Huali Jin

Based on DNA strand replacement reaction and aptamer-specific recognition, a simple dual-labeled DNA nanostructure is designed for the simultaneous detection of Ochratoxin A (OTA) and aflatoxin B1 (AFB₁). C1 is labeled with Cy3 and Cy5, while C2 and C3 are labeled with BHQ2. The fluorescence intensity of DNA nanostructure composed of C1, C2 and C3 is weak because of fluorescence resonance energy transfer. When OTA Aptamer (OTA-Apt) and AFB1 Aptamer (AFB₁-Apt) are added to the homogeneous system at the same time, C1 can be replaced with the help of toehold strand displacement, resulting in fluorescence enhancement. In the presence of both OTA and AFB₁, the toehold strand displacement reaction is inhibited due to preferential binding between the target and their corresponding aptamers. The limit of detection of OTA was 0.007 ng/mL and that of AFB₁ was 0.03 ng/mL. The recoveries of OTA and AFB₁ were 96%-101% and 97%-101% in the corn sample, and 99%-101% and 92%-106% in the wine sample. Compared with other sensors, the preparation of this aptasensor needs simpler experimental steps and a shorter total-preparing time, confirming the convenient, rapid, and time-saving operation process.

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.