Aug 28, 1996 - A monolithic inductively coupled plasma generator includes ..... selective chemical vapor deposition, and other techniques may be used.
THIRD TO CLINTON L. waLKER, of PIEDMoNT, CALLFoRNLA v. UNIFORM. ... relation between the intake manifold pressure ... bustion engine cylinder with a movable abut ment and ... abutments 7 are disposed in the cylinders 6, ... provided with a crank 18 w
Mar 23, 2010 - floating platform, a vessel, a spar, and the like) is added to form the complete .... anticipated that Such design features and others apparent to.
e$**'-6. 8X+1 = lente x= lub-1 loszx=2. 122 =X. X = 0.0990. 1444 = x x = t Jean x= + 7.3891. 7. e2x â 4e* - 5 = 0. (ex-sXe*+1)-0 ex 50ex+1=0 op te local alogizx=.
Nehemiah 8.1-4a, 8-12, Ps 119.9-16, Col 3.12-17, Matt 24.30-35 ... With my whole heart / have I / sought you : O let me not go a/stray from / your ... love. NEWSLETTER. St Catherine's | St John's | St Luke's. St Catherine's St John's St Luke's ... Ha
Jan 26, 2014 - 1. â» Analyse the content of a marketing plan. â» Assess the usefulness of marketing planning. â» Link the marketing plan to a coordinated.
Mar 1, 2007 - accordance With detection conditions such as Whether or not the object is in an area Which at least tWo detection assurance ranges cover and ...
BoysâSandwich Ziploc bags. â¢ GirlsâGallon Ziploc bags. **Money ($4.50) will be collected for Weekly. Readers after school starts.**. Please label supplies with.
I. Key TermsâExplain what each item is and WHY it is important. 1. Law of supply. 5. Raw materials. 9. Total revenue. 2. Supply. 6. Diminishing returns. 10.
Nov 26, 2013 - A roller comprises an exterior layer including one or more .... This liquid carrier, largely comprised of low molecular weight imaging oil, simply ...
(19) United States (12) Patent Application Publication (10) Pub. No.: US 2002/0168046 A1 (43) Pub. Date:
Amethod and apparatus for determination of properties of a medium of food or feed, such as the fat content of meat, by use of dual X-ray absorptiometry, the medium being a raW material of food or feed, a product or intermediary product
(76) Inventor: Per Waaben Hansen, Lyngby (DK) Correspondence Address: BIRCH STEWART KOLASCH & BIRCH PO BOX 747
of food or feed, or a batch, sample or section of the same,
FALLS CHURCH, VA 22040-0747 (US)
comprises—scanning substantially all of the medium by X-ray beams (16, 18) having at least tWo energy levels,
(21) Appl. No.:
Apr. 8, 2002
speci?cally for online use in a slaughter house. The method
including a low level and a high level,—detecting the X-ray beams having passed through the medium for a plurality of areas (pixels) of the medium,—for each area calculating a
Related US. Application Data
value, AIOW, representing the absorption in the area of the
(63) Continuation of application No. PCT/DKOO/OOSSS,
a value, Ahigh representing the absorption in the area of the medium at the high energy level. The accuracy of the
medium at the loW energy level,—for each area calculating
?led on Oct. 20, 2000.
determination is improved considerably by generating for each area a plurality of values being products of the type A1"W“*Ahighrn Wherein n and m are positive and/or negative integers or Zero, and predicting the properties of the medium in this area by applying a calibration model to the plurality of values, Wherein the calibration model de?nes relations betWeen the plurality of values and properties of the
Foreign Application Priority Data
Oct. 21, 1999
(DK) .............................. .. PA 1999 01512
Measure ldark and lair ARRANGE A BATCH OF MEAT ON A CONVEYOR
PASSING THROUGH THE X-ray APPARATUS
I FIRST SCANNING USING THE FIRST BEAM AND FIRST DETECTOR
SECOND SCANNING USING THE SECOND BEAM AND SECOND DETECTOR
T\ REGISTRATION OF VALUE OF EACH PIXEL
REGISTRATION OF VALUE OF 112
EACH PIXEL (Ihrgh)
STORAGE OF ALL VALUES,
Calculate Am, and Ahigh for all pixels, COORDINATION OF A|0W VALUES t0 Ahigh VALUES;
calculation of the expressions AIOW" * Ahigh’".
F116 Application of multivariate calibration models for the determination of the properties ot the batch of meat: For all points: Calculate the fat content.
Calculate the areal density. Multiply fat content and areal density, generating a "fat map". Add all points in the "lat map" to give the total fat weight, (Ftotal) ot the batch. Add all areal densities for the batch to give the total fat weight, (Wlolal ) of the batch. Calculate average fat content of batch as the ratio Ftotal/Wlotal. I18
METHOD AND APPARATUS FOR DETERMINATION OF PROPERTIES OF FOOD OR FEED TECHNICAL FIELD
 The presently applied apparatus in most slaughter houses is a Continuous Fat Analyser (Wolfking A/S, Den
mark) and Infratec 1265 (Foss Tecator AB, SWeden) using NIR technology. Also applied is Anyl-Ray (The Kartridg Pak Co., IoWa) using a single energy X-ray on a sample of Well-de?ned Weight or volume.
 The present invention relates to X-ray analysis, and more speci?cally to the determination of properties of food
or feed, such as the fat content of meat.
method and apparatus enabling a faster and more accurate determination than hitherto knoWn, of the fat content in a food or feed product, such as a batch of meat trimmings,
 X-ray analysis for determining the fat content of meat has been knoWn for several years. Such examples are described in numerous documents. US. Pat. No. 4,168,431
(Henriksen) discloses a multiple-level X-ray analysis for determining fat percentage. The apparatus includes at least three X-ray beams at different energy levels. DK PS 172 377 B1 discloses detection means for X-rays as Well as a system
for determination of properties of an item by use of X-rays. The system operates at a single energy level and applies tWo detection means separated by a X-ray attenuating material.  WO 92/05703 discloses a method and device for cutting food products. The positioning of suitable cuts are
guided by use of X-ray scanning shoWing the distribution of tissue type in the product. US. Pat. No. 5,585,603 discloses a method and system for Weighing objects using X-rays. A continuous X-ray analysis for a meat blending system is knoWn from US. Pat. No. 4,171,164 (Groves et al). 
The percentages of fat in tWo meat streams are
determined by passing a beam of polychromatic X-rays through the streams, measuring both the incident and the attenuated beams. US. Pat. No. 4,504,963 discloses an
apparatus, system and method for determining the percent
It is an object of the present invention to provide a
alloWing creation of speci?c products (such as sausages or minced meat) having a desired content of fat, Which is much more accurate than presently possible.
 The present invention also applies regression analysis and multivariate calibration. Such analysis is knoWn from eg the applicant’s oWn W0 95/ 16201 disclos ing the Determination of extraneous Water in milk samples
using regression analysis and multivariate calibration. Fur ther, the applicant’s WO 98/43070 discloses Measurement of acetone in milk using IR spectroscopy and multivariate calibration. US. Pat. No. 5,459,677 discloses a calibration
transfer for analytical instruments. The applicant’s WO 93/06460 discloses an infrared attenuation measuring sys tem, including data processing based on multivariate cali bration techniques, and the applicant’s US. Pat. No. 5,252, 829 discloses a determination of urea in milk With improved
accuracy using at least part of an infrared spectrum. DISCLOSURE OF THE INVENTION
 The present invention relates to a method of deter mining properties a medium of food or feed, such as the fat content of meat, by use of dual X-ray absorptiometry, the medium being a raW material of food or feed, a product or
age of fat in a meat sample through use of X-ray radiation techniques. An automatic calibration is obtained by use of three incident beams, all at same energy level. Validation of
intermediary product of food or feed, or a batch, sample or section of the same, the method comprising—scanning
body composition by dual energy X-ray absorptiometry is described in Clinical Physiology (1991) 11, 331-341. (J. Haarbo, A. Gotfredsen, C. Hassager and C. Christiansen).
least tWo energy levels, including a loW level and a high
Further studies on bodies are reported in Am. J. Clinical
Haarbo, Christian Hassager, and Claus Christiansen).
substantially all of the medium by X-ray beams having at
level,—detecting the X-ray beams having passed through the medium for a plurality of areas (pixels) of the medium,—for each area calculating a value, AIOW, repre senting the absorbance in the area of the medium at the loW
energy level,—for each area calculating a value, Ahigh representing the absorbance in the area of the medium at the
 Recently analysis of meat has been reported in Meat Science Vol. 47, No 1/2, 115-124, 1997 (A. D. Mitch
high energy level, characterised by for each area generating a plurality of values being products of the type A1OW“*Ahighrn
ell, M. B. Solomon & T. S. Rumsey). A thorough study on pork carcasses by use of dual-energy X-ray absorptiometry
Wherein n and m are positive and/or negative integers or
Was reported by P EloWsson et al (1998) J. Nutr. 128
1543-1549 An Evaluation of Dual-Energy X-Ray Absorpti ometry and UnderWater Weighing to estimate Body Com position by means of carcass Analysis in Piglets. (p. 1543, l.&r. col.; p. 1544, l.col.; p. 1547, l.col.). Another analysis on pork carcasses is reported by Mitchell et al., J. Anim. Sci. (1998), vol. 76, pp 2104-2113. HoWever, on page 2113 of this analysis it is speci?cally concluded that the X-ray analysis is too sloW for compatibility With on-line process ing. None of the above-mentioned prior art has so far lead to an efficient apparatus ful?lling the needs in a slaughter house. Generally the prior art shoWs dif?culties When mea
suring layers of varying thickness, speci?cally thin layers adjacent to thick layers. Further the prior art is unable to measure and provide results as fast as required to be useful
for online processing.
Zero, and predicting the properties of the medium in this area by applying a calibration model to the plurality of values, Wherein the calibration model de?nes relations betWeen the
plurality of values and properties of the medium.  The advantage over the prior art is a more accurate determination of the properties, such as the fat content in the medium. The accuracy is speci?cally improved over the
prior art When measuring layers of varying thickness. A further advantage is due to the fact that using the method according to the invention almost the Whole product is measured instead of a sampling. Generally, When using sampling in an inhomogeneous medium the extraction of a sample Will introduce an error, because the sample may not
be representative.  Preferably the plurality of values includes values Alownl/Ahighml, Wherein n1 and m1 are positive integers.
Nov. 14, 2002
US 2002/0168046 A1
Further on it is preferred that the plurality of values includes
the values AIOW, Ahigh, AIOWZ, Ahighz, and AIOW/Ahigh and/or at least one of the values Alow’kAhigh; Alowz’kAhigh; A1"W*Ahigh2 and/or at least one of the values Alow’kAhigh;
AloW2 *Ahigh>. AloW*Ahigh2.> Alow*Ahigh4 and AloW2 *Ahigh 4 and/or at least one of the values AIOWZ/Ahi h;A1OW/Ahigh2 and
 Practical experiments have proved that such values contribute considerably to improve the accuracy.  Preferably the calibration model is obtained by use of a regression method being included in the group com
prising Principal Component Regression (PCR), Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, and Arti?cial Neural Networks (ANN) .
 The present invention further relates to an appara tus for the determination of properties of a medium, such as the content of a component in the medium, the medium
Width of medium (20) and a ?rst X-ray detection means (22) arranged to be eXposed to the fan-shaped loW energy beam (16) and beloW the medium (20) a second X-ray detection
means (24) arranged to be eXposed to the fan-shaped high energy beam (18) and beloW the medium (20), and elec
tronic means (34, 38, 42) including the data processing means (38) and communicating With the detectors (22, 24) and arranged to store and process data representing signals from the detection means (22, 24), and further comprising means (10) for moving the medium (20) relative to the X-ray beams (16, 18) or visa versa.
 Preferably and according to claim 11 the apparatus may be characterised in that the data processing means include and/or communicate With means including data storage means comprising a calibration model prepared by use of multivariate calibration methods such as Arti?cial
Neural Networks (ANN), or PCR, MLR or PLS regression
comprising a raW material of food or feed, a product or
 Preferably and according to claim 12 the apparatus
intermediary product of food or feed, or a batch, sample or section of the same, the apparatus comprising means (12,
may be characterised by comprising at least tWo sources (12,
14) for emitting at least tWo X-ray beams (16, 18) at tWo different energy levels, means for directing the at least tWo
X-ray beams toWards and through the medium, X-ray detec tion means (22, 24) covering a plurality of areas for detect
ing the tWo beams (16, 18) after passing through the medium, means (27, 28, 34, 35) for transferring and con verting output signals from the detection means (22, 24) into digital data set for input to data processing means (38) for receiving, storing and processing the at least tWo data set representing X-ray images at the at least tWo different energy levels, the apparatus further comprising means for synchro nising the at least tWo data sets and the data processing means including means for calculating values representing the absorbances (AIOW, Ahigh) in each area of the medium at the at least tWo energy levels, characterised in that the data processing means comprise means for generating a plurality
of values being products of the type A1OW“*Ahighrn Wherein n and m are positive and/or negative integers or Zero, and
means for predicting the properties of the medium in this area by applying a calibration model to the plurality of values, Wherein the calibration model de?nes relations betWeen the plurality of values and properties of the medium. 
The advantage over the prior art is a faster and
more accurate determination, Which is so fast that it can be
applied continuously on a process line in a slaughterhouse.
Preferably and according to claim 8 the medium is arranged on a conveyor moving at substantially constant speed, and the at least tWo X-ray beams are fanshaped, and the loW level beam is detected by a ?rst linear array, being dedicated to the
detection of the loW energy beam, and the high level beam is detected by a second linear array being dedicated to the detection of the high energy beam, each comprising a
plurality of pixels.  Preferably and according to claim 10 the apparatus may be characterised by comprising at least one loW energy
X-ray source (12) arranged above the medium (20) for providing a fan-shaped loW energy beam (16) substantially covering the Width of medium and at least one high energy
X-ray source (14) arranged above the medium (20) for providing a fan-shaped loW energy beam (16) covering the
14) emitting X-rays of tWo different energy levels.  According to claim 13 the apparatus may be char acterised by the tWo energy levels comprising a loW energy level in a range betWeen 35 and 75 keV, preferably betWeen 45 and 70 keV and most preferred about 62 keV, and a high energy level in a range betWeen about 60 and 140 keV, preferably betWeen 80 and 130 keV and most preferred about 120 keV.
 According to claim 14 the apparatus may be char acterised by comprising ?lter means located in each of the
beams (16, 18).  According to claim 15 the apparatus may be char acterised by comprising one X-ray source and tWo ?lter means splitting the beam into tWo beams of X-rays at tWo different energy levels.
 According to claim 16 the apparatus may be char acterised in that the means (12, 14) for emitting at least tWo X-ray beams, the means for directing the at least tWo X-ray beams and the X-ray detection means (22, 24) are mutually ?Xed.
 According to claim 17 the apparatus may be char
acterised by comprising means (12, 14) for emitting spa
tially separated fan-shaped beams (16, 18).  According to claim 18 the apparatus may be char acterised in that the detection means (22, 24) are covered by a scintillating layer, e.g. cadmium telluride, mercury iodide,
and/or gadolinium oXysulphide.  According to claim 19the apparatus may be char acterised by comprising conveyor means (10) arranged to carry container means (20), such as a tray or an open boX,
adapted to accommodate a random number of meat lumps of various siZes to be analysed, the conveyor means being arranged to let the container means (20) pass the at least tWo
fan-shaped X-ray beams (16, 18).  According to claim 20 the apparatus may be char acterised by comprising conveyor means (10) Wherein the conveyor belt is made from a material shoWing a loW
absorption of X-rays, and/or is split into tWo separate,
Nov. 14, 2002
US 2002/0168046 A1
spaced parts, the detector means (22, 24) being arranged in an open space between the tWo parts.
 According to claim 21 the apparatus may be char acterised by comprising conveyor means (10) adapted to accommodate a continuous How of meat lumps of various siZes to be analysed, the conveyor means being arranged to let the meat lumps pass the at least tWo fan-shaped X-ray
beams (16, 18).  According to claim 22 an apparatus according to any of the claims 9-21 may be characterised by being arranged to perform the folloWing steps: scan at least a section of a medium by X-ray beams having at least tWo energy levels, store data representing at least tWo X ray images of the medium, calculate the fat content and/or areal
density for all points (pixels) obtained from the scanning by use of multivariate calibration models generated in a previ
ously performed calibration step, multiply the fat content and areal density at each point, in order to generate a “fat
tion model obtained by a method according to claim 23 or 24. The invention also relates to an apparatus according to any of the claims 9-22, comprising a calibration model determined by a method according to claim 23 or 24.
By use of the present invention it is possible—
more accurate and more rapidly than hitherto knoWn—to determine the fat content of a random number of meat lumps
(such as trimmings or cuts) of various siZes in a container (or similar means for enclosing or carrying a load of meat) or directly on a conveyor belt. The measurement may be performed Within a fairly short time, such as a feW seconds, eg about 4.5 or 9 seconds per container, each container having a volume of eg about 0.1 m3. Preferably, a smaller
volume, about eg 25 kg meat, is arranged in each container. Accordingly, the method and apparatus may be applied for on-line control of the production of various meat products, such as minced meat, and more speci?cally Where minced
meat is produced from meat trimmings of various siZes.
 According to the applicant’s best knoWledge mul
map” (in g/cm2) of the sample, add all points in the “fat map” to give the total fat Weight (Ftotal) of the sample, add
tivariate calibration techniques have never been applied to
all areal densities for the sample to give the total Weight (Wtotal) of the sample, calculate the average fat content of the sample as the ratio Ftotal/Wtotal.
use of multivariate techniques solves a speci?c problem
X-ray analysis of meat, nor to X-ray analysis in general. The
present When using the techniques according to the prior art. The knoWn apparatus becomes highly inaccurate When
 According to claim 23 the present invention further
measuring on a combination of thin and thick layers. When
relates to a method for calibration of an apparatus according
measuring meat lumps of various siZes the thickness of the layers through Which the X-ray has to pass Will vary
to any of the claims 9-22, characterised by comprising preparation of a plurality of calibration samples consisting of speci?ed food or feed products, such as minced pork meat, of various Well-de?ned heights and properties, mea suring the plurality of calibration samples in the apparatus,
considerably from 0 or almost 0 to a speci?ed maximum. The use of a plurality of values alloWs a better accuracy of such measurements than hitherto knoWn.
thereby obtaining data representing tWo X-ray responses of each sample, each response comprising a plurality of pixels,
BRIEF DESCRIPTION OF THE DRAWINGS
and Wherein the data of each pixel, or the mean of a number
 FIG. 1 shoWs as an example a system according to the invention
of neighbouring pixels, are processed using the formulas:
 FIG. 2 shoWs a preferred embodiment of an X-ray apparatus according to the invention 
FIG. 3 shoWs a simulation of a system comprising
one source and a combination of tWo ?lters.
 FIG. 4 shoWs cross-validated X-ray fat predictions versus reference fat content of 32 calibration samples When
performing a simple univariate regression of AIOW/Ahigh 
or similar expressions for calculation of values
representing the absorbance in an area of the medium above a pixel or a number of neighbouring pixels, generating a
plurality of values of the type Alown’kAhighm, Wherein n and m are positive and/or negative integers and/or Zero, corre lating—by use of multivariate calibration methods, such as Arti?cial Neural Networks (ANN), or PCR, MLR or PLS regression—the data set for all/or a plurality of calibration
samples to the properties determined by other means, such as a reference method,—in order to determine a number of
calibration coef?cients, providing a calibration model com
prising the number of determined calibration coef?cients.
 Preferably and according to claim 24 all calibration samples are prepared in such a manner that they are homo
against the reference fat content of the samples.
 FIG. 5 shoWs cross-validated X-ray fat predictions versus reference fat content of 32 calibration samples When performing a PLS calibration With 5 PLS factors (based on
11 variables) against the reference fat content of the samples.  FIG. 6 shoWs cross-validated X-ray areal densities versus reference areal density of 32 calibration samples When performing a simple univariate regression of Ahigh against the reference areal densities of the samples.  FIG. 7 shoWs cross-validated X-ray areal densities versus reference areal density of 32 calibration samples When performing a PLS calibration With 1 PLS factor (based on 2 variables) against the reference areal densities of the
geneous and of ?xed areal densities, and further by averag
ing each of the values over all pixels at least in a de?ned
 FIG. 8 shoWs Fat predicted by X-ray in 99 points
portion of the images.
of a meat sample.
 The invention further relates to a method of pre dicting the fat content of meat, comprising use of a calibra
 FIG. 9 shoWs Areal density predicted by X-ray in 99 points of a meat sample.
Nov. 14, 2002
US 2002/0168046 A1
 FIG. 10 shows Fat (in g/cm2) predicted by multi plication of the fat content (FIG. 8) by the areal density (FIG. 9) in 99 points of a meat sample.
 The equipment used in the present eXample con sists of tWo constant potential X-ray sources 12, 14, one at
loW energy (eg 62 kV/5 mA) and another at high energy (eg 120 kV/3 mA), both With an appropriate ?ltration (eg using 0.25 and 1.75 mm of copper, respectively) narroWing the spectral range of the radiation emitted from the poly
 FIG. 12 shoWs a typical meat sample in a plastic container.
to avoid interference betWeen them, i.e. to avoid that radia
FIG. 11 shows a flow diagram illustrating the
FIG. 13 shoWs a typical loW energy X-ray trans
mission image of a meat sample as shoWn in FIG. 12.
 FIG. 14 shoWs a typical high energy X-ray trans mission image of the same meat sample. 
FIG. 15 is an image illustrating a calculated areal
density for each individual piXel.  FIG. 16 is an image illustrating a calculated fat content for each individual piXel.  FIG. 17 is an image illustrating a calculated “fat map” for a meat sample of 36% fat. 
FIG. 18 shoWs a reference versus predicted plot for
chromatic sources. The tWo sources are spatially separated
tion from one source is detected as if it originated from the other. The radiation from either source is collimated by a
lead collimator. In this Way tWo fan-shaped beams of X-rays 16, 18 are directed through container 20 comprising a sample or batch of the food or feed product toWards detec tors 22, 24, eg Hamamatsu C 7390. Alternatively the meat lumps may be arranged loosely on a conveyor band.
Further, the tWo separate sources may be replaced
by a combination of one source and tWo ?lters emitting a
loW energy and a high energy beam. The resulting source spectra are shoWn in FIG. 3. HoWever the preferred embodi ment applies tWo separate sources 12, 14 driven by separate
poWer supplies 13, 37. 
Both X-ray sources 12, 14 are associated With an
DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT AND METHOD
array of detectors 22, 24 covered With a scintillating layer converting the transmitted radiation into visible light that can be measured by the detectors 22, 24. The scintillating layer may consist of e. g. cadmium telluride, mercury iodide,
 The folloWing description discloses as an eXample a preferred embodiment of the invention using tWo X-ray sources. The apparatus is designed for being installed in
and/or gadolinium oXysulphide. The piXels used in the presently preferred embodiment have the dimensions 1.6x
relation to a production line in a slaughterhouse. FIG. 1 shoWs a schematic diagram of an embodiment of a mea
surement system according to the invention. FIG. 2 illus
trates the principle of the presently preferred X-ray appara tus. FIG. 2 shoWs only the active operating portions of the X-ray equipment. For purpose of clarity, all protective shielding or screening and all casings are deleted from the draWing. The equipment comprises or is located in close relation to a conveyor 10. TWo X-ray sources 12, 14 are
arranged above the conveyor 10. From the tWo sources 12, 14 X-ray beams 16, 18 are directed toWards detectors 22, 24 arranged beloW the conveyor. The conveyor may be split into tWo separate conveyors spaced to alloW free pass of the
1.3 mm2 and are arranged as an array of 384 piXels With a pitch of 1.6 mm. These dimensions are only stated as an
eXample. Other dimensions may be applied. The piXels convert the amount of transmitted light into analogue signals that are passed through cables 27, 28 to an analogue-to digital converter 34 Which is connected through cable 35 to a computing means 38 capable of performing the successive calculations. A monitor 42 may be connected through cable 40 to the computing means to shoW results or details of the operation. The computing means 38 may include means for
controlling the supply of poWer through means 36, 37, 26 and 25, 13, 15 to the X ray sources 12, 14. The monitor 42 and the computing means 38 may comprise a Personal
X-rays and to leave an open space for location of detectors
Computer, preferably including at least one Pentium pro cessor and/or a number of digital signal processors.
22, 24. Alternatively the conveyor belt should be made from a material shoWing a loW absorbance of X-rays, e.g. poly
urethane or polypropylene. The food or feed to be measured
is arranged in an open container or boX 20, preferably also
composed by a material shoWing loW absorbance of X-rays. Obviously in an alternative arrangement the sources could be located beloW the conveyor and the detectors above the conveyor.
 The operational speed of the conveyor is preferably substantially constant. The items, motor 30, control boX 33, and cables 32, 39, shoWn by phantom lines in FIG. 1, indicate that the operation of the conveyor optionally may be
 A container 20, comprising e.g. meat trimmings from a cutting section of the slaughterhouse, is received on the conveyor 10. The container is moved With a fairly constant speed of eg about 5-100 cm per second, such as 10-50 cm, eg 30 cm per second past the fan shaped beams 16, 18 and the arrays of detectors 22, 24 in a controlled manner in order to generate tWo images of the sample or batch, one at a loW X-ray energy and another at a high energy. All data representing the tWo images are stored in the
controlled by the computing means 38. The conveyor may include position measuring means, eg an encoder installed
on a conveyor driving shaft. Alternative means may be a laser or radar detection or marks on the conveyor belt. It is
 FIG. 11 represents a flow chart illustrating the measurement and data treatment. As stated above, tWo X-ray images of each container, comprising a batch of food or feed e.g. meat, are obtained. The signals at the piXels are I10W and
essential to the present method that the data representing the
tWo X-ray images can be synchronised. Such synchronisa tion may hoWever be obtained in many Ways, including
mathematical post-processing of the images.
Treatment of the collected data
Ihigh at loW and high X-ray energies, respectively, (110, 112 in FIG. 11). Furthermore, the so-called “dark signals” (i.e.
Nov. 14, 2002
US 2002/0168046 Al
the signal from the detectors When no radiation reaches
determined by use of a Wet chemistry method. The heights
them), IdMkOOW) and Idmk(high) , and the “air signals” (ie
and fat contents (percentage), together With the fat-depen
the signal from the detectors When no sample is present in
dent density of meat, Were used for calculating the areal densities of all 32 samples, ranging from 4.8 to 21.0 g/cm2.
the sampling region), IaiI(lOW) and Iair(high), are collected for each pixel at both X-ray energies (102 in FIG. 11). Preferably these data are collected repetitively in the inter vals betWeen the passage/passing of meat containers, ie the dark signals and air signals are measured repetitively, eg at regular intervals during a day to adjust for any drift of
instrument performance. 
NoW referring to 114 in FIG. 11, these signals are
transformed into absorbance units by using the folloWing formulas:
The froZen meat blocks Were measured in the
aforementioned X-ray equipment, yielding tWo images of each sample. The data points (pixels) of these images Were treated according to the steps described above. To avoid random noise from in?uencing the calibration results, the 11 values generated from the original absorbance values Were averaged over all pixels in the image. This could only be done since the samples Were homogeneous and of ?xed height. This data set consisting of 11 variables obtained for all 32 samples Was correlated against the fat content (per centage) measured by a reference method and the areal
densities using the Partial Least Squares (PLS) regression
Isample?OW) — [dark (10W)
method. This, and other similar multivariate calibration methods are Well knoWn (Martens and Naes: Multivariate
Iair(1OW) — [dark (10W) ]
Isample(high) — [dark (high)]
Calibration, 2nd. ed., Wiley (1992)).
Iair(high) — [dark (high)
The calibrations Were validated using full cross
validation, ie one sample at a time Was removed from the
From these tWo values, a plurality of values can be
integers and/or Zero,  These values are used as the input for the calibra tion routine establishing a relationship betWeen the collected
data and the component (eg the fat content) or the property (eg the areal density) of interest. It is essential that a value A10W for a speci?c pixel measuring the loW energy trans
data set for validation While the remaining 31 samples Were used for calibration. This procedure Was repeated for all samples, and validation results Were generated by combin ing the validation results for all 32 samples.
 The traditional Way of building an X-ray calibra tion model for fat in meat is by correlating the AIOW/Ahigh ratio to the fat reference results (Haardbo et al., Clin. Phys. (1991), vol. 11, pp. 331-341 or Mitchell et al. J. Anim. Sci.
(1998), vol. 76, pp. 2104-2114). This method is, hoWever, sensitive to the thickness (or areal density) of the sample and is therefore not useful With the range of sample heights (from 5 to 20 cm) of interest in the present context. This is
evident from FIG. 4, Where X-ray fat predictions using only the AIOW/Ahigh ratio are plotted against the fat reference
mittance through a speci?c area of the medium is matched
results. The prediction error (expressed as the Root Mean
to, the value Ahigh for the pixel measuring the high energy
Square Error of Prediction, RMSEP) is 14.7% in this case.
transmission through exactly the same area of the medium.
This can be accomplished by ensuring a synchronisation of
 Using the method according to the invention, with
the pictures as mentioned beloW.
eg 11 or more variables generated from the original tWo absorbences in combination With a PLS regression With 5
 If the loW and high energy images are not perfectly aligned, ie if a speci?c region of the sample does not shoW up at exactly the same positions in the tWo images, large
case, the prediction error (RMSEP) is as loW as 1.0%, thus
errors may result. This problem may occur eg if the tWo
tion With the neW variables.
line scan detectors (22, 24) are not synchronised. Apossible solution to this problem is to calculate the correlation betWeen the tWo images using various shifts betWeen them and thereby ?nding the shift at Which the correlation is at a maximum, folloWed by a correction of one of the images by this shift. It is hoWever preferred to synchronise the line scanning eg by the use of a position measuring means, or
by tight control of the conveyor speed.
PLS-factors, the plot presented in FIG. 5 is obtained. In this shoWing the bene?ts of using the PLS method in combina  The method can also be applied for the determi nation of the areal density of the sample. According to the
prior art the areal density is determined by correlating Ahigh to the reference areal density. The result of such a calibration
model is presented in FIG. 6, Where the agreement betWeen the areal density determined by X-ray and the reference results is very good. The prediction error (RMSEP) is 0.30 g/cm2 in this case. When using the method according to the
 The folloWing example explains hoW to generate a
invention, ie using both measured absorbences, Alowand
Ahigh, in combination With a PLS regression With 1 factor, the result presented in FIG. 7, and a prediction error (RMSEP) of 0.28 g/cm2, is obtained. This is only a slight
 Example: Calibration against fat content and areal
density  A set of 32 calibration samples consisting of minced pork meat Were prepared. They Were froZen in
blocks of varying heights (5, 10, 15, and 20 cm) With horiZontal dimensions of 10x10 cm2. Their fat content (percentage) Which ranged from 2.6 to 70.9%, Were later
improvement, but the use of tWo variables instead of one
provides the user With a further advantage: the possibility of detecting incorrect measurements (eg if one of the tWo X-ray sources shoWs a sudden drop in intensity, or if a pixel is not responding). This is because discrepancies from the
relationship betWeen A10W and Ahigh can easily be detected
Nov. 14, 2002
US 2002/0168046 A1
by the PLS model. Such outlier detection is not possible if only one absorbance is used. This possibility is very relevant and advantageous When using CCD detector Wherein a
single pixel may deteriorate fairly abruptly.  The calibration models developed in this Way can be used for future predictions of the fat content and areal density in a given point in an inhomogeneous meat sample
correction is preferably made at the end of step 6, providing neW corrected values of A10W and Ahigh for
all pixels.  A further advantageous option is smoothing of the data, eg in the direction of the movement. Further experi ence has proved that it can be advantageous to include a
as Well as for determination of the mean fat content of a large
further data processing in step 9. In a presently preferred embodiment pixels having an areal density outside a speci
?ed interval are removed/deleted or at least disregarded in
the folloWing data processing, i.e. pixels for Which the
Prediction of the fat content of an unknoWn meat
calculated areal density is extremely loW or much too high,
 The folloWing example Will demonstrate the use of the calibration models in practice Where samples are inho
 The present example shoWs the calculation of the
mogeneous and of varying thickness. The purpose is to predict the mean fat content of the samples. Therefore the procedure involves the folloWing steps as shoWn in FIG. 11
fat content (percentage) for one roW of 99 points only. This is done in order to make the presented plots simpler, and is easily generalised to be performed on a tWo-dimensional image of a meat product.
1. Regular measuring of IClark and I air, 102
 2. Arranging a batch or stream of meat (or other food or feed product) on a conveyor passing
through the X-ray apparatus, 104  3. Scanning the batch or stream by X-ray beams at tWo different energy levels, 106, 108,
 4. Detecting signals representing a plurality of X-ray intensities, using the detectors 22, 24 in FIGS. 1, 2110, 112
 5. Recording data representing the detected signals 114.  6. Calculate A10W and Ahigh for all pixels 114,
Example 1  Ameat sample consisting of a cubic block (dimen sions: 10><10><10 cm3) of minced pork meat Was measured using the same X-ray equipment as Was used for measuring the calibration samples. The ?rst tWo steps of the prediction procedure are shoWn in FIG. 8 (predicted fat content (per
centage) ), FIG. 9 (predicted areal density, step 9), and FIG. 10 (the “fat map”, step 10). There is clearly a variation in both fat content and areal density over the sample, so the results from all sample points are needed in order to obtain an accurate estimate of the mean fat content of the sample.
 The sum of all points in the “fat map” (step 11), Ftotal, equals 464 g/(99 pixels), and the total Weight of the
(optionally, a smoothing of the picture may be
sample (step 12), Wtotal, is 963 g/(99 pixels). This, in turn,
results in a predicted mean fat content of 464/963=48.2% (step 13) , not far from the true fat content, Which Was determined later as 49.2% by a reference method.
 7. Co-ordinate (match) A10W values and Ahigh values 114, if necessary.
preferably the areal density for all points (pixels) obtained from the scannings, using a fat calibration model generated as described above 116.
10. Multiply the fat content (percentage) and
areal density at each point, in order to generate a “fat map” (in g/cm2) of the batch or stream of food or feed 116.
 11. Add all points in the “fat map” to give the total fat Weight (Ftotal) 116. 
12. Add all areal densities for the sample to
give the total Weight (Wtotal) 116.  13. Calculate the average fat content (percent age) as the ratio Ftotal/Wtotal 116.
This example is an extension of the results stated
above. The present example involves the prediction of the fat content of samples consisting of approximately 25 kg of meat in plastic containers. 
Ten samples of meat ranging from 11.5 to 84.6%
fat Were obtained from a meat processing plant. The amount
of meat in each container ranged from 20 to 30 kg, the
sample homogeneity ranging from ground meat to meat pieces of 5 kg each. A typical sample consisting of 36% fat trimmings arranged in a presently preferred container (dimensions: 70><40><17 cm3) is shoWn in FIG. 12. 
Each of these ten samples Were scanned by the
instrument ?ve times over a period of tWo days. Before each neW scan the contents of the container Was reorganised, i.e.
the meat pieces Were moved around Without changing the total content of the container. This Was done in order to
check the repeatability of the measurement. A total of 50 X-ray scans, each consisting of a loW and a high energy
 Optionally, tWo more steps may be included betWeen step 6 and 7:
image of 306>
 If the meat lumps are arranged in a container the data should be subjected to a correction for the absorption in the bottom of the container. Such
 All 50 scans Were subjected to the prediction steps according to the invention, using a calibration model based