Abstract:
A system and method for analyzing samples, such as biological samples, to accurately and effectively determine the susceptibility of the samples to antimicrobial materials, so as to determine minimum inhibitory concentration (MIC) values for the respective samples and antimicrobial materials. At each of a plurality of time intervals, the system and method directs a plurality of different analyzing light wavelengths, such as red, green and blue wavelengths, onto each of a plurality of sample wells, and detects a respective resultant light wavelength emanating from the respective sample wells for each of the analyzing light wavelengths. The system and method uses resultant light wavelengths to generate at least two growth indicator characteristic curves representing, for example, the redox state and turbidity characteristics of the sample wells. The system then uses the redox state and turbidity characteristics of sample wells containing the same antimicrobial material to determine the MIC value for that material in relation to the sample contained in those wells.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     Related subject matter is disclosed in a copending U.S. patent application of Clark et al., entitled “Automated Microbiological Testing Apparatus and Methods Therefor”, Ser. No. 09/083,130, filed May 22, 1998, the entire contents of which are incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a system and method for analyzing samples, such as biological samples, to determine the susceptibility of the samples to antimicrobial materials, such as antibiotics. More particularly, the present invention relates to a system and method which takes a plurality of optical readings of a biological sample contained in sample wells of a sample test panel having various types and concentrations of antimicrobial materials therein and, based on these readings, determines the respective minimum inhibitory concentrations (MICs) at which the respective antimicrobial materials will inhibit growth of the sample. 
     2. Description of the Related Art 
     Many conventional systems exist for performing tests on microbiological samples related to patient diagnosis and therapy. The microbiological samples may come from a variety of sources, including infected wounds, genital infections, cerebro-spinal fluids, blood, abscesses or any other suitable source. From the microorganism samples, an inoculum is prepared in accordance with established procedures which produce a bacterial or cellular suspension of a predetermined concentration. Further processing of the suspension may depend on the testing method employed, as can be appreciated by one skilled in the art. 
     The conventional systems are used, for example, to identify the types of microorganisms present in a patient&#39;s sample. Typically, in such systems, reagents are placed into cupules, or test wells, of identification trays, into which the sample is introduced. The reagents change color in the presence of an actively growing culture of microorganisms. Based on the color change, or lack thereof, the microorganism can be identified by the use of reference tables. 
     Other systems have been developed for susceptibility testing of microorganisms. These systems are used to determine the susceptibility of a microorganism in a sample to various therapeutics, such as antibiotics. Based on these test results, physicians can then, for example, prescribe an antimicrobial product which will be successful in eliminating or inhibiting growth of the microorganism. Qualitative susceptibility testing, in particular, provides an indication of whether a microorganism is resistant or sensitive to a particular antibiotic, but does not provide an indication on the degree of sensitivity or resistance of the microorganism. On the other hand, quantitative susceptibility testing provides an indication of the concentration of the antimicrobial agent needed to inhibit growth of the microorganism. The term minimum inhibitory concentration (MIC) is used to refer to the minimum concentration of the antimicrobial agent that is required to inhibit the growth of a microorganism. 
     Although the conventional systems can be somewhat useful in determining the MICs at which respective antimicrobial agents will inhibit growth of respective microorganisms, these systems have certain drawbacks. For example, when performing identification and susceptibility testing, the test trays are incubated at a controlled temperature for an extended period of time. At predetermined time intervals, the wells of the test trays are individually examined for an indication of color change or other test criteria. However, this process can be long and tedious when performed manually by a technician. In addition, the incubation times for identification and susceptibility test trays may differ, or the optimal time to read a test result from the test tray may not be known in advance. Thus, a technician may typically need to read and record results for a specimen at several different times, sometimes far apart, which may cause assignment or correlation errors. 
     Automated systems are desirable in performing these tests to minimize the technician handling time, as well as to minimize the possibility of human error. In addition, automated systems may be preferred because they generally can obtain results more rapidly and accurately than manual methods. One known microbiological testing apparatus for the automatic incubation and reading of microbiological samples employs a plurality of test trays having a plurality of wells which contain the samples or agents to be tested. The trays are first placed in an incubator, and are then moved to an inspection station after a sufficient incubation period. A light source is disposed above the tray and a pair of video cameras are disposed below the tray at the inspection station. Each video camera takes a video image of an entire tray, and the video image signal of the entire tray is sent to an image processor to be analyzed. 
     The image processor requires uniform lighting over the entire inspection station. Consequently, the processor records the background light level of each pixel within an area of interest corresponding to each well of the tray to account for variability in the light source. The image processor processes the video image of the tray and determines the number of pixels for a particular well whose intensity exceeds a predetermined threshold for that area of interest. If the number of pixels exceeds a predetermined number, a positive result is assigned to that well. The image processor analyzes the binary partial results from the wells to determine the possible identity of the microorganisms. The binary partial results are compared to prerecorded patterns of results for each type of test tray to identify the sample in question. 
     A microbiological testing apparatus for detecting the presence of a fluorescence emitting reaction resulting from the interaction of a reacting agent and a sample for detection, susceptibility, and identification testing, is also known. In this apparatus, multiple trays having a plurality of test chambers are contained within a carousel. This carousel is rotated to move one of the trays close to a detection area. A positioning mechanism then radially moves that tray out of the carousel and into the detection area, and a high-energy light source is disposed proximate to the tray. The light source provides narrow-band light sufficient to produce an emission fluorescence from the reaction within the test chambers, which in turn is detected by a video mechanism disposed opposite to the light source and behind the positioned tray. The video mechanism produces an image based on the emission wavelength. 
     Another test system is known for identifying bacteria using signals based on the intensity of monochromatic light reflected from specimens placed in a culture plate having a plurality of cells. A rotary disk containing six interference filters is interposed between a lamp and a group of optical fibers. The light from the lamp passes through a particular interference filter to produce monochromatic light of a certain wavelength. The filtered monochromatic light is guided by the optical fibers to be incident on respective cells of the culture plate. The disk is rotated so that the six different wavelength monochromatic lights are caused to be incident on the cells sequentially. The light reflected from the specimens is guided by additional optical fibers to corresponding phototransistors. A signal is derived for each specimen based on the intensity of the reflected monochromatic light. These signals are then analyzed to determine the identity of the specimen by calculating the difference, or ratio, between the signals and comparing that result with a reference value. 
     Although the above-described systems may be somewhat useful, each system fails to fulfill all of the requirements of a fully automated microbiological testing system. In particular, the known systems are not capable of simultaneously performing both colorimetric-type and fluorometric-type testing on multiple-well test panels, which is needed to obtain more accurate test results. Further, these systems are generally not designed to continuously gather test data from a plurality of multiple-well test panels in a quick and reliable manner. Moreover, the automated processing of these systems is limited. 
     In addition, the known systems do not examine multiple indicators of growth of the samples, and then base the MIC calculations on these multiple growth indicators. The use of data from multiple growth indicators is desirable to provide increased accuracy and integrity of the results. Furthermore, the known systems fail to employ a method of screening questionable MIC results. In particular, the known systems do not evaluate the quality and reliability of the MIC results to provide a probability or confidence value which indicates the level of certainty at which the MIC results are deemed to be correct. 
     Accordingly, a need exists for a system and method for an improved system and method for analyzing biological samples to determine the susceptibility of the samples to antimicrobial materials, and to provide MIC values for the antimicrobial materials with respect to the various samples. 
    
    
     SUMMARY OF THE INVENTION 
     An object of the present invention is to provide a system and method for analyzing samples, such as biological samples, to accurately and effectively determine the susceptibility of the samples to antimicrobial materials. 
     Another object of the present invention is to provide a system and method which measures multiple indicators of growth of the biological samples, and then uses these measurements to determine the susceptibility of the samples to the various antimicrobial materials to provide MIC values for the respective samples and antimicrobial materials. 
     A further object of the invention is to provide a system and method evaluating the calculated MIC values for respective samples and antimicrobial materials to provide a probability or confidence value which indicates the level of certainty at which the MIC values are deemed to be correct. 
     A still further object of the present invention is to provide a system and method for optically reading a biological sample contained in sample wells of a sample test panel having various types and concentrations of antimicrobial materials therein and, based on these readings, measuring a plurality of growth indicators of the samples that the system and method uses to determine the respective minimum inhibitory concentrations (MICs) at which the respective antimicrobial materials will inhibit growth of the sample. 
     These and other objects of the present invention are substantially achieved by providing a system and method for analyzing a sample contained in at least one sample well by directing a plurality of analyzing light waves of different wavelengths, such as red, green and blue, onto the sample contained in the sample well, and detecting a respective resultant light wave emanating from the sample for each of the analyzing light waves being directed onto the sample. The system and method then provides a result value representative of each respective resultant light wave, and mathematically combines the result values to provide at least two growth indicator values, such as the redox state and turbidity of the sample, each of which represents a respective growth characteristic of the sample. The method and system can perform the directing, detecting and mathematical combining steps on the sample in the sample well at a plurality of time intervals, such that each of the mathematical combining steps performed provides a set of growth indicator values for each of the time intervals. 
     The method and system can perform the above steps on a plurality of the sample wells at a plurality of time intervals to obtain a respective set of growth indicator values for each of the respective sample wells at each of the time intervals. The method and system can then further mathematically combine certain of the growth indicator values in the respective sets of growth indicator values for each of the sample wells to provide a respective sample well characteristic value, such as an MIC value, for each of the respective sample wells. The method and system can then group the sample well characteristic values into a plurality of groups, and compare the sample well characteristic values to each other in each of the respective groups to determine in which sample wells in each of the groups sample growth is inhibited. 
     Another aspect of the present invention lies in providing a system and method for determining at least one minimum inhibitory concentration (MIC) value for a sample contained in a sample container that includes a plurality of sample wells, each of which containing a portion of the sample and a respective material adapted to affect growth of the sample. The system and method take a respective set of readings of each respective sample well at each of a plurality of intervals of time to provide a respective set of values for each respective sample well at each of said intervals. The readings are taken, for example, by detecting a plurality of light waves of different wavelengths, such as red, green and blue, from each of the sample wells at each of said intervals to provide the respective sets of values for each respective sample well at each of the intervals. Also, in each of the respective sets of values, one of the values represents a redox state of its respective sample well and the other value represents a turbidity value of its respective sample well. 
     For each of the sample wells, the system and method mathematically combine the respective sets of values to provide a respective well characteristic value for each of the sample wells. The system and method then group the sample well characteristic values into a plurality of groups representative of respective groups of the sample wells, and compare the sample well characteristic values to each other in each of the respective groups to determine a respective MIC value for each of the groups of sample wells. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other objects, advantages and novel features of the invention will be more readily appreciated from the following detailed description when read in conjunction with the accompanying drawings, in which: 
         FIG. 1  illustrates a system for analyzing samples to determine their antimicrobial susceptibility according to an embodiment of the present invention; 
         FIG. 2  illustrates a carousel housing portion of the system shown in  FIG. 1 ; 
         FIG. 3  is a top view of the carousel housing portion shown in  FIG. 2  with the top of the enclosure removed; 
         FIGS. 4A-4C  are perspective, top and bottom views of an example of a test panel used in the system shown in  FIG. 1 ; 
         FIG. 5  is a diagrammatic view of the sample well reading components of the system shown in  FIG. 1 ; 
         FIG. 6  is a schematic diagram illustrating the interrelationship among the mechanical and electrical components of the system shown in  FIG. 1 ; 
         FIGS. 7A and 7B  are flowcharts showing the steps performed by the system shown in  FIG. 1  for analyzing samples contained in sample wells of the test panels as shown in  FIGS. 4A-4C ; 
         FIG. 8  is a graph illustrating redox states and turbidity values for a sample contained in one sample well of a test panel as calculated by the system shown in  FIG. 1 ; 
         FIG. 9  is a graph illustrating the relationship between a variable and its indication of the probability of growth of a sample, which is evaluated by the system shown in  FIG. 1  to derive an MIC value for the sample; 
         FIG. 10  is a table illustrating an example of MIC values and probabilities calculated according to an embodiment of the present invention; 
         FIG. 11  is a table illustrating another example of MIC values and probabilities calculated according to an embodiment of the present invention; and 
         FIG. 12  is a graph illustrating the relationship between redox values for wells having different antibiotic concentrations in relation to elapsed incubation time. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       FIG. 1  illustrates a system  100  according to an embodiment of the present invention for analyzing biological samples to identify the susceptibility of the samples to various types and concentrations of antimicrobial materials, and for calculating the minimum inhibitory concentration (MIC) at which the respective antibiotics or antimicrobial materials inhibit growth of the respective samples. The system  100  includes a measurement instrument  102  having an enclosure  104  which is divided into a carousel housing portion  106  and a controller housing portion  108 . The system  100  further includes a workstation  110 , such as a personal computer (PC) or the like, which is coupled to the controller housing portion  108  to communicate with the system  100  for purposes of transferring data to and from the system  100 , for example. 
     The carousel housing portion  106  includes a door  112  and a latch mechanism  114 . The latch mechanism  114  can maintain the door  112  in a closed state, and can be manipulated to allow the door  112  to be opened to expose an interior chamber  115  of the carousel housing portion  106 . The controller housing portion  108  includes a display panel  116 , a keyboard panel  118 , a computer readable medium drive  120 , and a barcode reader  122 , the purposes of which are described in detail below. 
     As shown in  FIGS. 2 and 3 , a carousel  124  is housed in the interior chamber  115  of the carousel housing portion  106 . The carousel  124  includes a plurality of rings and ribs bolted to a drive ring  126  to form a cylindrical cage, which is mounted vertically in the interior chamber  115 . The carousel housing portion  106  is insulated to provide a substantially uniform temperature incubation environment in the interior chamber  115 , and is light-tight under normal operation to prevent ambient light from entering the interior chamber  115 , as described in more detail in copending U.S. patent application Ser. No. 09/083,130, referenced above. 
     In this example, the carousel  124  is arranged to include four horizontal tiers with each tier having twenty-six panel positions, thus providing a total of one-hundred and four panel positions  128 . However, these numbers of tiers and panel positions  128  may be changed to accommodate the requirements of any specified application as will be appreciated by one skilled in the art. A panel carrier  130  is mounted in each of the panel positions  128 . Each panel carrier  130  is configured to receive a test panel  132 , an example of which is shown in  FIGS. 4A-4C . 
     As shown in  FIGS. 4A-4C , a test panel  132  is a disposable, transparent or semi-transparent device which is inoculated with materials or reagents needed for both identification (ID) and antimicrobial susceptibility determination (AST) testing of the samples. The testing is performed based on reactions that occur between the samples and reagents placed in individual wells  134  on each ID/AST test panel  132 . The wells  134  are arranged on the ID/AST test panels  132  as a two-dimensional array having rows and columns. The wells  134  are segregated into a ID section  136  and an AST section  138 . In this example, the ID section  136  includes fifty-one wells  134 , and the AST section includes eighty-five wells  134 . Each test panel  132  further includes a base  140 , a chassis  142 , a lid  144 , a cellulose acetate pad  146 , inoculation ports  148 , and a panel label (not shown) which includes information that identifies the complete manufacturing history of that test panel  132 . Further details of the test panels  132  used with the system  100  are described in copending U.S. patent application Ser. No. 09/083,130, referenced above, and in U.S. Pat. No. 5,922,593 to Livingston, the entire contents of which are incorporated herein by reference. 
     The panel carriers  130  are designed such that they will not retain improperly seated test panels  132 . When the test panels  132  are mounted in the four tiers of the carousel  124 , they are arranged to form substantially circular rows and vertical columns of wells  134 . That is, all the columns of wells  134  in all four tiers of the carousel  124  should be substantially aligned with each other in the vertical direction along the entire height of the carousel  124 , while all rows of wells  134  should be substantially aligned with each other around the entire circumference of the carousel  124 . In this example, panel positions  128  are numbered zero through twenty-five in each tier of the carousel  124 , with panel position zero being reserved for a normalization panel and thus not accessible by an operator during normal operation of the instrument  102 . 
     The carousel housing portion  106  also includes a drive module  150  that drives the carousel  124  to rotate in a clockwise or counter-clockwise manner, as desired, and a plurality of bearings  152  and a spring-loaded pivot  154  which rotatably secure the carousel  124  in the interior chamber  115  of the carousel housing portion  106  and facilitate rotation of the carousel  124 . Further details of the carousel  124  and its associated components, as well as the panel carriers  130  and test panels  132 , are described in copending U.S. patent application Ser. No. 09/083,130, referenced above. 
     As shown in  FIGS. 3 and 5 , and in the schematic diagram shown of  FIG. 6 , the carousel housing portion  106  in this example further includes a visible light source assembly  156  and an ultraviolet (UV) light source assembly  158 . The visible light source assembly  156  includes four visible light source modules  156 - 1  through  156 - 4  and a supporting tower  160 , while the ultra-violet light source assembly  158  includes ultraviolet light sources  158 - 1  and  158 - 2 . The supporting tower  160  aligns one visible light source module with each tier of the carousel  124  so that at any given time, one entire column of wells of the ID/AST test panels  132  in the four tiers of the carousel  124  can be illuminated by the visible light source modules. 
     In this example, each visible light source module  156 - 1  through  156 - 4  includes three parallel vertical columns of sixteen light-emitting diodes (LEDs) each. The first column consists of red LEDs, the second of green LEDs and the third of blue LEDs. A holographic diffuser plate  162  is disposed in close proximity to the ID/AST test panels  132  mounted in the carousel  124 . The holographic diffuser plate  162  diffuses the illumination energy from each column of LEDs, when the columns are energized. Each column of LEDs is mounted in the visible light source modules to maintain a fixed distance from the diffuser plate  162 . Cylindrical lenses (not shown) may be used to focus the illumination energy from each column of LEDs onto the vertical well columns of the ID/AST test panels  132 . The illumination axis for each column of LEDs is made coincident for the red, green and blue illumination. Thus, each well column sees a uniform stripe of either red, green or blue illumination, depending upon which column of LEDs is energized. 
     As further shown in  FIGS. 3 ,  5  and  6 , an optical measurement system  164  is disposed approximately within the center of the carousel  124  such that it is aligned to receive the visible light transmitted through each well  134  of the ID/AST test panels  132  during excitation by red, green or blue illumination from the visible light source modules of the visible light source assembly  156 . Visible fluorescent radiation is similarly detected from the wells  134  when the samples in the wells  134  are excited by the ultraviolet light emitted from the ultraviolet light source assembly  158 . As can be appreciated by one skilled in the art, excitation filters  166  eliminate unwanted spectral components present in the light emitted from the ultraviolet light source assembly  158 , and emission filters  168  eliminate unwanted spectral components that may be present in the output signal before detection by the optical measurement system  164 . 
     In this example, the optical measurement system  164  includes a plurality of CCD detector modules  170 - 1  through  170 - 4  and corresponding lens assemblies  172 - 1  through  1724 , with one CCD detector module  170  and one lens assembly  172  being aligned to receive readings from wells  134  of test panels  132  in a respective tier of the carousel  124 . Accordingly, because the carousel  124  includes four tiers in this example, the optical measurement system  164  includes four CCD detector modules  170 - 1  through  170 - 4  and four corresponding lens assemblies  172 - 1  through  172 - 4 , with each detector module/lens assembly pair arranged substantially in alignment in the vertical direction. The lens assemblies  172 - 1  through  172 - 4  focus light from of each panel well column of the test panels  132  in their respective tiers of the carousel  124  onto the CCD arrays of the corresponding CCD detector modules  170 - 1  through  170 - 4 . 
     Each CCD detector module  170  can include, for example, a 2048-pixel linear CCD array. The CCD arrays of the CCD detector modules  170  detect and measure the intensity of light transmitted through each well  134  of the test panels  132  in the corresponding tiers of the carousel  124  when the wells  134  are illuminated by the red, green and blue LEDs. Visible fluorescent light is similarly detected by the CCD arrays of the CCD detector modules  170  when the samples in the wells  134  are excited by the ultraviolet light emitted from the ultraviolet light source assembly  158 . Further details of the structure and operation of the visible light source assembly  156 , ultraviolet light source assembly  158 , optical measurement system  164 , and their related components can be found in copending U.S. patent application Ser. No. 09/083,130, referenced above. 
     As stated above,  FIG. 6  is an exemplary schematic diagram illustrating further components of the measurement instrument  102  described above. As shown, the carousel housing portion  106  and the controller housing portion  108  are separated by a divider panel  174  which can be, for example, part of the housing  106 . The ultraviolet light sources  158 - 1  and  158 - 2  are driven by a lamp driver  176 . The lamp driver  176 , visible light source modules  156 - 1  through  156 - 4 , CCD detector modules  170 - 1  through  170 - 4 , an ultraviolet light source cooling fan  178 , and an optical measurement system cooling fan  180  are coupled to an interconnect board  182 . A plurality of status indicator boards  184 , barcode readers  186  which read the barcodes on the test panels  132 , and panel flags and home flag reader  188 , are also coupled to the interconnect board  182 . Further details of the status indicator boards  184 , barcode readers  186 , and panel flags and home flag reader  188  can be found in copending U.S. patent application Ser. No. 09/083,130, referenced above. 
     The interconnect board  182  is coupled to an I/O interface board  190  of the controller module  191  that is present in the controller housing portion  108 . As described in more detail below and in copending U.S. patent application Ser. No. 09/083,130, referenced above, the control module  191  includes a controller  192  which controls the visible light source modules  156 - 1  through  156 - 4 , CCD detector modules  170 - 1  through  170 - 4 , lamp driver  176 , and all other components associated with performing the well reading process. The controller  191  further includes an ethernet  194  and an LCD driver  196 . The ethernet  194  can be coupled to a network port  198  to output and input data to and from the workstation  110  (see FIG.  1 ), for example. The LCD driver  196  is coupled to the display) panel  116  (see  FIG. 1 ) to display, for example, results of the well readings, and is further coupled to an external video connection  197 . The controller  192  is coupled to the computer readable medium drive  120  (see  FIG. 1 ) to output and input data to and from a computer readable disk, for example. 
     In addition, the controller module  191  is coupled to the computer readable medium drive  120 , to the display panel  116  via an inverter  200 , to the keyboard panel  118  via an indicator board  202 , to the barcode reader  121 , to an AT keyboard  204  and to a speaker  206 . The controller module  191  is further coupled to an auxiliary serial port  208 , a printer port  210 , a remote alarm port  212  and an auxiliary barcode reader port  214  which, along with the network port  198 , are housed in a connector panel  216 . In this example, the barcode reader port  214  is coupled to the barcode reader  122  (see FIG.  1 ). 
     The controller module  191  is also coupled to a drive and DC distribution module  218  and a power control and distribution module  220 . An ambient temperature sensor  222  and an incubation temperature sensor  224  sense the temperature inside the interior chamber  115  arid provide signals indicative of the temperature to the controller  192  of the controller module  191 . Furthermore, upper temperature cut-off sensor  226  provides a signal to the controller  192  via the power control and distribution module  220  indicating when the temperature of the interior chamber  115  has reached the maximum temperature. In response, the controller  192  will control the heater  228  via power control and distribution module  220 , and will control heater blower  230  via drive and distribution module  218 , to prevent the temperature in the interior chamber  115  from further increasing. The controller  192  further controls the door switch  232  and door solenoid  234  via drive and distribution module  218  to control the latch mechanism  114  of the door  112  (see  FIGS. 1 and 2 ) to either maintain the door  112  in the closed position or allow the door  112  to be opened. The controller  192  also controls the drive module  150  to control the rotation of carousel  124  as described in detail below. Further details of the temperature controlling operations and carousel rotation operations are set forth in copending U.S. patent application Ser. No. 09/083,130, referenced above. 
     As further shown in  FIG. 6 , the controller housing portion  108  includes a transformer  236  and cooling fans  248  that are coupled to the power control and distribution module  220 . Also, a 24 V power supply  240 , a 15 V power supply  242  and a 5 V power supply  244  provide power to the drive and distribution module  218  and power controller module  220 , as well as to the lamp driver  176 . These power supplies  240 ,  242  and  244  are powered from an A.C. input power that is received by the power control and distribution module  220  via filter  246  and the main on/off switch  248  of the system  100 . 
     The operation of the system  100  will now be described with reference to  FIGS. 1-6 , as well as the flow chart and graphs shown in  FIGS. 7A-9 . In Step  1000 , each test panel  132  is inoculated with a respective broth-suspended organism (i.e., a sample) before being placed into a respective panel carrier  130  of the carousel  124 . The separate innocula are added manually to the inoculation ports  148  of the test panels  132 , and allowed to flow into the wells  134  of the test panels  132  as described in copending U.S. patent application Ser. No. 09/083,130, referenced above. Only one type of sample is introduced into each respective test panel  132 . As discussed above, the wells  134  of the test panels  132  include various types and concentrations of antimicrobial materials, which affect the growth of the samples, along with indicators that indicate the presence or absence of sample growth. Also, at least one of the wells  134  of each test panel  132  is designated as a growth control well and does not include any antimicrobial material. 
     The inoculated test panels  132  are then inserted into the respective panel carriers  130  of the carousel  124  in step  1010 . The operator uses the barcode scanner  121  or barcode scanner  122  to scan the barcode of each test panel  132  as it is being inserted into a respective panel carrier  130 , to thus enter information pertaining to the sample in the test panel  132 , the antimicrobial materials in the test panel wells  134 , and so on, into the system  100 . The technician also can enter information pertaining to the tier level and position in the carousel  124  at which the test panel  132  is inserted via the keyboard  118 , for example. Once the test panels  132  have been loaded into the carousel  124 , the door  112  of the carousel housing portion  106  is closed and latched shut. In step  1020 , the controller  192  controls the carousel  124  to begin rotating, and controls the heater  228  and heater blower  230  to begin increasing the temperature of the interior chamber  115  to incubate the samples in the wells  134 . In this example, the operator can set the carousel  124  to rotate at one revolution per minute (RPM). However, the rotational speed can be set to any value as appropriate. 
     After a predetermined amount of time has passed, for example, two hours, the controller  192  controls the system  100  to begin taking measurements of the wells  134  of the test panels  132  in a manner as described in copending U.S. patent application Ser. No. 09/083,130, referenced above. In this example, measurements are taken at 20 minute intervals. Also, as can be appreciated from the discussion below, the following steps in the flowchart shown in  FIG. 7  are performed for each panel  132 , and the manner in which the processing proceeds for each respective panel  132  is dependent on the results of the well readings obtained for each respective panel  132 . Also, the operations described in these steps are controlled by controller  192 . 
     In step  1030 , the first readings of the wells  134  of the test panels  132  are taken as test readings, to determine whether the readings pass an initial criteria indicating that the samples are valid for analysis. The well readings are taken as the carousel is being rotated. The controller  192  waits until the home flag of the carousel  124  is detected by the home flag detector  188  before beginning to take the readings, to insure that the controller  192  can match the readings with the correct well  134  from which the readings were taken. 
     The controller  192  can first control the detector modules  170 - 1  through  170 - 4  to perform dark readings, during which neither the UV light sources  158  nor the visible light sources  156 - 1  through  156 - 4  are energized. The controller  192  can then control the lamp driver  176  to drive the ultraviolet light source assembly  158 . The controller  192  in this example waits until the carousel  124  has rotated two revolutions to allow the ultraviolet lights of the ultraviolet light source assembly  158  to warm up, so that the light intensity can stabilize, and then controls the detector modules  170 - 1  through  170 - 4  to take an ultraviolet light reading for an entire revolution of the carousel  124 . The controller  192  then controls the lamp driver  176  to turn off the ultraviolet light sources  158 , and processes the readings. As discussed in copending U.S. patent application Ser. No. 09/083,130 referenced above, the controller  192  uses the ultraviolet readings to identify the types of samples in the sample wells  134  of the test panels  132 . 
     After the above readings have been taken, the visible light readings are then taken. The controller  192  can then control the rate of rotation of the carousel  124  to remain the same, or can increase the rate of rotation of the carousel  124 , for example, to 2 RPM, or any other suitable rotation speed, while the visible light readings are being taken. In one example, the rotation speed is increased to 2 RPMs, and the red LEDs of the visible light source assembly  156  (see  FIGS. 3 ,  5  and  6 ) are activated. The carousel  124  can be rotated one revolution to allow the red LEDs to warm up so that light intensity can stabilize, and then “red” readings can be taken of the wells  134  by the detector modules  170 - 1  through  1704  while the carousel  124  rotates the second revolution. 
     Once the red readings have been taken, the red LEDs are turned off and the green LEDs of the visible light  156  can be energized. As with the red LEDs, the carousel  124  can be rotated one revolution to allow the green LEDs to warm up to allow the light intensity to stabilize. The “green” readings can then be taken of the wells  124  by the detector modules  170 - 1  through  170 - 4  while the carousel  124  is rotated another revolution. After the green readings have been taken, the green LEDs are turned off. In this example, the rotation speed of the carousel  124  is then reduced to 1 RPM, and the blue LEDs of the visible light source assembly  156  are energized. The carousel  124  is allowed to rotate for one revolution while the blue LEDs warm up to allow the light intensity to stabilize. Then, the “blue” readings of the wells  134  are taken by the detector modules  170 - 1  through  110 - 4  during the next revolution of the carousel  124 . 
     The red, green and blue readings taken for each well  134  of each test panel  132  are then stored by the controller  192  in a memory such that each well  134  has a specific red. green and blue reading for that particular time interval. The process then continues to step  1040  where the readings for each well  134  are evaluated to determine whether the further readings that are taken on a well  134  are to be considered valid. 
     In step  1040 , the red readings taken of each well  134  are evaluated to determine whether the wells have been properly filled. The readings can range from an intensity level of “0” to an intensity level of “4200” with 0 being zero intensity and 4200 being the maximum intensity reading for a particular color (e.g., red). In this example, the process identifies in step  1040  the wells  134  having a red reading above 2200. For those wells  134  having such a red reading, the processing continues to step  1050  where those wells  134  are failed or, in other words, the system  100  identifies all future readings from those wells  134  as being invalid. Accordingly, either no further readings of those wells  134  are taken, or any readings that are taken are ignored. 
     Furthermore, if a well  134  has been identified as a growth control well and has a red reading of over 2200, the entire side of the test panel  132  on which that control well resides is failed. Also, if that well contains a particular antimicrobial material, no results are reported by the system  100  for that antimicrobial material for the particular test panels  132  including the failed wells. 
     Once the red well readings have been evaluated in step  1040  and the appropriate wells  134  have been failed in step  1050 , the processing continues to step  1060  where a panel indicator determination is made. Specifically, in this step, the wells  134  identified as growth control wells for their respective test panels  132  are evaluated to determine whether the initial state of the growth indicator present in the samples in the control wells  134  of their respective test panels  132  are acceptable for evaluating those test panels  132 . In this example, the value of the respective red reading for each control well is divided by the value of the respective green reading for each control well. If the result of the division is less than 0.3692 or greater than 0.6464, controller  192  determines that the initial state of the growth indicator is unacceptable for the test panel  132  including the control well providing this result. Accordingly, no results obtained by the well measurements for that particular test panel  132  are reported. As stated above, step  1060  is carried out for each test panel  132 . 
     The processing then continues to step  1070  where the controller  192  will continue to rotate the carousel  124  and thus, the carousel housing portion  106  will continue to incubate the samples in the wells  134 . The processing will continue to step  1080  where the system  100  will take the red, green and blue readings of the wells in a manner similar to that described to above with regard to step  1030 , and as described in copending U.S. patent application Ser. No. 09/083,130, referenced above. The processing then proceeds to step  1090  where the controller  192  determines whether the minimum amount of incubation time has elapsed. The minimum incubation time at which readings of the wells  134  can begin to be analyzed to determine MIC values in this example is two hours. If the minimum incubation time has not elapsed, the processing returns to step  1070  and the incubation is continued. However, once the appropriate amount of incubation time has elapsed, the processing proceeds to step  1100  where the controller  192  will calculate the redox state and turbidity values for each well. 
     The system  100  in this example uses two indicators of growth, redox and turbidity, to evaluate the susceptibility of the samples to the antimicrobial materials in the wells  134 . The redox and turbidity values are calculated for each well  134  in each of the panels based on the red, green and blue readings taken of the respective wells at the respective  20  minute time intervals as discussed above. A simultaneous nonlinear algorithmic model was developed from experimentally obtained redox and turbidity readings, and this algorithm is used by the controller  192  to predict the redox state and organism density (turbidity) in each of the wells  132 . The controller  192  can arrange the calculated redox state and turbidity values for each respective well  132  in graph form with respect to incubation time. An example of the calculated redox and turbidity growth curves for  E.coli  for a single well  132  is shown in FIG.  8 . 
     As stated above, the redox state of a sample in a well  132  is measured by utilizing the change in red, green and blue readings that occurs over time as a result of the reduction of a growth indicator, such as resazurin, by the antimicrobial material in the well  132 . As the resazurin is reduced, the color of the sample in the well  132  changes from blue to red to clear. This change in redox is represented numerically as a continuum, with the value “0” indicating an unreduced growth indicator (blue=resazurin), the value “0.5” indicating that the indicator is 50% reduced (red=resorufin), and the value “1.0” indicating that the indicator has been completely reduced (clear=dihydroresorufin). 
     The turbidity is also estimated by using the red, green and blue reading in an equation similar to the redox calculation. The initial signal has a value of “0” and a maximum of 2.25 units can be estimated. The units for turbidity correspond to McFarland units (1 McFarland=3×10 8  cfu/ml). 
     An example of the manner in which the actual red, green and blue readings are used to calculate redox and turbidity values will now be demonstrated. In this example, the red, green and blue readings taken of a sample well at the first twenty minute interval are as follows: red=873, green=956 and blue=2705. The processing then generates a one-column, four-row input matrix as shown in Table 1 as follows: 
     
       
         
               
             
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Input Matrix Values 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1.0000 
               
               
                 2705.0000 
               
               
                 0.3227 
               
               
                 0.3534 
               
               
                   
               
             
          
         
       
     
     It is noted that the first row in the input matrix is always padded with the value 1.0000. The value 2705.0000 is equal to the blue reading, the value 0.3227 is calculated by dividing the red reading by the blue reading (i.e., red/blue), and the value 0.3534 is calculated by dividing the green reading by the blue reading (i.e., green/blue). It is also noted that in this example, the blue reading is clamped at a starting value of 2705 until 36 minutes has elapsed in the incubation. All points after 36 minutes are multiplied by the value (2705/(blue signal @ last point before 36 minutes)). The result is then clamped to limits of 558 and 4474. Furthermore, the value of red/blue is clamped to a minimum of 0.2012329 and a maximum of 1.8936959, while the value of green/blue is clamped to a minimum of 0.3091655 and a maximum of 1.3084112. 
     The processing then multiplies the one-column, four-row Input Matrix by the four-column, five-row Redox Input Weight Matrix according to the equation “Input Matrix*Redox Input Weight Matrix” and known matrix multiplication techniques to arrive at a one-column, five-row matrix of numbers as discussed below. The twenty values in the Redox Input Weight Matrix have been calculated and programmed into the controller  192  based on past empirical data and observations, and remain constant for all of the readings at all of the time intervals. An example of the values of the Redox Input Weight Matrix are shown in the following Table 2: 
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 Redox Input Weight Matrix Values 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 −0.673253 
                  0.000710423 
                 −1.623674164 
                  3.340127166 
               
               
                  2.445846 
                 −0.000572912 
                  1.4797837 
                 −6.311909249 
               
               
                  0.109425 
                  0.005775254 
                 −3.604370752 
                 −0.242927922 
               
               
                  1.356753 
                  0.000748697 
                 −2.139010636 
                 −1.067568082 
               
               
                  3.88E−05 
                  0.022713989 
                  3.80317E−05 
                  2.99302E−05 
               
               
                   
               
             
          
         
       
     
     The values of the Intermediate Matrix calculated according to the above equation “Input Matrix*Redox Input Weight Matrix” are shown in Table 3 as follows: 
     
       
         
               
             
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                 Intermediate Matrix Values 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1.9049 
               
               
                 −0.8571 
               
               
                 14.4824 
               
               
                 2.3143 
               
               
                 61.4414 
               
               
                   
               
             
          
         
       
     
     These values of the Intermediate Matrix, as well as the values of the Input Matrix, are used to create a one-column, nine-row Output Matrix. Specifically, the first row of the Output Matrix is padded with the value 1.0000, and rows two through six of the Output Matrix are calculated by taking the antilog value of each of the above values of the Input Matrix, respectively, according to the following equation:
 
antilog value= e   x /(1 +e   x )
 
with x being the respective value from the matrix. Rows seven through nine of the Output Matrix arc filled with the values in rows two through four of the Input Matrix. Accordingly, the values of the Output Matrix are shown in the following Table 4:
 
     
       
         
               
             
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                 Output Matrix Values 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1.0000 
               
               
                 0.8704 
               
               
                 0.2980 
               
               
                 1.0000 
               
               
                 0.9101 
               
               
                 1.0000 
               
               
                 2705.0000 
               
               
                 0.3227 
               
               
                 0.3534 
               
               
                   
               
             
          
         
       
     
     The redox value is then calculated by multiplying the one-column, nine-row Output Matrix by a nine-column, one-row Redox Output Weight Matrix according to the following equation and known matrix multiplication techniques.
 
Redox Value=Output Matrix*Output Weight Matrix
 
In this example, the values of the Redox Output Weight Matrix are shown in the following Table 5:
 
                                                           TABLE 5                   Redox Output Weight Matrix Values                    −0.633973   4.167218646   1.721677475   −0.389544272   −2.543872   −0.63391676   1.30610E−04   0.04646759   −0.842252                    
As with the values of the Redox Input Weight Matrix, the Redox Output Weight Matrix values have been calculated and programmed into the controller  192  based on past empirical data and observations, and remain constant for all of the readings at all of the time intervals. The Redox Value is thus calculated as 0.23843516. This value is then plotted on the graph as shown in FIG.  8 .
 
     The turbidity value based on these red, green and blue readings is calculated in a similar manner. That is, the processing then generates a one-column, four-row input matrix as shown in Table 6 as follows: 
                           TABLE 6               Input Matrix Values                                1.0000       2705.0000       0.3227       0.3534                    
As with the Input Matrix Values for the redox calculation, the Input Matrix Values for the turbidity calculations are based on the actual red, green and blue readings. The first row in the input matrix is always padded with the value 1.0000. The value 2705.0000 is equal to the blue reading, the value 0.3227 is calculated by dividing the red reading by the blue reading (i.e., red/blue), and the value 0.3534 is calculated by dividing the green reading by the blue reading (i.e., green/blue). It is also noted that in this example, the blue reading is clamped at a starting value of 2705 until 36 minutes has elapsed in the incubation. All points after 36 minutes are multiplied by the value (2705/(blue signal @ last point before 36 minutes)). The result is then clamped to limits of 558 and 4474. Furthermore, the value of red/blue is clamped to a minimum of 0.2012329 and a maximum of 1.8936959, while the value of green/blue is clamped to a minimum of 0.3091655 and a maximum of 1.3084112.
 
     The processing then multiplies the one-column, four-row Input Matrix by the four-column, five-row Turbidity Input Weight Matrix according to the equation “Input Matrix * Turbidity Input Weight Matrix” and known matrix multiplication techniques to arrive at a one-column, five-row matrix of numbers as discussed below. The twenty values in the Turbidity Input Weight Matrix have been calculated and programmed into the controller  192  based on past empirical data and observations, and remain constant for all of the readings at all of the time intervals. An example of the values of the Turbidity Input Weight Matrix are shown in the following Table 7: 
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 7 
               
               
                   
               
               
                 Turbidity Input Weight Matrix Values 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 −2.870675 
                  0.002111599 
                 −0.234543715 
                  0.334025395 
               
               
                 −1.306260 
                  0.00202755 
                  0.577175204 
                 −2.717689223 
               
               
                  3.477755 
                  0.001837992 
                 −4.028539894 
                  1.455268741 
               
               
                 −0.008775 
                 −0.004819911 
                 −0.027006746 
                 −0.01188475 
               
               
                  8.842011 
                  0.001408226 
                 −5.393142566 
                 −4.464335919 
               
               
                   
               
             
          
         
       
     
     The values of the Intermediate Matrix calculated according to the above equation “Input Matrix*Turbidity Input Weight Matrix” are shown in Table 8 as follows: 
     
       
         
               
             
               
             
           
               
                 TABLE 8 
               
               
                   
               
               
                 Intermediate Matrix Values 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 2.8836 
               
               
                 3.4041 
               
               
                 7.6637 
               
               
                 −13.0596 
               
               
                 9.3329 
               
               
                   
               
             
          
         
       
     
     These values of the Intermediate Matrix, as well as the values of the Input Matrix, are used to create a one-column, nine-row Output Matrix. Specifically, the first row of the Output Matrix is padded with the value 1.0000, and rows two through six of the Output Matrix are calculated by taking the antilog value of each of the above values of the Input Matrix, respectively, according to the following equation:
 
antilog value= e   x /(1 +e   x )
 
with x being the respective value from the matrix. Rows seven through nine of the Output Matrix are filled with the values in rows two through four of the Input Matrix. Accordingly, the values of the Output Matrix are shown in the following Table 9:
 
     
       
         
               
             
               
             
           
               
                 TABLE 9 
               
               
                   
               
               
                 Output Matrix Values 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1.0000 
               
               
                 0.9470 
               
               
                 0.9678 
               
               
                 0.9995 
               
               
                 0.0000 
               
               
                 0.9999 
               
               
                 2705.0000 
               
               
                 0.3227 
               
               
                 0.3534 
               
               
                   
               
             
          
         
       
     
     The turbidity value is then calculated by multiplying the one-column, nine-row Output Matrix by a nine-column, one-row Turbidity Output Weight Matrix according to the following equation and known matrix multiplication techniques.
 
Turbidity Value=Output Matrix*Output Weight Matrix
 
In this example, the values of the Turbidity Output Weight Matrix are shown in the following Table 10:
 
                                                           TABLE 10                   Turbidity Output Weight Matrix Values                    −0.107225   −2.957877127   2.378329542   1.866207268   0.012793   −1.741858375   1.38488E−04   −0.08976299   0.401581                    
As with the values of the Turbidity Input Weight Matrix, the Turbidity Output weight Matrix values have been calculated and programmed into the controller  192  based on past empirical data and observations, and remain constant for all of the readings at all of the time intervals. The Turbidity Value is thus calculated as 0.00459741. This value is then plotted on the graph as shown in FIG.  8 .
 
     The redox and turbidity values are calculated for each well based on the readings taken for each well at each time interval (i.e., each twenty minute time interval in this example), and the values are plotted on a graph as shown in  FIG. 8. A  local regression algorithm (LOESS) smoothes the time series data for both the redox and turbidity values calculated for each well  132  over the elapsed period of time. The LOESS in this example uses no more than seven readings for each local regression. In evaluating a time point, at least one reading is required past the time point being interpolated. From the LOESS equations any reading at any time point can be estimated. From the interpolated data a series of metrics that describe the growth in the well are calculated. All metrics will be based on the time or growth control values derived from these smoothed and interpolated points. The metrics are derived from the basic functions such as absolute value, first derivative (rate), second derivative (acceleration) and integral (area under the curve). The metrics are then used to derive a series of variables that are utilized by the generalized additive models (GAMs) as described in more detail below. These variables are a variety of absolutes, maximums and ratios to the growth control. A total of 27 variables are available to the GAMs, as listed below in Table 11. 
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 11 
               
             
             
               
                   
               
               
                 Variables Available for GAMs 
               
             
          
           
               
                   
                 Running Count 
                 Abbreviation 
                 Description 
               
               
                   
                   
               
               
                   
                  1 
                 CONC_LOG 
                 drug concentration 
               
               
                   
                  2 
                 T_AB 
                 turbidity value 
               
               
                   
                  3 
                 T_FD 
                 turbidity first 
               
               
                   
                   
                   
                 derivative 
               
               
                   
                  4 
                 T_SD 
                 turbidity second 
               
               
                   
                   
                   
                 derivative 
               
               
                   
                  5 
                 T_IN 
                 turbidity integral 
               
               
                   
                  6 
                 T_AB_M 
                 turbidity maximum 
               
               
                   
                   
                   
                 value 
               
               
                   
                  7 
                 T_FD_M 
                 turbidity maximum 
               
               
                   
                   
                   
                 first derivative 
               
               
                   
                  8 
                 T_SD_M 
                 turbidity maximum 
               
               
                   
                   
                   
                 second derivative 
               
               
                   
                  9 
                 T_AB_M_R 
                 turbidity maximum 
               
               
                   
                   
                   
                 value/turbidity 
               
               
                   
                   
                   
                 maximum value of 
               
               
                   
                   
                   
                 the growth control 
               
               
                   
                 10 
                 T_FD_M_R 
                 turbidity maximum 
               
               
                   
                   
                   
                 first derivative/ 
               
               
                   
                   
                   
                 turbidity maximum 
               
               
                   
                   
                   
                 first derivative of the 
               
               
                   
                   
                   
                 growth control 
               
               
                   
                 11 
                 T_SD_M_R 
                 turbidity maximum 
               
               
                   
                   
                   
                 second derivative/ 
               
               
                   
                   
                   
                 turbidity maximum 
               
               
                   
                   
                   
                 second derivative of 
               
               
                   
                   
                   
                 the growth control 
               
               
                   
                 12 
                 T_IN_R 
                 turbidity integral/ 
               
               
                   
                   
                   
                 turbidity integral of 
               
               
                   
                   
                   
                 the growth control 
               
               
                   
                 13 
                 T_FD_T 
                 time at turbidity 
               
               
                   
                   
                   
                 maximum first 
               
               
                   
                   
                   
                 derivative minus time 
               
               
                   
                   
                   
                 at turbidity maximum 
               
               
                   
                   
                   
                 first derivative of the 
               
               
                   
                   
                   
                 growth control 
               
               
                   
                 14 
                 T_SD_T 
                 time at turbidity 
               
               
                   
                   
                   
                 maximum second 
               
               
                   
                   
                   
                 derivative minus time 
               
               
                   
                   
                   
                 at turbidity maximum 
               
               
                   
                   
                   
                 second derivative of 
               
               
                   
                   
                   
                 the growth control 
               
               
                   
                 15 
                 R_AB 
                 redox value 
               
               
                   
                 16 
                 R_FD 
                 redox first derivative 
               
               
                   
                 17 
                 R_SD 
                 redox second 
               
               
                   
                   
                   
                 derivative 
               
               
                   
                 18 
                 R_IN 
                 redox integral 
               
               
                   
                 19 
                 R_AB_M 
                 redox maximum 
               
               
                   
                   
                   
                 value 
               
               
                   
                 20 
                 R_FD_M 
                 redox maximum first 
               
               
                   
                   
                   
                 derivative 
               
               
                   
                 21 
                 R_SD_M 
                 redox maximum 
               
               
                   
                   
                   
                 second derivative 
               
               
                   
                 22 
                 R_AB_M_R 
                 redox maximum 
               
               
                   
                   
                   
                 value/redox 
               
               
                   
                   
                   
                 maximum value of 
               
               
                   
                   
                   
                 the growth control 
               
               
                   
                 23 
                 R_FD_M_R 
                 redox maximum first 
               
               
                   
                   
                   
                 derivative/redox 
               
               
                   
                   
                   
                 maximum first 
               
               
                   
                   
                   
                 derivative of the 
               
               
                   
                   
                   
                 growth control 
               
               
                   
                 24 
                 R_SD_M_R 
                 redox maximum 
               
               
                   
                   
                   
                 second derivative/ 
               
               
                   
                   
                   
                 redox maximum 
               
               
                   
                   
                   
                 second derivative of 
               
               
                   
                   
                   
                 the growth control 
               
               
                   
                 25 
                 R_IN_R 
                 redox integral/redox 
               
               
                   
                   
                   
                 integral of the growth 
               
               
                   
                   
                   
                 control 
               
               
                   
                 26 
                 R_FD_T 
                 time at redox 
               
               
                   
                   
                   
                 maximum first 
               
               
                   
                   
                   
                 derivative minus time 
               
               
                   
                   
                   
                 at redox maximum 
               
               
                   
                   
                   
                 first derivative of the 
               
               
                   
                   
                   
                 growth control 
               
               
                   
                 27 
                 R_SD_T 
                 time at redox 
               
               
                   
                   
                   
                 maximum second 
               
               
                   
                   
                   
                 derivative minus time 
               
               
                   
                   
                   
                 at redox maximum 
               
               
                   
                   
                   
                 second derivative of 
               
               
                   
                   
                   
                 the growth control 
               
               
                   
                   
               
             
          
         
       
     
     The processing then proceeds to step  1110  during which the calculated redox state for each growth control well in each respective test panel  132  is analyzed. If the maximum redox value for the growth control well of a test panel  132  is not above a desired value which, in this example, is 0.07, the processing continues to step  1120 . In step  1120 , the processing determines whether the elapsed incubation time has reached a certain desired duration which, in this example, is 16 hours. If the processing determines in step  1120  that 16 hours of incubation time or less has elapsed, the processing returns to step  1070  for this panel  132 , and the process repeats as discussed above. However, if the processing determines in step  1110  that more than 16 hours of incubation time has elapsed for this particular panel  132 , the processing proceeds to step  1130  where the panel  132  is failed as being inoculated or containing a non-reactive sample, and no test results are reported for that panel. 
     If the processing in step  1110  determines that the maximum redox state for the growth control well of the test panel  132  are greater than 0.07, the processing proceeds to step  1140  for that panel  132 . In step  1140 , the processing determines whether the maximum redox state for the growth control well of that panel  132  is greater than a predetermined value which, in this example, is 0.2. If the maximum redox state for the growth control well in that panel  132  is not greater than 0.2, the processing continues to step  1150  for that panel  132  where it is determined whether the elapsed incubation time is greater than a predetermined value which, in this example, is 16 hours. If the elapsed incubation time is less than or equal to 16 hours, the processing returns to step  1070  for this panel  132 , and repeats as discussed above. However, if the processing determines in step  1150  that the elapsed incubation time has exceeded 16 hours, the processing continues to step  1160 , where the test panel  132  is failed as having insufficient sample growth, and no results are reported for that test panel  132 . 
     Concerning step  1140  discussed above, if the processing determines that the maximum redox state for the growth control well for the panel  132  is indeed greater than 0.2, the processing continues to step  1170  where the processing evaluates the type of curve represented by the calculated redox states plotted in graph form with respect to incubation time as shown, for example, in FIG.  8 . Specifically, based on the maximum redox value of the growth control well of the panel  132 , the processing determines whether the curve representing the redox values for the growth control well indicates that the sample is a slow or fast growing sample. If the processing determines in step  1170  that the curve is classified as a class “zero” curve, the sample is not yet classifiable as a slow or fast growing sample because a sufficient incubation time has not elapsed for that sample. Therefore, the processing returns to step  1070  for that panel  132 , and repeats as discussed above. However, if the processing determines in step  1170  that the curve classification is other than “zero”, the processing continues to step  1180 . 
     In step  1180 , the processing determines whether the curve representing the redox states can be classified as a class “one” curve. If so, the processing continues to step  1190  where the controller  192  will perform the appropriate GAM on the redox states and turbidity values measured for each of the wells  134  in the test panel  132  to determine the MIC values for the particular sample and the anti-microbial materials contained in the wells  134  of the test panel  132 . 
     In step  1200 , the probability of sample growth for each well  134  of the test panel  134  is calculated according to the appropriate GAM once the growth control is above a specified threshold. The GAMs were developed for each antibiotic by evaluating a spectrum of species, MIC values and resistance mechanisms. The GAMs are specific for each antibiotic and broad category of organisms (gram positive/gram negative). Each GAM requires approximately 5 of the 27 variables previously described above in Table 11 to predict growth, but can use as many variables as deemed appropriate. A GAM uses a polynomial equation as shown below to describe the relationship between each variable included in the model and the contribution of that variable in predicting growth in a well. The calculation of the well probability P k  is simply the sum of the polynomial functions for each variable and an intercept term. 
         log   ⁡     (     p     p   -   I       )       =     α   +       f   l     ⁡     (     x   i     )       +   …   +       f   l     ⁡     (     x   l     )             
 
Each polynomial function in the above equation represents the function associated with a respective variable chosen from Table 11. For example, f 1 (x 1 ) can represent the function for the first derivative of the turbidity curve at a particular time interval, f 2 (x 2 ) can represent the function for the second derivative of the turbidity curve at that time interval, and so on.
 
       FIG. 9  illustrates a graph showing an example of the relationship between a variable and its prediction. These probabilities are then used to determine the MIC and calculate the confidence value for the reported MIC as follows. 
     Once a set of growth probabilities for each well  134  in the test panel  132  is derived by the GAM, a probability is calculated for every possible MIC in step  1210 . This MIC probability is the product of the well probabilities with respect to the values obtained from the GAM. The example below shows the calculation for obtaining a probability for an MIC of 16 μg for one antimicrobial material with respect to the sample in the test panel  132 . In this example, the raw probability would be 0.525. It is noted that five wells  134  of the test panel  132  contain different concentrations of this antimicrobial material, and the redox and turbidity results for each of these five wells is used by the GAM to determine the MIC. 
                                                               TABLE 12                   An Example of an MIC Calculation for Five Wells            Antibiotic                                           Concentration   2 μg well       4 μg well       8 μg well       16 μg well       32 μg well               Pattern for   Growth       Growth       Growth       No Growth       No Growth       MIC = 16 μg       Well Probability   0.9       0.9       0.8       0.1       0.1       (p from the GAM)       Calculation   p 2     ×   p 4     ×   p 8     ×   l-p 16     ×   l-p 32                      
After a raw probability is obtained, the processing proceeds to step  1220  where a confidence value for the most probable MIC is calculated. This is simply the raw probability (P) of the MIC value (k) over the sum of all valid MIC probabilities as shown in the following equation: 
         MIC   ⁢           ⁢   Confidence   ⁢           ⁢   Value     =       P   k         ∑   n     ⁢           ⁢     P     k   ⁢                         
     Once an MIC and the associated confidence value are calculated, processing proceeds to step  1230  where this information is evaluated with respect to a threshold. If the threshold is exceeded, then the processing proceeds to step  1240  where the system  100  reports the MIC for the particular sample in the test panel  132  with respect to the particular antimicrobial material in the group of wells  134  of the test panel  132 . The system  100  can report the MIC on, for example, display screen  116  of  FIGS. 1 ,  2  and  6 , and can also control a printer (not shown) to generate a printed report. 
     However, if a low confidence value is obtained, the processing proceeds to step  1250  where it is determined whether the incubation protocol of a certain duration (e.g., 16 hours) has elapsed. If the incubation protocol has not elapsed, the processing returns to step  1070  where the test panel  132  continues to incubate and is reevaluated according to the processing discussed above after each 20 minute reading. On the other hand, if a minimum threshold is still not met at the end of the incubation protocol, the processing proceeds to step  1260  during which the system  100  does not report an MIC for that antimicrobial material, but rather, provides a message suggesting that the user check purity/viability and repeat the test. 
     A more detailed example of MIC probability calculations is shown in  FIG. 10  for four wells having antibiotic concentrations of 1 μg, 2 μg, 4 μg and 8 μg, respectively. As illustrated in this example, the probability of growth for a well having a 1 μg concentration of antibiotic as calculated by the polynomial equation discussed above for a set of readings taken at a particular interval in time is 0.9. Also, the probabilities of growth for the wells having 2 μg, 4 μg and 8 μg are 0.9, 0.1 and 0.1, respectively. The five different growth possibilities are then entered into the table as shown, with the value “0” representing no growth and the value “1” representing growth. That is, as shown in the first row of the table, the condition in which no growth occurs (i.e., “0” for each well) is considered, meaning that the MIC value would be less than the minimum concentration of 1 μg. The second row illustrates the condition in which growth occurs in the 1 μg well but in no other wells, the third row illustrates the condition in which growth occurs in the 1 μg well and in the 2 μg well, but not in the higher concentration wells, and so on. 
     The four growth probabilities are then multiplied for each row to arrive at the probability of valid growth pattern values on the right side of the table. It is noted that because the probabilities of 0.9 or 0.1 at the top of the table represent probabilities of growth, these values are subtracted from 1 for conditions of non-growth to provide a value that is used in the multiplication. Considering the first row, for example, the probability of growth for the well concentration of 1 μg is “0.9”. However, because no growth occurred in this well, the value used in the multiplication is “0.1” (i.e., 1−0.9). This is also the case for the 2 μg concentration well. Also, because the no growth occurred in the 4 μg and 8 μg wells, the values for these wells used in the multiplication are each “0.9” (i.e., 1−0.1). Accordingly, the multiplication values are 0.1*0.1*0.9*0.9=0.0081, which is the probability that this growth pattern in the first row is valid. 
     The above calculations are performed for each row to provide the values shown in the first column on right side of the table. In addition, the probabilities of the “local” growth patterns (i.e., the shaded wells in the graph) are multiplied to provide the probabilities of valid local growth patterns. This additional calculation is used to increase the accuracy of the results. As indicated, the row having the MIC possibility of “4” (the third row) provides the highest probabilities. 
     Using the MIC confidence value equation indicated above, the highest local growth pattern probability of 0.729 is divided by the sum of itself and the local growth pattern probabilities (i.e., 0.081+0.729+0.09) to arrive at a MIC probability of 0.81 as indicated. This value is then compared to a predetermined threshold. If the value exceeds the predetermined threshold, then the system can report the MIC value of “4” for this sample. 
     An example of another table in which wells having antibiotic concentrations of 0.25 and 0.50 taken into account is shown in FIG.  11 . The probabilities, MIC value and MIC probability are calculated in the same manner as described above. 
     It is also noted that prior to reporting the results in step  1240  shown in  FIG. 7B , the processing can delay the reporting until the same MIC value has been determined for a desired number of consecutive, for example, three time intervals. That is, as can be appreciated from the graph of  FIG. 12  showing redox values for wells having different antibiotic concentrations, the occurrence of growth in higher concentration wells can be delayed. For example, growth in a well having an antibiotic concentration of 1 μg can occur several hours after growth occurs in a well having an antibiotic concentration of 0.5 μg. Therefore, the accuracy of the results can be increased by refraining from reporting an MIC value until that value has been determined for a desired number of consecutive intervals, or a desired number of times within a certain number of consecutive intervals (e.g., 3 times out of 5 consecutive intervals). This delay reduces the possibility that a lower MIC value will be inadvertently reported. 
     It is noted that steps  1200  through  1260  of  FIG. 7B  are repeated as appropriate for each respective group of wells  134  containing a respective type of antimicrobial material, so that the MIC for each antimicrobial material in the test panel  132  can be reported for the sample. 
     Returning now to the discussion of step  1180  of  FIG. 7B , if the processing in step  1180  determines that the curve representing the redox values for the wells  134  is not a class “one” curve, the processing proceeds to step  1270  where the processing determines whether the maximum redox state for the growth control well of the panel  132  is less than or equal to a particular value which, in this example is 0.4. If the maximum value of the redox state of the growth control well is not less than 0.4, it is determined that the sample is a slow growing sample. Accordingly, the processing continues to step  1280 , where the controller  192  selects the appropriate GAM to be used to evaluate the redox and turbidity data for the wells  134  of the test panel. The processing then proceeds to step  1210  where the MIC values are determined as discussed above. 
     However, if the processing determines in step  1270  that the maximum redox state for the growth control well of the panel  132  is less than or equal to 0.4, the processing continues to step  1290  where the elapsed incubation time of the panel  132  is compared to predetermined value which, in this example, is 8 hours. If the elapsed incubation time is less than or equal to 8 hours, the processing returns to step  1070  and continues as discussed above. However, if the processing is greater than 8 hours, the processing continues to step  1300  where the controller  192  selects the appropriate GAM to be used to evaluate the redox and turbidity data for the wells  134  of the test panel. The processing then proceeds to step  1210  where the MIC values are determined as discussed above. 
     As mentioned previously, the processing discussed above is performed for each test panel  132  being rotated by the carousel  124  of  FIGS. 2 and 3 . Once all of the test panels  132  have been evaluated, and the MIC values relating to their respective samples have been reported, the controller  192  of Gi. 6 controls the heater  228  and heater blower  230  to discontinue heating the inner chamber  115 . The controller  192  also controls the carousel  124  to stop rotating, and unlatches the door  112 . The technician can then remove the test panels  132  and, if desired, commence a new series of tests using new test panels  132 . 
     Although only one exemplary embodiment of the present invention has been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiment without materially departing from the novel teachings and advantages of this invention. All such modifications are intended to be included within the scope of the invention as defined in the following claims.