CN105973814A - Laser near-infrared rapid detecting method for milk freshness - Google Patents
Laser near-infrared rapid detecting method for milk freshness Download PDFInfo
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- CN105973814A CN105973814A CN201510945211.7A CN201510945211A CN105973814A CN 105973814 A CN105973814 A CN 105973814A CN 201510945211 A CN201510945211 A CN 201510945211A CN 105973814 A CN105973814 A CN 105973814A
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- milk sample
- milk
- laser near
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- acidity value
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
- G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
- G01N21/78—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator producing a change of colour
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N31/00—Investigating or analysing non-biological materials by the use of the chemical methods specified in the subgroup; Apparatus specially adapted for such methods
- G01N31/16—Investigating or analysing non-biological materials by the use of the chemical methods specified in the subgroup; Apparatus specially adapted for such methods using titration
Abstract
The invention relates to a laser near-infrared rapid detecting method for milk freshness. The method comprises the following steps: a plurality of milk samples are collected and divided into the calibration set milk samples and the test set milk samples, the acidity values of the calibration set milk samples and the test set milk samples are measured; near infrared spectrums of the calibration set milk samples and the test set milk samples are measured; a quantitative model of the acidity values of the calibration set milk samples and the fused spectrum of the calibration set milk samples is established; the acidity values of the test set milk samples are predicted through the quantitative model, and compared with those of the calibration set milk samples; the near infrared spectrum of the milk sample to be detected is collected, and the acidity value of the milk sample to be detected is predicted through the quantitative model. The laser near-infrared rapid detecting method for milk freshness is safe, reliable, fast and accurate, and has very high practical application value.
Description
Technical field
The present invention relates to milk detection technique field, the method being specifically related to the infrared quick detection milk freshness of a kind of Laser Near.
Background technology
Milk is one of the most ancient natural drink, is described as " white blood ", well imagines the importance of human body；It is the best source of human calcium that milk contains abundant mineral, calcium, phosphorus, ferrum, zinc, copper, manganese, molybdenum, and milk, and calcium phosphorus ration is very suitable, the beneficially absorption of calcium.But milk is unfavorable for depositing for a long time, time one length will be stale, and people has once drunk discomfort will occurs, in some instances it may even be possible to can have serious consequences.
Summary of the invention
For weak point present in above-mentioned technology, the invention provides a kind of method safe and reliable, the infrared quick detection milk freshness of Laser Near fast and accurately.
The technical solution adopted for the present invention to solve the technical problems is: the method for the infrared quick detection milk freshness of a kind of Laser Near, comprise the steps: step one, sample collecting: the milk sample gathering the production of same kind not same date is some, is divided into calibration set milk sample and test set milk sample；Step 2, acidity value measure: measure calibration set milk sample and the acidity value of test set milk sample；Step 3, spectra collection: acquisition correction collection milk sample and the Laser Near infrared spectrum of test set milk sample, spectra collection condition is: milk sample temperature 20～30 DEG C, spectral region 1200～1800nm, and acquisition mode is transmission；Step 4, quantitative model are set up: set up the quantitative model of the acidity value of calibration set milk sample and the Laser Near infrared spectrum of calibration set milk sample；Step 5, quantitative model are verified: the acidity value of the quantitative model prediction test set milk sample by setting up in step 4, and compare with the acidity value of the test set milk sample measured in step 2, it is desirable to error is less than 10%；Step 6, milk sample freshness to be measured are analyzed: gather the Laser Near infrared spectrum of milk sample to be measured, use quantitative model to predict the acidity value of milk sample to be measured；Wherein, as acidity value >=25 ° T, then judge that milk sample to be measured is stale；As 25 ° of T of acidity value ＜, then judge that milk sample to be measured is fresh.
Preferably, acidity value assay method in described step 2 is as follows: draws 10ml milk sample and injects in 100ml triangular flask, and dilute with 20ml neutral distillation water, add 0.5% phenolphthalein indicator 0.5ml mixing, using the titration of 0.1mol/L standard solution of sodium hydroxide, shake is not till blush disappeared in 30 seconds constantly；Wherein, acidity value (° T)=10* (V1-V0) * C；V1 is the volume consuming standard solution of sodium hydroxide, and unit is ml；Consuming the volume of standard solution of sodium hydroxide when V0 is blank assay, unit is ml；C is the concentration of standard solution of sodium hydroxide, and unit is mol/L.
Preferably, in described step 2, the acidity value scope of calibration set milk sample and test set milk sample is 20 ° of T～30 ° of T.
Preferably, in described step 3 during spectra collection, repeated acquisition 5 times also takes its meansigma methods Laser Near infrared spectrum as this milk sample.
Preferably, the method setting up quantitative model in described step 4 is as follows: the Laser Near infrared spectrum of calibration set milk sample is carried out pretreatment, and the Laser Near infrared spectrum of pretreatment is carried out characteristic wavelength extraction, use multiplexed quantitative bearing calibration to set up the quantitative model between Laser Near infrared spectrum and the acidity value of calibration set milk sample that characteristic wavelength extracts.
Preferably, described preprocess method uses Savitzky-Golay to filter 11 smooth elimination spectral noise.
Preferably, use backward interval partial least square (BiPLS) that pretreated Laser Near infrared spectrum is carried out characteristic wavelength extraction, with validation-cross mean square deviation as standard, choose the minimum model interval of validation-cross mean square deviation for optimal interval.
Preferably, described multiplexed quantitative bearing calibration uses support vector regression (SVR) and combines grid-search algorithms (CV) and carry out parameter optimization.
Preferably, described support vector regression (SVR) method uses e-SVR regression model and radial direction base (RBF) kernel function carry out regression modeling, by grid-search algorithms (CV), the penalty factor in described support vector regression (SVR) model and RBF kernel functional parameter g are optimized.
Compared with prior art, it provides the benefit that the present invention: the method for the infrared quick detection milk freshness of Laser Near that the present invention provides, safe and reliable, quick and precisely, has good actual application value.
Detailed description of the invention
The method that the invention provides the infrared quick detection milk freshness of a kind of Laser Near, comprises the steps:
Step one, sample collecting: the milk sample gathering the production of same kind not same date is some, is divided into calibration set milk sample and test set milk sample；
Step 2, acidity value measure: measure calibration set milk sample and the acidity value of test set milk sample, the acidity value scope of calibration set milk sample and test set milk sample is 20 ° of T～30 ° of T, acidity value assay method is as follows: draws 10ml milk sample and injects in 100ml triangular flask, and dilute with 20ml neutral distillation water, add 0.5% phenolphthalein indicator 0.5ml mixing, using the titration of 0.1mol/L standard solution of sodium hydroxide, shake is not till blush disappeared in 30 seconds constantly；Wherein, acidity value (° T)=10* (V1-V0) * C；V1 is the volume consuming standard solution of sodium hydroxide, and unit is ml；Consuming the volume of standard solution of sodium hydroxide when V0 is blank assay, unit is ml；C is the concentration of standard solution of sodium hydroxide, and unit is mol/L；
Step 3, spectra collection: acquisition correction collection milk sample and the Laser Near infrared spectrum of test set milk sample, acquisition condition is: milk sample temperature 20～30 DEG C, spectral region 1200～1800nm, metering system is transmission, and repeated acquisition 5 times also takes its meansigma methods Laser Near infrared spectrum as this milk sample；
Step 4, quantitative model are set up: set up the quantitative model of the acidity value of calibration set milk sample and the Laser Near infrared spectrum of calibration set milk sample, the method setting up quantitative model is as follows: the Laser Near infrared spectrum of calibration set milk sample is carried out pretreatment, and the Laser Near infrared spectrum of pretreatment is carried out characteristic wavelength extraction, use multiplexed quantitative bearing calibration to set up the quantitative model between Laser Near infrared spectrum and the acidity value of calibration set milk sample that characteristic wavelength extracts；
Wherein, described preprocess method uses Savitzky-Golay to filter 11 smooth elimination spectral noise, use backward interval partial least square (BiPLS) that pretreated Laser Near infrared spectrum is carried out characteristic wavelength extraction, with validation-cross mean square deviation as standard, choose the minimum model interval of validation-cross mean square deviation for optimal interval, described multiplexed quantitative bearing calibration uses support vector regression (SVR) and combines grid-search algorithms (CV) and carry out parameter optimization, described support vector regression (SVR) method use e-SVR regression model and radial direction base (RBF) kernel function carry out regression modeling, by grid-search algorithms (CV), the penalty factor in described support vector regression (SVR) model and RBF kernel functional parameter g are optimized；
Step 5, quantitative model are verified: the acidity value of the quantitative model prediction test set milk sample by setting up in step 4, and compare with the acidity value of the test set milk sample measured in step 2, it is desirable to error is less than 10%；
Step 6, milk sample freshness to be measured are analyzed: gather the Laser Near infrared spectrum of milk sample to be measured, use quantitative model to predict the acidity value of milk sample to be measured；
Wherein, as acidity value >=25 ° T, then judge that milk sample to be measured is stale；As 25 ° of T of acidity value ＜, then judge that milk sample to be measured is fresh.
Claims (9)
1. the method for the infrared quick detection milk freshness of Laser Near, it is characterised in that include walking as follows
Rapid:
Step one, sample collecting: the milk sample gathering the production of same kind not same date is some, by its point
Become calibration set milk sample and test set milk sample；
Step 2, acidity value measure: measure calibration set milk sample and the acidity value of test set milk sample；
Step 3, spectra collection: the Laser Near of acquisition correction collection milk sample and test set milk sample is infrared
Spectrum, spectra collection condition is: milk sample temperature 20～30 DEG C, spectral region 1200～1800nm,
Acquisition mode is transmission；
Step 4, quantitative model are set up: set up acidity value and the calibration set milk sample of calibration set milk sample
The quantitative model of Laser Near infrared spectrum；
Step 5, quantitative model are verified: by the quantitative model prediction test set milk sample set up in step 4
The acidity value of product, and compare with the acidity value of the test set milk sample measured in step 2, it is desirable to error is little
In 10%；
Step 6, milk sample freshness to be measured are analyzed: gather the Laser Near infrared spectrum of milk sample to be measured,
Quantitative model is used to predict the acidity value of milk sample to be measured；
Wherein, as acidity value >=25 ° T, then judge that milk sample to be measured is stale；As acidity value ＜ 25 °
During T, then judge that milk sample to be measured is fresh.
2. the method for the infrared quick detection milk freshness of Laser Near as claimed in claim 1, its feature exists
In, the acidity value assay method in described step 2 is as follows: draws 10ml milk sample and injects 100ml triangle
In Ping, and dilute with 20ml neutral distillation water, add 0.5% phenolphthalein indicator 0.5ml mixing, use
0.1mol/L standard solution of sodium hydroxide titrates, and shake is not till blush disappeared in 30 seconds constantly；
Wherein, acidity value (° T)=10* (V1-V0) * C；V1 is the body consuming standard solution of sodium hydroxide
Long-pending, unit is ml；Consuming the volume of standard solution of sodium hydroxide when V0 is blank assay, unit is ml；C
For the concentration of standard solution of sodium hydroxide, unit is mol/L.
3. the method for the infrared quick detection milk freshness of Laser Near as claimed in claim 2, its feature exists
In, in described step 2 the acidity value scope of calibration set milk sample and test set milk sample be 20 ° of T～
30°T。
4. the method for the infrared quick detection milk freshness of Laser Near as claimed in claim 1, its feature exists
In, in described step 3 during spectra collection, repeated acquisition 5 times also takes its meansigma methods as this milk sample
Laser Near infrared spectrum.
5. the method for the infrared quick detection milk freshness of Laser Near as claimed in claim 1, its feature exists
In, the method setting up quantitative model in described step 4 is as follows: infrared to the Laser Near of calibration set milk sample
Spectrum carries out pretreatment, and the Laser Near infrared spectrum of pretreatment is carried out characteristic wavelength extraction, uses polynary
Quantitative correction method sets up the acidity of the Laser Near infrared spectrum through characteristic wavelength extraction and calibration set milk sample
Quantitative model between value.
6. the method for the infrared quick detection milk freshness of Laser Near as claimed in claim 5, its feature exists
In, described preprocess method uses Savitzky-Golay to filter 11 smooth elimination spectral noise.
7. the method for the infrared quick detection milk freshness of Laser Near as claimed in claim 6, its feature exists
In, use backward interval partial least square that pretreated Laser Near infrared spectrum is carried out characteristic wavelength and carry
Taking, with validation-cross mean square deviation as standard, the model interval choosing validation-cross mean square deviation minimum is optimal
Interval.
8. the method for the infrared quick detection milk freshness of Laser Near as claimed in claim 7, its feature exists
Use support vector regression in, described multiplexed quantitative bearing calibration and combine grid-search algorithms to carry out parameter excellent
Change.
9. the method for the infrared quick detection milk freshness of Laser Near as claimed in claim 8, its feature exists
In, described support vector regression method use e-SVR regression model and Radial basis kernel function return
Modeling, by grid-search algorithms to the penalty factor in described support vector regression model and RBF core letter
Number parameter g is optimized.
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| CN107247026A (en) * | 2017-07-26 | 2017-10-13 | 成都九维云联科技有限公司 | A kind of pre-judging method of perishable items |
| CN109540838A (en) * | 2019-01-24 | 2019-03-29 | 广东产品质量监督检验研究院（国家质量技术监督局广州电气安全检验所、广东省试验认证研究院、华安实验室） | A kind of method of acidity in quick detection acidified milk |
| CN111678884A (en) * | 2020-07-27 | 2020-09-18 | 浙江工商大学 | Method for detecting estrogen in milk |
| CN111795932A (en) * | 2020-06-15 | 2020-10-20 | 杭州电子科技大学 | Hyperspectrum-based nondestructive testing method for sugar acidity of waxberry fruits |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107247026A (en) * | 2017-07-26 | 2017-10-13 | 成都九维云联科技有限公司 | A kind of pre-judging method of perishable items |
| CN109540838A (en) * | 2019-01-24 | 2019-03-29 | 广东产品质量监督检验研究院（国家质量技术监督局广州电气安全检验所、广东省试验认证研究院、华安实验室） | A kind of method of acidity in quick detection acidified milk |
| CN109540838B (en) * | 2019-01-24 | 2021-03-30 | 广东产品质量监督检验研究院（国家质量技术监督局广州电气安全检验所、广东省试验认证研究院、华安实验室） | Method for rapidly detecting acidity in fermented milk |
| CN111795932A (en) * | 2020-06-15 | 2020-10-20 | 杭州电子科技大学 | Hyperspectrum-based nondestructive testing method for sugar acidity of waxberry fruits |
| CN111795932B (en) * | 2020-06-15 | 2022-11-15 | 杭州电子科技大学 | Hyperspectrum-based nondestructive testing method for sugar acidity of waxberry fruits |
| CN111678884A (en) * | 2020-07-27 | 2020-09-18 | 浙江工商大学 | Method for detecting estrogen in milk |
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Application publication date: 20160928 |