infrared thermographic equipment and related computer software used in association with the present invention is known in the art . the infrared camera may be , for example , an inframetrics 760 broadband camera ( inframetrics corp . north billerica , mass .). this camera can be fitted with a number of different lenses such as a 0 . 5 × lens . suitable software for analysing the thermographic images includes thermogram image software ( inframetrics inc ., north billerica , mass .) and viewscan software ( viewscan ltd ., concord , ont ). in developing a predictive model of the invention , each animal in a sample population is scanned from a range of about 1 - 3 m . the preferred range is 175 - 185 cm . the thermographic images should be obtained from steady - state unstressed animals . stress in animals can be caused by such factors as transport and mixing of animals that have been reared in different groups . stressed animals exhibit aberrant thermal expression which may interfere with the collection of data suitable for making the predictive models of the invention . infrared thermographic images are collected from a number of views which may include the dorsal ( top ), distal ( rear ), lateral ( side ), proximal ( front ) and ventral ( bottom ) views of the animal . the scans showing the greatest utility , in the experience of the inventors , are the dorsal , distal and lateral views . the images can include the whole animal or can be limited to a portion of the animal . images from the various selected views are preferably taken of each animal both before slaughter , and of each animal carcass within approximately 24 hours after slaughter . the image area and the selected image temperature statistics are calculated for each of the infrared thermographc images obtained of the sample population . an uncalibrated , digitized thermographic image ( the image as obtained , without further processing ) may consist of , say , 135 × 256 pixels . the relative radiant surface temperature represented by each pixel of the uncalibrated image may be represented by assigning each pixel a numerical value in the range from , for instance , 0 to 255 . the pixel values are mapped to actual celsius temperature by relating them to the maximum and minimum temperature settings of the infrared camera through the following formula : ## equ1 ## to assist a human operator viewing the infrared thermographic images on a computer monitor , pseudo - colours can be generated by assigning a specific colour to all pixels with temperature values within a certain range . for example , purple may identify pixels representing temperatures less than 16 ° c ., blue for temperatures from 16 to 19 ° c ., and light blue for temperatures from 19 to 21 ° c . though the entire image may be processed , preferably a matrix within each image , measuring about 50 × 100 pixels , is selected for further analysis . let t ij represent temperature value in the i - th row and j - th column of this selected matrix of temperature , i from 1 to r and j from 1 to c , and r = 50 rows and c = 100 columns . sample mean , standard deviation , minimum , maximum and mode of this temperature matrix are calculated as follows : ## equ2 ## the selected statistical measures for each selected view become input variables used in the development of the predictive model . as discussed previously , other input variables , representing animal properties which may not be derived from the infrared thermographic analysis , may be included in developing the predictive model . such additional input variables include , without limitation , properties which are size or quantity measurements such as animal live weight , carcass weight and fat thickness . standardized procedures for measuring various physical characteristics of live animals and carcasses are known , allowing consistent measurements of these additional input variables to be obtained ( jones et al ., 1995 ). these additional input variables can also include animal properties that are not size or quantity measurements , such as , for example the sex of the animal . a value for the selected tissue composition characteristic ( output variable ) of each scanned animal in the sample population is measured . the measurement technique and units in which the measurement is expressed will vary depending on the tissue composition characteristic selected . for instance , if the output variable is lean body mass , this may be measured by total dissection of the carcass , and expressed in kilograms , or as a proportion of live weight . conversely , if the output variable is a non - parametric measure such as a beef grading distinction between aaa - grade and aa / a - grade carcasses , measurement of the value of this variable in the sample population might be determined by visual observation of rib eye marbling , and the value expressed as a rank or grade . using the accumulated data , a relationship between the statistical measures for the selected infrared thermographic image views and additional input variables such as live weight , and the selected tissue composition characteristic ( output variable ) is determined to provide a predictive model whereby , given a value for each of the input variables in the model , the value of the output variable can be predicted . thus , as shown in the figures once a predictive model for a selected tissue composition characteristic for a sample population has been established , the value for the selected tissue composition characteristic for a subsequent animal for which the sample population is statistically appropriate can be predicted by : ( a ) obtaining infrared thermographic images of the subject animal from the selected views 10 ; ( b ) calculating a value for each selected statistical measure to obtain a data point for each input variable for the subject animal 12 ; ( c ) substituting the data points obtained for each input variable for the subject animal into the predictive mode 14 ; and ( d ) calculating the predicted value of the selected tissue composition characteristic for the subject animal 16 , see fig1 . the relationship between the input variables and the output variable can be determined by any of a number of known statistical methods such as multiple linear regression , artificial neural net learning , cluster analysis and discriminant analysis . in a preferred embodiment , the multiple regression procedure of sas ( sas institute inc ., cary n . c .) is used . where there are multiple input variables , a solution utilizing matrix algebra may be used . for instance , where nine input variables are being analysed , and the output variable is lean body mass , the multiple regression model may be : ## equ3 ## where : y j = the percentage lean body mass of the j - th animal , j = 1 , 2 . . . n , b i = the i - th regression coefficient , i = 1 , 2 . . . 9 , where e denotes the expectation operator and σ 2 is a constant . the objective of this example was to determine whether infrared thermography could be used to predict the lean body mass of cattle . sixteen market weight ( 500 kg ) crossbred steers raised at the agriculture and agri - food canada lacombe research centre ( lacombe , alberta , canada ) beef unit facilities on conventional cereal - silage diets were used in this study . the cattle were transported to the lacombe research centre meats research facility in the morning and within two hours of arrival they were scanned by an infrared camera ( inframetrics model 760 with a 0 . 5 × lens ). scans were obtained from dorsal , lateral and distal views of the live animals at a distance of 175 - 193 cm . tpi image software ( ottawa , canada ) was used for the subsequent resolution and printing of the individual thermographs as described in example 2 herein . the live weight of the animals was also determined . the animals were subsequently processed at the lacombe research centre meats research facility abattoir and thermographic scans were obtained of the carcasses of the animals from the lateral view approximately one hour after slaughter . the total lean body mass for the animals was measured by total dissection into lean , bone and fat according to the methods described by jones et al . ( 1987 ). the image area and values for temperature statistics including the mean , mode and standard deviation as well as the product of the image mean temperature × image area ( previously defined as total temperature ) were calculated for each of the infrared images . for each animal , the following information was compiled : 3 . lean body mass of the carcass as determined by dissection ; 4 . for the image of the dorsal view of the live animal , the image area in cm 2 , maximum image temperature , mean image temperature , minimum image temperature , mode of image temperature , standard deviation of image temperature , and total temperature for image ; 5 . for the image of the distal view of the live animal , the image area in cm 2 , maximum image temperature , mean image temperature , minimum image temperature , mode of image temperature , standard deviation of image temperature , and total temperature for image ; 6 . for the image of the lateral view of the live animal , the image area in cm 2 , maximum image temperature , mean image temperature , minimum image temperature , mode of image temperature , standard deviation of image temperature , and total temperature for image ; and , 7 . for the image of the lateral view of the carcass of the animal one hour after slaughter , the image area in cm 2 , maximum image temperature , mean image temperature , minimum image temperature , mode of image temperature , standard deviation of image temperature , and total temperature for image . the lean body mass was not determined for the carcasses of four of the 20 animals . these were condemned animals that did not undergo further abattoir processing . the degree of linear association between the selected statistical measures or groups of the selected statistical measures of the infrared images of the selected views and animal live weight ( input variables ) and lean body mass ( output variable ) was determined using multiple regression procedures as defined by steel and torrie ( 1960 ). the degree of linear association was calculated as the coefficient of determination ( r 2 value ) shown in table 1 . the value of r 2 defines the fraction of the total variation that can be ascribed to the linearity relationship . as shown in table 1 , the highest r 2 value for any set of input variables tested was 0 . 89 . this indicates that 89 % of variation in lean body mass can be explained by the linearity relationship between the set of input variables tested and lean body mass . table 1__________________________________________________________________________coefficients of determination ( r . sup . 2 values ) of lean body masspredictorswith actual dissected lean mass in 500 kg beef steersmeasurement r . sup . 2 value__________________________________________________________________________mean temp . of dorsal view of live animal + stnd . dev . of temp . of dorsalview of live 0 . 37animalmean temp . of distal view of live animal + stnd . dev . of temp . of distalview of live 0 . 49animalmean temp . of lateral view of live animal + stnd . dev . of temp . oflateral view of live 0 . 30animalmean temp . of dorsal view of live animal + stnd . dev . of temp . of dorsalview of live 0 . 46animal + live weightmean temp . of distal view of live animal + stnd . dev . of temp . of distalview of live 0 . 68animal + live weightmean temp . of lateral view of live animal + stnd . dev . of temp . oflateral view of live 0 . 47animal + live weightmean temp . of lateral view of carcass 1 h after slaughter + stnd . dev . oftemp . of lateral 0 . 15view of carcass 1 h after slaughtermean temp . of lateral view of carcass 1 h after slaughter + stnd . dev . oftemp . of lateral 0 . 40view of carcass 1 h after slaughter + carcass weightmean temp . of dorsal view of live animal + mean temp . of distal view oflive animal + 0 . 74mean temp . of lateral view of live animal + stnd . dev . of temp . of thedorsal view ofthe live animal + stnd . dev . of temp . of distal view of live animal + stnd . dev . of temp . of lateral view of live animal + live weighttotal temp . of dorsal view of live animal + tota1 temp . of distal view oflive animal + 0 . 72total temp . of lateral view of live animal + stnd . dev . of temp . of thedorsal view of thelive animal + stnd . dev . of temp . of distal view of live animal + stnd . dev . of temp . oflateral view of live animal + live weighttotal temp . of dorsal view of live animal + total temp . of distal view oflive animal + 0 . 89total temp . of lateral view of live animal + total temp . of lateral viewof carcass 1 hafter slaughter + stnd . dev . of temp . of the dorsal view of the liveanimal + stnd . dev . of temp . of distal view of live animal + stnd . dev . of temp . of lateralview of liveanimal + stnd . dev . of temp . of lateral view carcass 1 h after slaughter + live weight__________________________________________________________________________ using the set of predictors ( input variables ) exhibiting the highest r 2 value , the following predictive model for predicting lean body mass in cattle was determined using multiple regression procedures as defined by steel and torrie ( 1960 ): % lean body mass = 566 . 8795 + 0 . 0014x . sub . 1 - 30 . 9181x . sub . 2 0 . 0015x . sub . 3 + 29 . 5325x . sub . 4 + 0 . 0009x . sub . 5 - 15 . 9213x . sub . 6 + 0 . 0005x . sub . 7 - 31 . 1438x . sub . 8 - 0 . 05625x . sub . 9 wherein x 1 is the total temperature of the dorsal view of the live animal , x 2 is the standard deviation of temperature of the dorsal view of the live animal , x 3 is the total temperature of the distal view of the live animal , x 4 is the standard deviation of temperature of the distal view of the live animal , x 5 is the total temperature of the lateral view of the live animal , x 6 is the standard deviation of temperature of the lateral view of the live animal , x 7 is the total temperature of the lateral view of the carcass one hour after slaughter , x 8 is the standard deviation of the temperature of the lateral view of the carcass one hour after slaughter , and x 9 is the live weight of the animal . to use this predictive model in practice , thermographic images of the animal for which lean body mass is to be determined are taken from each of the dorsal , distal and lateral views of the animal prior to slaughter . the live weight of the animal is also measured . about one hour after slaughter , a thermographic image is obtained of the lateral view of the carcass . the appropriate statistical measures are determined for the temperature data provided by each image to provide a value for each input variable . the value of each input variable for the subject animal are substituted into the predictive model and the predictive model solved to provide a prediction of the lean body mass of the animal . as noted above , this example demonstrating the prediction of lean body mass in cattle was based on a cut - out study of 16 animals . the cost and labour involved in studies involving the total dissection of large animal carcasses such as cattle to determine lean body mass place constraints on the size of sample populations that can be used in a research setting . in a commercial setting , it may be possible to include data from a much larger cut - out study . it will be appreciated that those skilled in the art that superior accuracy may be achieved using the processes of the present invention with predictive models based on larger sample populations , ideally around 1500 animals . it is apparent in table 1 that high r 2 values were obtained with sets of input variables other than the preferred set of input variables used to derive the above predictive model . for instance , r 2 values of 0 . 68 , 0 . 72 and 0 . 74 were also obtained with different sets of input variables . those skilled in the art will appreciate that within the scope of the invention , there may be applications where a lower degree of accuracy in the prediction of the tissue composition characteristic of interest will be acceptable and it may therefore be possible to use a predictive model with fewer input variables . for example , although greater accuracy might be achieved in predicting lean body mass in cattle if statistical measures of temperature data obtained from the lateral view of the carcass one hour after slaughter are included in the predictive model , circumstances may make it impractical to obtain thermographic images of the cattle after slaughter . it may prove sufficient in those circumstances to use a predictive model that does not include statistical measures of temperature data obtained from the lateral view of the carcass one hour after slaughter as input variables , even if such a model does not provide the optimum degree of predictive accuracy , in order to avoid the need to obtain thermographic images in the post - mortem environment . the objective of this example was to determine whether infrared thermography can be used to distinguish differences in carcass fat composition or grade in live animals . a total of 866 crossbred steers and heifers weighing approximately 500 kg weight were dorsally scanned with an infrared camera within one hour prior to slaughter in the antemortem holding area at the ibp inc . beef abattoir located at brooks , alberta , canada . the infrared thermographic images were taken with an inframetrics 760 broadband camera ( inframetrics corp . north billerica , mass .). subsequent resolution and printing of the individual thermographs was accomplished using viewscan software ( viewscan ltd , concord , ontario , canada ) as set out below . the video signal from the camera was converted to digital data with an a / d converter before being processed by a computer as follows . the image was saved as a raw , uncalibrated data file . the area of the image itself was divided into pixels . the raw pixel data was digital data proportional to voltage signals from the infrared camera . in order to analyse the thermograph , the digital data was converted to temperature data using a calibration procedure with the viewscan software . after calibration , the pixels were displayed in fifteen different colours plus a background colour , representing fifteen temperature ranges of 1 . 2 +/- 0 . 2 ° c ., ranging from 15 . 0 to 32 . 0 ° c . the viewscan software allowed for analysis of the pixel data by different zones or by the entire image . the following information was obtained for each image using the viewscan software : absolute pixel counts and pixel counts as a percentage of the total pixels in the image falling into each temperature range ; maximum and minimum temperatures in the image ; the overall range of temperatures in the image ; the median , the mean , and standard deviations of temperatures in the image ; and the total area of the image ( in pixels ). the live weight of each animal was also measured prior to slaughter . after slaughter , the carcasses from all animals were classified according to canadian beef grading standards ( alberta agriculture feedlot handbook , 1997 ) and all carcasses were in the highest standards or a grade categories . the percentage yield of each carcass was also determined . as described previously , carcass yield is the combined mass of skeletal muscle , bone and associated fat as a proportion of live animal weight and is commonly expressed as a percentage ( e . g . 60 % carcass yield ) or as a weight relationship ( e . g . 600 g / kg live weight ). the carcass classification , average infrared thermography dorsal temperatures and standard errors ( se ) are shown in table 2 . table 2______________________________________average infrared dorsal temperatures +/- se for three classes of cattlecarcassclassification a aa aaa______________________________________sample size n = 143 n = 522 n = 201mean live animal 26 . 7 . sup . a 26 . 85 . sup . a 27 . 6 . sup . bdorsal temperature (° c . )+/- standard error 0 . 22 0 . 11 0 . 18______________________________________ . sup . a , b statistically different at p & lt ; 0 . 01 apparent in table 2 is the observation that the animals producing carcasses classed as triple a ( aaa ) were observed to display infrared thermography temperatures that were statistically different from cattle displaying carcasses of either single a ( a ) or double a ( aa ) grades . as described in the canadian beef grading system , carcasses with different grade classification between a - aa and aaa differ in fat composition . a - grade cattle exhibit trace levels of marbling . aa - grade cattle exhibit slight levels of marbling , and aaa - grade cattle exhibit small levels (& gt ; slight ) of marbling . grades a , aa and aaa are otherwise substantially identical . marbling is the common name given to the flecks of intramuscular fat exposed on the cut surface of a muscle , typically viewed on the longissimus dorsi muscle , cut between the 11th and 12th rib of the carcass . thus , animals having different levels of intramuscular fat were distinguished by infrared thermography . hence , this example illustrates that infrared thermography can be used on live animals to differentiate fat composition . given that a statistically significant distinction in mean temperature based upon single dorsal scans of each animal was detected between a / aa and aaa - grade cattle having slightly different levels of marbling , it is apparent that it is possible to develop predictive models in which the relative or absolute fat composition of a live animal may be predicted from infrared thermography data . this will hold particularly true if the input variables include various statistical measures other than only the mean for infrared thermographic images from views other than the dorsal view as well as other non - thermographic measures . though in a commercial setting , a predictive model would likely be developed which might include input variables derived from thermographic data obtained from other views of the animal than the dorsal view , and might also include input variables derived from non - thermographic data such as animal weight , it is nevertheless possible to provide an example of a very simple predictive model based on the observed mean temperature distinction in the dorsal scans of a , aa versus aaa - grade cattle . such a predictive model that might be applied would be such a predictive model would have application in canada , for instance , where there is only a small price premium for aaa - grade cattle over aa or a - grade cattle . in the united states , the department of agriculture grade equivalent to canada - aaa is termed choice or prime , and there is a price premium for such animals . if a canadian producer were to determine from the application of the present invention using the above predictive model which of its cattle were likely to grade aaa rather than aa or a , it would be able to market the aaa grade animals to the united states to realize the price premium . it was the objective of this example to determine whether infrared thermography values collected from live pigs would display a statistically significant relationship with the lean body mass in pigs , as was determined to be the case with other species such as cattle . such information could be used in predictive models as provided in example 1 . 27 market weight ( 100 kg ) crossbred pigs raised at the swine unit of the agriculture and agri - food canada lacombe research centre at lacombe , alberta were used in this trial . the pigs were transported to the lacombe research centre meats research facility in the morning and within two hours of arrival were scanned with an infrared camera ( inframetrics model 760 with a 0 . 5 × lens ). scans were obtained from the dorsal surface only . tpi image software ( ottawa , canada ) was used for the subsequent resolution and printing of the individual thermographs as described in example 2 . the pigs were subsequently processed at the meats research facility and the total lean body mass for the animals was directly measured by total dissection into lean , bone , and fat according to the methods described by dugan et al . ( 1997 ). the image area and values for temperature statistics including the mean , mode and standard deviation were determined . table 3 provides the values for the lean yield of muscle from these animals ( calculated as the lean dissected weight expressed as g / kg of cold side weight × the side weight of the cold carcass ) and the total temperature ( image area in number of pixels × mean image temperature ). the degree of association between the lean yield value and the total temperature is expressed in table 3 as the coefficient of linear correlation ( r ) determined by pearson correlation coefficients ( sas , 1985 ). in table 3 , r expresses the strength of the linear relationship between lean yield and total temperature . the value of r always ranges between - 1 and 1 inclusive . that is , - 1 ≦ r ≦ 1 . when r = 1 , there is a perfect positive correlation . for a perfect positive correlation with r = 1 , in a scatter diagram plotting dorsal total temperature against lean yield , all the points would lie on a straight line rising upward to the right . a value of r =- 1 means that there is a perfect negative correlation and that all the points lie on a straight line falling downward to the right . any value of r in the vicinity of + 1 or - 1 implies that the points are scattered closely around a straight line . table 3 reports an r value of 0 . 72 at p = 0 . 0001 . it is therefore evident in table 3 that there is a highly statistically significant probability that total temperature is correlated with lean yield in the pig . the thermal expression of a pig having been demonstrated to be closely associated with its lean yield , it is apparent to those skilled in the art that the processes of the present invention could be used to develop predictive models for tissue composition characteristics in pigs . the results presented in table 3 can be used to derive a predictive model wherein total temperature of the dorsal view of the live pig is the input variable , and lean yield is the tissue composition characteristic forming the output variable . as with example 2 , those of skill in the art will recognize , based on the results reported in example 1 , that the accuracy of the predictive model in pigs could be increased by including additional input variables constituting statistical measures of temperature data obtained from thermographic images of views of the animals other than the dorsal view as well as non - temperature related input variables such as live animal weight . table 3______________________________________lean yield , total temperature and correlation between lean yield andtotaltemperature in market weight pigs . lean yield . sup . 1 ( kg ) total temperature . sup . 2mean ± stand . dev . mean r value______________________________________43 . 5 ± 3 . 76 233 , 823 ° c . 0 . 72______________________________________ . sup . 1 lean yield of muscle as g / kg of cold side weight × side weight of cold carcass . sup . 2 image area in pixels × mean image temperature . sup . 3 p = 0 . 0001 clark , j . a . and cena , k . 1972 . thermographic measurements of the surface temperatures of animals . j . of mammalogy 54 : 1003 - 1007 . dugan , m . e . r ., a . k . w . tong , j . p . carlson , b . r . schricker , j . l . aalhus , a . l . schaefer , a . p . sather , a . c . murray and s . d . m . jones . 1997 . the effects of porcine somatotropin , gender and porcine stress syndrome on growth , carcass composition and pork quality . canadian j . animal science . 77 : 233 - 240 . forrest , j . c . 1995 . new techniques for estimation of carcass composition . pp 157 - 171 in . quality and grading of carcasses in meat animals ed . by . s . d . m . jones . crc press . new york . goll , d . e ., stromer , m . h . and robson , r . m . 1977 . skeletal muscle , nervous system , temperature regulation and special senses . p 504 - 548 . in . dukes physiology of domestic animals . 9th ed . ed by m . j . swenson . comstock pub . cornell univ . press . ithaca . hayward , j . a ., eckerson , j . d . and collis , m . 1975 . thermal balance and survival time prediction of men in cold water . can . j . physiol . pharmacol . 53 : 21 - 32 . jones , s . d . m ed . 1995 . quality and grading of carcasses of meat animals . crc press . new york . jones , s . d . m ., tong , a . k . w . and robertson , w . m . 1987 . the effect of carcass grade and sex on lean content of beef carcasses . can . j . anim . sci . 67 : 205 - 208 . kleiber , m . 1975 . the fire of life . r . e . krieger pub . comp . new york . neilson , n . p . and jensen , c . r . 1972 . measurement and statistics in physical education . woodsworth pub . comp . inc . belmont , calif . o &# 39 ; grady , j . f . 1989 . new techniques in pig carcass evaluation . editor eaap pub . no 41 . pudoc , wageningen . sas institute inc . 1985 . sas user &# 39 ; s guide : statistics . 5th ed . sas institute , inc . cary , n . c . steel , r . g . d . and torrie , j . h . 1960 . principles and procedures of statistics . mcgraw - hill book company ltd . new york . steel , r . g . d . and torrie , j . h . 1980 . principles and procedures of statistics , a biometrical approach . 2d edition . mcgraw - hill book company ltd . new york . all publications mentioned in this specification are indicative of the level of skill of those skilled in the art to which this invention pertains . all publications are herein incorporated by reference to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference . although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding , it should be understood that certain changes and modifications may be practised within the scope of the appended claims .
a process for determining a tissue composition characteristic of a live animal or carcass involves the steps of : obtaining either or both of at least one infrared thermographic image of the animal while it is alive , taken from at least one view , or at least one image of the carcass of the animal after slaughter , taken from at least one view , each thermographic image being capable of being represented as an array of pixels providing temperature data representative of temperature information at the corresponding part of the image ; calculating the value of at least one statistical measure of the temperature data for each thermographic image ; providing a predictive model wherein the tissue composition characteristic is included as an output variable , and the statistical measures of temperature data for each thermographic image are included as input variables ; and solving the predictive model to provide the value of the tissue composition characteristic . the process of the invention is useful in a wide variety of homoeotherinic animals . tissue composition characteristics of interest include , without limitation , lean body mass , grade , carcass yield and rib eye area . an apparatus for performing the processes of the invention includes : image acquisition means for obtaining the infrared thermographic images ; computing and storage means for storing each image as an array of pixels , calculating the value of statistical measures of the temperature data for each thermographic image , and providing and solving the predictive model ; and output means for providing an output of the value of the tissue composition characteristic .