METHOD AND SYSTEM FOR ANALYSING MATERIALS

Abstract

Methods and systems for analysing products comprising marked materials and marking and tracking such materials are provided. A method of quantifying the proportion of a marked material comprising luminescent markers in a product comprises (i) obtaining a composite signal associated with the product, the composite signal including spectroscopic data and imaging data collected from the product, the spectroscopic and imaging data associated with a luminescent signal of the one or more luminescent markers in the marked material; (ii) identifying the marked material based on spectroscopic data associated with the one or more luminescent markers; (iii) quantifying the proportion of the marked material that is present in the product based at least in part on said imaging data of the composite signal, wherein said quantifying is based at least in part on the relative positions of and/or the number of luminescent markers detected in each image of the product.

Claims

1. A method of quantifying the proportion of a marked material that is present in a product, wherein the product comprises a mixture of one or more materials and the marked material contains one or more luminescent markers, the method comprising: (i) obtaining a composite signal associated with the product, the composite signal including spectroscopic data and imaging data collected from the product, the spectroscopic and imaging data associated with a luminescent signal of the one or more luminescent markers in the marked material; (ii) identifying the marked material based on spectroscopic data associated with the one or more luminescent markers; (iii) quantifying the proportion of the marked material that is present in the product based at least in part on said imaging data of the composite signal, wherein said quantifying is based at least in part on the relative positions of and/or the number of luminescent markers detected in each image of the product.

2. The method of claim 1, further comprising automatically uploading information corresponding to the quantified proportion of a marked material that is present in the product as an entry to a distributed ledger associated with the product, and/or where the quantified proportion deviates from an expected value, providing an alert signal to a user.

3. The method of claim 1 or claim 2, wherein the composite signal includes spectroscopic and/or imaging data collected from a plurality of different portions of the product.

4. The method of any one of the preceding claims, wherein obtaining a composite signal associated with the product comprises scanning the product to collect spectroscopic and/or imaging data associated with the luminescent signal of the one or more luminescent markers in the marked material.

5. The method of claim 4, comprising scanning the product using an in-line detector arranged to scan a process flow of the product, or using a movable detector, for example a handheld detector or a system configured to move a detector over the surface of the product.

6. The method of any one of claims 1 to 3, wherein obtaining a composite signal associated with the product comprises receiving the composite signal from a remote detector that is used to scan the product, for example receiving the composite signal from a server in communication with the detector.

7. The method of any one of the preceding claims, wherein the one or more luminescent markers comprise one or more inorganic luminescent compounds, preferably an inorganic carrier material doped with one or more metal ions or a quantum dot material, for example an inorganic carrier material doped with one or more ions selected from the group consisting of In.sup.+, In.sup.3+, Sn.sup.2+, Pb.sup.2+, Sb.sup.3+, Bi.sup.+, Bi.sup.2+, Bi.sup.3+, As.sup.3+, Ce.sup.3+, Ce.sup.4+, Pr.sup.3+, Nd.sup.3+, Sm.sup.2+, Sm.sup.3+, Eu.sup.2+, Eu.sup.3+, Gd.sup.3+, Tb.sup.3+, Dy.sup.3+, Ho.sup.3+, Er.sup.3+, Tm.sup.2+, Tm.sup.3+, Tl.sup.+, Yb.sup.2+, Yb.sup.3+, Ti.sup.+, Ti.sup.3+, V.sup.2+, V.sup.3+, V.sup.4+, Cr.sup.2+, Cr.sup.3+, Cr.sup.4+, Cr.sup.5+, Mn.sup.2+, Mn.sup.3+, Mn.sup.4+, Fe.sup.3+, Fe.sup.4+, Fe.sup.5+, Co.sup.3+, Co.sup.4+, Ni.sup.2+, Cu.sup.+, Ru.sup.2+, Ru.sup.3+, Pd.sup.2+, Ag.sup.+, Ir.sup.3+, Pt.sup.2+, Zn.sup.2+ and Au.sup.+.

8. The method of any one of the preceding claims, wherein the one or more inorganic luminescent compounds comprise a halide, oxide, oxyhalide, sulfide, oxysulfide, sulfate, oxysulfate, nitride, siliconitride, selenide, oxynitride, nitrate, oxynitrate, arsenate, borate, phosphide, phosphate, halophosphate, carbonate, aluminate, silicate, halosilicate, oxysilicate, vanadate, molybdate, tungstate, germinate, oxygermanate, stannate or combinations thereof of one or more of the elements Li, Na, K, Ba, Rb, Mg, Ca, Cd, Ce, Cs, Sc, Sr, Se, Y, La, Ti, Zr, Hf, Nb, Ta, Tb, Zn, Gd, Lu, Al, Ga or In, optionally doped with one or more metal ions as defined in claim 7.

9. The method of any one of the preceding claims, further comprising determining the identity and/or quantity of the one or more materials mixed with the marked material using spectroscopic data collected from the product.

10. The method of claim 9, wherein the spectroscopic data used in said determining the identity and/or quantity comprises spectroscopic data of the composite signal obtained in part (i), for example wherein the spectroscopic data used in said determining the identity and/or quantity comprises a background signal in spectroscopic data of the composite signal.

11. The method of claim 9, wherein the spectroscopic data used in said determining the identity and/or quantity comprises additional spectroscopic data different to spectroscopic data of the composite signal.

12. The method of any one of the preceding claims, wherein the quantifying step comprises comparing characteristics of said composite signal associated with the product to said one or more reference signals and, based on the comparison, quantifying the proportion of the marked material that is present in the product.

13. The method of any one of the preceding claims, wherein the quantifying step comprises applying a pattern recognition or machine learning element associated with one or more reference signals collected from one or more respective reference materials to the composite signal to quantify the proportion of the marked material that is present in the product.

14. The method of any one of the preceding claims, wherein the product is in the form of natural or synthetic fibres, yarn, woven or non-woven fabric made from synthetic or natural fibres, pellets, powder, granulate, extruded material stream such as filament, moulded materials, foams, or a material formed from pulp such as paper or cardboard, or product streams for forming such products or processed material formed from such products, for example shredded or ground materials, or fibres or yarns obtained from fabrics.

15. The method of any one of the preceding claims, wherein the marked material comprises natural fibres, for example plant-based fibres such as cotton, kapok, flax, hemp, jute, rami, sisal, coconut, bamboo or animal-derived fibres such as from sheep's wool, goat hair (mohair), cashmere, tibetan wool, alpaca, llama, vicuña wool, camel hair, angora, horsehair, silks including mulberry silk or wild silk (tussah silk), or feathers such as down or duck feathers, or inorganic fibres such as glasswool or mineral wool, preferably the marked material comprises recycled material.

16. The method of any one of claims 1 to 15, wherein the marked material comprises plastic, for example polyesters such as polyethylene terephthalate or polybutylene terephthalate, polyolefins such as polyethylene or polypropylene, polystyrene, polyvinylchloride, polyamide, polycarbonate, polyurethane, polyacrylonitriles formaldehyde resins, epoxy resins, or mixtures thereof, preferably the marked material comprises a recycled plastic.

17. The method of any one of the preceding claims, wherein quantifying the proportion of the marked material that is present in the product comprises indicating that the proportion falls within a range of values, for example indicating that the product comprises less than a threshold proportion of the marked material.

18. A system for quantifying the proportion of a marked material that is present in a product, wherein the product comprises a mixture of one or more materials and the marked material contains one or more luminescent markers, the system comprising: a controller configured to obtain a composite signal associated with the product, the composite signal including spectroscopic data and imaging data collected from the product, the spectroscopic and imaging data associated with a luminescent signal of the one or more luminescent markers in the marked material; wherein the controller is further configured to: (i) identify the marked material based on spectroscopic data associated with the one or more luminescent markers; and (ii) quantify the proportion of the marked material that is present in the product based at least in part on said imaging data of the composite signal, wherein said quantifying is based at least in part on the relative positions of and/or the number of luminescent markers detected in each image of the product.

19. The system of claim 18, further comprising a detector communicatively coupled to the controller, the detector configured to provide the composite signal by scanning a plurality of different portions of the product to detect the one or more luminescent markers associated with the marked material.

20. The system of claim 19, wherein the detector is an in-line detector for scanning a process flow of the product, or a movable detector, for example a handheld detector or a system configured to move a detector over the surface of the product.

21. The system of any of claims 18 to 20, wherein the controller is configured to perform the method of any one of claims 2 to 17.

22. A scanning system for obtaining spectroscopic data and imaging data from a product, the system comprising: a first spectrometer for detecting the luminescent signal of one or more luminescent markers present in the product; a camera for collecting images of the luminescent markers in the product; and a second spectrometer, such as a near-IR spectrometer, for determining the identity and/or quantity of the one or more materials mixed with the marked material.

23. The scanning system of claim 22, wherein the scanning system is configured for in-line scanning of a product flow, or configured for moving the first spectrometer, the camera, and the second spectrometer across the surface of a product in order to scan multiple portions of the product.

24. The scanning system of claim 23, comprising a motion control frame configured to move the spectrometers and camera across the surface of the product.

25. The scanning system of any of claims 22 to 24, further comprising a controller configured to quantify the proportion of a marked material that is present in the product according to the method of any of claims 1 to 17.

26. A method of quantifying the proportion of a marked material that is present in a product, wherein the product comprises a mixture of one or more materials and the marked material contains one or more luminescent markers, the method comprising: (i) obtaining a composite signal associated with the product, the composite signal including spectroscopic and/or imaging data collected from the product, the spectroscopic and/or imaging data associated with a luminescent signal of the one or more luminescent markers in the marked material; (ii) identifying the marked material based on spectroscopic data associated with the one or more luminescent markers; (iii) based on spectroscopic data collected from the product, automatically selecting reference data associated with one or more reference materials comprising the marked material; and (iv) quantifying the proportion of the marked material that is present in the product based on said composite signal and said reference data.

27. The method of claim 26, wherein the method is as further defined in any one of claims 1 to 17.

28. The method of claim 26 or claim 27, wherein the spectroscopic data used in part (iii) comprises spectroscopic data of the composite signal obtained in part (i), for example wherein the spectroscopic data used in part (iii) comprises a background signal in spectroscopic data of the composite signal.

29. The method of any one of claims 26 to 28, wherein the spectroscopic data used in part (iii) comprises additional spectroscopic data different to spectroscopic data of the composite signal.

30. The method of any one of claims 26 to 29, wherein the reference data comprises one or more reference signals collected from one or more respective reference materials, and the quantifying step comprises comparing characteristics of said composite signal associated with the product to said one or more reference signals and, based on the comparison, quantifying the proportion of the marked material that is present in the product.

31. The method of any one of claims 26 to 30, wherein the reference data comprises a pattern recognition or machine learning element associated with one or more reference signals collected from one or more respective reference materials and the quantifying step comprises applying the pattern recognition or machine learning element to the composite signal to quantify the proportion of the marked material that is present in the product.

32. A system for quantifying the proportion of a marked material that is present in a product, wherein the product comprises a mixture of one or more materials and the marked material contains one or more luminescent markers, the system comprising: a controller configured to obtain a composite signal associated with the product, the composite signal including spectroscopic and/or imaging data collected from the product, the spectroscopic and/or imaging data associated with a luminescent signal of the one or more luminescent markers in the marked material; wherein the controller is further configured to: (i) identify the marked material based on spectroscopic data associated with the one or more luminescent markers; (ii) based on spectroscopic data collected from the product, automatically select reference data associated with one or more reference materials comprising the marked material; and (iii) quantify the proportion of the marked material that is present in the product based on said composite signal and said reference data.

33. The system of claim 32, further comprising a detector as defined in claim 19 or 20, and/or wherein the controller is configured to perform the method of any one of claims 26 to 31.

34. A method of quantifying the proportion of a marked material that is present in a product, wherein the product comprises a mixture of one or more materials and the marked material contains one or more luminescent markers, the method comprising: obtaining a composite signal associated with the product, the composite signal including spectroscopic and/or imaging data collected from a plurality of different portions of the product, the spectroscopic and/or imaging data associated with the one or more luminescent markers in the marked material; quantifying the proportion of the marked material that is present in the product based on said composite signal and one or more composite reference signals comprising spectroscopic and/or imaging data collected from a plurality of different portions of one or more respective reference materials that comprise the marked material.

35. A method of producing one or more composite reference signals associated with a marked material, for quantifying the proportion of the marked material that is present in a product, the method comprising: (i) providing a reference material comprising a known proportion of a marked material, the marked material containing one or more luminescent markers; (ii) scanning a plurality of different portions of the reference material to obtain spectroscopic and/or imaging data associated with said one or more luminescent markers; (iii) associating the spectroscopic and/or imaging data from a plurality of different portions of the reference material with a common identifier to provide a composite reference signal associated with the reference material; repeating steps (i) to (iii) for one or more further reference materials that comprise the marked material to obtain a plurality of composite reference signals associated with the further reference materials.

36. A method of tracking a marked material and quantifying the proportion of a marked material that is present in a product, wherein the product comprises a mixture of one or more materials and the marked material contains one or more luminescent markers, the method comprising: obtaining a composite signal associated with the product, the composite signal including spectroscopic and/or imaging data collected from the product, the spectroscopic and/or imaging data associated with the one or more luminescent markers in the marked material; quantifying the proportion of the marked material that is present in the product based on said composite signal and reference data associated with one or more respective reference materials that comprise the marked material; and automatically uploading information corresponding to the quantified proportion of the marked material that is present in the product as an entry to a distributed ledger associated with the product and/or the marked material.

37. A method of marking a recycled material for subsequent identification, wherein the method comprises distributing inorganic luminescent markers into the recycled material during the recycling process.

38. The method of claim 37, wherein the recycled material is recycled plastic or recycled cotton.

39. A system for marking a recycled plastic for subsequent identification, wherein the system is configured to continuously incorporate one or more luminescent markers into a flow of recycled plastic, preferably wherein the system is configured to heat and extrude or mould the recycled plastic such that the luminescent markers are incorporated into the recycled plastic prior to extruding or moulding the recycled plastic.

40. A computer program product comprising program instructions to cause a processor to perform the method of any one of claims 1 to 17, 26 to 31, or 34 to 38.

Description

[0160] The invention will now be described by reference to the following non-limiting examples and drawings, in which:

[0161] FIG. 1 shows a flow diagram for a method of quantifying the proportion of a marked material that is present in a product;

[0162] FIG. 2 shows a flow diagram for an alternative method of quantifying the proportion of a marked material that is present in a product;

[0163] FIG. 3 shows a diagram showing a comparison of imaging data obtained for two materials containing luminescent markers;

[0164] FIG. 4 shows a schematic for a system for quantifying the proportion of a marked material that is present in a product.

[0165] FIG. 5 shows a scanning system for quantifying the proportion of a marked material that is present in a product.

[0166] The flow diagram of FIG. 1 shows an exemplary method of quantifying the proportion of a marked material that is present in a product, wherein the product comprises a mixture of one or more materials and the marked material contains one or more luminescent markers. In step 101, a composite signal is obtained, the composite signal including spectroscopic and/or imaging data collected from the product, the spectroscopic and/or imaging data associated with a luminescent signal of the one or more luminescent markers in the marked material. Step 102 comprises identifying the marked material based on spectroscopic data associated with the one or more luminescent markers. Step 103 comprises automatically selecting reference data associated with one or more reference materials comprising the marked material based on spectroscopic data collected from the product. Step 104 comprises quantifying the proportion of the marked material that is present in the product based on said composite signal and said reference data.

[0167] By way of specific example, step 101 may comprise receiving multidimensional data representing spectroscopic and image data, for example collected by scanning a product with a hyperspectral camera multiple times to generate multiple images and associated spectra from different portions of the product. The multidimensional data thus forms a composite signal that represents the features of the multiple images and associated spectra collected from different portions of the product. If the frequency of measurement of spectroscopic data is fast enough, the luminescent response/lifetime of the luminescent marker(s) over time may be measured and included as a further variable/dimension in the multidimensional data. In other examples, instead of a hyperspectral camera separate imaging and spectroscopic apparatus may be used to collect multidimensional data.

[0168] In a specific example, the multidimensional data may have been collected by moving a scanner comprising a hyperspectral camera over a fabric or garment to scan multiple portions of the fabric, or moving a fabric past a fixed in-line hyperspectral camera and taking readings at defined intervals that may be coordinated with the speed of the fabric movement past the camera. In other instances, the product may comprise other forms of material such as a yarn or thread that is moved past a detector such as a hyperspectral camera.

[0169] Step 102 may then comprise analysing the multidimensional data by identifying the unique spectral response of a luminescent marker in the spectroscopic data, for example by the luminescent marker's response at a particular wavelength. The identity of the marker is then compared to a database in which the specific marker is linked to the previous marking of a specific material, for example organic cotton from a particular manufacturer. In order to broaden the scope for uniquely identifying multiple different sources, the unique spectral response may relate to the presence of two or more different luminescent markers in the material in a specific ratio. The identity of the material that was marked with the detected luminescent marker(s) is then associated with the multidimensional data.

[0170] In step 103, the spectroscopic data of the multidimensional data may then be analysed to determine the identity of materials that are present in the product. For example the near-IR region of the spectroscopic data, or separate spectroscopic data such as near-IR spectroscopic data, may be used to identify that the product is primarily cotton, a mixture of cotton and PET fibres or primarily PET fibres, or the visible region of the spectroscopic data may be used to identify that a particular colour of dye is present, which may be done by processing the spectroscopic data using pattern recognition to classify the product based on previously measured materials. The identity of materials that are present, and relative proportions, may then also be associated with the multidimensional data to refine the reference data that is to be used subsequently, for example by assigning a further variable/dimension to the multidimensional data that represents the materials that are present. Alternatively or in addition, the identity of materials that are present, identified spectroscopically, may be used to determine the nature of any blending identified by the quantification measurement, for example determining the nature of materials other than the marked material when the amount of marked material is less than expected. In this way, the source of blending or error in a supply chain may be identified.

[0171] In an example, the product is identified from the spectroscopic data as only or almost exclusively containing cotton. A reference data set that relates to different amounts of marked organic cotton blended with non-marked cotton may then be selected as the relevant reference data to compare to, which may comprise assigning a further variable to the multidimensional data that represents the presence of cotton. In a different example, the product is identified from the spectroscopic data as containing a mixture of PET and cotton. A reference data set that relates to different amounts of marked organic cotton blended with non-marked PET fibres may then be selected as the relevant reference data to compare to. In this way, compared to where only cotton is present, any interference of signals relating to PET in the spectroscopic data with the intensity of luminescent signals from the luminescent markers may be accounted for in the quantification to improve reliability.

[0172] Then, in step 104, using the reference data set selected, for example using the additional variables in the multidimensional data identifying the marked material as organic cotton and other materials present as cotton or PET fibres, the multidimensional data is compared to the reference data to determine how much of the marked organic cotton is present based on previously measured blends containing different amounts of marked organic cotton. In particular, a pattern recognition process using dimensional reduction to identify significant features of the data may be used to classify the amount of the material marked with the luminescent marker that is present.

[0173] In a different example, the product may be a fabric or a moulded plastic product, and in step 102 the luminescent marker(s) are identified as relating to recycled PET that was previously marked. In step 101 multidimensional data corresponding to a composite signal as described may be obtained from a process of scanning a melt flow of the PET, or by scanning a product such as a PET filament, fibres or fabric containing PET or a moulded PET product.

[0174] Step 103 may then comprise identifying from the spectroscopic data of the multidimensional data that the product contains essentially only PET. For example, the product may be a fabric that is made from PET or a moulded product. A reference data set that relates to different amounts of marked PET blended with non-marked PET may then be selected as the relevant reference data. Step 104 then comprises comparing the multidimensional data for the scanned product the reference data, for example as described previously, to determine how much of the marked recycled PET is present based on previously measured blends containing different amounts of marked recycled PET.

[0175] The above steps, including identifying the marked material, selecting reference data and quantifying the proportion of the marked material present may be simultaneously achieved by providing the multidimensional data to a pattern recognition process that processes the data to identify its characteristics, for example by using dimensional reduction techniques such as principal component analysis, where the pattern recognition process then classifies the product as containing a particular marked material in a particular amount based on comparing, for example by covariance analysis, to reference data that contains an entry relating to the same marked material, e.g. organic cotton, blended with a corresponding quantity of a second material, e.g. PET fibres. The comparison may in some cases simply comprise comparison with the “knowledge” of a trained machine learning element such as an artificial neural network that classifies the product rather than comparison with an explicit list of recorded data for different materials.

[0176] The method may also optionally comprise a further step 105 of automatically uploading information corresponding to the quantified proportion of the marked material that is present in the product as an entry to a distributed ledger such as automatically uploading an entry to a blockchain associated with the product and/or the marked material.

[0177] Although specific materials such as fabrics, cotton and PET and specific equipment such as a hyperspectral camera are mentioned above, these are merely illustrative examples and it will be appreciated that the method may be applied to other materials using other means as described herein.

[0178] The flow diagram of FIG. 2 shows an exemplary method of quantifying the proportion of a marked material that is present in a product, wherein the product comprises a mixture of one or more materials and the marked material contains one or more luminescent markers. Step 201 comprises obtaining a composite signal associated with the product, the composite signal including spectroscopic data and imaging data collected from the product, the spectroscopic and imaging data associated with a luminescent signal of the one or more luminescent markers in the marked material. Step 202 comprises identifying the marked material based on spectroscopic data associated with the one or more luminescent markers. Step 203 comprises quantifying the proportion of the marked material that is present in the product based at least in part on said imaging data of the composite signal, wherein said quantifying is based at least in part on the relative positions of and/or the number of luminescent markers detected in each image of the product.

[0179] By way of specific example, step 201 may comprise receiving multidimensional data representing spectroscopic and image data, for example collected by scanning a product with a hyperspectral camera multiple times to generate multiple images and associated spectra from different portions of the product. The multidimensional data forms a composite signal that represents the features of the spectrum of the luminescent markers and the multiple images collected from different portions of the product. In other examples, instead of a hyperspectral camera, separate imaging and spectroscopic apparatus may be used to collect multiple images from the product and at least one spectrum for identifying the luminescent markers.

[0180] In a specific example, the multidimensional data may have been collected by moving a scanner comprising a hyperspectral camera over a fabric or garment to scan multiple portions of the fabric, or moving a fabric past a fixed in-line hyperspectral camera and taking readings at defined intervals that may be coordinated with the speed of the fabric movement past the camera. In other instances, the product may comprise other forms of material such as a yarn or thread that is moved past a detector such as a hyperspectral camera.

[0181] Step 202 may then comprise analysing the multidimensional data by identifying the unique spectral response of a luminescent marker in the spectroscopic data, for example as described in relation to FIG. 1. The identity of the material that was marked with the detected luminescent marker(s) is then associated with the multidimensional data. For example, the markers may be associated with organic cotton or recycled PET.

[0182] Then, in step 203, the imaging data is analysed based on the relative positions of and/or the number of luminescent markers detected each image of the product, and compared to known results from previously measured blends to determine how much of the marked organic cotton or recycled PET. In particular, a pattern recognition process using dimensional reduction to identify significant features of the image data may be used to classify the amount of the material marked with the luminescent marker that is present. The pattern recognition may for example be based on reduced multidimensional data representing the relative positions of the markers in the images or may be based on image processing based pattern recognition for example using artificial neural network such as a convolutional neural network to analyse the images and classify the product as containing a particular proportion of the marked material. Information relating to the quantification may optionally also be automatically uploaded to a distributed ledger as described previously.

[0183] FIG. 3 shows an example of imaging data obtained from marked materials. Inset 301 shows an image representing the positions of luminescent markers detected in a first material containing a first proportion of a marked material. Inset 302 shows an image representing the positions of luminescent markers detected in a second material containing a second proportion of a marked material that is less than the first proportion of marked material in the first material shown in 301.

[0184] As shown by inset 303, the imaging data may be multidimensional data comprising spectral data across the image, for example obtained using a hyperspectral camera. Inset 303 shows spectra relating to single point in the image 301, although it will be appreciated that spectral data may be recorded from across the entire image or only selected portions of the image such as where luminescent spots are observed. Inset 303 also shows a time-dependent spectral signal for a point in the image 301, where the luminescent response of a luminescent marker present in the marked material is recorded to further characterise the luminescent marker.

[0185] The schematic in FIG. 4 shows a system for quantifying the proportion of a marked material that is present in a product. At 401 a camera collects images showing the presence of luminescent markers in the material. At 402 a first spectrometer collects spectroscopic data for detecting the luminescent signal of the one or more luminescent markers. At 403 a second spectrometer collects second spectroscopic data, for example using a near-IR spectrometer, for identifying the composition of the material.

[0186] The data from 401, 402, and 403 is then passed to a pattern recognition or machine learning element 404, whereby quantifying the proportion of the marked material present may be simultaneously achieved by processing the data from 401, 402 and 403 to identify its characteristics, for example by using dimensional reduction techniques such as principal component analysis, where the pattern recognition or machine learning element 404 then provides an output 405. The output may classify the product as containing a particular marked material in a particular amount, as well as identifying the material composition, from which the source of a deficiency in marked material may be identified. The output may also contain additional information that can be identified from the input data 401, 402 and 403 by pattern recognition or machine learning element 404, such as colour or physical form of the material, for example in the case of a fabric, yarn count or weaving type.

[0187] FIG. 5 shows a scanning system 500 for quantifying the proportion of a marked material that is present in a product. The system comprises a sample material holder 505 arranged to support a material sample 504, for example a portion of a fabric. The system also comprises a camera 501 for imaging luminescent markers present in material sample 504, a first spectrometer 502 for detecting the luminescent signal of luminescent markers present in the material sample 504, and a second spectrometer 503, for example a near-IR spectrometer, for identifying the overall composition of material 504. The spectrometers/camera 501, 502 and 503 are affixed to a motion control frame 505 configured for moving the spectrometers/camera 501, 502 and 503 across the surface of the material sample 504 on the sample material holder 505 so as to scan different portions of the material sample 504. In this way, the system 500 can simultaneously move spectrometers/camera 501, 502 and 503 over the surface to obtain a composite signal comprising data from the spectrometers/camera that varies with time/position on the material 504.

[0188] In certain examples a controller described herein may be configured to perform any of the methods, or particular steps of said methods described herein. A controller described herein may refer to a single controller and/or processor or control may be distributed between multiple controllers and/or processors. The activities and apparatus outlined herein may be implemented using controllers and/or processors which may be provided by fixed logic such as assemblies of logic gates or programmable logic such as software and/or computer program instructions executed by a processor. Other kinds of programmable logic include programmable processors, programmable digital logic (e.g., a field programmable gate array (FPGA), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM)), an application specific integrated circuit, ASIC, or any other kind of digital logic, software, code, electronic instructions, flash memory, optical disks, CD-ROMs, DVD ROMs, magnetic or optical cards, other types of machine-readable mediums suitable for storing electronic instructions, or any suitable combination thereof.

[0189] The above embodiments are to be understood as illustrative examples. Further embodiments are envisaged. It is to be understood that any feature described in relation to any one embodiment may be used alone, or in combination with other features described, and may also be used in combination with one or more features of any other of the embodiments, or any combination of any other of the embodiments. Furthermore, equivalents and modifications not described above may also be employed without departing from the scope of the invention, which is defined in the accompanying claims.

[0190] Other variations and modifications of the apparatus will be apparent to persons of skill in the art in the context of the present disclosure.