【作者】 张成飞；

【导师】 张贵林；

【作者基本信息】 吉林大学 ， 农产品加工及贮藏工程， 2009， 硕士

【摘要】 本文以芝麻油、花生油、玉米油、大豆油、葵花油、棕榈油、及其二元混合油为研究对象,对单一和二元混合植物油种类的识别及掺混量的检测方法进行了探索性研究。经检验,本研究形成的方法识别单一和二元混合植物油的准确率分别为100%和92.3%,二元混合植物油掺混量的最低检出限为8%,检测范围为8%-92%,最大检测误差为5.4%。具体研究内容包括:通过考察不同稀释浓度对紫外吸收光谱的显著性和稳定性的影响,确定了最佳扫描样本浓度为2mg/mL。在实测单一植物油紫外吸收光谱的基础上,进行聚类分析,对识别的可行性进行了探讨,结果表明利用一阶导吸光度对植物油进行聚类的效果最为理想;进行主成分分析,获得了包含紫外吸收光谱信息的主成分得分函数;通过判别分析,获得了六种植物油的判别函数。在实测大量二元混合油紫外吸收光谱的基础上,应用模板匹配法研究了二元混合油的识别问题,获得了二元混合油种类的识别方法;对实测数据进行回归分析,获得了二元混合油掺混量与紫外吸光度的回归方程。在Matlab平台上编写了单一与二元混合植物油的识别和掺混量计算程序,建立了用于植物油识别及掺混量检测的人机交互操作界面。更多还原

【Abstract】 As one of main nutriments in people’s daily life, edible vegetable oils have many kinds and categories. Their components, physical and chemical characteristics are diversified by effect of different raw species, origin, production process and other factors. So the detection of vegetable oil is difficult. Conventional physical and chemical detection methods are difficult to meet the requirements of contemporary laws and regulations,well, modern apparatus detection methods exist some other problems, such as more complex operations, the high cost of testing, the less universal in our country. Therefore, it is very important to research and develope a simple, accurate and rapid detection method of vegetable oils, to achieve qualitative and quantitative detection.In this paper, peanut oil, maize oil, soybean oil, sunflower oil, palm oil and their binary blended oils were taken as main research objects. Relyed on the ultraviolet spectrum of vegetable oils, cluster analysis, principal component analysis, discriminant analysis were used to study the recognition method of single vegetable oils; template matching method was applicated to recognise the binary blended vegetable oils; the blended proportion were analyzed through linear regression, multiple linear regression, principal component regression and partial least-squares regression. The recognition program of single and binary blended vegetable oils and the detection program of blended proportion were established in Matlab platform; the identification and detection interface of vegetable oils was founded; the identification and detection of vegetable oils was achieved rapidly.The main contents and specific conclusions of this paper as follows:1) Theoretical analysis of the UV spectra of the nature of vegetable oils was discussed, conjugated diene acids and polyunsaturated acids contained in vegetable oils have peaks detection between 220nm and 300nm, the vitamin A at 325nm, vitamin D at 265nm, vitamin E at 285nm have peaks detected, sterols of vegetable oils absorbe ultraviolet light too. The interaction and mutual influence of these components form the UV absorption spectrum of vegetable oils.2) The UV absorption spectrum characteristics of vegetable oils were gotten through scan of UV absorption spectrum curve. Six vegetable oils had peaks detection at 271, 267, 258, 252nm, acromion was detected at 245nm. Sesame oil’s peak detected at 282nm,while other oils’at 279nm. peanut oil, soybean oil, sunflower oil and palm oil had peak detection at 227nm,while corn oil and sesame oil at 230nm.3) The first derivative and second derivative processing were used to analyze the UV absorption spectra of six kinds of vegetable oils. The first derivative spectrometry of sesame oil had characteristic trough detection at 297nm, second derivative spectrometry had characteristic peak detection at 299nm and characteristic trough at 294nm, while other five kinds of vegetable oils had no peak or trough detection at these wavelengths. The first derivative spectra of six kinds of vegetable oils had peaks detection at 221,264nm and troughs detection at 242, 248, 284nm; The second derivative spectra had peaks detection at 219, 249, 262nm and troughs detection at 223, 246, 266nm.4) Comparison of the ultraviolet absorption spectrum and derivative spectrum of vegetable oils, except characteristic peaks and troughs of sesame oil, the other peaks and troughs of six kinds of vegetable oils appeared in the same or similar position, however, absorbance values were different, which show that oils contain the same absorption material, well difference in content. At the same concentration, Corn oil has the strongest absorbance between 220 and 250nm; between 250 and 300nm, the absorption spectra of corn oil and sunflower oil was close to each other. In the entire scan range, the absorbance of peanut oil was the smallest.5) Under the concentration of 2 mg/mL, the UV absorption spectrum of solution could reflect the absorption characteristics of six vegetable oils, all the peaks and troughs of spectrometry were detected. The absorbance value was appropriate and fluctuation scope of absorbance was small, the deviation was less than 5%, so, 2 mg/mL was selected as the best experimental concentration.6) Based on the original absorbance spectra, first derivative absorbance and second derivative absorbance, cluster analysis was applied to analyze six kinds of vegetable oils. Comparison result, cluster analysis of the first derivative absorbance was the best, corn oil and sesame oil were clustered respectively, soybean oil and sunflower oil were clustered together for a class, peanut oil and palm oil for a class.7) The absorbance under wavelength of 290, 279, 271, 267, 258, 252, 245, 227nm was analysed through the method of principal component analysis, three main components was got and their cumulative contribution rate was 97.31%, containing most information of sample. The first principal component took up 76.47%, the second 12.7%, the third 8.14%. A271, A267, A252, A258, A245 and A279 had the significant contribution to the first principal component, A290 contributed more to the second principal component, A227 had significant contribution to the third principal component. Three principal component score function were founded. According to principal component scores, the distribution of the vegetable oil samples was gotten,According to the regional distribution of scores points, the type of vegetable oil. could be jundged.8) The absorbance of six kinds of vegetable oils was analysed by means of progressive discrimination analyses. A290、A279、A252、A245、A227 were selected as the main variable and got three kinds of discriminant function. Through comparison, the best discriminant function was linear discriminant function.. Substituted the absorbance into the discriminant function to calculate the function value, the maximum determined the category of vegetable oil. Verification by experiment, the accuracy of discriminant function was 100%.9) This paper set sesame peanut blended oil as example. Through the establishment of one dimension regression equation, multiple regression equation, principal component regression equation and partial least-squares regression equation, analysed the best way to calculate the mixing amount of peanut oil.The best regression equation, identified by comparison, was one dimension regression equation, which was based on the original UV-absorbance. The equation was y=-2.8706A290+1.1319. Validation results were satisfactory and the detection limit for peanut oil was 8%, the detection range was 8% -92%, the maximum detection error was 5.4%.10) According to the principles of template matching method, template file of the UV absorbance of six single oils was founded in Matlab platform, contained UV absorbance of six kinds of vegetable oil. Pattern recognition program of vegetable oils was established, the recognition of vegetable oil was realized and the accuracy was 100%.11) The structure template, contained the name of binary mixtures of oil, UV absorb- ance and coefficient of the regression equation, was founded in Matlab platform. Pattern recognition program was complied and the blended proportion was calculated in program, achieved qualitative identification and quantitative analysis of binary blended oils, the accuracy qualitative identification was 92.3%.12) The recognition interface of vegetable oils was founded through GUIDE editor of Matlab. The interface structure was smart and operated conveniently. The identification of vegetable oils and the detection of blended proportion were achieved rapidly.更多还原