CN105044014A - Method for detecting low-quality starch-doped potato starch fast - Google Patents
Method for detecting low-quality starch-doped potato starch fast Download PDFInfo
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Abstract
The invention discloses a method for detecting low-quality starch-doped potato starch fast. The method comprises the following steps: qualitatively distinguishing whether sweet potato starch, tapioca, corn starch and wheat starch are doped in potato starch or not through electronic microscope imaging and starch grain shape and grain size analysis; quantitatively analyzing the doping quantity of low-quality starch doped in the potato starch through infrared spectroscopic analysis. Test results show that the method for qualitatively and quantitatively detecting the low-quality starch doped potato starch is simple, fast and high in forecasting capability and stability, thereby having an excellent market application prospect.
Description
Technical field
The present invention relates to a kind of farina adulteration detection method, particularly the method for qualitative and quantitative detection of the adulterated starch from sweet potato of farina, tapioca, wheaten starch, cornstarch.
Background technology
The negative event that field of food occurs causes the worry of domestic and international people from all walks of life to China's food security, rectifies food service industry, improves the quality of products, to renew consumer confidence extremely urgent.Current China is still in the food safety risk high-incidence season and contradiction highlights the phase, and food-safety problem has become profound, trans-regional, inter-trade important social concern.Country, while gradual perfection legislation, still needs to carry out variation, omnibearing supervision and management.
Food is forged or adulterated behavior has become in modern society except the Traditional Factors such as agricultural and veterinary chemicals is residual, chemical contamination, abuse growth hormone, the key factor of another threat food security.Such as, the harm such as melamine milk, tonyred food food safety affair, not only grievous injury consumer health, and cause severe social influence.
Along with the development of producing, various powdered food gets more and more.Due to the feature of its contour structures, adulteration in food starch is also more and more rampant, this kind of adulteration not only compromises the physical and mental health of consumer, also upset normal market order, serious economic loss is caused to the producer abide by the law, according to statistics, mainly there is the problem of the following aspects in this phenomenon: (1) utilizes market price difference to be adulterated basic law.Because adulterated material is cheap, easily obtain, mix in the high food of price, though or kind is identical mixes the inferior material of food quality, the net content of food is increased.(2) in solid food, non-material of the same race like some outer appearnces is mixed, such as: in flour, be mixed into talcum powder, zeyssatite etc.(3) people is for being added with harmful substances, adds sodium formaldehyde sulfoxylate etc., to obtain larger profit as: wheat flour product has people even to add in whitening agent, ground rice.
Use the behaviors such as violated adjuvant in food starch, country has formulated a series of test stone.But, for the adulteration between variety classes food starch, due to its mode of appearance and physicochemical property closely, organoleptic examination or conventional physico-chemical analysis detection method is all difficult to by its rapid identification out, and there is no national test stone for being suitable for.
Many physical and chemical indexs of farina are better than other starch, some special performances that farina has, and are that other starch can not be compared, as: potato starch particle is large, and degree of expansion is high, and water retention property is good, be applicable to dilated food, meat products, the products such as instant noodles; The amylose molecule amount of farina is higher than other starch of great majority, pliable and tough film can be produced, and farina contains natural phosphate group, starch paste transparency is high, taste is gentle, non-stimulated etc., these characteristics are all very suitable for food processing and production.
Farina is widely used in field of food industry with its unique use value, its market price is also high than other common food starch such as cornstarch, for this reason, illegal retailer is often had to be spiked in the higher farina of price, to seek illegal profit by low-cost cornstarch, starch from sweet potato, tapioca or wheaten starch etc.Owing to belonging to starch together, all closely, organoleptic examination or physico-chemical analysis detect and are all difficult to rapid identification for mode of appearance and physicochemical property.This behavior not only compromises the legitimate interests of food processing producer and consumer, and destroys fair and just market order, and even some outlet problem commodity have also ruined " made in China " commodity prestige in the international market and national image.
Summary of the invention
For the adulterated technological deficiency being difficult to differentiate of existing food starch, the invention discloses a kind of method that fast qualitative quantitatively detects the adulterated low-quality starch of farina.Wherein, above-mentioned alleged low-quality starch refers to any food starch conventional in the adulterated process of farina, includes but not limited to starch from sweet potato, tapioca, cornstarch and wheaten starch.The method disclosed in the present utilizes the difference of farina and starch from sweet potato, tapioca, cornstarch and wheaten starch grain type, granularity and near infrared spectrum, can be used for the adulterated qualitative analysis of farina and quantitative test.
For achieving the above object, the present invention is achieved through the following technical solutions:
Fast qualitative quantitatively detects the adulterated method of farina, comprises following two stages: (1) carries out quantitative test with (2) to adulterated in potato starch sample to qualitative detection of carrying out adulterated in potato starch sample.
Wherein, the qualitative detection stage adopts scanning electron microscope to carry out scanning to potato starch sample and obtains nanotopographical characteristic image, identifies in potato starch sample to be measured whether be mixed with starch from sweet potato, tapioca, wheaten starch by nanotopographical characteristic image; Adopt particle size analyzer to detect the size-grade distribution of potato starch sample to be measured, judge whether be mixed with cornstarch in farina according to Average Particle Diameters and bulky grain volume parts;
Wherein, the quantitative test stage adopts near-infrared analyzer to carry out near infrared spectrum scanning with the potato starch sample of the low-quality starch of adulterated different quality content and obtain near infrared spectrum collection of illustrative plates, carries out process conversion set up optimal scaling model and quantitatively differentiate detection model as adulterated farina the best according near infrared spectrum profile information; Choose and be accredited as through qualitative detection the potato starch sample to be measured being mixed with low-quality starch, gather the near infrared spectrum of this sample, utilize the near infrared spectrum of the best quantitatively discriminating detection model of described adulterated farina to potato starch sample to be measured to differentiate, thus detect the incorporation of low-quality starch in this testing sample.
In above-mentioned qualitative detection, judge according to as farina pattern be irregular polyhedrons, starch from sweet potato, tapioca are spherical in shape, and wheat flour is pancake.
In order to strengthen accuracy and the readability in qualitative detection stage, avoid the collimation error, imaging contrast can be carried out, specifically comprise the following steps: 1) collect farina, cornstarch, starch from sweet potato, tapioca and wheaten starch sample respectively, obtained the scanning electron microscope nanotopographical characteristic image of above-mentioned starch by scanning electron microscope; 2) potato starch sample to be measured is collected, obtain its scanning electron microscope image by scanning electron microscope, by with step 1) obtain in the scanning electron microscope nanotopographical characteristic image identify potato starch sample to be measured of starch whether be mixed with starch from sweet potato, tapioca, wheaten starch and cornstarch;
In qualitative detection, by scanning electron microscope (SEM) photograph based on farina pattern be irregular polyhedron by itself and spherical starch from sweet potato, tapioca, the difference of the wheaten starch of flat pattern.Because cornstarch is more similar with the nanotopographical of farina, the domain size distribution of starch granules is utilized to carry out determining whether cornstarch in addition.Wherein, potato starch particle particle size range is: 5 ~ 61 μm, mean grain size is about 15 μm, the particle size range of corn starch granules is: 5 ~ 35 μm, mean diameter is about 14 μm, percent by volume shared by farina bulky grain is 3.5% ~ 4.5%, and percent by volume shared by corn starch granules bulky grain is 0.06 ~ 0.10%.Judge whether be mixed with cornstarch in farina qualitatively according to the Average Particle Diameters of starch sample and bulky grain volume parts height.
On the basis of the above, can carry out the quantitative test of adulterated low-quality starch in potato starch sample, this analysis is mainly for the quantitative test that cornstarch carries out.In the method, in quantitatively detecting, near infrared spectrum scanning parameter is: sweep limit is 570 ~ 1098nm, and travelling belt light path selects 25, and wavelength interval is 2nm, and scanning times is 10 times.
Above-mentioned scanning times, light path, wavelength interval all reasonably can adjust according to the performance index of near infrared spectrometer used.
In above process, under farina, cornstarch, starch from sweet potato, wheaten starch, tapioca are placed in scanning electron microscope, adjustment enlargement factor is carried out scanning to above-mentioned starch granules and is taken pictures, wherein, enlargement factor controls 1000 × to 3000 × between effect ideal.
In order to improve the accuracy of quantitative test, prevent the data exception in modeling, can also by the order of accuarcy of setting checking collection inspection modeling method.Specifically, comprise the following steps: obtained low-quality starch addition content accounts for the adulterated potato starch sample collection of 0-99% (w/w) respectively, choose this sample concentrated part sample as calibration set, remaining sample is as checking collection, carry out near infrared spectrum scanning using adulterated farina calibration set sample as calibration collection sample, obtain calibration collection sample near infrared spectrum collection of illustrative plates; To verify that best predictive ability and the stability quantitatively differentiating detection model of adulterated farina verified by collection sample.
Preferably, the quantity of calibration set accounts for 50% ~ 90% of sample sets, and remaining is checking collection.
Further, the step rejecting abnormal sample in sample near infrared spectrum collection of illustrative plates is also comprised: CSD format conversion is become NIR form by described sample near infrared spectrum collection of illustrative plates IFT software in quantitatively detecting, analyze with WinISI III software again, reject the abnormal sample in described sample sets, generate new calibration modeling data collection.
Undertaken in the process of modeling by near infrared spectrum data, the existence of exceptional value will produce larger impact to the Stability and veracity of model, so must reject exceptional value before Modling model, and then effectively improves the predictive ability of calibration model.The present invention adopts the method staying a crosscheck, predict that in each sample score be retained and file, all samples score averages (as central point) compares calculating mahalanobis distance, the mode calculating mahalanobis distance has two kinds, one is Mean (central point), another kind is Sample1, using certain sample as central point, the present invention have selected Mean as the mode calculating mahalanobis distance, and in the three-dimensional plot representing score, the distance GH arranging each sample distance center sample spot is 3.0, in the spectrum intervals result of calculation of equalization point, the sample that GH is greater than 3.0 is marked as redness and is considered to abnormal sample, sample sets after rejecting abnormalities value can generate new calibration modeling data collection.
Because infrared diaphanoscopy spectral information degree of overlapping is larger, so before with NIR quantitative model, in order to improve the forecast test ability of calibration model, stability and sensitivity, suitable process or conversion must be carried out to measured spectrum, select best regression analysis, spectral dispersion disposal route and mathematical processing methods, eliminate interference and the impact of various Aimless factors in spectrum, increase the difference between sample.
In addition, because NIR collection of illustrative plates is not only by the impact of article chemical composition to be measured, also can be subject to the impact of the physicochemical property such as density, superficial makings, granularity, viscosity of test substance simultaneously.Therefore, when with WinISI III software calibration modeling, the information that the chemical composition extracted from spectrum is relevant, the interference of other influences factor must be eliminated, so the application have selected different regression analysis, spectral dispersion disposal route and mathematical processing methods, determine the impact effect of each preprocessing procedures to calibration model stability with SEC, RSQ, SECV and 1-VR.SEC representative calibration standard deviation row, namely the standard deviation evaluation of calibration sample laboratory data and near infrared predicted data difference is participated in, RSQ represents the facies relationship ordered series of numbers between near infrared predicted data and laboratory standard data, when SECV representative calibration modeling adopts cross validation to calculate, the prediction standard deviation mean value do not participated between calibration sample its near infrared value and chemical analysis value arranges, 1-VR represents cross validation related coefficient, namely when cross validation is carried out in calibration modeling, the mean value of related coefficient between calibration sample its near infrared value and chemical analysis value is not participated in prediction.So SEC value and SECV value lower, higher to represent calibration model more stable for RSQ value and 1-VR value.
By research, applicant adopts partial least square method (PLS) and principal component regression (PCR) analytical approach, spectral dispersion disposal route adopts standard normalization in conjunction with scattering facture (SNVandDetrend), go scattering facture (DetrendOnly), standard normalization facture (SNV), without scattering facture (None), multiplicative scatter correction (MSC) etc., mathematical processing methods adopts derivative processing (1, 2, 3, 4 order derivative process), derivative processing spectrum interval point 4nm, once level and smooth spectrum interval point 4nm, do not do secondary level and smooth.
Test findings shows, SEC, SECV value is minimum and the calibration model preprocess method that RSQ, 1-VR value is the highest selects without scattering facture (None), mathematical processing methods adopts first order derivative process, derivative processing spectrum interval point 4nm, once level and smooth spectrum interval point 4nm, do not do secondary smoothing processing, regression analysis adopts partial least square method (PLS).Wherein SEC and SECV be respectively 5.4958,7.7785, RSQ, 1-VR is respectively 0.9511,0.9017, shows to carry out pre-service calibration model precision of prediction by the method the highest.
For evaluating stability and the predictive ability of this model, except evaluating by RSQ value and SEC value, also need the checking carrying out model with the remaining checking collection sample having neither part nor lot in modeling.The present invention adopts the actual value of the adulterated starch of potato and calibration model to the comparative result of the predicted value of unknown sample, namely reliability and the predictive ability of calibration model is evaluated by the correlativity size of actual value and predicted value, result of study shows, calibration model is Y=0.9157X+1.7082 to the regression equation between the predicted value of sample and actual value, coefficient R
2be 0.9129, this shows that this calibration model has higher predictive ability and stability.
After above-mentioned work completes, choose and be accredited as through qualitative detection the potato starch sample to be measured being mixed with low-quality starch, gather the near infrared spectrum of this sample, utilize the near infrared spectrum of the best quantitatively discriminating detection model of described adulterated farina to potato starch sample to be measured to differentiate, thus differentiate the incorporation of low-quality starch in this testing sample.
In sum, the adulterated method of fast quantification qualitative detection farina disclosed by the invention is simple, fast and predictive ability and stability high, there is good market application foreground.
Accompanying drawing explanation
The scanning electron microscope (SEM) photograph of Fig. 1 amylum body.
The near infrared light spectrogram of Fig. 2 potato and adulterated potato.
Relation between the chemical score of the adulterated farina of Fig. 3 and predicted value.
Embodiment
Embodiment 1: the sweep measuring of amylum body nanotopographical
Get about 0.1g farina, cornstarch, starch from sweet potato, wheaten starch and tapioca sample respectively, be sprinkling upon above the metal objective table with conducting resinl one by one uniformly, with the starch granules blowout that ear washing bulb is scattered surrounding, utilize conventional vacuum metallikon to carry out metal spraying process under vacuum, make its plated surface last layer conducting film.Under being placed in scanning electron microscope, adjustment enlargement factor is carried out scanning to particle and is taken pictures, wherein, the enlargement factor of farina, cornstarch, starch from sweet potato, wheaten starch, tapioca 1000 × to 3000 × between effect ideal.
As shown in Figure 1, wherein, farina pattern is irregular polyhedrons to test findings, and starch from sweet potato, tapioca are spherical in shape, and wheat flour is pancake; Therefore can identify in potato starch sample to be measured whether be mixed with starch from sweet potato, tapioca or wheaten starch by scanning electron microscope image.
Embodiment 2: size-grade distribution method is to the detection of adulterated starch
After Mastersizer2000 laser particle analyzer preheating half an hour, getting about 0.5g farina, cornstarch, starch from sweet potato, wheaten starch and tapioca sample is respectively placed on sample cell, start Mastersizer2000 software design patterns parameter auto injection, measure the size-grade distribution situation of farina, cornstarch, starch from sweet potato, wheaten starch, tapioca particle.
The size-grade distribution of table 1 different cultivars starch
As shown above, potato starch particle particle size range is: 5 ~ 61 μm, mean grain size is about 15 μm, the particle size range of corn starch granules is: 5 ~ 35 μm, mean diameter is about 14 μm, farina bulky grain percent by volume is 3.5% ~ 4.5%, and corn starch granules bulky grain percent by volume is 0.06 ~ 0.10%.So, can judge whether be mixed with cornstarch in farina qualitatively according to the average particle size of starch sample and bulky grain volume parts height.
Embodiment 3: for corn, illustrates the detection of near infrared spectroscopy to the adulterated cornstarch of farina
(1) material and process:
In farina, add the cornstarch of different quality, and mix with mixer.Obtained adulterated amount is respectively 100 parts, the sample of 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,35,40,45,50,55,60,70,80,90%, choose 84 parts as calibration set, all the other 16 parts as checking collection.
(2) data processing and modeling analysis:
This experiment uses the InfratecTM1241 type near infrared grain analyzer of FOSS company, and sweep limit is the shortwave scanning of 570 ~ 1098nm, and travelling belt light path selects 25, and wavelength interval is 2nm, and scanning times is 10 times.
Mensuration obtains near-infrared analysis collection of illustrative plates, is NIR form, then analyzes CSD format conversion former spectrum data IFT3.20 software with WinISI III software, rejects the abnormal sample in calibration collection sample, to residue calibration collection sample modeling analysis.
Because infrared diaphanoscopy spectral information degree of overlapping is larger, so before with NIR quantitative model, in order to improve the forecast test ability of calibration model, stability and sensitivity, suitable process or conversion must be carried out to measured spectrum, select best regression analysis, spectral dispersion disposal route and mathematical processing methods, eliminate interference and the impact of various Aimless factors in spectrum, increase the difference between sample.Regression analysis adopts partial least square method (PLS) and principal component regression (PCR); spectral dispersion disposal route adopts standard normalization in conjunction with scattering facture (SNVandDetrend), goes scattering facture (DetrendOnly), standard normalization facture (SNV), without scattering facture (None); multiplicative scatter correction (MSC) etc.; mathematical processing methods adopts derivative processing (1,2,3,4 order derivative process); derivative processing spectrum interval point 4nm; once level and smooth spectrum interval point 4nm, does not do secondary level and smooth.
(3) near infrared spectrum of farina and adulterated farina (mixing cornstarch) compares
Carry out near infrared spectrum scanning to 101 increment product, each sample obtains 10 spectrum lines, is averaging processing it, obtains 101 spectrograms, sees Fig. 2.The frequency multiplication of chemical bond oscillations and sum of fundamental frequencies absorbs and Fermi resonance produces absorption peak in the mainly molecule of near infrared spectrum reflection, so scanning optical spectrum information overlap degree is larger.As shown in Figure 2, in 500-1100nm wavelength coverage, the peak type of farina and adulterated cornstarch is all comparatively similar with position, is difficult to directly differentiate adulterated sample from collection of illustrative plates.If extract the discriminating of Weak Information for detection of adulterations from the near infrared collection of illustrative plates that degree of overlapping is larger, in conjunction with the method for Chemical Measurement, to feeble signal and multi information processing, the model that adulterated farina is quantitatively differentiated to detect must be set up.
(4) rejecting of exceptional value
Undertaken in the process of modeling by near infrared spectrum data, the existence of exceptional value will produce larger impact to the Stability and veracity of model, so must reject exceptional value before Modling model, and then effectively improves the predictive ability of calibration model.Employ the method staying a crosscheck herein, predict that in each sample score be retained and file, all samples score averages (as central point) compares calculating mahalanobis distance, the mode calculating mahalanobis distance has two kinds, one is Mean (central point), another kind is Sample1, using certain sample as central point, have selected Mean herein as the mode calculating mahalanobis distance, and in the three-dimensional plot representing score, the distance GH arranging each sample distance center sample spot is 3.0, in the spectrum intervals result of calculation of equalization point, the sample that GH is greater than 3.0 is marked as redness and is considered to abnormal sample, sample sets after rejecting abnormalities value can generate new calibration modeling data collection.
(5) different pretreatments method is on the impact of calibration model
Table 2 different pretreatments method is on the impact of calibration model
Because NIR collection of illustrative plates is not only by the impact of article chemical composition to be measured, also can be subject to the impact of the physicochemical property such as density, superficial makings, granularity, viscosity of test substance simultaneously.Therefore, when with WinISI III software calibration modeling, the information that the chemical composition extracted from spectrum is relevant, the interference of other influences factor must be eliminated, so have selected different regression analysis, spectral dispersion disposal route and mathematical processing methods herein, determine the impact effect of each preprocessing procedures to calibration model stability with SEC, RSQ, SECV and 1-VR.SEC representative calibration standard deviation row, namely the standard deviation evaluation of calibration sample laboratory data and near infrared predicted data difference is participated in, RSQ represents the facies relationship ordered series of numbers between near infrared predicted data and laboratory standard data, when SECV representative calibration modeling adopts cross validation to calculate, the prediction standard deviation mean value do not participated between calibration sample its near infrared value and chemical analysis value arranges, 1-VR represents cross validation related coefficient, namely when cross validation is carried out in calibration modeling, the mean value of related coefficient between calibration sample its near infrared value and chemical analysis value is not participated in prediction.So SEC value and SECV value lower, higher to represent calibration model more stable for RSQ value and 1-VR value.
As seen from the above table, SEC, SECV value is minimum and the calibration model preprocess method that RSQ, 1-VR value is the highest selects without scattering facture (None), mathematical processing methods adopts first order derivative process, derivative processing spectrum interval point 4nm, once level and smooth spectrum interval point 4nm, do not do secondary smoothing processing, regression analysis adopts partial least square method (PLS).Wherein SEC and SECV be respectively 5.4958,7.7785, RSQ, 1-VR is respectively 0.9511,0.9017, shows to carry out pre-service calibration model precision of prediction by the method the highest.
(6) modelling verification
Evaluate stability and the predictive ability of this model, except evaluating by RSQ value and SEC value, also need the checking carrying out model with remaining 16 samples having neither part nor lot in modeling.Lower Fig. 3 be the actual value of the adulterated starch of potato and calibration model to the comparative result of the predicted value of unknown sample, evaluate reliability and the predictive ability of calibration model by the correlativity size of actual value and predicted value.In Fig. 3, calibration model is Y=0.9157X+1.7082 to the regression equation between the predicted value of sample and actual value, coefficient R
2be 0.9129, illustrate that this calibration model has higher predictive ability and stability.
Claims (9)
1. one kind is detected the method for the adulterated low-quality starch of farina fast, it is characterized in that, described low-quality starch refers to starch from sweet potato adulterated in potato starch sample, tapioca, wheaten starch, cornstarch, and described method comprises qualitative detection and two stages of quantitative test; Wherein, the qualitative detection stage adopts scanning electron microscope to carry out scanning to potato starch sample and obtains nanotopographical characteristic image, identifies in potato starch sample to be measured whether be mixed with starch from sweet potato, tapioca, wheaten starch by nanotopographical characteristic image; Adopt particle size analyzer to detect the size-grade distribution of potato starch sample to be measured, judge whether be mixed with cornstarch in farina according to Average Particle Diameters and bulky grain volume parts; Wherein, the quantitative test stage adopts near-infrared analyzer to carry out near infrared spectrum scanning with the potato starch sample of the low-quality starch of adulterated different quality content and obtain near infrared spectrum collection of illustrative plates, carries out process conversion set up optimal scaling model and quantitatively differentiate detection model as adulterated farina the best according near infrared spectrum profile information; Choose and be accredited as through qualitative detection the potato starch sample to be measured being mixed with low-quality starch, gather the near infrared spectrum of this sample, utilize the near infrared spectrum of the best quantitatively discriminating detection model of described adulterated farina to potato starch sample to be measured to differentiate, thus detect the incorporation of low-quality starch in this testing sample.
2. method according to claim 1, is characterized in that, in qualitative detection, farina pattern is irregular polyhedrons, and starch from sweet potato, tapioca are spherical in shape, and wheat flour is pancake.
3. method according to claim 1, it is characterized in that, in qualitative detection, 1) collect farina, starch from sweet potato, tapioca and wheaten starch sample respectively, the scanning electron microscope nanotopographical characteristic image of above-mentioned four kinds of starch is obtained by scanning electron microscope; 2) potato starch sample to be measured is collected, obtain its scanning electron microscope image by scanning electron microscope, by with step 1) whether be mixed with starch from sweet potato, tapioca, wheaten starch in the scanning electron microscope nanotopographical characteristic image identify potato starch sample to be measured of four kinds of starch that obtains.
4. method according to claim 1, it is characterized in that, in qualitative detection, potato starch particle particle size range is: 5 ~ 61 μm, mean grain size is about 15 μm, and the particle size range of corn starch granules is: 5 ~ 35 μm, and mean diameter is about 14 μm, percent by volume shared by farina bulky grain is 3.5% ~ 4.5%, and percent by volume shared by corn starch granules bulky grain is 0.06 ~ 0.10%.
5. method according to claim 1, is characterized in that, in quantitatively detecting, near infrared spectrum scanning parameter is: sweep limit is 570 ~ 1098nm, and travelling belt light path selects 25, and wavelength interval is 2nm, and scanning times is 10 times.
6. method according to claim 1, it is characterized in that, in quantitatively detecting, in pure potato starch, add the low-quality starch of different quality and mix, obtained low-quality starch addition content accounts for the adulterated potato starch sample collection of 0-99% (w/w) respectively, choose this sample concentrated part sample as calibration set, remaining sample is as checking collection, carry out near infrared spectrum scanning using adulterated farina calibration set sample as calibration collection sample, obtain calibration collection sample near infrared spectrum collection of illustrative plates; To verify that best predictive ability and the stability quantitatively differentiating detection model of adulterated farina verified by collection sample.
7. method according to claim 6, is characterized in that, the quantity of calibration set accounts for 50% ~ 90% of sample sets, and remaining is checking collection.
8. method according to claim 1, it is characterized in that, the step rejecting abnormal sample in sample near infrared spectrum collection of illustrative plates is also comprised: CSD format conversion is become NIR form by described sample near infrared spectrum collection of illustrative plates IFT software in quantitatively detecting, analyze with WinISI III software again, reject the abnormal sample in described sample sets, generate new calibration modeling data collection.
9. method according to claim 1, it is characterized in that, in quantitative test, adulterated farina is best quantitatively differentiates that the modeling parameters of detection model is: without scattering facture, first order derivative mathematical processing methods, derivative processing spectrum interval point 4nm, once level and smooth spectrum interval point 4nm, do not do secondary smoothing processing, partial least square method carries out regretional analysis.
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| CN106442587A (en) * | 2016-08-31 | 2017-02-22 | 安徽紫山农业科技有限公司 | Fast measuring method of starch mass |
| CN106525825A (en) * | 2016-10-12 | 2017-03-22 | 安徽佛子岭面业有限公司 | Method for rapidly detecting adulteration amount of kudzu vine root powder |
| CN107044967A (en) * | 2017-04-18 | 2017-08-15 | 江苏大学 | A kind of method of potato starch near infrared spectrum quick discriminating |
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