Method and Device for Bone Scan in Meat

Abstract

A method and device for detection of bone in meat identifies fragments larger than about 1 mm using spectral optical imaging and ultrasound. Spectral imaging can detect foreign material proximate to the surface and ultrasound can detect material within the sample. The sample is irradiated by light and reflected light or Raman scattered light measured. The sample is similarly irradiated by ultrasound and reflected or transmitted sound waves give a set of amplitude data points, which include temporal delay. These data points are then processed by statistical methods to derive a set of vectors in n-dimensional space, which are compared to a calibrated data set of derived vectors which have distinct identifying loci for each type of surface, are indicative of the presence or absence of defects.

1 . A method for detecting defects in a meat sample on a production line, comprising the steps of
 emitting at least one wavelength of light onto an area of said meat sample;   receiving light reflected from said area of said meat sample measuring the amplitude of said reflected light.;   and in a data processor comparing the amplitudes of said reflected light for each area of said meat sample by multivariate analysis and   determining from said multivariate analysis the presence of surface defects in said meat sample.   
     
     
         2 . The method of  claim 1  wherein a single wavelength of light is emitted and the reflected light is Raman scattered comprising the additional step of dispersing said Raman scattered light through a wavelength selector to separate the Raman scattered light into distinct wavelengths and the further additional step of measuring the amplitudes of said distinct wavelengths. 
     
     
         3 . The method of  claim 1  or  2  wherein said single wavelength is quasi monochromatic and selected from ultraviolet in the wavelength range of 200 to 220 nm and visible light and infrared light at 488, 515, 532, 594, 633, 635, 650, 660, 670, 780, 808, 830, 850, 980, and 1064 nm. 
     
     
         4 . The method of  claim 3  wherein said single wavelength ultraviolet is in the wavelength range of 200 to 220 nm. 
     
     
         5 . The method of any preceding claim wherein single wavelength is selected from visible light and infrared light at 488, 515, 532, 594, 633, 635, 650, 660, 670, 780, 808, 830, 850, 980, and 1064 nm. 
     
     
         6 . The method of any preceding claim wherein said at least one wavelength of light is broad band white light and comprising the additional step of dispersing said reflected light through a wavelength selector to separate the reflected light into distinct wavelengths and the further additional step of measuring the amplitudes of said distinct wavelengths. 
     
     
         7 . The method of any preceding claim wherein said at least one wavelength of light is near infrared wavelength selected from the range of 900 to 2600 nm. 
     
     
         8 . The method of any preceding claim wherein said at least one wavelength of light comprises at least two separate wavelengths. 
     
     
         9 . The method of  claim 8  wherein comprising the steps of emitting said at least two separate wavelengths at separate times for each wavelength, and the steps of measuring said amplitudes of reflected light at separate times for each wavelength. 
     
     
         10 . The method of  claim 9  wherein said at least two separate wavelengths comprise 620 to 640 and 720 to 760 nm. 
     
     
         11 . The method of  claim 9  wherein said at least two separate wavelengths comprise three wavelengths of 540 to 570, 620 to 640 and 720 to 760 nm. 
     
     
         12 . The method of  claim 9 ,  10  or  11  wherein the steps of measuring the amplitudes of each separate wavelength are measured by the same focal plane array of pixels. 
     
     
         13 . The method of  claim 9 ,  10  or  11  wherein the steps of measuring the amplitudes of each separate wavelength are measured by two separate focal plane arrays of pixels, each focal plane array measuring a different wavelength. 
     
     
         14 . The method of any preceding claim wherein said data processor receives a plurality of light amplitudes corresponding to a sample area of said meat sample, said data processor generates n-dimensional data vectors from light amplitudes and compares said data vectors with a calibration set generated by multivariate analysis, to determine whether they correspond to bone, cartilage, fat, flesh or skin, or contaminant for each sample area of the sample, when undesired matter is identified, a logic signal is sent to actuate a pass-fail gate stopping the sample, otherwise no logic signal is sent. 
     
     
         15 . The method of  claim 14  wherein additionally said data processor identifies the amplitudes of neighboring areas to said sample area, abutting directly and diagonally, for each wavelength, calculates the gradient across said sample area and said abutting areas for all wavelengths and generates n-dimensional data vectors from said gradients and amplitudes, and compares said data vectors with a calibration set, generated by multivariate analysis, which additionally determines the presence of edges between sample areas, when undesired matter is identified, a logic signal is sent to actuate a pass-fail gate stopping the sample, otherwise no logic signal is sent. 
     
     
         16 . The method of any preceding claim comprising the additional steps of:
 emitting at least one frequency of ultrasound onto an area of said meat sample receiving ultrasound returned from said meat sample;   measuring the amplitudes and times of flight of said returned ultrasound;   comparing the amplitudes and times of flight of said returned ultrasound for each area of said meat sample by multivariate analysis;   determining from said multivariate analysis the presence of surface and internal defects in said meat sample.   
     
     
         17 . The method of  claim 16  comprising the additional steps of comparing the amplitudes of said reflected light for each area of said meat sample and the amplitudes and times of flight of said returned ultrasound for each area of said meat sample by multivariate analysis and determining from said multivariate analysis the presence of surface and internal defects in said meat sample. 
     
     
         18 . The method of  claim 16  or  17  wherein said data processor receives a plurality of acoustic amplitudes and times of flight of said amplitudes corresponding to a sample area of said meat sample, said data processor compares said amplitudes to standard amplitudes to determine the presence of bone in said sample, when bone is present, a logic signal is sent to actuate a fail gate stopping the sample, otherwise no logic signal is sent. 
     
     
         19 . The method of  claim 16 ,  17  or  18  wherein said data processor mean centers and normalizes said amplitudes to standard deviation for each wavelength. 
     
     
         20 . The method of any preceding claim wherein each type of LED of the same wavelength has a power converter controlled by a switching circuit, in combination with a logic processor, whereby each type of LED of the same wavelength is strobed separately. 
     
     
         21 . A method for detecting defects in a meat sample on a production line comprising the steps of:
 emitting at least one frequency of ultrasound onto an area of said meat sample;   receiving ultrasound returned from said meat sample;   measuring the amplitudes and times of flight of said returned ultrasound;   comparing the amplitudes and times of flight of said returned ultrasound for each area of said meat sample by multivariate analysis;   determining from said multivariate analysis the presence of surface and internal defects in said meat sample.   
     
     
         22 . The method of  claim 21  comprising the additional steps of comparing the amplitudes of said reflected light for each area of said meat sample and the amplitudes and times of flight of said returned ultrasound for each area of said meat sample by multivariate analysis and determining from said multivariate analysis the presence of surface and internal defects in said meat sample. 
     
     
         23 . The method of  claim 21  or  22  wherein said data processor receives a plurality of acoustic amplitudes and times of flight of said amplitudes corresponding to a sample area of said meat sample, said data processor compares said amplitudes to standard amplitudes to determine the presence of bone in said sample, when bone is present, a logic signal is sent to actuate a fail gate stopping the sample, otherwise no logic signal is sent. 
     
     
         24 . The method of  claim 21 ,  22  or  23  wherein said data processor mean centers and normalizes said amplitudes to standard deviation for each wavelength.