干豇豆的腌渍工艺优化

Optimization of Pickling Processing of Dried Cowpea

中文关键词:  豇豆  腌渍  感官评分  响应面  多目标优化

英文关键词:cowpea  pickled  sensory score  response surface  multi-objective optimization

中文摘要:

      本研究以干豇豆为原料进行浸泡腌渍调味，开发即食豇豆制品，通过设计响应面试验，讨论浸泡液中白砂糖、食盐与醋酸添加比例对浸泡腌渍后豇豆总酸度、硬度、L*值、复水比、感官评分的影响，分析影响各指标的主次因素及因素间的交互作用并建立二次回归模型，利用熵权法对各个响应值赋权值进行多目标优化，得到最佳工艺参数并加以验证。结果表明：建立总酸度、硬度、感官评分3个指标的回归方程模型均极显著（p<0.01），L*值指标的回归方程模型显著（p<0.05），复水比指标的回归方程模型则不显著，可用于对干豇豆浸泡腌渍工艺指标进行分析和预测；熵权法综合评分的回归方程显著（p<0.05），可用于腌渍工艺的多目标优化，得到最佳工艺配方：食盐4%、醋酸1.4%、白砂糖11.8%，在此条件下进行验证试验，腌渍后豇豆的总酸度0.44、硬度217.03 g、L*值42.31、复水比2.94、感官评分85.29分，与理论预测值接近，说明响应面结合熵权法优化具有较好的准确性和可靠性，可为后续研究提供理论依据。

英文摘要:

      In order to obtain instant cowpea products, the dried cowpea was used as raw material for soaking and pickling seasoning, the effects of salt, acetic acid, sugar contents and their interactions on total acidity, hardness, L* value and rehydration ratio were explored, and the sensory evaluation of cowpea after soaking and picking was also investigated, using three-factor response surface design. The factors and their interactions between the various factors were analyzed, quadratic regression models were established, multi-objective optimization was performed by entropy weight method, which were verified by applying three optimization methods. The results showed that the established regression model of total acidity, hardness, and sensory evaluation was very significant (p<0.01), the regression model of L* value was significant (p<0.05) and the regression model of rehydration ratio was not significant, suggesting that the model could be used to analyze and predict the pickling processing of dried cowpea parameters. The optimum parameters were 4% of salt content, 1.4% of acetic acid content, and 11.8% of sugar content. With these parameters, the hardness, L* value, rehydration ratio and sensory score were 0.44, 217.03 g, 42.31, 2.94, 85.29, respectively, which were close to the theoretical prediction. The parameters of pickling processing of dried cowpea were optimized by response surface design combined with entropy weight method, which were accurate and reliable, providing a theoretical basis for future study.