College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China; College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230038, China.
Food Chem. 2022 Apr 16;374:131658. doi: 10.1016/j.foodchem.2021.131658. Epub 2021 Nov 28.
Vinegar is a kind of traditional fermented food, there are significant variances in quality and flavor due to differences in raw ingredients and processes. The quality assessment and flavor characteristics of 69 vinegar samples with 5 brewing processes were analyzed by physicochemical parameters combined with flash gas chromatography (GC) e-nose. The evaluation system of quality and the detection method of flavor profile were established. 17 volatile flavor compounds and potential flavor differential compounds of each brewing process were identified. The artificial neural network (ANN) analysis model was established based on the physicochemical parameters and the analysis of flash GC e-nose. Although the physicochemical parameters were more intuitive in quality evaluating, the flash GC e-nose could better reflect the flavor characteristics of vinegar samples and had better fitting, prediction and discrimination ability, the correct rates of training and prediction of flash GC e-nose trained ANN model were 98.6% and 96.7%, respectively.
醋是一种传统的发酵食品，由于原料和工艺的不同，其质量和风味有很大的差异。采用物理化学参数与快速气相色谱（GC）电子鼻相结合的方法，对 5 种酿造工艺的 69 个醋样进行了质量评估和风味特征分析。建立了质量评价体系和风味图谱检测方法。确定了 17 种挥发性风味化合物和各酿造工艺的潜在风味差异化合物。基于理化参数和快速 GC 电子鼻分析，建立了人工神经网络（ANN）分析模型。虽然理化参数在质量评价中更直观，但快速 GC 电子鼻能更好地反映醋样的风味特征，且具有更好的拟合、预测和判别能力，快速 GC 电子鼻训练的 ANN 模型的训练和预测正确率分别为 98.6%和 96.7%。
