CN101710071A - Sampling method for establishing tobacco near-infrared model - Google Patents

Sampling method for establishing tobacco near-infrared model Download PDF

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Publication number
CN101710071A
CN101710071A CN 200910216655 CN200910216655A CN101710071A CN 101710071 A CN101710071 A CN 101710071A CN 200910216655 CN200910216655 CN 200910216655 CN 200910216655 A CN200910216655 A CN 200910216655A CN 101710071 A CN101710071 A CN 101710071A
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sample
sample sets
infrared
sets
spectrum
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CN101710071B (en
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邓发达
吴艳
郑建
胡兴峰
李朝荣
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China Tobacco Chuanyu Industrial Co Ltd
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China Tobacco Chuanyu Industrial Co Ltd
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Abstract

The invention discloses a sampling method for establishing a tobacco near-infrared model, comprising the following steps of: obtaining near-infrared scanning spectrograms of tobacco samples in two mutually independent sample sets (A) and (B); carrying out spectrum preprocessing on the near-infrared scanning spectrograms; obtaining the spectrum Mahalanobis distance di from a sample i to the sample set (A); obtaining the component value distance di' from the sample i to the sample set (A); obtaining the integral Euclidean distance Di from the sample i in the sample set (B) to the sample set (A); selecting samples in the sample set (B) according to specific principles and then adding to the sample set (A) to form a new sample set (A'), and finally obtaining a new near-infrared model A' by the establishment of the sample set (A'). The invention overcomes the problem that the prior art can not give attention to predictive ability and model authenticity and provides a sampling method being capable of getting rid of invalid exceptional data; the established model has good predictive ability and applicability and can be widely applied to tobacco industry.

Description

Set up the sampling method of tobacco near-infrared model
Technical field
The present invention relates to a kind of method that is used to set up tobacco near-infrared model, be specifically related to a kind of sampling method of setting up tobacco near-infrared model.
Technical background
Since nineteen nineties, the application of near infrared technology in tobacco more and more widely, each tobacco enterprise has all been set up the near-infrared model of oneself according to actual conditions separately.After entering this century, domestic tobacco business has started the tide of merging and reorganization, and the original cigar mill of Fen Saning has constituted jointly the industrial group of cross-region, large group's formula.In the research of near infrared technology, thing followed problem has occurred: the near-infrared model set up of each cigarette factory originally can not adapt to the needs of studying under large group's pattern.Reality but is, different cigarette factories have set up different near-infrared models, but these models are independent of each other, and these models may adopt different nir instrument scanning optical spectrums, also might obtain raw data with different analytical approachs simultaneously.Generally speaking, the sample message between the model is to be difficult to share in this case.In this case, it is unpractical rebuliding near-infrared model, must find suitable method, and existing model is fully utilized.
At present, in setting up the tobacco near-infrared model process, the selection of sample is commonly used to experience selection, concentration identification and three kinds of methods of spectrum identification.The experience back-and-forth method is selected representative tobacco sample and is set up model as calibration collection sample normally according to the character such as kind, time, position, grade and purposes of tobacco; The concentration method of identification mainly is the concentration difference according to sample component, selects representative sample to set up model as calibration collection sample; The spectrum method of identification is mainly selected sample according to the SPECTRAL DIVERSITY of sample and is set up model.The shortcoming that employing experience back-and-forth method is selected sample is that its required sample size is huge, and simultaneously this method has been ignored the physics of sample itself and chemical property and the error that causes easily; The shortcoming of concentration method of identification is its SPECTRAL DIVERSITY of having ignored sample, thereby may cause the predictive ability of model on the low side; The shortcoming of spectrum method of identification is its difference of having ignored the sample inherent quality, causes the model distortion easily.At these situations, the present invention proposes a kind of sampling method that is used to set up tobacco near-infrared model.
Summary of the invention
The present invention has overcome the deficiencies in the prior art, and a kind of sampling method of setting up tobacco near-infrared model that can overcome the said method shortcoming is provided.
Technical scheme of the present invention is:
A kind of sampling method of setting up tobacco near-infrared model, adopt step 1 to step 7:
Step 1 obtains two separate sample sets { A} and { the near infrared scanning spectrogram of the tobacco sample among the B};
{ { the near infrared scanning spectrogram of B} carries out the spectrum pre-service to step 2 for A} and sample sets with sample sets;
Step 3 collects { the near infrared spectrum X of sample i among the B} per sample iAnd sample sets { the averaged spectrum X of sample among the A} Avg, obtain sample i to sample sets { the spectrum mahalanobis distance d of A} i
Step 4 collects { the component concentration Y of sample i among the B} per sample iAnd sample sets { the component concentration mean value Y of sample among the A} Avg, { the component value of A} is apart from d to sample sets to obtain sample i i';
The spectrum mahalanobis distance d that step 5 is tried to achieve according to step 3 iThe component value of trying to achieve with step 4 is apart from d i', { sample i is to sample sets { the whole Euclidean distance D of A} among the B} to obtain sample sets i
Step 6 is chosen sample sets by rule of specialty, and { sample among the B} adds sample sets { form among the A} new sample sets { A ' } to, is set up by sample sets { A ' } and obtains new near-infrared model A ';
Step 7 is estimated model A ' with internal chiasma proof method and external certificate method.
Further technical scheme is that the spectrum pre-service in the step 2 is to adopt one or more methods in level and smooth, quadratic interpolattion, first order derivative method or the second derivative method that original near infrared spectrum is carried out pre-service.
Further technical scheme is to ask for sample sets in the step 3 { sample i is to sample sets { the spectrum mahalanobis distance d of A} among the B} iMethod adopt following formula:
d i = ( X i - X avg ) T S - 1 ( X i - X avg )
X iFor sample sets the spectrum vector of sample i (n * 1) among the B},
X AvgFor sample sets the averaged spectrum vector of sample (n * 1) among the A},
S be covariance matrix (n * n),
(X i-X Avg) TBe (X i-X Avg) transposed matrix,
N is spectrum X iThe data point number.
Further technical scheme is to ask for sample sets in the step 4 { { the component value of A} is apart from d to sample sets for sample i among the B} i' method adopt following formula:
d′ i＝|Y i-Y avg|
Y iFor sample sets the component concentration of sample i among the B},
Y AvgFor sample sets the average composition content of sample among the A},
Further technical scheme is to ask for whole Euclidean distance D in the step 5 iMethod adopt following formula:
D i = d i 2 + d i ′ 2
Further technical scheme is the described rule of specialty of step 6, is according to whole Euclidean distance D i{ sample among the B} adds sample sets { form among the A} new sample sets { A ' } to order from small to large with sample sets.
Further technical scheme is that cross validation root-mean-square error (RMSECV), the coefficient of determination (R are adopted in the internal chiasma checking in the step 7 2), predicted root mean square error (RMSEP), the coefficient of determination (R are adopted in external certificate 2) model is estimated.
The present invention is provided with two independently tobacco sample collection { A} and sample sets { B}, belong to two independently near-infrared model A and Model B successively respectively, to { choose effective sample among the B} and add sample sets { form among the A} sample sets { A ' } to, set up new near-infrared model A ' in sample sets with sample sets { A ' }.{ { B} is separate, does not have association between them, if choose at random sample, then these samples become the abnormity point in the sample sets { A ' } probably, and then influences the precision of model A ' for A} and sample sets because sample sets.Useful technique effect of the present invention is { to choose effective sample the B} and join sample sets { obtain among the A} sample sets { A ' } from sample sets, avoid the appearance of abnormity point in the sample sets { A ' } effectively, thereby solved the problem that sample message is shared between independent tobacco near-infrared model, adopt sampling method of the present invention can set up the model of rejecting invalid abnormal data, guarantee that model has good predictive ability and applicability.
Description of drawings
Fig. 1 the present invention is used for testing sample sets { the near infrared scanning spectrogram of A1} among the embodiment 1 of protein tobacco;
Fig. 2 the present invention is used for testing sample sets { the near infrared scanning spectrogram of B1} among the embodiment 1 of protein tobacco;
Fig. 3 the present invention is used for testing sample sets { the pretreated near infrared spectrogram of A1} among the embodiment 1 of protein tobacco;
Fig. 4 the present invention is used for testing sample sets { the pretreated near infrared spectrogram of B1} among the embodiment 1 of protein tobacco;
Fig. 5 the present invention is used for testing near-infrared model A1 ' internal chiasma proof diagram among the embodiment 1 of protein tobacco;
Fig. 6 the present invention is used for testing near-infrared model A1 ' external certificate figure among the embodiment 1 of protein tobacco;
Fig. 7 the present invention is used for testing sample sets { the near infrared scanning spectrogram of A2} among the embodiment 2 of tobacco ligroin extraction;
Fig. 8 the present invention is used for testing sample sets { the near infrared scanning spectrogram of B2} among the embodiment 2 of tobacco ligroin extraction;
Fig. 9 the present invention is used for testing sample sets { the pretreated near infrared spectrogram of A2} among the embodiment 2 of tobacco ligroin extraction;
Figure 10 the present invention is used for testing sample sets { the pretreated near infrared spectrogram of B2} among the embodiment 2 of tobacco ligroin extraction;
Figure 11 the present invention is used for testing near-infrared model A2 ' internal chiasma proof diagram among the embodiment 2 of tobacco ligroin extraction;
Figure 12 the present invention is used for testing near-infrared model A2 ' external certificate figure among the embodiment 2 of tobacco ligroin extraction;
Embodiment
Embodiment 1
Test with protein near-infrared model A1 in the tobacco and Model B 1, carry out according to following steps 8 to step 14:
Step 8: { A1} is with { near infrared of tobacco sample scans spectrogram among the B1}, and { near infrared of sample scanning spectrogram is seen Fig. 1 to sample sets among the A1}, and { near infrared of sample scanning spectrogram is seen Fig. 2 to sample sets among the B1} to obtain two separate sample sets;
Step 9: { near infrared of sample scanning spectrogram carries out pre-service among the A1}, and the near infrared spectrogram after the processing is seen Fig. 3 with sample sets for utilization quadratic interpolattion and first order derivative method; { near infrared of sample scanning spectrogram carries out pre-service among the B1}, and the near infrared spectrogram after the processing is seen Fig. 4 with sample sets with quadratic interpolattion and first order derivative method;
Step 10: collect { the near infrared spectrum X of sample i among the B1} per sample iAnd sample sets { the averaged spectrum X of sample among the A1} Avg, obtain sample i to sample sets { the spectrum mahalanobis distance d of A1} i
Step 11: collect { the component concentration Y of sample i among the B1} per sample iAnd sample sets { the component concentration mean value Y of sample among the A1} Avg, { the component value of A1} is apart from d to sample sets to obtain sample i i';
Step 12: the spectrum mahalanobis distance d that tries to achieve according to step 10 iThe component value of trying to achieve with step 11 is apart from d i', { sample i is to sample sets { the whole Euclidean distance D of A1} among the B1} to obtain sample sets iSpectrum mahalanobis distance d i, whole Euclidean distance D iSee Table 1 protein example distance table:
Table 1 protein example distance table
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.001 ??10.96 ??2.61 ??11.27 ??CQ.283 ??12.18 ??2.39 ??12.41
??CQ.002 ??11.19 ??2.82 ??11.54 ??CQ.284 ??8.96 ??2.21 ??9.23
??CQ.003 ??3.56 ??2.07 ??4.12 ??CQ.285 ??11.09 ??2.17 ??11.30
??CQ.004 ??3.98 ??1.95 ??4.43 ??CQ.286 ??9.31 ??2.13 ??9.55
??CQ.005 ??4.57 ??2.46 ??5.20 ??CQ.287 ??9.56 ??2.25 ??9.82
??CQ.006 ??6.72 ??0.39 ??6.73 ??CQ.288 ??10.21 ??1.78 ??10.37
??CQ.007 ??10.93 ??1.85 ??11.08 ??CQ.289 ??9.03 ??2.19 ??9.29
??CQ.008 ??11.92 ??2.08 ??12.10 ??CQ.290 ??9.01 ??1.57 ??9.15
??CQ.009 ??11.63 ??2.10 ??11.82 ??CQ.291 ??9.10 ??2.21 ??9.36
??CQ.010 ??3.42 ??2.30 ??4.12 ??CQ.292 ??9.59 ??1.26 ??9.68
??CQ.011 ??12.87 ??1.24 ??12.93 ??CQ.293 ??8.61 ??2.30 ??8.91
??CQ.012 ??18.37 ??2.06 ??18.48 ??CQ.294 ??9.65 ??2.29 ??9.92
??CQ.013 ??12.65 ??2.80 ??12.96 ??CQ.295 ??10.25 ??2.40 ??10.53
??CQ.014 ??12.49 ??2.41 ??12.72 ??CQ.296 ??9.52 ??2.27 ??9.79
??CQ.015 ??3.65 ??2.53 ??4.45 ??CQ.297 ??9.28 ??2.95 ??9.74
??CQ.016 ??4.14 ??1.88 ??4.55 ??CQ.298 ??8.82 ??1.83 ??9.01
??CQ.017 ??3.72 ??2.46 ??4.46 ??CQ.299 ??10.61 ??0.47 ??10.62
??CQ.018 ??4.40 ??0.42 ??4.42 ??CQ.300 ??7.70 ??1.70 ??7.88
??CQ.019 ??6.62 ??1.25 ??6.74 ??CQ.301 ??17.90 ??0.38 ??17.91
??CQ.020 ??4.39 ??1.77 ??4.74 ??CQ.302 ??16.71 ??1.78 ??16.81
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.021 ??15.63 ??0.98 ??15.66 ??CQ.303 ??17.17 ??1.71 ??17.25
??CQ.022 ??18.05 ??1.57 ??18.12 ??CQ.304 ??20.75 ??0.03 ??20.75
??CQ.023 ??10.89 ??1.57 ??11.00 ??CQ.305 ??11.82 ??0.96 ??11.86
??CQ.024 ??12.74 ??1.63 ??12.85 ??CQ.306 ??13.39 ??1.87 ??13.52
??CQ.025 ??12.87 ??1.44 ??12.95 ??CQ.307 ??22.23 ??1.14 ??22.26
??CQ.026 ??12.94 ??1.46 ??13.03 ??CQ.308 ??13.51 ??0.49 ??13.52
??CQ.027 ??17.20 ??1.68 ??17.28 ??CQ.309 ??12.80 ??0.46 ??12.80
??CQ.028 ??15.18 ??1.89 ??15.30 ??CQ.310 ??22.07 ??1.01 ??22.09
??CQ.029 ??14.49 ??1.00 ??14.53 ??CQ.311 ??21.85 ??1.42 ??21.89
??CQ.030 ??11.32 ??2.44 ??11.58 ??CQ.312 ??14.71 ??0.11 ??14.71
??CQ.031 ??9.58 ??0.98 ??9.63 ??CQ.313 ??22.30 ??0.87 ??22.31
??CQ.032 ??12.53 ??1.75 ??12.66 ??CQ.314 ??14.86 ??0.68 ??14.87
??CQ.033 ??14.81 ??1.88 ??14.93 ??CQ.315 ??24.49 ??0.68 ??24.50
??CQ.034 ??14.83 ??1.39 ??14.90 ??CQ.316 ??20.78 ??0.51 ??20.78
??CQ.035 ??19.11 ??1.57 ??19.17 ??CQ.317 ??14.63 ??1.13 ??14.67
??CQ.036 ??16.66 ??1.54 ??16.74 ??CQ.318 ??22.72 ??0.76 ??22.73
??CQ.037 ??11.32 ??2.40 ??11.57 ??CQ.319 ??21.26 ??1.04 ??21.28
??CQ.038 ??15.38 ??0.86 ??15.40 ??CQ.320 ??16.05 ??0.51 ??16.06
??CQ.039 ??14.76 ??1.44 ??14.83 ??CQ.321 ??15.97 ??0.30 ??15.97
??CQ.040 ??16.46 ??1.72 ??16.55 ??CQ.322 ??5.57 ??0.00 ??5.57
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.041 ??11.21 ??1.26 ??11.28 ??CQ.323 ??2.73 ??0.02 ??2.73
??CQ.042 ??7.28 ??1.17 ??7.37 ??CQ.324 ??2.88 ??0.15 ??2.89
??CQ.043 ??5.10 ??2.31 ??5.60 ??CQ.325 ??5.06 ??0.14 ??5.06
??CQ.044 ??6.73 ??1.71 ??6.95 ??CQ.326 ??3.80 ??1.61 ??4.13
??CQ.045 ??5.60 ??0.76 ??5.65 ??CQ.327 ??4.36 ??1.64 ??4.66
??CQ.046 ??11.82 ??2.58 ??12.10 ??CQ.328 ??8.15 ??2.32 ??8.48
??CQ.047 ??14.78 ??1.95 ??14.91 ??CQ.329 ??8.63 ??2.58 ??9.01
??CQ.048 ??20.27 ??0.89 ??20.29 ??CQ.330 ??7.21 ??1.59 ??7.39
??CQ.049 ??18.30 ??1.79 ??18.38 ??CQ.331 ??6.62 ??1.57 ??6.81
??CQ.050 ??18.07 ??1.83 ??18.17 ??CQ.332 ??6.58 ??2.18 ??6.93
??CQ.051 ??7.55 ??1.06 ??7.63 ??CQ.333 ??9.16 ??1.86 ??9.35
??CQ.052 ??4.85 ??1.68 ??5.13 ??CQ.334 ??8.31 ??0.93 ??8.36
??CQ.053 ??2.65 ??1.76 ??3.18 ??CQ.335 ??6.33 ??0.73 ??6.37
??CQ.054 ??6.68 ??2.39 ??7.09 ??CQ.336 ??6.78 ??0.89 ??6.84
??CQ.055 ??18.71 ??1.41 ??18.76 ??CQ.337 ??16.66 ??3.84 ??17.10
??CQ.056 ??13.10 ??1.67 ??13.21 ??CQ.338 ??7.05 ??2.48 ??7.47
??CQ.057 ??13.96 ??1.35 ??14.03 ??CQ.339 ??5.41 ??1.11 ??5.52
??CQ.058 ??14.50 ??1.54 ??14.58 ??CQ.340 ??5.72 ??1.81 ??6.00
??CQ.059 ??20.63 ??1.02 ??20.65 ??CQ.341 ??5.12 ??1.71 ??5.40
??CQ.060 ??15.24 ??1.29 ??15.30 ??CQ.342 ??4.71 ??2.02 ??5.13
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.061 ??16.41 ??1.90 ??16.52 ??CQ.343 ??4.59 ??2.10 ??5.05
??CQ.062 ??17.20 ??2.08 ??17.33 ??CQ.344 ??5.29 ??2.14 ??5.71
??CQ.063 ??16.09 ??0.47 ??16.10 ??CQ.345 ??4.56 ??2.21 ??5.07
??CQ.064 ??16.51 ??1.46 ??16.57 ??CQ.346 ??5.26 ??1.39 ??5.44
??CQ.065 ??15.33 ??1.44 ??15.39 ??CQ.347 ??4.93 ??2.30 ??5.44
??CQ.066 ??15.12 ??1.87 ??15.23 ??CQ.348 ??5.29 ??3.17 ??6.17
??CQ.067 ??15.43 ??1.53 ??15.51 ??CQ.349 ??4.54 ??1.84 ??4.90
??CQ.068 ??18.75 ??1.75 ??18.84 ??CQ.350 ??6.81 ??1.93 ??7.07
??CQ.069 ??20.52 ??1.61 ??20.58 ??CQ.351 ??5.06 ??1.80 ??5.38
??CQ.070 ??18.66 ??1.89 ??18.75 ??CQ.352 ??5.52 ??2.22 ??5.95
??CQ.071 ??15.26 ??1.20 ??15.30 ??CQ.353 ??5.03 ??1.99 ??5.41
??CQ.072 ??16.78 ??2.18 ??16.92 ??CQ.354 ??5.14 ??2.32 ??5.64
??CQ.073 ??15.05 ??1.95 ??15.17 ??CQ.355 ??4.05 ??2.19 ??4.61
??CQ.074 ??12.37 ??1.96 ??12.52 ??CQ.356 ??4.07 ??2.26 ??4.66
??CQ.075 ??5.85 ??1.42 ??6.02 ??CQ.357 ??6.63 ??2.50 ??7.09
??CQ.076 ??5.78 ??1.97 ??6.11 ??CQ.358 ??3.86 ??0.24 ??3.87
??CQ.077 ??6.01 ??1.93 ??6.32 ??CQ.359 ??5.36 ??0.31 ??5.37
??CQ.078 ??5.45 ??1.15 ??5.57 ??CQ.360 ??2.85 ??0.80 ??2.96
??CQ.079 ??5.79 ??1.65 ??6.02 ??CQ.361 ??2.55 ??0.20 ??2.56
??CQ.080 ??8.22 ??1.42 ??8.34 ??CQ.362 ??2.35 ??1.30 ??2.68
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.081 ??6.10 ??2.04 ??6.43 ??CQ.363 ??2.16 ??0.81 ??2.31
??CQ.082 ??5.17 ??2.25 ??5.64 ??CQ.364 ??2.68 ??1.17 ??2.93
??CQ.083 ??5.75 ??1.44 ??5.92 ??CQ.365 ??2.10 ??2.27 ??3.10
??CQ.084 ??6.08 ??2.29 ??6.50 ??CQ.366 ??7.69 ??1.35 ??7.81
??CQ.085 ??10.21 ??2.47 ??10.51 ??CQ.367 ??1.98 ??1.02 ??2.23
??CQ.086 ??6.13 ??2.36 ??6.57 ??CQ.368 ??2.99 ??0.87 ??3.12
??CQ.087 ??6.66 ??1.80 ??6.90 ??CQ.369 ??7.67 ??1.38 ??7.79
??CQ.088 ??7.17 ??1.46 ??7.31 ??CQ.370 ??7.92 ??0.90 ??7.97
??CQ.089 ??3.17 ??2.20 ??3.86 ??CQ.371 ??4.58 ??0.18 ??4.58
??CQ.090 ??4.78 ??1.79 ??5.10 ??CQ.372 ??7.24 ??1.45 ??7.38
??CQ.091 ??5.87 ??2.35 ??6.32 ??CQ.373 ??5.23 ??1.49 ??5.43
??CQ.092 ??5.50 ??2.22 ??5.93 ??CQ.374 ??3.15 ??1.41 ??3.45
??CQ.093 ??5.33 ??2.11 ??5.73 ??CQ.375 ??6.85 ??1.17 ??6.95
??CQ.094 ??5.84 ??2.43 ??6.32 ??CQ.376 ??9.67 ??0.10 ??9.67
??CQ.095 ??5.88 ??1.59 ??6.09 ??CQ.377 ??10.92 ??1.04 ??10.97
??CQ.096 ??5.04 ??0.71 ??5.09 ??CQ.378 ??4.03 ??1.61 ??4.34
??CQ.097 ??5.18 ??2.08 ??5.58 ??CQ.379 ??7.70 ??1.93 ??7.94
??CQ.098 ??9.01 ??2.30 ??9.30 ??CQ.380 ??5.39 ??0.43 ??5.41
??CQ.099 ??6.31 ??2.24 ??6.70 ??CQ.381 ??6.62 ??0.99 ??6.70
??CQ.100 ??3.66 ??1.54 ??3.97 ??CQ.382 ??2.94 ??0.94 ??3.09
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.101 ??5.45 ??1.41 ??5.63 ??CQ.383 ??5.29 ??1.31 ??5.45
??CQ.102 ??5.46 ??1.52 ??5.67 ??CQ.384 ??4.72 ??0.88 ??4.80
??CQ.103 ??5.02 ??1.71 ??5.31 ??CQ.385 ??4.99 ??1.05 ??5.10
??CQ.104 ??5.20 ??1.80 ??5.50 ??CQ.386 ??6.55 ??1.52 ??6.72
??CQ.105 ??5.66 ??1.68 ??5.91 ??CQ.387 ??5.85 ??2.35 ??6.31
??CQ.106 ??5.63 ??2.02 ??5.98 ??CQ.388 ??3.45 ??1.05 ??3.61
??CQ.107 ??5.98 ??0.97 ??6.06 ??CQ.389 ??2.92 ??1.17 ??3.15
??CQ.108 ??6.65 ??0.27 ??6.66 ??CQ.390 ??5.87 ??1.27 ??6.00
??CQ.109 ??5.24 ??0.16 ??5.24 ??CQ.391 ??5.48 ??2.31 ??5.95
??CQ.110 ??9.06 ??0.50 ??9.08 ??CQ.392 ??4.66 ??2.20 ??5.16
??CQ.111 ??8.12 ??1.81 ??8.32 ??CQ.393 ??8.40 ??2.76 ??8.85
??CQ.112 ??6.81 ??1.70 ??7.02 ??CQ.394 ??7.55 ??2.35 ??7.91
??CQ.113 ??8.75 ??2.14 ??9.01 ??CQ.395 ??3.53 ??2.12 ??4.12
??CQ.114 ??8.83 ??2.16 ??9.09 ??CQ.396 ??7.95 ??1.54 ??8.10
??CQ.115 ??6.77 ??1.75 ??6.99 ??CQ.397 ??5.91 ??1.47 ??6.09
??CQ.116 ??5.88 ??1.85 ??6.17 ??CQ.398 ??7.53 ??1.67 ??7.71
??CQ.117 ??5.91 ??2.42 ??6.39 ??CQ.399 ??9.55 ??1.62 ??9.69
??CQ.118 ??5.63 ??2.03 ??5.99 ??CQ.400 ??6.13 ??0.40 ??6.15
??CQ.119 ??5.04 ??1.02 ??5.14 ??CQ.401 ??4.29 ??1.07 ??4.42
??CQ.120 ??5.26 ??0.60 ??5.29 ??CQ.402 ??4.17 ??1.51 ??4.44
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.121 ??5.74 ??0.69 ??5.78 ??CQ.403 ??2.82 ??0.17 ??2.82
??CQ.122 ??13.13 ??1.33 ??13.20 ??CQ.404 ??4.78 ??1.23 ??4.93
??CQ.123 ??4.74 ??2.45 ??5.34 ??CQ.405 ??7.14 ??2.95 ??7.73
??CQ.124 ??4.69 ??2.58 ??5.36 ??CQ.406 ??3.65 ??2.02 ??4.17
??CQ.125 ??6.23 ??2.29 ??6.64 ??CQ.407 ??5.29 ??1.98 ??5.65
??CQ.126 ??6.99 ??0.58 ??7.01 ??CQ.408 ??4.16 ??1.42 ??4.40
??CQ.127 ??6.94 ??1.45 ??7.09 ??CQ.409 ??2.71 ??0.86 ??2.84
??CQ.128 ??5.45 ??1.42 ??5.63 ??CQ.410 ??4.26 ??1.13 ??4.41
??CQ.129 ??5.91 ??1.48 ??6.09 ??CQ.411 ??4.51 ??0.85 ??4.59
??CQ.130 ??5.15 ??1.68 ??5.42 ??CQ.412 ??2.47 ??1.34 ??2.81
??CQ.131 ??6.53 ??1.98 ??6.82 ??CQ.413 ??4.77 ??0.10 ??4.77
??CQ.132 ??4.78 ??2.02 ??5.19 ??CQ.414 ??5.48 ??0.38 ??5.49
??CQ.133 ??5.87 ??1.34 ??6.02 ??CQ.415 ??5.55 ??1.99 ??5.90
??CQ.134 ??6.20 ??1.45 ??6.37 ??CQ.416 ??4.88 ??1.33 ??5.06
??CQ.135 ??5.93 ??1.38 ??6.09 ??CQ.417 ??4.00 ??1.31 ??4.21
??CQ.136 ??5.06 ??1.91 ??5.41 ??CQ.418 ??4.47 ??0.26 ??4.47
??CQ.137 ??5.13 ??1.76 ??5.42 ??CQ.419 ??6.90 ??0.83 ??6.95
??CQ.138 ??4.66 ??1.48 ??4.89 ??CQ.420 ??4.35 ??0.71 ??4.41
??CQ.139 ??10.68 ??2.17 ??10.90 ??CQ.421 ??6.92 ??0.84 ??6.97
??CQ.140 ??6.45 ??1.74 ??6.68 ??CQ.422 ??3.66 ??1.45 ??3.94
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.141 ??4.60 ??1.99 ??5.01 ??CQ.423 ??3.74 ??0.23 ??3.75
??CQ.142 ??5.51 ??1.07 ??5.61 ??CQ.424 ??4.22 ??0.55 ??4.25
??CQ.143 ??6.95 ??1.37 ??7.08 ??CQ.425 ??4.76 ??0.38 ??4.77
??CQ.144 ??4.56 ??2.18 ??5.06 ??CQ.426 ??3.96 ??1.29 ??4.17
??CQ.145 ??6.19 ??1.63 ??6.40 ??CQ.427 ??3.55 ??1.25 ??3.76
??CQ.146 ??6.41 ??1.25 ??6.53 ??CQ.428 ??4.19 ??1.73 ??4.53
??CQ.147 ??10.04 ??1.70 ??10.19 ??CQ.429 ??4.03 ??1.17 ??4.19
??CQ.148 ??5.47 ??2.34 ??5.95 ??CQ.430 ??4.05 ??1.28 ??4.25
??CQ.149 ??4.55 ??1.69 ??4.86 ??CQ.431 ??3.71 ??0.90 ??3.82
??CQ.150 ??9.84 ??1.18 ??9.91 ??CQ.432 ??3.76 ??1.16 ??3.94
??CQ.151 ??5.22 ??1.79 ??5.52 ??CQ.433 ??3.52 ??1.09 ??3.68
??CQ.152 ??6.37 ??1.64 ??6.58 ??CQ.434 ??5.28 ??1.06 ??5.39
??CQ.153 ??6.84 ??1.42 ??6.99 ??CQ.435 ??5.79 ??1.11 ??5.89
??CQ.154 ??4.63 ??1.75 ??4.95 ??CQ.436 ??5.70 ??1.95 ??6.02
??CQ.155 ??7.25 ??1.59 ??7.43 ??CQ.437 ??4.49 ??2.00 ??4.91
??CQ.156 ??5.27 ??1.51 ??5.49 ??CQ.438 ??4.83 ??0.63 ??4.87
??CQ.157 ??6.86 ??2.15 ??7.19 ??CQ.439 ??5.24 ??0.71 ??5.29
??CQ.158 ??5.31 ??1.15 ??5.43 ??CQ.440 ??6.43 ??0.86 ??6.48
??CQ.159 ??5.25 ??1.64 ??5.50 ??CQ.441 ??5.55 ??1.01 ??5.64
??CQ.160 ??5.17 ??0.90 ??5.25 ??CQ.442 ??5.12 ??0.65 ??5.16
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.161 ??5.30 ??1.68 ??5.56 ??CQ.443 ??4.72 ??0.25 ??4.72
??CQ.162 ??4.70 ??0.49 ??4.73 ??CQ.444 ??4.81 ??0.62 ??4.85
??CQ.163 ??6.55 ??0.84 ??6.60 ??CQ.445 ??4.61 ??0.82 ??4.69
??CQ.164 ??6.74 ??1.76 ??6.97 ??CQ.446 ??4.82 ??0.81 ??4.88
??CQ.165 ??6.26 ??1.80 ??6.51 ??CQ.447 ??5.30 ??0.13 ??5.30
??CQ.166 ??5.98 ??0.10 ??5.98 ??CQ.448 ??7.97 ??0.65 ??8.00
??CQ.167 ??10.06 ??0.79 ??10.10 ??CQ.449 ??6.93 ??1.87 ??7.18
??CQ.168 ??10.08 ??1.97 ??10.27 ??CQ.450 ??4.56 ??1.02 ??4.67
??CQ.169 ??6.20 ??0.72 ??6.24 ??CQ.451 ??9.06 ??1.23 ??9.14
??CQ.170 ??5.81 ??0.44 ??5.82 ??CQ.452 ??9.38 ??0.87 ??9.42
??CQ.171 ??4.69 ??0.79 ??4.76 ??CQ.453 ??3.64 ??1.77 ??4.05
??CQ.172 ??6.65 ??0.41 ??6.66. ??CQ.454 ??3.70 ??0.93 ??3.82
??CQ.173 ??8.40 ??1.13 ??8.48 ??CQ.455 ??3.98 ??0.32 ??4.00
??CQ.174 ??3.80 ??1.18 ??3.98 ??CQ.456 ??6.43 ??1.39 ??6.58
??CQ.175 ??3.01 ??1.48 ??3.36 ??CQ.457 ??7.82 ??0.68 ??7.85
??CQ.176 ??6.43 ??0.53 ??6.45 ??CQ.458 ??4.52 ??1.01 ??4.63
??CQ.177 ??4.86 ??1.20 ??5.01 ??CQ.459 ??4.09 ??1.58 ??4.38
??CQ.178 ??3.86 ??1.85 ??4.28 ??CQ.460 ??4.22 ??1.49 ??4.47
??CQ.179 ??4.78 ??1.81 ??5.12 ??CQ.461 ??5.18 ??1.18 ??5.31
??CQ.180 ??8.46 ??2.44 ??8.81 ??CQ.462 ??5.53 ??0.40 ??5.55
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.181 ??6.84 ??2.66 ??7.34 ??CQ.463 ??4.91 ??1.34 ??5.09
??CQ.182 ??8.58 ??2.54 ??8.95 ??CQ.464 ??4.08 ??0.94 ??4.18
??CQ.183 ??7.15 ??3.10 ??7.79 ??CQ.465 ??5.12 ??1.13 ??5.24
??CQ.184 ??8.42 ??2.70 ??8.85 ??CQ.466 ??3.40 ??0.21 ??3.41
??CQ.185 ??6.41 ??3.25 ??7.18 ??CQ.467 ??4.13 ??0.62 ??4.18
??CQ.186 ??7.18 ??3.17 ??7.85 ??CQ.468 ??5.94 ??0.63 ??5.98
??CQ.187 ??8.05 ??3.10 ??8.63 ??CQ.469 ??3.84 ??0.88 ??3.94
??CQ.188 ??7.27 ??2.61 ??7.73 ??CQ.470 ??4.29 ??0.99 ??4.41
??CQ.189 ??7.38 ??3.02 ??7.97 ??CQ.471 ??4.75 ??0.51 ??4.77
??CQ.190 ??10.05 ??2.84 ??10.45 ??CQ.472 ??3.54 ??1.09 ??3.70
??CQ.191 ??6.24 ??2.44 ??6.70 ??CQ.473 ??4.23 ??0.27 ??4.24
??CQ.192 ??7.51 ??2.27 ??7.84 ??CQ.474 ??4.20 ??0.16 ??4.20
??CQ.193 ??8.76 ??2.26 ??9.05 ??CQ.475 ??4.34 ??0.78 ??4.41
??CQ.194 ??7.35 ??2.17 ??7.66 ??CQ.476 ??4.55 ??0.59 ??4.59
??CQ.195 ??8.17 ??2.64 ??8.59 ??CQ.477 ??4.16 ??0.79 ??4.23
??CQ.196 ??5.65 ??1.89 ??5.96 ??CQ.478 ??4.55 ??0.06 ??4.55
??CQ.197 ??9.04 ??2.32 ??9.34 ??CQ.479 ??4.40 ??1.07 ??4.53
??CQ.198 ??7.98 ??1.78 ??8.18 ??CQ.480 ??4.85 ??1.35 ??5.03
??CQ.199 ??7.81 ??2.44 ??8.19 ??CQ.481 ??5.09 ??0.04 ??5.09
??CQ.200 ??3.47 ??0.25 ??3.48 ??CQ.482 ??4.09 ??0.27 ??4.09
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.201 ??4.25 ??1.45 ??4.49 ??CQ.483 ??4.81 ??0.23 ??4.81
??CQ.202 ??14.44 ??1.17 ??14.48 ??CQ.484 ??4.45 ??0.25 ??4.46
??CQ.203 ??12.39 ??0.89 ??12.42 ??CQ.485 ??3.83 ??0.29 ??3.84
??CQ.204 ??8.35 ??2.22 ??8.64 ??CQ.486 ??4.91 ??0.76 ??4.97
??CQ.205 ??8.17 ??2.55 ??8.56 ??CQ.487 ??4.89 ??0.14 ??4.89
??CQ.206 ??7.26 ??2.78 ??7.77 ??CQ.488 ??4.72 ??0.84 ??4.79
??CQ.207 ??7.35 ??2.59 ??7.79 ??CQ.489 ??4.81 ??0.01 ??4.81
??CQ.208 ??7.95 ??1.40 ??8.08 ??CQ.490 ??4.44 ??0.02 ??4.44
??CQ.209 ??10.06 ??1.57 ??10.18 ??CQ.491 ??3.67 ??0.00 ??3.67
??CQ.210 ??8.31 ??3.44 ??9.00 ??CQ.492 ??3.96 ??1.41 ??4.20
??CQ.211 ??7.82 ??1.37 ??7.94 ??CQ.493 ??4.28 ??1.03 ??4.41
??CQ.212 ??8.03 ??1.95 ??8.26 ??CQ.494 ??4.88 ??0.87 ??4.95
??CQ.213 ??8.73 ??1.85 ??8.93 ??CQ.495 ??4.16 ??1.01 ??4.28
??CQ.214 ??6.71 ??2.21 ??7.07 ??CQ.496 ??3.74 ??1.49 ??4.03
??CQ.215 ??8.10 ??1.34 ??8.21 ??CQ.497 ??5.19 ??0.59 ??5.22
??CQ.217 ??9.47 ??1.74 ??9.63 ??CQ.498 ??3.06 ??0.29 ??3.08
??CQ.218 ??8.08 ??1.73 ??8.26 ??CQ.499 ??5.27 ??0.77 ??5.33
??CQ.219 ??8.43 ??1.04 ??8.49 ??CQ.500 ??4.33 ??0.70 ??4.39
??CQ.220 ??4.69 ??1.20 ??4.84 ??CQ.501 ??4.68 ??0.96 ??4.78
??CQ.221 ??3.46 ??0.98 ??3.59 ??CQ.502 ??4.21 ??0.37 ??4.23
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.222 ??4.91 ??0.80 ??4.98 ??CQ.503 ??4.58 ??0.30 ??4.59
??CQ.223 ??9.57 ??1.77 ??9.73 ??CQ.504 ??4.85 ??1.36 ??5.04
??CQ.224 ??9.50 ??2.37 ??9.79 ??CQ.505 ??5.46 ??0.32 ??5.47
??CQ.225 ??7.90 ??2.25 ??8.22 ??CQ.506 ??8.05 ??0.89 ??8.10
??CQ.226 ??8.28 ??1.04 ??8.35 ??CQ.507 ??5.36 ??0.65 ??5.40
??CQ.227 ??8.31 ??1.04 ??8.38 ??CQ.508 ??6.30 ??0.32 ??6.31
??CQ.228 ??9.26 ??1.56 ??9.40 ??CQ.509 ??4.55 ??0.06 ??4.55
??CQ.229 ??10.04 ??1.86 ??10.21 ??CQ.510 ??5.24 ??0.10 ??5.24
??CQ.230 ??7.58 ??0.83 ??7.63 ??CQ.511 ??5.56 ??0.85 ??5.62
??CQ.231 ??11.41 ??2.27 ??11.64 ??CQ.512 ??4.55 ??0.20 ??4.56
??CQ.232 ??11.80 ??0.68 ??11.82 ??CQ.513 ??6.96 ??1.50 ??7.12
??CQ.233 ??10.54 ??1.63 ??10.67 ??CQ.514 ??3.67 ??0.23 ??3.68
??CQ.234 ??9.77 ??1.11 ??9.83 ??CQ.515 ??6.64 ??0.83 ??6.69
??CQ.235 ??11.31 ??0.20 ??11.31 ??CQ.516 ??5.59 ??0.50 ??5.62
??CQ.236 ??3.66 ??1.98 ??4.17 ??CQ.517 ??4.83 ??0.26 ??4.84
??CQ.237 ??3.19 ??1.35 ??3.47 ??CQ.518 ??6.93 ??1.93 ??7.19
??CQ.238 ??17.41 ??1.61 ??17.48 ??CQ.519 ??6.67 ??0.93 ??6.74
??CQ.239 ??4.52 ??1.51 ??4.77 ??CQ.520 ??4.86 ??0.10 ??4.86
??CQ.240 ??13.59 ??0.04 ??13.59 ??CQ.521 ??3.85 ??0.14 ??3.85
??CQ.241 ??8.69 ??1.62 ??8.84 ??CQ.522 ??4.20 ??0.63 ??4.24
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.242 ??4.64 ??1.53 ??4.89 ??CQ.523 ??8.10 ??0.48 ??8.12
??CQ.243 ??6.22 ??0.41 ??6.24 ??CQ.524 ??6.37 ??0.68 ??6.41
??CQ.244 ??4.96 ??0.75 ??5.02 ??CQ.525 ??5.01 ??0.80 ??5.08
??CQ.245 ??6.87 ??1.38 ??7.01 ??CQ.526 ??5.97 ??0.20 ??5.98
??CQ.246 ??8.25 ??1.01 ??8.31 ??CQ.527 ??5.52 ??0.03 ??5.52
??CQ.247 ??9.22 ??0.93 ??9.27 ??CQ.528 ??7.32 ??0.80 ??7.36
??CQ.248 ??7.14 ??0.15 ??7.14 ??CQ.529 ??5.33 ??1.70 ??5.59
??CQ.249 ??8.59 ??0.54 ??8.61 ??CQ.530 ??5.55 ??0.11 ??5.55
??CQ.250 ??7.42 ??2.68 ??7.89 ??CQ.531 ??4.17 ??0.38 ??4.18
??CQ.251 ??8.86 ??2.71 ??9.27 ??CQ.532 ??6.11 ??0.36 ??6.12
??CQ.252 ??13.65 ??2.97 ??13.97 ??CQ.533 ??4.16 ??0.40 ??4.18
??CQ.253 ??8.50 ??2.18 ??8.78 ??CQ.534 ??5.86 ??0.32 ??5.87
??CQ.254 ??7.20 ??2.47 ??7.62 ??CQ.535 ??6.30 ??0.78 ??6.35
??CQ.255 ??7.02 ??2.65 ??7.51 ??CQ.536 ??7.29 ??0.09 ??7.29
??CQ.256 ??7.01 ??2.52 ??7.45 ??CQ.537 ??5.48 ??0.52 ??5.50
??CQ.257 ??7.41 ??2.51 ??7.82 ??CQ.538 ??6.45 ??0.87 ??6.51
??CQ.258 ??8.30 ??2.53 ??8.68 ??CQ.539 ??6.47 ??0.00 ??6.47
??CQ.259 ??7.72 ??2.64 ??8.16 ??CQ.540 ??4.14 ??1.81 ??4.51
??CQ.260 ??8.30 ??1.68 ??8.47 ??CQ.541 ??6.73 ??1.96 ??7.01
??CQ.261 ??6.28 ??1.53 ??6.46 ??CQ.542 ??6.39 ??1.13 ??6.49
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.262 ??7.32 ??2.67 ??7.80 ??CQ.543 ??6.73 ??0.05 ??6.73
??CQ.263 ??5.38 ??1.61 ??5.62 ??CQ.544 ??5.91 ??0.57 ??5.94
??CQ.264 ??6.26 ??2.61 ??6.78 ??CQ.545 ??6.34 ??0.78 ??6.39
??CQ.265 ??5.31 ??2.09 ??5.71 ??CQ.546 ??6.02 ??0.80 ??6.07
??CQ.266 ??6.77 ??1.02 ??6.84 ??CQ.547 ??6.07 ??0.85 ??6.13
??CQ.267 ??4.39 ??1.12 ??4.53 ??CQ.548 ??3.03 ??0.95 ??3.18
??CQ.268 ??6.94 ??1.74 ??7.16 ??CQ.549 ??4.67 ??0.73 ??4.73
??CQ.269 ??7.49 ??1.90 ??7.73 ??CQ.550 ??5.26 ??0.11 ??5.26
??CQ.270 ??8.12 ??1.33 ??8.23 ??CQ.551 ??5.48 ??0.42 ??5.50
??CQ.271 ??7.12 ??1.79 ??7.34 ??CQ.552 ??5.81 ??0.42 ??5.82
??CQ.272 ??8.61 ??0.91 ??8.66 ??CQ.553 ??5.05 ??0.95 ??5.14
??CQ.273 ??7.26 ??0.73 ??7.30 ??CQ.554 ??3.51 ??1.00 ??3.65
??CQ.274 ??7.56 ??2.02 ??7.83 ??CQ.555 ??5.26 ??0.26 ??5.27
??CQ.275 ??7.10 ??1.26 ??7.21 ??CQ.556 ??6.02 ??0.47 ??6.04
??CQ.276 ??5.02 ??1.48 ??5.24 ??CQ.557 ??5.70 ??0.52 ??5.73
??CQ.277 ??8.62 ??1.10 ??8.69 ??CQ.558 ??6.06 ??0.18 ??6.06
??CQ.278 ??6.98 ??0.06 ??6.98 ??CQ.559 ??6.03 ??1.75 ??6.27
??CQ.279 ??9.47 ??1.90 ??9.66 ??CQ.560 ??5.49 ??0.11 ??5.49
??CQ.280 ??9.89 ??1.90 ??10.08 ??CQ.561 ??4.65 ??0.33 ??4.66
??CQ.281 ??8.78 ??2.16 ??9.04 ??CQ.562 ??4.78 ??1.01 ??4.88
The sample name ??d i ??d i’ ??D i The sample name ??d i ??d i’ ??D i
??CQ.282 ??9.77 ??2.29 ??10.04 ??CQ.563 ??4.61 ??0.63 ??4.65
Step 13: according to D iPrinciple is from small to large chosen sample sets, and { 320 samples among the B1} add sample sets { form among the A1} new sample sets { A1 ' } to, are set up by sample sets { A1 ' } and obtain new near-infrared model A1 '.
Step 14: model A1 ' is carried out the internal chiasma checking, and its cross validation root-mean-square error (RMSECV) is 0.267, the coefficient of determination (R 2) be 92.33, near-infrared model A1 ' internal chiasma proof diagram is seen Fig. 5; Model A1 ' is carried out external certificate, and its predicted root mean square error (RMSEP) is 0.179, the coefficient of determination (R 2) be 94.52, near-infrared model A1 ' external certificate figure sees Fig. 6.
New model A1 ' satisfies application request.
Embodiment 2
Test with ether extract near-infrared model A2 of tobacco PetroChina Company Limited. and Model B 2, carry out according to following steps 15 to step 20:
Step 15: { A2} is with { near infrared of tobacco sample scans spectrogram among the B2}, and { near infrared of sample scanning spectrogram is seen Fig. 7 to sample sets among the A2}, and { near infrared of sample scanning spectrogram is seen Fig. 8 to sample sets among the B2} to obtain two separate sample sets;
Step 16: { near infrared of sample scanning spectrogram carries out pre-service among the A2}, and the near infrared spectrogram after the processing is seen Fig. 9 with sample sets for utilization quadratic interpolattion and first order derivative method; { near infrared of sample scanning spectrogram carries out pre-service among the B2}, and the near infrared spectrogram after the processing is seen Figure 10 with sample sets with quadratic interpolattion and first order derivative method;
Step 17: collect { the near infrared spectrum X of sample i among the B2} per sample iAnd sample sets { the averaged spectrum X of sample among the A2} Avg, obtain sample i to sample sets { the spectrum mahalanobis distance d of A2} i
Step 18: collect { the component concentration Y of sample i among the B2} per sample iAnd sample sets { the component concentration mean value Y of sample among the A2} Avg, { the component value of A2} is apart from d to sample sets to obtain sample i i';
2 steps 19: the spectrum mahalanobis distance d that tries to achieve according to step 17 iThe component value of trying to achieve with step 18 is apart from d i', { sample i is to sample sets { the whole Euclidean distance D of A2} among the B2} to obtain sample sets iSpectrum mahalanobis distance d i, whole Euclidean distance D iSee Table 2 Petroleum ether extraction matter sample distance tables:
Table 2 Petroleum ether extraction matter sample distance table
Filename ??d i ??d i’ ??D i Filename ??d i ??d i’ ??D i
??CD.001 ??2.79 ??1.01 ??2.97 ??CD.076 ??6.17 ??1.56 ??6.37
??CD.002 ??4.13 ??1.18 ??4.29 ??CD.077 ??5.75 ??1.02 ??5.84
Filename ??d i ??d i’ ??D i Filename ??d i ??d i’ ??D i
??CD.003 ??3.50 ??0.53 ??3.54 ??CD.078 ??6.61 ??1.10 ??6.70
??CD.004 ??0.77 ??0.69 ??1.04 ??CD.079 ??3.31 ??4.27 ??5.40
??CD.005 ??0.81 ??0.55 ??0.98 ??CD.080 ??5.76 ??2.23 ??6.17
??CD.006 ??0.86 ??0.85 ??1.21 ??CD.081 ??6.28 ??1.95 ??6.58
??CD.007 ??0.87 ??0.01 ??0.87 ??CD.082 ??5.75 ??0.96 ??5.83
??CD.008 ??2.86 ??0.98 ??3.03 ??CD.083 ??4.57 ??3.65 ??5.84
??CD.009 ??3.11 ??0.67 ??3.18 ??CD.084 ??8.45 ??1.71 ??8.62
??CD.010 ??6.96 ??0.12 ??6.96 ??CD.085 ??6.51 ??1.91 ??6.78
??CD.011 ??3.43 ??0.49 ??3.46 ??CD.086 ??7.42 ??1.57 ??7.59
??CD.012 ??3.52 ??0.76 ??3.60 ??CD.087 ??7.25 ??1.98 ??7.52
??CD.013 ??4.21 ??0.76 ??4.28 ??CD.088 ??6.49 ??1.16 ??6.59
??CD.014 ??3.68 ??1.83 ??4.11 ??CD.089 ??3.52 ??0.04 ??3.52
??CD.015 ??3.86 ??1.66 ??4.20 ??CD.090 ??4.53 ??1.89 ??4.91
??CD.016 ??2.23 ??0.78 ??2.36 ??CD.091 ??3.99 ??2.25 ??4.58
??CD.017 ??1.57 ??0.28 ??1.59 ??CD.092 ??5.20 ??0.92 ??5.28
??CD.018 ??2.32 ??0.08 ??2.32 ??CD.093 ??6.36 ??3.23 ??7.14
??CD.019 ??2.46 ??0.11 ??2.46 ??CD.094 ??5.69 ??1.33 ??5.85
??CD.020 ??2.04 ??1.01 ??2.28 ??CD.095 ??5.42 ??0.54 ??5.44
??CD.021 ??2.12 ??0.50 ??2.18 ??CD.096 ??4.44 ??3.23 ??5.49
??CD.022 ??3.74 ??0.88 ??3.84 ??CD.097 ??4.96 ??1.77 ??5.26
??CD.023 ??4.34 ??0.75 ??4.40 ??CD.098 ??2.11 ??3.43 ??4.03
??CD.024 ??4.19 ??2.71 ??4.99 ??CD.099 ??1.91 ??2.19 ??2.91
Filename ??d i ??d i’ ??D i Filename ??d i ??d i’ ??D i
??CD.025 ??4.00 ??2.07 ??4.51 ??CD.100 ??2.03 ??1.39 ??2.46
??CD.026 ??4.13 ??1.16 ??4.29 ??CD.101 ??1.24 ??1.39 ??1.86
??CD.027 ??3.58 ??0.08 ??3.58 ??CD.102 ??2.00 ??4.02 ??4.49
??CD.028 ??3.27 ??1.75 ??3.71 ??CD.103 ??2.26 ??2.20 ??3.15
??CD.029 ??4.22 ??0.27 ??4.22 ??CD.104 ??1.94 ??2.41 ??3.09
??CD.030 ??3.36 ??1.33 ??3.62 ??CD.105 ??2.15 ??1.12 ??2.43
??CD.031 ??1.70 ??0.73 ??1.85 ??CD.106 ??1.47 ??2.54 ??2.93
??CD.032 ??7.02 ??5.29 ??8.79 ??CD.107 ??1.56 ??1.97 ??2.51
??CD.033 ??2.23 ??0.21 ??2.24 ??CD.108 ??1.79 ??1.84 ??2.57
??CD.034 ??6.06 ??2.64 ??6.61 ??CD.109 ??1.62 ??1.07 ??1.94
??CD.035 ??1.62 ??2.20 ??2.73 ??CD.110 ??2.45 ??1.68 ??2.97
??CD.036 ??3.56 ??1.75 ??3.97 ??CD.111 ??2.65 ??1.75 ??3.18
??CD.037 ??5.29 ??1.02 ??5.39 ??CD.112 ??2.16 ??1.53 ??2.64
??CD.038 ??4.69 ??1.34 ??4.88 ??CD.113 ??1.99 ??1.75 ??2.65
??CD.039 ??5.06 ??4.98 ??7.10 ??CD.114 ??1.73 ??3.38 ??3.80
??CD.040 ??3.76 ??5.53 ??6.69 ??CD.115 ??1.87 ??2.03 ??2.77
??CD.041 ??3.29 ??6.72 ??7.48 ??CD.116 ??2.82 ??2.01 ??3.47
??CD.042 ??3.76 ??2.74 ??4.66 ??CD.117 ??1.88 ??1.03 ??2.14
??CD.043 ??9.10 ??4.49 ??10.15 ??CD.118 ??2.55 ??1.30 ??2.87
??CD.044 ??6.53 ??5.42 ??8.48 ??CD.119 ??2.32 ??1.76 ??2.91
??CD.045 ??6.63 ??5.88 ??8.86 ??CD.120 ??2.18 ??1.04 ??2.42
??CD.046 ??4.09 ??2.98 ??5.06 ??CD.121 ??2.33 ??1.25 ??2.64
Filename ??d i ??d i’ ??D i Filename ??d i ??d i’ ??D i
??CD.047 ??4.70 ??2.70 ??5.43 ??CD.122 ??1.77 ??2.80 ??3.31
??CD.048 ??3.65 ??1.69 ??4.02 ??CD.123 ??1.78 ??1.93 ??2.62
??CD.049 ??3.69 ??1.68 ??4.05 ??CD.124 ??2.22 ??1.57 ??2.72
??CD.050 ??2.87 ??0.60 ??2.93 ??CD.125 ??2.65 ??1.21 ??2.91
??CD.051 ??2.64 ??2.72 ??3.79 ??CD.126 ??2.89 ??2.66 ??3.93
??CD.052 ??3.92 ??1.90 ??4.35 ??CD.127 ??2.64 ??1.92 ??3.26
??CD.053 ??3.21 ??1.61 ??3.59 ??CD.128 ??2.15 ??2.06 ??2.98
??CD.054 ??3.12 ??0.96 ??3.26 ??CD.129 ??2.20 ??1.24 ??2.52
??CD.055 ??3.55 ??0.89 ??3.66 ??CD.130 ??1.93 ??3.09 ??3.64
??CD.056 ??4.48 ??0.33 ??4.49 ??CD.131 ??1.69 ??2.19 ??2.76
??CD.057 ??3.27 ??0.27 ??3.29 ??CD.132 ??1.48 ??1.54 ??2.14
??CD.058 ??3.48 ??0.06 ??3.48 ??CD.133 ??1.48 ??1.42 ??2.06
??CD.059 ??3.22 ??1.89 ??3.73 ??CD.134 ??2.32 ??1.36 ??2.69
??CD.060 ??6.61 ??1.67 ??6.82 ??CD.135 ??1.75 ??1.97 ??2.63
??CD.061 ??5.46 ??3.26 ??6.36 ??CD.136 ??1.81 ??1.22 ??2.18
??CD.062 ??7.09 ??2.12 ??7.40 ??CD.137 ??1.93 ??0.44 ??1.98
??CD.063 ??8.28 ??2.58 ??8.67 ??CD.138 ??5.08 ??1.83 ??5.40
??CD.064 ??8.89 ??1.24 ??8.97 ??CD.139 ??6.16 ??0.54 ??6.18
??CD.065 ??6.24 ??3.66 ??7.23 ??CD.140 ??7.36 ??4.04 ??8.40
??CD.066 ??4.30 ??1.66 ??4.61 ??CD.141 ??4.86 ??1.97 ??5.25
??CD.067 ??7.48 ??1.63 ??7.66 ??CD.142 ??6.41 ??2.00 ??6.71
??CD.068 ??4.68 ??0.76 ??4.74 ??CD.143 ??3.61 ??2.22 ??4.24
Filename ??d i ??d i’ ??D i Filename ??d i ??d i’ ??D i
??CD.069 ??3.19 ??3.33 ??4.61 ??CD.144 ??3.59 ??0.94 ??3.71
??CD.070 ??3.12 ??0.38 ??3.14 ??CD.145 ??5.61 ??1.08 ??5.71
??CD.071 ??5.64 ??2.49 ??6.17 ??CD.146 ??2.65 ??0.83 ??2.78
??CD.072 ??4.25 ??1.94 ??4.67 ??CD.147 ??2.53 ??0.70 ??2.62
??CD.073 ??7.33 ??1.67 ??7.52 ??CD.148 ??2.77 ??0.02 ??2.77
??CD.074 ??6.01 ??0.90 ??6.08 ??CD.149 ??2.22 ??1.10 ??2.48
??CD.075 ??6.44 ??3.94 ??7.55 ??CD.150 ??5.09 ??1.41 ??5.28
Step 6: according to D iPrinciple is from small to large chosen sample sets, and { 35 samples among the B2} add sample sets { form among the A2} new sample sets { A2 ' } to, are set up by sample sets { A2 ' } and obtain new near-infrared model A2 '.
Step 20: model A2 ' is carried out the internal chiasma checking, and its cross validation root-mean-square error (RMSECV) is 0.243, the coefficient of determination (R 2) be 94.95, near-infrared model A2 ' internal chiasma proof diagram is seen Figure 11; Model A2 ' is carried out external certificate, and its predicted root mean square error (RMSEP) is 0.193, the coefficient of determination (R 2) be 97.19, near-infrared model A2 ' external certificate figure sees Figure 12.
New model A2 ' satisfies application request.

Claims (7)

1. sampling method of setting up tobacco near-infrared model is characterized in that adopting step 1 to step 7:
Step 1 obtains two separate sample sets { A} and the sample sets { near infrared of tobacco sample scanning spectrogram among the B};
{ { the near infrared scanning spectrogram of B} carries out the spectrum pre-service to step 2 for A} and sample sets with sample sets;
Step 3 collects { the near infrared spectrum X of sample i among the B} per sample iAnd sample sets { the averaged spectrum X of sample among the A} Avg, obtain sample i to sample sets { the spectrum mahalanobis distance d of A} i
Step 4 collects { the component concentration Y of sample i among the B} per sample iAnd sample sets { the component concentration mean value Y of sample among the A} Avg, { the component value of A} is apart from d to sample sets to obtain sample i i';
The spectrum mahalanobis distance d that step 5 is tried to achieve according to step 3 iThe component value of trying to achieve with step 4 is apart from d i', { sample i is to sample sets { the whole Euclidean distance D of A} among the B} to obtain sample sets i
Step 6 is chosen sample sets by rule of specialty, and { sample among the B} adds sample sets { form among the A} new sample sets { A ' } to, is set up by sample sets { A ' } and obtains new near-infrared model A ';
Step 7 is estimated model A ' with internal chiasma proof method and external certificate method.
2. the sampling method of setting up tobacco near-infrared model according to claim 1 is characterized in that spectrum pre-service in the described step 2 is to adopt one or more methods in level and smooth, quadratic interpolattion, first order derivative method or the second derivative method that original near infrared spectrum is carried out pre-service.
3. the sampling method of setting up tobacco near-infrared model according to claim 1, { sample i is to sample sets { the spectrum mahalanobis distance d of A} among the B} to it is characterized in that asking in the described step 3 sample sets iMethod adopt following formula:
Figure F2009102166551C00021
X iFor sample sets the spectrum vector of sample i (n * 1) among the B},
X AvgFor sample sets the averaged spectrum vector of sample (n * 1) among the A},
S be covariance matrix (n * n),
(X i-X Avg) T is (X i-X Avg) transposed matrix,
N is spectrum X iThe data point number.
4. the sampling method of setting up tobacco near-infrared model according to claim 1, { { the component value of A} is apart from d to sample sets for sample i among the B} to it is characterized in that asking in the described step 4 sample sets i' method adopt following formula:
d i′＝|Y i-Y avg|
Y iFor sample sets the component concentration of sample i among the B},
Y AvgBe sample sets { the average composition content of sample among the A}.
5. the sampling method of setting up tobacco near-infrared model according to claim 1 is characterized in that asking in the described step 5 whole Euclidean distance D iMethod adopt following formula:
。
6. the sampling method of setting up tobacco near-infrared model according to claim 1 is characterized in that the rule of specialty in the described step 6, is according to whole Euclidean distance D i{ sample among the B} adds sample sets { form among the A} new sample sets { A ' } to order from small to large with sample sets.
7. the sampling method of setting up tobacco near-infrared model according to claim 1, it is characterized in that cross validation root-mean-square error (RMSECV), the coefficient of determination (R2) are adopted in the internal chiasma checking in the described step 7, external certificate adopts predicted root mean square error (RMSEP), the coefficient of determination (R2) that model is estimated.
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