Methods for detecting and evaluating quality of peanuts suitable for soluble protein processing
A protein, soluble technology, applied in biological testing, testing food, material testing, etc., to reduce the number of analysis steps
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Embodiment 1
[0029] Example 1. Establishment of a peanut quality measurement model suitable for soluble protein processing
[0030] (1) Determination of peanut quality
[0031] Take the peanut samples harvested in 2011 as the standard, 64 samples (in line with the normal distribution of peanut populations, as shown in Table 1);
[0032] Table 1 64 peanut varieties
[0033]
[0034] A total of 44 indicators of sensory quality, physicochemical and nutritional quality and processing quality indicators of each variety were determined; among them, each indicator and its measurement methods and standards are as follows:
[0035] Physical traits of flowers: Fruit shape: When the fruit shape of the peanut sample is hockey stick, the fruit shape score is 1; when the fruit shape of the peanut sample is hump-shaped, the fruit shape score is 2; when the fruit shape of the peanut sample is beaded When the fruit shape of the peanut sample is normal, the fruit shape score is 4; when the fruit shape ...
Embodiment 2
[0098] Example 2. Determination of the solubility of peanut samples
[0099] The remaining 21 peanut varieties in Example 1 were subjected to protein solubility assays.
[0100] Outlier analysis found that Zhongnong 108 was an outlier in solubility, and it was deleted. The crude fat content, total protein content, total sugar content, cystine content, and arginine content of the remaining 20 varieties were analyzed. , Conarabinin I content, the subunit with a molecular weight of 37.5kDa accounted for the mass percentage of the protein, the subunit with a molecular weight of 23.5kDa accounted for the protein mass percentage, the subunit with a molecular weight of 15.5kDa accounted for the protein mass percentage Substitute 11 indicators such as content, protein extraction rate and kernel yield into formula (1) to calculate the solubility of 20 varieties; the comparison between the model predicted value of the peanut gelatinity and the chemically measured value is shown in Table...
Embodiment 3
[0103] Example 3. Establishment of peanut quality evaluation method suitable for soluble protein processing
[0104] Using K-means cluster analysis method, the comprehensive solubility value of peanut protein is classified into three categories, the cluster center of each category is determined, and the gelatinity is divided into three grades. The 64 peanut varieties were classified as shown in Table 12.
[0105] Table 12 Classification of 64 peanut varieties
[0106]
[0107]
[0108] According to the regression coefficient of each index in formula (1), the weight of each index is determined, and K-means cluster analysis and the actual situation are used to divide each evaluation index into grade I, grade II and grade III, and the weight value of each index is regarded as the score of grade I. ,And so on.
[0109] Table 13 Weights of indicators in formula (1)
[0110]
[0111] K-means cluster analysis was performed on 11 qualities of 64 peanut varieties, and each ...
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