CN101504363A - Edible fatty acid value detection method based on near-infrared spectrum analysis - Google Patents
Edible fatty acid value detection method based on near-infrared spectrum analysis Download PDFInfo
- Publication number
- CN101504363A CN101504363A CN 200910071567 CN200910071567A CN101504363A CN 101504363 A CN101504363 A CN 101504363A CN 200910071567 CN200910071567 CN 200910071567 CN 200910071567 A CN200910071567 A CN 200910071567A CN 101504363 A CN101504363 A CN 101504363A
- Authority
- CN
- China
- Prior art keywords
- sample
- spectrum
- acid value
- calibration
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The invention discloses a method for detecting an acid value of edible fat based on near infrared spectrum analysis. The invention relates to a method for detecting an acid value of edible fat by utilizing near infrared spectrum analysis technology, which aims to solve the problems that in the practical production, the prior laboratory detection and test method can only perform intermittent operation, fail to realize accurate and quick on-line detection and the like. The method for detecting the acid value of the edible fat by utilizing the near infrared spectrum analysis technology is realized through the following steps of: 1, establishment of a calibration set sample spectrum; 2, pretreatment of spectrum data; 3, determination of essential data; 4, establishment of a calibration model; 5, verification of the calibration model; and 6, analysis of a sample to be tested. The method can effectively eliminate personal error, shorten detection period, and realize the on-line detection and control of the acid value in the process of processing the fat.
Description
Technical field:
The present invention relates to a kind of method of near infrared spectrum nondestructive analysis edible fatty acid value, belong to edible oil and fat quality analysis field.
Background technology:
Grease is exposed in the air can produce the phenomenon of becoming sour, its chemical nature be since grease under the enzyme effect that fat hydrolase or microbial reproduction produce, partial glyceride decomposition generation free fatty acid causes, therefore, acid value has reflected the variation of quality in the quality of oil and fat product and the storage, being the leading indicator of measure oil quality, is the essential items for inspection of oil and fat product.In addition, in the grease process, when also being the oil alkali refining depickling, the size of acid value calculates the foundation of alkali charge, also need often to detect, regulate and control, best depickling condition in the hope of grease processing technique, improve the quality of products, thereby obtain maximum profit, so acid value of lipids is determined at crucial effect is arranged in the actual production.
In China, according to the GB requirement, what the mensuration of acid value of lipids adopted is titrimetry, this method belongs to a kind of assay method of subjectivity, have operating personnel are required height, testing process complexity, sense cycle length, need be unfavorable for that real-time online detects and regulation and control in the product processing with characteristics such as chemical reagent, the pre-service of being damaged property of sample.Therefore, press for a kind of new method and realize that the real-time online of acid value of lipids detects.
Near infrared spectrum (NIRS) analyze have no pre-treatment, pollution-free, convenient and swift, do not have destructive, can realize advantage such as online detection, be the ideal scheme of realizing the detection of acid value of lipids real-time online.
Summary of the invention:
The present invention is directed to fatty acid value detection method traditional in the actual production and can only carry out intermittent operation, can not realize problems such as serialization and robotization, and proposed to measure the method for edible fatty acid value with near-infrared spectral analysis technology.
The acid value of measuring edible oil and fat with near-infrared spectrum analysis realizes by following steps:
One, the foundation of calibration set sample spectrum: collect representational sample and in 780～2500nm spectrum district scope, scan the standard spectrum that obtains the calibration samples collection, same sample needs repeatedly duplicate measurements, with averaged spectrum as the standard spectrum of this sample (when correcting sample is collected, preferably collect a sample and just carry out spectra collection immediately, and as far as possible not time of concentration measure spectrum, so that factors such as instrument and environmental variations are all comprised wherein, improve the robustness of model);
Two, the pre-service of calibration set spectrum: obtaining needs carry out pre-service to sample set spectrum behind the sample spectrum, adopts methods such as level and smooth, differential, differentiate or small echo denoising here, to offset background interference, improves the resolution of spectrum;
Three, the mensuration of basic data: the acid value of pressing the whole samples of national standard method titration, each sample detection three times, average (when correcting sample is collected, preferably collect a sample and just measure acid value with national standard method immediately, and as far as possible not time of concentration measure, to get rid of because the error that factor such as become sour causes);
Four, calibration model is set up: the spectrum after handling and the standard acid value of sample are set up calibration model by the multiple regression algorithm, the multiple regression algorithm comprises multiple linear regression algorithm and nonlinear multivariable regression algorithm, the occasion difference that both use can be selected the method that is fit to as required; Simultaneously, select characteristic wave bands very important, the progressively Return Law commonly used is sought characteristic wave bands;
Five, the checking of calibration model: the grease of getting known acid value is as the checking collection, use the spectrometer scanning optical spectrum under the same conditions, according to the Model Calculation acid value value of having set up, each checking collection sample error of empirical tests all less than after 10%, can determine that this calibration model is suitable for; If some checking sample error is then carried out regressing calculation to correction parameter again greater than 10%, so repeatedly, until obtaining satisfied quantitative model;
Six, the analysis of testing sample: the spectrum that scans grease to be analyzed with spectrometer, carry out after the pre-service spectroscopic data input model can being determined acid value of lipids (scanning process of testing sample and pretreatment condition should be consistent with the calibration samples collection, to eliminate error).
This problem has proposed the new method that a kind of acid value of lipids detects, this method is introduced oil processing industry with advanced person's spectral analysis technique, chemometrics method first, solved the problem that exists in traditional laboratory assay detection method, acid value of lipids online in real time check and analysis have fast and accurately been realized, eliminate personal error effectively, shortened sense cycle, can effectively improve the quality of products.
Description of drawings
Accompanying drawing is the theory diagram of the method for near-infrared spectral analysis technology fast detecting edible fatty acid value
Embodiment
Whole implementation process of the present invention is as shown in drawings:
One, the foundation of calibration set sample spectrum: collect representational sample and in 780-2500nm spectrum district scope, scan the standard spectrum that obtains the calibration samples collection, same sample needs repeatedly duplicate measurements, with averaged spectrum as the standard spectrum of this sample (when correcting sample is collected, preferably collect a sample and just carry out spectra collection immediately, and as far as possible not time of concentration measure spectrum, so that factors such as instrument and environmental variations are all comprised wherein, improve the robustness of model);
Two, the pre-service of calibration set spectrum: obtaining needs carry out pre-service to sample set spectrum behind the sample spectrum, adopts methods such as level and smooth, differential, differentiate or small echo denoising here, to offset background interference, improves the resolution of spectrum;
Three, the mensuration of basic data: the acid value of pressing the whole samples of national standard method titration, each sample detection three times, average (when correcting sample is collected, preferably collect a sample and just measure acid value with national standard method immediately, and as far as possible not time of concentration measure, to get rid of because the error that factor such as become sour causes);
Four, calibration model is set up: the spectrum after handling and the standard acid value of sample are set up calibration model by the multiple regression algorithm, the multiple regression algorithm comprises multiple linear regression algorithm and nonlinear multivariable regression algorithm, the occasion difference that both use can be selected the method that is fit to as required.Simultaneously, select characteristic wave bands very important, the progressively Return Law commonly used is sought characteristic wave bands;
Five, the checking of calibration model: the grease of getting known acid value is as the checking collection, use the spectrometer scanning optical spectrum under the same conditions, according to the Model Calculation acid value value of having set up, each checking collection sample error of empirical tests all less than after 10%, can determine that this calibration model is suitable for; If some checking sample error is then carried out regressing calculation to correction parameter again greater than 10%, so repeatedly, until obtaining satisfied quantitative model;
Six, the analysis of testing sample: the spectrum that scans grease to be analyzed with spectrometer, carry out after the pre-service spectroscopic data input model can being determined acid value of lipids (scanning process of testing sample and pretreatment condition should be consistent with the calibration samples collection, to eliminate error).
Claims (1)
1, a kind of method that detects based on the edible fatty acid value of near-infrared spectrum analysis is characterized in that the acid value of measuring edible oil and fat with near-infrared spectrometers realizes by following steps:
The foundation of step 1, calibration set sample spectrum: collect representational sample and in 780～2500nm spectrum district scope, scan the standard spectrum that obtains the calibration samples collection, same sample needs repeatedly duplicate measurements, with averaged spectrum as the standard spectrum of this sample (when correcting sample is collected, preferably collect a sample and just carry out spectra collection immediately, and as far as possible not time of concentration measure spectrum, so that factors such as instrument and environmental variations are all comprised wherein, improve the robustness of model);
The pre-service of step 2, calibration set spectrum: obtaining needs carry out pre-service to sample set spectrum behind the sample spectrum, adopts methods such as level and smooth, differential, differentiate or small echo denoising here, to offset background interference, improves the resolution of spectrum;
The mensuration of step 3, basic data: the acid value of pressing the whole samples of national standard method titration, each sample detection three times, average (when correcting sample is collected, preferably collect a sample and just measure acid value with national standard method immediately, and as far as possible not time of concentration measure, to get rid of because the error that factor such as become sour causes);
Step 4, calibration model are set up: the spectrum after handling and the standard acid value of sample are set up calibration model by the multiple regression algorithm, the multiple regression algorithm comprises multiple linear regression algorithm and nonlinear multivariable regression algorithm, the occasion difference that both use can be selected the method that is fit to as required.Simultaneously, select characteristic wave bands very important, the progressively Return Law commonly used is sought characteristic wave bands;
The checking of step 5, calibration model: the grease of getting known acid value is as the checking collection, use the spectrometer scanning optical spectrum under the same conditions, according to the Model Calculation acid value value of having set up, each checking collection sample error of empirical tests all less than after 10%, can determine that this calibration model is suitable for; If some checking sample error is then carried out regressing calculation to correction parameter again greater than 10%, so repeatedly, until obtaining satisfied quantitative model;
The analysis of step 6, testing sample: the spectrum that scans grease to be analyzed with spectrometer, carry out after the pre-service spectroscopic data input model can being determined acid value of lipids (scanning process of testing sample and pretreatment condition should be consistent with the calibration samples collection, to eliminate error).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200910071567 CN101504363A (en) | 2009-03-18 | 2009-03-18 | Edible fatty acid value detection method based on near-infrared spectrum analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200910071567 CN101504363A (en) | 2009-03-18 | 2009-03-18 | Edible fatty acid value detection method based on near-infrared spectrum analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101504363A true CN101504363A (en) | 2009-08-12 |
Family
ID=40976670
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 200910071567 Pending CN101504363A (en) | 2009-03-18 | 2009-03-18 | Edible fatty acid value detection method based on near-infrared spectrum analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101504363A (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102252995A (en) * | 2011-06-22 | 2011-11-23 | 中国林业科学研究院林产化学工业研究所 | Method for rapid detection and authenticity identification of fatty acid of racy camellia oil by near infrared transmission spectroscopy (NITS) |
| CN103728267A (en) * | 2014-01-17 | 2014-04-16 | 华东理工大学 | Method for correcting baseline of spectrogram in near infrared analysis of gasoline |
| CN104345055A (en) * | 2013-07-26 | 2015-02-11 | 丰益(上海)生物技术研发中心有限公司 | Mixed indicator, utilization and rapid detection method for grease acid value |
| CN105181641A (en) * | 2015-10-12 | 2015-12-23 | 华中农业大学 | Infrared detection method for rapeseed oil quality and application |
| CN105181633A (en) * | 2015-08-24 | 2015-12-23 | 河南省农业科学院 | Nondestructive detection method for identifying F1 seed true/false between species of peanut with high oleic acid content and peanut with normal oleic acid content |
| CN106841083A (en) * | 2016-11-02 | 2017-06-13 | 北京工商大学 | Sesame oil quality detecting method based on near-infrared spectrum technique |
| CN104764699B (en) * | 2015-01-22 | 2018-05-15 | 四川大学 | A kind of method for measuring edible oil acid value |
| CN108195789A (en) * | 2017-12-05 | 2018-06-22 | 广州讯动网络科技有限公司 | A kind of hand-held device of quick detection grain index, method |
| CN108362659A (en) * | 2018-02-07 | 2018-08-03 | 武汉轻工大学 | Edible oil type method for quick identification based on multi-source optical spectrum parallel connection fusion |
| CN108489934A (en) * | 2018-03-26 | 2018-09-04 | 山东省花生研究所 | A method of detection peanut oil quality |
| CN110084262A (en) * | 2018-01-26 | 2019-08-02 | 唯亚威通讯技术有限公司 | The wrong report identification of reduction for spectrum quantization |
| CN110108667A (en) * | 2019-06-05 | 2019-08-09 | 浙江工业大学 | A method of utilizing Near Infrared Spectroscopy for Rapid waste grease acid value |
| CN110785661A (en) * | 2017-04-21 | 2020-02-11 | 英索特有限公司 | Method for detecting rancidity in oil-containing fruits, seeds and nuts |
| CN111650154A (en) * | 2020-05-27 | 2020-09-11 | 温氏食品集团股份有限公司 | Grease quantitative analysis method based on near-infrared transmission and reflection spectrum technology |
| CN113092405A (en) * | 2021-04-08 | 2021-07-09 | 晨光生物科技集团股份有限公司 | Method for rapidly predicting induction period of vegetable oil under normal temperature condition |
| US11656175B2 (en) | 2018-01-26 | 2023-05-23 | Viavi Solutions Inc. | Reduced false positive identification for spectroscopic classification |
| US11656174B2 (en) | 2018-01-26 | 2023-05-23 | Viavi Solutions Inc. | Outlier detection for spectroscopic classification |
-
2009
- 2009-03-18 CN CN 200910071567 patent/CN101504363A/en active Pending
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102252995A (en) * | 2011-06-22 | 2011-11-23 | 中国林业科学研究院林产化学工业研究所 | Method for rapid detection and authenticity identification of fatty acid of racy camellia oil by near infrared transmission spectroscopy (NITS) |
| CN104345055A (en) * | 2013-07-26 | 2015-02-11 | 丰益(上海)生物技术研发中心有限公司 | Mixed indicator, utilization and rapid detection method for grease acid value |
| CN103728267A (en) * | 2014-01-17 | 2014-04-16 | 华东理工大学 | Method for correcting baseline of spectrogram in near infrared analysis of gasoline |
| CN104764699B (en) * | 2015-01-22 | 2018-05-15 | 四川大学 | A kind of method for measuring edible oil acid value |
| CN105181633A (en) * | 2015-08-24 | 2015-12-23 | 河南省农业科学院 | Nondestructive detection method for identifying F1 seed true/false between species of peanut with high oleic acid content and peanut with normal oleic acid content |
| CN105181641B (en) * | 2015-10-12 | 2017-12-22 | 华中农业大学 | A kind of near infrared detection method of rapeseed oil quality and application |
| CN105181641A (en) * | 2015-10-12 | 2015-12-23 | 华中农业大学 | Infrared detection method for rapeseed oil quality and application |
| CN106841083A (en) * | 2016-11-02 | 2017-06-13 | 北京工商大学 | Sesame oil quality detecting method based on near-infrared spectrum technique |
| CN110785661A (en) * | 2017-04-21 | 2020-02-11 | 英索特有限公司 | Method for detecting rancidity in oil-containing fruits, seeds and nuts |
| CN108195789A (en) * | 2017-12-05 | 2018-06-22 | 广州讯动网络科技有限公司 | A kind of hand-held device of quick detection grain index, method |
| US11775616B2 (en) | 2018-01-26 | 2023-10-03 | Viavi Solutions Inc. | Reduced false positive identification for spectroscopic quantification |
| US11656174B2 (en) | 2018-01-26 | 2023-05-23 | Viavi Solutions Inc. | Outlier detection for spectroscopic classification |
| CN110084262A (en) * | 2018-01-26 | 2019-08-02 | 唯亚威通讯技术有限公司 | The wrong report identification of reduction for spectrum quantization |
| US11656175B2 (en) | 2018-01-26 | 2023-05-23 | Viavi Solutions Inc. | Reduced false positive identification for spectroscopic classification |
| CN110084262B (en) * | 2018-01-26 | 2022-07-05 | 唯亚威通讯技术有限公司 | Reduced false positive identification for spectral quantification |
| CN108362659B (en) * | 2018-02-07 | 2021-03-30 | 武汉轻工大学 | Edible oil type rapid identification method based on multi-source spectrum parallel fusion |
| CN108362659A (en) * | 2018-02-07 | 2018-08-03 | 武汉轻工大学 | Edible oil type method for quick identification based on multi-source optical spectrum parallel connection fusion |
| CN108489934A (en) * | 2018-03-26 | 2018-09-04 | 山东省花生研究所 | A method of detection peanut oil quality |
| CN110108667A (en) * | 2019-06-05 | 2019-08-09 | 浙江工业大学 | A method of utilizing Near Infrared Spectroscopy for Rapid waste grease acid value |
| CN111650154A (en) * | 2020-05-27 | 2020-09-11 | 温氏食品集团股份有限公司 | Grease quantitative analysis method based on near-infrared transmission and reflection spectrum technology |
| CN113092405A (en) * | 2021-04-08 | 2021-07-09 | 晨光生物科技集团股份有限公司 | Method for rapidly predicting induction period of vegetable oil under normal temperature condition |
| CN113092405B (en) * | 2021-04-08 | 2023-06-16 | 晨光生物科技集团股份有限公司 | Method for rapidly pre-judging induction period of vegetable oil under normal temperature condition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101504363A (en) | Edible fatty acid value detection method based on near-infrared spectrum analysis | |
| CN103674838B (en) | A kind of fish fats content distribution detection method based on high light spectrum image-forming technology | |
| CN103630499B (en) | A kind of fish protein content distribution detection method based on high light spectrum image-forming technology | |
| CN102879353B (en) | The method of content of protein components near infrared detection peanut | |
| CN102590129B (en) | Method for detecting content of amino acid in peanuts by near infrared method | |
| CN103398970A (en) | Method for qualitatively and quantitatively analyzing edible oil and further detecting hogwash oil | |
| CN101413885A (en) | Near-infrared spectrum method for rapidly quantifying honey quality | |
| CN107817223A (en) | The construction method of quick nondestructive real-time estimate oil property model and its application | |
| CN103674864A (en) | Fish water content distribution detection method based on hyperspectral imaging technology | |
| CN103983595A (en) | Water quality turbidity calculating method based on ultraviolet-visible spectroscopy treatment | |
| CN107703097B (en) | Method for constructing model for rapidly predicting crude oil property by using near-infrared spectrometer | |
| CN102636450A (en) | Method for detecting wolfberry polyose content in Chinese wolfberry in a nondestructive way based on near infrared spectrum technology | |
| CN103645155A (en) | Quick nondestructive testing method for tenderness of fresh mutton | |
| CN104764699A (en) | Method for determination of edible oil acid value | |
| CN104596979A (en) | Method for measuring cellulose of reconstituted tobacco by virtue of near infrared reflectance spectroscopy technique | |
| CN104596975A (en) | Method for measuring lignin of reconstituted tobacco by paper-making process by virtue of near infrared reflectance spectroscopy technique | |
| CN108072627A (en) | It is a kind of that the method for amino-acid nitrogen and total acid content in soy sauce is quickly detected with middle infrared spectrum | |
| Hirri et al. | The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and estimation of extra virgin olive oil adulteration with old olive oil | |
| CN104596976A (en) | Method for determining protein of paper-making reconstituted tobacco through ear infrared reflectance spectroscopy technique | |
| CN105954228A (en) | Method for measuring content of sodium metal in oil sand based on near infrared spectrum | |
| CN106338503B (en) | The peroxide value rapid detection method of vegetable oil | |
| CN105092526A (en) | Rapid determination method for content of binary adulterated sesame oil based on near-infrared spectroscopy | |
| CN104198430A (en) | Detecting method of substituent content of CAB (cellulose acetate butyrate) | |
| CN104568811B (en) | The new quick determination method of caloric value in coal sample | |
| CN104596982A (en) | Method for measuring pectin of paper-making reconstituted tobacco by near-infrared diffuse reflection spectrum technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Open date: 20090812 |