CN101504362A - Fast detection of trans-fatty acid content in edible fat based on near infrared spectrum technology - Google Patents
Fast detection of trans-fatty acid content in edible fat based on near infrared spectrum technology Download PDFInfo
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Abstract
The invention discloses a method for quickly detecting the content of trans-fatty acid in edible fat based on near infrared spectrum technology. The near infrared spectrum analysis technology is selected to detect the content of the trans-fatty acid in the edible fat, which aims to solve the problems that in the practical production, the detection of the fat by the prior gas-phase chromatography (GC) needs methyl esterification, the chromatographic resolution needs long time, standard products needed by qualitative and quantitative analysis have large quantity and high cost and the like. The method for detecting the content of the trans-fatty acid in the edible fat by the near infrared spectrum analysis technology is realized through the 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 for detecting the content of the trans-fatty acid in the edible fat can effectively shorten detection period, and the whole process realizes data acquisition, storage, display and processing functions under the control of a computer.
Description
Technical field
The present invention relates to a kind of detection method of the content of trans-fatty acid in edible fat based on near-infrared spectrum technique.
Background technology
(the Trans Fatty Acids of trans-fatty acid in the food, TFA) be unsaturated fatty acid (Unsaturated FattyAcids), comprise at least one two key on transconfiguration, be divided into 16 carbon trans-fatty acids, 18 carbon trans-fatty acids, 20 carbon trans-fatty acids etc. according to carbon atom number; Be divided into trans monoenoic acid, trans diene acid etc. according to the double key number order.The trans-fatty acid major part derives from finished hydrogenated oil and fat of part and goods thereof in the food industry in the food, with trans 9-ElaidicAcid (t9-C
18:1) be main; Small part is present in the nature ruminant body, mainly is trans 11-VaccenicAcid (t11-C
18:1).TFA can increase the occurrence probability of heart disease and obesity, may cause tumour (breast cancer etc.), can also give fetus through placental transport, influences growing of baby by the metabolism of disturbing essential fatty acid, the function that suppresses essential fatty acid etc.Because TFA is to the many-sided negative effect of human body, some developed countries have made corresponding regulation to the sign of the TFA in grease and the fatty foods in the world.The most vapor-phase chromatographies (GC) that adopt of TFA analytical approach commonly used at present.It is lower that the GC method detects lower bound, but grease needs esterification, the chromatographic resolution required time is long, the required standard items quantity of qualitative, quantitative is many and cost an arm and a leg, and the employing near-infrared spectral analysis technology, can realize qualitative or quantitative test to trans-fatty acid in the unknown sample by setting up calibration model, overcoming the GC method needs loaded down with trivial details shortcomings such as pre-treatment, realizes the fast detecting of content of trans-fatty acid in edible fat.
Summary of the invention
For simple, quick, non-destruction, detect content of trans-fatty acid in edible fat in real time, the invention provides a kind of method based on near-infrared spectrum technique fast detecting content of trans-fatty acid in edible fat.
The technical solution used in the present invention step is as follows:
1) foundation of calibration set sample spectrum: at first will select the calibration samples collection: utilize near infrared spectrometer scanning to obtain calibration samples collection standard spectrum then at different qualities, various processes, representational edible oil and fat product; Same sample needs repeatedly duplicate measurements, and is approximate as this sample standard spectrum with averaged spectrum;
2) 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;
3) mensuration of basic data: adopt vapor-phase chromatography that the content of trans fatty acids of calibration set sample is measured;
4) calibration model is set up: the standard content of trans fatty acids measured value to pretreated spectroscopic data and correcting sample collection is 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;
5) checking of calibration model: the grease of getting known TFA content is as the checking collection, obtain spectrum with spectrometer scanning under the same conditions, according to the Model Calculation TFA content 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;
6) analysis of testing sample: the spectrum that obtains grease to be analyzed with spectrometer scanning, carry out after the pre-service spectroscopic data input model can being determined content of trans fatty acids in the grease (scanning process of testing sample and pretreatment condition should be consistent with the calibration samples collection, to eliminate error).
So the present invention, just can realize quick, Non-Destructive Testing to content of trans fatty acids in the unknown grease as long as set up calibration model on the basis of representational grease sample.
Description of drawings
Accompanying drawing is based on the theory diagram of near-infrared spectral analysis technology fast detecting content of trans-fatty acid in edible fat
Embodiment
Whole implementation process of the present invention is as shown in drawings:
Embodiment
Utilizing near-infrared spectral analysis technology to detect content of trans-fatty acid in edible fat in the present embodiment realizes by following steps:
1) foundation of calibration set sample spectrum: at first will select the calibration samples collection at different qualities, various processes, representational edible oil and fat product; Utilize near infrared spectrometer scanning to obtain calibration samples collection standard spectrum then; Same sample needs repeatedly duplicate measurements, and is approximate as this sample standard spectrum with averaged spectrum;
2) 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;
3) mensuration of basic data: adopt vapor-phase chromatography that the content of trans fatty acids of calibration set sample is measured;
4) calibration model is set up: the standard content of trans fatty acids measured value to pretreated spectroscopic data and correcting sample collection is 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;
5) checking of calibration model: the grease of getting known TFA content is as the checking collection, obtain spectrum with spectrometer scanning under the same conditions, according to the Model Calculation TFA content 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;
6) analysis of testing sample: the spectrum that obtains grease to be analyzed with spectrometer scanning, carry out after the pre-service spectroscopic data input model can being determined content of trans fatty acids in the grease (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 based on near-infrared spectrum technique fast detecting content of trans-fatty acid in edible fat is characterized in that measuring content of trans-fatty acid in edible fat with near-infrared spectral analysis technology realizes by following steps:
1) foundation of calibration set sample spectrum: at first will select the calibration samples collection at different qualities, various processes, representational edible oil and fat product; Utilize near infrared spectrometer scanning to obtain calibration samples collection standard spectrum then; Same sample needs repeatedly duplicate measurements, and is approximate as this sample standard spectrum with averaged spectrum;
2) 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;
3) mensuration of basic data: adopt vapor-phase chromatography that the content of trans fatty acids of calibration set sample is measured;
4) calibration model is set up: the standard content of trans fatty acids measured value to pretreated spectroscopic data and correcting sample collection is 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;
5) checking of calibration model: the grease of getting known TFA content is as the checking collection, obtain spectrum with spectrometer scanning under the same conditions, according to the Model Calculation TFA content 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;
6) analysis of testing sample: the spectrum that obtains grease to be analyzed with spectrometer scanning, carry out after the pre-service spectroscopic data input model can being determined content of trans fatty acids in the grease (scanning process of testing sample and pretreatment condition should be consistent with the calibration samples collection, to eliminate error).
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| CN101936893A (en) * | 2010-07-30 | 2011-01-05 | 华中农业大学 | Method for detecting protein and amino acid in rapeseeds |
| CN102252972A (en) * | 2011-04-20 | 2011-11-23 | 湖南省农产品加工研究所 | Near infrared spectrum based detection method for rapid discrimination of oil-tea camellia seed oil real property |
| CN102435574A (en) * | 2011-09-01 | 2012-05-02 | 中国农业科学院农产品加工研究所 | Nondestructive grading method for lamb carcass output |
| CN102680416A (en) * | 2012-04-19 | 2012-09-19 | 江苏大学 | Method and device for fast detecting caffeine content of summer and autumn tea |
| CN102692391A (en) * | 2011-03-24 | 2012-09-26 | 河南省产品质量监督检验院 | Method for rapid determination of trans-fatty acid in food |
| CN103926216A (en) * | 2014-04-24 | 2014-07-16 | 江西农业大学 | Method and device for rapidly detecting trans-fatty acids in edible vegetable oil |
| CN103940774A (en) * | 2014-05-08 | 2014-07-23 | 江苏物联网研究发展中心 | Edible oil detection device and edible oil detection method |
| CN104020131A (en) * | 2014-06-09 | 2014-09-03 | 蓝星化工新材料股份有限公司江西星火有机硅厂 | Method for analyzing content of vinyl in methyl vinyl polysiloxane by using near infrared spectrum |
| CN104132905A (en) * | 2014-05-05 | 2014-11-05 | 河南科技大学 | Detection method for adulterated sesame oil |
| CN104730031A (en) * | 2015-03-31 | 2015-06-24 | 中国林业科学研究院亚热带林业研究所 | Method for determining chemical components of rosin by using near infrared spectrum technology |
| CN104914068A (en) * | 2015-03-19 | 2015-09-16 | 哈尔滨商业大学 | Spectrum rapid detection method of trans-fatty acid content in grease |
| CN105092512A (en) * | 2015-08-21 | 2015-11-25 | 广东省粮食科学研究所 | Fourier transform infrared spectroscopy technology-based method for detecting camellia oleosa seed oil |
| CN105784951A (en) * | 2014-12-24 | 2016-07-20 | 九芝堂股份有限公司 | Multiple indicator rapid detection method for raw medicinal powder of condensed pill of six drugs with rehmannia |
| CN106290665A (en) * | 2016-11-02 | 2017-01-04 | 百奥森（江苏）食品安全科技有限公司 | A kind of detection method of Trans-fatty Acids in Foods |
| 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 |
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| CN101936893A (en) * | 2010-07-30 | 2011-01-05 | 华中农业大学 | Method for detecting protein and amino acid in rapeseeds |
| CN102692391A (en) * | 2011-03-24 | 2012-09-26 | 河南省产品质量监督检验院 | Method for rapid determination of trans-fatty acid in food |
| CN102252972A (en) * | 2011-04-20 | 2011-11-23 | 湖南省农产品加工研究所 | Near infrared spectrum based detection method for rapid discrimination of oil-tea camellia seed oil real property |
| CN102435574A (en) * | 2011-09-01 | 2012-05-02 | 中国农业科学院农产品加工研究所 | Nondestructive grading method for lamb carcass output |
| CN102435574B (en) * | 2011-09-01 | 2013-12-18 | 中国农业科学院农产品加工研究所 | Nondestructive grading method for lamb carcass output |
| CN102680416A (en) * | 2012-04-19 | 2012-09-19 | 江苏大学 | Method and device for fast detecting caffeine content of summer and autumn tea |
| CN103926216A (en) * | 2014-04-24 | 2014-07-16 | 江西农业大学 | Method and device for rapidly detecting trans-fatty acids in edible vegetable oil |
| CN104132905A (en) * | 2014-05-05 | 2014-11-05 | 河南科技大学 | Detection method for adulterated sesame oil |
| CN103940774B (en) * | 2014-05-08 | 2017-09-29 | 贵州黔香园油脂有限公司 | edible oil detecting device and method |
| CN103940774A (en) * | 2014-05-08 | 2014-07-23 | 江苏物联网研究发展中心 | Edible oil detection device and edible oil detection method |
| CN104020131A (en) * | 2014-06-09 | 2014-09-03 | 蓝星化工新材料股份有限公司江西星火有机硅厂 | Method for analyzing content of vinyl in methyl vinyl polysiloxane by using near infrared spectrum |
| CN105784951B (en) * | 2014-12-24 | 2019-03-29 | 九芝堂股份有限公司 | A kind of Liuwei Dihuang Wan condensed pill crude drug powder multiple index quick detecting method |
| CN105784951A (en) * | 2014-12-24 | 2016-07-20 | 九芝堂股份有限公司 | Multiple indicator rapid detection method for raw medicinal powder of condensed pill of six drugs with rehmannia |
| CN104914068B (en) * | 2015-03-19 | 2019-02-19 | 哈尔滨商业大学 | The spectrum rapid detection method of content of trans fatty acids in a kind of grease |
| CN104914068A (en) * | 2015-03-19 | 2015-09-16 | 哈尔滨商业大学 | Spectrum rapid detection method of trans-fatty acid content in grease |
| CN104730031A (en) * | 2015-03-31 | 2015-06-24 | 中国林业科学研究院亚热带林业研究所 | Method for determining chemical components of rosin by using near infrared spectrum technology |
| CN105092512A (en) * | 2015-08-21 | 2015-11-25 | 广东省粮食科学研究所 | Fourier transform infrared spectroscopy technology-based method for detecting camellia oleosa seed oil |
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| CN106290665A (en) * | 2016-11-02 | 2017-01-04 | 百奥森（江苏）食品安全科技有限公司 | A kind of detection method of Trans-fatty Acids in Foods |
| CN110865046A (en) * | 2019-11-28 | 2020-03-06 | 浙江农林大学 | Method for rapidly detecting content of trans-fatty acid isomer of edible oil |
| CN110865046B (en) * | 2019-11-28 | 2022-01-07 | 浙江农林大学 | Method for rapidly detecting content of trans-fatty acid isomer of edible oil |
| CN111650137A (en) * | 2020-04-26 | 2020-09-11 | 深圳市人工智能与机器人研究院 | Spectrum file generation method and device, computer equipment and storage medium |
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| CN112213282A (en) * | 2020-09-15 | 2021-01-12 | 吉林省农业科学院 | Method for detecting content of crude fat in cyperus esculentus by applying near-infrared grain analyzer |
| CN114965836A (en) * | 2022-06-01 | 2022-08-30 | 国网湖北省电力有限公司超高压公司 | Background gas correction method based on ultraviolet infrared SF6 decomposed gas detection method |
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