Patent Description:
An important part of the meat industry is making an assessment of the quality of meat.

Assessment of meat quality may involve assessing a variety of characteristics to ensure that meat provided to the consumer is of a desired quality. In addition, assessing the quality of meat informs a producer as to how animal characteristics, animal management and/or processing of animals influence the final quality of a meat product.

Determining meat quality often involves methods of directly assessing characteristics of the meat, such as a colour assessment, an analysis of texture and a determination of muscle pH. In some cases, this requires a sample of the meat to be tested, which imposes an additional burden and/or constraint on the processing of meat products.

In addition, processing of meat is typically undertaken on a large scale, so as to provide economic advantages associated with bulk processing. However, methods involving testing samples of meat for quality impose further burdens on bulk processing, such as introducing delays in the production process, the need for integration into the production process, and increased costs.

<CIT> discloses methods and systems for the determination of meat tenderness using high resolution imaging of meat surfaces.

<CIT> discloses a measuring device to measure a surface coating on a foodstuff, such as meat, and in particular discloses the surface measurement of meat to assess metabolites of bacteria present on the surface, by measuring fluorescent radiation, in order detect bacterial contamination.

Jordan et al. is directed to surface measurement of meat freshness and discloses the non- invasive mobile monitoring of meat quality. The monitoring is non-invasive and uses fluorescent measurements to determine the freshness of meat and recognises that the surface measurement involves fluorescence from contaminating bacteria on the surface of the meat. Jordan et al. also discloses the use of a lamp producing ultraviolet and visible light.

<CIT> discloses the determination of the tenderness of meat by using optical measurement in the visible and near infrared ranges, discloses the measurement of light in either a transmission mode or a reflectance mode from the meat, and discloses the measurement of reflected light from the meat product.

<CIT> discloses a system for simultaneously detecting quality parameters of raw meat from multiple points.

<CIT> discloses a computer-implemented method for evaluating an animal product. In particular, this document discloses receiving measurement data of the animal product and selecting a generic mathematical model built from a plurality of corresponding animal products representing a variability of at least one aspect of a whole population.

Accordingly, methods of assessing quality of a meat product that do not require sampling would be advantageous, and in particular methods where the product does not need to be physically interrogated.

The present invention is directed to the subject-matter set out in the appended claims.

In particular, the present invention is directed to method (<NUM>) of assessing quality of a meat product (<NUM>), the method comprising: receiving (<NUM>) data representative of autofluorescent light emitted from the meat product (<NUM>) upon application of incident laser light from a probe (<NUM>) inserted into the meat product (<NUM>) analysing (<NUM>) the data to determine one or more parameters indicative of quality of the meat product (<NUM>); and assessing (<NUM>) the quality of the meat product (<NUM>) on the basis of the one or more parameters.

In certain embodiments, the meat product (<NUM>) is a carcass, a part of a carcass, a cut of meat from the carcass, or a processed product derived from the carcass or the cut of meat.

In certain embodiments, the meat product (<NUM>) is a red meat product (<NUM>).

In certain embodiments, the meat product (<NUM>) is a product derived from a sheep, a lamb, a cow, a calf, a pig, a goat, a deer, or a horse. Other types of meat products (<NUM>) are contemplated.

In certain embodiments, the meat product (<NUM>) is an ovine meat product (<NUM>), a bovine meat product (<NUM>), a porcine meat product (<NUM>), a caprine meat product (<NUM>), a cervine meat product (<NUM>), or an equine meat product (<NUM>).

In certain embodiments, the meat product (<NUM>) is a beef meat product (<NUM>), a veal meat product (<NUM>), a lamb meat product (<NUM>), a mutton meat product (<NUM>), a pig meat product (<NUM>), a goat meat product (<NUM>), a deer meat product (<NUM>), or a horse meat product (<NUM>).

The term "quality of a meat product (<NUM>)" as used herein refers to a selected characteristic of a meat product (<NUM>). Examples of quality of a meat product (<NUM>) may comprise one or more of eating quality, price point, grading, pH, fat content, tenderness, and suitability for specific purposes.

In certain embodiments, the quality of the meat product (<NUM>) comprises eating quality.

In certain embodiments, the quality of the meat product (<NUM>) comprises pH.

In certain embodiment, the quality of the meat product (<NUM>) comprises a grading or scoring system. For example, the method (<NUM>) may be used to grade or score the eating quality of the meat product (<NUM>).

In certain embodiments, the quality of the meat product (<NUM>) comprises a threshold value, a minimum value, a maximum value, an assigned value, or a range of values for a quality of the meat product (<NUM>). The quality of the meat product (<NUM>) may comprise one of a number of different grades, for example low, medium or high grade.

In certain embodiments, the incident laser light comprises light of one or more specific wavelengths. In certain embodiments, the incident laser light comprises one or more wavelengths over a specific range of wavelengths.

Methods and sources for producing incident laser light of one or more wavelengths are known in the art, and commercially available.

In certain embodiments, the incident laser light comprises a wavelength in the range of <NUM> to <NUM>. Other wavelength ranges are contemplated.

In certain embodiments, the incident laser light comprises a wavelength in the range of one of <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, or <NUM> to <NUM>, or about one of the aforementioned ranges.

In certain embodiments, the incident laser light comprises a wavelength in the range of one of <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, and <NUM> to <NUM>, or about one of the aforementioned ranges.

In certain embodiments, the incident laser light comprises a wavelength of <NUM> + <NUM>.

In certain embodiments, the incident laser light comprises a wavelength of about <NUM> or about <NUM>.

The term "about" or "approximately" means an acceptable error for a particular value, which depends in part on how the value is measured or determined. In certain embodiments, "about" can mean <NUM> or more standard deviations. When the antecedent term "about" is applied to a recited range or value it denotes an approximation within the deviation in the range or value known or expected in the art from the measurements method.

In certain embodiments, the incident laser light is transmitted to the meat product (<NUM>) via an optical fibre. In certain embodiments, the incident laser light is transmitted to a probe (<NUM>) via an optical fibre.

In certain embodiments, the incident laser light is applied to the meat product (<NUM>) via a probe (<NUM>). In certain embodiments, the incident laser light is applied to the meat product (<NUM>) via a probe (<NUM>) below the surface of the meat product (<NUM>).

In certain embodiments, the incident laser light is applied to the meat product (<NUM>) via an optical fibre. In certain embodiments, the incident laser light is applied to the meat product (<NUM>) via an optical fibre probe (<NUM>). In certain embodiments, the incident laser light is applied to the meat product (<NUM>) via an optical fibre in a needle.

In certain embodiments, the incident laser light is applied to the surface of the meat product (<NUM>). In certain embodiments, the incident laser light is applied to below the surface of the meat product (<NUM>).

In certain embodiments, the light emitted from the meat product (<NUM>) comprises autofluorescent excited light.

The incident laser light induces autofluorescence in the meat product (<NUM>). The emitted light comprises autofluorescent light excited in the meat product (<NUM>) by application of the incident laser light to the meat product (<NUM>).

In certain embodiments, the autofluoresence is excited by the application of laser light to the meat product.

In certain embodiments, the method (<NUM>) comprises detecting autofluorescent emitted light from the meat product (<NUM>) upon application of the incident laser light to the meat product (<NUM>). Methods for detecting light and converting it into data are known in the art.

In certain embodiments, the received (<NUM>) data from the meat product (<NUM>) comprises data associated with a spectrum of light. In certain embodiments, the received (<NUM>) data from the meat product (<NUM>) comprises data associated with autofluorescence excited light. In certain embodiments, the received (<NUM>) data from the meat product (<NUM>) comprises data associated with spectral autofluorescent light.

In certain embodiments, the received (<NUM>) data from the meat product (<NUM>) comprises data associated with light with a wavelength in the range from <NUM> to <NUM>.

In certain embodiments, the received (<NUM>) data comprises spectral data emitted from the meat product (<NUM>). In certain embodiments, the received (<NUM>) data comprises spectral data representative of autofluoresence excited in the meat product (<NUM>).

In certain embodiments, the spectral data comprises data associated with light in the range of <NUM> to <NUM>.

In certain embodiments, the spectral data comprises data associated with light with a wavelength in the range selected from one of <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, or <NUM> to <NUM>, or about one of the aforementioned ranges.

In certain embodiments, the one or more parameters indicative of quality of the meat product (<NUM>) comprises a single parameter. In certain embodiments, the one or more parameters indicative of quality of the meat product (<NUM>) comprise two parameters. In certain embodiments, the one or more parameters indicative of quality of the meat product (<NUM>) comprise a plurality of parameters.

Examples of parameters include a parameter indicative of carcass weight (or part of a carcass), a parameter indicative of fat content in the meat product (<NUM>), a parameter indicative of fat mass in the meat product (<NUM>), a parameter indicative of a measurement (size, area, and/or depth) of a specific muscle or region in a carcass, a parameter indicative of acidity and/or alkalinity (such as pH) in the meat product (<NUM>), one or more colours of the meat product (<NUM>), shear force (SF), intramuscular fat (IMF), species of animal, time of year, or a combination of any one or more of the aforementioned parameters.

In certain embodiments, the one or more parameters comprises one or more of the following:.

In certain embodiments, the one or more parameters comprise a parameter indicative of the temperature of the meat product (<NUM>).

In certain embodiments, the one or more parameters comprise a parameter indicative of intra-muscular fat (IMF parameter) and a parameter indicative of shear force (SF parameter).

In certain embodiments, the data is analysed using one or more models to predict the one or more parameters.

In certain embodiments, the one or more models comprise linear statistical models. In certain embodiments, the one or more models comprise non-linear models.

In certain embodiments, the one or more models are created using a data minimisation approach. In certain embodiments, the data minimisation approach includes employing Akaike's Information Criterion. Other methods for data minimisation are contemplated.

In certain embodiments, the one or more models comprise non-linear models.

In certain embodiments, the one or more models comprise logistic regression.

In certain embodiments, the one or more models are created using training data including data representative of light emitted from a plurality of sample meat products (<NUM>) upon application of incident laser light to the sample meat products (<NUM>), each sample meat product (<NUM>) having pre-determined values for the one or more parameters.

In certain embodiments, the one or more models are created using machine learning.

In certain embodiments, the one or more models are created using neural networks.

In certain embodiments, the one or models are created using deep learning.

In certain embodiments, the data comprises spectral data which is processed prior to analysis to reduce a number of data points across the spectral range.

In certain embodiments, the method (<NUM>) is used to grade, score or classify a meat product (<NUM>) for quality.

Certain embodiments of the present invention include software (<NUM>) for use with a computer (<NUM>) comprising a processor (<NUM>) and memory (<NUM>) for storing the software (<NUM>), the software (<NUM>) comprising a series of instructions executable by the processor (<NUM>) to carry out the methods (<NUM>) as described herein.

Certain embodiments of the present invention provide a system (<NUM>) for assessing quality of a meat product (<NUM>).

Certain embodiments of the present invention provide a system (<NUM>) for assessing quality of a meat product (<NUM>), the system comprising: a laser light source (<NUM>) for applying incident light from a probe (<NUM>) inserted into the meat product (<NUM>); a measuring device (<NUM>) for producing data representative of autofluorescent light emitted from the meat product (<NUM>) upon application of the incident light to the meat product (<NUM>); a processor (<NUM>); a memory (<NUM>); and software (<NUM>) resident in the memory (<NUM>) accessible to the processor (<NUM>), the software (<NUM>) comprising a series of instructions executable by the processor (<NUM>) to analyse the data to determine one or more parameters indicative of quality of the meat product (<NUM>), and provide a measure of the quality of the meat product (<NUM>) on the basis of the one or more parameters.

Meat products (<NUM>), and the quality of meat products (<NUM>), are as described herein.

Sources of light for producing incident laser light are known in the art. Details of incident laser light are as described herein.

In certain embodiments, the source of incident laser light produces light comprising a wavelength in the range of one of <NUM> to <NUM>. Other wavelength ranges are contemplated.

In certain embodiments, the source of incident laser light produces light comprising a wavelength in the range of one of <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, or <NUM> to <NUM>, or about one of the aforementioned ranges.

In certain embodiments, the source of incident laser light produces light comprising a wavelength in the range of <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, and <NUM> to <NUM>, or about one of the aforementioned ranges.

In certain embodiments, the source of incident laser light produces light comprising a wavelength of <NUM> ± <NUM>.

In certain embodiments, the source of incident laser light produces light comprising a wavelength of about <NUM> or about <NUM>.

In certain embodiments, the measuring device (<NUM>) detects and measures the emitted light.

Details of light emitted from a meat product (<NUM>) are as described herein.

In certain embodiments, the measuring device (<NUM>) measures light comprising a wavelength in the range selected from one of <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, or <NUM> to <NUM>, or about one of the aforementioned ranges.

In certain embodiments, the measuring device (<NUM>) measures light comprising a wavelength in the range from <NUM> to <NUM>.

In certain embodiments, the light source (<NUM>) and/or the measuring device (<NUM>) comprise part of a probe (<NUM>). Other arrangements are contemplated.

In certain embodiments, the light emitted from the meat product (<NUM>) comprises light with a wavelength in the range from <NUM> to <NUM>.

In certain embodiments, the light emitted from the meat product (<NUM>) comprises light with a wavelength in the range from <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, or <NUM> to <NUM>, or about one of the aforementioned ranges.

Methods and devices for measuring light and converting the light measured into data are known in the art.

In certain embodiments, the measuring device (<NUM>) comprises a spectrometer.

Processors (<NUM>), memory (<NUM>) and software (<NUM>) are as described herein.

In certain embodiments, the processor (<NUM>) is in physical data connection with the measuring device (<NUM>).

Processing of data is as described herein.

In certain embodiments, the light source (<NUM>) and the measuring device (<NUM>) comprise part of a probe (<NUM>), and the processor (<NUM>), the memory (<NUM>) and the software (<NUM>) are located remotely from the probe (<NUM>) and receive the data over the internet.

In certain embodiments, the software (<NUM>) comprises a series of instructions executable by the processor (<NUM>) using the method (<NUM>) described herein.

One or more parameters indicative of quality, and methods for their determination, are as described herein. The use of the one or more parameters to provide a measure of the quality of a meat product (<NUM>) is as described herein.

In certain embodiments, the quality of meat product (<NUM>) comprises fat content (such as intramuscular fat content) and/or tenderness (such as shear force).

In certain embodiments, the system is used to grade, score or classify a meat product (<NUM>) for quality.

While not part of the present invention, which is set out in the appended claims, the following provides additional disclosure to better understand the context of the present invention.

Certain embodiments of the present disclosure provide a meat product graded, scored or classified using a system as described herein.

Certain embodiments of the present disclosure provide a method of creating one or more models for assessing quality of a meat product.

Certain embodiments of the present disclosure provide a method of creating one or more models for assessing quality of a meat product, the method comprising:.

Meat products, and the quality of meat products, are as described herein.

Details of incident light are as described herein.

In certain embodiments, the incident light comprises light with a wavelength in the range of one of <NUM> to <NUM>. Other wavelength ranges are contemplated.

In certain embodiments, the incident light comprises light with a wavelength in the range of one of <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, or <NUM> to <NUM>, or about one of the aforementioned ranges.

In certain embodiments, the incident light comprises light with a wavelength in the range of <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, and <NUM> to <NUM>, or about one of the aforementioned ranges.

In certain embodiments, the incident light comprises light with wavelength of <NUM> ± <NUM>.

In certain embodiments, the light comprises light with a wavelength of about <NUM> or about <NUM>.

In certain embodiments, the light emitted from the sample comprises light with a wavelength in the range from <NUM> to <NUM>.

In certain embodiments, the light emitted from the sample comprises light with a wavelength in the range selected from <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, <NUM> to <NUM>, or <NUM> to <NUM>, or about one of the aforementioned ranges.

Details of light emitted from a meat product are as described herein.

Methods and devices for measuring light and converting the light measured into data are as described herein.

In certain embodiments, the pre-determined values comprise a parameter indicative of carcass weight (or part of a carcass), a parameter indicative of fat content in the meat product, a parameter indicative of fat mass in the meat product, a parameter indicative of a measurement (size, area, an/or depth) of a specific muscle in a carcass, a parameter indicative of acidity and/or alkalinity (such as pH) in the meat product, one or more colours of the meat product, shear force (SF), intramuscular fat (IMF), or a combination of any one or more of the aforementioned parameters.

In certain embodiments, the pre-determined values comprise one or more parameters as follows:.

In certain embodiments, the predetermined values comprise one or more parameters as follows:.

In certain embodiments, the data comprises spectral data which is processed prior to using the spectral data to create one or more models.

In certain embodiments, the one or more models comprise linear statistical models. In certain embodiments, the one or more models comprise non-linear statistical models.

In certain embodiments, the one or more models are created using a data minimisation approach. In certain embodiments, the data minimisation approach comprises employing Akaike's Information Criterion.

In certain embodiments, the one or more models are created using training data including data representative of light emitted from a plurality of sample meat products upon application of incident light to the sample meat products, each sample meat product having pre-determined values for the one or more parameters.

In certain embodiments, a model as created by a method described herein is used in a system to grade, score or classify a meat product for quality.

Embodiments of the present disclosure will now be described, by way of example only, with reference to the accompanying drawings, in which:.

The present invention is defined by the subject-matter set out in the appended claims. The following disclosure provides teachings which go beyond the disclosure of the invention as such, which is defined exclusively by the appended claims. The teachings are provided to place the actual invention in a broader technical context and to illustrate possible related technical developments. Such additional technical information which does not fall within the scope of the appended claims is not part of the invention. In particular, the terms "embodiment", "invention", and "aspect" are not to be construed as necessarily referring to the claimed invention, unless the subject-matter in question falls within the scope of the claims.

An embodiment of a system <NUM> for assessing quality of a meat product <NUM> is shown in <FIG>.

The system <NUM> includes a light source, which in this embodiment is a blue laser <NUM> having a wavelength of about <NUM>. The laser <NUM> is connected to a fibre probe <NUM> through a bifurcated optical fibre <NUM> and fibre connector <NUM>. The fibre probe <NUM> is shown inserted into the meat product <NUM> in order to apply incident light from the laser <NUM> to the meat product <NUM>. Typically, the probe is inserted to a depth of <NUM> to <NUM>, but other depths are applicable, and the present disclosure contemplates the use of the probe externally to the meat product.

The meat product <NUM> may be a whole carcass, a side of meat or any cut of meat, for example meat suitable for wholesale or retail sale. The present disclosure may be used to assess the quality of a red meat product, for example lamb, beef, pork, venison, goat, or horse.

In an embodiment, the fibre probe <NUM> is inserted around the rib eye (the outer side of the rib) of a carcass. Penetration depths of approximately <NUM>-<NUM> for lamb and <NUM>-<NUM> for beef have been successfully trialled. Multi-probing of the carcass to assess multiple muscle groups may also be performed.

The application of incident light from the laser <NUM> to the meat product <NUM> causes the meat product <NUM> to autofluoresce and emit light. The fibre probe <NUM> has a sensing tip <NUM>, which receives the light emitted from the meat product <NUM>. This emitted light passes through the bifurcated fibre <NUM> to a measuring device, which in this embodiment is a spectrometer <NUM>. A long pass filter <NUM> is used to supress laser light background.

The spectrometer <NUM> converts the emitted light into spectral data representative of the autofluorescence excited in the meat product <NUM>. The spectral data may comprise a measurement of intensity of the emitted light across a range of wavelengths, for example <NUM> to <NUM>. Such measurements may be taken at different intervals across the range of wavelengths and multiple measurements may be taken for each interval.

The system <NUM> further includes a processor <NUM>, a memory <NUM> and software <NUM> resident in the memory <NUM> and accessible to the processor <NUM>. In this embodiment, the processor <NUM> and memory <NUM> are part of a computer <NUM>, which is in data communication <NUM> with the spectrometer <NUM>.

The computer <NUM> may be co-located with the other components of the system <NUM> (hereafter referred to as the optical apparatus <NUM>), or may be located remotely and in data communication with the spectrometer <NUM> over a data network, such as a LAN or the Internet. It may be physically connected to the spectrometer by a cable or in wireless communication. Alternatively, data from the spectrometer <NUM> may be saved, for example, on a memory card and later transferred to the computer <NUM> for analysis and/or stored on the cloud. It will be appreciated that the disclosure covers all means of transferring data from the spectrometer <NUM> to the computer <NUM>, and all different forms the computer <NUM> may take including a desktop computer, laptop or mobile device.

The optical apparatus <NUM>, with or without the computer <NUM> may be portable. This may enable a user to walk alongside a continuously moving abattoir chain carrying meat products and probe the meat products, or to probe meat products in a chiller without removing them from the chiller. For example, components of the optical apparatus <NUM> and control hardware <NUM> may be mounted into a pelican style case and attached to a harness. This allows the complete setup to be worn, for example as a backpack, while the measurements are being taken. A continuous connection to mains power is not required.

The optical fibre probe <NUM> may be housed in a gun shaped housing <NUM> as shown in <FIG>. The housing body <NUM> and top cover <NUM> in this embodiment is made from CNC machined ABS plastic. The front plate <NUM> and probes <NUM> are made from Stainless Steel. The probes <NUM> are <NUM> outer diameter x <NUM> inner diameter x <NUM> length tubes with a taper and a M3 thread. A clear polycarbonate window <NUM> is included in the top cover <NUM>, sealed with a food grade silicone. The housing <NUM> may facilitate ease of inserting and operating the probes <NUM>. For example, the length of the probes <NUM> extending beyond the housing <NUM> may be set to a desired depth of insertion for the meat product being analysed. It will be appreciated that other shapes and materials of housing <NUM> may alternatively be used.

Other hardware or equipment may be used in conjunction with the system <NUM>, for example, a barcode scanner for reading barcodes identifying the meat products <NUM>, so that a particular product <NUM> and its measured spectral data can be associated.

Different embodiments of the optical apparatus <NUM> have been trialled. In a first version, the optical apparatus <NUM> included a <NUM> continuous-wave (CW) laser <NUM> delivering 15mw of power, a UV/Vis Flame spectrometer <NUM> (integration time <NUM>-<NUM>) collecting all wavelengths from <NUM>-<NUM>, a <NUM> long pass filter <NUM>, a <NUM> multimodal bifurcated fibre <NUM> to combine the laser <NUM> and spectrometer <NUM>, a <NUM> multimodal fibre for combined delivery and collection of the signal and a stainless steel needle <NUM> for delivery of the fibre into the meat product <NUM>.

In a second version, the optical apparatus <NUM> was designed for taking multiple measurements at once. The optical apparatus <NUM> included the components of the first version except that four <NUM> multimobal fibres for combined delivery and collection of signal were used, and also a PS Jena 1x6 optical splitter for multiple samples. The components were all mounted in a pelican style case for portability.

In a third version, also designed for taking multiple measurements at once, the optical apparatus <NUM> included a <NUM> CW laser <NUM> for each needle <NUM>, the lasers <NUM> delivering <NUM>-40mw of power, a UV/Vis Flame spectrometer <NUM> collecting all wavelengths from <NUM>-<NUM>, a <NUM> bifurcated bundle <NUM> (4xfibres) delivering light to the spectrometer <NUM> and a <NUM> filter set <NUM>, all mounted in a pelican style case for portability.

Different versions of the control hardware <NUM> were also trialled to control turning the laser/s <NUM> on/off, collect data from the spectrometer <NUM> and control a barcode scanner. The code was custom made and controlled using a beaglebone. One version of the control hardware <NUM> includes a <NUM> LiPo Battery <NUM>. 7V, a beaglebone for software control of components, an additional custom board for control of lasers (inputs controlled by the beaglebone), voltage regulated to ~<NUM>-6V and integrated with a wireless barcode scanner. In operation, spectral data measurements are taken using the optical apparatus <NUM> of <FIG>. As meat is moved along a meat processing line, the probe <NUM> may be inserted into the meat at a depth of <NUM>-<NUM>, the laser <NUM> activated and resulting autofluoresence in the meat measured by the spectrometer <NUM>. A barcode associated with the meat may be scanned to obtain an identification number and the spectral data generated by the spectrometer <NUM> may be labelled using the identification number.

Trials were performed probing hot carcasses (less than <NUM> minutes since kill) and cold carcasses (<NUM>-<NUM> hours in a <NUM> chiller). The hot and cold scanning took around <NUM> hours, and a transition time of around <NUM>-<NUM> minutes was used when transitioning the probe from hot to cold and vice versa. It is expected that end use will likely be <NUM>-<NUM> hours and the probe will likely remain in the hot or cold environment for at least <NUM> hours. It is expected that a scanning run will be unlikely to experience frequent thermal cycles (in/out of the chiller in less than <NUM> minutes).

The meat was probed as it travelled on a continuously moving abattoir chain at a speed of approximately <NUM>-<NUM> carcasses per minute. The environmental temperature during hot scanning was approximately <NUM>-<NUM> and the environmental temperature during cold scanning was approximately <NUM>-<NUM>. The scanning of cold meat products was done either as carcasses exited the chiller on an abattoir chain or while the carcasses were stationary in the chiller. Four scans were taken on the hot carcass and four scans taken <NUM> hours post mortem on the cold carcass. A <NUM> meat sample was taken, labelled and aged at <NUM>. The meat sample was assessed for defined variables including IMF, shear force, pH and colour. Details of some of the variables are given in the table below.

The spectral data and identification number may then be sent to a computer in which the software <NUM> is installed (for example, at a remote location).

The spectral data may then be processed utilising one or more of the following steps:.

The software <NUM> includes a series of instructions executable by the processor <NUM> to carry out a method to analyse the data to determine one or more parameters indicative of quality of the meat product <NUM>. With reference to <FIG>, the method <NUM> includes the steps of receiving <NUM> data representative of light emitted from the meat product <NUM> upon application of incident light to the meat product <NUM>; analysing <NUM> the data to determine one or more parameters indicative of quality of the meat product <NUM>; and assessing <NUM> the quality of the meat product <NUM> on the basis of the one or more parameters.

The analysis <NUM> of data may involve using one or more models to predict the one or more parameters from the data output from the spectrometer <NUM>. Based on these predictions, the quality of the meat product <NUM> can be assessed <NUM>.

The one or more parameters may be predicted values of properties of the meat product, or a range of likely values for these properties (for example, a value +/- an error). Alternatively, the one or more parameters may be a categorisation of a property of the meat product into one of a plurality of categories relating to a property of the meat product, or as being above or below a threshold.

<FIG> depicts a method <NUM> of creating one or more models for assessing quality of a meat product. The method <NUM> may be implemented in software and comprises, for a plurality of sample meat products, receiving data <NUM> representative of light emitted from the sample meat product upon application of incident light to the sample meat product; for the sample meat products, receiving one or more pre-determined values <NUM>; and using the data and one or more pre-determined values to create one or more models <NUM> to predict one or more parameters indicative of quality of the meat product. The models may be created using linear statistical methods or supervised machine learning algorithms. Other models are contemplated. A prediction model may thus be created for each variable by spectral signals/signatures/fingerprints. Neural network and deep learning approaches may be applied to the data <NUM> to increase predictability beyond what the linear models can achieve.

Using the system and software described, it was found that parameters of lamb meat and beef meat, utilised as meat products indicative of other red meats, could be predicted at line speed (in hot, cold and chilled samples) with sufficient accuracy to assess the quality of the meat product.

Persons skilled in the art will appreciate that the software <NUM> could be supplied in a number of ways; for example on a computer readable medium, such as a disc or a memory of the computer <NUM>, or as a data signal, such as by transmission from a server.

It will also be appreciated that variations on the above system and method are possible. For example, the probe <NUM> may be inserted into the meat product in multiple positions, with measurements taken in each position and/or multiple measurements taken with the probe <NUM> in the same position. In another embodiment, multiple probes may be used to take a number of simultaneous measurements of autofluoresence.

Examples of the development of the system and method are given below. It is to be understood that the following description is for the purpose of describing particular embodiments only and is not intended to be limiting with respect to the above description.

The following measurements were obtained for <NUM> lamb carcasses:.

These measurements are given in Appendix A (Table <NUM>) below.

The measurements were obtained as follows:.

Hot carcase weight (HCWT), depth of tissue at the GR site (GR depth), cfat thickness, and eye muscle area (EMA) at the 12th rib were measured. HCWT was provided by the processing plant. GR depth (mm) was measured with a GR knife <NUM>-<NUM> post-mortem at the <NUM>th rib, <NUM> from the spinal column on the right-hand side of the carcass.

Following overnight chilling, at approximately <NUM> post-mortem, <NUM> - <NUM> of the left section of the loin (m. longissimus thoracic et lumborum; LL) was removed from above the <NUM>th rib. From this section of LL, fresh eye muscle colour was measured after the exposed section of LL was allowed to 'bloom' for <NUM>-<NUM> minutes. A Minolta Chromameter was used to measure lightness (L*), redness (a*) and yellowness (b*) of the loin. pH levels were recorded as an estimate of ultimate pH (pHu loin) using TPS WP-<NUM> pH meter linked to temperature sensor and an lonode pH probe. Eye muscle width (EMW; mm), eye muscle depth (EMD; mm) and cFat (mm) were measured with digital callipers on the exposed surface of the LL. EMA was calculated from EMW and EMD according to the equation: EMA = EMW*EMD*<NUM>.

At approximately <NUM> post-mortem, the fat and epimysium were removed from the section of the LL that was previously removed from the carcass. Samples for shear force (SF5; <NUM>) and intramuscular fat (IMF; <NUM>) were collected. IMF samples were frozen immediately after collection.

The SF5 samples were vacuum packed and aged at <NUM>-<NUM> for five days prior to freezing at -<NUM>.

Frozen <NUM> LL samples were placed into a water bath at <NUM> for <NUM> to cook, and then immersed in chilled water prior to processing. The samples were processed according to the methods of <NPL>, and a Lloyd LRX machine was used to measure <NUM>-<NUM><NUM><NUM> sub-samples from each <NUM> LL sample.

IMF samples were freeze dried and the IMF content was determined using a near infrared procedure (as described in <NPL>).

The number of measurements and simple statistics are shown in Table <NUM>. A supplier was used to source a large range of carcases, representative of what would be typical in the Australian lamb industry. Carcases ranged from <NUM>-<NUM> with <NUM> to <NUM> fat at the GR site, with an average carcase weight of <NUM> and average fatness of <NUM>. Likewise, IMF, SF5, fresh colour and pHu reflected normal industry ranges.

The range in carcass and eating quality parameters provided a source for proof of concept of meat eating quality solutions analysis.

This example describes the results of data processing of measurements taken using a fibre probe.

Both linear and categorical statistical approaches were employed in the analysis.

Analysis of the data was used to determine one or more parameters indicative of quality of the meat product, and thereby assess the quality of the meat product on the basis of those one or more parameters.

Of the parameters investigated, it was found that meat quality measures of IMF (intramuscular fat) and SF (shear force) could be predicted statistically significantly using both modeling approaches.

It was also found that knowledge of the state of the meat at the time of measurement improves modeling, as the spectra changes on the condition of the meat.

Based on this data it is apparent that a measurement of surrogates of meat quality can be obtained at line speed.

Spectral data measurements were taken using the optical apparatus <NUM> of <FIG>.

The fibre probe <NUM> was inserted into each of the <NUM> lamb carcasses, the laser <NUM> was activated to apply incident light to the carcass via the probe <NUM> and the spectrometer <NUM> generated spectral data representative of the autofluorescence excited in the lamb carcass. Twelve to twenty samples were taken for each carcass, and this was repeated at different temperatures, so that a hot data set, chilled data set and cold data set were obtained.

The spectral data was then processed as follows, to reduce the complexity of the data and increase prediction accuracy:.

Examples of the processed spectral data at hot, chilled and cold temperatures obtained after processing described in points <NUM> to <NUM> above are shown in Figures 5A-5C.

Predictor variables: The following external parameters were considered as predictor variables in the analysis:.

The studies identified SF and IMF as two measures of importance to meat quality.

Initially two methods were used to find correlation between all of the external parameters and the response variables (spectral data): <NUM>) linear model with Akaike's Information Criterion, and <NUM>) k-fold Cross Validation. Each of the methods provided a number of output parameters that were used to estimate how well the model described the data. Those output parameters included:.

The results of this analysis created a model that allowed for the estimation of a continuous variable for the prediction score. That is a value +/- an error rate was returned by the optimal model.

An alternative categorical approach was also applied using machine learning which fragmented the dataset into categorical gradings for each variable. For example, IMF scores of <NUM>-<NUM> were categorised B, <NUM>-<NUM> were C etc. Thus a measurement was provided that allowed categorisation rather than quantification of measurements.

<FIG>, present the output parameters of the statistical model. pHu_temp predictor was excluded from the analysis as this variable varied in steps and was found to produce artificial statistical output. Moreover, this parameter was physically irrelevant to the spectral response variable considered in the analysis.

<FIG> shows the mean R-squared values for all data sets, averaged across three methods: linear model, Akaike's Information Criterion, and <NUM>-fold Cross Validation.

Considering the R-squared and Adjusted R-squared values, the following predictor variables showed better results relative to the other parameters: HCWT, GRfat, FatC, SF, and IMF. This was relatively consistent across all three data sets, however, lightness parameter, L, appeared to have better R-squared value for the Cold data set. pHu_loin parameter showed higher Adjusted R-squared value for the Hot data set.

F-Statistic values were almost twice as large for the same parameters: HCWT, GRfat, FatC, SF, and IMF, and smaller p-values were attributed to the same parameters. Here, the lightness parameter, L, also showed better statistics for the Cold data set, while pHu_loin parameter showed better values for Hot data set.

<FIG> shows adjusted R-squared values for all data sets for linear model and Akaike's Information Criterion.

<FIG> shows mean Relative Residual Standard Error for all data sets averaged across linear model and AIC; and F-Statistic values for all data sets for linear model. Values much larger than <NUM> (shown in black line) indicate there is relationship between predictor and response variables.

<FIG> shows log<NUM>(p-value) for all data sets for linear model and Akaike's Information Criterion. Values below the threshold of log<NUM>(<NUM>) = -<NUM> indicate that we can reject the null hypothesis, and a relationship between predictor and response variables exists.

Analysis incorporating categorical statistical approaches allowed greater collecting of the data, such that all hot, chilled and cold measurements were analysed together, with a categorical assignment of their measurement temperature. The data set was randomly split into an <NUM>:<NUM> / build:test set to allow independent validation of the model. A parallel assessment of <NUM> different types of machine learning approaches was performed with the best selected in a head to head test. For SF K nearest neighbour approach created the best predictive model with an accuracy of <NUM>% in the test dataset. That is, out of <NUM> C grading's of SF the model incorrectly assigned <NUM> as a D.

For IMF, the head to head test yielded a <NUM>% accuracy using a random forest approach.

R-squared: Overall, the R-squared values were relatively low, even for the fat-related parameters, and were around <NUM>, with the adjusted R-squared values expectedly lower. Akaike's Information Criterion resulted in an improved model once the best fitting response variables were left in the model. On average, <NUM> - <NUM>% of the initial response variables were left after AIC was applied. The k-fold Cross Validation method showed similar R-squared values to the linear model, or the AIC.

Number of response variables: When reducing the number of response variables, the overall statistical results became worse, while an increase in the number of response variables lead to the overall improvement of the statistical model. This was to be expected as it indicated that the total amount of data available to calculate the maximal model had not been reached. This confirmed that an <NUM>/<NUM> and kfold approach was justified. When considering the relative results in the statistical analysis, the same parameters showed better correlation than others, independent of the number of the response variables considered.

Variation in spectral data: When intervals of less variation in the spectral data were chosen instead of the most variable range, the statistical model outcome improved, even when the same number of response variables were considered.

Relative Residual Standard Errors: RSEs for the fat-related parameters were as follows: ≈ <NUM>% for HCWT, ≈ <NUM>% for GRfat, ≈ <NUM>% for FatC, and ≈ <NUM>% for IMF.

Relationship between predictor variables: Analysis of the correlation between predictor variables themselves revealed there were internal correlations between several parameters. For example, Table <NUM> shows the statistical output of the linear models created based on relationship between some external parameters.

Linear model conclusions: The models created for the following parameters were shown to be more statistically significant when compared to other analysed parameters: HCWT, GRfat, FatC, SF and IMF. Some internal relationship between those parameters can also be assumed. Thus there exists a model that can statistically significantly predict HCWT, GRfat, FatC, SF and IMF. Albeit each model being different.

Categorical model conclusions: The increased accuracy of the categorical approach was proportional to the loss of information gained in the linear model.

Overall conclusions: It would appear that a statistically significant model predicting meat quality surrogate measures using a fibre based approach collected at line speed is possible.

Further data is provided in Appendices A and B, provided below.

<FIG> shows R-squared and Adjusted R-squared values for all data sets. Im - linear model, aic - Akaike's Information Criterion, 5fold - k-fold Cross Validation method with <NUM> folds.

<FIG> shows relative Residual Standard Error (RSE) and F-Statistics values for all data sets. Im - linear model, aic - Akaike's Information Criterion. F-Statistic value is considered good if it is much larger than <NUM> (shown as black lines).

<FIG> shows log<NUM>(p-value) for all data sets. Im - linear model, aic - Akaike's Information Criterion. Black line shows the position of threshold = log<NUM>(<NUM>) ≈ -<NUM>, where values below it allow to reject the null-hypothesis.

Measurements were performed across two days. On the first day, <NUM> hot beef carcasses were scanned using the first version of the optical apparatus <NUM> as described above with the probe needle <NUM> attached directly to the bifurcated fibre <NUM>. <NUM> body analyses were obtained with <NUM> bodies excluded due to either poor scan or missing variables. The integration time was approximately <NUM> and spectral signatures were not normalised. The average spectral signature from the hot beef plus a variance is shown in <FIG>. Any spectral signature with an intensity of <<NUM> at <NUM> was excluded as this was likely due to an accidental scan outside of the meat.

On the second day, the same <NUM> carcasses, now cold, were scanned using the same apparatus except that the probe needle <NUM> was attached to the bifurcated fibre <NUM> via a patch cable. <NUM> body analyses were obtained with <NUM> bodies excluded due to either poor scan or missing variable data. The integration time was approximately <NUM>. The average spectral signature from the cold beef plus a variance is shown in <FIG>. Any spectral signature with an intensity of <<NUM> at <NUM> was excluded as this was likely due to an accidental scan outside of the meat. Despite an integration time of approximately <NUM>-<NUM>, the signal from meat the meat was extremely low. This made background subtraction problematic. Also, the use of a patch cable reduced the collection efficiency compared to the measurements made on the first day.

After processing of the data and linear model generation with Akaike's Information Criterion, it was found that IMF, SF, and pH have an approximate R2 value of <NUM> - <NUM> in hot carcasses (<FIG>) and IMF, SF, and pH have an approximate R2 value of <NUM> - <NUM> in cold carcasses (<FIG>).

<FIG> shows measured percentage of intra-muscular fat vs predicted percentage of intra-muscular fat for the hot carcass. The prediction of intra-muscular fat percentage has an R2 of <NUM> using a Linear model with AIC.

<FIG> shows measured shear force vs predicted shear force for the hot carcass. The prediction of shear force has an R2 of <NUM> using Linear model with AIC.

<FIG> shows measured pH vs predicted pH for the hot carcass. The prediction of pH has an R2 of <NUM> using Linear model with AIC.

<FIG> shows measured percentage of intra-muscular fat vs predicted percentage of intra-muscular fat for the cold carcass. The prediction of intra-muscular fat percentage has an R2 of <NUM> using a Linear model with AIC.

<FIG> shows measured shear force vs predicted shear force for the cold carcass. The prediction of shear force has an R2 of <NUM> using Linear model with AIC.

<FIG> shows measured pH vs predicted pH for the cold carcass. The prediction of pH has an R2 of <NUM> using Linear model with AIC.

As used herein, the singular forms "a," "an," and "the" may refer to plural articles unless specifically stated otherwise.

Throughout this specification, unless the context requires otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or integer or group of elements or integers but not the exclusion of any other element or integer or group of elements or integers.

All methods described herein can be performed in any suitable order unless indicated otherwise herein or clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the example embodiments and does not pose a limitation on the scope of the claimed invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential.

The description provided herein is in relation to several embodiments which may share common characteristics and features. It is to be understood that one or more features of one embodiment may be combinable with one or more features of the other embodiments. In addition, a single feature or combination of features of the embodiments may constitute additional embodiments.

Claim 1:
A method (<NUM>) of assessing quality of a meat product (<NUM>), the method comprising:
receiving (<NUM>) data representative of autofluorescent light emitted from the meat product (<NUM>) upon application of incident laser light from a probe (<NUM>) inserted into the meat product (<NUM>);
analysing (<NUM>) the data to determine one or more parameters indicative of quality of the meat product (<NUM>); and
assessing (<NUM>) the quality of the meat product (<NUM>) on the basis of the one or more parameters.