DE112014004052B4 - NMR sample analysis - Google Patents

Abstract

A method for identifying animal tissue using an NMR spectrometer with a permanent magnet with a magnetic field strength on a sample holder of the spectrometer between 1.0 and 2.5 Tesla, the method comprising: (i) contacting the animal tissue with a solvent in order to Dissolve triglyceride molecules from animal tissue in the solvent to obtain a sample; (ii) place the sample in the sample holder of the NMR spectrometer; (iii) apply a radio frequency (RF) pulse sequence to the sample and detect free induction decay (FID ) Signal using the NMR spectrometer; (iv) converting the detected FID signal into the frequency domain in order to obtain a frequency spectrum with a triglyceride signature, and (v) analyzing the triglyceride signature and outputting characteristic data for the animal tissue, - wherein the characteristic data indicate the identity of the animal tissue and - where: - the term 'identity' is used relates to the species of the animal tissue analyzed and / or- in a case that the sample is prepared using a mixture of different animal tissues, the characteristic data indicate a plurality of identities with relative weights for different identities of animal tissue contained in the sample.

Description

Field of the invention

The present invention relates to a method for animal tissue analysis using a “benchtop” NMR spectrometer with a permanent magnet.

Background of the invention

The issue of meat contamination has recently come to the fore. In January 2013 it was found that a number of European supermarkets were selling meat products advertised as containing beef, but what was actually found to contain undeclared horse meat, in some cases up to 100% of the meat content. Similar cases have been discovered where undeclared pork was found in what was advertised as beef balls. The presence of undeclared pork is particularly annoying to those who deliberately do not eat pork. In the light of this scandal, there has been a growing need for testing the origin of meat products.

It should first be noted that the terms "meat" and "animal tissue" are used interchangeably in this document, and both are intended to refer to the meat of a non-human animal (including mammals, fish, birds, reptiles, molluscs, crustaceans, etc.) ).

The standard method for testing meat products is a polymerase chain reaction (PCR), which amplifies the DNA in a sample, allowing the identification of a contaminating species.

The test requires trained personnel and expensive equipment to run, and the fastest time that results can be obtained is typically between 24 and 48 hours. This procedure is also a significant expense as the cost is more than £ 100 per sample.

The scientific publication "Fast and Simple Nuclear Magnetic Resonance Method to Measure Conjugated Linoleic Acid in Beef," J. Agric. Food Chem. 2010, Vol. 58, pages 6562 to 6564 , by Maria et al. from 2010 describes a method for determining the proportion of conjugated linoleic acids (CLA) in beef using a method of nuclear magnetic resonance on a chloroform extract of a beef sample. The identity of the animal tissue that is used to form the samples is assumed to be already known.

There is a need for a cheaper and faster solution to testing animal tissues.

Summary of the invention

• (i) Inkontaktbringen des Tiergewebes mit einem Lösungsmittel, um Triglyceridmoleküle aus dem Tiergewebe in dem Lösungsmittel aufzulösen, um eine Probe zu erhalten;
• (ii) Platzieren der Probe in den Probenhalter des NMR-Spektrometers;
• (iii) Anwenden einer Hochfrequenz(HF)-Pulssequenz auf die Probe und Detektieren eines freien Induktionszerfall(FID)-Signals unter Verwendung des NMR-Spektrometers;
• (iv) Umwandeln des detektierten FID-Signals in den Frequenzraum, um ein Frequenzspektrum mit einer Triglyceridsignatur zu erhalten, und
• (v) Analysieren der Triglyceridsignatur und Ausgeben von charakteristischen Daten für das Tiergewebe,
  • - wobei die charakteristischen Daten die Identität des Tiergewebes anzeigen und
  • - wobei:
    • - der Ausdruck ‚Identität‘ sich auf die Spezies des analysierten Tiergewebes bezieht und/oder
    • - in einem Fall, dass die Probe unter Verwendung einer Mischung verschiedener Tiergewebe hergestellt wird, die charakteristischen Daten eine Mehrzahl von Identitäten mit relativen Wichtungen für verschiedene Identitäten von in der Probe enthaltenem Tiergewebe anzeigen.

According to the first aspect of the present invention there is provided a method for identifying animal tissue using an NMR spectrometer having a permanent magnet with a magnetic field strength on a sample holder of the spectrometer between 1.0 and 2.5 Tesla, the method comprising:

• (i) contacting the animal tissue with a solvent to dissolve triglyceride molecules from the animal tissue in the solvent to obtain a sample;
• (ii) placing the sample in the sample holder of the NMR spectrometer;
• (iii) applying a radio frequency (RF) pulse sequence to the sample and detecting a free induction decay (FID) signal using the NMR spectrometer;
• (iv) converting the detected FID signal to frequency space to obtain a frequency spectrum with a triglyceride signature, and
• (v) analyzing the triglyceride signature and outputting characteristic data for the animal tissue,
  • - the characteristic data indicating the identity of the animal tissue and
  • - in which:
    • - the term 'identity' refers to the species of animal tissue analyzed and / or
    • in a case that the sample is prepared using a mixture of different animal tissues, the characteristic data indicate a plurality of identities with relative weights for different identities of animal tissues contained in the sample.

The present invention provides an inexpensive, quick, and effective method for analyzing the nature or origin of non-human animal tissues. In animals, fats are mainly stored as triglycerides. Triglyceride molecules consist of a glycerol molecule (referred to as the "backbone"), which is bonded to three fatty acid molecules via esterification. Numerous types of fatty acids occur naturally, and different fatty acid molecules can be attached to the three respective sites on the glycerol backbone. The presence of triglycerides with Fatty acids with different saturations (i.e., monounsaturated, polyunsaturated, or saturated) will vary between different species of animals and to a certain extent between different cuts of meat, that is, tissues from different parts of the same type of animal. A fatty acid is saturated if there are only single bonds between adjacent carbon atoms in the chain, monounsaturated if there is a double bond, and polyunsaturated if there is more than one double bond. The present applicant has recognized that by analyzing the triglyceride components in a meat sample, a simpler solution than DNA sequencing after PCR can be found to determine the origin of the meat.

Nuclear magnetic resonance (NMR) spectroscopy is a method for studying the electronic environment of NMR-active nuclei and the resulting structure of the molecules to which these nuclei are bound. NMR spectroscopy involves placing a sample with NMR-active nuclei (such as hydrogen-1) in a static external magnetic field B ₀ , which is generated by an NMR spectrometer. A radio frequency (RF) electromagnetic pulse is then applied to the sample by the spectrometer at a frequency characteristic of the NMR active nuclei of interest. The energy delivered by this pulse is 'absorbed' by these nuclei and re-emitted as radio waves with the resonance frequency of the active nuclei. This signal is known as the free induction decay signal (FID) and is detected and analyzed by the spectrometer in order to be able to infer the chemical structure of this sample.

In the context of meat sampling, we found that the relative triglyceride compositions differ significantly between different species of animals and between different cuts of meat. We have also recognized that the large abundance of hydrogen nuclei and their diverse chemical environments in triglycerides make them suitable candidates for analysis using NMR. We have also discovered that these triglycerides can be extracted from animal tissue samples by a simple solvent-based process with few impurities. Triglyceride samples can therefore be obtained from an unknown meat with an unknown identity and analyzed using NMR to obtain characteristic data for the animal tissue indicative of the identity of the animal tissue.

The term "identity" is intended to refer to the origin, type or species of meat as regards the animal of origin and, in some cases, the particular type or cut of meat from that animal. In the case that the sample is produced using animal tissue, that is to say actually a mixture of different animal tissues, several identities can be given by the characteristic data. Preferably, however, the relative weightings of the identities can also be specified.

The identity can be given as a marker, for example the word “pork” is displayed on a monitor, or it can be given in a more abstract way by triggering a sound, light indicator, mechanical output, or outputting raw data as long as it was possible to easily identify the identity of the animal tissue with this output. In the event that the device is configured to recognize only an animal tissue identity, the output of the characteristic data can include a simple yes / no answer which indicates whether the triglyceride signature shows a predetermined or preset answer.

When analyzing in the frequency domain, the part of the free induction decay signal that originates only from the triglyceride content is referred to herein as the “triglyceride signature”. The triglyceride signature is preferably the response of the sample analyzed in the frequency space to at least two of the olefinic, bis-allylic and glyceride regions (defined below) of the spectrum, and most preferably each of these three regions. In some cases, the signature can also contain the reaction in the linolenic region.

The triglyceride signature varies between different triglycerides because of the different levels of satiety of the fatty acids in the triglyceride. For example, a hydrogen nucleus near a single double bond between adjacent carbon atoms will resonate at a different frequency than one surrounded by only single bonds. This is because the resonance frequency of the nucleus is proportional to the magnetic field to which this nucleus is exposed, and this magnetic field itself is influenced by the electron density in the surrounding region. Orbiting electrons create a small magnetic field that typically acts in the opposite direction to the external magnetic field; the higher the electron density, the weaker the magnetic field on the nucleus. Double bonds have a higher electron density than single bonds and thus protect the external magnetic field to a greater extent. Hydrogen atoms in a fatty acid that are bonded to a carbon atom that has a single bond and a double bond between the adjacent two carbon atoms in the chain (-CH = CH- ) resonate in a specific region of the frequency spectrum known as the olefinic region. Similarly, hydrogen atoms located in the region of two double bonds (= CH-CH ₂ -CH =) resonate in a different region of the spectrum known as the bis-allylic region. In the same way, the two hydrogen atom pairs in a glycerol molecule, which are individually bonded to an 'end' carbon atom, resonate in another region, which is different from the olefinic and the bis-allylic region and is known as the glyceride region.

"Benchtop" low-field NMR spectrometers have recently been developed as a cheaper, more compact, and portable device for performing certain types of NMR experiments that have otherwise been performed in large NMR spectrometers with superconducting magnets. Instead of requiring a specific infrastructure, trained personnel and extensive installations, benchtop NMR spectrometers use permanent magnets that generate a magnetic field between 1.0 and 2.5 Tesla on the specimen holder of the spectrometer. They are generally stand-alone units that can be placed directly on a laboratory bench or other surface and moved as needed. These spectrometers are also typically much easier to use by first-time users since they are used for some of the simpler NMR experiments. An example of such a benchtop NMR spectrometer is the Pulsar ™ supplied by the Oxford Instruments Group. The present invention should provide an alternative, simpler and cheaper solution than current meat sampling. In the present invention, magnetic fields having a field strength of less than 1 Tesla are undesirable because it is difficult to distinguish between the peaks in the triglyceride signature at such weak fields. A magnetic field strength above 2.5 Tesla is also undesirable, since the spectrometer is then no longer a benchtop instrument at such high field strengths because superconducting magnets are required.

The accuracy with which the triglyceride signature can be analyzed is an important consideration in practicing the method. One effective approach involves incorporating an olefinic region of the triglyceride signature in step (v). The chemical shift of the olefinic region is typically between 5.0 and 5.6 ppm. Preferably, step (v) also includes integrating a bis-allylic region of the triglyceride signature. The chemical shift of the bis-allylic region is typically between 2.5 and 3.0 ppm. Further preferably, if realizable, step (v) comprises integrating a linolenic region of the triglyceride signature. The chemical shift of the linolenic region is typically between 0.96 and 1.02 ppm. These chemical shift values can be treated as limits on the integrals above. Although the actual response of the triglycerides varies for different animal tissues (i.e. different intensities are registered at different chemical shifts in the triglyceride signature), the chemical shift regions in which the triglycerides can be detected are common within the animal tissues. By comparing the integrals obtained for these regions, one can estimate the proportion of saturated, monounsaturated and polyunsaturated fat in the animal tissue. This data can indicate the type or origin of the animal tissue. Characteristic peaks in these regions can also be identified, for example, by integrating partial areas of these regions in order to provide a more detailed analysis. The presence of a characteristic peak, usually associated with only one particular tissue type, can be particularly useful in identifying trace contamination of that tissue type in a composite meat product (or animal tissue), i.e. primarily of another type.

Another method that can be used to analyze the triglyceride signature is a method of principal component analysis. This method can typically be used in place of the peak integration and comparison method, but it can also be used in combination with this method. Principal component analysis is a standard data processing technique and we have found it to be particularly effective in distinguishing between different types of tissue, particularly between different species of animals. In addition, the major components can preferably be calibrated to determine contamination or adulteration levels using a method known as major component regression. We have found that principal component analysis is a very effective technique in the present application because of the specific regions of interest in the chemical shift spectrum and the strong relationships that exist between the triglyceride content of the tissue and the animal species or tissue type from which they are derived . Such relationships are not always straightforward, and in such cases principal component analysis is an effective technique for finding more complex relationships. Further statistical analysis processes such as neural network analysis can in principle also be used, although some such techniques may require a well-structured data set of sample data determined by trained personnel.

The spectrometers used in the present invention have relatively small magnetic field strengths; this typically means that, compared with a device with a stronger field, there are higher noise values in the signal. To deal with this, the method of step (iii) preferably comprises comparing a set of peaks in signal intensity at the beginning of the FID signal detected in the period with a set of peaks at the end of the FID signal detected in the period and calculating one Signal-to-noise ratio from this comparison. The signal-to-noise ratio increases with the square root of the number of scans (ie the number of individual FID signals that are added up). The signal-to-noise ratio can be measured and then an appropriate number of scans can be calculated and performed to achieve a desired overall signal-to-noise ratio / data quality in the final data. Typically, step (iii) is repeated according to the calculated signal-to-noise ratio to achieve a target signal-to-noise ratio for the data used in the following analysis.

Preferably, the output characteristic data indicate a candidate animal tissue for the animal tissue used to obtain the sample. In the event that the spectrometer has been set up or 'calibrated' to assign several different triglyceride signatures to different animal tissues, the characteristic data can be used to suggest a candidate identity of the animal tissue used to generate the sample. This candidate animal tissue (or identity candidate) may be the particular species of animal from which the tissue was obtained, or it may indicate the particular cut or quality of the meat. This information can be output as a marker, for example the word "pork" is displayed on a monitor, or it can be presented in a more abstract manner, such as triggering a sound, a light indicator or a mechanical output. In the event that the signature analysis indicates the presence of more than one animal tissue, multiple candidate animal tissue identities can be displayed, and preferably their relative amounts can be reported (e.g. 80% cattle, 20% horse).

Preferably, step (iv) further comprises carrying out a phase correction on the frequency spectrum. With ideal (theoretical) NMR acquisition, all frequency components contained in the FID signal begin exactly in phase, and a phase correction to the frequency spectrum is not necessary. However, in typical data acquisition, the frequency components are shifted from one another by unknown and different amounts, and phase correction is necessary.

The selection of the solvent to be used is important because it is effective in extracting the triglyceride molecules, is ineffective in extracting other molecules which can cause noise in the signal and itself does not have an NMR spectrum which interferes with the analysis of the triglyceride signature. Preferably the solvent is deuterated chloroform. Chloroform is an effective solvent for organic, carbon-based, hydrophobic molecules such as triglycerides. When mixed with meat, chloroform only slightly dissolves the fat molecules known as triglycerides from the meat sample and other components. Other types of fat (such as free fatty acids, phospholipids, monoglycerides, or diglycerides) in the solvent are less than 1 percent. Typically, deuterated chloroform is used in the NMR instead of chloroform in order to avoid the spectrum being “falsified” by the appearance of a chloroform peak at 7.26 ppm. Deuterated chloroform has a similar composition to chloroform, but the hydrogen atom in chloroform has been replaced by a deuterium. Deuterium resonates at a different frequency, i.e. not stimulated by the HF pulse applied by the spectrometer with typical hydrogen frequencies. In practice, however, a sample of deuterated chloroform is never 100% deuterated and thus a reduced peak still occurs at 7.26 ppm, corresponding to the trace presence of chloroform. Fortunately, this is not a problem in this context because this signal does not overlap the triglyceride signature. Other solvents such as toluene or benzene can in principle also be used, but it has been found that these are less effective in extracting triglycerides and can produce undesirable peaks in the frequency spectrum in the regions of interest.

The carrier frequency of the RF pulse is preferably between 40 and 110 MHz. This range corresponds roughly to the frequency required to stimulate the hydrogen nuclei in a magnetic field with a strength between 1.0 and 2.5 Tesla. This frequency range is sufficient to obtain a triglyceride signature which can be effectively analyzed and yet delivered by a spectrometer which is small in size and relatively low in cost.

Preferably, step (iv) further comprises integrating a glyceride region of the frequency spectrum. Has preferred the region indicating the glycerol content in the triglyceride molecules has a chemical shift between 4.0 to 4.5 ppm. The frequency spectra are also preferably adjusted based on a region of the spectra indicative of the glycerol content in the triglyceride molecules. This glycerol signal is present in all samples that contain triglyceride and has a common spectral response for each sample. It is therefore a suitable choice with which spectra of different samples can be compared.

Preferably, step (iv) further comprises normalizing the spectrum based on the integral of the glyceride region. The intensity of the signal indicating the glycerol content is proportional to the total triglyceride content in the sample, and thus the spectrum is preferably always scaled in such a way that the intensity of the peaks originating from the glycerol “triglyceride backbone” is the same in each of the spectra. This process explains the variation in the amount of triglyceride molecules between samples.

Advantageously, step (iv) is carried out using a fast Fourier transform technique. This is a mathematical technique that is implemented and used using software (such as MNova ™) to significantly reduce the processing time for performing a Fourier transform on the data obtained.

Before step (i), the animal tissue is preferably first cut into small pieces, finely chopped or otherwise homogenized. This ensures that the lean and fatty parts of the meat are mixed well together so as to reduce the variation in triglyceride levels between different samples from the same source.

Advantageously, steps (i) through (iv) are initially carried out using known animal tissues of known type so as to obtain target triglyceride signature data for use in the analysis of step (v). This procedure effectively calibrates the spectrometer and in particular the analytical method used so that certain triglyceride signatures can be assigned to certain animal tissues. The characteristic data from a variety of different animal tissues can be entered into a database so that when the spectrometer is later used on unknown animal tissues, the database can be referenced to find a likely candidate for the animal tissue from which the sample is being sampled was obtained. The animal tissues for which the device has been calibrated to recognize the triglyceride signatures form a list of potential animal tissue candidates to be dispensed. Alternatively, if the signature does not match stored data in the database, the user can be informed so that the animal tissue can be subjected to more detailed analysis such as DNA sequencing after PCR. In this case, the identity of the animal tissue used to generate the sample is displayed as unknown, but no candidate animal tissue is output. A database can be formed which includes the spectral data itself, or it can contain information gathered therefrom after some form of analysis has been performed on it.

Advantageously, the results stored in the database can be used to define triglyceride signature information that is characteristic of a known type of meat. This may be the case when a number of meat samples have been obtained from the same carcass of known origin. Variations in the fat content and thus in the triglyceride signature in this animal body can be explained by building a database with information that is characteristic of the triglyceride signatures obtained from the various meat samples for this animal body. An average expected triglyceride signature contains triglyceride signature information that is characteristic of a known type of meat, such as where the resonance frequency peaks can be expected and the intensity, shape, and width of those peaks. This database can be used to carry out a principal component analysis method. In some cases the NMR spectrometer cannot be used to carry out the analysis step itself, for example the spectrometer can be connected to a computer which can operate with suitable software to carry out this analysis. Alternatively, the analysis and database of known characteristic triglyceride signatures with relevant markings for animal tissue candidates can be present in a remote control device which can be accessed via an Internet connection.

Figure list

• 1 eine Perspektive eines Benchtop-NMR-Spektrometers, das mit einem Computer zur Verwendung gemäß den diskutierten Beispielen verbunden ist;
• 2 ein Flussdiagramm, das die Verfahrensschritte darstellt, die zum Kalibrieren eines NMR-Spektrometers gemäß den beschriebenen Beispielen ausgeführt werden;
• 3 ein Beispiel eines Frequenzspektrums, das eine Triglyceridsignatur umfasst, die aus Rindfleisch gemäß dem Kalibrierungsbeispiel erhalten wurde;
• 4 ein Flussdiagramm, das die Verfahrensschritte zum Analysieren von Tiergewebe gemäß einem ersten Beispiel der Erfindung darstellt;
• 5 eine graphische Darstellung, die durch ein Verfahren der Hauptkomponentenanalyse von Triglyceridsignaturen gemäß einem zweiten Beispiel der Erfindung erhalten wurde; und
• 6 ein Beispiel für ein Frequenzspektrum, das eine Triglyceridsignatur umfasst, die aus Rindfleisch und Schweinefleisch gemäß einem dritten Beispiel der Erfindung erhalten wurde.

Some examples of a method for analyzing animal tissue will now be described with reference to the accompanying drawings. Show it:

• 1 Figure 3 is a perspective of a benchtop NMR spectrometer connected to a computer for use in accordance with the examples discussed;
• 2 a flow chart illustrating the method steps that are carried out for calibrating an NMR spectrometer according to the examples described;
• 3 an example of a frequency spectrum that includes a triglyceride signature derived from Beef obtained according to the calibration example;
• 4th a flow chart illustrating the method steps for analyzing animal tissue according to a first example of the invention;
• 5 Fig. 8 is a graph obtained by a method of principal component analysis of triglyceride signatures according to a second example of the invention; and
• 6th an example of a frequency spectrum comprising a triglyceride signature obtained from beef and pork according to a third example of the invention.

Detailed description

To aid in understanding the invention, the following discussion contains a number of examples of implementing the present invention. An example is first given for calibrating a benchtop NMR spectrometer with a known type of animal tissue. Examples of identifying unknown tissues are then discussed, along with typical experimental data that can be obtained.

contraption

1 is an illustration of a system 1 that one with a computer 10 connected “benchtop” NMR spectrometer 2 for use in accordance with the examples of the invention to be discussed. The spectrometer 2 includes a sample holder 5 , shown as a cylindrical cavity in the spectrometer 2 , and two NdFeB permanent magnets 7a and 7b . The magnets 7a and 7b have opposite north and south poles, so that a static, primary magnetic field B ₀ between 1.0 and 2.5 Tesla on the sample holder 5 works. In use, a sample (not shown) is placed in the sample holder 5 brought in the primary magnetic field B ₀ . Two magnets are not necessarily required, and other magnet geometries can also be used, such as a cylindrical magnet.

The NMR spectrometer also includes a set of radio frequency (RF) coils 4th operating between 40 and 110 MHz to generate RF magnetic fields perpendicular to the direction of the B ₀ field, and a set of B ₀ auxiliary coils (not shown) which are used to compensate for _{the inhomogeneities of the B 0 field} compensate. The RF coils 4th are capable of detecting RF FID signals from a sample in the sample holder 5 originate, as well as for the application of RF fields to the sample. Other features such as heaters, a vacuum chamber, and vibration dampers, etc. typically found in benchtop NMR spectrometers may also be provided, but are not shown here for clarity.

The benchtop spectrometer 2 is an independent unit that can be set up and moved directly on a laboratory bench or other surface (unlike superconducting NMR spectrometers). The spectrometer 2 is with a computer 10 connected, which the by the spectrometer 2 results obtained analyzed. Alternatively, this analysis can be carried out remotely by a server (not shown), for example via access to the Internet. The computer 10 includes a display for the user to view the results of the analysis.

Calibrate the spectrometer

2 Figure 3 is a flow chart showing the process steps for calibrating the spectrometer 2 with high quality triglyceride signature data according to a first example. In the present case, the method is intended to be used for the subsequent checking of minced beef for the presence of substitute meat of lower quality.

In step 101 a 50g serving of ground beef is obtained from a guaranteed source and further finely chopped so that the lean and fat parts are mixed together. Optionally, the origin or DNA of each serving is verified using PCR techniques to ensure that the tissue is identified. A 3 g subsample is obtained and placed in a vial containing 1 mL of deuterated chloroform. The vial is shaken and left for 5 minutes to dissolve the triglyceride molecules from the meat. The remaining solid animal tissue is then filtered from the solvent and the liquid solvent, now containing triglyceride molecules from the meat, is transferred to a 5 mm NMR sample tube using a pipette to form a calibration sample.

In step 102 the calibration sample is placed in a sample holder 5 of the benchtop NMR spectrometer 2 brought. In the present case, the NMR spectrometer 2 a permanent magnet system ( 7a and 7b ), which generates a magnetic field of 1 Tesla at a target location where the sample holder is in use 5 is located. The magnet system generates a magnetic field B ₀ , which is very homogeneous, so that each part of a sample in the sample holder is exposed to an essentially identical magnetic field strength with a common direction. As soon as the sample is in the NMR spectrometer 2 has been stabilized, for example by stabilizing the temperature and, if necessary, applying "shimming" of the magnetic field, an NMR signal is obtained from the calibration sample.

In step 103 is made by the RF coils 4th of the spectrometer 2 a “90 degree” RF pulse is applied to the sample. This is achieved using a radio frequency signal of 50 MHz which is pre-calibrated with reference to the known magnetic field strength to which the sample is exposed so as to cause the hydrogen nuclei in the sample to be deflected out of alignment with the applied magnetic field and precess around the main field direction. This precession decreases, and the resulting free induction decay (FID) back into alignment causes a release of energy to be released by the RF coils 4th of the spectrometer is detected as an FID signal. The FID signal is generated by the spectrometer 2 digitized and sent to the computer 10 communicated for analysis.

The signal-to-noise ratio in the detected FID signal is in step 104 calculated using a set of peaks at the beginning and at the end of the signal. In the present example, the NMR spectrometer acquires 32,000 points during the acquisition time of the signal. In this example, an average of the first and last five percent of the points in the signal (i.e. 1,600) is taken as the signal-to-noise ratio. It was found that the signal-to-noise ratio increases with the square root of the number of times the acquisition is repeated using the applied RF signal (known as "scans"), at least less than 100 scans, and so becomes the Total number of scans needed to achieve a target signal-to-noise ratio is calculated and then performed by the spectrometer. The target signal-to-noise ratio varies between different spectrometers and the required quality of the spectrum, although for a given spectrometer and experiment a suitable number of scans can easily be derived by the operator.

The resulting FID signals from the scans are detected by software on the computer 10 processed and combined to form a composite FID signal.

In step 105 the composite FID signal is converted into the frequency space by suitable frequency analysis software in order to obtain a frequency spectrum. Appropriate Fast Fourier Transform software can be used to accomplish this in order to reduce the time it takes to perform this conversion into frequency space.

When the time domain FID signal is Fourier transformed, it creates “real” and “imaginary” components. Ideally, the real components contain all information about the absorption spectrum. However, in the actual NMR acquisition (where the hydrogen atoms do not resonate in phase with each other) a phase correction must be carried out in order to obtain their absorption spectrum as a real result of the Fourier transform. This phase correction is carried out by the software which fits the real and imaginary components of the Fourier transform by different amounts for each sample.

The frequency spectrum is then subjected to further processing so that the chemical shift for the spectrum is calculated using tetramethylsilane as the reference point (this is routine with NMR). This forms the abscissa of the spectrum while the intensity forms the ordinate. The chemical shift data provide information about the specific chemical environments in which the hydrogen nuclei are located in the sample, so that there is a relationship between a specific chemical shift and a specific part of a molecule with hydrogen atoms. One such part is the glyceride backbone region of the triglyceride molecules.

An example of a frequency spectrum that can be generated for a sample using ground beef is shown in 3 shown. In this case the triglyceride signature is the intensity of the frequency spectrum in the olefinic region 150 (5.0 to 5.6 ppm), the glyceride region 151 (4.0 to 4.5 ppm) and the bis-allylic region 152 (2.5 to 3.0 ppm). In addition, a strong resonance between 1.0 and 2.5 ppm can be seen, which is due to the methylene groups (-CH ₂ -) in the fatty acid chain (except for the bis-allylene). The reaction between 0.5 to 1.0 ppm is due to the terminal methyl (-CH ₃ ) groups of the fatty acid chain. The region of the spectrum between 0.5 and 2.5 ppm is typically not analyzed because the peaks in this region overlap significantly and make it difficult to obtain any useful information about the sample. However, if linolenic acid is present in the sample in sufficient quantities so that a characteristic signal is visible in the spectrum, this signal region (0.96 to 1.02 ppm) can be included in the triglyceride signature for the purposes of analysis.

The resulting frequency spectrum for this sample is shown in step 106 concatenated into a series of a data matrix so that it can be analyzed mathematically. The glyceride region 151 is integrated so as to provide a measure of the number of glycerol-containing molecules present. Glycerol is in everyone. Triglyceride molecule, and so the integral of the signal associated with glycerol provides a measure of the number of triglyceride molecules present in the sample (since it is assumed that other glycerol-containing molecules are not present in significant amounts). The Spectrum is normalized by this integral to accommodate varying amounts of triglycerides among the samples. The glyceride region 151 can also be used as an internal reference with which the spectra are to be compared, if necessary.

In addition to normalizing the spectra, step 107 the peak integration method of analysis is also applied to other display parts of the obtained spectra. In particular, the bis-allylic region 152 and the olefin region 150 of the spectrum and adjusted according to the glycerol normalization. While in this example the olefinic, bis-allylic and glyceride regions are integrated, in other cases the triglyceride signature is based on only two regions of the olefinic, glyceride, bis-allylic and linolenic regions, and only these two regions are integrated.

The values for these integrals are then inserted into pre-installed or user-defined equations to obtain characteristic data for the triglyceride signature. These equations can be based on pre-existing knowledge of, for example, the number of protons that contribute to each of the peaks in the spectrum and can be used to estimate the percentage of monounsaturated fatty acids, polyunsaturated fatty acids and saturated fatty acids in the animal tissue. An exemplary consideration that should be made concerns the signal generated by the hydrogen atom, that is, attached to the middle carbon in glycerol, as this is just below the olefinic band 150 occurs. This should be taken into account and negated when calculating peak areas for estimating component concentrations. These characteristic data are then used in step 108 output and stored in a database and related to the relevant sample for which the spectrum was obtained.

The steps 101 to 108 are repeated for a number of subsamples (such as five) from the original 50g sample of tissue. This process is then repeated for 6 different batches of ground beef, each from a known and approved source. In total, data from 30 samples are therefore obtained. Samples are obtained from different batches to ensure that all natural variations between batch contents are taken into account. The average characteristic data obtained from each sample is calculated and stored in the database with the associated animal tissue tag “minced meat”.

In step 109 statistical analysis is performed on the data to mathematically analyze the variance in the data and allow confidence limits associated with the characteristic data. The results of the statistical analysis are also stored in the database, providing, for example, expected “average” values for the integrals, their internal ratios and associated “tolerance levels” of the variance.

Analyze an unknown animal tissue

First example

4th Fig. 3 is a flow chart showing the essential method steps according to the first example of the invention. The first example provides a process for checking ground beef for the presence of lower quality substitute meat.

The benchtop NMR spectrometer 2 will start in the step 200 calibrated according to the above method using a source of verified ground beef. A serving of finely chopped, unknown animal tissue, advertised as ground beef, is obtained and in the crotch 201 to a sample according to step 101 of the calibration example. The sample is placed in the sample holder 5 of the spectrometer 2 brought and in crotch 202 according to the steps 103 to 106 of the calibration procedure to obtain a frequency spectrum for the sample based on the integral of the glyceride region 151 has been normalized. In step 203 characteristic data are obtained for the sample using a method of peak integration. The olefinic and bis-allylic regions ( 150 and 151 ) of the frequency spectrum (between 5.0 to 5.6 ppm and 2.5 to 3.0 ppm, respectively) are integrated and these values are entered into a set of predefined equations to obtain characteristic data (as before) for the animal tissue used get in the sample. In step 204 the computer compares 10 this characteristic data with the average characteristic data for minced meat, which are stored in a database during the calibration process to see if they are within the accepted tolerance ranges. If so, take a step 205a running, displaying the "ground beef" mark, which the user can see on a display on the computer 10 can read so that the identity can be determined. If the characteristic data for the sample are outside the permitted ranges, however, the user is warned that the sample does not meet the predetermined response and thus may contain substitute meat. This is step 205b . The sample will then likely require further testing, such as by DNA analysis.

In some cases, the percentages of monounsaturated, polyunsaturated and saturated fat may also be displayed to the user if calculated with an acceptable level of accuracy.

Second example

In a second example of the invention, triglyceride signatures formed by several different animal tissues (in this case beef, lamb, pig and goose) can be identified and distinguished. In this example principal component analysis is used in place of the peak integration method used in the first example.

First, it is necessary to create an appropriate database of characteristic data for each type of animal tissue that the user wishes to identify. The system 1 will be in crotch 200 calibrated, but in this example characteristic data are obtained for meat from beef, lamb, pork and goose. 30 samples are made using guaranteed finely minced beef. Various "cuts" are used to give representative average data for beef. The steps 101 to 106 are then performed to obtain a scaled / normalized frequency spectrum for each sample. These spectra are compared on the basis of the glyceride region and stored in a database as a marker for the animal tissue. This process is repeated for the remaining tissues (using verified sources of lamb, pork and goose meat) to obtain spectra which are also stored in the database.

The characteristic data are then generated for each tissue type (lamb, pork and goose meat) using the Principal Components (PC) analysis. This is a standard data processing technique and has been found to be particularly effective in distinguishing between different types of tissue, particularly between different species of animals. The data set is broken down into the main components that describe systematic sources of variability in the data set in decreasing order of significance. The spectra from the various tissue types in the database are effectively compared to one another to determine the components which vary between each spectrum. This is used to classify the tissue types based on an appropriate subset of the principal components, by constructing tolerance intervals that define each group in pc room by a statistical method, for example Hotelling's T-square distribution or the Mahalanobis distance.

An animal tissue sample of unknown meat (with an unknown identity) to be tested is then prepared into a sample and according to the steps 101 to 106 scanned to get a frequency spectrum with a triglyceride signature. The frequency spectrum (or part of it) is then analyzed as a single data set. Appropriate major components as defined by the calibration process are then extracted from the spectrum as characteristic data for the animal tissue. These main components are compared to the predefined values in the database to see if they fall within one of the predefined tolerances. If so, the appropriate marker indicating an identity candidate for this data is output so that the user can read it on a display (step 205a ). If not, the user is warned on a display that the animal tissue is unknown and requires further testing, such as by DNA analysis, to confirm his identity (step 205b ). Alternatively, the characteristic data can simply be entered in a graphic representation, such as 5 in which case the user can make the decision as to the origin of the animal tissue.

An example of data that can be generated according to the second example is shown in FIG 5 shown. Two major components (PCA1 and PCA2) are shown and data obtained from several samples made using beef, lamb, pork and goose meat are shown by the various dots on the graph. The variance or 'tolerance level' expected for each of these tissues is shown by the ring areas of the graph and is calculated through the calibration process using verified sources of animal tissue. The samples that produce points that occur in a particular ring area (ie, tolerance) are identified as containing the tissue type or candidate animal tissue associated with that area. It can be seen from the graph that, for example, results obtained from the goose appear in a range different from those obtained from the lamb. 5 also shows that in some cases, such as pork and beef, there may be an overlap between the tolerance regions. If the characteristic data output by a sample is found to occur in one of these areas with overlap, the sample is marked for the user as requiring further testing to confirm its identity.

Principal components can also be calibrated to determine contamination or adulteration levels using a method known as principal component regression. This is a multivariate regression technique that is suitable for applying spectral data sets and is discussed, for example, in Jolliffe, I. T. 2002, Principal Component Analysis, Springer.

Third example

In a third example of the invention, the presence of trace pork contamination in a product containing primarily beef is identified by analyzing subregions of the triglyceride signature. Certain tissue types produce characteristic peaks in the olefinic or bis-allylic region which can be used to identify this tissue. If this tissue is only present in a trace amount, these peaks cannot significantly change the total integral for this region, but they can still be detectable in the total spectrum due to the presence of a narrow peak. Quantitative analysis of this peak can also be performed using a computer 10 according to the following example.

First, a calibration process is carried out. A database of averaged, normalized spectra is obtained using several guaranteed tissue types (including beef and pork) according to the second example. The olefinic and bis-allylic regions ( 150 and 152 ) each spectrum is then checked by a computer 10 displayed on a graph and analyzed by a user. The presence of a characteristic peak (or multiple peaks) in a spectrum which is believed to be sufficiently different from the equivalent region for other tissue types is manually identified by a user and the chemical shift limits for that peak are entered into a database, around a sub-region 600 define.

6th shows an example of an average spectrum obtained using verified beef compared to an average spectrum obtained using verified pork. In this example, a characteristic peak occurring for pork and not for beef (or for the other tissue types analyzed) is identified by the user between 2.6 and 2.7 ppm, and a sub-region 600 is defined. This peak occurs because of the relatively high presence of polyunsaturated fatty acids in pork.

The sub-region 600 is integrated for each tissue type and the value is stored in the database as a reference value for this tissue type. A "tolerance" or variance for the integral of this sub-region is also calculated. If the user is only interested in the possible presence of pork, then only a sub-region is needed 600 entered into the database. Alternatively, the process can be repeated for equivalent subregions assigned to other animal tissues as desired. A calibration procedure using principal component analysis is also carried out according to the second example to obtain a database of characteristic data for each tissue type analyzed.

An unknown animal tissue to be analyzed is then prepared into a sample and a normalized frequency spectrum is obtained as before. The triglyceride signature for the sample is then analyzed using principal component analysis and the characteristic data identified as falling within the beef tolerance limit. In addition, the specific subregions of interest (as defined during the calibration process) are now also analyzed to identify the possible presence of trace contaminants from other tissue types in the sample. The integral for the sub-region 600 of the unknown meat is made by the computer 10 compared to the values stored during the calibration process for pork and beef. In this case, although the unknown tissue has previously been identified as beef, it turns out that the integral of the subregion 600 is above what is typically expected for beef and closer to what is expected for pork. Using predefined algorithms, the approximate relative weighting of beef and pork is then calculated and processed by the computer 10 displayed. In addition, there is also a graphic representation of the sub-region 600 through the computer 10 for verified pork and beef samples and for the unknown tissues visually displayed to the user. This allows the user to manually check to see if it matches the analysis. Thus, a candidate animal tissue is indicated by showing the approximate weights of the animal tissues used in the sample, and the raw data is shown to support this analysis.

Claims (21)

A method for identifying animal tissue using an NMR spectrometer with a permanent magnet with a magnetic field strength on a sample holder of the spectrometer between 1.0 and 2.5 Tesla, - the method comprising: (i) contacting the animal tissue with a solvent in order to Triglyceride molecules from the Dissolving animal tissue in the solvent to obtain a sample; (ii) placing the sample in the sample holder of the NMR spectrometer; (iii) applying a radio frequency (RF) pulse sequence to the sample and detecting a free induction decay (FID) signal using the NMR spectrometer; (iv) converting the detected FID signal into the frequency domain in order to obtain a frequency spectrum with a triglyceride signature, and (v) analyzing the triglyceride signature and outputting characteristic data for the animal tissue, - the characteristic data indicating the identity of the animal tissue and - wherein: - the term 'identity' refers to the species of animal tissue analyzed and / or - in a case that the sample is prepared using a mixture of different animal tissues, the characteristic data a plurality of identities with relative weights for different identities of Show animal tissue contained in the sample. Procedure according to Claim 1 wherein the analysis of step (v) comprises integrating an olefinic region of the triglyceride signature. Procedure according to Claim 2 , the chemical shift of the olefinic region being between 5.0 and 5.6 ppm. Method according to one of the Claims 1 to 3 wherein the analysis of step (v) comprises integrating a bis-allylic region of the triglyceride signature. Procedure according to Claim 4 , the chemical shift of the bis-allylic region being between 2.5 and 3.0 ppm. Procedure according to Claim 1 wherein the analysis of step (v) comprises performing a principal component analysis method. Method according to one of the preceding claims, wherein after step (iii) the following steps are carried out: - comparing a set of signal intensity peaks detected at the beginning of the FID signal in the time period with a set of peaks detected at the end of the FID signal in the time period, and - Calculating a signal-to-noise ratio from this comparison. Procedure according to Claim 7 wherein step (iii) is repeated according to the calculated signal-to-noise ratio to achieve a target signal-to-noise ratio for the frequency spectrum. A method according to any one of the preceding claims, wherein the output characteristic data indicate one or more candidate animal tissue for the animal tissue used to obtain the sample. Method according to one of the preceding claims, wherein step (iv) further comprises performing a phase correction on the frequency spectrum. A method according to any one of the preceding claims, wherein the solvent is deuterated chloroform. Method according to one of the preceding claims, wherein the frequency of the RF pulse is between 40 and 110 MHz. A method according to any one of the preceding claims, wherein step (iv) further comprises integrating a glyceride region of the frequency spectrum. Procedure according to Claim 13 where the glyceride region has a chemical shift between 4.0 and 4.5 ppm. Procedure according to Claim 13 or 14th wherein step (iv) further comprises normalizing the spectrum based on the integral of the glyceride region. Method according to one of the preceding claims, wherein step (iv) is carried out using a fast Fourier transform technique. A method according to any one of the preceding claims, wherein the animal tissue is first chopped or finely chopped prior to step (i). A method according to any preceding claim, wherein steps (i) through (iv) are initially carried out using known animal tissue so as to obtain target triglyceride signature data for use in the analysis of step (v). Method according to one of the preceding claims, which further comprises storing the results of the analysis of step (v) in a database. Procedure according to Claim 19 , the database being used to carry out a principal component analysis method. The method according to any one of the preceding claims, wherein the triglyceride signature at least comprises two of an olefinic, bis-allylic and glyceride region of the frequency spectrum.

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