CN101539545A - Identification method of precious rosewood based on chemical fingerprint - Google Patents

Abstract

The invention relates to an identification method of precious rosewood based on chemical fingerprint. The method provided by the invention is based on the chemical composition of the wood and can identify the precious rosewood at molecular level by virtue of chemometrics. The method includes ultrasonic extraction, nitrogen-blow concentration, etc. Sample chromatogram of the precious rosewood is collected by GC-FID, then PCA projection is carried out on the obtained chromatogram after being pretreated, thus obtaining identification result. The method realizes identification on unknown rosewood sample, and the result is accurate and reliable.

Description

A kind of identification method of precious rosewood based on chemical fingerprint

Technical field

The present invention relates to a kind of identification method of precious rosewood based on chemical fingerprint.

Background technology

Redwood is the starting material of valuable furniture and artistic handicraft using, need to go up century-old time because it is become a useful person, and self material is durable in addition, can distribute certain fragrance, has very high marketable value and practical value.Yet different cultivars redwood outward appearance is similar, but function and value differs greatly, and redwood that sell in market usually shoddy phenomenon can occur, therefore need carry out real and fake discrimination to redwood not of the same race.At present, the method that precious redwood is differentiated, the traditional botanical classification that mainly is based on artificial experience is biology microstructure key and Computer Image Processing method, and based on the mode of appearance artificial cognition method of physical indexs such as the glossiness of wood surface, colourity, these methods not only waste time and energy, the result also has subjective uncertain factor, and unavoidable the appearance judges by accident.

Summary of the invention

The object of the present invention is to provide a kind of based on the contained chemical substance Global Information of redwood, by data mining technology and Chemical Measurement algorithm, can accurately differentiate the identification method of precious rosewood based on chemical fingerprint of judgement to precious redwood timber.

The identification method of precious rosewood that the present invention proposes based on chemical fingerprint, concrete steps are as follows:

1. the foundation of precious redwood standard finger-print

1.1 The pretreatment

The redwood heartwood of the botany ownership being determined with icking tool is cut into the thin slice shape, takes by weighing 1.0g in 50ml tool plug triangular flask, adds the 20ml absolute ethyl alcohol, filter behind the ultrasonic Extraction 25min, add the 20ml absolute ethyl alcohol in the filter residue, filter behind the ultrasonic Extraction 25min once more, merge filtrate twice, as stoste; Get 10ml stoste in sample bottle, after nitrogen blows the whole solvent absolute ethyl alcohols of concentrated removal, add the 1ml absolute ethyl alcohol, filter, place 2ml standard model bottle through organic phase miillpore filter (0.45 μ m);

1.2 the gas chromatography finger-print obtains

To carry out gas chromatographic detection through pretreated sample, obtain the finger-print of collection of illustrative plates as this sample; The gas chromatography parameter is provided with as follows: injector temperature is 250-280 ℃; Detector temperature is 280-300 ℃; Split ratio is 10-20: 1; Flow rate of carrier gas is 1.0ml/min; Sample size is 1 μ l;

Column temperature adopts temperature programme: initial column temperature is 80 ℃, rises to 240 ℃ with the heating rate of 20 ℃/min, keeps 5 minutes under this temperature, and then the heating rate with 5 ℃/min rises to 280 ℃, keeps 3 minutes under this temperature, and the whole service time is 24 minutes;

2. real and fake discrimination decision method

2.1 chromatographic peak alignment

Different samples require the retention time unanimity of same component in different samples when carrying out the follow-up data processing, therefore need carry out pre-service to chromatographic data, i.e. the influence of chromatographic peak drift to analysis result avoided in chromatographic peak alignment.Chromatographic data with step (1.2) is obtained behind the peak area integration, carries out the automatic registration process of chromatographic peak according to peak area value, and for the peculiar peak of a certain sample, the relevant position of other samples is mended " zero " automatically.

2.2 data are from scaleization

Setting up before pattern recognition model carries out discriminator, to carry out pre-service to the supplemental characteristic that characterizes sample earlier, make data satisfy certain requirement in type, dimension, aspect big or small.Data preprocessing method commonly used in the middle of scaleization (Autoscaling) is a kind of statistics, the new variables y after the scaleization _InBe defined as

$y_{\mathrm{in}}=\frac{y_{\mathrm{ik}}-\stackrel{\‾}{y_k}}{s_k}---\left(1\right)$

Y in its Chinese style _kBe proterotype component of a vector y _IkMean value, s _kBe proterotype component y _IkAmplitude of variation.

2.3PCA projection classification

PCA is a kind of multivariate statistical analysis technology of classics.Its main thought is to get rid of overlapped information in numerous chemical information coexistences, and former variable by linear combination, is obtained the new variables of a few quadrature, that is major component, thereby makes former data dimensionality reduction.Major component is set up the standard projection space matrix by how many orderings of its contained quantity of information; Before several major components promptly comprise the major part of gross information content, this just makes the variable that newly obtains characterize the data structure feature of former variable as much as possible and drop-out not.

According to nonlinear iterative partial least square algorithm (NIPALS), decompose measuring matrix Y

Y＝TP ^t (2)

(subscript " t " expression transposition) chooses first principal component to Second principal component, projection mapping, and the sample with similar features produces the effect directly perceived of " things of a kind come together, people of a mind fall into the same group ", as classification foundation.

2.4 the judgement of unknown sample

(a) unknown sample is carried out sample pretreatment, and its disposal route is with step 1.1;

(b) to carrying out gas chromatographic analysis through the unknown sample that obtains after the pre-service, obtain the data collection of illustrative plates, its disposal route is with step 1.2;

(c) will go up that the unknown sample gas chromatography raw data that obtains of step is carried out the chromatographic peak alignment and from the pre-service of scaleization, its disposal route sees 2.1 and 2.2 respectively, obtains Y thus _Unknown

(d) with treated data Y _UnknownBe projected in the standard projection space matrix of setting up in 2.3.

According to the linear algebra principle, (2) formula is represented the linear transformation (Y with the column vector of T open into same linear space) of T to the Y mapping, P ^tBe transformation matrix.Because P ^tBe the nonsingular square formation (P of quadrature ^t=P '), so there is inverse transformation (subscript " ' " expression inverse transformation) in above-mentioned conversion,

T _Unknown=Y _UnknownP ^t(P ^tP) ' (3)

Its transformation matrix,

P ^t(P ^tP)′＝P (4)

So with unknown sample Y _UnknownIn 2.3, have the linear space projection, obtain the projection score of unknown sample,

T _Unknown=Y _UnknownP (5)

The major component perspective view of unknown sample and known sample is compared, carry out the true and false and subordinate kind judging with this.

The invention has the beneficial effects as follows that can differentiate kind under the similar redwood sample of outward appearance, thereby the true and false of redwood sample is made discriminating, the result accurately and reliably.

Description of drawings

Fig. 1 is the GC-FID finger-print of the broad-leaved yellow wingceltis redwood set up of the present invention.

Fig. 2 is the GC-FID finger-print of East Africa rosewood redwood of setting up of the present invention.

Fig. 3 is the GC-FID finger-print of the Burma padauk redwood set up of the present invention.

Fig. 4 is the GC-FID finger-print of India's red sandalwood redwood of setting up of the present invention.

Fig. 5 is 4 kinds of precious redwood space distribution plane figures.

To be embodiment 1 and embodiment 2 set up distribution in the perspective view at Fig. 6 to Fig. 6.Wherein: zero is the broad-leaved yellow wingceltis, and ◇ is East Africa rosewood, and △ is a Burma padauk, and is the India red sandalwood ,+be unknown sample one, * be unknown sample two.

Embodiment

Below in conjunction with drawings and Examples invention is further specified.

Embodiment 1

1 obtains the finger-print of standard model and unknown sample

1.1 The pretreatment

A. with icking tool 4 kinds of standard redwood samples and 2 kinds of unknown redwood heartwood samples as embodiment are cut into the thin slice shape, take by weighing 1.0g in 50ml tool plug triangular flask;

B. add the 20ml absolute ethyl alcohol, filter behind the ultrasonic Extraction 25min, add the 20ml absolute ethyl alcohol in the filter residue, filter behind the ultrasonic Extraction 25min once more, merge filtrate twice, as stoste;

C. get 10ml stoste in sample bottle, blow to concentrate through nitrogen and remove whole solvents;

D. add the 1ml absolute ethyl alcohol, filter, place 2ml standard model bottle through organic phase miillpore filter (0.45 μ m).

1.2 gas chromatography (GC-FID) finger-print obtains

The GC-FID parameter is provided with as follows:

280 ℃ of injector temperatures (Injector Temperature);

300 ℃ of detector temperatures (Detector Temperature);

Split ratio (Split Ratio) 10: 1;

Flow rate of carrier gas (Flow Rate) 1.0ml/min;

Sample size (Injection Volume) 1 μ l.

Concrete column temperature temperature programme setting sees Table 1.

Table 1GC-FID temperature programme table

2. embodiment being carried out real and fake discrimination judges

2.1 chromatographic peak alignment

Obtaining chromatographic data is carried out the peak area integration, according to peak area value, all samples data (comprising standard model and unknown sample) are carried out the automatic registration process of chromatographic peak, for the peculiar peak of a certain sample, the relevant position of other samples is mended " zero " automatically.

2.2 data are from scaleization

All chromatographic datas that obtain are carried out following processing respectively:

Calculate a chromatographic curve data (y _Ik) mean value y _k, and standard deviation s _k, the chromatographic data after scaleization passes through formula,

$y_{\mathrm{in}}=\frac{y_{\mathrm{ik}}-\stackrel{\‾}{y_k}}{s_k}---\left(1\right)$

Calculate.

2.3 Standard PC A standard projection space is set up

4 kinds of standard redwood samples (measuring 3 times for every kind) are measured gained data matrix Y carry out the PCA decomposition

Y＝TP ^t (2)

Choose first principal component (PC1) to Second principal component, (PC2) projection mapping, set up standard projection space (see figure 5).The get score value of 4 kinds of redwood standard models in this space sees Table 2.

4 kinds of redwood standard models of table 2 and 2 kinds of unknown samples foundation space the score value table

2.4 the judgement of unknown sample

2 kinds of unknown sample are carried out projection to foundation normed space,

T _Unknown=Y _UnknownP (5)

The get score value of 2 kinds of unknown sample in this space sees Table 2, sees Fig. 6 at the plane figure of this space distribution.

In Fig. 5, broad-leaved yellow wingceltis (zero), East Africa rosewood (◇), Burma padauk (△), 4 kinds of redwood of India red sandalwood () distribute in the plane projection of the normed space that is constituted, and wherein, all kinds of redwood samples place circle is its distribution range, be judged to be such redwood in the circle, be judged to be non-such redwood outside the circle.

In Fig. 6, unknown sample one (+) is distributed in the regional extent of India red sandalwood () in the normed space plane figure that Fig. 5 set up, so be judged as India red sandalwood () redwood; Unknown sample two (*) is distributed in broad-leaved yellow wingceltis (zero) in the normed space plane figure that Fig. 5 set up, East Africa rosewood (◇), and Burma padauk (△) is outside 4 kinds of redwood kinds of India red sandalwood () zone, so be judged to be non-above-mentioned 4 kinds of redwood.

Claims (1)

1, a kind of identification method of precious rosewood based on chemical fingerprint is characterized in that concrete steps are as follows:

(1) obtains the finger-print of precious redwood

(1.1) The pretreatment

(1.2) the gas chromatography finger-print obtains

To carry out gas chromatographic detection through pretreated sample, obtain the finger-print of collection of illustrative plates as this sample; The gas chromatography parameter is provided with as follows: injector temperature is 250-280 ℃; Detector temperature is 280-300 ℃; Split ratio is 10-20: 1; Flow rate of carrier gas is 1.0ml/min; Sample size is 1 μ l;

Column temperature adopts temperature programme: initial column temperature is 80 ℃, rises to 240 ℃ with the heating rate of 20 ℃/min, keeps 5 minutes under this temperature, and then the heating rate with 5 ℃/min rises to 280 ℃, keeps 3 minutes under this temperature, and the whole service time is 24 minutes;

(2) real and fake discrimination decision method

(2.1) chromatographic peak alignment

The finger-print that step (1.2) is obtained carries out the peak area integration, carries out the automatic registration process of chromatographic peak according to peak area value, and for the peculiar peak of a certain sample, mend automatically the relevant position of other samples is zero;

(2.2) data are from scaleization

To carrying out from the scale pre-service new variables y after the scaleization through the finger-print after the chromatographic peak alignment _InBe defined as

$y_{\mathrm{in}}=\frac{y_{\mathrm{ik}}-\stackrel{\‾}{y_k}}{s_k}---\left(\text{1}\right)$

Y in its Chinese style _kBe proterotype component of a vector y _IkMean value, s _kBe proterotype component y _IkAmplitude of variation;

(2.3) PCA projection classification

Adopt PCA multivariate statistical analysis method, will obtain the new variables of the quadrature of negligible amounts, i.e. major component through the variable after the scaleization by linear combination; Major component is set up the standard projection space matrix by how many orderings of its contained quantity of information;

According to the nonlinear iterative partial least square algorithm, decompose measuring matrix Y

Y＝TP ^t (2)

Wherein, subscript t represents transposition, chooses first principal component to Second principal component, projection mapping, as classification foundation;

(2.4) judgement of unknown sample

(a) unknown sample is carried out sample pretreatment;

(b) to carrying out gas chromatographic analysis, obtain the data collection of illustrative plates through the unknown sample that obtains after the pre-service; The same step of method (1.2);

(c) will go up the unknown sample gas chromatography raw data that obtains of step carries out the chromatographic peak alignment and from the pre-service of scaleization, obtains Y _UnknownThe same step of method (2.1) and (2.2);

(d) with treated data Y _UnknownBe projected in the standard projection space matrix of setting up in the step (2.3);

Formula (2) expression T is to the linear transformation of Y mapping, P ^tBe transformation matrix, P ^tBe the nonsingular square formation P of quadrature ^t=P ', so there is inverse transformation in formula (2) conversion,

T _Unknown=Y _UnknownP ^t(P ^tP) ' (3)

Subscript ' the expression inverse transformation,

Its transformation matrix is,

P ^t(P ^tP)′＝P (4)

So unknown sample is invested existing standard projection space matrix

T _Unknown=Y _UnknownP (5)

Major component perspective view to unknown sample and known sample compares, and carries out the true and false and subordinate kind judging with this.

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