On-site analysis system with central processor and method of analyzing

• US 20030154044A1
• Filed: 07/05/2002
• Published: 08/14/2003
• Est. Priority Date: 07/23/2001
• Status: Active Grant

• Current Assignee: Eurofins QTA, Inc. (Eurofins Scientific SE)
• Original Assignee: Cognis International GmbH (BASF SE)
• Inventors: Tseng, Ching-Hui, Hall, Allen L., Lundstedt, Alan P.
• Granted Patent: US 7,194,369 B2
• Time in Patent Office: Days
• US Class Current: 702/104
• CPC Class Codes: G01N 2035/00881 network configurations
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/12753 and storage
• CPC Class Codes: G01N 2201/12792 Compensating own radiation in apparatus
• CPC Class Codes: G01N 35/00712 Automatic status testing, e.g. at start-up or periodic
• CPC Class Codes: G01N 35/00871 Communications between instruments or with remote terminals

Abstract

A method of analysis, analysis system, program product, apparatus, and method of supplying analysis of value incorporating the use of at least one data acquisition device, a central processor, and a communication link that is connectable between the data acquisition device and the central processor. The central processor is loaded with multivariate calibration models developed for predicting values for various properties of interest, wherein the calibration models are capable of compensating for variations in an effectively comprehensive set of measurement conditions and secondary material characteristics. As so configured, the calibration models can compensate for instrument variance without instrument-specific calibration transfer. Measurement results generated by the central processor can be transmitted to an output device of a user interface.

Claims

1. A method of predicting a value of a property of interest of a material comprising:

using a calibration model to process data obtained from a sample of the material, wherein the calibration model is configured to compensate for instrument variance in predicting the value of the property of interest.

2. The method of claim 1 wherein the calibration model is also configured to compensate for sample variance.

3. The method of claim 1 wherein the calibration model is also configured to compensate for environmental variance.

4. The method of claim 1 wherein the calibration model is also configured to compensate for sample presentation variance.

5. The method of claim 2 wherein the calibration model is also configured to compensate for environmental variance.

6. The method of claim 2 wherein the calibration model is also configured to compensate for sample presentation variance.

7. The method of claim 5 wherein the calibration model is also configured to compensate for sample presentation variance.

8. The method of claim 1 wherein the calibration model is configured to compensate for variations in orientation of an excitation source relative to the sample.

9. The method of claim 1 wherein the calibration model is configured to compensate for variations in age of an excitation source.

10. The method of claim 1 wherein the calibration model is configured to compensate for variations in mechanical alignment of optical components in the data acquisition device.

11. The method of claim 1 wherein the calibration model is configured to compensate for variations in the output of a detector.

12. The method of claim 1 wherein the calibration model is configured to compensate for variations in the output of an excitation source.

13. The method of claim 1 wherein the calibration model is configured to compensate for variations in transmission of a data acquisition probe.

14. The method of claim 1 wherein the data are transmitted over a communication link to a central processor.

15. The method of claim 14 wherein the data is acquired using a data acquisition device at a location remote from the central processor.

16. The method of claim 14 wherein the calibration model is configured to compensate for variance in more than one data acquisition device connectable to the central processor by the communication link.

17. The method of claim 1 further comprising pretreating the data.

18. The method of claim 14 further comprising pretreating the data after transmission over the communication link.

19. The method of claim 1 further comprising pre-processing the data.

20. The method of claim 1 wherein the data are pretreated with a refined filter.

21. The method of claim 14 wherein the data are pretreated with a refined filter after transmission over a communication link.

22. The method of claim 20 wherein the data are pre-processed.

23. The method of claim 21 wherein the data are pre-processed.

24. The method of claim 1 wherein the data are the result from electromagnetic radiation detected from the sample.

25. The method of claim 24 wherein the data are pre-processed.

26. The method of claim 24 wherein the data are pre-processed using Fourier transformation.

27. The method of claim 1 wherein the data are the result of non-stimulated emission radiation detected from the sample.

28. The method of claim 1 wherein the data are the result from the sample being subjected to mass spectrometry.

29. The method of claim 1 wherein the data are the result from the sample being subjected to chromatography.

30. The method of claim 14 further comprising transmitting a predicted value from the central processor to at least one user interface.

31. The method of claim 14 further comprising transmitting a predicted value to a user interface in the vicinity of the data acquisition device.

32. The method of claim 1 wherein the data are an accumulation of individual repetitive runs.

33. The method of claim 32 wherein the data are pre-processed.

34. The method of claim 1 wherein at least one probable outlier is used in connection with updating the calibration model.

35. The method of claim 1 wherein at least one probable outlier detected from one data acquisition device is used in connection with updating the calibration model for all data acquisition devices using the calibration model.

36. The method of claim 34 wherein the outlier is good.

37. The method of claim 35 wherein the outlier is good.

38. The method of claim 1 wherein the calibration model is configured to compensate for non-quantified instrument variance.

39. The method of claim 1 wherein the calibration model is also configured to compensate for non-quantified sample variance.

40. The method of claim 1 wherein the calibration model is also configured to compensate for non-quantified environmental variance.

41. The method of claim 1 wherein the calibration model is also configured to compensate for non-quantified sample presentation variance.

42. The method of claim 39 wherein the calibration model is also configured to compensate for non-quantified environmental variance.

43. The method of claim 39 wherein the calibration model is also configured to compensate for non-quantified sample presentation variance.

44. The method of claim 42 wherein the calibration model is also configured to compensate for non-quantified sample presentation variance.

45. The method of claim 1 wherein the calibration model is configured to compensate for non-quantified variations in orientation of an excitation source relative to the sample.

46. The method of claim 1 wherein the calibration model is configured to compensate for non-quantified variations in age of an excitation source.

47. The method of claim 1 wherein the calibration model is configured to compensate for non-quantified variations in mechanical alignment of optical components in the data acquisition device.

48. The method of claim 1 wherein the calibration model is configured to compensate for non-quantified variations in the output of a detector.

49. The method of claim 1 wherein the calibration model is configured to compensate for non-quantified variations in the output of an excitation source.

50. The method of claim 1 wherein the calibration model is configured to compensate for non-quantified variations in transmission of a data acquisition probe.

51. The method of claim 1 wherein at least a portion of the measurement data is stored in a database of the central processor.

52. The method of claim 1 wherein at least a portion of the measurement results is stored in a database of the central processor.

53. The method of claim 1 wherein two or more measurement results acquired from at least one data acquisition device are transmitted to at least one user interface as aggregated results.

54. The method of claim 19 wherein the preprocessing uses a background spectrum.

55. The method of claim 54 wherein the background spectrum is acquired from a single data acquisition device.

56. The method of claim 55 wherein the background spectrum is stored at a location, and is used to generate more than one predicted value of a property of interest using the data acquisition device.

57. The method of claim 54 wherein the background spectrum is generated from an accumulation of background spectra acquired from one or more data acquisition devices.

58. The method of claim 57 wherein the background spectrum is stored at a location, and is used to generate more than one predicted value of a property of interest using the data acquisition device.

59. The method of claim 7 wherein the data are transmitted over a communication link to a central processor.

60. The method of claim 59 wherein the data is acquired using a data acquisition device at a location remote from the central processor.

61. The method of claim 59 wherein the calibration model is configured to compensate for variance in more than one data acquisition device connectable to the central processor by the communication link.

62. The method of claim 7 further comprising pretreating the data.

63. The method of claim 59 further comprising pretreating the data after transmission over the communication link.

64. The method of claim 7 further comprising pre-processing the data.

65. The method of claim 7 wherein the data are pretreated with a refined filter.

66. The method of claim 59 wherein the data are pretreated with a refined filter after transmission over a communication link.

67. The method of claim 65 wherein the data are pre-processed.

68. The method of claim 66 wherein the data are pre-processed.

69. The method of claim 7 wherein the data are the result from electromagnetic radiation detected from the sample.

70. The method of claim 56 wherein the background spectrum is stored in a local processor connected to the data acquisition device.

71. The method of claim 56 wherein the background spectrum is stored in the central processor.

72. The method of claim 56 wherein the background spectrum is stored at a location remote from the data acquisition device, and connected to the data acquisition device by a communication link.

73. The method of claim 1 wherein parameters defining a calibration model are transmitted from a central processor along a communication link to a local processor.

74. The method of claim 1 wherein parameters defining a property model are transmitted from a central processor along a communication link to a local processor.

75. The method of claim 73 wherein the parameters defining the calibration model are transmitted to the local processor of at least one data acquisition device remote from the central processor.

76. The method of claim 74 wherein the parameters defining the calibration model are transmitted to the local processor of at least one data acquisition device remote from the central processor.

77. A system for analyzing a material by predicting a value of a property of interest of the material comprising:

at least one data acquisition device for obtaining data on a sample of the material; and

a central processor connectable to the data acquisition device over a communication link, the central processor loaded with a calibration model configured to compensate instrument variance in predicting the value of the property of interest.

78. The system of claim 77 further comprising a local processor to receive the predicted values for the property of interest.

79. The system of claim 78 wherein the local processor outputs the predicted values for the property of interest to a user interface.

80. The system of claim 77 wherein a user interface is located in the vicinity of the data acquisition device.

81. The system of claim 77 wherein the data acquisition device is in a location geographically removed from the central processor.

82. The system of claim 77 wherein the data acquisition device includes a local processor for pre-processing the data prior to transmitting the data to the central processor.

83. The system of claim 77 wherein the communication link is an Internet connection.

84. The system of claim 77 wherein the communication link is a private link.

85. The system of claim 78 wherein the local processor is configured to pre-process the data.

86. The system of claim 77 wherein the central processor is configured to pretreat the data.

87. The system of claim 77 wherein the central processor is configured to pretreat the data with a refined filter.

88. The system of claim 78 wherein the local processor is configured to pretreat the data with a refined filter.

89. The system of claim 78 wherein a user interface is located in the vicinity of the data acquisition device.

90. The system of claim 78 wherein the data acquisition device is in a location geographically removed from the central processor.

91. The system of claim 78 wherein the data acquisition device includes a local processor for pre-processing the data prior to transmitting the data to the central processor.

92. The system of claim 78 wherein the communication link is an Internet connection.

93. The system of claim 78 wherein the communication link is a private link.

94. The system of claim 78 wherein the central processor is configured to pretreat the data.

95. The system of claim 78 wherein the central processor is configured to pretreat the data with a refined filter.

96. The system of claim 77 wherein the communication link is a combination of public and private links.

97. The system of claim 78 wherein the communication link is a combination of public and private links.

98. A method of generating a calibration model for predicting a value of a property of interest from data acquired on an unknown sample of material comprising:

obtaining a preliminary model for predicting the property of interest, the model developed from a training set using at least one instrument;

identifying at least one factor which may influence the predictive ability of the preliminary model for the property of interest;

determining the at least one factor which influences the predictive ability of the preliminary model outside a limit of defined precision; and

revising the preliminary model to compensate for variation in the at least one factor which influences the property of interest to generate the calibration model, the models predicting the value of the property of interest within the limits of defined precision.

99. The method of claim 98 further comprising pretreating at least a portion of data in the training set.

100. The method of claim 99 further wherein pretreating at least a portion of the training set precedes creating the preliminary model.

101. The method of claim 99 further wherein pretreating at least a portion of the training set follows creating the preliminary model.

102. The method of claim 99 wherein pretreating at least a portion of the training set comprises mathematically transforming the training set.

103. The method of claim 99 wherein pretreating at least a portion of the training set comprises filtering the training set.

104. A method of updating a calibration model to compensate for a new influential factor for predicting a property of interest, comprising:

obtaining a calibration model for predicting the property of interest, the model developed from a training set using at least one instrument;

identifying a new factor which may influence the predictive ability of the generated calibration model for the property of interest using a validation set spanning at least a portion of a range of the new factor;

calculating the RMSEP of the validation set to determine whether the new factor is influential;

determining whether the new influential factor causes the predicted ability of the generated calibration model to fall outside a limit of precision defined in terms of RMSEP; and

updating the generated calibration model to compensate for variance in the new influential factor to generate a revised calibration model having a predictive ability within the limit of precision.

105. A method of updating a calibration model to compensate for a modified range of an existing factor for predicting a property of interest, comprising:

obtaining a calibration model for predicting the property of interest, the model developed from a training set using at least one instrument;

identifying the modified range of the existing factor which may influence the predictive ability of the generated calibration model for the property of interest using a validation set spanning at least a portion of the modified range;

calculating the RMSEP of the validation set to determine whether the new factor is influential;

determining whether the modified range causes the predicted ability of the generated calibration model to fall outside a limit of precision defined in terms of RMSEP; and

updating the generated calibration model to compensate for variance in the new influential factor to generate a revised calibration model having a predictive ability within the limit of precision.

106. A method of updating a calibration model to compensate for a new influential factor for predicting a property of interest, comprising:

obtaining a calibration model for predicting the property of interest, the model developed from a first training set using at least one instrument;

identifying a new factor which may influence the predictive ability of the generated calibration model for the property of interest using a second training set spanning at least a portion of a range of the new factor;

calculating the RMSECV of the validation set to determine whether the new factor is influential;

determining whether the new influential factor causes the predicted ability of the generated calibration model to fall outside a limit of precision defined in terms of RMSECV; and

updating the generated calibration model to compensate for variance in the new influential factor to generate a revised calibration model having a predictive ability within the limit of precision.

107. A method of updating a calibration model to compensate for a modified range of an existing factor for predicting a property of interest, comprising:

obtaining a calibration model for predicting the property of interest, the model developed from a first training set using at least one instrument;

identifying the modified range of the existing factor which may influence the predictive ability of the generated calibration model for the property of interest using a second training set spanning at least a portion of the modified range;

calculating the RMSECV of the validation set to determine whether the new factor is influential;

determining whether the modified range causes the predicted ability of the generated calibration model to fall outside a limit of precision defined in terms of RMSECV; and

updating the generated calibration model to compensate for variance in the new influential factor to generate a revised calibration model having a predictive ability within the limit of precision.

108. A method of defining at least one acceptable region of data wherein the data are generated from an instrument response prior to evaluation of the data by a calibration model to determine if pretreatment can compensate for an influential factor in predicting a property of interest using the calibration model, comprising:

identifying at least one trial region containing at least one subregion of data within an entire region of data from a plurality of trial regions comprising discrete combinations of subregions;

evaluating the calibration model for each identified trial region using a validation set spanning at least a portion of a range of the influential factor;

calculating a RMSEP for each identified trial region; and

selecting at least one acceptable region from the at least one identified trial region having the RMSEP within a limit of precision identified in terms of RMSEP.

109. A method of defining a refined filter using a validation set, comprising:

identifying at least one acceptable filter for the validation set;

selecting a set of at least one acceptable filter wherein each filter within the set has the lowest number of subregions;

selecting a subset of the set wherein each filter within the subset has the lowest rank; and

defining the refined filter as the filter within the subset which further has the lowest RMSEP calculated from the validation set.

110. A method of defining a refined filter using a validation set, comprising:

identifying at least one acceptable filter for the validation set;

selecting a set of at least one acceptable filter wherein each filter within the set has the lowest rank;

selecting a subset of the set wherein each filter within the subset has the lowest number of subregions; and

defining the refined filter as the filter within the subset which further has the lowest RMSEP calculated from the validation set.

111. A method of defining a refined filter using a validation set, comprising:

identifying at least one acceptable filter for the validation set;

selecting a set of at least one acceptable filter wherein each filter within the set has the lowest rank;

defining the refined filter as the filter within the set which further has the lowest RMSEP calculated from the validation set.

112. A method of defining at least one acceptable region of data wherein the data are generated from an instrument response prior to evaluation of the data by a calibration model to determine if pretreatment can compensate for an influential factor in predicting a property of interest using the calibration model, comprising:

identifying at least one trial region containing at least one subregion of data within an entire region of data from a plurality of trial regions comprising discrete combinations of subregions;

evaluating the calibration model for each identified trial region using a training set spanning at least a portion of a range of the influential factor;

calculating a RMSECV for each identified trial region; and

selecting at least one acceptable region from the at least one identified trial region having the RMSECV within a limit of precision identified in terms of RMSECV.

113. A method of defining a refined filter using a training set, comprising:

identifying at least one acceptable filter for the training set;

selecting a set of at least one acceptable filter wherein each filter within the set has the lowest number of subregions;

selecting a subset of the set wherein each filter within the subset has the lowest rank; and

defining the refined filter as the filter within the subset which further has the lowest RMSECV calculated from the training set.

114. A method of defining a refined filter using a training set, comprising:

identifying at least one acceptable filter for the training set;

selecting a set of at least one acceptable filter wherein each filter within the set has the lowest rank;

selecting a subset of the set wherein each filter within the subset has the lowest number of subregions; and

defining the refined filter as the filter within the subset which further has the lowest RMSECV calculated from the training set.

115. A method of defining a refined filter using a training set, comprising:

identifying at least one acceptable filter for the training set;

selecting a set of at least one acceptable filter wherein each filter within the set has the lowest rank;

defining the refined filter as the filter within the set which further has the lowest RMSECV calculated from the training set.

116. A method of revising a calibration model developed from a training set for a calibration model development for predicting a value of a property of interest comprising:

detecting at least one probable outlier in the training set;

identifying at least one good probable outlier from a group of at least one detected probable outlier; and

extending the training set using the good probable outlier to develop a revised calibration model.

117. The method of claim 116 further comprising detecting at least one probable outlier using Mahalanobis distance and a first threshold value as a lower limit for identifying the probable outlier.

118. The method of claim 116 further comprising identifying at least one good probable outlier using Mahalanobis distance, a first threshold value as a lower limit for identifying the probable outlier, and a second threshold value as an upper limit for identifying the good probable outlier.

119. A method of revising a calibration model development from a training set for calibration model development for predicting a value of a property of interest comprising:

detecting at least one probable outlier in a validation set;

identifying at least one good probable outlier from a group of at least one detected probable outlier; and

extending the training set using the good probable outlier to develop a revised calibration model.

120. The method of claim 119 further comprising detecting at least one probable outlier using Mahalanobis distance and a first threshold value as a lower limit for identifying the probable outlier.

121. The method of claim 119 further comprising identifying at least one good probable outlier using Mahalanobis distance, a first threshold value as a lower limit for identifying the probable outlier, and a second threshold value as an upper limit for identifying the good probable outlier.

122. A method of revising a calibration model developed from a training set for use in predicting a value of a property of interest on at least one unknown sample comprising:

detecting at least one probable outlier in at least one predicted value from measurements on the at least one unknown sample;

identifying at least one good probable outlier from a group comprising at least one detected probable outlier; and

extending the training set using the good probable outlier to develop a revised calibration model.

123. The method of claim 122 further comprising detecting at least one probable outlier using Mahalanobis distance and a first threshold value as a lower limit for identifying the probable outlier.

124. The method of claim 122 further comprising identifying at least one good probable outlier using Mahalanobis distance, a first threshold value as a lower limit for identifying the probable outlier, and a second threshold value as an upper limit for identifying the good probable outlier.

125. A method of revising a calibration model developed from a training set for a calibration model development for predicting a value of a property of interest comprising:

detecting at least one probable outlier in the training set;

identifying at least one good probable outlier from a group of at least one detected probably outlier; and

improving the training set by replacing one or more observations in the training set using the good probable outlier to develop a revised calibration model.

126. The method of claim 125 further comprising detecting at least one probable outlier using Mahalanobis distance and a first threshold value as a lower limit for identifying the probable outlier.

127. The method of claim 125 further comprising identifying at least one good probable outlier using Mahalanobis distance, a first threshold value as a lower limit for identifying the probable outlier, and a second threshold value as an upper limit for identifying the good probable outlier.

128. A method of revising a calibration model developed from a training set for calibration model development for predicting a value of a property of interest comprising:

detecting at least one probable outlier in a validation set;

identifying at least one good probable outlier from a group of at least one detected probable outlier; and

improving the training set by replacing one or more observations in the training set using the good probable outlier to develop a revised calibration model.

129. The method of claim 128 further comprising detecting at least one probable outlier using Mahalanobis distance and a first threshold value as a lower limit for identifying the probable outlier.

130. The method of claim 128 further comprising identifying at least one good probable outlier using Mahalanobis distance, a first threshold value as a lower limit for identifying the probable outlier, and a second threshold value as an upper limit for identifying the good probable outlier.

131. A method of revising a calibration model developed from a training set for use in predicting a value of a property of interest on at least one unknown sample comprising:

detecting at least one probable outlier in at least one predicted value from measurements on the at least one unknown sample;

identifying at least one good probable outlier from a group comprising at least one detected probable outlier; and

improving the training set by replacing one or more observations in the training set using the good probable outlier to develop a revised calibration model.

132. The method of claim 131 further comprising detecting at least one probable outlier using Mahalanobis distance and a first threshold value as a lower limit for identifying the probable outlier.

133. The method of claim 131 further comprising identifying at least one good probable outlier using Mahalanobis distance, a first threshold value as a lower limit for identifying the probable outlier, and a second threshold value as an upper limit for identifying the good probable outlier.

134. A method of evaluating an instrument for acceptability as a data acquisition device in combination with a calibration model such that a limit of precision is met by the instrument in combination with the calibration model in generating a result, comprising:

generating a validation set with the instrument in combination with the calibration model;

computing a RMSEP value from the validation set; and

accepting the instrument if the RMSEP value is within the limit of precision.

135. A method of evaluating a component of an instrument for acceptability as a data acquisition device in combination with a calibration model such that a limit of precision is met by the instrument in combination with the calibration model in generating a result, comprising:

generating a validation set with the component of the instrument in combination with the calibration model;

computing a RMSEP value from the validation set; and

accepting the component of the instrument if the RMSEP value is within the limit of precision.

136. A method of providing analysis services, comprising:

providing analysis services on behalf of a plurality of customers using a plurality of data acquisition devices connected to a central processor into which is loaded at least one calibration model configured to generate a predicted result of a property of interest from data acquired from a plurality of samples using the data acquisition devices, wherein providing analysis services includes transmitting the predicted value of the property of interest to a customer for which analysis services is required for a particular sample of a material.

138. The method of claim 136, further comprising receiving a fee from a customer in response to providing analysis services on behalf thereof.

137. The method of 136 further comprising updating one or more calibration models for all of the data acquisition devices from time to time in response to detecting good outliers when processing data acquired on a plurality of samples by at least one data acquisition device.

139. A program product, comprising:

a program configured to define at least one acceptable region of data, wherein the data are generated from an instrument response prior to evaluation of the data by a calibration model to determine if pretreatment can compensate for an influential factor in predicting a property of interest using the calibration model, by identifying at least one trial region containing at least one subregion of data within an entire region of data from a plurality of trial regions comprising discrete combinations of subregions;

evaluating the calibration model for each identified trial region using a validation set spanning at least a portion of a range of the influential factor;

calculating a RMSEP for each identified trial region; and

selecting at least one acceptable region from the at least one identified trial region having the RMSEP within a limit of precision identified in terms of RMSEP, wherein the at least one acceptable region is selected based on a comparative evaluation of RMSEP, number of subregions, and rank of the calibration model, wherein lower values for each of RMSEP, number of regions and rank are preferred; and

a signal bearing medium bearing the program.

140. The method of claim 139 wherein the at least one acceptable region is selected based on lowest RMSEP.

141. The method of claim 139 wherein the at least one acceptable region is selected based on lowest number of subregions.

142. The method of claim 139 wherein the at least one acceptable region is selected based on lowest rank of the calibration model.

143. A program product comprising:

a program configured to predict a value of a property of interest of a material by using a calibration model to process data obtained from a sample of the material, wherein the calibration model is configured to compensate for instrument variance in predicting the value of the property of interest; and

a signal bearing medium bearing the program.

144. The method of claim 143 wherein the calibration model is also configured to compensate for sample variance.

145. The method of claim 143 wherein the calibration model is also configured to compensate for environmental variance.

146. The method of claim 143 wherein the calibration model is also configured to compensate for sample presentation variance.

147. The method of claim 144 wherein the calibration model is also configured to compensate for environmental variance.

148. The method of claim 144 wherein the calibration model is also configured to compensate for sample presentation variance.

149. The method of claim 147 wherein the calibration model is also configured to compensate for sample presentation variance.

150. The program product of claim 143, wherein the signal bearing medium includes at least one of a transmission medium and a recordable medium.

151. A product program comprising:

a program configured to generate a calibration model for predicting a value of a property of interest from data acquired on an unknown sample of material by obtaining a preliminary model for predicting the property of interest, the model developed from a training set using at least one instrument;

identifying at least one factor which may influence the predictive ability of the preliminary model for the property of interest;

determining the at least one factor which influences the predictive ability of the preliminary model outside a limit of defined precision; and

revising the preliminary model to compensate for variation in the at least one factor which influences the property of interest to generate the calibration model, the models predicting the value of the property of interest within the limits of defined precision; and

a signal bearing medium bearing the program.

152. The method of claim 151 further comprising pretreating at least a portion of data in the training set.

153. The method of claim 152 further wherein pretreating at least a portion of the training set precedes creating the preliminary model.

154. The method of claim 152 further wherein pretreating at least a portion of the training set follows creating the preliminary model.

155. The method of claim 152 wherein pretreating at least a portion of the training set comprises mathematically transforming the training set.

156. The method of claim 152 wherein pretreating at least a portion of the training set comprises filtering the training set.

157. A program product comprising:

a program configured to evaluate a component of an instrument for acceptability as a data acquisition device in combination with a calibration model such that a limit of precision is met by the instrument in combination with the calibration model in generating a result, by generating a validation set with the component of the instrument in combination with the calibration model;

computing a RMSEP value from the validation set; and

accepting the component of the instrument if the RMSEP value is within the limit of precision; and

a signal bearing medium bearing the program.

158. An apparatus, comprising:

at least one microprocessor; and

a program configured to execute on the at least one microprocessor to define at least one acceptable region of data, wherein the data are generated from an instrument response prior to evaluation of the data by a calibration model to determine if pretreatment can compensate for an influential factor in predicting a property of interest using the calibration model, by identifying at least one trial region containing at least one subregion of data within an entire region of data from a plurality of trial regions comprising discrete combinations of subregions;

evaluating the calibration model for each identified trial region using a validation set spanning at least a portion of a range of the influential factor;

calculating a RMSEP for each identified trial region; and

selecting at least one acceptable region from the at least one identified trial region having the RMSEP within a limit of precision identified in terms of RMSEP, wherein the at least one acceptable region is selected based on a comparative evaluation of RMSEP, number of subregions, and rank of the calibration model, wherein lower values for each of RMSEP, number of regions and rank are preferred.

159. The apparatus of claim 158 wherein the at least one acceptable region is selected based on lowest RMSEP.

160. The apparatus of claim 158 wherein the at least one acceptable region is selected based on lowest number of subregions.

161. The apparatus of claim 158 wherein the at least one acceptable region is selected based on lowest rank of the calibration model.

162. An apparatus, comprising:

at least one microprocessor; and

a program configured to execute on the at least one microprocessor to predict a value of a property of interest of a material by using a calibration model to process data obtained from a sample of the material, wherein the calibration model is configured to compensate for instrument variance in predicting the value of the property of interest.

163. The apparatus of claim 162 wherein the calibration model is also configured to compensate for sample variance.

164. The apparatus of claim 162 wherein the calibration model is also configured to compensate for environmental variance.

165. The apparatus of claim 162 wherein the calibration model is also configured to compensate for sample presentation variance.

166. The apparatus of claim 163 wherein the calibration model is also configured to compensate for environmental variance.

167. The apparatus of claim 163 wherein the calibration model is also configured to compensate for sample presentation variance.

168. The apparatus of claim 166 wherein the calibration model is also configured to compensate for sample presentation variance.

169. An apparatus, comprising:

at least one microprocessor; and

a program configured to execute on the at least one microprocessor to generate a calibration model for predicting a value of a property of interest from data acquired on an unknown sample of material by obtaining a preliminary model for predicting the property of interest, the model developed from a training set using at least one instrument;

identifying at least one factor which may influence the predictive ability of the preliminary model for the property of interest;

determining the at least one factor which influences the predictive ability of the preliminary model outside a limit of defined precision; and

revising the preliminary model to compensate for variation in the at least one factor which influences the property of interest to generate the calibration model, the models predicting the value of the property of interest within the limits of defined precision.

170. The apparatus of claim 169 further comprising pretreating at least a portion of data in the training set.

171. The apparatus of claim 170 further wherein pretreating at least a portion of the training set precedes creating the preliminary model.

172. The apparatus of claim 170 further wherein pretreating at least a portion of the training set follows creating the preliminary model.

173. The apparatus of claim 170 wherein pretreating at least a portion of the training set comprises mathematically transforming the training set.

174. The apparatus of claim 170 wherein pretreating at least a portion of the training set comprises filtering the training set.

175. An apparatus, comprising:

at least one microprocessor; and

a program configured to execute on the at least one microprocessor to evaluate a component of an instrument for acceptability as a data acquisition device in combination with a calibration model such that a limit of precision is met by the instrument in combination with the calibration model in generating a result, by generating a validation set with the component of the instrument in combination with the calibration model;

computing a RMSEP value from the validation set; and

accepting the component of the instrument if the RMSEP value is within the limit of precision.

176. An apparatus comprising:

a memory;

a calibration model resident in the memory and configured to compensate for instrument variance in predicting a value of a property of interest for a material; and

a program configured to process data obtained from a sample of the material by using the calibration model, wherein the calibration model is configured to compensate for instrument variance in predicting the value of the property of interest.

177. An apparatus comprising:

a memory;

a calibration model resident in the memory for predicting a value of a property of interest from data acquired on an unknown sample of material; and

a program configured to generate the calibration model by obtaining a preliminary model for predicting the property of interest, the model developed from a training set using at least one instrument;

identifying at least one factor which may influence the predictive ability of the preliminary model for the property of interest;

determining the at least one factor which influences the predictive ability of the preliminary model outside a limit of defined precision; and

revising the preliminary model to compensate for variance in the at least one factor which influences the property of interest to generate the calibration model, the models predicting the value of the property of interest within the limits of defined precision.

178. An apparatus, comprising:

a memory;

a calibration model resident in the memory and configured for use in evaluating data; and

a program configured to define at least one trial region of data wherein the data are generated from an instrument response prior to evaluation of the data by the calibration model to determine if pretreatment can compensate for an influential factor in predicting a property of interest using the calibration model by identifying at least one trial region containing at least one subregion of data within an entire region of data from a plurality of trial regions comprising discrete combinations of subregions;

evaluating the calibration model for each identified trial region using a validation set spanning at least a portion of a range of the influential factor;

calculating a RMSEP for each identified trial regions; and

selecting at least one identified trial region used for generating a RMSEP within a limit of precision defined in terms of RMSEP, wherein the at least one trial region is selected based on a comparative evaluation of RMSEP, number of subregions, and rank of the calibration model, wherein lower values for each of RMSEP, number of subregions, and rank of the calibration model, wherein lower values for each of RMSEP, number of subregions and rank are preferred.

179. An apparatus, comprising:

a memory;

a calibration model resident in the memory; and

a program configured to evaluate a component of an instrument for acceptability as a data acquisition devise in combination with the calibration model such that a limit of precision is met by the instrument in combination with the calibration model in generating a result, by generating a validation set with a component of the instrument in combination with the calibration model;

computing a RMSEP value from the validation set; and

accepting the component of the instrument if the RMSEP value is within the limit of precision.