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Studieson Effects of Soybean Species on Yuba and Initial Establishment of Quality Evalution System f

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Tutor: SongLianJun
School: Henan Agricultural University
Course: Of Food Science
Keywords: soybean,Yuba,quality indicators,sensory evaluation,relevance,cluster analysis,fa
CLC: TS214.2
Type: Master's thesis
Year:  2011
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Abstract:
Yuba is one of the famous national characteristics foods with high nutritional value.20 soybean varieties were selected in this paper to determinate the ingredients of soybean and qualities of Yuba. The relationships between the ingredients of soybean and qualities of Yuba were analyzed by means of correlation and regression analysis.Then suitable soybean varieties were preliminary screened for processing Yuba. The qualities of Yuba were researched by the methods of instruments and sensory evaluation to get the correlation between sensory indicators and instruments measured values, and established Yuba sensory prediction model.The main results were stated as follows:1. Physical and chemical composition of different soybean varieties existed large differences, causing the difference between the qualities of Yuba. Fat content of soybean was significantly negative correlated with cooking nature, water absorption, tensile strength, elongation, protein content of Yuba, and positive correlated with yield, fat content of Yuba. Protein content of soybean was significantly positive correlated with protein content of Yuba, and negative correlated with yield, fat content of Yuba. Sugar of soybean was significantly negative correlated with tensile strength, and significantly positive correlated with yield, fat content of Yuba.Protein/ Sugar of soybean was positive correlated with tensile strength and protein, and negative correlated with yield, fat content of Yuba. Protein/Fat was negative correlated with yield, fat, and was significantly positive correlated with protein content of Yuba. Phytic acid content of soybean was significantly negative correlated with elongation.¦Â-eonglyeinin(7S) content of soybean was significantly positive correlated with L* of Yuba, glyeinin(11S) content of soybean was significantly correlated with L*, tensile strength, elongation, 7S/11S of soybean was negative correlated with tensile strength.2. Through regression analysis, the results showed value F in the equation and multiple correlation coefficient value R was significant or highly significant. From the selected indicators, Yuba¡¯s qualities were affected by soybean ash, fat, sugar, protein/sugar and protein/fat etc. By clustering analysis, 20 soybean varieties are divided into three groups and three soybeans, ZhouDou 11, Zheng 98120-5, Soybean 24(82-24) were optimal elected to processe Yuba with good quality.3. The relationships between sensory evaluation indicators and instruments value were good. L* of Yuba was significantly positive correlated with many sensory evaluation indexs (color, smell, overall score), and was significantly negative correlated with remove speed. Yellowness parameters b* was negative correlated with color of sensory evaluation. Water absorption of Yuba was significantly positive correlated with color, rehydration, overall score. Cooking nature of Yuba was negative correlated with color, smell of sensory evaluation indexs, and was positive correlated with rehydration. Tensile strength and Elongation of Yuba was significantly positive correlated with color, overall score, and was significantly negative correlated with smell. Fat content of Yuba was significantly positive correlated with color and overall score.4. Six sensory evaluation indicators were analysised by factor analysis and get colour and overall score as two main influencing factors. Sensory evaluation and instruments value were analyzed by stepwise regression for establishment of a sensory evaluation prediction model of Yuba. The models were significant or highly significant to the evaluation of Yuba.Then Nine quality indexes of Yuba were researched by factor analysis and get three main components. Tensile strength, elongation, protein and fat content, L*of Yuba could as the main influencing factors. According to three main components values, Yuba samples can be divided into four classes by clustering analysis.
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