CN104132865A - Method for predicting density of wood of loblolly pine by utilizing near-infrared spectrum technology - Google Patents
Method for predicting density of wood of loblolly pine by utilizing near-infrared spectrum technology Download PDFInfo
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Abstract
The invention discloses a method for predicting the density of wood of loblolly pine by utilizing a near-infrared spectrum technology. The method comprises the following steps: establishing a predicting model for the basic density of the wood of the loblolly pine on the basis of the near-infrared spectrum technology, and utilizing the model to fast and accurately measuring the basic density of the wood of the loblolly pine. The method disclosed by the invention has the beneficial effects that the defects of long determination time, large error due to artificial operation, high cost and the like in the conventional determination method for the density of the wood are overcome; chemicals do not need to be consumed, so that the harm of the chemicals to a human body is reduced; a sample is not consumed in the measurement process, the appearance and the internal quality of the sample can not be affected, and thus the method realizes typical nondestructive analysis and measurement; the testing repeatability is good, the analysis efficiency is high, the stability of a result is good, and a fast, simple and accurate testing method with low cost is provided for selective breeding of the loblolly pine in China.
Description
Technical field
The present invention relates to a kind of method of predicting pine tree density of wood, specifically a kind of method of utilizing near-infrared spectrum technique prediction torch pine timber basic density.
Background technology
Torch pine originates in southeastern US, is the Major Tree Species Planted of southeastern US, and China introduces a fine variety the torch pine history of existing more than 60 year.Its form is perfectly straight satisfactory, and wood property is good, is one of seeds of successful introduction, belongs to the timber that in structure purposes, material is the most tough, purposes is the most various.Its timber can be built, paper pulp, Fibre Wood.Torch pine is rich in rosin, can be processed into rosin for resin tapping, and its quality is higher, has now become the essential industry material reproducting tree species of China's wide geographic area.In addition, the tall and straight grace of tree performance, hat is like torch, and trunk end is straight, can be used for viewing and admiring green tree species.
The basic density of torch pine timber is the key index of its performance of reflection, it is a critical nature in wood property, closely related with chemical composition and the cell structure of timber, can estimate the quality of timber, the technological property that judges timber and physico-mechanical properties (hardness, intensity, drying shrinkage and wet rise etc.) according to it.Its variability and Changing Pattern thereof be other performance change of left and right often, can reflect the growth rhythm of torch pine simultaneously, is the common counter of the wood property utilization of research torch pine and fine-variety breeding correlativity.
The assay method of density of wood is mainly to adopt GB/T 1933-2009 Method for determination of the density of wood at present, and this standard is applicable to the mensuration of air-dry density, oven-dry density and the basic density of small clear specimen of wood.The mensuration of torch pine timber basic density, use be drainage (with reference to GB/T 1933-2009 Method for determination of the density of wood).In the time adopting the method method to carry out the mensuration of torch pine timber density, sampling, the preparation etc. of sample are had to stricter requirement, there is complex steps, consuming time, large to sample damage in mensuration process, and the shortcoming such as manual operation error is large, efficiency is low.
There is scholar once the moisture of the air-dry density under timber different water cut and Chinese littleleaf box to be measured, also the correlative study that has the basic density of pair blue gum and longleaf pine to measure, but not yet see can carry out fast the density of torch pine, accurately, simple and Forecasting Methodology cheaply.
Summary of the invention
The object of this invention is to provide a kind of method of utilizing near-infrared spectral analysis technology prediction torch pine timber density, the method is to set up the forecast model of torch pine timber basic density based on near-infrared spectrum technique, by this model realization torch pine timber basic density fast, Accurate Determining, for the extensive Wood Properties Within parameter that detects torch pine breeding population and progeny test woods provides fast a kind of, accurately, simply, and method cheaply, more fully hold the hereditary variation rule of torch pine timber wood property, to disclosing the great potential of torch pine Breeding for Wood, carry out more accurately and select, enrich between China's torch pine wood property factor and and growth factor, the correlation analysis research of form factor, have important practical significance to accelerating torch pine genetic improvement process.
The technical solution adopted in the present invention is as follows: a kind of method by near-infrared spectrum technique prediction torch pine timber density, comprises the following steps:
(1) sample collection and basic density conventional determining thereof: gather the growth cone reel of torch pine as sample, adopt the basic density value of conventional method working sample; Described conventional method is GB/T 1993-2009 Method for determination of the density of wood; The near infrared spectra collection of sample: the sample collecting is scanned with near infrared spectrometer, obtain the near infrared spectrum data of sample;
(2) set up model: gathered sample is divided into two groups, i.e. forecast set and checking collection; The near infrared spectrum data of the sample first step (1) being obtained is carried out spectrum pre-service, then based on partial least square method (PLS), the conventional method of forecast set sample being measured to the basic density value obtaining is associated and carries out matching through pretreated near infrared spectrum data with it, through regretional analysis, set up torch pine timber basic density near-infrared model;
(3) checking of model: verify and evaluate with the torch pine timber basic density near-infrared model that the external sample of checking collection has been set up step (2), concrete grammar is: the basic density value that the conventional method of external sample is measured is carried out respectively comparison with the predicted value that adopts the model prediction of having set up, using coefficient R, checking collection prediction standard deviation S EP and absolute deviation as major parameter difference more between the two, model is carried out to the evaluation of external certificate and precision of prediction;
(4) use set up torch pine timber basic density near-infrared model to predict the timber basic density of torch pine sample to be measured: to torch pine to be measured, gather its growth cone reel as testing sample, gather its near infrared light spectrogram with near-infrared spectrometers, the characteristic spectrum data that collect are input in model, obtain the basic density predicted value of this torch pine timber to be measured;
Condition and parameter that described step (1) and step (4) sample carry out near infrared spectra collection are: collection SPECTRAL REGION is 950nm ~ 1650nm, spot diameter is 3.5cm, resolution is 5nm, and environment temperature is controlled at 22 DEG C ~ 23 DEG C, and ambient humidity is controlled at 30% ~ 70%; Adopt scanning 2 times and repeat to fill the spectrum collection mode that sample is averaged for 2 times, with universal stage, to increase sampling area, the diffuse reflection spectrum of collected specimens, gets the mean value of scanning result and preserves;
Described preprocessing procedures is: first order derivative differentiate (1
stder), standard normal variable conversion (SNV) and smoothing algorithm (SG) combine.
The process of establishing of described model, while setting up model, determines best number of principal components based on partial least square method.
The checking of described step (3) model is with model tuning related coefficient (R
c), model tuning related coefficient standard deviation (R
mSEC), the related coefficient (R that obtains of validation-cross
cV), the prediction standard deviation (R that obtains of validation-cross
mSECV) weigh forecast result of model, wherein R
cplay leading role, R
c, R
cVhigher, R
mSEC, R
mSECVlower, forecast result of model is better; Wherein coefficient R
c=1-
, standard deviation R
mSEC=
,
In formula: the sample number that n is modeling; M is the selected number of principal components of modeling; Y is standard method measured value; y
pfor predicted value;
for mean value is measured in standard method.
The collection of described sample, the acquisition method that is torch pine growth cone reel is: choose 15 years and choose in raw Pinus taeda plantations growth vigorous, and more than diameter of a cross-section of a tree trunk 1.3 meters above the ground 16cm, the good torch pine plant that dry type is more perfectly straight, use the arboreal growth cone that diameter is 12mm at height of tree 1-1.5 rice, be parallel to ground, from northwest to southeastern direction, drill through full reel, penetrate trunk, avoid tree knot as far as possible, carry out mark with pencil, put into the transparent plastic bag of sealing, and put into 4 DEG C of Refrigerator stores.
Before the near infrared spectrum data of described collected specimens, the disposal route of sample is: the segment that the reel drilling through is cut into 3.5cm, before sample is carried out near infrared spectrum scanning, more than all samples that need gather spectrum are placed to 24h in laboratory, near infrared spectrometer place.
The present invention has following beneficial effect: the foundation of near infrared technology torch pine timber basic density forecast model, overcome that conventional assay method minute is long, manual operation error large, high in cost of production shortcoming, technique does not need loss chemicals, has reduced the harm of chemicals to human body; In measuring process, not consuming sample, from outward appearance to inherence, can not exert an influence to sample, is that typical nondestructive analysis is measured; And test favorable reproducibility, analysis efficiency is high, result good stability.For China's torch pine fine-variety breeding provide a kind of fast, simply, accurately, method of testing cheaply.
figure of description
The histogram of Fig. 1 torch pine timber basic density measured value
The near infrared light spectrogram of Fig. 2 torch pine timber
Spectrogram after Fig. 3 first order derivative+smoothing algorithm+normalized
Near infrared correction and the cross-validation model of Fig. 4 torch pine timber basic density
The basic density calculating value distribution Butut of 270 samples of Fig. 5 progeny test woods
The basic density calculating value distribution Butut of 56 samples of Fig. 6 core population
The basic density calculating value distribution Butut of 36 samples of Fig. 7 breeding garden.
Embodiment
Further explain the present invention below in conjunction with embodiment, but embodiment does not limit in any form to the present invention.
The torch pine rosin sample that the present embodiment uses in the time setting up model is taken from: the artificial pilot forest of torch pine of city of Lechang county Longshan Forest Farm; The torch pine sample of three different groups predicting is 270 samples of progeny test woods, 56 samples of core population and 36 samples of breeding garden sample, takes from Germany and Britain of Yingde City of Guangdong Province forest farm.
Adopt following steps to realize the present invention.
1, sample collection and basic density conventional determining thereof
(1) sample collection: choose 15 years and choose in raw Pinus taeda plantations growth vigorous, more than diameter of a cross-section of a tree trunk 1.3 meters above the ground 16cm, the good torch pine plant that dry type is more perfectly straight, is used the arboreal growth cone that diameter is 12mm at 1.3 meters of of the height of tree, be parallel to ground, from northwest to southeastern direction, drill through full reel, penetrate trunk, avoid tree knot as far as possible, carry out mark with pencil, put into the transparent plastic bag of sealing, and put into 4 DEG C of Refrigerator stores; The present embodiment is total to 93 of collected specimens, wherein 72 of forecast sets, 21 of checking collection.
(2) sample basic density conventional determining: adopt GB/T 1993-2009 Method for determination of the density of wood,
1. first sample is taken out from 4 DEG C of refrigerators, put on emgloves, be placed in large beaker, add water, reel is pressed in water with weight, allow it fully absorb water, by the time when all samples all sink to beaker bottom, soak its water states that reach capacity of 2 angels more more, change during this time water every day, prevent from going mouldy;
2. survey volume with drainage: clean beaker is contained to distilled water to appropriate depth, be positioned on electronic balance tray, on electronic balance limit, place an iron stand, the cotton cord of one root knot reality is fixed on iron stand, pinion metal needle with cotton cord, make metal needle in suspension status, then metal needle is immersed under water after 1 ~ 2cm, putting electronic balance is zero, makes it balance; Metal needle point is solidly plugged on sample and is immersed in the water, and immersion depth is that whole reel and metal needle submerge in water completely, reel not with at the bottom of beaker and walls of beaker contact, rapidly reading out data also makes a record;
3. dry: in baking oven, first with 60 DEG C of dry 4 h, then dry 8 to 10 hours with 100 DEG C, prevent after timber fully absorbs water unexpected during with too high temperature oven dry, the situation that timber may burst;
4. first weigh, then dry after 2 hours, then weigh, if weighing of front and back 2 hours differs less than 0.5 gram, take out one by one sample weighing over dry and weigh and make a record, until claimed all samples, use Excle logging data, then obtain its density and preserve document.
Finally the histogram of the basic density measured value of 72 samples in mensuration forecast set as shown in Figure 1, can be drawn by Fig. 1, and numerical value major part is at 0.409g/cm
3to 0.437 g/cm
3between, mean value is 0.424986 g/cm
3, total data becomes normal distribution on the whole, can find out that calibration sample has good continuity with representative, meets the requirement of calibration condition.
(3) sample near infrared spectrum data gathers
1. sample preparation: the reel drilling through is cut into the segment of 3.5cm, before sample is carried out near infrared spectrum scanning, more than all samples that need gather spectrum are placed to 24h in laboratory, near infrared spectrometer place;
2. the near infrared spectra collection of sample: with the following conditional parameter of near infrared spectrometer employing, the sample collecting is scanned, obtain the near infrared spectrum data of sample: collection SPECTRAL REGION is 950nm ~ 1650nm, spot diameter is 3.5cm, resolution is 5nm, environment temperature is controlled at 22 DEG C ~ 23 DEG C, and ambient humidity is controlled at 30% ~ 70%; Adopt scanning 2 times and repeat to fill the spectrum collection mode that sample is averaged for 2 times, with universal stage, to increase sampling area, the diffuse reflection spectrum of collected specimens, gets the mean value of scanning result and preserves.The near infrared spectrum data figure of the sample obtaining as shown in Figure 2.
2, set up model:
(1) the present embodiment is total to 93 of collected specimens, wherein 72 of forecast sets, 21 of checking collection; Select 72 samples of forecast set;
(2) in model process of establishing, respectively by method and first order derivatives (1 such as standardization method (Normalization), standard normal variable transformation approach (SNV), smoothing algorithm (SG), product dispersion corrected methods (MSC)
stder) process and combine, spectrum is carried out to pre-service, and select best preprocess method according to the prediction effect of calibration model., major component is analyzed meanwhile, determined best number of principal components.
From table 1, data can be found out, MSC is that product dispersion corrected method, 1st Der+MSC are that first order derivative+product dispersion corrected method and 1st Der+SG+ MSC are the R of first order derivative+smoothing algorithm+product dispersion corrected method
c(model tuning related coefficient) value is all below 0.95, and number of principal components is more, belongs to level on the low side, can directly give up; The R of other spectrum pretreatment combination size
cvalue, more than 0.95, is all the R of first order derivative+standardization method but SNV is standard normal variable transformation approach and 1st Der+Normalization
cV(related coefficient that validation-cross obtains) is worth on the low side, all at 0.8g/cm
3below, also can give up; In remaining spectrum pretreatment combination size, can draw by correlation data, 1st Der+SG+SNV is the R of first order derivative+smoothing algorithm+standard normal variable transformation approach
cvalue and R
cVbeing worth all highlyer, is respectively 0.969978 and 0.897213, R
mSEC(model tuning related coefficient standard deviation) value and R
mSECV(the prediction standard deviation that validation-cross obtains) value is all minimum, is respectively 0.003752g/cm
3and 0.007186g/cm
3, and its number of principal components is minimum, is 11.Consider R
cvalue and R
cVbe worth more approachingly, model is better, therefore, selects the spectrum pre-service of first order derivative+smoothing algorithm+standard normal variable transformation approach as best spectrum pretreatment combined method.
Pre-service Umscrambler software to the spectrum that obtains sample adopts first order derivative differentiate (1
stder), the method that combines of standard normal variable conversion (SNV) and smoothing algorithm (SG) carries out, spectrogram after pretreatment is as shown in Figure 3.
(3) based on partial least square method (PLS), the conventional method of forecast set sample being measured to the basic density value obtaining is associated and carries out matching through pretreated near infrared spectrum data with it, through regretional analysis, set up torch pine timber basic density near-infrared model, as shown in Figure 4, the straight line that wherein in figure, slope is larger represents calibration model, and the straight line that slope is less represents cross-validation model, can find out very intuitively the degree of fitting of model from figure.With R
c(model tuning related coefficient), R
mSEC(model tuning related coefficient standard deviation), R
cV(related coefficient that validation-cross obtains), R
mSECV(the prediction standard deviation that validation-cross obtains), as the major parameter of weighing forecast result of model, the value that calculates each parameter is as shown in table 2.We know result from table 2, and number of principal components is 11 o'clock, the R of model
cbe 0.957679, R
mSECfor 0.004611g/cm
3, R
cVbe 0.881581, R
mSECVfor 0.007816g/cm
3, the related coefficient of model is higher, and the prediction standard deviation that correction related coefficient standard deviation and validation-cross obtain is all lower, and the prediction effect of model is relatively good.
The model parameter of the torch pine timber basic density that the different preprocessing procedures of table 1 draws
The near-infrared model major parameter table of table 2 torch pine timber basic density
。
3, the checking of model: the torch pine timber basic density near-infrared model of having set up is verified and evaluated by 21 external sample of checking collection.
Concrete grammar is: the basic density value that the conventional method of external sample is measured is carried out respectively comparison with the predicted value that adopts the model prediction of having set up, using coefficient R, checking collection prediction standard deviation S EP and absolute deviation as major parameter difference more between the two, model is carried out to the evaluation of external certificate and precision of prediction.
Table 3 is comparisons of predicted value, measured value, absolute deviation and the relative deviation of the external sample of torch pine timber basic density, the result arranging, draws from table 4, and the coefficient R of external sample checking is 0.85, and prediction standard deviation S EP is 0.033g/cm
3, meet the error requirements of standard method, illustrate that the mass ratio of model is better.
Predicted value, measured value, absolute deviation and the relative deviation of the external sample of table 3 torch pine timber basic density
The major parameter table of the checking of the near-infrared model of table 4 external sample to torch pine timber basic density
。
4, use set up torch pine timber basic density near-infrared model to predict the timber basic density of torch pine sample to be measured: to torch pine to be measured, gather its growth cone reel as testing sample, gather its near infrared light spectrogram with near-infrared spectrometers, the characteristic spectrum data that collect are input in model, obtain the basic density predicted value of this torch pine timber to be measured.
The torch pine timber basic density near infrared forecast model that the present embodiment utilization is set up has carried out the basic density prediction of timber to 270 samples of progeny test woods, 56 samples of core population and 36 samples of breeding garden sample respectively.
Fig. 5,6,7 is respectively the distribution plan of corresponding basic density predicted value, can see intuitively that the distribution of three population datas is all normal distribution substantially from scheming, and meets the essential characteristic that data distribute.Through data preparation, obtain table 5, the mean value of known three colony's basic densities is respectively 0.418802g/cm
3, 0.444432g/cm
3, 0.408417g/cm
3, variance is respectively 0.002175,0.001299,0.000302, standard deviation is respectively 0.0468g/cm
3, 0.0357g/cm
3, 0.0171g/cm
3, the basic demand of coincidence loss, the model predication value of setting up is more accurate, utilize near-infrared spectrum technique can be fast, harmless, time saving and energy saving, the basic density of predicting more exactly torch pine timber.
Mean value, variance and the standard deviation of three population sample basic densities of table 5
Claims (6)
1. by a method for near-infrared spectrum technique prediction torch pine timber density, it is characterized in that: comprise the following steps:
(1) sample collection and basic density conventional determining thereof: gather the growth cone reel of torch pine as sample, adopt the basic density value of conventional method working sample; Described conventional method is GB/T 1993-2009 Method for determination of the density of wood; The near infrared spectra collection of sample: the sample collecting is scanned with near infrared spectrometer, obtain the near infrared spectrum data of sample;
(2) set up model: gathered sample is divided into two groups, i.e. forecast set and checking collection; The near infrared spectrum data of the sample first step (1) being obtained is carried out spectrum pre-service, then based on partial least square method, the conventional method of forecast set sample being measured to the basic density value obtaining is associated and carries out matching through pretreated near infrared spectrum data with it, through regretional analysis, set up torch pine timber basic density near-infrared model;
(3) checking of model: verify and evaluate with the torch pine timber basic density near-infrared model that the external sample of checking collection has been set up step (2), concrete grammar is: the basic density value that the conventional method of external sample is measured is carried out respectively comparison with the predicted value that adopts the model prediction of having set up, using coefficient R, checking collection prediction standard deviation S EP and absolute deviation as major parameter difference more between the two, model is carried out to the evaluation of external certificate and precision of prediction;
(4) use set up torch pine timber basic density near-infrared model to predict the timber basic density of torch pine sample to be measured: to torch pine to be measured, gather its growth cone reel as testing sample, gather its near infrared light spectrogram with near-infrared spectrometers, the characteristic spectrum data that collect are input in model, obtain the basic density predicted value of this torch pine timber to be measured;
Condition and parameter that described step (1) and step (4) sample carry out near infrared spectra collection are: collection SPECTRAL REGION is 950nm-1650nm, spot diameter is 3.5cm, resolution is 5nm, and environment temperature is controlled at 22 DEG C-23 DEG C, and ambient humidity is controlled at 30% ~ 70%; Adopt scanning 2 times and repeat to fill the spectrum collection mode that sample is averaged for 2 times, with universal stage, to increase sampling area, the diffuse reflection spectrum of collected specimens, gets the mean value of scanning result and preserves.
2. the method by near-infrared spectrum technique prediction torch pine timber density according to claim 1, is characterized in that: described preprocessing procedures is: first order derivative differentiate, standard normal variable conversion and smoothing algorithm combine.
3. the method by near-infrared spectrum technique prediction torch pine timber density according to claim 1, is characterized in that: the process of establishing of described model, while setting up model based on partial least square method, determine best number of principal components.
4. the method by near-infrared spectrum technique prediction torch pine timber density according to claim 1, is characterized in that: the checking of described step (3) model is with model tuning coefficient R
c, model tuning related coefficient standard deviation R
mSEC, the coefficient R that obtains of validation-cross
cV, the prediction standard deviation R that obtains of validation-cross
mSECVweigh forecast result of model, wherein R
cplay leading role, R
c, R
cVhigher, R
mSEC, R
mSECVlower, forecast result of model is better; Wherein, coefficient R
c=1-
, standard deviation R
mSEC=
,
In formula: the sample number that n is modeling; M is the selected number of principal components of modeling; Y is standard method measured value; y
pfor predicted value;
for mean value is measured in standard method.
5. the method by near-infrared spectrum technique prediction torch pine timber density according to claim 1, it is characterized in that: the collection of described sample, the acquisition method that is torch pine growth cone reel is: choose 15 years and choose in raw Pinus taeda plantations growth vigorous, more than diameter of a cross-section of a tree trunk 1.3 meters above the ground 16cm, the good torch pine plant that dry type is more perfectly straight, using diameter at 1 ~ 1.5 meter of of the height of tree is the arboreal growth cone of 12 mm, be parallel to ground, from northwest to southeastern direction, drill through full reel, penetrate trunk, avoid tree knot as far as possible, carry out mark with pencil, put into the transparent plastic bag of sealing, and put into 4 DEG C of Refrigerator stores.
6. the method for near-infrared spectrum technique prediction torch pine timber density according to claim 1, it is characterized in that: before the near infrared spectrum data of described collected specimens, the disposal route of sample is: the segment that the reel drilling through is cut into 3.5cm, before sample is carried out near infrared spectrum scanning, more than all samples that need gather spectrum are placed to 24h in laboratory, near infrared spectrometer place.
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| CN106442382A (en) * | 2016-07-15 | 2017-02-22 | 中国林业科学研究院热带林业研究所 | Method for rapid prediction of Eucapyptus urophylla * E. tereticornis wood basic density |
| CN108776079A (en) * | 2018-06-13 | 2018-11-09 | 上海建为历保科技股份有限公司 | Utilize the method for information prediction wood component density of damaging by worms |
| CN111811991A (en) * | 2020-07-16 | 2020-10-23 | 西安航天化学动力有限公司 | Near infrared spectrum analysis method for non-contact testing density of composite solid propellant |
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| CN108776079A (en) * | 2018-06-13 | 2018-11-09 | 上海建为历保科技股份有限公司 | Utilize the method for information prediction wood component density of damaging by worms |
| CN111811991A (en) * | 2020-07-16 | 2020-10-23 | 西安航天化学动力有限公司 | Near infrared spectrum analysis method for non-contact testing density of composite solid propellant |
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