A method and device for categorising cheese

Abstract

A method for categorising cheese, comprises: exposing a surface of the cheese to light; collecting spectral data from the surface of the cheese for at least two, at least three, or at least four wavel

Description

Field of the Invention The invention relates to a method and a device for use in categorising cheese. In particular, the method and device can be used to categorise cheese using spectral data. The invention further relates to cheese categorised by the method and cheese products made therefrom. Background Cheddar cheese is one of the world’s most popular cheeses, renowned for its distinctive flavour, versatility, and rich history. Originating in the English village of Cheddar in Somerset, this cheese was first made in the 12th century, where the region's ideal climate and naturally occurring limestone caves provided optimal conditions for aging cheese. Traditionally produced from cow’s milk, Cheddar cheese undergoes a unique process called "cheddaring", where curds are stacked, flipped, and pressed to remove moisture and develop its firm texture. Over time, Cheddar cheese has evolved into a global cheese staple, with varieties ranging from mild to vintage, reflecting differences in aging and intensity. Its flavour profile, ranging from smooth and creamy to sharp and tangy, and its adaptable texture make it a favourite for cooking, snacking, and culinary pairings. Today, Cheddar cheese is produced worldwide, including in regions like the UK, the United States, Canada, and Australia, each lending subtle local variations while maintaining the essential qualities that define this beloved cheese. Once produced, Cheddar cheese is aged until it reaches the desired maturity of mild, medium, mature, extra mature, or vintage, as determined by a grading expert. Current grading techniques are time consuming and expensive. In addition, cheese manufacturers regularly store vast quantities of cheese stock in ageing houses, awaiting final grading before being cut and packaged for retail. In many cases, cheese stock is retained for longer periods than required, to the detriment of storage space and associated costs. To determine the grade of a cheese, skilled graders, often trained for years, use established techniques to assess characteristics like flavour, texture, aroma, and appearance, which indicate when the cheese has reached a particular maturity level. It is thus necessary for the grader to physically inspect and, typically also remove a portion of the cheese for their grading analysis. Inevitably some of the product is thereby forfeited by the manufacturer for the grading process. There is thereby a need for improved methods for categorising and grading cheese in a more automated, less time-consuming fashion, and less wasteful manner. The present invention seeks to obviate or mitigate the problems associated with grading Cheddar cheese. The present invention is not only applicable to Cheddar cheese but could be used for any cheese to determine when a cheese is ready to sell, or when it has reached a particular maturity, for example. Summary of the Invention According to an aspect of the invention there is provided a method for categorising cheese, involving the steps of: i. exposing a surface of the cheese to light; ii. collecting spectral data from the surface of the cheese for at least two, at least three, or at least four wavelengths within the range of 900 to 2500 nm; and iii. assigning the cheese to a predetermined category based on the spectral data. Optionally, the step i. of exposing a surface to cheese involves or consists of exposing the cheese to infra-red light across the waveband of 900 nm to 2500 nm. Optionally, the step iii. of assigning the cheese to a predetermined category involves comparing the spectral data to reference data. Optionally, the reference data is obtained by first obtaining cheeses of two or more different predetermined categories, exposing a surface of the cheeses to light, and collecting spectral data from the surface of the cheeses for at least two, at least three, or at least four wavelengths within the range of 900 to 2500 nm. Optionally, the step of assigning the cheese to a predetermined category involves comparing transformed spectral data to reference data. Optionally, spectral data is collected for one or more wavelengths, each wavelength being selected from a different one of the following ranges: 945 to 965 nm; 951 to 971 nm; 964 to 984 nm; 982 to 1002 nm; 995 to 1015 nm; 1001 to 1021 nm; 1013 to 1033 nm; 1026 to 1046 nm; 1032 to 1052 nm; 1057 to 1077 nm; 1063 to 1083 nm; 1069 to 1089 nm; 1076 to 1096 nm; 1082 to 1102 nm; 1088 to 1108 nm; 1132 to 1152 nm; 1150 to 1170 nm; 1157 to 1177 nm; 1163 to 1183 nm; 1175 to 1195 nm; 1182 to 1202 nm; 1194 to 1214 nm; 1200 to 1220 nm; 1207 to 1227 nm; 1213 to 1233 nm; 1219 to 1239 nm; 1225 to 1245 nm; 1232 to 1252 nm; 1250 to 1270 nm; 1263 to 1283 nm; 1282 to 1302 nm; 1326 to 1346 nm; 1332 to 1352 nm; 1338 to 1358 nm; 1357 to 1377 nm; 1363 to 1383 nm; 1369 to 1389 nm; 1376 to 1396 nm; 1382 to 1402 nm; 1388 to 1408 nm; 1395 to 1415 nm; 1401 to 1421 nm; 1407 to 1427 nm; 1420 to 1440 nm; 1432 to 1452 nm; 1438 to 1458 nm; 1445 to 1455 nm; 1457 to 1477 nm; 1470 to 1490 nm; 1482 to 1502 nm; 1489 to 1509 nm; 1501 to 1521 nm; 1507 to 1527 nm; 1514 to 1534 nm; 1526 to 1546 nm; 1533 to 1553 nm; 1551 to 1571 nm; 1564 to 1584 nm; 1576 to 1596 nm; 1589 to 1609 nm; 1595 to 1615 nm; 1602 to 1622 nm; 1608 to 1628 nm; 1614 to 1634 nm; 1620 to 1640 nm; 1627 to 1647 nm; 1633 to 1653 nm; 1639 to 1659 nm; 1645 to 1665 nm; 1652 to 1672 nm; 1658 to 1678 nm; 1664 to 1684 nm; 1677 to 1697 nm; 1683 to 1703 nm; 1696 to 1716 nm; 1714 to 1734 nm; 1721 to 1741 nm; 1727 to 1747 nm; 1746 to 1766 nm; 1764 to 1784 nm; 1771 to 1791 nm; 1783 to 1803 nm; 1789 to 1809 nm; 1814 to 1834 nm; 1827 to 1847 nm; 1833 to 1853 nm; 1845 to 1865 nm; 1858 to 1878 nm; 1870 to 1890 nm; 1876 to 1896 nm; 1914 to 1934 nm; 2013 to 2033 nm; 2062 to 2082 nm; 2093 to 2113 nm; 2099 to 2119 nm; 2117 to 2137 nm; 2123 to 2143 nm; 2142 to 2162 nm; 2154 to 2174 nm; 2185 to 2205 nm; 2191 to 2211 nm; 2209 to 2229 nm; 2233 to 2253 nm; 2270 to 2290 nm; 2276 to 2296 nm; and 2306 to 2326 nm. Optionally, spectral data is collected for one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, or nine wavelengths, each wavelength being selected from a different one of the following ranges: 982 to 1002 nm; 1213 to 1233 nm; 1470 to 1490 nm; 1526 to 1546 nm; 1564 to 1584 nm; 1639 to 1659 nm; 1645 to 1665 nm; 1876 to 1896 nm; and 2191 to 2211 nm. Optionally, spectral data is collected for one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, or nine wavelengths, each wavelength being selected from a different one of the following ranges: 1382 to 1402 nm; 1407 to 1427 nm; 1614 to 1634 nm; 1627 to 1647 nm; 1639 to 1659 nm; 1645 to 1665 nm; 1652 to 1672 nm; 2093 to 2113 nm; and 2306 to 2326 nm. Optionally, spectral data is collected for one or more, two or more, three or more, four or more, five or more, six or more, or seven wavelengths, each wavelength being selected from a different one of the following ranges: 945 to 965 nm; 1001 to 1021 nm; 1013 to 1033 nm; 1063 to 1083 nm; 1157 to 1177 nm; 1250 to 1270 nm; and 1401 to 1421 nm. Optionally, spectral data is collected for one or more, two or more, three or more, four or more, five or more, six or more, or wavelengths, each wavelength being selected from a different one of the following ranges: 945 to 965 nm; 964 to 984 nm; 1013 to 1033 nm; 1063 to 1083 nm; 1157 to 1177 nm; 1250 to 1270 nm; and 1401 to 1421 nm. Optionally, spectral data is collected for one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or ten wavelengths, each wavelength being selected from a different one of the following ranges: 1376 to 1396 nm; 1432 to 1452 nm; 1445 to 1455 nm; 1482 to 1502 nm; 1664 to 1684 nm; 2117 to 2137 nm; 2123 to 2143 nm; 2185 to 2205 nm; 2209 to 2229 nm; and 2233 to 2253 nm. Optionally, the spectral data comprises reflectance data. Optionally, the method comprises a transformation of the reflectance data. Optionally, the transformation comprises one or more of the following: regression, polynomial regression, second-order polynomial regression, fourth-order polynomial regression, Savitzky-Golay filter, Standard Normal Variate, Reflectance Ansiotropy, first-order Reflectance Ansiotropy, second-order Reflectance Ansiotropy, Kramers-Kronig, or Multiplicative Scatter Correction. Optionally, the cheese is hard cheese or semi-hard cheese, such as Cheddar cheese. Optionally, the predetermined categories are selected from two or more of: Mild, Medium, Mature, Extra Mature, Vintage, or Low-Fat. Optionally, the predetermined categories are defined by free amino acid concentration in the cheese. Optionally, Mild is cheese having a total free amino acid content in the range of 62 to 122 mmol / kg. Optionally, Mild is cheese having a total free amino acid content in the range of 72 to 112 mmol / kg. Optionally, Mild is cheese having a total free amino acid content in the range of 82 to 102 mmol / kg. Optionally, Medium is cheese having a total free amino acid content in the range of 123 to 183 mmol / kg. Optionally, Medium is cheese having a total free amino acid content in the range of 133 to 173 mmol / kg. Optionally, Medium is cheese having a total free amino acid content in the range of 143 to 163 mmol / kg. Optionally, Mature is cheese having a total free amino acid content in the range of 176 to 256 mmol / kg. Optionally, Mature is cheese having a total free amino acid content in the range of 186 to 246 mmol / kg. Optionally, Mature is cheese having a total free amino acid content in the range of 196 to 236 mmol / kg. Optionally, Mature is cheese having a total free amino acid content in the range of 206 to 226 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 358 to 458 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 368 to 448 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 378 to 438 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 388 to 428 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 398 to 418 mmol / kg. According to a further aspect of the invention there is provided a device for collecting spectral data from the surface of a cheese, the device comprising a sensor configured to collect spectral data for at least two, at least three, or at least four wavelengths within the range of 900 to 2500 nm, the device further comprising a data analysis means for analysing spectral data. Optionally, the data analysis means is further configured to assign the cheese to a predetermined category. Optionally, the device is configured to collect spectral data for one or more wavelengths, each wavelength being selected from a different one of the following ranges: 945 to 965 nm; 951 to 971 nm; 964 to 984 nm; 982 to 1002 nm; 995 to 1015 nm; 1001 to 1021 nm; 1013 to 1033 nm; 1026 to 1046 nm; 1032 to 1052 nm; 1057 to 1077 nm; 1063 to 1083 nm; 1069 to 1089 nm; 1076 to 1096 nm; 1082 to 1102 nm; 1088 to 1108 nm; 1132 to 1152 nm; 1150 to 1170 nm; 1157 to 1177 nm; 1163 to 1183 nm; 1175 to 1195 nm; 1182 to 1202 nm; 1194 to 1214 nm; 1200 to 1220 nm; 1207 to 1227 nm; 1213 to 1233 nm; 1219 to 1239 nm; 1225 to 1245 nm; 1232 to 1252 nm; 1250 to 1270 nm; 1263 to 1283 nm; 1282 to 1302 nm; 1326 to 1346 nm; 1332 to 1352 nm; 1338 to 1358 nm; 1357 to 1377 nm; 1363 to 1383 nm; 1369 to 1389 nm; 1376 to 1396 nm; 1382 to 1402 nm; 1388 to 1408 nm; 1395 to 1415 nm; 1401 to 1421 nm; 1407 to 1427 nm; 1420 to 1440 nm; 1432 to 1452 nm; 1438 to 1458 nm; 1445 to 1455 nm; 1457 to 1477 nm; 1470 to 1490 nm; 1482 to 1502 nm; 1489 to 1509 nm; 1501 to 1521 nm; 1507 to 1527 nm; 1514 to 1534 nm; 1526 to 1546 nm; 1533 to 1553 nm; 1551 to 1571 nm; 1564 to 1584 nm; 1576 to 1596 nm; 1589 to 1609 nm; 1595 to 1615 nm; 1602 to 1622 nm; 1608 to 1628 nm; 1614 to 1634 nm; 1620 to 1640 nm; 1627 to 1647 nm; 1633 to 1653 nm; 1639 to 1659 nm; 1645 to 1665 nm; 1652 to 1672 nm; 1658 to 1678 nm; 1664 to 1684 nm; 1677 to 1697 nm; 1683 to 1703 nm; 1696 to 1716 nm; 1714 to 1734 nm; 1721 to 1741 nm; 1727 to 1747 nm; 1746 to 1766 nm; 1764 to 1784 nm; 1771 to 1791 nm; 1783 to 1803 nm; 1789 to 1809 nm; 1814 to 1834 nm; 1827 to 1847 nm; 1833 to 1853 nm; 1845 to 1865 nm; 1858 to 1878 nm; 1870 to 1890 nm; 1876 to 1896 nm; 1914 to 1934 nm; 2013 to 2033 nm; 2062 to 2082 nm; 2093 to 2113 nm; 2099 to 2119 nm; 2117 to 2137 nm; 2123 to 2143 nm; 2142 to 2162 nm; 2154 to 2174 nm; 2185 to 2205 nm; 2191 to 2211 nm; 2209 to 2229 nm; 2233 to 2253 nm; 2270 to 2290 nm; 2276 to 2296 nm; and 2306 to 2326 nm. Optionally, the device is configured to collect spectral data for one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, or nine wavelengths, each wavelength being selected from a different one of the following ranges: 982 to 1002 nm; 1213 to 1233 nm; 1470 to 1490 nm; 1526 to 1546 nm; 1564 to 1584 nm; 1639 to 1659 nm; 1645 to 1665 nm; 1876 to 1896 nm; and 2191 to 2211 nm. Optionally, the device is configured to collect spectral data for one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, or nine wavelengths, each wavelength being selected from a different one of the following ranges: 1382 to 1402 nm; 1407 to 1427 nm; 1614 to 1634 nm; 1627 to 1647 nm; 1639 to 1659 nm; 1645 to 1665 nm; 1652 to 1672 nm; 2093 to 2113 nm; and 2306 to 2326 nm. Optionally, the device is configured to collect spectral data is collected for one or more, two or more, three or more, four or more, five or more, six or more, or seven wavelengths, each wavelength being selected from a different one of the following ranges: 945 to 965 nm; 1001 to 1021 nm; 1013 to 1033 nm; 1063 to 1083 nm; 1157 to 1177 nm; 1250 to 1270 nm; and 1401 to 1421 nm. Optionally, the device is configured to collect spectral data is collected for one or more, two or more, three or more, four or more, five or more, six or more, or seven wavelengths, each wavelength being selected from a different one of the following ranges: 945 to 965 nm; 964 to 984 nm; 1013 to 1033 nm; 1063 to 1083 nm; 1157 to 1177 nm; 1250 to 1270 nm; and 1401 to 1421 nm. Optionally, the device is configured to collect spectral data is collected for one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or ten wavelengths, each wavelength being selected from a different one of the following ranges: 1376 to 1396 nm; 1432 to 1452 nm; 1445 to 1455 nm; 1482 to 1502 nm; 1664 to 1684 nm; 2117 to 2137 nm; 2123 to 2143 nm; 2185 to 2205 nm; 2209 to 2229 nm; and 2233 to 2253 nm. Optionally, the spectral data comprises reflectance data. Optionally, the data analysis means is operable to transform the reflectance data. Optionally, the data analysis means is capable of comparing the spectral data to reference data and then assigning to cheese to a predetermined category based on the comparison. Optionally, the transformation comprises one or more of the following: regression, polynomial regression, second-order polynomial regression, fourth-order polynomial regression, Savitzky-Golay filter, Standard Normal Variate, Reflectance Ansiotropy, first-order Reflectance Ansiotropy, second-order Reflectance Ansiotropy, Kramers-Kronig, or Multiplicative Scatter Correction. Optionally, the predetermined categories are selected from two or more of: Mild, Medium, Mature, or Extra Mature, Vintage, or Low-Fat. Optionally, the cheese is Cheddar cheese. Optionally, the predetermined categories are defined by free amino acid concentration in the cheese. Optionally, Mild is cheese having a total free amino acid content in the range of 62 to 122 mmol / kg. Optionally, Mild is cheese having a total free amino acid content in the range of 72 to 112 mmol / kg. Optionally, Mild is cheese having a total free amino acid content in the range of 82 to 102 mmol / kg. Optionally, Medium is cheese having a total free amino acid content in the range of 123 to 183 mmol / kg. Optionally, Medium is cheese having a total free amino acid content in the range of 133 to 173 mmol / kg. Optionally, Medium is cheese having a total free amino acid content in the range of 143 to 163 mmol / kg. Optionally, Mature is cheese having a total free amino acid content in the range of 176 to 256 mmol / kg. Optionally, Mature is cheese having a total free amino acid content in the range of 186 to 246 mmol / kg. Optionally, Mature is cheese having a total free amino acid content in the range of 196 to 236 mmol / kg. Optionally, Mature is cheese having a total free amino acid content in the range of 206 to 226 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 358 to 458 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 368 to 448 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 378 to 438 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 388 to 428 mmol / kg. Optionally, Vintage is cheese having a total free amino acid content in the range of 398 to 418 mmol / kg. The total free amino acid content is the total concentration in mmol / kg of: aspartic acid, glutamic acid, asparagine, serine, glutamine, histidine, glycine, threonine, citrulline, arginine, alanine, tyrosine, valine, methionine, phenyl alanine, isoleucine, ornithine, leucine, lysine, and proline. Optionally, the device is a handheld device. Optionally, the device is configured to be supported by a cheese. According to a further aspect of the invention there is provided a cheese that has been assigned to a predetermined category using the method herein disclosed, wherein the cheese has been assigned to the category without any portion of the cheese having been removed for the purpose of categorising the cheese. According to a further aspect of the invention there is provided cheese product that has been provided from a cheese that has been assigned to a predetermined category using the method herein disclosed, wherein the cheese has been assigned to the category without any portion of the cheese having been removed for the purpose of categorising the cheese. Optionally, the cheese product is packaged in consumer packaging. List of Figures Specific implementations of the present disclosure will now be described, by way of example only, and with reference to the accompanying drawings. Figure 1. Flow diagram of the method for categorising cheese according to the invention. Figure 2. Changes in free amino acid content as Cheddar cheese matures (most abundant free amino acids). Free amino acid concentrations measured in mmol / kg fresh weight cheese. Standard deviations included. For each amino acid, the order of the bars shown from left to right is: Mild, Medium, Mature, Vintage. Figure 3. Changes in free amino acid content as Cheddar cheese matures (most abundant free amino acids). Free amino acid concentrations measured in mmol / kg fresh weight cheese. Standard deviations included. For each amino acid, the order of the bars shown from left to right is: Mild, Medium, Mature, Vintage. Figure 4. Schematic of a device for categorising cheese according to the invention, and a cheese according to the invention. Detailed Description Figure 1 displays a flow chart of the method of the present invention. In step 101, the surface of a cheese is exposed to light. The light may be infra-red light across the waveband of 900 nm to 2500 nm. This exposure could be simply as a result of exposing the cheese, directly or indirectly, to sunlight. Alternatively, if there is no access to sunlight, the cheese could be exposed to artificial light. Step 102 involves then collecting spectral data from the surface of the cheese. The term “spectral data” as used herein is to mean information obtained by analysing the electromagnetic radiation, in this case infra-red light, as it is reflected or emitted from the surface of the cheese. These data typically include characteristics such as intensity and distribution across the spectrum. Step 102 further involves collecting spectral data relating to at least two, at least three, or at least four wavelengths within the range of 900 to 2500 nm. The suitability of this range and analysis for categorising cheese is demonstrated by way of the following experimental study. Discriminant Analysis (DA) using hyperspectral data Discriminant analysis using hyperspectral imaging data was conducted on 150 Cheddar cheese samples of varying maturities of Mild, Medium, Mature, or Vintage, as determined by a grading expert. Cheddar cheese is a semi-hard, cow’s milk cheese, characterised by a pH range of about 4.9 to 5.3, a moisture content of approximately 30-39%, and a salt concentration of 1.5-2% (w / w). Cheeses were scanned either “InPack”, wherein the cheese was within a plastic wrapping, or “Open-Pack”, wherein the cheese was removed from packaging. In addition, the wavelengths tested were all within the Shortwave Infrared Region (SWIR) of 900 - 2500 nm. Table 1 shows the best performing DA models. Table 1: DA models using hyperspectral data Type Transformation n values # Terms Fitted %Classifiedc %Classifiedv In Pack R4 98 / 52 1392, 1417.09, 1624.1, 1636.63, 1649.16, 1655.43, 1661.69, 2102.79 &2315.87 85 71 In Pack SNV 98 / 52 1379.46, 1385.73, 1479.84, 1605.3, 1630.37, 1667.95, 1880.22, 2022.75 & 2133.45 83 75 In Pack R2 98 / 52 1185.34, 1373.18, 1398.27, 1498.66, 1724.28, 1736.78, 1824.18, 1842.87 & 2151.82 82 75 In Pack SG2 98 / 52 1172.85, 1379.46, 1392, 1561.4, 1573.94, 1624.1, 1686.74, 1705.51 & 2285.67 81 69 In Pack RA1 98 / 52 1066.9, 1085.56, 1429.64, 1467.29, 1661.69, 1836.64, 2127.32, 2194.57 & 2279.62 80 65 In Pack RA 98 / 52 1073.12, 1222.85, 1392, 1536.31, 1686.74, 1692.99 & 2315.87 79 75 Type Transformation n values # Terms Fitted %Classifiedc %Classifiedv In Pack SG4 98 / 52 955.248, 973.811, 1023.4, 1073.12, 1166.61, 1260.39 & 1410.82 76 62 In Pack R 98 / 52 961.434, 1004.79, 1010.99, 1035.82, 1066.9, 1073.12, 1091.79 & 1141.65 74 58 In Pack MSC 98 / 52 1235.36, 1404.55, 1611.56 & 1780.51 74 65 In Pack RA2 98 / 52 1511.21, 2127.32, 2133.45 & 2151.82 68 56 In Pack KS 98 / 52 1404.55, 1836.64, 1842.87 & 1855.33 65 56 Open Pack R 98 / 52 992.393, 1222.85, 1479.84, 1536.31, 1573.94, 1649.16, 1655.43, 1886.44 & 2200.67 87 73 Open Pack SG2 98 / 52 1210.34, 1216.59, 1442.19, 1448.47, 1605.3 &1655.43 82 71 Open Pack SG4 98 / 52 955.248, 1010.99, 1023.4, 1079.34, 1085.56, 1098.01, 1166.61, 81 73 Type Transformation n values # Terms Fitted %Classifiedc %Classifiedv 1291.7 & 1348.1 Open Pack KS 98 / 52 955.248, 1204.09, 1392, 1410.82, 1417.09, 1448.47, 1617.83 & 1636.63 80 67 Open Pack RA2 98 / 52 992.393, 1341.83, 1517.49, 1523.76, 1730.53, 2108.92 & 2164.05 80 62 Open Pack SNV 98 / 52 1210.34, 1216.59, 1542.58, 1624.1, 1642.9, 1649.16 &1867.78 80 71 Open Pack RA1 98 / 52 1035.82, 1042.03, 1098.01, 1160.37, 1241.61, 1260.39, 1272.91, 1755.53 & 1836.64 79 65 Open Pack RA 98 / 52 1216.59, 1586.49, 1674.21, 1692.99, 1774.27, 1793, 1836.64 & 2072.05 78 63 Open Pack R2 98 / 52 1216.59, 1599.03, 1611.56, 1649.16, 1799.24 & 1923.71 77 73 Open Pack R4 98 / 52 1141.65, 1191.59, 1366.91, 77 69 Type Transformation n values # Terms Fitted %Classifiedc %Classifiedv 1624.1 & 1636.63 Open Pack In Pack R&W MSC SG4 98 / 52 1023.4, 1229.1, 1335.56 & 1655.43 1385.73, 1442.19, 1454.74, 1492.39, 1674.21, 2127.32, 2133.45, 2194.57, 2218.94 & 2243.25 77 88 69 85 In Table 1, “In-Pack” refers to cheese scanned inside a vacuum pack; “Open Pack” refers to cheese scanned outside of vacuum pack; “R&W’ refers to red and white cheese; “R4” is the mean of 9 wavelengths, the target wavelength and 4 either side, “SG4” refers to a Savitzky-Golay filter of 4th order, “R2” refers to the mean of 5 wavelengths, the target wavelength and 2 either side, “SN V” refers to Standard Normal Variate, “SG2” refers to a Savitzky-Golay filter of 2nd order, “RA1” refers to Reflectance attenuance (absorbance) 1st derivative, “KS” refers to K / S ratio (Ku be Ika-Munk), “RA2” refers to is Reflectance attenuance (absorbance) 2nd derivative “RA” refers to Reflectance attenuance (absorbance), “MSC” refers to Multiplicative Scatter Correction; %Classifiedc is % cheese samples correctly classified in calibration data set; and %Classifiedv is % cheese samples correctly classified in validation data set. Referring again to Figure 1, step 103 involves assigning the cheese to a predetermined category based on the spectral data. The wavelength groups in each row of Table 1 represent the best wavelengths that can be used to categorise Cheddar cheese into Mild, Medium, Mature and Vintage. Recording and analysing the reflectance of these wavelengths from the surface of a cheese can thereby be used to determine the category of a cheese. As can be seen from Table 1, the use of a mathematical transformation of the collected data can also improve the ability of the model to determine between different cheese categories. Free amino acid analysis To determine if there was underlying chemical distinction between the different maturity of the cheeses, the inventors analysed the free amino acid content of the sampled cheeses using methods within the common general knowledge of the skilled person. 5 Table 2 describes the free amino acid content of Mild, Medium, Mature and Vintage cheeses, in mmol / kg, following ANOVA analysis. The a, b, c, d lettering identifies where significant differences between cheese maturities occur for each of the free amino acids analysed (P<0.05). There were significant differences between the maturity grades with Vintage cheeses generally having higher levels of free amino 10 acids. Mild cheeses had significantly the lowest levels of free amino acids. These results highlight the importance of proteolysis during the maturation process. Table 2: ANOVA results of free amino acid analysis Identifier Mild Medium Mature Vintage avSED Prob Aspartic acid 1.20a 2.49b 4.17c 10.98d 0.409 P<0.001 Glutamic acid 14.71a 23.04b 33.50c 66.67d 2.091 P<0.001 Asparagine 7.70a 9.83b 12.51c 20.37d 0.557 P<0.001 Serine 2.43a 3.90a 7.00b 19.81c 0.89 P<0.001 Glutamine 3.68a 7.54b 9.07c 11.95d 0.487 P<0.001 Histidine 1.33a 4.88b 6.92c 11.44d 0.509 P<0.001 Glycine 3.64a 5.71b 7.95c 13.92d 0.465 P<0.001 Threonine 1.99a 3.00b 5.02c 12.21d 0.466 P<0.001 Citruline 3.500a 3.374a 4.429a 7.787b 0.552 P<0.001 Arginine 0.213a 2.970b 3.731b 3.230b 0.577 P<0.001 Alanine 2.51a 5.55b 8.08c 15.43d 0.522 P<0.001 Tyrosine 1.94a 2.72a 4.65b 11.29c 0.525 P<0.001 Valine 6.62a 9.86b 14.01c 27.34d 0.898 P<0.001 Methionine 1.809a 2.498b 3.984c 8.411d 0.309 P<0.001 Phenyl alanine 6.63a 8.82b 11.22c 20.02d 0.591 P<0.001 Isoleucine 2.44a 6.11b 10.37c 23.31d 0.902 P<0.001 Ornithine 2.517a 3.354ab 4.129b 8.095c 0.521 P<0.001 Leucine 14.81a 20.51b 26.25c 44.02d 1.212 P<0.001 Lysine 6.27a 13.74b 19.43c 37.19d 1.431 P<0.001 Proline 5.94a 12.69b 19.09c 34.46d 1.407 P<0.001 TOTAL 91.88 152.59 215.51 407.93 Figures 2 and 3 show how the most and least abundant free amino acids change as cheese matures. Glutamic acid is known to contribute to the umami taste of food and in the case of cheese, was found to be one of the more abundant free amino acids, with highest concentration found in Vintage cheeses. Aspartic acid is also known to contribute to umami taste, though in this case, concentrations were as much as 6-fold less than for glutamic acid. The content of the sweet tasting free amino acids, glycine, serine and alanine varied between 5 and 10 mmol / kg fresh weight, but each of these compounds showed a near linear increase as cheese matures from Mild to Vintage. The present invention may involve assigning a cheese to a predetermined category by comparing the obtained spectral data to reference data. To categorise a cheese, the measured reflectance at chosen wavelengths of Table 1, for example, is compared to reference data that represents known categories (such as Mild, Medium, Mature or Vintage). Reference data may comprise reflectance profiles for each category, generated under controlled conditions to establish a baseline. Statistical or machine learning methods may be implemented to assess the similarity of the measured data to each reference profile. If the reflectance values at the chosen wavelengths match closely with those of a known category, the surface can be assigned to that category with confidence. By using only a few wavelengths, rather than hyperspectral imaging, this method allows rapid, cost-effective analysis while retaining sufficient specificity to categorise surfaces accurately. The invention further includes a cheese that has been assigned using the method of categorising cheese as herein disclosed, or a cheese product derived from such a cheese. Such cheese may be assigned to its category (for example, of Mild, Medium, Mature, or Vintage) without the requirement to remove a portion of the cheese for categorising the cheese. This has the advantage of avoiding any physical interface and waste of the cheese. As shown above, for example, from the data of Table 1, the method of the present invention can be used to categorise cheese whether it is packaged or not. As such, consumer-packaged cheese may also be categorised using the method. Further disclosed herein, and as illustrated schematically in Figure 4, is a device 401 for collecting spectral data from the surface of a cheese 402. The device 401 has a sensor 403 configured to collect spectral data for wavelengths within the range of 900 to 2500 nm. The sensor may be a CCD or CMOS sensor. The device 401 further has a data analysis means 404 for analysing spectral data. The device 401 may further have optical components 405 such as lenses, mirrors, or filters to direct and focus the light onto the sensor 403 and to filter out unwanted wavelengths. The device 401 may further have a data acquisition system 406. This converts the analog signals from the sensor into digital data that can be processed. It may include an analog-to-digital converter (ADC). The data analysis means 404 may be capable of carrying out several steps. For example, the data analysis means 404 may be configured for signal processing, wherein it can filter and amplify the signal to remove noise and enhance the quality of the data. Further, the data analysis means 404 may be configured to adjust the data based on known standard to ensure accuracy. The data analysis means 404 may also be configured to store raw and processed data for further analysis. Yet further, the data analysis means may comprise algorithms or software tools for data analysis, which may include statistical analysis, pattern recognition, or machine learning techniques to interpret the reflectance data. This configuration further enables the comparison of the reflectance data to reference data and subsequent categorisation of the cheese. The device 401 may further have a user interface 407. The user interface 407 may be a display or interface for users to interact with the device, view results, and control the measurement process. The device 401 may further have a power source 408 to power the components of the device 401. The power source may be a battery or rechargeable battery. Alternatively, or additionally, the power source may comprise AC power. A further alternative or additional power source may be solar power. In use, light 410, which may include infra-red light, contacts the surface of the cheese 402 and is reflected from the surface. The reflected light 410 is then collected by the optical components 405, before contacting the sensor 403. The light 410 may be filtered and re-directed as it passes through the optical components 405. The sensor 403 functions to convert the light into an electrical signal. The raw electrical signals from the sensor are then processed by the data acquisition system 406, converting analog signals from the sensor into digital data that can be processed. The data analysis means 404 then processes the data. The transformation may comprise comprises one or more of the following: regression, polynomial regression, second-order polynomial regression, fourth-order polynomial regression, Savitzky-Golay filter, Standard Normal Variate, Reflectance Ansiotropy, first-order Reflectance Ansiotropy, second-order Reflectance Ansiotropy, Kramers-Kronig, or Multiplicative Scatter Correction. Processing may involve signal amplification, filtering, or transformation of the data, or combinations thereof. The data processing may also involve a comparison of the data to reference data and categorisation of a cheese based on the comparison. The user interface 407 then operates to display the raw or processed data and a categorisation of the cheese, or both. The display on the user interface 407 may be by use of graphs or charts, numerical values or visual representations, for example. The user can interact with the user interface 407 to view results, adjust settings, or perform further analysis. The user interface 407 might include touchscreens, buttons, or software applications on a connected computer. The device 401 can be configured to collect spectral data for specific wavelengths. In particular, the device 401 could be configured to collect spectral data for a group of wavelengths, such as those identified in Table 1, which are capable of determining between different maturities of Cheddar cheese. The device 401 may have a handle (not shown). The device 401 can be operated to assign a cheese to a predetermined category, such as Mild, Medium, Mature, or Vintage. The device 401 is a handheld device. The term “handheld” is to mean a device having: a width not greater than 30 cm, ora width not greater than 20 cm, or a width not greater than 10 cm; and, a height not greater than 30 cm, or a height not greater than 20 cm, or a height not greater than 10 cm; and, a depth not greater than 30 cm, or a depth not greater than 20 cm, or a depth not greater than 10 cm. The device 401 may further have an arrangement for attaching the device 401 to the surface of the cheese 402 or a surface nearby the cheese. The arrangement (not shown) may be, for example, a suction cup, an adhesive pad, hook-and-loop fastening arrangements, a clamp, a spring-loaded holder, a tripod mount, elastic strap, or puttylike adhesive. Advantageously, the device 401 can be temporarily set up directed at a cheese 402, and can be arranged to relay data to signal when the cheese 402 has reached the desired level of maturity, for example. In this embodiment, the device 401 may have a means for indicating when the cheese has reached the desired maturity. Such means may include an audible or visual signal, or means for transmitting data to a secondary device, such as a PC or smartphone, which can then trigger an alert to the user. The device 401 may be attached to a mobile apparatus (not shown) for remote categorisation of cheese. The mobile apparatus may be configured or configurable to be autonomous, semi-autonomous or manually remotely operated. Advantageously, the mobile apparatus may be programmed to inspect cheese around a storage house, for example, at regular intervals and to transmit collected data to a remote computing device, such as a PC, for processing, analysis and storage. This enables remote and autonomous determination of the category of cheese in storage as they mature. Data transmission may occur wirelessly via a secure network protocol, or through a physical connection if required. Additional sensors, such as LIDAR or infrared, may be integrated to assist with navigation and ensure safe operation in dynamic factory settings. Where a range of values is provided, for example, concentration ranges, percentage ranges, or ratio ranges, it is understood that each intervening value, to the tenth of the unit of the lower limit, unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the described subject matter. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and such embodiments are also encompassed within the described subject matter, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the described subject matter. It should be understood that the terms "a" and "an" as used above and elsewhere herein refer to "one or more" of the enumerated components. It will be clear to one of ordinary skill in the art that the use of the singular includes the plural unless specifically stated otherwise. Therefore, the terms “a,” “an” and “at least one” are used interchangeably in this application. Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as size, weight, reaction conditions and so forth used in the specification and claims are to the understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present subject matter. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Throughout the application, descriptions of various embodiments use "comprising" language; however, it will be understood by one of skill in the art, that in some instances, an embodiment can alternatively be described using the language "consisting essentially of or "consisting of."

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