CN100561194C - A kind of AOTF near infrared spectrometer that utilizes detects method of microorganism in the Chinese medicine - Google Patents

Abstract

The present invention relates to a kind of AOTF of utilization near infrared spectrometer and detect method of microorganism in the Chinese medicine, may further comprise the steps: A. utilizes the spectroscopic data of AOTF near infrared spectrometer acquisition correction collection sample; B. set up qualitative calibration model according to the spectroscopic data of gathering; C. be associated with microbiological data according to spectroscopic data and set up the quantitative correction model; D. gather the near infrared spectrum data of unknown sample; E. by calling the qualitative and quantitative analysis result that calibration model draws related microorganism.The present invention utilizes the AOTF-NIR technology, realizes the fast detecting work of laboratory microorganism, and fast detecting that also can online microorganism can also be differentiated activation of microorganism at an easy rate simultaneously.The present invention has does not need The pretreatment, and detection speed fast (second step velocity) does not consume reagent, pollution-free environmental protection analysis, accuracy advantages of higher.

Description

A kind of AOTF near infrared spectrometer that utilizes detects method of microorganism in the Chinese medicine

Technical field

The present invention relates to the detection method of a kind of microorganism, specifically a kind of AOTF near infrared spectrometer that utilizes detects method of microorganism in the Chinese medicine.

Background technology

At present in the herbal pharmaceutical industry, to microorganisms such as bacterium detection be a test item that must carry out according to the regulation of state-promulgated pharmacopoeia, conventional detection method is to utilize the mode of medium culture counting to detect, this method complicated operation, waste time and energy, at least need 48 hours just can obtain final testing result, and can not keep the production run of a smoothness.

Summary of the invention

The present invention is for overcoming above-mentioned the deficiencies in the prior art, a kind of The pretreatment that do not need is provided, detection speed fast (second step velocity) does not consume reagent, pollution-free environmental protection analysis, the high AOTF near infrared spectrometer that utilizes of accuracy and detects method of microorganism in the Chinese medicine.

The objective of the invention is to adopt following technical proposals to realize:

A kind of AOTF near infrared spectrometer that utilizes detects method of microorganism in the Chinese medicine, utilize the AOTF NIR technology, near infrared light is produced the principle of characteristic absorption according to microorganism species, near infrared spectrum data by analytic sample, use partial least square method and principal component regression method to set up quantitative, qualitative analysis model respectively, unknown sample is carried out fast qualitative, detection by quantitative, specifically may further comprise the steps:

A. utilize the near infrared spectrum data of the whole samples of AOTF near infrared spectrometer collection, do reference with the qualified sample of micro organism quantity and the sample of nonactive thalline;

B. set up biological qualitative calibration model according to the data of near infrared spectrum data and content of microorganisms, the qualified qualitative analysis model of sample that micro organism quantity was set up when whether qualified biological qualitative calibration model be respectively per sample and according to the active active thalline qualitative analysis model of setting up of thalline;

C. further set up microorganism quantitative correction model according to the associated data of near infrared spectrum data and content of microorganisms;

D. gather the near infrared spectrum data of Chinese medicine sample to be measured;

E. the microorganism in the Chinese medicine sample to be measured is carried out qualitative, quantitative test according to the above-mentioned calibration model of setting up, determine having or not and/or its content of microorganism in the Chinese medicine sample to be measured;

Collection spectroscopic data in the described steps A adopts the test sample mode of diffuse reflection or transmission, and each Zhang Guangpu is the average result of 1～1000 scanning, and wavelength coverage is from 780nm～2500nm, and wavelength increment is 0.3～20nm.

Described each Zhang Guangpu of steps A is the average result of 280～500 scannings, and wavelength coverage is 880nm～2300nm, and wavelength increment is 1～10nm.

Described diffuse reflection test sample mode is applicable to the mensuration of solid state sample, assay method is: after the sample of solid particle state being positioned in the groove of sample box, with lid sample is wipeed off again, be positioned on the support together with lid, the probe card of spectrometer is in the circular hole of sample lid, vertical chucking, the spectroscopic data of collected specimens then.

Described transmission test sample mode is applicable to the test sample of liquid condition sample, and transmission mode is meant the only transmitted light of instrument detecting, and transmitted light is meant that light enters sample interior, passes the light of sample after repeatedly refraction of sample interior, scattering and absorption.

The method for building up of the qualitative calibration model among the described step B is: spectroscopic data through 9 smoothing processing of single order differential, is imported analysis software, utilize principal component regression method that spectroscopic data is calculated to set up then and form.

The foundation of the quantitative correction model among the described step C is with 9 smoothing processing of spectroscopic data process single order differential, imports analysis software, and spectroscopic data is corresponding one by one with the data of microorganism, adopts partial least square method to calculate foundation and forms.

Described qualitative calibration model is used for the qualitative detection that whether qualified canbe used on line is to the microorganism of semi-manufacture and final drug and whether have activity.

Described quantitative correction model is used in the laboratory fast that the micro organism quantity to medicine detects, and it comprises three models that are complementary with concrete test item.

Described microorganism is bacterium or mould.

AOTF among the present invention is the abbreviation of Acousto-optic tunable filter, and Chinese is an acousto-optic tunable filter, belongs to existing equipment; AOTF NIR technology and transmission test sample mode all belong to prior art.

Other hardware device involved in the present invention, software and mathematics manipulation mode all belong to existing routine techniques, do not repeat them here.

The present invention utilizes the AOTF NIR technology, near infrared light is produced the principle of characteristic absorption according to microorganism species, near infrared spectrum data by analytic sample, utilization PLS1 (partial least square method) and PCA (principal component analysis (PCA)) set up quantitatively, qualitative analysis model, and unknown sample is carried out fast qualitative, detection by quantitative.

The present invention can realize the fast detecting work of laboratory microorganism, and simultaneously fast detecting that also can online microorganism can also be differentiated the activation of microorganism in the medicine at an easy rate.The present invention has does not need The pretreatment, and detection speed fast (second step velocity) does not consume reagent, pollution-free environmental protection analysis, accuracy advantages of higher.

Description of drawings

Fig. 1 is the original absorption spectrum of new Huang sheet sample;

Fig. 2 is the single order differential smoothing of new Huang sheet sample;

Fig. 3 is spectroscopic data and an achievement data corresponding tables in the The Unscrambler software;

Fig. 4 is the PLS1 regression model of bacterium;

Fig. 5 is the PLS1 regression model of mould;

Fig. 6 is the regression model of mould number below 100;

Fig. 7 is the regression model of mould number below 1000;

Fig. 8 is the PCA major component spatial distribution map of mould qualified samples;

Fig. 9 is the PCA major component spatial distribution map of mould failed test sample;

Figure 10 is the classification forms of two models to qualified samples;

Figure 11 is the classification forms of two models to failed test sample;

Figure 12 be two models to qualified samples apart from view;

Figure 13 be two models to failed test sample apart from view;

Figure 14 is the PCA major component spatial distribution map of active thalline sample;

Figure 15 is the classification form of life model to the 1-4 sample;

Figure 16 be the life model to the 1-4 sample apart from view;

Embodiment

The present invention is further described below in conjunction with drawings and Examples.

Instrument condition and sample preparation:

Instrument: the portable AOTF technology of the Luminar 5030 types near infrared spectrometer that U.S. BRIMROSE company produces, critical piece comprises: opticator, control section, power supply adaptor, notebook computer.The instrument wavelength coverage be 1100nm to 2300nm, the wavelength increment of 2nm, scanning times is 300, adopts the InGaAs detecting device.Norway CAMO company's T heUnscrambler analysis software.

Sample: 20 in new Huang sheet irregular particle shape sample, be numbered 1-20, wherein the microorganism in No. 1 and No. 2 samples kills, and is nonactive thalline, and the microorganism in the 3-20 sample is active thalline.And the data of each sample bacterial population and fungi count are provided, unit is: individual/gram.

Experimental technique:

20 of the new Huang sheet sample sizes of this experiment scanning, sample state is irregular graininess.Use the spectroscopic data of the U.S. Luminar of Brimrose company 5030 type AOTF near infrared spectrometer collected specimens.Sample is positioned in the groove of sample box, with lid sample is wipeed off, be positioned on the support together with lid, the probe card of spectrometer is in the circular hole of sample lid, and vertical chucking adopts irreflexive test sample mode to gather spectrum.Each Zhang Guangpu is the average result of 300 scannings.Wavelength coverage is from 1100nmm to 2300nm, and wavelength increment is 2nm.5 spectrum of the equal continuous sweep of each sample obtain 100 spectrum altogether.100 spectroscopic datas are handled (level and smooth) at 9 through the single order differential, import The Unscrambler analysis software, utilize PCA that spectroscopic data is calculated then and create qualitative calibration model; The data of bacterial population and fungi count are corresponding one by one with sample, adopt the PLS1 mode to calculate and set up the quantitative correction model.

Experimental result and analysis:

1. spectrum

The original absorption spectrum (see figure 1) of new Huang sheet sample, as can be seen from Figure 1, all spectral arrangement are in good order, do not have unusual sample spectra.The single order differential smoothing (see figure 2) of new Huang sheet sample, in good order equally, apparent in view absorption peak is arranged, spectral arrangement is tightr, and the similarity between spectrum and the spectrum is stronger, and the spectral information amount that collects is big.

2. set up the PLS1 Quantitative Analysis Model

(1) foundation of model

Table 1: the numbering of a sample and microorganism numerical value

New Huang sheet lot number	1	2	3	4	5	6	7	8	9	10
											Bacterial population (individual/g)	10	10	10	10	10	10	500	10	10	10
Fungi count (individual/g)	50	10	1050	1400	1630	170	100	10	10	50
											New Huang sheet lot number	11	12	13	14	15	16	17	18	19	20
Bacterial population (individual/g)	300	1000	500	500	1000	1700	10	300	500	10
											Fungi count (individual/g)	30	10	100	3550	600	250	4500	200	50	100

Table 1 is the numbering of institute's sampling and the data of pairing bacterial population of each sample and fungi count.In The Unscrambler software, the spectroscopic data of each sample is corresponding one by one with the data of bacterium and mould number, as shown in Figure 3.

Adopt partial least square method (PLS1), the mode of validation-cross (Full Cross Validation) is set up the recurrence Quantitative Analysis Model of bacterium and mould fully.

(2) interpretation of result

Regression model from bacterium and the mould of Fig. 4 Fig. 5: the both has good correlativity, and related coefficient is respectively 0.9791 and 0.9895.Therefore, we can tentatively judge and utilize near infrared spectrum can obtain the effective information of microorganism.

Because it is fewer to set up the used sample size of model, can't carry out the checking of unknown sample, therefore, we use the model of being set up the spectrum of all scannings to be carried out the checking of an inside, resulting result is similar to external certificate, and identical problem can be described.Table 2 is to call the checking result of model to bacterium and mould:

Table 2. model predicts the outcome to bacterium and mould

Sample number into spectrum	The bacterium predicted value	The bacterium actual value	The mould predicted value	The mould actual value
					11	23	10	510	50
12	-42	10	33	50
					13	105	10	66	50
14	17	10	205	50
					15	44	10	373	50
21	-121	10	-192	10
					22	8	10	59	10
23	32	10	-13	10
					24	34	10	-85	10
25	-187	10	-82	10
					31	38	10	1086	1050
32	214	10	1117	1050
					33	13	10	1255	1050
34	-39	10	993	1050
					35	52	10	1152	1050

41	41	10	1666	1400
					42	-126	10	1593	1400
43	-61	10	1407	1400
					44	-256	10	1409	1400
45	-9	10	1409	1400
					51	59	10	1483	1630
52	32	10	1645	1630
					53	45	10	1429	1630
54	-27	10	1652	1630
					55	-17	10	1378	1630
61	13	10	84	170
					62	-29	10	366	170
63	16	10	388	170
					64	14	10	182	170
65	263	10	570	170
					71	759	500	218	100
72	487	500	-709	100
					73	704	500	-50	100
74	548	500	57	100
					75	761	500	-311	100
81	211	10	114	10
					82	38	10	-97	10
83	80	10	377	10
					84	-44	10	394	10
85	18	10	78	10
					91	3	10	648	10
92	-9	10	108	10
					93	129	10	646	10
94	-216	10	479	10
					95	28	10	-56	10
101	-9	10	527	50
					102	80	10	340	50
103	1	10	102	50
					104	5	10	-32	50
105	9	10	70	50
					111	235	300	77	30
112	257	300	52	30
					113	240	300	-136	30
114	299	300	59	30
					115	286	300	129	30
121	986	1000	292	10
					122	1147	1000	324	10
123	712	1000	-47	10
					124	975	1000	86	10

125	1032	1000	239	10
					131	450	500	527	100
132	245	500	9	100
					133	414	500	250	100
134	459	500	171	100
					135	530	500	74	100
141	502	500	2525	3550
					142	502	500	2776	3550
143	518	500	2125	3550
					144	454	500	2626	3550
145	500	500	2427	3550
					151	960	1000	721	600
152	718	1000	690	600
					153	1036	1000	690	600
154	1001	1000	586	600
					155	1002	1000	615	600
161	1674	1700	-334	250
					162	1729	1700	-163	250
163	1675	1700	-284	250
					164	1720	1700	-226	250
165	1518	1700	-381	250
					171	85	10	4231	4500
172	-9	10	4386	4500
					173	-6	10	4424	4500
174	34	10	4499	4500
					175	-36	10	4496	4500
181	284	300	211	200
					182	283	300	7	200
183	292	300	201	200
					184	280	300	170	200
185	183	300	-147	200
					191	471	500	-157	50
192	653	500	-355	50
					193	499	500	-70	50
194	496	500	-461	50
					195	521	500	70	50
201	79	10	886	100
					202	298	10	984	100
203	221	10	873	100
					204	63	10	714	100
205	237	10	662	100

As can be seen from Table 2: bacterial number all approaches actual value greater than 300 sample and mould quantity greater than the model prediction result of 1000 sample, relatively accurately.But the sample prediction result difference fewer to quantity is very big.This is because the data gradient of two models is very big, and data are from several to several thousand, in so wide data area, because sample size is limited, there is not good gradient at interval, and, few its signal reaction of sample of microorganism number also relatively a little less than, therefore, be difficult to its number accurately of prediction.

(3) improvement of model

Directed toward bacteria if our net result just requires the number of bacterium is controlled under the quantity, such as being less than 7000 for qualified, though so above model is not accurate enough to the few sample prediction of bacterium, can reach the requirement of control.For mould, it is to be less than 100 for qualified that national standard requires, and we change a thinking so, are less than the model that the sample that equals 100 is set up mould with mould quantity, see to reach which type of prediction effect.In 100 spectrum of scanning, the spectrum number of the sample of mould quantity below 100 is 55 altogether.Utilize these 55 spectroscopic datas and a corresponding mould numerical value, set up the model of 100 following moulds, see Fig. 6.

As can be seen from Figure 6, mould model among a small circle has better correlativity, and related coefficient has reached 0.9913, and 55 spectrum that utilize this model that modeling is used are predicted, obtain the result of table 3.

The comparison of table 3. mould model prediction result among a small circle below 100 and actual value

Sample number into spectrum	Predicted value	Actual value	Absolute deviation	Sample number into spectrum	Predicted value	Actual value	Absolute deviation
								11	50	50	0	104	53	50	-3
12	46	50	4	105	51	50	-1
								13	54	50	-4	111	32	30	-2
14	48	50	2	112	27	30	3
								15	51	50	-1	113	31	30	-1
21	12	10	-2	114	36	30	-6
								22	11	10	-1	115	30	30	0
23	9	10	1	121	9	10	1
								24	10	10	0	122	11	10	-1
25	7	10	3	123	11	10	-1
								71	98	100	2	124	11	10	-1
72	100	100	0	125	7	10	3
								73	101	100	-1	131	100	100	0
74	97	100	3	132	101	100	-1
								75	100	100	0	133	96	100	4
81	11	10	-1	134	98	100	2
								82	14	10	-4	135	97	100	3
83	9	10	1	191	51	50	-1
								84	6	10	4	192	53	50	-3
85	12	10	-2	193	50	50	0
								91	8	10	2	194	53	50	-3
92	8	10	2	195	51	50	-1
								93	10	10	0	201	99	100	1

94	8	10	2	202	100	100	0
								95	7	10	3	203	105	100	-5
101	53	50	-3	204	98	100	2
								102	48	50	2	205	97	100	3
103	54	50	-4

As can be seen from Table 3: mould model is very high to the sample prediction result accuracy below 100 among a small circle below 100, have only the absolute deviation of positive and negative several quantity.

The sample that model prediction mould quantity is few is more accurate among a small circle, predict so how mould quantity returns greater than the result of 100 sample? table 4 the predicting the outcome that be mould quantity greater than 100 sample.

Table 4. among a small circle model prediction mould quantity greater than the result of 100 sample

Sample number into spectrum	Predicted value	Actual value	Sample number into spectrum	Predicted value	Actual value
						31	103	1050	141	90	3550
32	112	1050	142	97	3550
						33	105	1050	143	102	3550
34	103	1050	144	90	3550
						35	107	1050	145	94	3550
41	149	1400	151	89	600
						42	151	1400	152	93	600
43	158	1400	153	90	600
						44	149	1400	154	94	600
45	142	1400	155	93	600
						51	92	1630	161	89	250
52	85	1630	162	89	250
						53	91	1630	163	91	250
54	84	1630	164	84	250
						55	90	1630	165	90	250
61	21	170	171	106	4500
						62	15	170	172	91	4500
63	12	170	173	108	4500
						64	17	170	174	103	4500
65	13	170	175	101	4500

As can be seen from Table 4: the prediction of the test model among a small circle mould quantity that mould quantity is set up less than 100 sample differs greatly greater than the result and the actual value of 100 sample, this is normal, because the sample scope of being tested is not within the modeling scope, prediction result is inaccurate certainly.We can attempt setting up a model with the quantity of mould again less than 1000 all samples.In 100 spectrum of scanning, mould quantity is 75 altogether less than the spectrum number of 1000 sample.Utilize these 75 spectroscopic datas and a corresponding mould numerical value, set up the model of 1000 following moulds, see Fig. 7.

As can be seen from Figure 7, the model of mould below 1000 also has good correlativity, and related coefficient is 0.9823, and 75 spectrum that utilize this model that modeling is used are predicted, obtain the result of table 5.

The model prediction mould quantity of table 5. mould below 1000 is less than the result of 1000 sample

Sample number into spectrum	Predicted value	Actual value	Sample number into spectrum	Predicted value	Actual value
						11	69	50	114	32	30
12	50	50	115	56	30
						13	44	50	121	-2	10
14	30	50	122	48	10
						15	52	50	123	-76	10
21	18	10	124	18	10
						22	-92	10	125	-3	10
23	30	10	131	111	100
						24	7	10	132	38	100
25	-96	10	133	123	100
						61	179	170	134	73	100
62	189	170	135	112	100
						63	173	170	151	607	600
64	147	170	152	589	600
						65	165	170	153	597	600
71	190	100	154	606	600
						72	101	100	155	589	600
73	97	100	161	231	250
						74	97	100	162	246	250
75	147	100	163	226	250
						81	124	10	164	272	250
82	17	10	165	247	250
						83	31	10	181	217	200
84	28	10	182	131	200
						85	-7	10	183	180	200
91	31	10	184	201	200
						92	16	10	185	190	200
93	13	10	191	43	50
						94	-4	10	192	46	50
95	-23	10	193	36	50
						101	50	50	194	38	50
102	101	50	195	-1	50
						103	70	50	201	183	100
104	69	50	202	204	100
						105	26	50	203	100	100
111	15	30	204	95	100
						112	58	30	205	108	100
113	10	30

As can be seen from Table 5: the model of mould below 1000 is that four sample prediction result accuracy of 170,200,250,600 is very high to mould quantity; The sample prediction accuracy of mould quantity below 100 reduced, but the mean value of each sample still can approach actual value.

(4) comprehensive solution

Comprehensive above analysis, in so big data gradient scope, we have no idea only just can measure accurately all samples with a model.But, analyzing this tests us and can find to set up three models and just can accurately predict the mould quantity of each sample (bacterium similarly, do not remake labor), these three models are: all samples participates in the unified model of the wide data area of foundation, and we might as well be called model-all; The model that mould quantity is set up less than 1000 samples is called model-1000; The model among a small circle that mould quantity is set up less than 100 samples is called model-100.

Analytical table 2, the mould number is very accurate the sample more than 1000 prediction, and number does not surpass 1000 in the sample predicted value below 1000, therefore, by the prediction of model-all, can effectively the mould number accurately be detected at the sample more than 1000.

Analytical table 5 utilizes the model-1000 model, can accurately predict the sample of mould quantity more than 100.The sample prediction of mould quantity below 100 is not accurate enough.

Analytical table 3 utilizes the model-100 model, and is very high in the sample accuracy for predicting below 100 to mould quantity.

Sum up according to above analysis, can utilize the AOTF-NIR technology fully, realize the work of quick bacterium inspection.Step is as follows: the spectrum of scanning unknown sample, predict with the model-all model that at first predicted value is greater than 1000, and so, the mould quantity of this sample is this numerical value; If the predicted value of sample is less than 1000, again the spectrum of this sample is predicted with the model-1000 model, if predicted value is within 200～1000 scopes, we can affirm the actual value of the predicted value of this sample for this sample so, if predicted value is in 100～200 scopes, we need with the mean value of 5 spectrum of model-1000 model prediction, be the actual value of this sample to this sample multiple scanning 5 times so; If, so, call the model-100 model again this sample is predicted accurately less than 100 with the predicted value of this sample spectra of model-1000 model prediction, the result who obtains is exactly the actual value of this sample.

3. set up the PCA qualitative analysis model

(1) foundation of qualutative model

The multi-model substep very suitable AOTF-NIR technology of bacterium inspection method fast detects the microorganism in the medicine fast in the laboratory, but, this method relative complex, can not adapt to online fast microbiological detects, below we discuss and use qualitative analysis methods, can solve the difficult problem of online bacterium inspection.

Still be example with the mould.Qualitative analysis is two different notions with quantitative test, and quantitative test can detect the concrete number of the mould that contains in the sample; Qualitative analysis is to judge the problem whether be, suppose that the number that contains mould in the regulation medicine surpasses 100 for defective, is less than 100 and is certified products, and whether qualified qualitative analysis be exactly the medicine that detected of judgement problem so.

In existing 20 samples, the mould number of 11 samples is no more than the mould number of 100,9 samples more than 100.Utilize principal component analysis (PCA) (PCA) that the single order differential smoothing data of 11 samples are carried out cluster, as the qualified samples collection; Single order differential smoothing data to 9 samples are carried out cluster, as closing not lattice sample sets.Because we have scanned 5 spectrum each sample, we can branch away first spectrum of each sample, and as checking usefulness, remaining 4 spectrum participates in setting up the model of qualitative analysis.Like this, the qualified samples collection has 11 checking spectrum, and the failed test sample collection has 9 checking spectrum.The qualutative model of 44 certified products spectrum foundation is yes, and the qualutative model of 36 unacceptable product sample foundation is no, sees Fig. 8 and Fig. 9.

(2) prediction

Good yes model (Figure 10) and no model (Figure 11) predicted the checking collection sample of certified products and the checking collection sample of unacceptable product respectively to utilize foundation.The result is shown in Figure 12,13: have the expression sample and the model of " * " number to belong to same class, the expression inhomogeneity that does not have in the classification form.

Among Figure 12,13, the model dot matrix of " zero " expression failed test sample, the model dot matrix of " ● " expression qualified samples, 11 qualified samples or 9 failed test samples that " △ " expression is used for verifying.

From the classification form analysis of Figure 10, Figure 11,11 qualified samples that are used for verifying all are determined and belong to certified products, and neither one belongs to the unacceptable product scope.And 9 failed test samples that are used for verifying have 7 to be determined and to belong to unacceptable product, have 2 can't determine to belong to qualified or defective, but as can be seen from Figure 13, the zone of these 2 more close unacceptable products of sample should belong to unacceptable product.

(3) conclusion

By above result, we can pass through qualitative analysis methods easily, qualified and underproof sample is made a distinction, thereby can use the AOTF-NIR qualitative analysis methods, canbe used on line is to the work of the microorganism detection of semi-manufacture and final drug, judge whether the index of microorganism is qualified fast, if qualified horse back enters next operation, the defective measures areput of taking immediately.Stable smoothness to the Chinese medicine production run is significant, and saves a large amount of time, reduces cost, and increases benefit.

4. the qualitative discrimination of active and nonactive thalline

At present, the thalline that does not have instrument can differentiate in the medicine as yet is that active thalline still is nonactive thalline.In this experiment in 20 samples providing, the thalline in No. 1 and No. 2 samples is nonactive thalline (thalline that kills by certain mode), and thalline is active thalline in all the other 18 samples.Utilize the AOTF-NIR technology can differentiate the activity of thalline effectively, verify by following experiment.

(1) foundation of qualutative model

With No. 5 to No. 20 totally 16 samples gather as active thalline, set up the qualitative analysis model of active thalline, be used for verifying the 1-4 sample then, see and whether 1-2 number the nonactive thalline sample and the sample of 3-4 number active thalline effectively can be differentiated.Utilize principal component analysis (PCA) (PCA) that the single order differential smoothing data of 16 active thalline samples are carried out cluster, obtain active thalline sample major component spatial distribution map shown in Figure 14, model name is life.

(2) prediction

Good life model is predicted the 1-4 sample to utilize foundation.The results are shown in Figure 15 and Figure 16:

As can be seen from Figure 15, No. 1 No. 2 sample does not all have * number, illustrates that 1, No. 2 sample does not belong to active thalline sample; No. 3 No. 4 sample all indicates * number, illustrates that 3, No. 4 samples belong to active thalline sample fully.Can find out clearly also that from Figure 16 1, No. 2 sample drops on outside the model, 3, No. 4 samples are within model area.

(3) conclusion

Utilize the AOTF-NIR technology can differentiate activation of microorganism in the medicine at an easy rate!

Claims (9)

1. one kind is utilized the AOTF near infrared spectrometer to detect method of microorganism in the Chinese medicine, it is characterized in that, utilize NIR technology, near infrared light is produced the principle of characteristic absorption according to microorganism species, near infrared spectrum data by analytic sample, use partial least square method and principal component regression method to set up quantitative, qualitative analysis model respectively, unknown sample carried out fast qualitative, detection by quantitative, specifically may further comprise the steps:

A. utilize the near infrared spectrum data of the whole samples of AOTF near infrared spectrometer collection;

B. set up biological qualitative calibration model according to the data of near infrared spectrum data and content of microorganisms;

C. further set up microorganism quantitative correction model according to the associated data of near infrared spectrum data and content of microorganisms;

D. gather the near infrared spectrum data of Chinese medicine sample to be measured;

E. the microorganism in the Chinese medicine sample to be measured is carried out qualitative, quantitative test according to the above-mentioned calibration model of setting up, determine having or not and/or its content of microorganism in the Chinese medicine sample to be measured;

Wherein, described microorganism is bacterium or mould.

2. a kind of AOTF near infrared spectrometer that utilizes according to claim 1 detects method of microorganism in the Chinese medicine, it is characterized in that: the collection spectroscopic data in the described steps A adopts the test sample mode of diffuse reflection or transmission, each Zhang Guangpu is the average result of 1～1000 scanning, wavelength coverage is from 780nm～2500nm, and wavelength increment is 0.3～20nm.

3. a kind of AOTF near infrared spectrometer that utilizes according to claim 2 detects method of microorganism in the Chinese medicine, it is characterized in that: described each Zhang Guangpu of steps A is the average result of 280～500 scannings, wavelength coverage is 880nm～2300nm, and wavelength increment is 1～10nm.

4. detect method of microorganism in the Chinese medicine according to claim 2 or 3 described a kind of AOTF near infrared spectrometers that utilize, it is characterized in that, described diffuse reflection test sample mode is applicable to the mensuration of solid state sample, assay method is: after the sample of solid particle state being positioned in the groove of sample box, with lid sample is wipeed off, be positioned on the support together with lid, the probe card of spectrometer is in the circular hole of sample lid, vertical chucking, the spectroscopic data of collected specimens then.

5. detect method of microorganism in the Chinese medicine according to claim 2 or 3 described a kind of AOTF near infrared spectrometers that utilize, it is characterized in that: described transmission test sample mode is applicable to the test sample of liquid condition sample, transmission mode is meant the only transmitted light of instrument detecting, transmitted light is meant that light enters sample interior, passes the light of sample after repeatedly refraction of sample interior, scattering and absorption.

6. a kind of AOTF near infrared spectrometer that utilizes according to claim 1 detects method of microorganism in the Chinese medicine, it is characterized in that, the method for building up of the qualitative calibration model among the described step B is: with 9 smoothing processing of spectroscopic data process single order differential, import analysis software, utilize principal component regression method that spectroscopic data is calculated to set up then and form.

7. a kind of AOTF near infrared spectrometer that utilizes according to claim 1 detects method of microorganism in the Chinese medicine, it is characterized in that: the foundation of the quantitative correction model among the described step C is through 9 smoothing processing of single order differential with spectroscopic data, import analysis software, spectroscopic data is corresponding one by one with the data of microorganism, adopt partial least square method to calculate foundation and form.

8. detect method of microorganism in the Chinese medicine according to claim 1 or 6 described a kind of AOTF near infrared spectrometers that utilize, it is characterized in that: described qualitative calibration model is used for that canbe used on line has or not the microorganism of semi-manufacture and final drug and whether microorganism has active qualitative detection.

9. detect method of microorganism in the Chinese medicine according to claim 1 or 7 described a kind of AOTF near infrared spectrometers that utilize, it is characterized in that: described quantitative correction model is used in the laboratory fast the micro organism quantity of medicine is carried out detection by quantitative, and it comprises three models that are complementary with concrete test item.

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