampling repetition time TW = 4000 ms; the collected signal is processed by nuclear magnetic resonance T2 inversion software to obtain a T2 inversion spectrum and corresponding NMR parameters.
6. The method according to claim 4, wherein in the step (3), a CPMG pulse sequence is used in low-field nuclear magnetic resonance analysis for signal collection; the parameters used in the CPMG sequence are: number of sampling points TD = 784794, spectral width 100 kHz, number of echoes 18000, number of repetitive scans NS=4, and sampling repetition time TW = 4000 ms; the collected signal is processed by nuclear magnetic resonance T2 inversion software to obtain a T2 inversion spectrum and corresponding NMR parameters.
7. The method according to claim 3, wherein in the step (4), the changes of different types of flavor substances in the same species of spiced vegetable are measured by using the electronic nose, and the sample is placed in a sealed vial and is allowed to stand for 60 min; and the collection time is 150 s.
8. The method according to claim 4, 5 or 6, wherein in the step (4), the changes of different types of flavor substances in the same species of spiced vegetable are measured by using the electronic nose, and the sample is placed in a sealed vial and is allowed to stand for 60 min;
and the collection time is 150 s.
and the collection time is 150 s.
9. The method according to claim 3, wherein in the step (5), when a relationship equation between the peak area of different components of water in nuclear magnetic response signal parameters and the sensor response value of the electronic nose for the sample is established, the peak area needs to be normalized by mass.
10. The method according to claim 4, 5, 6 or 7, wherein in the step (5), when a relationship equation between the peak area of different components of water in nuclear magnetic response signal parameters and the sensor response value of the electronic nose for the sample is established, the peak area needs to be normalized by mass.
Date Recue/Date Received 2021-07-08
Date Recue/Date Received 2021-07-08
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| CN201910157019.XA CN109799256A (en) | 2019-03-01 | 2019-03-01 | A kind of device and method of the microwave drying condiment vegetable flavor intelligent measurement based on low field nuclear-magnetism |
| PCT/CN2019/123520 WO2020177423A1 (en) | 2019-03-01 | 2019-12-06 | Low-field nuclear magnetism-based device and method for intelligent detection of microwave-dried spicy vegetable flavor |
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