Abstract:
A produce recognition system and method which use an internal reference to calibrate a produce data collector. The produce data collector collects first data from an external reference, collects second and third data from an internal reference, and collects fourth data from a produce item. A computer determines a first calibration value from the first and second data and a second calibration value from the third data and applies the first and second calibration values to the fourth data to produce fifth data. The computer further obtains sixth data from reference produce data and compares the fifth and sixth data to identify the produce item.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present invention is related to the following commonly assigned and co-pending U.S. application: 
     “Produce Data Collector And Produce Recognition System”, filed Nov. 10, 1998, invented by Gu et al., and having a Ser. No. 09/189,783. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates to product checkout devices and more specifically to a produce recognition system and method. 
     Bar code readers are well known for their usefulness in retail checkout and inventory control. Bar code readers are capable of identifying and recording most items during a typical transaction since most items are labeled with bar codes. 
     Items which are typically not identified and recorded by a bar code reader are produce items, since produce items are typically not labeled with bar codes. Bar code readers may include a scale for weighing produce items to assist in determining the price of such items. But identification of produce items is still a task for the checkout operator, who must identify a produce item and then manually enter an item identification code. Operator identification methods are slow and inefficient because they typically involve a visual comparison of a produce item with pictures of produce items, or a lookup of text in table. Operator identification methods are also prone to error, on the order of fifteen percent. 
     Therefore, it would be desirable to provide a produce recognition system and method. It would also be desirable to provide a produce data collector with a reference apparatus that makes calibration easier. 
     SUMMARY OF THE INVENTION 
     In accordance with the teachings of the present invention, a produce recognition system and method are provided. 
     The produce recognition system includes a produce data collector and a computer. The produce data collector collects first data from an external reference, collects second and third data from an internal reference, and collects fourth data from a produce item. A computer determines a first calibration value from the first and second data and a second calibration value from the third data and applies the first and second calibration values to the fourth data to produce fifth data. The computer further obtains sixth data from reference produce data and compares the fifth and sixth data to identify the produce item. 
     A method of identifying a produce item includes the steps of obtaining calibration information for a produce data collector, collecting first data describing the produce item by the produce data collector, applying the calibration information to the first data to produce second data, obtaining a number of previously stored third data associated with a plurality of produce items, comparing the second data to the third data to determine fourth data and a corresponding produce item from the third data which is most like the second data, and identifying the produce item to be the corresponding produce item. 
     A method of calibrating produce data collected by a produce data collector includes the steps of obtaining a first calibration value for the produce data collector using an external reference and an internal reference, obtaining a second calibration value for the produce data collector using only the internal reference, and applying the first and second calibration values to the produce data. 
     It is accordingly an object of the present invention to provide a produce recognition system and method. 
     It is another object of the present invention to provide a produce recognition system and method which identifies produce items by comparing their spectral data with those in a spectral data library. 
     It is another object of the present invention to provide the produce data collector with a reference apparatus that makes calibration easier. 
     It is another object of the present invention to provide the produce data collector with an internal reference for automatic calibration. 
     It is another object of the present invention to provide a produce data collector which uses an internal reference for indirect inter-device calibration. 
     It is another object of the present invention to provide an indirect inter-device calibration method for a produce data collector. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Additional benefits and advantages of the present invention will become apparent to those skilled in the art to which this invention relates from the subsequent description of the preferred embodiments and the appended claims, taken in conjunction with the accompanying drawings, in which: 
     FIG. 1 is a block diagram of a transaction processing system including a produce recognition system; 
     FIG. 2 is a block diagram of a type of produce data collector which collects spectral data; 
     FIG. 3 is a perspective view of the produce data collector illustrating placement of external and internal references; 
     FIGS. 4A and 4B are top and bottom views of a housing of the produce data collector illustrating a placement and operation of the internal reference; 
     FIG. 5 is a flow diagram illustrating a produce recognition method of the present invention; and 
     FIG. 6 is a flow diagram illustrating a method of obtaining an internal reference calibration value. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring now to FIG. 1, transaction processing system  10  includes bar code data collector  12 , produce data collector  14 , and scale  16 . 
     Bar code data collector  12  reads bar code  22  on merchandise item  32  to obtain an item identification number, also know as a price look-up (PLU) number, associated with item  32 . Bar code data collector  12  may be any bar code data collector, including an optical bar code scanner which uses laser beams to read bar codes. Bar code data collector  12  may be located within a checkout counter or mounted on top of a checkout counter. 
     Produce data collector  14  collects data for produce item  18  or any other non-barcoded merchandise item. Such data preferably includes color or spectral data, but may also include size data, shape data, surface texture data, and aromatic data. 
     Produce data collector  14  includes memory  36  for storing device-specific calibration data  34 . Memory  36  may include a flash read-only-memory (ROM). 
     Classification library  30  is a data library derived from previously collected and processed produce data. It contains information about different produce items, or types of produce items called classes, each of which is associated with a PLU number. 
     During a transaction, operation of produce data collector  14  may be initiated by placement of produce item  18  on the data collector window  60  (FIG. 2) or by operator-initiated commands from transaction terminal  20 . Window  60  is integrated into the cover plate of scale  16 , such that produce item  18  is weighed by scale  16  and viewed by produce data collector  14  at the same time. 
     Scale  16  determines a weight for produce item  18 . Scale  16  works in connection with bar code data collector  12 , but may be designed to operate and be mounted separately. Scale  16  sends weight information for produce item  18  to transaction terminal  20  so that transaction terminal  20  can determine a price for produce item  18  based upon the weight information. 
     Bar code data collector  12  and produce data collector  14  operate separately from each other, but may be integrated together. Bar code data collector  12  works in conjunction with transaction terminal  20  and transaction server  24 . 
     In the case of bar coded items, transaction terminal  20  obtains the item identification number from bar code data collector  12  and retrieves a corresponding price from PLU data file  28  through transaction server  24 . 
     In the case of non-bar coded produce items, transaction terminal  20  executes produce recognition software  21  which obtains produce characteristics from produce data collector  14 , identifies produce item  18  by comparing the collected produce data with classification library  30 , retrieves a corresponding price from PLU data file  28 . 
     Produce recognition software  21  manages calibration of produce data collector  14  and maintains calibration data  34 . Calibration data  34  includes device-specific calibration data on each produce data collector  14  in system  10 . 
     In an alternative embodiment, identification of produce item  18  may be handled by transaction server  24 . Transaction server  24  receives collected produce characteristics and identifies produce item  18  using classification library  30 . Following identification, transaction server  24  obtains a price for produce item  18  and forwards it to transaction terminal  20 . 
     Storage medium  26  preferably includes one or more hard disk drives. PLU data file  28 , classification library  30 , and calibration data  34  are stored within storage medium  26 , but each may also be located instead at transaction terminal  20 . PLU data file  28  may be located in bar code data collector  12 . Calibration data  34  may also be stored within individual produce data collectors  14 . 
     To assist in proper identification of produce items, produce recognition software  21  may additionally display candidate produce items for operator verification. Produce recognition software  21  preferably arranges the candidate produce items in terms of probability of match and displays them as text and/or color images on an operator display of transaction terminal  20 . The operator may accept the most likely candidate returned by or override it with a different choice. 
     Turning now to FIGS. 2 and 3, produce data collector  14  primarily includes light source  40 , spectrometer  51 , control circuitry  56 , transparent window  60 , internal reference  62 , and housing  66 . 
     Light source  40  produces light  70 . Light source  40  preferably produces a white light spectral distribution, and preferably has a range from 400 nm to 700 nm, which corresponds to the visible wavelength region of light. 
     Light source  40  preferably includes one or more light emitting diodes (LED&#39;s). A broad-spectrum white light producing LED, such as the one manufactured by Nichia Chemical Industries, Ltd., is preferably employed because of its long life, low power consumption, fast turn-on time, low operating temperature, good directivity. Alternate embodiments include additional LED&#39;s having different colors in narrower wavelength ranges and which are preferably used in combination with the broad-spectrum white light LED to even out variations in the spectral distribution and supplement the spectrum of the broad-spectrum white light LED. 
     Other types of light sources  40  are also envisioned by the present invention, although they may be less advantageous than the broad spectrum white LED. For example, a tungsten-halogen light may be used because of its broad spectrum, but produces more heat. 
     A plurality of different-colored LEDs having different non-overlapping wavelength ranges may be employed, but may provide less than desirable collector performance if gaps exist in the overall spectral distribution. 
     Spectrometer  51  includes light separating element  52 , photodetector array  54 . 
     Light separating element  52  splits light  76  in the preferred embodiment into light  80  of a continuous band of wavelengths. Light separating element  52  is preferably a linear variable filter (LVF), such as the one manufactured by Optical Coating Laboratory, Inc., or may be any other functionally equivalent component, such as a prism or a grating. 
     Photodetector array  54  produces spectral signals  82 . The pixels of the array spatially sample the continuous band of wavelengths produced by light separating element  52 , and produce a set of discrete signals. Photodetector array  54  is preferably a complimentary metal oxide semiconductor (CMOS) array, but could be a Charge Coupled Device (CCD) array. 
     Control circuitry  56  controls operation of produce data collector  14  and produces digitized spectral signals  84 . The digitized spectrum represent a series of data points for narrow wavelength bands. These data points make up the measured spectrum F(λ) of produce item  18 , where λ is the center wavelength of various wavelength bands. For this purpose, control circuitry  56  includes an on-board digital controller/processor, which contains multiple analog-to-digital (A/D) and digital-to-analog (D/A) converters. For a detector array with 1000:1 signal-to-noise ratio, a 12-bit A/D converter with a sampling rate of 22-44 kHz produces acceptable results. 
     Transparent window  60  includes an anti-reflective surface coating to prevent light  72  reflected from window  60  from contaminating reflected light  74 . 
     Internal reference  62  is used for purposes of indirectly calibrating produce data collector  14 . External reference  64  is used for direct calibration. Both internal and external references are made of materials which are diffusely reflective, and are white or gray in color. The material and its color should be stable in time and against changes in environmental conditions. Commercially available ceramic references may be used as external references. Internal reference materials should be light in weight and easy to work with. Certain types of white or gray plastic material (e.g., ABS polycarbon) are suitable for use as internal references. 
     Calibration data  34  includes correction function C dev (λ) and the measured spectrum F′ ref (λ) of internal reference  62 . Correction function C dev (λ) is determined during manufacture or field installation of produce data collector  14  using measured spectrum F′ ref (λ) of internal reference  62  and measure spectrum F ref (λ) of external reference  64 . Internal measured spectrum F′ ref (λ) is also determined subsequently during an internal calibration procedure. Calibration data  34  may also include mapping and/or interpolation data specific to each produce data collector  14 . 
     Housing  66  contains light source  40 , spectrometer  51 , photodetector array  54 , control circuitry  56 , transparent window  60 , and internal reference  62 . 
     In operation, an operator places produce item  18  on window  60 . Control circuitry  56  turns on light source  40 . Light separating element  52  separates reflected light  74  into different wavelengths to produce light  80  of a continuous band of wavelengths. Photodetector array  54  produces spectral signals  82  containing produce data. Control circuitry  56  produces digitized produce data signals  84  which it sends to transaction terminal  20 . Control circuitry  56  turns off light source  40  and goes into a wait state. 
     Transaction terminal  20  uses produce data in digitized produce data signals  84  to identify produce item  18 . Here, produce data consists of digitized spectra which transaction terminal  20  processes and identifies using information provided in classification library  30 . After identification, transaction terminal  20  obtains a unit price from PLU data file  28  and a weight from scale  16  in order to calculate a total cost of produce item  18 . Transaction terminal  20  enters the total cost into the transaction. 
     From time to time, produce data collector  14  must be calibrated. Preferably, produce recognition software  21  controls operation of internal reference  62  in order to minimize operator involvement. Calibration may be conducted during each produce transaction or based upon a predetermined schedule. However, switch  104  may be used by an employee or technician to signal control circuitry  56  to initiate calibration. 
     Normally, a common external reference  64  or references identical to each other in terms of their reflective properties are needed for inter-device calibration. 
     For ideal linear devices, the measured spectra F(λ) for any external object (a produce item or external reference  64 ) may be expressed as 
     
       
           F (λ)= T (λ) S (λ) R (λ);  (1) 
       
     
     where T(λ) is the system transfer function, S(λ) is the source illumination function at window  60 , and R(λ) is the average diffuse reflection coefficient of the external object. 
     If the object is external reference  64 , the measured spectrum F ref (λ) has the same form: 
     
       
           F   ref (λ)= T (λ) S (λ) R   ref (λ);  (2) 
       
     
     where R ref (λ) is the average diffuse reflection coefficient of external reference  64 . Therefore when the sampled spectrum of an external object is normalized by the external reference spectrum F ref (λ), a device-independent measurement of spectral data results:                    F   NORM          (   λ   )       ≡       F        (   λ   )           F   ref          (   λ   )           =         R        (   λ   )           R   ref          (   λ   )         .             (   3   )                                
     Obviously, if the same external reference  64  or identical references are used, the normalized spectra for different produce data collectors  14  will be identical: since there is no device-dependent factors, i.e., T(λ) and S(λ), on the right-hand side of Equation (3). 
     For most practical devices, frequent calibration is required, since both the transfer function T(λ) and source function S(λ) of produce data collector  14  may vary with time and the environment. An external reference measurement using external reference  64  requires operator involvement and can be inconvenient to checkout operations. Internal reference  62  is preferred because it improves operability and reliability by minimizing operator involvement. However, since both the source illumination function S(λ) and the system transfer function T(λ) are different for internal reference  62  than for the external reference  64 , internal reference  62  cannot be used for direct inter-device calibration. Internal reference  62  can be used for indirect inter-device calibration, but only under special conditions. 
     Indirect calibration is preformed by first calibrating internal reference  62 . The measured spectrum F′ ref (λ) of internal reference  62  is 
     
       
           F′   ref (λ)= T ′(λ)× S ′(λ)× R′   ref (λ).  (4) 
       
     
     An initial calibration of internal reference  62  determines                    F   ref          (   λ   )           F   ref   ′          (   λ   )         =         T        (   λ   )           T   ′          (   λ   )         ×       S        (   λ   )           S   ′          (   λ   )         ×           R   ref          (   λ   )           R   ref   ′          (   λ   )         .               (   5   )                                
     As mentioned above, special conditions must be met in order to use internal reference  62  for indirect inter-device calibration. One condition is that internal reference  62  must be located and oriented so that its system transfer function T′(λ) only differs by a constant factor t from the system transfer function T(λ) of external reference  64 .                    T        (   λ   )           T   ′          (   λ   )         =     t        (   λ   )         ;           (   6   )                                
     where t(λ) is in general a function of wavelength λ but independent of any system characteristics that may vary with time or environmental conditions. For a spectrometer  51  using a linear variable filter for light separating element  52  combined with a linear diode array detector for photodetector array  54 , one way of achieving a constant factor t(λ) is by placing internal reference  62  in the direct light path between window  60  and light separating element  52 . The only difference between T(λ) and T′(λ) is now due to the transmission of window  60  and the geometric factors. These differences are, or can be made, very stable factors. 
     Another condition which must be met in order to use internal reference  62  for indirect inter-device calibration is that the source illumination function S′(λ) of internal reference  62  only differs by a factor s from the source illumination function S(λ) of external reference  64 :                    S        (   λ   )           S   ′          (   λ   )         =     s        (   λ   )         ;           (   7   )                                
     where s(λ) represents the difference due to geometric parameters, which can be made stable against time and environmental changes. 
     A final condition which must be met in order to use internal reference  62  for indirect inter-device calibration is that the diffuse-reflection coefficient R(λ) of internal reference  62  is stable in time. This is achieved by proper selection of reference material. 
     In general, the equation for indirect inter-device calibration is:                    F   NORM   ′          (   λ   )       ≡       F        (   λ   )           F   ref   ′          (   λ   )           =             F   ref          (   λ   )           F   ref   ′          (   λ   )         ×       F        (   λ   )           F   ref          (   λ   )           =         C   dev          (   λ   )       ×         F   NORM          (   λ   )       .                 (   8   )                                
     Thus, the device-independent spectral measurement as defined in Equation (3) can be obtained through an internal reference by                    F   NORM          (   λ   )       =         1       C   dev          (   λ   )         ×       F   NORM   ′          (   λ   )         =       F        (   λ   )             C   dev          (   λ   )       ×       F   ref   ′          (   λ   )               ;           (   9   )                                
     where correction function C dev (λ) equals:                  C   dev          (   λ   )       =           F   ref          (   λ   )           F   ref   ′          (   λ   )         =       t        (   λ   )       ×     s        (   λ   )       ×           R   ref          (   λ   )           R   ref   ′          (   λ   )         .                 (   10   )                                
     External reference  64  is only needed for initial calibration to determine the correction function C dev (λ). This initial calibration may be during manufacture or field installation of produce data collector  14 . 
     In equations (1) through (10), all measurements and factors are expressed as functions of wavelength λ. In reality, however, measurements obtained as raw data are functions of pixel positions. To transform these functions of pixels to functions of wavelength, produce data collector  14  needs to be wavelength-calibrated at manufacture. For the spectrometer  51  described in this invention which uses an LVF, the relationship between wavelength and pixel position is linear, and the wavelength-calibration can be easily obtained from a measured spectrum of a line source, such as a mercury-argon (HgAr) lamp. 
     Let x=1,2, . . . , N be the pixel positions, where N is the total number of pixels, the linear relation between x and wavelength λ can be expressed as 
     
       
         λ= C   0   +C   1   ×x;   (11) 
       
     
     where C 0  and C 1  are two constant factors. By determining the center-positions of two or more spectral lines in the wavelength range of the linear-variable-filter, the linear mapping parameters C 0  and C 1  can be computed. 
     If an LVF and a linear diode array, as taught in example spectrometer  51  above, are permanently fixed together at manufacture, the wavelength mapping will be fixed too. Therefore, wavelength mapping parameters C 0  and C 1 , along with correction function C dev (λ), can be determined at manufacture and permanently stored on the produce data collector board, e.g., into memory  36  of the controller/processor chip along with calibration values C dev (λ) and F′ ref (λ). Produce recognition software  21  loads, wavelength mapping parameters C 0  and C 1  during startup and/or as necessary. 
     While one type of spectrometer and corresponding mapping function have been disclosed, the present invention anticipates that other types of spectrometers and mapping functions may be employed in a similar fashion. 
     Equation (11) defines a one-to-one relationship between the pixel position and a device-dependent wavelength grid. By interpolating the normalized spectrum from this grid onto a common wavelength grid, say, from 400 nm to 700 nm with 5 nm intervals, makes the resulting data truly device independent. 
     With reference to FIG. 3, produce data collector  14  is shown in further detail. 
     Light source  40  preferably includes a number of white LED&#39;s which are specially arranged so that the illumination is uniform in both luminosity and spectrum over the entire surface of window  60  for illuminating produce item  18 . 
     Housing  66  contains window  60  and internal reference  62 . External reference  64  is shown above window  64 . External reference may be a separate element or mounted to the top surface of housing  66  and activated in a manner similar to internal reference  62 . 
     Turning mirrors  90  and  92  direct reflected light  74  to spectrometer  51 . 
     Light baffle  96  minimizes contamination of reflected light  74  by light  72  from light source  40 . 
     Printed circuit board  98  contains control circuitry  56  and forms a base for mounting light source  40 , spectrometer  51 , turning mirror  90 , turning mirror  92 , and light baffle  96 . Printed circuit board  98  fastens to housing  66 . 
     Turning now to FIGS. 4A and 4B, internal reference  62  is shown in further detail. Internal reference  62  is mounted below and adjacent window  60 . FIG. 4A shows both housing  66  and printed circuit board  98 , while FIG. 4B shows only printed circuit  98 . 
     Internal reference assembly  63  includes motor  100  and shutter  102 . Motor  100  is mounted to printed circuit board  90 . Shutter  102  is mounted to the shaft of motor  100 . Internal reference  62  is either formed as part of shutter  102  or attached to inner surface  103  of shutter  102 . 
     Control circuitry  56  energizes motor  100  to place shutter  102  in an open position (FIG. 4A) and a closed position (FIG.  4 B). Calibration readings are taking while shutter  102  is closed. Control circuitry  56  responds to commands from produce recognition software  21  in the automatic mode of operation and from switch  104  in the manual mode of operation. 
     Turning now to FIG. 5, the produce recognition method of the present invention begins with START  108 . 
     In step  109 , produce recognition software  21  loads classification library  30  and calibration data  34 . Classification library  30  may be loaded from storage medium  26  through transaction server  24  or from transaction terminal  20 . 
     Calibration data  34  may be loaded from storage medium  26 , transaction terminal  20 , and/or memory  36 . Values C 0 , C 1 , C dev (λ) are preferably loaded from memory  36 . If a previously measured internal reference spectrum F′ ref (λ) is available for the same produce data collector  14 , it may be loaded as initial calibration data until a new calibration is performed. 
     In step  110 , produce recognition software  21  determines whether a new calibration is necessary. During normal operations, produce recognition  21  software and/or produce data collector  14  constantly monitors system performance and stability and automatically determines if a new calibration is needed. Upon system startup, if there is no previously measured internal reference data F′ ref (λ) available, then a new calibration is required. Produce recognition software  21  may periodically initiate calibration based upon a preset schedule. Alternatively, an operator may force a calibration by issuing a command through transaction terminal  20  or by using switch  104 . If a new calibration is necessary, operation proceeds to step  112 . If not, operation proceeds to step  113 . 
     In step  112 , produce recognition software  21  initiates calibration to obtain more recent internal reference spectrum F′ ref (λ) (FIG.  6 ). Following calibration, operation proceeds to step  114 . 
     In step  114 , produce recognition software  21  waits for a signal from produce data collector  14  to identity produce item  18 . Preferably, produce data collector  14  is self-activated. Control circuitry  56  continuously monitors the ambient illumination at window  60  to determine if produce item  18  is placed on window  60 . Alternatively, if produce data collector  14  is integrated with scale  16 , scale  16  may signal control circuitry  56  when there is a stable weight reading. As another alternative, an operator may manually signal control circuitry  56  to begin data collection through an input device (e.g., keyboard) of transaction terminal  20 . 
     In detail, produce data collector  14  illuminates produce item  18 , splits light collected from produce item  18  into a plurality of different light portions in different wavelength bands, converts energy in the plurality of light portions into a plurality of electrical signals, and digitizes the plurality of electrical signals to produce sample spectrum F(λ). 
     If a signal is received from produce data collector  14  by produce recognition software  21 , operation proceeds to step  116 . 
     In step  116 , produce recognition software  21  normalizes sample spectrum F(λ) by dividing it by the product of internal reference spectrum F′ ref (λ) and the correction function C dev (λ) according to equation (9). As mentioned above, internal reference spectrum F′ ref (λ) and correction function C dev (λ) are obtained from memory  36 . Internal reference spectrum F′ ref (λ) may be one which was recently obtained in step  112 . 
     In step  118 , produce recognition software  21  maps and interpolates normalized spectrum F NORM (λ) onto a fixed wavelength grid, for example, a grid in the visible range from 400 to 700 nm, with 5 nm intervals. For an LVF, equation (11) and a standard linear interpolation method are used for this data reduction step. 
     In step  120 , produce recognition software  21  performs further data reduction that may be required to optimize the identification result. For example, by linearly transforming the spectral data into a lower dimensional space in which the distinguishing features between different classes within library  30  are weighted according to their importance, and the less and non-distinguishing features are disregarded. 
     In step  122 , produce recognition software  21  compares the processed sample data against library  30  and classifies the unknown produce item  18 . 
     The data reduction detail in step  120  and the data format in classification library  30  are all related to the classification process of step  122 . One simple classification algorithm uses the nearest-neighbor method, which compares the distances between the unknown sample or instance and all the known instances in classification library  30 . The class containing the instance with the shortest distance from the unknown instance is the closest match and may be chosen as the identity of the unknown instance. Many more sophisticated classification algorithms may also be used. Some of these algorithms may be used in conjunction with the nearest-neighbor method. 
     Produce recognition software  21  may automatically choose the identity of produce item  18  or display a short list of possible identifications for operator selection through a graphic user interface or other type of interface. For example, the operator may pick the correct identification by touching one of a number of color pictures of possible identifications on a touch-screen display. Transaction terminal  20  uses the identification information to obtain a unit price for produce item  18  from transaction server  24 . Transaction terminal  20  then determines a total price by multiplying the unit price by weight information from scale  16  and, if necessary, by count information entered by the operator. 
     Operation returns to step  110  to await another signal from produce data collector  14 . 
     Referring now to FIG. 6, the method of obtaining an internal reference calibration value (measured spectrum F′ ref (λ)) for step  112  in FIG. 5 begins with START  150 . 
     In step  152 , produce recognition software  21  closes shutter  102  thereby placing internal reference  62  in the light path. 
     step  154 , produce recognition software  21  causes control circuitry  56  to activate light source  40 . Light source  40  illuminates internal reference  62 . 
     In step  156 , produce recognition software  21  collects measured spectrum F′ ref (λ) of internal reference  62  from control circuitry  56 . 
     In step  158 , produce recognition software  21  stores measured spectrum F′ ref (λ) of internal reference  62  in calibration data  34 . 
     In step  160 , produce recognition software  21  opens shutter  102 . 
     In step  162 , operation ends. 
     Advantageously, the present invention facilitates inter-device calibration without operator involvement. 
     Although the invention has been described with particular reference to certain preferred embodiments thereof, variations and modifications of the present invention can be effected within the spirit and scope of the following claims.