Assessment of Taste Attributes of Peanut Meal Enzymatic-Hydrolysis Hydrolysates Using an Electronic Tongue

Sensors

Li Wang²  Qunfeng Niu²  Yanbo Hui²  Huali Jin¹  Shengsheng Chen² 

[1] School of Food Science & Engineering, Henan University of Technology, Zhengzhou 450007, China; E-Mail:;School of Electrical Engineering, Henan University of Technology, Zhengzhou 450007, China; E-Mails:

关键词: peanut meal DEH;    taste assessment;    electronic tongue;    qualitative analysis;    discrimination;    quantitative prediction;    correlation;    PCA;    DFA;    PLS;    RBFNN;   

DOI  :  10.3390/s150511169

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.