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Analysis Of Thermal Characteristics In Soy Textured Protein And The Relation Between Quality

Posted on:2009-02-22Degree:MasterType:Thesis
Country:ChinaCandidate:Y YuFull Text:PDF
GTID:2121360245472629Subject:Agricultural products processing and storage
Abstract/Summary:PDF Full Text Request
To aim directly at the issue that shortage thermal characteristics assay method and characteristics research of textured soybean protein products, the 4 sample as research object in the experiment, was applied monofactorial test obtained optimization determination parameter, the 1~15 sample was determined by optimization determin parameter, analyzing obtain thermal characteristics nature of textured soybean protein products. Now because our country lacks of the characteristics relationship among the textured soybean protein, the experiment measured and analyzed the composition and the characteristic of 1~15 sample,according to the principal component analysis, using canonical correlation analytical method to establish the relationship of characteristic index.The material is sample 16~20, measured each index and using clustering methods test the established relationship. Thus, the theory of fast inspection of textured soybean protein is supplied.According to single factor test, the best parameter of the thermal characteristics of textured soybean protein is particle size 20 target,the weight 5mg and the raised temperature velocity 10℃/min. The DSC showed, textured soybean protein production has no vitrum inversion temperature, before 100℃the product appears a absorb the heat peak, among the denaturation temperature 70℃~85℃. There is little influence textured soybean protein,raise or lower the denaturation temperature has on infiuence of the moisture of the production,but the denaturation needed quantity of heat increase gradually.According to the principal component analysis,there is little difference between variable modulus of the first reconstituent,they are positive all, they can be the index of total body mass of textured soybean protein products.a* and water absorption modulus comparatively large and are positive in the second reconstituent,temperature of the peak,peak area,enthalpy change, rigidity,tenderness are all negative.And the modulus are fairly mean.Indicate that the second reconstituent reflect the difference of physical characteristic,functional characteristic,thermal characteristics and characteristics.L* modulus is fairly large in the third reconstituent,indicate that the third reconstituent reflect the physical characteristic of the products.Id est the second and the third reconstituent can reflect the index of organoleptic property of textured soybean protein products.Liquid~binding power modulus is fairly large in the forth reconstituent,and the thermal characteristics variation coefficients are all positive.So the forth reconstituent can be as the index of practical application effect and the thermal characteristics of the textured soybean protein products.Peak area and tenderness,hardness have the positive correlation though the canonical correlation analysis.Peak area is bigger the hardness and tenderness are more well.Enthalpy change have the positive relationship with aspiration oiliness and hydrophilia.Enthalpy change is bigger the aspiration oiliness and hydrophilia display more well.Peak area and enthalpy change commonly reflect the moisture content.They are appearant positive correlation.The peak area and enthalpy change are bigger the moisture content is higher.Cluster analysis test results that the correlation accurate rate between peak area and tenderness,hardness can reach 60%.The correlation accurate rate between enthalpy change and aspiration oiliness,moisture content can reach 80%.The super tendency of peak area,enthalpy change and moisture content in test are completely same with prediction,but the specific appearance is a little different.Three canonical correlation analysis results all have the practical meaning.
Keywords/Search Tags:extured soybean protein, thermal characteristics, principal component analysis, canonical correlation analysis
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