CN101655454A - Rapid determination method for evaluation of storage quality of grain - Google Patents
Rapid determination method for evaluation of storage quality of grain Download PDFInfo
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- CN101655454A CN101655454A CN 200910092693 CN200910092693A CN101655454A CN 101655454 A CN101655454 A CN 101655454A CN 200910092693 CN200910092693 CN 200910092693 CN 200910092693 A CN200910092693 A CN 200910092693A CN 101655454 A CN101655454 A CN 101655454A
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Abstract
The invention relates to the technical field of quality detection and provides a rapid determination method for the evaluation of the storage quality of grain, which comprises: S1: grain samples having different storage years are acquired according to the grain samples to be evaluated, thus obtaining a modeling sample set; S2: a near-infrared spectrum of the modeling sample set is obtained by means of acquisition and is pretreated; S3: a fatty acid value of the modeling sample set is obtained by means of measurement; S4: a correction relation for the modeling sample set is established according to the near-infrared spectrum and the fatty acid value, thus obtaining a correction model; and S5: the storage quality of the grain samples to be evaluated is evaluated according to the correction model. On the basis of near-infrared spectrum technique, the rapid determination method is in no need of the sample pretreatment, fast in detection, great in convenience and simplicity and suitable forthe determination of the fatty acid value in kernel of grain crop such as paddy rice, wheat, corn, soybean and the like, thus technically supporting the real-time detection and surveillance of the storage quality of grain.
Description
Technical field
The present invention relates to agricultural product quality detection technique field, relate to the evaluation of grain storage quality, relate in particular to a kind of rapid assay methods of judging based on the grain storage quality of near infrared spectrum.
Background technology
Along with improving constantly of rapid economy development and living standards of the people, agricultural product quality and safety is subjected to extensive concern.The decline of grain storage quality is the pernicious qualitative change that takes place in the foodstuff preservation process, is not suitable for processing human edible finished product of grain, relates to a plurality of links of grain reserves, transportation, sale, processing.Because its professional and disguise, the grain storage quality is difficult for being discovered by the consumer, adds industry and commerce and also is difficult for detecting, and the grain storage quality is that the grain that severe should not be deposited even slightly should not be deposited can be detrimental to health by the food chain accumulation, therefore, its monitoring is just particularly important.
Grain is subjected to the influence of temperature, moisture and enzyme in storage, its lipid material generation hydrolysis and oxidation reaction.When grain went mouldy, the fatty acid enzyme that mould produces can impel the grain hydrolysis.Hydrolysis causes the grain free fatty acid content to increase, and uses quality and edible quality to produce harmful effect to the kind of grain.Grain fatty acid value (mgKOH/100g), promptly in and the milligram number of the required potassium hydroxide of free fatty acid in the 100g grain sample, be one of leading indicator of judging the grain storage quality." paddy storage quality decision rule " GB/T20569-2006, " corn storage quality decision rule " GB/T20570-2006, " wheat storage quality decision rule " GB/T20571-2006 stipulate to the storage quality controlling index of paddy rice, corn, wheat that respectively wherein fatty acid value is one of important indicator.Therefore, the mensuration for the grain fatty acid value just seems particularly important.The mensuration of grain fatty acid value adopts national standard method usually, comprises the method for titrimetry, colourimetry and stratographic analysis.These methods are often all carried out in the laboratory for the mensuration of fatty acid value, and accuracy of analysis is than higher, and still, program is loaded down with trivial details in sample pre-treatments and mensuration process, and the time is longer, and the cost height is difficult more for the analysis of grain great amount of samples.
Summary of the invention
At the deficiencies in the prior art, the invention provides a kind of rapid assay methods that the grain storage quality is judged that is used for based near infrared spectrum, can carry out real-time, online, quick evaluation to the storage quality of a large amount of grains.
The purpose of this invention is to provide a kind of grain storage quality and judge rapid assay methods, described method comprises:
S1: gather the different grain samples that store the time limit according to grain samples to be determined, obtain the correcting sample collection;
S2: gather and obtain the also near infrared spectrum of the described correcting sample collection of pre-service;
S3: measure the fatty acid value that obtains described correcting sample collection;
S4: set up the correction relationship of described correcting sample collection according to described near infrared spectrum and described fatty acid value, obtain calibration model;
S5: the storage quality of judging grain samples to be determined according to described calibration model.
Wherein, correction relationship is described in the step S4: y
i=A
iX+a
0, wherein x is the near infrared spectrum vector, each element in the vector is the absorbance under the different wave length; y
iBe the fatty acid predicted value; A
iBe coefficient vector, its length value is identical with the independent variable number of spectrum x, a
0Be constant term; A
iAnd a
0Can export by the partial least square method program.
Wherein, adopt one or more the chemometrics method be selected from multiple linear regression, partial least squares regression, artificial neural network, the support vector machine to set up described correction relationship.
Wherein, select 4000-12500cm
-1Diffuse reflection near infrared spectrum in the wave-number range is the zone of setting up described correction relationship.
Wherein, described step S5 further comprises the following steps:
S501: gather the also near infrared spectrum of pre-service grain samples to be determined;
S502: the fatty acid value that obtains described grain samples to be measured according to described near infrared spectrum and described correction relationship;
S503: the storage quality of judging described grain samples to be determined according to described fatty acid value according to national standard.
Wherein, described step S5 also comprises the process that described calibration model is carried out error correction and optimizes repeatedly.
Wherein, described error correction and repeatedly optimizing process comprise the following steps:
Step S701: set up the verification sample collection according to grain samples to be determined;
Step S702: gather and obtain the also near infrared spectrum of the described verification sample collection of pre-service;
Step S703: the fatty acid value predicted value that obtains described verification sample collection according to described calibration model;
Step S704: measure and obtain the fatty acid value chemical score that described verification sample is concentrated grain samples;
Step S705: by more described predicted value and described chemical score, rectification error value;
Repeating step S701～S705 optimizes described calibration model.
Wherein, described near infrared spectrum preprocess method be selected from that centralization, canonical variable conversion, additional scatter correction, orthogonal signal are proofreaied and correct, in level and smooth, small echo denoising, differentiate conversion and the genetic algorithm Wavelength optimization one or more.
Wherein, the measurement of described fatty acid value realizes by the KOH titrimetry.
Method of the present invention need not sample pre-treatments, detects fast, and is easy, is fit to the mensuration of fatty acid value in the staple food crop seeds such as paddy rice, corn, wheat, for the real-time detection and the monitoring of grain storage quality provides technical support.
Compared with prior art, advantage of the present invention is:
Sample pre-treatments is simple, and that grain samples only need simply be got is assorted, purify and pulverize;
Fast detecting.Set up after the model, the collected specimens near infrared spectrum can pass through calibration model calculation sample fatty acid value;
Can be the online in real time detection technical support is provided.Near infrared spectrometer is connected with computing machine and can be implemented in line and detect, and the processing that detects and detect data for great amount of samples has positive effect.
Description of drawings
Fig. 1 is the near infrared spectrum of paddy rice sample in the embodiment of the invention;
Fig. 2 is the prediction scatter diagram that the embodiment of the invention adopts fatty acid value rating model in the paddy rice sample that characteristic wave bands sets up.
Embodiment
The rapid assay methods that the grain storage quality that the present invention proposes is judged is described as follows in conjunction with the accompanying drawings and embodiments.
S101: gather representative grain samples and form the modeling sample collection;
Particularly, according to the range of application of calibration model, gather the different grain samples that store the time limit and form the modeling sample collection;
S102: the near infrared spectrum of collected specimens also carries out pre-service;
Particularly, described preprocess method can be selected from that centralization, canonical variable conversion, additional scatter correction, orthogonal signal are proofreaied and correct, in level and smooth, small echo denoising, differentiate conversion and the genetic algorithm Wavelength optimization one or more; Pretreated purpose is the influence of removing system noise, the random noise in the spectrum and following the irrelevant wavelength of fatty acid, proofreaies and correct the inhomogeneous scattering of light that causes of grain size in addition.Thereby acquisition signal to noise ratio (S/N ratio) height, the spectrum that effective information is big, interference is little;
S103: measure fatty acid value in the grain samples according to standard method of analysis;
Particularly, can realize by the KOH titrimetry;
S104: set up the calibration model between near infrared spectrum and the grain fatty acid value;
Particularly, select 4000-12500cm
-1Diffuse reflection near infrared spectrum in the wave-number range is the zone of setting up correction relationship;
In the specific implementation process, described correction relationship can be y
i=A
iX+a
0, wherein x is the near infrared spectrum vector, each element in the vector is the absorbance under the different wave length; y
iBe the fatty acid predicted value; A
iBe coefficient vector, its length value is identical with the independent variable number of spectrum x, a
0Be constant term; A
iAnd a
0Can export by the partial least square method program;
Can adopt one or more the chemometrics method that is selected from multiple linear regression, partial least squares regression, artificial neural network, the support vector machine to set up described correction relationship; Wherein multiple linear regression and offset minimum binary method can be set up the linear model between spectrum and the fatty acid, and the independent variable in spectrum uses multiple linear regression more after a little while, and independent variable uses offset minimum binary more for a long time.If in the time of may having nonlinear relationship between spectrum and the fatty acid, end user's artificial neural networks and support vector machine are set up correction relationship;
S105: checking calibration model;
Gather common grain samples, gather and its near infrared spectrum of pre-service, it is predicted, obtain predicted value according to the calibration model that step S104 sets up according to step S102; Simultaneously measure its fatty acid value according to step S103; The relatively fatty acid value of this grain samples and predicted value, and according to the error requirements in the actual production, calibration model is optimized repeatedly;
It should be noted that in specific implementation process according to the concrete situation of calibration model, this step can be omitted; During actual modeling, often be difficult to determine there is which kind of correction relationship on earth between spectrum and the fatty acid in advance, therefore need to adopt above-mentioned number of chemical metrology method set up model respectively, its error is relatively then finally determined optimum correction relationship;
Particularly, optimization method mainly comprises optimization process and the rejecting of corresponding exceptional value and the optimization of model algorithm of near infrared spectrum; Optimization aim is to make the predicted value of model more near the chemical score of sample, promptly makes calibration model more excellent in the parameter performance to the prediction of verification sample collection by checking and optimization;
S106: gather the also near infrared spectrum of pre-service grain samples to be measured, its fatty acid value is made detection by quantitative with the calibration model of empirical tests;
S107: the storage quality of the mensuration of grain fatty acid value being judged grain according near infrared.
Particularly, judge according to " paddy storage quality decision rule " GB/T20569-2006, " corn storage quality decision rule " GB/T20570-2006, " wheat storage quality decision rule " GB/T20571-2006.
That the near-infrared spectrum technique that the present invention utilized has is easy to use, characteristics fast, and need not to carry out sample pre-treatments, and the minute of each sample only needs 1-3 minute.
To enumerate specific embodiment below is further elaborated to method of the present invention.
In the present embodiment, the foundation of calibration model and range of application are positioned at the paddy rice sample of 1-10 storage life.
The collection of sample and preparation: respectively from Beijing, ground grain depots such as Heilungkiang, Jilin have collected 200 parts in the paddy rice sample of 1-10 storage life, be respectively applied for the modeling sample collection and test the mould sample sets; Sample dries in dark ventilation appropriateness through the impurity elimination purified treatment; Every duplicate samples get 200g with FOSS cutter formula abrasive dust broken be placed in the valve bag standby; Measure spectrum in the 1h, measure chemical score in the 2h;
Sample near infrared spectra collection: use BuChi N-200 Fourier near infrared spectrometer to gather paddy rice sample diffuse reflectance IR, instrument work wave-number range 4000-10000cm
-1, resolution 2cm
-1, be averaged spectrum after the multiple scanning 3 times.Spectrometer is furnished with quartz curette, adorns sample at every turn and completely smooths with glass sheet the back, to avoid the influence of sample-loading amount to the spectra collection effect; The near infrared spectrum that collects as shown in Figure 1.
Fatty acid value in the standard chemical process analyzing rice sample: measure according to the KOH solution titrimetry among the GB GB/T15684-1995 " mensuration of cereal abrasive article-fatty acid ";
The foundation of calibration model: the fatty acid value of calibration model set up to(for) 58 parts of paddy rice samples.Removing earlier during modeling has obviously unusual sample, adopts E-test to divide calibration set, checking collection then.
Obviously referring to unusually in the grain sample of the same storage time limit in the present embodiment, fatty acid value can be rejected so that model is representative apparently higher than the sample data of other sample fatty acid values.
The calibration set sample is used for setting up model, and checking collection sample is used for model is estimated.To the sample infrared spectrum carry out smoothly, the small echo denoising, carry out second order differentiate conversion pre-service simultaneously, set up calibration model between near infrared spectrum and the standard chemical value with partial least-squares regression method.
The checking of calibration model:, all adopt checking collection sample (in the present embodiment being 20 parts) to verify for the calibration model of setting up.Fatty acid value with calibration model prediction sample obtains predicted value, standard chemical value with KOH solution titrimetry working sample, predicted value and chemical score are compared, the result as shown in Figure 2, predicted value and true value related coefficient higher (all greater than 0.8) show that the model of being set up can predict paddy rice sample fatty acid value exactly.In addition, as shown in table 1, the validation-cross standard deviation of model is more approaching with the value of prediction standard deviation, and the error between predicted value and the standard chemical value is less, illustrates that modelling effect is better, and model performance can be better weighed in the distribution of checking collection sample.Above modeling result explanation near infrared spectrum can be measured fatty acid value in the paddy rice sample rapidly and accurately.
Table 1
| Parameter (Parameters) | LV# (number of principal components) | Nc (calibration set sample number) | Np (checking collection sample number) | R (related coefficient) | SECV (validation-cross standard deviation) | SEP (prediction standard deviation) |
| Fatty acid value | ??4 | ?58 | ?20 | ??0.9301 | ??12.7766 | ??9.1636 |
Paddy rice sample storage quality is judged: last, according to the calibration model of setting up to carry out the mensuration of fatty acid value from 12 parts of paddy rice samples of different grain depots, carry out the judgement of its storage quality according to " (paddy storage quality decision rule " GB/T20569-2006, paddy storage quality index is as shown in table 2:
Table 2
For the japonica rice paddy sample in the present embodiment, result of determination is as shown in table 3.
Table 3
| Sample number into spectrum | ??1 | ??2 | ??3 | ??4 | ??5 | ??6 | ??7 | ??8 | ??9 | ??10 | ??11 | ??12 |
| Fatty acid value (mgKOH/100g) | ??26.88 | ??33.87 | ??34.35 | ??31.32 | ??35.73 | ??62.96 | ??29.79 | ??65.24 | ??59.2 | ??57.21 | ??62.99 | ??113.47 |
| Storage quality | Slightly should not deposit | Slightly should not deposit | Slightly should not deposit | Slightly should not deposit | The severe degree should not be deposited | The severe degree should not be deposited | Slight degree should not be deposited | The severe degree should not be deposited | The severe degree should not be deposited | The severe degree should not be deposited | The severe degree should not be deposited | The severe degree should not be deposited |
Above embodiment only is used to illustrate the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; under the situation that does not break away from the spirit and scope of the present invention; can also make various variations and modification; therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (9)
1, a kind of rapid assay methods of grain storage quality judgement comprises the following steps:
S1: gather the different grain samples that store the time limit according to grain samples to be determined, obtain the modeling sample collection;
S2: gather and obtain the also near infrared spectrum of the described modeling sample collection of pre-service;
S3: measure the fatty acid value that obtains described modeling sample collection;
S4: set up the correction relationship of described modeling sample collection according to described near infrared spectrum and described fatty acid value, obtain calibration model;
S5: the storage quality of judging grain samples according to described calibration model.
2, the method for claim 1 is characterized in that, correction relationship described in the step S4 is: y
i=A
iX+a
0, wherein x is the near infrared spectrum vector, each element in the vector is the absorbance under the different wave length; y
iBe the fatty acid predicted value; A
iBe coefficient vector, its length value is identical with the independent variable number of spectrum x, a
0Be constant term; A
iAnd a
0Can export by the partial least square method program.
3, the method for claim 1 is characterized in that, adopts one or more the chemometrics method be selected from multiple linear regression, partial least squares regression, artificial neural network, the support vector machine to set up described correction relationship.
4, the method for claim 1 is characterized in that, selects 4000-12500cm
-1Diffuse reflection near infrared spectrum in the wave-number range is the zone of setting up described correction relationship.
5, the method for claim 1 is characterized in that, described step S5 further comprises the following steps:
S501: gather the also near infrared spectrum of pre-service grain samples to be determined;
S502: the fatty acid value that obtains described grain samples to be determined according to described near infrared spectrum and described correction relationship;
S503: the storage quality of judging described grain samples to be determined according to described fatty acid value according to national standard.
6, the method for claim 1 is characterized in that, described step S5 also comprises the process that described calibration model is carried out error correction and optimizes repeatedly.
7, method as claimed in claim 5 is characterized in that, described error correction and repeatedly optimizing process comprise the following steps:
S701: set up the verification sample collection according to grain samples to be determined;
S702: gather and obtain the also near infrared spectrum of the described verification sample collection of pre-service;
S703: the fatty acid value predicted value that obtains described verification sample collection according to described calibration model;
S704: measure and obtain the fatty acid value chemical score that described verification sample is concentrated grain samples;
S705: by more described predicted value and described chemical score, rectification error value;
Repeating step S701～S705 optimizes described calibration model.
8, as claim 1 or 5 or 7 described methods, it is characterized in that described near infrared spectrum preprocess method is selected from that centralization, canonical variable conversion, additional scatter correction, orthogonal signal are proofreaied and correct, in level and smooth, small echo denoising, differentiate conversion and the genetic algorithm Wavelength optimization one or more.
As claim 1 or 7 described methods, it is characterized in that 9, the measurement of described fatty acid value realizes by the KOH titrimetry.
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