阅读说明：本技术 一种基于核磁共振技术的食用油种类鉴定和品质检测方法 (Edible oil type identification and quality detection method based on nuclear magnetic resonance technology ) 是由 姚叶锋 王嘉琛 李毅 辛家祥 朱晶 王雪璐 于 2020-04-27 设计创作，主要内容包括：本发明公开了一种基于核磁共振技术的食用油种类鉴定和品质检测方法,通过对食用油样品施加特别设计的组合脉冲序列,获得包含食用油～(1)H T-(1)和T-(2)弛豫特征的两维弛豫信号,突破了传统方法无法有效区分不同食用油的缺陷,并建立了食用油指纹谱,其与食用油本质特征相联系,可作为特定食用油与其他油类区别的标准,同时其数字化形式适合于构建食用油大数据和基于人工智能的食用油品质检测和真伪判断。该测定方法具有无须对待测物进行预处理、可对待测物进行无损检测的特点,具有方便快捷,可操作性强,稳定性和重现性好等优点,可用于食用油种类鉴定和品质检测,同时也可以用于其他类似流质样品生产、质量控制以及掺伪鉴定等领域,具有广泛的应用价值。(The invention discloses a method for identifying edible oil types and detecting quality based on nuclear magnetic resonance technology, which obtains edible oil by applying a specially designed combined pulse sequence to an edible oil sample 1 H T 1 And T 2 The two-dimensional relaxation signal of the relaxation characteristic breaks through the defect that different edible oils cannot be effectively distinguished by the traditional method, an edible oil fingerprint spectrum is established, the fingerprint spectrum is associated with the essential characteristics of the edible oil and can be used as a standard for distinguishing specific edible oil from other oils, and meanwhile, the digitization form of the fingerprint spectrum is suitable for constructing edible oil big data and edible oil quality detection and authenticity judgment based on artificial intelligence. The determination method has the characteristics of no need of pretreatment on the object to be determined and capability of carrying out nondestructive detection on the object to be determined, has the advantages of convenience, rapidness, strong operability, good stability and reproducibility and the like, can be used for type identification and quality detection of edible oil, can also be used in the fields of production, quality control, adulteration identification and the like of other similar fluid samples, and has wide application value.)

1. A kind identification and quality detection method of edible oil based on nuclear magnetic resonance technology is characterized by comprising the following steps:

step 1: design includes¹Pulse or combination of pulses and T for H spin echo function₁A pulse sequence of pulses or pulse combinations of filtering functions;

step 2: applying the pulse sequence to a target object to obtain the target object¹H two-dimensional relaxation signals;

and step 3: will obtain¹H two-dimensional relaxation signal conversionAnd (4) converting the fingerprint spectrum into a fingerprint spectrum of the target object for fluid substance type identification and quality detection.

2. The method of claim 1, wherein the fluidic mass comprises: edible oil, yogurt, beverage, and oil.

3. The method according to claim 1, characterized in that in step 1, the pulse sequence comprises the following design and substeps:

step 11: using pulses or combinations of pulses to excite the system to be measured¹H magnetic resonance signals;

step 12: will have¹The pulse or the pulse combination of the H spin echo function acts on a system to be tested, and the pulse or the pulse combination comprises 1 or more variables;

step 13: will have¹H T₁The pulse or pulse combination of the filtering function acts on a system to be measured, and the pulse or pulse combination comprises 1 or more variables;

step 14: by pulses or combinations of pulses¹And converting the H magnetic resonance signal into a detectable signal of a magnetic resonance instrument for signal acquisition.

4. The method of claim 1, wherein, in the pulse sequence,

the first step is as follows: using a 90 DEG pulse with phase x to excite the system to be measured¹H magnetic resonance signals;

the second step is that: combining pulses [ tau ]₁-(180°)_y-τ₁]_nN is the number of repetitions acting on the object, the pulse combination comprising a time variable τ₁And a repetition number variable n;

the third step: combining pulses [ tau ]₂-(90°)_x-τ₃]Acting on the system to be measured, in combination with the pulses₂Is a time constant, τ₂Is 10-20 mus, tau₃Is a time variable;

the fourth step: the magnetic resonance signals of the target object are converted into detectable signals of a magnetic resonance instrument through 90-degree pulses with phases of x, y, -x and y, and then the signals are acquired.

5. The method according to claim 1, wherein in step 2, the variable sum in the pulse or pulse combination with spin echo function in the pulse sequence is controlled to have T₁And filtering the functional pulse or pulse combination variable to obtain a two-dimensional relaxation signal of the target object.

6. The method according to claim 1, wherein in step 3, the two-dimensional relaxation signal f (x, y) is subjected to intensity normalization to obtain f_n(x,y)；f_nSubtracting the reference function F (x, y) from the (x, y) to obtain a target fingerprint spectrum; the reference function F (x, y) is based on the object¹H relaxation property design, or by p-f_n(x, y) is obtained by surface fitting, or F is obtained by fitting a plurality of two-dimensional relaxation signals_m(x, y), m ═ 1,2, …, i, obtained on average.

7. The method of claim 1, wherein the same reference function is used in the generation of fingerprint spectra for different objects in step 3 when comparing fingerprint spectra for object species detection and quality determination.

8. The method of claim 1, wherein τ is fixed by the time t is measured when the pulse sequence is used to acquire the two-dimensional relaxation signal of the corn germ oil₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of the maize germ oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) obtaining the maize germ oil t by subtracting the selected reference function F (x, y) after normalization₂Distributing fingerprint spectrums; and/or the presence of a gas in the gas,

by fixing tau when using said pulse sequence to acquire a two-dimensional relaxation signal of peanut oil₃Changing τ₁And n, obtaining a peanut oil two-dimensional relaxation signal f (tau)₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) normalizing and subtracting the selected reference function F (x, y) to obtain the peanut oil t₂Distributing fingerprint spectrums; and/or the presence of a gas in the gas,

by fixing τ when acquiring a two-dimensional relaxation signal of soybean oil using said pulse sequence₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of the soybean oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) normalizing and subtracting the selected reference function F (x, y) to obtain the soybean oil t₂Distributing fingerprint spectrums; and/or the presence of a gas in the gas,

when the pulse sequence is used for acquiring the two-dimensional relaxation signal of the linseed oil, tau is fixed₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of linseed oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) obtaining the linseed oil t by subtracting the selected reference function F (x, y) after normalization₂Distributing fingerprint spectrums; and/or the presence of a gas in the gas,

by fixing τ when acquiring a two-dimensional relaxation signal of olive oil using said pulse sequence₃Changing τ₁And n, obtaining two-dimensional relaxation signal f (tau) of olive oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) obtaining the olive oil t by subtracting the selected reference function F (x, y) after normalization₂And (5) distributing fingerprint spectrums.

9. The method of claim 1, wherein the pulse sequence is used to collect two-dimensional corn germ oilBy fixing τ on relaxation of the signal₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

by fixing tau when using said pulse sequence to acquire a two-dimensional relaxation signal of peanut oil₁Changing τ₃And n, obtaining a peanut oil two-dimensional relaxation signal f_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain the peanut oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

by fixing τ when acquiring a two-dimensional relaxation signal of soybean oil using said pulse sequence₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the soybean oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain soybean oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

when the pulse sequence is used for acquiring the two-dimensional relaxation signal of the linseed oil, tau is fixed₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of linseed oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃,n)Normalized and selected reference function F_aSubtracting (x, y) to obtain linseed oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

by fixing τ when acquiring a two-dimensional relaxation signal of olive oil using said pulse sequence₁Changing τ₃And n, obtaining two-dimensional relaxation signal f of olive oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain olive oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

when the pulse sequence is used to collect two-dimensional relaxation signals of corn germ oil doped with 1% by weight of water, tau is fixed₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil doped with 1 weight percent of water_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t doped with 1% by weight of water₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

when the pulse sequence is used to collect two-dimensional relaxation signals of corn germ oil doped with 1 wt% lard oil, tau is fixed₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil doped with 1 weight percent of lard_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t doped with 1 wt% lard₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

when two-dimensional relaxation signals of corn germ oil doped with 1% by weight of tallow are acquired by using the pulse sequence, tau is fixed₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil doped with 1% by weight of tallow_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t doped with 1 wt% of beef tallow₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

by fixing tau when using said pulse sequence to acquire two-dimensional relaxation signals of corn germ oil incorporating 1% by weight of butter₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the oil of the maize germ doped with 1% by weight of butter_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t containing 1 wt% butter₁-t₂And (4) correlating fingerprint spectrums.

Technical Field

The invention relates to the technical field of magnetic resonance and edible oil identification, in particular to a method for realizing type identification and quality detection of edible oil by utilizing a nuclear magnetic resonance relaxation technology.

Background

The edible oil is a necessary product in life, consists of a plurality of saturated fatty acids and unsaturated fatty acids, has unique fragrance and rich nutrition, the unsaturated fatty acids (mainly oleic acid) can reduce cholesterol, the essential fatty acids (such as linolenic acid, linoleic acid, arachidonic acid and the like) can soften blood vessels, reduce blood fat and blood pressure, promote microcirculation and have reasonable fatty acid intake proportion, so that a human body can ensure that energy is provided sufficiently and simultaneously effectively prevent and treat various cardiovascular diseases, and the edible oil is widely applied to daily cooking and is also a large producing and consuming country of the edible oil in China. However, illegal vendors exist in the market, and in order to seek violence, edible oil with lower price or different processing degrees is added into the edible oil to be sufficient again, so that the health and the rights and interests of consumers are seriously damaged.

At present, the edible oil identification technology used in the national standard is mainly based on the detection of sensory experience and physical and chemical indexes as initial measurement, and the composition of edible oil fatty acid and phytosterol is determined by gas chromatography for final identification. In addition, characteristic marker detection technology and photoelectric sensory signal-based detection technology exist. However, the above methods are inferior and superior, and it is difficult to satisfy the requirements of accuracy, stability, multivariate measurement and operability at the same time. Meanwhile, the detection by the traditional chromatography, mass spectrometry and spectroscopic methods requires the pretreatment of the sample, and the sample can be damaged during the detection, so that the nondestructive detection of the sample cannot be realized. More importantly, detection schemes based on traditional chromatography, mass spectrometry and spectroscopic methods generally rely on large instruments and cannot achieve rapid detection in the field.

The prior patent CN108982570A discloses an edible oil type identification method based on nuclear magnetic resonance technology, which is based on hydrogen spectrum analysis, but not on relaxation analysis of edible oil. The method of patent CN108982570A has a completely different detection principle from the method described in this patent. FIG. 3 is a hydrogen spectrum of 5 edible oil samples. It can be seen from the figure that the hydrogen spectra of the edible oils of the various varieties do not differ much. It is difficult to effectively distinguish the types of the edible oil from the hydrogen spectrum. Moreover, the method disclosed in patent CN108982570A requires a high-resolution nuclear magnetic instrument, and cannot be applied to a low-field nuclear magnetic instrument based on relaxation property measurement. The method is not only suitable for high-resolution nuclear magnetic instruments, but also suitable for low-field nuclear magnetic instruments based on relaxation property measurement. The method is different from the nuclear magnetic instrument applicable to the patent CN 108982570A.

The measurement of edible oil is carried out in the existing literature (Wangxin et al, 2011, 37, 177-¹H T₂And (4) comparing different edible oils and identifying adulteration according to relaxation properties. The method can be used in the existing low-field and high-field magnetic resonance spectrometers. However, the method uses a test system obtained by a conventional method (CPMG sequence)¹H T₂Relaxation properties. From experimental data, the edible oil is used singly¹H T₂Relaxation properties do not allow for an effective discrimination between different edible oils and adulteration of edible oils.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a method for realizing the type identification and quality detection of edible oil by amplifying the difference of relaxation properties of the edible oil by utilizing the relaxation properties of the edible oil. The main chemical components in the edible oil are various saturated fatty acids and unsaturated fatty acids. These saturated and unsaturated fatty acids are similar in chemical structure, but differ in kind, content, relative proportions depending on the type of edible oil. Because different saturated fatty acids and unsaturated fatty acids have different nuclear magnetic relaxation properties, the characteristics can be utilized to distinguish and detect different edible oil varieties and qualities.

The invention develops a method for identifying the type and detecting the quality of edible oil based on a nuclear magnetic resonance relaxation technology. The core design idea of the method is to measure the content of the edible oil by designing a new nuclear magnetism method¹H T₁And T₂Relaxation ofTwo-dimensional data of characteristics amplifies the difference of different edible oils in nuclear magnetic relaxation property, and realizes the identification of edible oil types and quality detection. Meanwhile, the invention does not need sample pretreatment, and can realize nondestructive detection of the sample. The method is realized on a low-field magnetic resonance instrument, and the vehicle-mounted mobile field rapid detection can be realized.

Specifically, the implementation process of the method comprises the following steps:

step 1: design includes¹Pulse or combination of pulses and T for H spin echo function₁A pulse sequence of pulses or pulse combinations of filtering functions;

step 2: applying the pulse sequence to a target object to obtain the target object¹H two-dimensional relaxation signals;

and step 3: will obtain¹Converting the H two-dimensional relaxation signal into a fingerprint spectrum of a target object for edible oil type identification and quality detection;

wherein the target is a fluid, the fluid comprising: edible oil, yogurt, beverage, and oil.

In step 1 of the present invention, the pulse sequence includes the following design and substeps:

step 11: using pulses or combinations of pulses to excite the system to be measured¹H magnetic resonance signals;

step 12: will have¹The pulse or the pulse combination of the H spin echo function acts on a system to be tested, and the pulse or the pulse combination comprises 1 or more variables;

step 13: will have¹H T₁The pulse or pulse combination of the filtering function acts on a system to be measured, and the pulse or pulse combination comprises 1 or more variables;

step 14: by pulses or combinations of pulses¹And converting the H magnetic resonance signal into a detectable signal of a magnetic resonance instrument for signal acquisition.

In the pulse train of the present invention,

the first step is as follows: using a 90o pulse with phase x to excite the system under test¹H magnetic resonance signals;

the second step is that: combining pulses [ tau ]₁-(180o)_y-τ₁]_nN is the number of repetitions acting on the object, the pulse combination comprising a time variable τ₁And a repetition number variable n;

the third step: combining pulses [ tau ]₂-(90o)_x-τ₃]Acting on the system to be measured, in combination with the pulses₂Is a time constant, τ₂Is 10-20 mus, tau₃Is a time variable;

the fourth step: the signal acquisition is carried out by converting the magnetic resonance signal of the target into a detectable signal of a magnetic resonance instrument through 90-degree pulses with phases of x, y, -x and-y.

In step 2 of the invention, the variable in the pulse or the pulse combination with spin echo function in the pulse sequence is controlled and T is contained₁And filtering the functional pulse or pulse combination variable to obtain a two-dimensional relaxation signal of the target object.

In step 3 of the invention, the two-dimensional relaxation signal f (x, y) obtained above is subjected to intensity normalization processing to obtain f_n(x,y)；f_nSubtracting the reference function F (x, y) from the (x, y) to obtain a target fingerprint spectrum; the reference function F (x, y) is based on the object¹H relaxation property design, or by p-f_n(x, y) is obtained by surface fitting, or F is obtained by fitting a plurality of two-dimensional relaxation signals_m(x, y), m ═ 1,2, …, i, obtained on average.

When the fingerprint spectra are compared to realize the object species detection and quality identification, the same reference function is used in the generation process of different object fingerprint spectra according to the step 3.

The invention adopts the pulse sequence to acquire the two-dimensional relaxation signal of the maize germ oil by fixing tau₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of the maize germ oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) obtaining the maize germ oil t by subtracting the selected reference function F (x, y) after normalization₂Distributing fingerprint spectrums;and/or the presence of a gas in the gas,

by fixing tau when using said pulse sequence to acquire a two-dimensional relaxation signal of peanut oil₃Changing τ₁And n, obtaining a peanut oil two-dimensional relaxation signal f (tau)₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) normalizing and subtracting the selected reference function F (x, y) to obtain the peanut oil t₂Distributing fingerprint spectrums; and/or the presence of a gas in the gas,

by fixing τ when acquiring a two-dimensional relaxation signal of soybean oil using said pulse sequence₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of the soybean oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) normalizing and subtracting the selected reference function F (x, y) to obtain the soybean oil t₂Distributing fingerprint spectrums; and/or the presence of a gas in the gas,

when the pulse sequence is used for acquiring the two-dimensional relaxation signal of the linseed oil, tau is fixed₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of linseed oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) obtaining the linseed oil t by subtracting the selected reference function F (x, y) after normalization₂Distributing fingerprint spectrums; and/or the presence of a gas in the gas,

by fixing τ when acquiring a two-dimensional relaxation signal of olive oil using said pulse sequence₃Changing τ₁And n, obtaining two-dimensional relaxation signal f (tau) of olive oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n; signal f (tau)₁N) obtaining the olive oil t by subtracting the selected reference function F (x, y) after normalization₂Distributing fingerprint spectrums; and/or the presence of a gas in the gas,

the invention adopts the pulse sequence to acquire the two-dimensional relaxation signal of the maize germ oil by fixing tau₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

by fixing tau when using said pulse sequence to acquire a two-dimensional relaxation signal of peanut oil₁Changing τ₃And n, obtaining a peanut oil two-dimensional relaxation signal f_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain the peanut oil t₁-t₂A correlation fingerprint spectrum;

by fixing τ when acquiring a two-dimensional relaxation signal of soybean oil using said pulse sequence₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the soybean oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain soybean oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

when the pulse sequence is used for acquiring the two-dimensional relaxation signal of the linseed oil, tau is fixed₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of linseed oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a radical corresponding toτ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain linseed oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

by fixing τ when acquiring a two-dimensional relaxation signal of olive oil using said pulse sequence₁Changing τ₃And n, obtaining two-dimensional relaxation signal f of olive oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain olive oil t₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

when the pulse sequence is used to collect two-dimensional relaxation signals of corn germ oil doped with 1% by weight of water, tau is fixed₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil doped with 1 weight percent of water_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t doped with 1% by weight of water₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

when the pulse sequence is used to collect two-dimensional relaxation signals of corn germ oil doped with 1 wt% lard oil, tau is fixed₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil doped with 1 weight percent of lard_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t doped with 1 wt% lard₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

when two-dimensional relaxation signals of corn germ oil doped with 1% by weight of tallow are acquired by using the pulse sequence, tau is fixed₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil doped with 1% by weight of tallow_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t doped with 1 wt% of beef tallow₁-t₂A correlation fingerprint spectrum; and/or the presence of a gas in the gas,

by fixing tau when using said pulse sequence to acquire two-dimensional relaxation signals of corn germ oil incorporating 1% by weight of butter₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the oil of the maize germ doped with 1% by weight of butter_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n; will signal f_a(τ₃N) normalized with a selected reference function F_aSubtracting (x, y) to obtain corn germ oil t containing 1 wt% butter₁-t₂And (4) correlating fingerprint spectrums.

Two technical features of the invention are important:

1. the pulse sequence and the data acquisition scheme thereof are completely new and designed pulse sequences and data acquisition schemes for measuring edible oil¹H T₁And T₂Two-dimensional data of relaxation characteristics, amplifying different edible oils¹H T₁And T₂Difference in relaxation;

2. data processing method (two-dimensional data is subtracted from a reference function to obtain a fingerprint spectrum and its variants) -this is a completely new data processing method. The fingerprint spectrum obtained by the method can be used as a standard for distinguishing different edible oil types, and meanwhile, the digitalized form of the fingerprint spectrum is very suitable for constructing edible oil big data and judging authenticity based on artificial intelligence.

The present invention is different from the innovative concepts of previous inventions and literature work:

1. based on different edible oils¹H T₁And T₂The relaxation can be different, breaking through the prior work that only uses¹H T₂The limitation of detecting and identifying edible oil is innovatively proposed by measuring the content of edible oil¹H T_{1 and}T₂two-dimensional data of relaxation characteristics, amplifying different edible oils¹H T₁And T₂Relaxation difference, so as to realize detection and identification of different edible oils;

2. based on the above concept, different edible oils capable of being amplified have been developed¹H T₁And T₂A novel pulse sequence of relaxation differences and corresponding data acquisition method;

3. comprises edible oil¹H T₁And T₂"fingerprint spectra" of relaxation features. The fingerprint spectrum can be used as a standard for distinguishing different edible oil types, and meanwhile, the digitalized form of the fingerprint spectrum is very suitable for constructing edible oil big data and judging authenticity based on artificial intelligence.

4. The method can be used for a high-resolution nuclear magnetic spectrometer and a low-field magnetic resonance spectrometer, and overcomes the dependence of the patent CN108982570A on the resolution of nuclear magnetic signals. At the same time pass through¹H T₁And T₂Relaxation property measurement overcomes the defect that the traditional method only has to¹H T₂Relaxation measurements lead to the disadvantage of low discrimination.

5. Compared with the traditional chromatographic method, the mass spectrum method and the spectroscopic method, the invention can realize the nondestructive detection of the sample without sample pretreatment. The method is realized on a low-field magnetic resonance instrument, and the vehicle-mounted mobile field rapid detection can be realized.

In the existing reports, two methods for detecting and identifying edible oil by utilizing nuclear magnetism exist. One is to use high resolution magnetic resonance spectrum to detect and identify edible oil (CN 108982)570A) In that respect The principle of this technique is based on the identification of the molecular signals of edible oil in high-resolution magnetic resonance spectrograms, and therefore relies on high-resolution nuclear magnetic spectrometers. Because high resolution nuclear magnetometers are generally bulky and cannot move. The method based on patent CN108982570A can not realize quick detection of edible oil. Meanwhile, in practical test operation, the method disclosed in patent CN108982570A cannot easily realize effective differentiation of different edible oils (fig. 3). The method disclosed by the patent overcomes the limitations that a high-resolution nuclear magnetic spectrometer is required to be used in the patent CN108982570A and the division of different edible oils is not high. The second nuclear magnetism method for detecting and identifying edible oil is through comparison¹H T₂The difference of relaxation properties realizes the detection and identification of the edible oil. The method uses a system to be detected obtained by a conventional nuclear magnetic method (CPMG sequence)¹H T₂Relaxation property, can be used in the existing low-field and high-field magnetic resonance spectrometer. From experimental data, due to different edible oils¹H T₂The relaxation properties do not differ much. The method does not have good discrimination on different edible oils (Wangxin and the like, food and fermentation industry 2011, 37, 177 and 181, DOI:10.13995/j.cnki.11-1802/ts.2011.03.020, Wangxin and the like, food safety quality detection bulletin 2013, 4, 1428 and 1436, DOI: 10.19812/j.cnki.jfsq11-5956/ts.2013.05.026). The method of the invention is based on different edible oils¹H T₁And T₂The relaxation can be different, breaking through the prior work that only uses¹H T₂The limitation of detecting and identifying edible oil is innovatively proposed by measuring the content of edible oil¹H T₁And T₂Two-dimensional data of relaxation characteristics, amplifying different edible oils¹H T₁And T₂And the relaxation difference further realizes the detection and identification of different edible oils. Based on the above conception, the invention develops the edible oil capable of amplifying different edible oils¹H T₁And T₂Novel pulse sequences of relaxation differences and corresponding data acquisition methods. Aiming at the relaxation data, the invention develops a data processing method and constructs edible oil¹H T₁And T₂"fingerprint spectra" of relaxation features. According to the inventionThe method can be used for a high-resolution nuclear magnetic spectrometer and a low-field magnetic resonance spectrometer, and overcomes the dependence of the patent CN108982570A on the resolution of nuclear magnetic signals. At the same time pass through¹H T₁And T₂Relaxation property measurement overcomes the defect that the traditional method only has to¹H T₂Relaxation measurements lead to the disadvantage of low discrimination. Meanwhile, the fingerprint spectrum provided by the invention not only can be used as a standard for distinguishing different edible oil types, but also is very suitable for constructing edible oil big data and judging authenticity based on artificial intelligence in a digital form. In conclusion, the invention designs a novel pulse sequence and a corresponding data acquisition method to amplify different edible oils¹H T₁And T₂The relaxation difference can realize the identification of the edible oil types and the quality detection. With good accuracy, sensitivity and reproducibility. The method can be applied to the type identification and quality detection of edible oil, and can also be applied to the type identification and quality detection of fluid samples such as yoghourt, beverage, oil and the like. The method has the advantages of high detection speed, no need of pretreatment on the detected sample, no need of sample destruction before and after detection, capability of realizing on-site quick detection on edible oil or other fluid samples by vehicle-mounted movement by combining with a low-field nuclear magnetic resonance spectrometer, and is a novel original technology.

Drawings

FIG. 1: the schematic diagram of the pulse sequence framework for collecting the fingerprint spectrum of the edible oil.

FIG. 2: schematic diagram of an example of a pulse sequence for collecting a fingerprint spectrum of edible oil.

FIG. 3: high resolution nuclear magnetism of corn germ oil, peanut oil, soybean oil, linseed oil and olive oil¹And (4) H spectrum. The instrument used was a Bruker 500M nmr spectrometer.

FIG. 4: corn germ oil t₂And (5) distributing fingerprint spectrums.

FIG. 5: peanut oil t₂And (5) distributing fingerprint spectrums.

FIG. 6: soybean oil t₂And (5) distributing fingerprint spectrums.

FIG. 7: linseed oil t₂And (5) distributing fingerprint spectrums.

FIG. 8: olive oil t₂And (5) distributing fingerprint spectrums.

FIG. 9: corn germ oil t₁-t₂And (4) correlating fingerprint spectrums.

FIG. 10: peanut oil t₁-t₂And (4) correlating fingerprint spectrums.

FIG. 11: soybean oil t₁-t₂And (4) correlating fingerprint spectrums.

FIG. 12: linseed oil t₁-t₂And (4) correlating fingerprint spectrums.

FIG. 13: olive oil t₁-t₂And (4) correlating fingerprint spectrums.

FIG. 14: corn germ oil (blended with 1% by weight of water) t₁-t₂And (4) correlating fingerprint spectrums.

FIG. 15: corn germ oil (blended with 1 wt% lard) t₁-t₂And (4) correlating fingerprint spectrums.

FIG. 16: corn germ oil (blended with 1% by weight of beef tallow) t₁-t₂And (4) correlating fingerprint spectrums.

FIG. 17: corn germ oil (blended with 1% by weight butter) t₁-t₂And (4) correlating fingerprint spectrums.

Detailed Description

The invention is further described in detail with reference to the following specific examples and the accompanying drawings. The procedures, conditions, experimental methods and the like for carrying out the present invention are general knowledge and common general knowledge in the art except for the contents specifically mentioned below, and the present invention is not particularly limited. Meanwhile, these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention.

The invention discloses a method for identifying edible oil types and detecting quality based on nuclear magnetic resonance technology, which obtains edible oil by applying a specially designed combined pulse sequence to an edible oil sample¹H T₁And T₂Two-dimensional relaxation signals of relaxation characteristics break through the traditional method which only utilizes¹H T₂And the weaknesses of different edible oils cannot be effectively distinguished, and thus, the fingerprint spectrum of the edible oil is established. The fingerprint spectrum is associated with the essential characteristics of the edible oil, can be used as a standard for distinguishing specific edible oil from other oils, and is very suitable for constructing edible oil big data and based on people in a digital formAnd (4) carrying out industrial and intelligent edible oil quality detection and authenticity judgment. The determination method has the characteristics of no need of pretreatment on the object to be determined, capability of carrying out nondestructive detection on the object to be determined, convenience, rapidness, strong operability, good stability and reproducibility and the like, can be used for type identification and quality detection of edible oil, can also be used in the fields of production, quality control, adulteration identification and the like of other similar fluid samples, and has wide application value.

The main steps of the implementation method are as follows:

step 1: designing pulses or pulse combinations and T comprising spin echo function₁The pulse sequence of the pulse or pulse combination of the filtering function is shown in the figure 1;

step 2: applying the pulse sequence designed in the step 1 to a target object to obtain a two-dimensional relaxation signal of the target object;

and step 3: and (3) converting the two-dimensional relaxation signal obtained in the step (2) into a fingerprint spectrum of a target object, and using the fingerprint spectrum for identification of the type of the edible oil and quality detection.

Wherein the target is various edible oils or similar fluid.

In step 1, the pulse sequence includes the following design and steps:

the first step is as follows: exciting a magnetic resonance signal of a system to be tested by utilizing a pulse or a pulse combination; the second step is that: applying a pulse or a pulse combination with a spin echo function to a system to be tested, wherein the pulse or the pulse combination can contain 1 or more variables; the third step: will have T₁A pulse or a pulse combination of the filtering function acts on the target object, and the pulse or the pulse combination can contain 1 or more variables; the fourth step: and converting the magnetic resonance signal of the target object into a detectable signal of a magnetic resonance instrument through pulse or pulse combination, and acquiring the signal.

Fig. 2 shows an example of the above-described pulse sequence for obtaining a two-dimensional relaxation signal of edible oil. In this pulse sequence, the first step: exciting a magnetic resonance signal of a system to be tested by using a 90-degree pulse with the phase x; the second step is that: combining pulses [ tau ]₁-(180o)_y-τ₁]_nFunction ofIn the system under test, the pulse combination includes a time variable tau₁And cycle number n; the third step: combining pulses [ tau ]₂-(90o)_x-τ₃]Acting on the system to be measured, in combination with the pulses₂Is a time constant, usually 10-20 μ, τ₃Is a time variable; the fourth step: the signal acquisition is carried out by converting the magnetic resonance signal of the target into a detectable signal of a magnetic resonance instrument through 90-degree pulses (the phase can be x, y, -x, -y).

In the step 2, the variable in the pulse or the pulse combination with the spin echo function in the pulse sequence is controlled, and the variable T is included₁And filtering the variables of the functional pulse or the pulse combination to obtain a two-dimensional relaxation signal of the target object. By designing the variables, different types of two-dimensional relaxation signals can be obtained.

In the step 3, the intensity normalization processing is performed on the obtained two-dimensional relaxation signal f (x, y) to obtain f_n(x,y)。f_nAnd (x, y) is subtracted from the reference function F (x, y) to obtain the fingerprint spectrum of the target object. The reference function F (x, y) may be based on the target object¹H relaxation property design, or by p-f_n(x, y) is obtained by surface fitting, or F is obtained by fitting a plurality of two-dimensional relaxation signals_m(x, y), m ═ 1,2, …, i, obtained on average.

When the fingerprint spectra are compared to realize the object species detection and quality identification, the same reference function is used in the generation process of the fingerprint spectra of different objects according to the step 3.

FIG. 4 shows maize germ oil t₂An example of a distribution fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire two-dimensional relaxation signals of corn germ oil. By fixing τ₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of the maize germ oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n. Signal f (tau)₁N) normalizing and then performing surface fitting to obtain a reference function F (x, y), and fitting F (tau)₁N) subtracting the reference function F (x, y) to obtain the maize germ oil t₂Distribution fingerprint spectra (fig. 4).

FIG. 5 shows peanut oil t₂An example of a distribution fingerprint spectrum. This example uses the pulse sequence shown in figure 2 to acquire a two-dimensional relaxation signal of peanut oil. By fixing τ₃Changing τ₁And n, obtaining a peanut oil two-dimensional relaxation signal f (tau)₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n. Signal f (tau)₁N) obtaining peanut oil t by subtracting the normalized corn germ oil reference function F (x, y)₂Distribution fingerprint spectra (fig. 5).

FIG. 6 shows soybean oil t₂An example of a distribution fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire a two-dimensional relaxation signal of soybean oil. By fixing τ₃Changing τ₁And n, obtaining a peanut oil two-dimensional relaxation signal f (tau)₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n. Signal f (tau)₁N) obtaining soybean oil t by subtracting the normalized corn germ oil reference function F (x, y)₂Distribution fingerprint spectra (fig. 6).

FIG. 7 shows linseed oil t₂An example of a distribution fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire two-dimensional relaxation signals of linseed oil. By fixing τ₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of linseed oil₁N) in which₁Is a series of set time values, n is a set number of cycles, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n. Signal f (tau)₁N) obtaining linseed oil t by subtracting the normalized reference function F (x, y) of the maize germ oil₂Distribution fingerprint spectra (fig. 7).

FIG. 8 shows olive oil t₂An example of a distribution fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire two-dimensional relaxation signals of olive oil. By fixing τ₃Changing τ₁And n, obtaining a two-dimensional relaxation signal f (tau) of olive oil₁N) in which₁Is a series ofA fixed time value, n is a set cycle number, f (tau)₁N) is a number corresponding to τ₁And the signal strength of n. Signal f (tau)₁N) obtaining the olive oil t by subtracting the normalized value from the maize germ oil reference function F (x, y)₂Distribution fingerprint spectra (fig. 8).

FIG. 9 shows maize germ oil t₁-t₂An example of a correlation fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire two-dimensional relaxation signals of corn germ oil. By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalizing and then performing surface fitting to obtain a reference function F_a(x, y), mixing f_a(τ₃N) and a reference function F_aSubtracting (x, y) to obtain corn germ oil t₁-t₂Correlation fingerprint spectra (fig. 9).

FIG. 10 shows peanut oil t₁-t₂An example of a correlation fingerprint spectrum. This example uses the pulse sequence shown in figure 2 to acquire a two-dimensional relaxation signal of peanut oil. By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the peanut oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalized and corn germ oil reference function F_aSubtracting (x, y) to obtain the peanut oil t₁-t₂Correlation fingerprint spectra (fig. 10).

FIG. 11 shows soybean oil t₁-t₂An example of a correlation fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire a two-dimensional relaxation signal of soybean oil. By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the soybean oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cyclesNumber f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalized and corn germ oil reference function F_aSubtracting (x, y) to obtain soybean oil t₁-t₂Correlation fingerprint spectra (fig. 11).

FIG. 12 shows linseed oil t₁-t₂An example of a correlation fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire two-dimensional relaxation signals of linseed oil. By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of linseed oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalized and corn germ oil reference function F_aSubtracting (x, y) to obtain linseed oil t₁-t₂Correlation fingerprint spectra (fig. 12).

FIG. 13 shows olive oil t₁-t₂An example of a correlation fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire two-dimensional relaxation signals of olive oil. By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of olive oil_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalized and corn germ oil reference function F_aSubtracting (x, y) to obtain olive oil t₁-t₂Correlation fingerprint spectra (fig. 13).

FIG. 14 shows the t of maize germ oil (spiked with 1% by weight water)₁-t₂An example of a correlation fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire a two-dimensional relaxation signal of corn germ oil (spiked with 1% by weight water). By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil (doped with 1 weight percent water)_a(τ₃N) in which₃Is a series of set time values, n is setFixed number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalized and corn germ oil reference function F_aSubtracting (x, y) to obtain corn germ oil (mixed with 1% by weight of water) t₁-t₂Correlation fingerprint spectra (fig. 14).

FIG. 15 shows the t of maize germ oil (1% by weight lard incorporation)₁-t₂An example of a correlation fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire two-dimensional relaxation signals of corn germ oil (1% by weight lard incorporation). By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil (doped with 1 weight percent of lard)_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalized and corn germ oil reference function F_aSubtracting (x, y) to obtain corn germ oil (blended with 1 wt% lard) t₁-t₂Correlation fingerprint spectra (fig. 15).

FIG. 16 shows the t of maize germ oil (spiked with 1% by weight of tallow)₁-t₂An example of a correlation fingerprint spectrum. This example uses the pulse sequence shown in fig. 2 to acquire two-dimensional relaxation signals of corn germ oil (spiked with 1% by weight tallow). By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil (spiked with 1% by weight of tallow)_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalized and corn germ oil reference function F_aSubtracting (x, y) to obtain corn germ oil (added with 1 wt% of beef tallow) t₁-t₂Correlation fingerprint spectra (fig. 16).

FIG. 17 shows the t of corn germ oil (spiked with 1% by weight butter)₁-t₂An example of a correlation fingerprint spectrum. This example uses FIG. 2The pulse sequence shown acquires two-dimensional relaxation signals of corn germ oil (1% by weight butter incorporation). By fixing τ₁Changing τ₃And n, obtaining a two-dimensional relaxation signal f of the maize germ oil (doped with 1% by weight of butter)_a(τ₃N) in which₃Is a series of set time values, n is a set number of cycles, f_a(τ₃N) is a number corresponding to τ₃And the signal strength of n. Will signal f_a(τ₃N) normalized and corn germ oil reference function F_aSubtracting (x, y) to obtain corn germ oil (blended with 1 wt% butter) t₁-t₂Correlation fingerprint spectra (fig. 17).

In the examples there is a sample formulation. The configuration methods and steps are well known in the art.

Example 1-corn germ oil t2 distribution fingerprint Spectroscopy

Experimental samples: corn germ oil is commercially available.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁20, 30, 50, 100, 200, 500, and 1ms,. tau.₂＝20μs，τ₃2ms, the number of cycles n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Thereby obtaining a two-dimensional relaxation curved surface f (tau) of the maize germ oil₁N), normalized to f_n(τ₁,n)。

In the embodiment, the corn germ oil two-dimensional relaxation curved surface f is normalized by fitting the reference surface function_n(τ₁And n) obtaining. The surface function is:

f(x,y)＝18.54-17.11·x-6.667·y+6.41·x²+4.15·x·y+0.3606·y²-1.232·x³-1.024·x²·y-0.08823·x·y²+0.04467·y³+0.1212·x⁴+0.1138·x³·y+0.02533·x²·y²-0.02851·x·y³+0.003422·y⁴-0.00484·x⁵-0.004637·x⁴·y-0.002608·x³·y²+0.001928·x²·y³+0.001074·x·y⁴+0.0000372·y⁵

will f is_n(τ₁And n) is subtracted from the reference curved surface f (x, y), and then the distribution fingerprint spectrum of the maize germ oil t2 can be obtained (figure 4).

Example 2-peanut oil t2 distribution fingerprint Spectrum

Experimental samples: commercially available peanut oil.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁20, 30, 50, 100, 200, 500, and 1ms,. tau.₂＝20μs，τ₃2ms, the number of cycles n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Thereby obtaining a two-dimensional relaxor surface f (τ)₁N), normalized to f_n(τ₁,n)。

The reference surface function in this example is the maize germ oil reference surface function:

f(x,y)＝18.54-17.11·x-6.667·y+6.41·x²+4.15·x·y+0.3606·y²-1.232·x³-1.024·x²·y-0.08823·x·y²+0.04467·y³+0.1212·x⁴+0.1138·x³·y+0.02533·x²·y²-0.02851·x·y³+0.003422·y⁴-0.00484·x⁵-0.004637·x⁴·y-0.002608·x³·y²+0.001928·x²·y³+0.001074·x·y⁴+0.0000372·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₁N) is subtracted from the reference curved surface f (x, y), and the distribution fingerprint spectrum of the peanut oil t2 can be obtained (figure 5).

Example 3-Soybean oil t2 distribution fingerprint Spectroscopy

Experimental samples: commercially available soybean oil.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁20, 30, 50, 100, 200, 500, and 1ms,. tau.₂＝20μs，τ₃2ms, the number of cycles n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Thereby obtaining a two-dimensional relaxor surface f (τ)₁N), normalized to f_n(τ₁,n)。

The reference surface function in this example is the maize germ oil reference surface function:

f(x,y)＝18.54-17.11·x-6.667·y+6.41·x²+4.15·x·y+0.3606·y²-1.232·x³-1.024·x²·y-0.08823·x·y²+0.04467·y³+0.1212·x⁴+0.1138·x³·y+0.02533·x²·y²-0.02851·x·y³+0.003422·y⁴-0.00484·x⁵-0.004637·x⁴·y-0.002608·x³·y²+0.001928·x²·y³+0.001074·x·y⁴+0.0000372·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₁N) is subtracted from the reference curved surface f (x, y), and the distribution fingerprint spectrum of the soybean oil t2 can be obtained (fig. 6).

Example 4-Linseed oil t2 distribution fingerprint Spectrum

Experimental samples: commercially available linseed oil.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁20, 30, 50, 100, 200, 500, and 1ms,. tau.₂＝20μs，τ₃2ms, the number of cycles n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, or,1500. 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Thereby obtaining a two-dimensional relaxor surface f (τ)₁N), normalized to f_n(τ₁,n)。

The reference surface function in this example is the maize germ oil reference surface function:

f(x,y)＝18.54-17.11·x-6.667·y+6.41·x²+4.15·x·y+0.3606·y²-1.232·x³-1.024·x²·y-0.08823·x·y²+0.04467·y³+0.1212·x⁴+0.1138·x³·y+0.02533·x²·y²-0.02851·x·y³+0.003422·y⁴-0.00484·x⁵-0.004637·x⁴·y-0.002608·x³·y²+0.001928·x²·y³+0.001074·x·y⁴+0.0000372·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₁N) is subtracted from the reference surface f (x, y), and the distribution fingerprint spectrum of linseed oil t2 (fig. 7) can be obtained.

Example 5 olive oil t2 distribution fingerprint Spectrum

Experimental samples: commercially available olive oil.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁20, 30, 50, 100, 200, 500, and 1ms,. tau.₂＝20μs，τ₃2ms, the number of cycles n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Thereby obtaining a two-dimensional relaxor surface f (τ)₁N), normalized to f_n(τ₁,n)。

The reference surface function in this example is the maize germ oil reference surface function:

f(x,y)＝18.54-17.11·x-6.667·y+6.41·x²+4.15·x·y+0.3606·y²-1.232·x³-1.024·x²·y-0.08823·x·y²+0.04467·y³+0.1212·x⁴+0.1138·x³·y+0.02533·x²·y²-0.02851·x·y³+0.003422·y⁴-0.00484·x⁵-0.004637·x⁴·y-0.002608·x³·y²+0.001928·x²·y³+0.001074·x·y⁴+0.0000372·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₁N) is subtracted from the reference surface f (x, y), and then the distribution fingerprint spectrum of the olive oil t2 (fig. 8) is obtained.

Example 6 fingerprint spectra associated with maize germ oil t1-t2

Experimental samples: corn germ oil is commercially available.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s, and 3s, and the cycle number n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference surface function in this example is obtained by fitting a two-dimensional relaxation surface of the maize germ oil. The surface function is:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

will experimentObtained two-dimensional relaxor surface f_n(τ₃And n) is subtracted from the reference curved surface f (x, y), so that the related fingerprint spectrum of the maize germ oil t1-t2 can be obtained (figure 9).

Example 7-peanut oil t1-t2 related fingerprint spectra

Experimental samples: commercially available peanut oil.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s, and 3s, and the cycle number n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference curved surface in the example is a corn germ oil reference curved surface:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₃And n) is subtracted from the reference curved surface f (x, y), and the fingerprint spectrum related to the peanut oil t1-t2 can be obtained (figure 10).

Example 8 fingerprint spectra associated with Soybean oil t1-t2

Experimental samples: commercially available soybean oil.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s, and 3s, and the cycle number n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference curved surface in the example is a corn germ oil reference curved surface:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₃And n) is subtracted from the reference curved surface f (x, y), and the fingerprint spectrum related to the soybean oil t1-t2 can be obtained (figure 11).

Example 9 Linseed oil t1-t2 related fingerprint spectra

Experimental samples: commercially available linseed oil.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s, and 3s, and the cycle number n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference curved surface in the example is a corn germ oil reference curved surface:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₃N) is subtracted from the reference surface f (x, y), and the fingerprint spectrum of linseed oil t1-t2 is obtained (fig. 12).

Example 10 fingerprint spectra associated with olive oil t1-t2

Experimental samples: commercially available olive oil.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s, and 3s, and the cycle number n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference curved surface in the example is a corn germ oil reference curved surface:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₃N) is subtracted from the reference curved surface f (x, y), and then the fingerprint spectrum related to the olive oil t1-t2 can be obtained (fig. 13).

Example 11 fingerprint spectra associated with corn germ oil (spiked with 1% by weight water) t1-t2

Experimental samples: commercially available corn germ oil was blended with 1% water by weight.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s, and 3s, and the cycle number n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference curved surface in the example is a corn germ oil reference curved surface:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₃N) is subtracted from the reference curved surface f (x, y), and the corn can be obtainedFingerprint spectra associated with germ oil (spiked with 1% wt water) t1-t2 (FIG. 14).

Example 12 fingerprint spectra associated with t1-t2 corn germ oil (1% by weight lard incorporation)

Experimental samples: commercially available corn germ oil was blended with 1% by weight lard.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s, and 3s, and the cycle number n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference curved surface in the example is a corn germ oil reference curved surface:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₃And n) is subtracted from the reference curved surface f (x, y), and then the t1-t2 related fingerprint spectrum of the maize germ oil (which is doped with 1 weight percent lard) can be obtained (figure 15).

Example 13 fingerprint spectra associated with T1-t2 corn germ oil (spiked with 1% by weight of tallow)

Experimental samples: commercially available corn germ oil was spiked with 1% by weight of tallow.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s, and 3s, and the cycle number n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, 400, 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference curved surface in the example is a corn germ oil reference curved surface:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₃And n) is subtracted from the reference curved surface f (x, y), and then the related fingerprint spectrum of the maize germ oil (added with 1 weight percent of beef tallow) t1-t2 can be obtained (figure 16).

Example 14 fingerprint spectra associated with corn germ oil (1% by weight butter incorporation) t1-t2

Experimental samples: commercially available corn germ oil was blended with 1% by weight butter.

The measuring instrument: bruker AVANCE III 500MHz NMR spectrometer. The test temperature was room temperature.

The determination method comprises the following steps: the pulse sequence used is shown in figure 2. In experiment τ₁＝20μs，τ₂＝20μs，τ₃10ms, 50ms, 100ms, 200ms, 500ms, 1s, 2s and 3s, and the number of cycles n is 1,2, 5, 10, 15, 20, 30, 50, 80, 100, 150, 200, 300, m,400. 500, 700, 900, 1200, 1500, 2000, 3000, 5000, 7000, 10000, 15000, 20000, 30000, 40000. Obtaining a two-dimensional relaxation curved surface f (tau) through experiments₃N), normalized to f_n(τ₃,n)。

The reference curved surface in the example is a corn germ oil reference curved surface:

f(x,y)＝-2.281+2.317·x-0.4775·y-0.9071·x²-0.001151·x·y+0.2854·y²+0.1461·x³+0.09387·x²·y-0.1417·x·y²-0.007143·y³-0.00962·x⁴-0.01736·x³·y+0.01505·x²·y²+0.00902·x·y³-0.002398·y⁴+0.0001928·x⁵+0.0008389·x⁴·y-0.0003571·x³·y²-0.0006947·x²·y³+0.000005745·x·y⁴+0.00008974·y⁵

two-dimensional relaxation curved surface f obtained by experiment_n(τ₃And n) is subtracted from the reference curved surface f (x, y), and then the related fingerprint spectrum t1-t2 of the maize germ oil (which is mixed with 1 weight percent of butter) can be obtained (figure 17).

In the above summary, the present invention has the following features different from the conventional methods and techniques:

1. based on different edible oils¹H T₁And T₂The relaxation can be different, breaking through the prior work that only uses¹H T₂The limitation of detecting and identifying edible oil is innovatively proposed by measuring the content of edible oil¹H T₁And T₂Two-dimensional data of relaxation characteristics, amplifying different edible oils¹H T₁And T₂Relaxation difference, so as to realize detection and identification of different edible oils;

2. based on the above concept, different edible oils capable of being amplified have been developed¹H T₁And T₂A novel pulse sequence of relaxation differences and corresponding data acquisition method;

3. comprises edible oil¹H T₁And T₂"fingerprint spectra" of relaxation features. The fingerprint spectrumThe method can be used as a standard for distinguishing different edible oil types, and meanwhile, the digitalized form of the method is very suitable for constructing edible oil big data and judging authenticity based on artificial intelligence.

4. The method can be used for a high-resolution nuclear magnetic spectrometer and a low-field magnetic resonance spectrometer, and overcomes the dependence of the patent CN108982570A on the resolution of nuclear magnetic signals. At the same time pass through¹H T₁And T₂Relaxation property measurement overcomes the defect that the traditional method only has to¹H T₂Relaxation measurements lead to the disadvantage of low discrimination.

5. Compared with the traditional chromatographic method, the mass spectrum method and the spectroscopic method, the invention can realize the nondestructive detection of the sample without sample pretreatment. The method is realized on a low-field magnetic resonance instrument, and the vehicle-mounted mobile field rapid detection can be realized.

It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

The protection of the present invention is not limited to the above embodiments. Variations and advantages that may occur to those skilled in the art may be incorporated into the invention without departing from the spirit and scope of the inventive concept, and the scope of the appended claims is intended to be protected.