A METHOD FOR SPECIES IDENTIFICATION AND QUALITY DETECTION OF LIQUID-LIKE SAMPLES BASED ON NUCLEAR MAGNETIC RESONANCE TECHNOLOGY

Abstract

The present invention discloses a method of species identification and quality detection of liquid-like samples based on nuclear magnetic resonance technology. In this invention, a two-dimensional relaxation signal containing the .sup.1H T.sub.1 and T.sub.2 relaxation properties of liquid-like samples is obtained by applying a specially designed composite pulse sequence to liquid-like samples, and a fingerprint spectrum from this signal is established. The fingerprint spectrum can be associated with the essential characteristics of the tested sample, thus can be used to distinguish a specific liquid-like sample from the others. The fingerprint spectrum can be easily converted into a digital form, which is not only suitable for constructing big data of fingerprint spectrum for liquid-like samples, but also can be used for quality detection and authenticity judgment of liquid-like samples based on artificial intelligence. The present invention has the advantages of no need for sample pretreatment, non-destructive sample testing, convenience, quickness, excellent operability, good stability and reproducibility, etc., which can be used for species identification and quality detection of a variety of liquid-like samples, which has a wide application value.

Claims

1. A method for species identification and quality detection of liquid-like samples based on nuclear magnetic resonance technology, wherein, it comprises the following steps: step 1: designing a pulse sequence that includes pulse block or composite pulses containing .sup.1H spin-echo function and pulse block or composite pulses containing T.sub.1 filter function; step 2: applying pulse sequence to the targeted liquid-like samples to obtain their .sup.1H two-dimensional relaxation signals; step 3: converting the obtained .sup.1H two-dimensional relaxation signals into fingerprint spectra of the targeted liquid-like samples, so as to be used for species identification and quality detection of liquid-like samples.

2. The method of claim 1, wherein, the said liquid-like samples comprise: edible oil, cow and goat milk, donkey-hide gelatin, scorpion powder solution, yogurt, beverage, oil in general.

3. The method of claim 1, wherein, in step 1, the said pulse sequence comprises the following designs and the sub-steps: step 1-1: using a pulse block or composite pulses to excite .sup.1H magnetic resonance signal of the system under test; step 1-2: applying the pulse block or the composite pulses containing .sup.1H spin-echo function to the system under test, and the pulse block or the composite pulses may contain one or more variables; step 1-3: applying the pulse block or the composite pulses containing .sup.1H T.sub.1 filter function to the system under test, and the pulse block or the composite pulses may contain one or more variables; step 1-4: converting the .sup.1H magnetic resonance signal of the targeted samples into a signal detectable by magnetic resonance instrument through the pulse block or the composite pulses, and then collecting the signals.

4. The method of claim 1, wherein, in the said pulse sequence in the present invention: the first step: exciting the .sup.1H magnetic resonance signal of the system under test with a 90° pulse with a phase of x; the second step: applying the composite pulse block [τ.sub.1−(180°).sub.y−τ.sub.1].sub.n, wherein n is the number of repetitions, on the system under test, the composite pulse block includes the time variable τ.sub.1 and the number repetition variable n; the third step: applying the composite pulse block [τ.sub.2−(90°).sub.x−τ.sub.3] to the system under test, wherein τ.sub.2 is the time constant ranging from 10 μs to 20 μs, and τ.sub.3 is the time variable; the fourth step: converting the .sup.1H magnetic resonance signal of the system under test into a signal detectable by magnetic resonance instrument with a 90° pulse with the phases of x, y, −x, −y, and then collecting the signals.

5. The method of claim 1, wherein, in step 2 of the present invention, the .sup.1H two-dimensional relaxation signal of the targeted sample can be obtained by controlling the variables in the pulse block or the composite pulses containing .sup.1H spin-echo function and the variables in the pulse block or the composite pulses containing T.sub.1 filter function in the pulse sequence.

6. The method of claim 1, wherein, in step 3 of the present invention, f.sub.n(x,y) is obtained by normalizing the signal intensity of the above-mentioned two-dimensional relaxation signal f(x,y); the fingerprint spectrum can be obtained by subtracting the reference function F(x,y) from f.sub.n(x,y); the reference function F(x,y) is obtained by designing according to the .sup.1H relaxation properties of the target sample, or performing surface fitting of f.sub.n(x,y), or averaging F.sub.m(x, y), m=1, 2, . . . , i, which is acquired from surface fitting of the multiple two-dimensional relaxation signals.

7. The method of claim 1, wherein, when comparing the fingerprint spectrum for species identification and quality detection of liquid-like samples of the same type, the same reference function is used in the generation process of fingerprint spectrum in step 3 for those belonging to the same sample type but in different qualities.

8. The method of claim 1, wherein, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of corn germ oil, the two-dimensional relaxation signal of corn germ oil, f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of corn germ oil is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of peanut oil, the two-dimensional relaxation signal of peanut oil f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of peanut oil is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of soybean oil, the two-dimensional relaxation signal of soybean oil f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of soybean oil is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of linseed oil, the two-dimensional relaxation signal of linseed oil f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of linseed oil is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of olive oil, the two-dimensional relaxation signal of olive oil f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values and n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of olive oil is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of the commercially available cow milk, the two-dimensional relaxation signal of the commercially available cow milk f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is a set of cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of the commercially available cow milk is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of the commercially available goat milk, the two-dimensional relaxation signal of the commercially available goat milk f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of the commercially available goat milk is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of pig hide gelatin, the two-dimensional relaxation signal of pig hide gelatin f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of pig hide gelatin is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of cow hide gelatin, the two-dimensional relaxation signal of cow hide gelatin f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of cow hide gelatin is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of Liaoning scorpion powder solution, the two-dimensional relaxation signal of Liaoning scorpion powder solution f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values and n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of Liaoning scorpion powder solution is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of Shanxi scorpion powder solution, the two-dimensional relaxation signal of Shanxi scorpion powder solution f(τ.sub.1, n) can be obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n; the t.sub.2 distribution fingerprint spectrum of Shanxi scorpion powder solution is obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

9. The method of claim 1, wherein, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of corn germ oil, the two-dimensional relaxation signal of corn germ oil f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of corn germ oil is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of peanut oil, the two-dimensional relaxation signal of peanut oil f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of peanut oil is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of soybean oil, the two-dimensional relaxation signal of soybean oil f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of soybean oil is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of linseed oil, the two-dimensional relaxation signal of linseed oil f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of linseed oil is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of olive oil, the two-dimensional relaxation signal of olive oil f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and meanwhile changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of olive oil is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% water by weight, the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% water by weight f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% water by weight is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% lard by weight, the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% lard by weight f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% lard by weight is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% beef tallow by weight, the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% beef tallow by weight f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% beef tallow by weight is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n); and/or, when using the above-mentioned pulse sequence to acquire the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% butter by weight, the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% butter by weight f.sub.a(τ.sub.3, n) can be obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n; the t.sub.1-t.sub.2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% butter by weight is obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

Description

DESCRIPTION OF THE DRAWINGS

[0058] FIG. 1: A schematic diagram of the pulse sequence used to acquire fingerprint spectrum of edible oils or other liquid-like samples.

[0059] FIG. 2: Example of a pulse sequence used to acquire fingerprint spectrum of edible oils or other liquid-like samples.

[0060] FIG. 3: High-resolution .sup.1H NMR spectrum of corn germ oil, peanut oil, soybean oil, linseed oil and olive oil samples with Bruker 500 M NMR spectrometer.

[0061] FIG. 4: The t2 distribution fingerprint spectrum of a corn germ oil sample.

[0062] FIG. 5: The t2 distribution fingerprint spectrum of a peanut oil sample.

[0063] FIG. 6: The t2 distribution fingerprint spectrum of a soybean oil sample.

[0064] FIG. 7: The t2 distribution fingerprint spectrum of a linseed oil sample.

[0065] FIG. 8: The t2 distribution fingerprint spectrum of an olive oil sample.

[0066] FIG. 9: The t1-t2 correlation fingerprint spectrum of a corn germ oil sample.

[0067] FIG. 10: The t1-t2 correlation fingerprint spectrum of a peanut oil sample.

[0068] FIG. 11: The t1-t2 correlation fingerprint spectrum of a soybean oil sample.

[0069] FIG. 12: The t1-t2 correlation fingerprint spectrum of a linseed oil sample.

[0070] FIG. 13: The t1-t2 correlation fingerprint spectrum of an olive oil sample.

[0071] FIG. 14: The t1-t2 correlation fingerprint spectrum of a corn germ oil sample mixed with 1% water by weight.

[0072] FIG. 15: The t1-t2 correlation fingerprint spectrum of a corn germ oil sample mixed with 1% lard by weight.

[0073] FIG. 16: The t1-t2 correlation fingerprint spectrum of a corn germ oil sample mixed with 1% beef tallow by weight.

[0074] FIG. 17: The t1-t2 correlation fingerprint spectrum of a corn germ oil sample mixed with 1% butter by weight.

[0075] FIG. 18: The t2 distribution fingerprint spectrum of the commercially available cow milk 1.

[0076] FIG. 19: The t2 distribution fingerprint spectrum of the commercially available cow milk 2.

[0077] FIG. 20: The t2 distribution fingerprint spectrum of the commercially available goat milk.

[0078] FIG. 21: The t2 distribution fingerprint spectrum of a pig hide gelatin sample.

[0079] FIG. 22: The t2 distribution fingerprint spectrum of a cow hide gelatin sample.

[0080] FIG. 23: The t2 distribution fingerprint spectrum of Liaoning scorpion powder solution.

[0081] FIG. 24: The t2 distribution fingerprint spectrum of Shanxi scorpion powder solution.

PREFERRED EMBODIMENTS OF THE INVENTION

[0082] The following examples are given to further illustrate the specific solutions of the present invention. The implementation of the present invention process, including the conditions, experimental methods, etc., are common knowledge and known knowledge in the field. The present invention has no special limitations. Meanwhile, the embodiments are only used to illustrate the present invention and not to limit the scope of the present invention.

[0083] The invention discloses a method based on nuclear magnetic resonance technology that can carry out species identification and quality detection of liquid-like samples. The present invention applies a specially designed combined pulse sequence to liquid-like sample to obtain a two-dimensional relaxation signal containing the .sup.1H T.sub.1 and T.sub.2 relaxation characteristics of the sample, which breaks through the weakness of the traditional methods that only uses .sup.1H T.sub.2 and cannot effectively distinguish different liquid-like samples. From the two-dimensional relaxation signal, the fingerprint of the liquid-like sample can be established. The fingerprint spectrum is related to the essential characteristics of the liquid-like sample, and can be used as a standard for distinguishing a specific liquid-like sample from the others. Meanwhile, the digital form of the fingerprint spectrum obtained by the present invention is very suitable for constructing the big data of a sample and the quality detection and authenticity judgment of a sample based on artificial intelligence. This method has the characteristics of no need for pretreatment of the test sample, and non-destructive testing of the test object. It also has the advantages of convenience and quickness, strong operability, good stability and reproducibility, etc., and can be used for the identification of a variety of fluid samples. And quality inspection, has a wide range of application value.

[0084] The main steps of the implementation process are as follows:

[0085] Step 1: Designing a pulse sequence that includes pulse block or composite pulses containing .sup.1H spin-echo function and pulse block or composite pulses containing T.sub.1 filter function. The scheme of pulse sequence is referred to FIG. 1;

[0086] Step 2: Applying the pulse sequence obtained from Step 1 to the targeted liquid-like samples to obtain their .sup.1H two-dimensional relaxation signals of the targeted liquid-like samples;

[0087] Step 3: Converting the obtained .sup.1H two-dimensional relaxation signals from Step 2 into the fingerprint spectrum of the targeted liquid-like samples, so as to be used for species identification and quality detection of edible oil or the liquid-like samples.

[0088] Wherein, the said liquid-like samples refer to liquid and gel substances with a certain fluidity, including edible oil, cow and goat milk, donkey-hide gelatin, scorpion powder solution, yogurt, beverages, oils in general, etc.

[0089] In Step 1 of the present invention, the pulse sequence comprises the following designs and the sub-steps:

[0090] Step 1-1: Using a pulse block or composite pulses to excite .sup.1H magnetic resonance signal of the system under test; Step 1-2: Applying the pulse block or the composite pulses containing .sup.1H spin-echo function to the system under test, and the pulse block or the composite pulses may contain one or more variables; Step 1-3: Applying the pulse block or the composite pulses containing .sup.1H T.sub.1 filter function to the system under test, and the pulse block or the composite pulses may contain one or more variables; Step 1-4: Converting the .sup.1H magnetic resonance signal of the targeted samples into a signal detectable by the magnetic resonance instrument through the pulse block or the composite pulses, and then collecting the signals.

[0091] FIG. 2 shows an example of the above-mentioned pulse sequence which can be used to obtain the two-dimensional relaxation signal of edible oil. In this pulse sequence, there are four steps: The first step: exciting the .sup.1H magnetic resonance signal of the system under test with a 90° pulse with a phase of x; The second step: Applying the composite pulse block [τ.sub.1−(180°).sub.y−τ.sub.1].sub.n, which comprises the time variable τ.sub.1 and the number repetition variable n; The third step: Applying the composite pulse block [τ.sub.2−(90°).sub.x−τ.sub.3] to the system under test, wherein τ.sub.2 is the time constant ranging from 10 μs to 20 μs, and τ.sub.3 is the time variable; The fourth step: Converting the .sup.1H magnetic resonance signal of the system under test into a signal detectable by the magnetic resonance instrument with a 90° pulse with the phases of x, y, −x, −y, and then collecting the signals.

[0092] In Step 2 of the present invention, the .sup.1H two-dimensional relaxation signal of the targeted sample can be obtained by controlling the variables in the pulse block or the composite pulses containing the .sup.1H spin-echo function and the variables in the pulse block or the composite pulses containing T.sub.1 filter function in the pulse sequence. Through the design of the variables, different types of two-dimensional relaxation signal can be obtained.

[0093] In Step 3 of the present invention, f.sub.n(x,y) is obtained by normalizing the signal intensity of the above-mentioned two-dimensional relaxation signal f(x,y); the fingerprint spectrum can be obtained by subtracting the reference function F(x,y) from f.sub.n(x,y); the reference function F(x,y) is obtained by designing according to the .sup.1H relaxation properties of the target sample, or performing surface fitting of f.sub.n(x,y), or averaging F.sub.m(x, y), m=1, 2, . . . , i, which is acquired from surface fitting of the multiple two-dimensional relaxation signals.

[0094] In the present invention, when comparing the fingerprint spectrum for species identification and quality detection of liquid-like samples of the same type, the same reference function is used in the generation process of fingerprint spectrum in Step 3 for those belonging to the same sample type but in different quality.

[0095] FIG. 4 shows an example of the t2 distribution fingerprint spectrum of a corn germ oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the corn germ oil sample. The two-dimensional relaxation signal of corn germ oil, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The reference function F(x, y) is obtained by performing surface fitting of f(τ.sub.1, n). The t.sub.2 distribution fingerprint spectrum of corn germ oil in FIG. 4 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0096] FIG. 5 shows an example of the t.sub.2 distribution fingerprint spectrum of a peanut oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the peanut oil sample. The two-dimensional relaxation signal of peanut oil, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of peanut oil in FIG. 5 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0097] FIG. 6 shows an example of the t.sub.2 distribution fingerprint spectrum of a soybean oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the soybean oil sample. The two-dimensional relaxation signal of soybean oil, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of soybean oil in FIG. 6 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0098] FIG. 7 shows an example of the t.sub.2 distribution fingerprint spectrum of a linseed oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the linseed oil sample. The two-dimensional relaxation signal of linseed oil, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of linseed oil in FIG. 7 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0099] FIG. 8 shows an example of the t.sub.2 distribution fingerprint spectrum of an olive oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the olive oil sample. The two-dimensional relaxation signal of olive oil, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of olive oil in FIG. 8 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0100] FIG. 9 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of a corn germ oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the corn germ oil sample. The two-dimensional relaxation signal of corn germ oil, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The reference function F.sub.a(x, y) is obtained by performing surface fitting of f.sub.a(τ.sub.3, n). The t.sub.1-t.sub.2 correlation fingerprint spectrum of corn germ oil in FIG. 9 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0101] FIG. 10 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of a peanut oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the peanut oil sample. The two-dimensional relaxation signal of peanut oil, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The t.sub.1-t.sub.2 correlation fingerprint spectrum of peanut oil in FIG. 10 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0102] FIG. 11 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of a soybean oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the soybean oil sample. The two-dimensional relaxation signal of soybean oil, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The t.sub.1-t.sub.2 correlation fingerprint spectrum of soybean oil in FIG. 11 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0103] FIG. 12 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of a linseed oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the linseed oil sample. The two-dimensional relaxation signal of linseed oil, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The t.sub.1-t.sub.2 correlation fingerprint spectrum of linseed oil in FIG. 12 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0104] FIG. 13 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of an olive oil sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the olive oil sample. The two-dimensional relaxation signal of olive oil, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The t.sub.1-t.sub.2 correlation fingerprint spectrum of olive oil in FIG. 13 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0105] FIG. 14 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of a corn germ oil sample mixed with 1% water by weight. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% water by weight. The two-dimensional relaxation signal of the corn germ oil sample mixed with 1% water by weight, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The t.sub.1-t.sub.2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% water by weight in FIG. 14 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0106] FIG. 15 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of a corn germ oil sample mixed with 1% lard by weight. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% lard by weight. The two-dimensional relaxation signal of the corn germ oil sample mixed with 1% lard by weight, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The t.sub.1-t.sub.2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% lard by weight in FIG. 15 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0107] FIG. 16 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of a corn germ oil sample mixed with 1% beef tallow by weight. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% beef tallow by weight. The two-dimensional relaxation signal of the corn germ oil sample mixed with 1% beef tallow by weight, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The t.sub.1-t.sub.2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% beef tallow by weight in FIG. 16 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0108] FIG. 17 shows an example of the t.sub.1-t.sub.2 correlation fingerprint spectrum of a corn germ oil sample mixed with 1% butter by weight. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the corn germ oil sample mixed with 1% butter by weight. The two-dimensional relaxation signal of the corn germ oil sample mixed with 1% butter by weight, f.sub.a(τ.sub.3, n), was obtained by fixing τ.sub.1 and changing τ.sub.3 and n, wherein τ.sub.3 is a set of time values, n is cycle number, and f.sub.a(τ.sub.3, n) is the signal intensity corresponding to τ.sub.3 and n. The t.sub.1-t.sub.2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% butter by weight in FIG. 17 was obtained by subtracting the selected reference function F.sub.a(x, y) from the normalized f.sub.a(τ.sub.3, n).

[0109] FIG. 18 shows an example of the t.sub.2 distribution fingerprint spectrum of the commercially available cow milk 1. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the commercially available cow milk 1. The two-dimensional relaxation signal of the commercially available cow milk 1, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of the commercially available cow milk 1 in FIG. 18 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0110] FIG. 19 shows an example of the t.sub.2 distribution fingerprint spectrum of the commercially available cow milk 2. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the commercially available cow milk 2. The two-dimensional relaxation signal of the commercially available cow milk 2, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of the commercially available cow milk 2 in FIG. 19 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0111] FIG. 20 shows an example of the t.sub.2 distribution fingerprint spectrum of the commercially available goat milk. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the commercially available goat milk. The two-dimensional relaxation signal of the commercially available goat milk, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of the commercially available goat milk in FIG. 20 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0112] FIG. 21 shows an example of the t.sub.2 distribution fingerprint spectrum of the pig hide gelatin sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the pig hide gelatin sample. The two-dimensional relaxation signal of the pig hide gelatin sample, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of the pig hide gelatin sample in FIG. 21 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0113] FIG. 22 shows an example of the t.sub.2 distribution fingerprint spectrum of the cow hide gelatin sample. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of the cow hide gelatin sample. The two-dimensional relaxation signal of the cow hide gelatin sample, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of the cow hide gelatin sample in FIG. 22 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0114] FIG. 23 shows an example of the t.sub.2 distribution fingerprint spectrum of Liaoning scorpion powder solution. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of Liaoning scorpion powder solution. The two-dimensional relaxation signal of Liaoning scorpion powder solution, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of Liaoning scorpion powder solution in FIG. 23 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0115] FIG. 24 shows an example of the t.sub.2 distribution fingerprint spectrum of Shanxi scorpion powder solution. The pulse sequence shown in FIG. 2 was used to acquire the two-dimensional relaxation signal of Shanxi scorpion powder solution. The two-dimensional relaxation signal of Shanxi scorpion powder solution, f(τ.sub.1, n), was obtained by fixing τ.sub.3 and changing τ.sub.1 and n, wherein τ.sub.1 is a set of time values, n is cycle number, and f(τ.sub.1, n) is the signal intensity corresponding to τ.sub.1 and n. The t.sub.2 distribution fingerprint spectrum of Shanxi scorpion powder solution in FIG. 24 was obtained by subtracting the selected reference function F(x, y) from the normalized f(τ.sub.1, n).

[0116] There is some sample preparation processes in the examples. The methods and steps of the sample preparation processes are well-known in the field.

EXAMPLE 1—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A CORN GERM OIL

[0117] Sample: a commercially available corn germ oil.

[0118] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0119] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, 30 μs, 50 μs, 100 μs, 200 μs, 500 μs and 1 ms, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 2 ms. The number of repetition, n, was set to 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 and 40000. Thus the two-dimensional relaxation surface f(τ.sub.1, n) of the corn germ oil was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0120] In this example, the reference surface function was obtained by fitting and normalizing two-dimensional relaxation surface f.sub.n(τ.sub.1, n) of corn germ oil. The reference surface function is:

F(x,y)=18.54−17.11.Math.x−6.667.Math.y+6.41.Math.x.sup.2+4.15.Math.x.Math.y+0.3606.Math.y.sup.2−1.232.Math.x.sup.3−1.024.Math.x.sup.2.Math.y−0.08823.Math.x.Math.y.sup.2+0.04467.Math.y.sup.3+0.1212.Math.x.sup.4+0.1138.Math.x.sup.3.Math.y+0.02533.Math.x.sup.2.Math.y.sup.2−0.02851.Math.x.Math.y.sup.3+0.003422.Math.y.sup.4−0.00484.Math.x.sup.5−0.004637.Math.x.sup.4.Math.y−0.002608.Math.x.sup.3.Math.y.sup.2+0.001928.Math.x.sup.2.Math.y.sup.3+0.001074.Math.x.Math.y.sup.4+0.0000372.Math.y.sup.5

[0121] The t2 distribution fingerprint spectrum of the corn germ oil (FIG. 4) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 2—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A PEANUT OIL

[0122] Sample: a commercially available peanut oil.

[0123] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0124] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, 30 μs, 50 μs, 100 μs, 200 μs, 500 μs and 1 ms, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 2 ms. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f(τ.sub.1, n) of the peanut oil was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0125] In this example, the reference surface function was that of the peanut oil:

[0126] The t2 distribution fingerprint spectrum of the peanut oil (FIG. 5) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 3—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A SOYBEAN OIL

[0127] Sample: a commercially available .soybean oil

[0128] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0129] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, 30 μs, 50 μs, 100 μs, 200 μs, 500 μs and 1 ms, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 2 ms. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f(τ.sub.1, n) of the soybean oil was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0130] In this example, the reference surface function was that of the corn germ oil:

[0131] The t2 distribution fingerprint spectrum of the soybean oil (FIG. 6) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 4—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A LINSEED OIL

[0132] Sample: a commercially available linseed oil.

[0133] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0134] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, 30 μs, 50 μs, 100 μs, 200 μs, 500 μs and 1 ms, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 2 ms. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f(τ.sub.1, n) of the linseed oil was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0135] In this example, the reference surface function was that of the corn germ oil:

[0136] The t2 distribution fingerprint spectrum of the linseed oil (FIG. 7) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 5—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF AN OLIVE OIL

[0137] Sample: a commercially available olive oil.

[0138] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0139] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, 30 μs, 50 μs, 100 μs, 200 μs, 500 μs and 1 ms, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 2 ms. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f(τ.sub.1, n) of the olive oil was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0140] In this example, the reference surface function was that of the corn germ oil:

[0141] The t2 distribution fingerprint spectrum of the olive oil (FIG. 8) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 6—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF A CORN GERM OIL

[0142] Sample: a commercially available corn germ oil.

[0143] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0144] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the corn germ oil was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0145] In this example, the reference surface function was obtained by fitting and normalizing the two-dimensional relaxation surface f.sub.n(τ.sub.3, n) of the corn germ oil. The reference surface function is:

F.sub.a(x,y)=−2.281+2.317.Math.x−0.4775.Math.y−0.9071.Math.x.sup.2−0.001151.Math.x.Math.y+0.2854.Math.y.sup.2+0.1461.Math.x.sup.3+0.09387.Math.x.sup.2.Math.y−0.1417.Math.x.Math.y.sup.2−0.007143.Math.y.sup.3−0.00962.Math.x.sup.4−0.01736.Math.x.sup.3.Math.y+0.01505.Math.x.sup.2.Math.y.sup.2+0.00902.Math.x.Math.y.sup.3−0.002398.Math.y.sup.4+0.0001928.Math.x.sup.5+0.0008389.Math.x.sup.4.Math.y−0.0003571.Math.x.sup.3.Math.y.sup.2−0.0006947.Math.x.sup.2.Math.y.sup.3+0.000005745.Math.x.Math.y.sup.4+0.00008974.Math.y.sup.5

[0146] The t1-t2 correlation fingerprint spectrum of the corn germ oil (FIG. 9) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 7—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF A PEANUT OIL

[0147] Sample: a commercially available peanut oil.

[0148] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0149] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the peanut oil was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0150] In this example, the reference surface function was that of the corn germ oil:

[0151] The t1-t2 correlation fingerprint spectrum of the peanut oil (FIG. 10) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 8—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF A SOYBEAN OIL

[0152] Sample: a commercially available soybean oil.

[0153] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0154] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the soybean oil was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0155] In this example, the reference surface function was that of corn germ oil:

[0156] The t1-t2 correlation fingerprint spectrum of the soybean oil (FIG. 11) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 9—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF A LINSEED OIL

[0157] Sample: a commercially available linseed oil.

[0158] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0159] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the linseed oil was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0160] In this example, the reference surface function was that of the corn germ oil:

[0161] The t1-t2 correlation fingerprint spectrum of the linseed oil (FIG. 12) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 10—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF AN OLIVE OIL

[0162] Sample: a commercially available olive oil.

[0163] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0164] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the olive oil was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0165] In this example, the reference surface function was that of corn germ oil:

[0166] The t1-t2 correlation fingerprint spectrum of the olive oil (FIG. 13) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 11—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF A CORN GERM OIL SAMPLE MIXED WITH 1% WATER BY WEIGHT

[0167] Sample: a commercially available corn germ oil sample mixed with 1% water by weight.

[0168] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0169] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the corn germ oil was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0170] In this example, the reference surface function was that of the corn germ oil:

[0171] The t1-t2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% water by weight (FIG. 14) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 12—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF A CORN GERM OIL SAMPLE MIXED WITH 1% LARD BY WEIGHT

[0172] Sample: a commercially available corn germ oil sample mixed with 1% lard by weight.

[0173] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0174] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the corn germ oil sample mixed with 1% lard by weight was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0175] In this example, the reference surface function was that of the corn germ oil:

[0176] The t1-t2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% lard by weight (FIG. 15) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 13—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF A CORN GERM OIL SAMPLE MIXED WITH 1% BEEF TALLOW BY WEIGHT

[0177] Sample: a commercially available corn germ oil sample mixed with 1% beef tallow by weight.

[0178] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0179] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the corn germ oil sample mixed with 1% beef tallow by weight was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0180] In this example, the reference surface function was that of the corn germ oil:

[0181] The t1-t2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% beef tallow by weight (FIG. 16) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 14—THE T1-T2 CORRELATION FINGERPRINT SPECTRUM OF A CORN GERM OIL SAMPLE MIXED WITH 1% BUTTER BY WEIGHT

[0182] Sample: a commercially available corn germ oil sample mixed with 1% butter by weight.

[0183] NMR Instrument: Bruker AVANCE III 500 NMR spectrometer. The experimental temperature is room temperature.

[0184] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 20 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 10 ms, 50 ms, 100 ms, 200 ms, 500 ms and 1 s, 2 s and 3 s. The number of repetition, n, was set to 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 and 40000. The two-dimensional relaxation surface f.sub.a(τ.sub.3, n) of the corn germ oil was obtained, which then was normalized to f.sub.n(τ.sub.3, n).

[0185] In this example, the reference surface function was obtained by fitting the normalized two-dimensional relaxation surface f.sub.n(τ.sub.3, n) of corn germ oil. The reference surface function is:

[0186] The t1-t2 correlation fingerprint spectrum of the corn germ oil sample mixed with 1% butter by weight (FIG. 17) can be obtained by subtracting the reference surface F.sub.a(x,y) from f.sub.n(τ.sub.3, n).

EXAMPLE 15—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A COMMERCIALLY AVAILABLE COW MILK 1

[0187] Sample: a commercially available cow milk 1.

[0188] NMR Instrument: VTMR20-010V-I relaxometry, Niumag Corp., Ltd., Shanghai, China. The B.sub.0 field is 0.5±0.05 T and the .sup.1H Larmor frequency is 21.3 MHz. The experimental temperature is room temperature.

[0189] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 50 μs, 100 μs, 150 μs, 200 μs, 250 μs, 300 μs and 350 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 100 μs. The number of repetition, n, was set to 3, 5, 10, 20, 40, 80, 100, 150, 200, 250, 300, 400, 500, 700, 900, 1200, 1500, and 2500. The two-dimensional relaxation surface f(τ.sub.1, n) of the commercially available cow milk 1 was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0190] In this example, the reference surface function was obtained by fitting and normalizing the average value of two-dimensional relaxation surface f.sub.n(τ.sub.1, n) of the two commercially available cow milks and the goat milk. The reference surface function is:

F(x,y)=−0.4201−0.2412.Math.x−0.7473.Math.y−0.0085.Math.x.sup.2−0.2879.Math.x.Math.y−0.4974.Math.y.sup.2+0.0201.Math.x.sup.3+0.0121.Math.x.sup.2.Math.y−0.0027.Math.x.Math.y.sup.2+0.0213.Math.y.sup.3−0.0195.Math.x.sup.4−0.0061.Math.x.sup.3.Math.y+0.0379.Math.x.sup.2.Math.y.sup.2+0.1243.Math.x.Math.y.sup.3+0.1041.Math.y.sup.4−0.0102.Math.x.sup.5−0.0052.Math.x.sup.4.Math.y−0.0047.Math.x.sup.3.Math.y.sup.2+0.0137.Math.x.sup.2.Math.y.sup.3+0.0411.Math.x.Math.y.sup.4+0.0214.Math.y.sup.5

[0191] The t2 distribution fingerprint spectrum of the commercially available cow milk 1 (FIG. 18) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 16—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A COMMERCIALLY AVAILABLE COW MILK 2

[0192] Sample: a commercially available cow milk 2.

[0193] NMR Instrument: VTMR20-010V-I relaxometry, Niumag Corp., Ltd., Shanghai, China. The B.sub.0 field is 0.5±0.05 T and the .sup.1H Larmor frequency is 21.3 MHz. The experimental temperature is room temperature.

[0194] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 50 μs, 100 μs, 150 μs, 200 μs, 250 μs, 300 μs and 350 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 100 μs. The number of repetition, n, was set to 3, 5, 10, 20, 40, 80, 100, 150, 200, 250, 300, 400, 500, 700, 900, 1200, 1500, and 2500. The two-dimensional relaxation surface f(τ.sub.1, n) of the commercially available cow milk 2 was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0195] In this example, the reference surface function was obtained by fitting and normalizing the average value of two-dimensional relaxation surface f.sub.n(τ.sub.1, n) of the two commercially available cow milks and the goat milk. The reference surface function is:

[0196] The t2 distribution fingerprint spectrum of the commercially available cow milk 2 (FIG. 19) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 17—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A COMMERCIALLY AVAILABLE GOAT MILK

[0197] Sample: a commercially available goat milk.

[0198] NMR Instrument: VTMR20-010V-I relaxometry, Niumag Corp., Ltd., Shanghai, China. The B.sub.0 field is 0.5±0.05 T and the .sup.1H Larmor frequency is 21.3 MHz. The experimental temperature is room temperature.

[0199] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 50 μs, 100 μs, 150 μs, 200 μs, 250 μs, 300 μs and 350 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 100 μs. The number of repetition, n, was set to 3, 5, 10, 20, 40, 80, 100, 150, 200, 250, 300, 400, 500, 700, 900, 1200, 1500, and 2500. The two-dimensional relaxation surface f(τ.sub.1, n) of the commercially available goat milk was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0200] In this example, the reference surface function was obtained by fitting and normalizing the average value of two-dimensional relaxation surface f.sub.n(τ.sub.1, n) of the commercially available cow milks and goat milk. The reference surface function is:

[0201] The t2 distribution fingerprint spectrum of the commercially available goat milk (FIG. 20) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 18—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A PIG HIDE GELATIN

[0202] Sample: a pig hide gelatin.

[0203] NMR Instrument: VTMR20-010V-I relaxometry, Niumag Corp., Ltd., Shanghai, China. The B.sub.0 field is 0.5±0.05 T and the .sup.1H Larmor frequency is 21.3 MHz. The experimental temperature is room temperature.

[0204] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 100 μs, 200 μs, 300 μs, 400 μs, 500 μs, 650 μs and 800 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 200 ms. The number of repetition, n, was set to 3, 5, 10, 20, 40, 80, 100, 150, 200, 250, 300, 400, 500, 700, 900, 1200, 1500, and 2500. The two-dimensional relaxation surface f(τ.sub.1, n) of the pig hide gelatin was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0205] In this example, the reference surface function was obtained by fitting and normalizing the average value of two-dimensional relaxation surface f.sub.n(τ.sub.1, n) of the pig hide gelatin and cow hide gelatin. The reference surface function is:

F(x,y)=−0.2675−0.1528.Math.x−0.3691.Math.y−0.0574.Math.x.sup.2−0.2720.Math.x.Math.y−0.4243.Math.y.sup.2−0.0024.Math.x.sup.3−0.0270.Math.x.sup.2.Math.y−0.0552.Math.x.Math.y.sup.2−0.0768.Math.y.sup.3+0.00054.Math.x.sup.4+0.0133.Math.x.sup.3.Math.y+0.0302.Math.x.sup.2.Math.y.sup.2+0.1050.Math.x.Math.y.sup.3+0.0899.Math.y.sup.4−0.00077.Math.x.sup.5+0.0025.Math.x.sup.4.Math.y+0.0071.Math.x.sup.3.Math.y.sup.2+0.0280.Math.x.sup.2.Math.y.sup.3+0.0402.Math.x.Math.y.sup.4+0.0299.Math.y.sup.5

[0206] The t2 distribution fingerprint spectrum of the pig hide gelatin sample (FIG. 21) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 19—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF A COW HIDE GELATIN

[0207] Sample: a cow hide gelatin.

[0208] NMR Instrument: VTMR20-010V-I relaxometry, Niumag Corp., Ltd., Shanghai, China. The B.sub.0 field is 0.5±0.05 T and the .sup.1H Larmor frequency is 21.3 MHz. The experimental temperature is room temperature.

[0209] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 100 μs, 200 μs, 300 μs, 400 μs, 500 μs, 650 μs and 800 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 200 ms. The number of repetition, n, was set to 3, 5, 10, 20, 40, 80, 100, 150, 200, 250, 300, 400, 500, 700, 900, 1200, 1500, and 2500. The two-dimensional relaxation surface f(τ.sub.1, n) of the cow hide gelatin was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0210] In this example, the reference surface function was obtained by fitting and normalizing the average of two-dimensional relaxation surface f.sub.n(τ.sub.1, n) of the pig hide gelatin and cow hide gelatin. The reference surface function is:

[0211] The t2 distribution fingerprint spectrum of the cowhide gelatin sample (FIG. 22) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 20—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF LIAONING SCORPION POWDER SOLUTION

[0212] Sample: Liaoning scorpion powder solution. The weight ratio between the scorpion powder and the water is 1:1.

[0213] NMR Instrument: VTMR20-010V-I relaxometry, Niumag Corp., Ltd., Shanghai, China. The B.sub.0 field is 0.5±0.05 T and the .sup.1H Larmor frequency is 21.3 MHz. The experimental temperature is room temperature.

[0214] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 100 μs, 200 μs, 300 μs, 400 μs, 500 μs, 650 μs and 800 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 200 ms. The number of repetition, n, was set to 3, 5, 10, 20, 40, 80, 100, 150, 200, 250, 300, 400, 500, 700, 900, 1200, 1500, and 2500. The two-dimensional relaxation surface f(τ.sub.1, n) of the Liaoning scorpion powder solution was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0215] In this example, the reference surface function was obtained by fitting and normalizing the average value of the two-dimensional relaxation surface f.sub.n(τ.sub.1, n) of the Liaoning scorpion powder solution and the Shanxi scorpion powder solution. The reference surface function is:

[0216] The t2 distribution fingerprint of Liaoning scorpion powder solution (FIG. 23) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

EXAMPLE 21—THE T2 DISTRIBUTION FINGERPRINT SPECTRUM OF SHANXI SCORPION POWDER SOLUTION

[0217] Sample: Shanxi scorpion powder solution. The weight ratio between the scorpion powder and the water is 1:1.

[0218] NMR Instrument: VTMR20-010V-I relaxometry, Niumag Corp., Ltd., Shanghai, China. The B.sub.0 field is 0.5±0.05 T and the .sup.1H Larmor frequency is 21.3 MHz. The experimental temperature is room temperature.

[0219] Method: The pulse sequence used in this experiment is shown in FIG. 2. In the experiment, τ.sub.1 was set to 100 μs, 200 μs, 300 μs, 400 μs, 500 μs, 650 μs and 800 μs, τ.sub.2 was set to 20 μs and τ.sub.3 was set to 200 ms. The number of repetition, n, was set to 3, 5, 10, 20, 40, 80, 100, 150, 200, 250, 300, 400, 500, 700, 900, 1200, 1500, and 2500. The two-dimensional relaxation surface f(τ.sub.1, n) of the Shanxi scorpion powder solution was obtained, which then was normalized to f.sub.n(τ.sub.1, n).

[0220] In this example, the reference surface function was obtained by fitting and normalizing the average value of two-dimensional relaxation surface f.sub.n(τ.sub.1, n) of the Liaoning scorpion powder solution and the Shanxi scorpion powder solution. The reference surface function is:

[0221] The t2 distribution fingerprint of Shanxi scorpion powder solution (FIG. 24) can be obtained by subtracting the reference surface F(x,y) from f.sub.n(τ.sub.1, n).

[0222] The present invention has the following features which are different from previous methods and technologies:

[0223] 1. Based on the feature that the .sup.1H T.sub.1 and T.sub.2 relaxation properties are different in different edible oils or other liquid-like samples, the method in the present invention breaks through the limitation of previous works using only .sup.1H T.sub.2 relaxation to detect and identify edible oils or other liquid-like samples, innovatively proposes to amplify .sup.1H T.sub.1 and T.sub.2 relaxation differences of edible oils or other liquid-like samples by measuring two-dimensional .sup.1H T.sub.1 and T.sub.2 relaxation data containing relaxation properties of edible oils or other liquid-like samples, thus realize the detection and identification of edible oils or other liquid-like samples;

[0224] 2. Based on the above idea, a new pulse sequence and the corresponding data acquisition method are developed to be used to amplify the .sup.1H T.sub.1 and T.sub.2 relaxation differences of different edible oils or other liquid-like samples;

[0225] 3. A ‘fingerprint spectrum’ containing the .sup.1H T.sub.1 and T.sub.2 relaxation properties of edible oils or other liquid-like samples is constructed. The fingerprint can be used as a standard to distinguish different types of edible oil or other liquid-like samples, meanwhile, its digital form is very suitable for constructing the big data of edible oils or other liquid-like samples and the authenticity judgments based on artificial intelligence.

[0226] 4. The method in the present invention can be used om high-resolution nuclear magnetic instruments and low-field magnetic resonance relaxometry, overcoming the dependence of the patent CN108982570A on the nuclear magnetic resonance signal resolution. At the same time, the measurement of .sup.1H T.sub.1 and T.sub.2 relaxation properties overcomes the low discrimination of the traditional method caused by only measuring the individual .sup.1H T.sub.2 relaxation.

[0227] 5. Compared with the traditional chromatographic, mass spectrometry and the optical spectroscopy, the method in the present invention can realize a non-destructive sample testing without sample pretreatment. The method can be implemented on a low-field magnetic resonance instrument which can achieve a rapid on-site detection by moving on board.

[0228] It should be understood that the term “and/or” used in the present invention is only to describe a relationship between associated objects, which means that there can be three relationships, i.e A and/or B can mean three conditions: A alone, A and B, and B alone. In addition, the character “/” in the present invention generally indicates that the associated objects before and after being in an ‘or’ relationship.

[0229] The protection of the present invention is not limited to the following embodiments. Without departing from the spirit and scope of the idea of the invention, all changes and advantages that can be thought of by a person skilled in the field are included in the present invention and are protected by the attached claims.

A METHOD FOR SPECIES IDENTIFICATION AND QUALITY DETECTION OF LIQUID-LIKE SAMPLES BASED ON NUCLEAR MAGNETIC RESONANCE TECHNOLOGY

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G01R33/448

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G01R33/465

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G01R33/4633

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G01N24/08

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G01N33/02

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G01N24/08

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Abstract

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