CN104807777A - Rapid detection method for areca-nut water content based on near infrared spectrum analysis technology - Google Patents
Rapid detection method for areca-nut water content based on near infrared spectrum analysis technology Download PDFInfo
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- CN104807777A CN104807777A CN201510208303.7A CN201510208303A CN104807777A CN 104807777 A CN104807777 A CN 104807777A CN 201510208303 A CN201510208303 A CN 201510208303A CN 104807777 A CN104807777 A CN 104807777A
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Abstract
The invention relates to a rapid detection method for areca-nut water content based on a near infrared spectrum analysis technology. The detection method comprises the following steps: obtaining the near infrared spectrum diagram information of a representative areca-nut sample under a set modeling condition, and utilizing a traditional standard physiochemical method to measure the water content of the sample; selecting a proper chemometric resolution method, and establishing a mathematic relation between the diagram information and the water content of the sample, namely a quantitative model; adopting a verified sample set to carry out verification assessment, completing model performance, and finally enabling the model to be used for measuring the water content of the sample to be measured. The detection method is simple, accurate and rapid in operation, the measurement of a single sample can be completed within 30 s, the practicability is higher, and the urgent need of areca-nut production and processing enterprises for real-time product quality control can be solved.
Description
Technical field
The invention belongs to Food Quality and Safety rapid detection technical field, be specifically related to a kind of betel nut moisture method for quick based on near-infrared spectral analysis technology.
Background technology
Betelnut tree all has extensive cultivation in tropical and subtropical region, is mainly distributed in the ground such as Hainan Island, Taiwan, Yunnan, Guangxi, Guangdong and Fujian in China.Betel nut is one of China four great Nan medicine, containing multiple pharmacological component, mainly comprises alkaloid, phenolic compound, fat oil and several amino acids and mineral matter etc., has permanent tooth sterilization, the stagnated food that disappears, disappear the effect such as beriberi and expelling parasite.Betel nut is the major ingredient of betel quid (Betel Quid, BQ), is the 4th kind of preference be widely used in the world being only second to tobacco, alcohol and caffeine.According to estimates, the whole world about has 600,000,000 people to chew food betel nut, and this numeral is also in constantly increasing, and especially in the Hunan of China, chewing cuisine canon, to cross the commodity betel nut of processing all the fashion.
The important step that moisture control is enterprise's process of manufacture is carried out to betel nut product, the height of moisture can to the mouthfeel of betel nut product and processing and storage have an impact further, therefore, betel nut process moisture is controlled in time very important to guarantee betel nut production of articles quality.
The determination of moisture of current betel nut is mainly oven drying method, but, the method exist power consumption, consuming time, operate the defects such as more complicated, and the moisture content value of betel nut cannot be reflected fast.These defects are all unfavorable for carrying out fast mass analysis in the production run of betel nut goods, enhance productivity, and are not suitable for the needs quick and precisely detected during enterprise produces.Therefore, current in the urgent need to study a kind of fast, efficiently, the analyzing detecting method of new accurately betel nut moisture.
Near infrared spectrum (Near Infrared, being called for short NIR) analytical technology is a kind of Fast nondestructive evaluation analytical technology developed rapidly in the later stage eighties 20th century, by utilizing chemometrics method, the material information (frequency multiplication and the combination of the mainly hydric group such as C-H, O-H, N-H vibration absorb frequently) extracting near infrared spectrum district record come material quantitatively, qualitative analysis.This technology has sample without the need to advantages such as pre-treatment, simple to operate, quick, efficient, low costs, has been widely used in the industries such as agricultural, food, medicine, petrochemical complex, and good market prospect.But the relevant report of betel nut moisture is detected up to now there are no application near infrared technology.
Summary of the invention
Technical matters solved by the invention how to overcome the shortcomings such as complex operation in existing betel nut existing for moisture detection method, time-consuming and effort.
In order to solve the problems of the technologies described above, the invention provides a kind of betel nut moisture method for quick based on near-infrared spectral analysis technology.
Betel nut moisture method for quick provided by the present invention, comprises the steps:
1) collect the betel nut sample of different process and different batches, random selecting is some as Calibration, detects the measured value obtaining moisture in betel nut sample;
2) utilize near infrared spectrometer acquisition step 1) in the near infrared light spectrogram of Calibration, and by the near infrared spectrum data of betel nut sample in Calibration and the measured value one_to_one corresponding of moisture, set up the quantitative calibration models of moisture in betel nut sample by chemo metric software;
3) get testing sample, by step 2) in spectral measurement condition gather the near infrared spectrum data of testing sample, import in quantitative calibration models, moisture in betel nut can be obtained.
In above-mentioned detection method, step 1) in, the method for described detection is the direct drying method of the GB5009.3-2010 mensuration of moisture " in the food ".
Described betel nut sample is contained choosing, cleans, dries, sends out son, is dried, upper table is fragrant, cut son, put bittern, dry and pack the sample in 10 manufacturing procedures.
In above-mentioned detection method, step 2) in, in described acquisition correction sample sets, the method for the near infrared light spectrogram of betel nut sample is as follows: betel nut sample is carried out pulverization process, adopts irreflexive metering system, scanning times is 32 times, and collection wavelength coverage is 4000-10000cm
-1near infrared spectrum data as the near infrared spectrum characteristic of moisture in betel nut sample, each sample repeats dress sample and gathers 2 spectrum.
Described near infrared spectrometer is QC-LEADER IP54 near infrared detection instrument.
Described chemo metric software is provided by Zhong An Xinda, Beijing Science and Technology Ltd..
Described quantitative calibration models adopts following steps successively and sets up and obtains:
A, preprocessing procedures: described preprocessing procedures is selected from following at least one: multiplicative scatter correction (MSC), Savitzky-Golay first order derivative, Savitzky-Golay second derivative and standard normal variable conversion (SNV);
B, variable compression method: described variable compression method be selected from following any one: principal component analysis (PCA) (PCA) or partial least square method (PLS);
C, Chemical Measurement modeling method: described Chemical Measurement modeling method be selected from following any one: partial least square method (PLS), main composition return (PCR), support vector machine (SVM) or artificial neural network method (ANN).
Described quantitative calibration models specifically adopts following steps and setting up to obtain successively:
A, preprocessing procedures: standard normal variable conversion (SNV);
B, variable compression method: partial least square method (PLS);
C, Chemical Measurement modeling method: partial least square method (PLS).
In above-mentioned detection method, step 2) in, also comprise to set up quantitative calibration models carry out evaluate checking step: random selecting step 1) in some betel nut samples as verification sample collection, by step 2) in spectral measurement condition gather the near infrared spectrum data of described verification sample collection, imported in set up quantitative calibration models, be verified the predicted value of sample moisture in sample sets, by itself and step 1) described in verification sample concentrate compared with the measured value of the moisture of betel nut sample, if the error of predicted value and measured value is in the scope set, then described quantitative calibration models can be used, otherwise, then repetition step 2 is needed), Optimization Modeling condition is until described quantitative calibration models can be used.
The present invention provides a kind of accurate, quick, simple detection method completely newly for betel nut determination of moisture, improves the production efficiency of betel nut product, significant to whole betel nut production processing industry.
Compared with the prior art, beneficial effect of the present invention is embodied in following aspect: (1) sample analysis speed is fast, efficiency is high, adopts this method can complete the mensuration of betel nut moisture in 30 seconds, greatly improves detection efficiency; (2) operation steps of the present invention is simple, accurate, has greatly saved human and material resources cost; (3) testing process does not use any chemical reagent, provides a kind of betel nut Moisture Method of reliable green.
Accompanying drawing explanation
Fig. 1 is the spectrogram of the betel nut sample moisture of modeling.
Fig. 2 is betel nut moisture near infrared forecast model design sketch.
Embodiment
Be described method of the present invention below by specific embodiment, but the present invention is not limited thereto, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.
Experimental technique described in following embodiment, if no special instructions, is conventional method; Described reagent and material, if no special instructions, all can obtain from commercial channels.
Embodiment 1, betel nut moisture method for quick based on near-infrared spectrum technique
Concrete operation step is as follows:
(1) collect sample and detect moisture in sample: collecting the betel nut sample in different process, be divided into Calibration and verification sample collection, final selection 768 parts of sample composition Calibrations, 387 increment product are verification sample collection.Sample sets to contain in production run choosing, cleans, dries, sends out son, dries, upper table is fragrant, cut son, put bittern, dry and pack the sample in 10 manufacturing procedures;
Record the measured value of moisture in sample according to the direct drying method in the GB5009.3-2010 mensuration of moisture " in the food ", build the reference value data set of the near-infrared model of Calibration and verification sample collection.
(2) spectrum and modeling is gathered: after being used by Calibration comminutor pulverization process evenly, direct importing rotary sample cup, adopts near infrared spectrometer (near infrared spectrometer is QC-LEADER IP54 ft-nir spectrometer) with diffuse reflectance sweep measuring.Spectra collection scope 4000-10000cm
-1, scanning times 32 times, each sample repeats dress sample and gathers 2 spectrum, collects the near infrared spectrum picture library that gained 2 spectrum all add betel nut sample, final obtains the spectrum of Calibration as shown in Figure 1.
And the correcting sample of gained in the near infrared light spectrogram of Calibration and step (1) is concentrated the measured value one_to_one corresponding of the moisture of sample, set up the quantitative calibration models of moisture in betel nut by chemo metric software (Science and Technology Ltd. provides by Zhong An Xinda, Beijing), described quantitative calibration models adopts following steps successively and sets up and obtains:
A, preprocessing procedures: standard normal variable conversion (SNV);
B, variable compression method: partial least square method (PLS);
C, Chemical Measurement modeling method: partial least square method (PLS).
From Fig. 2, corresponding betel nut moisture near infrared forecast model design sketch as shown in Figure 2, can be learnt that the near infrared predicted value of betel nut moisture and measured value are evenly distributed on diagonal line both sides, illustrate that model can be predicted sample moisture.
(3) verification sample set pair quantitative calibration models carries out evaluating checking: concentrated by verification sample sample to pulverize evenly according to the processing mode identical with sample in Calibration in step (2), and the near infrared spectrum data of checking collection sample is obtained in spectra collection mode same in step (2), imported in built quantitative calibration models, be verified the predicted value of sample moisture in sample sets, compared with it is concentrated the measured value of the moisture of sample with verification sample described in step (1), if the error of predicted value and measured value is in setting range, as: according to enterprise requirements, if the absolute value of predicted value (%) and measured value (%) difference is within 2%, then described quantitative calibration models can be used, otherwise then need repetition step (2), Optimization Modeling condition is until described quantitative calibration models can be used.Add up this routine quantitative model the result as shown in table 1.Can see, modelling verification is dry straight, and model accuracy satisfies the demands.
The interpretation of result of collection near infrared moisture verified by table 1
| Reference method | Testing requirement | Total number of samples (individual) | Acceptance number (individual) | Qualification rate |
| Enterprise requirements | | predicted value-measured value |≤2% | 387 | 387 | 100％ |
(4) testing sample test: the betel nut sample composition test sample sets 95 being had neither part nor lot in modeling, utilizes the direct drying method in the standard GB/T 5009.3-2010 mensuration of moisture " in the food " to obtain its moisture, as measured value.Pulverize evenly with method identical in step (2), near infrared spectrum scanning is carried out under the near infrared spectrum scanning condition that same step (2) is identical, by in the quantitative calibration models of moisture in gained spectroscopic data input step (2) gained betel nut, obtain the predicted value of the moisture of betel nut sample to be measured, according to inner controlling standard of enterprise, betel nut water model testing result is as shown in table 2 below, as known from Table 2: the test result of 98% meets inner controlling standard of enterprise, the effect that the present invention is good in betel nut determination of moisture is described.
Table 2 betel nut water model testing result
| Reference method | Testing requirement | Total number of samples (individual) | Acceptance number (individual) | Qualification rate |
| Company standard | | predicted value-measured value |≤2% | 95 | 93 | 98％ |
Claims (7)
1., based on a betel nut moisture method for quick for near-infrared spectrum technique, comprise the steps:
1) collect the betel nut sample of different process and different batches, random selecting is some as Calibration, detects the measured value obtaining moisture in betel nut sample;
2) utilize near infrared spectrometer acquisition step 1) in the near infrared light spectrogram of Calibration, and by the near infrared spectrum data of betel nut sample in Calibration and the measured value one_to_one corresponding of moisture, set up the quantitative calibration models of moisture in betel nut sample by chemo metric software;
3) get testing sample, by step 2) in spectral measurement condition gather the near infrared spectrum data of testing sample, import in quantitative calibration models, moisture in betel nut can be obtained.
2. detection method according to claim 1, is characterized in that: step 1) in, the method for described detection is the direct drying method of the GB5009.3-2010 mensuration of moisture " in the food ";
Described betel nut sample is contained choosing, cleans, dries, sends out son, is dried, upper table is fragrant, cut son, put bittern, dry and pack the sample in 10 manufacturing procedures.
3. detection method according to claim 1 and 2, it is characterized in that: step 2) in, in described acquisition correction sample sets, the method for the near infrared light spectrogram of betel nut sample is as follows: betel nut sample is carried out pulverization process, adopt irreflexive metering system, scanning times is 32 times, and collection wavelength coverage is 4000-10000cm
-1near infrared spectrum data as the near infrared spectrum characteristic of moisture in betel nut sample, each sample repeats dress sample and gathers 2 spectrum.
4. the detection method according to any one of claim 1-3, is characterized in that: step 2) in, described quantitative calibration models adopts following steps successively and sets up and obtains:
A, preprocessing procedures: described preprocessing procedures is selected from following at least one: multiplicative scatter correction, Savitzky-Golay first order derivative, Savitzky-Golay second derivative and standard normal variable convert;
B, variable compression method: described variable compression method be selected from following any one: principal component analysis (PCA) or partial least square method;
C, Chemical Measurement modeling method: described Chemical Measurement modeling method be selected from following any one: partial least square method, main composition return, support vector machine or artificial neural network method.
5. detection method according to claim 4, is characterized in that: step 2) in, described quantitative calibration models adopts following steps successively and sets up and obtains:
A, preprocessing procedures: standard normal variable converts;
B, variable compression method: partial least square method;
C, Chemical Measurement modeling method: partial least square method.
6. the detection method according to any one of claim 1-5, it is characterized in that: step 2) in, also comprise to set up quantitative calibration models carry out evaluate checking step: random selecting step 1) in some betel nut samples as verification sample collection, by step 2) in spectral measurement condition gather the near infrared spectrum data of described verification sample collection, imported in set up quantitative calibration models, be verified the predicted value of sample moisture in sample sets, by itself and step 1) described in verification sample concentrate compared with the measured value of the moisture of betel nut sample, if the error of predicted value and measured value is in the scope set, then described quantitative calibration models can be used, otherwise, then repetition step 2 is needed), Optimization Modeling condition is until described quantitative calibration models can be used.
7. the detection method according to any one of claim 1-6, is characterized in that: described near infrared spectrometer is QC-LEADER IP54 near infrared detection instrument.
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| CN105004690A (en) * | 2015-07-30 | 2015-10-28 | 合肥工业大学 | Rapid and nondestructive testing method of sclereid content in pear pulp based on multi-spectral imaging technology |
| CN106092959A (en) * | 2016-06-30 | 2016-11-09 | 上海仪器仪表研究所 | A kind of near-infrared food quality based on cloud platform monitoring system |
| CN106248613A (en) * | 2016-08-19 | 2016-12-21 | 中国林业科学研究院热带林业研究所 | A kind of method measuring the Eucalyptus cloeziana mechanical property of wood |
| CN106323689A (en) * | 2016-08-22 | 2017-01-11 | 中国食品发酵工业研究院 | Water quality monitoring-orientated polar organic trace pollutant trap |
| CN108051417A (en) * | 2017-12-15 | 2018-05-18 | 湖南科技大学 | A kind of fluorescent inspection method of edible areca-nut brine |
| CN108760668A (en) * | 2018-06-01 | 2018-11-06 | 南京林业大学 | Pinus massoniana Seedlings root moisture method for fast measuring based on weighting autocoder |
| CN110567889A (en) * | 2019-09-12 | 2019-12-13 | 中国计量大学 | Nondestructive testing method for water content of fresh cocoons based on spectral imaging and deep learning technology |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105004690A (en) * | 2015-07-30 | 2015-10-28 | 合肥工业大学 | Rapid and nondestructive testing method of sclereid content in pear pulp based on multi-spectral imaging technology |
| CN106092959A (en) * | 2016-06-30 | 2016-11-09 | 上海仪器仪表研究所 | A kind of near-infrared food quality based on cloud platform monitoring system |
| CN106092959B (en) * | 2016-06-30 | 2019-03-19 | 上海仪器仪表研究所 | A kind of near-infrared food quality monitoring system based on cloud platform |
| CN106248613A (en) * | 2016-08-19 | 2016-12-21 | 中国林业科学研究院热带林业研究所 | A kind of method measuring the Eucalyptus cloeziana mechanical property of wood |
| CN106323689A (en) * | 2016-08-22 | 2017-01-11 | 中国食品发酵工业研究院 | Water quality monitoring-orientated polar organic trace pollutant trap |
| CN106323689B (en) * | 2016-08-22 | 2020-02-21 | 中国食品发酵工业研究院有限公司 | Trace polar organic pollutant trap with water quality monitoring as guide |
| CN108051417A (en) * | 2017-12-15 | 2018-05-18 | 湖南科技大学 | A kind of fluorescent inspection method of edible areca-nut brine |
| CN108760668A (en) * | 2018-06-01 | 2018-11-06 | 南京林业大学 | Pinus massoniana Seedlings root moisture method for fast measuring based on weighting autocoder |
| CN110567889A (en) * | 2019-09-12 | 2019-12-13 | 中国计量大学 | Nondestructive testing method for water content of fresh cocoons based on spectral imaging and deep learning technology |
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