Wang Li, Niu Qunfeng, Hui Yanbo, Jin Huali, Chen Shengsheng
School of Electrical Engineering, Henan University of Technology, Zhengzhou 450007, China.
School of Food Science & Engineering, Henan University of Technology, Zhengzhou 450007, China.
Sensors (Basel). 2015 May 13;15(5):11169-88. doi: 10.3390/s150511169.
Peanut meal is the byproduct of high-temperature peanut oil extraction; it is mainly composed of proteins, which have complex tastes after enzymatic hydrolysis to free amino acids and small peptides. The enzymatic hydrolysis method was adopted by using two compound proteases of trypsin and flavorzyme to hydrolyze peanut meal aiming to provide a flavor base. Hence, it is necessary to assess the taste attributes and assign definite taste scores of peanut meal double enzymatic hydrolysis hydrolysates (DEH). Conventionally, sensory analysis is used to assess taste intensity in DEH. However, it has disadvantages because it is expensive and laborious. Hence, in this study, both taste attributes and taste scores of peanut meal DEH were evaluated using an electronic tongue. In this regard, the response characteristics of the electronic tongue to the DEH samples and standard five taste samples were researched to qualitatively assess the taste attributes using PCA and DFA. PLS and RBF neural network (RBFNN) quantitative prediction models were employed to compare predictive abilities and to correlate results obtained from the electronic tongue and sensory analysis, respectively. The results showed that all prediction models had good correlations between the predicted scores from electronic tongue and those obtained from sensory analysis. The PLS and RBFNN prediction models constructed using the voltage response values from the sensors exhibited higher correlation and prediction ability than that of principal components. As compared with the taste performance by PLS model, that of RBFNN models was better. This study exhibits potential advantages and a concise objective taste assessment tool using the electronic tongue in the assessment of DEH taste attributes in the food industry.
花生粕是高温提取花生油后的副产品；它主要由蛋白质组成，经酶水解成游离氨基酸和小肽后具有复杂的味道。采用胰蛋白酶和风味酶这两种复合蛋白酶对花生粕进行酶水解，旨在提供一种风味基料。因此，有必要评估花生粕双酶水解物（DEH）的味觉属性并给出明确的味觉评分。传统上，采用感官分析来评估DEH中的味觉强度。然而，它存在缺点，因为既昂贵又费力。因此，在本研究中，使用电子舌对花生粕DEH的味觉属性和味觉评分进行了评估。在此方面，研究了电子舌对DEH样品和标准五种味觉样品的响应特性，以使用主成分分析（PCA）和判别因子分析（DFA）定性评估味觉属性。分别采用偏最小二乘法（PLS）和径向基函数神经网络（RBFNN）定量预测模型来比较预测能力，并关联从电子舌和感官分析获得的结果。结果表明，所有预测模型在电子舌预测得分与感官分析获得的得分之间都具有良好的相关性。使用传感器电压响应值构建的PLS和RBFNN预测模型比主成分具有更高的相关性和预测能力。与PLS模型的味觉性能相比，RBFNN模型的性能更好。本研究展示了在食品工业中使用电子舌评估DEH味觉属性的潜在优势和一种简洁客观的味觉评估工具。
