College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing 210095, PR China.
College of Food Science and Technology, Nanjing Agricultural University, No. 1, Weigang Road, Nanjing 210095, PR China.
Meat Sci. 2017 Nov;133:1-9. doi: 10.1016/j.meatsci.2017.05.017. Epub 2017 May 25.
A portable electronic nose was used for extracting flavour fingerprint map of Chinese-style sausage during processing and storage, in parallel with detection of acid value (AV) and peroxide value (POV) for evaluating lipid oxidation. Sausage samples during processing and storage were divided into three and five quality phases, respectively. After comparison of sensors response to lipid oxidation, optimal sensor array was determined. Several classification and regression models were developed to classify samples into their respective quality phase and predict lipid oxidation using full and optimal sensor array. Results indicated classification accuracy for sausage samples were, respectively, above 95% and 82% during the processing and storage. For support vector machine (SVM) and artificial neural networks (ANN) regression models, good performance in predicting AV and POV were obtained, with the coefficients of determination (Rs) >0.914 and 0.814 during processing and storage, respectively. Thus, E-nose demonstrated acceptable feasibility in evaluating the degree of lipid oxidation of Chinese-style sausage during processing and storage.
一种便携式电子鼻用于提取中式香肠加工和贮藏过程中的风味指纹图谱,并结合酸值(AV)和过氧化物值(POV)检测来评估脂质氧化。将加工和贮藏过程中的香肠样品分别分为三个和五个质量阶段。比较传感器对脂质氧化的响应后,确定了最佳传感器阵列。开发了几种分类和回归模型,使用全传感器阵列和最佳传感器阵列将样品分类到各自的质量阶段,并预测脂质氧化。结果表明,在加工和贮藏过程中,香肠样品的分类准确率均高于 95%和 82%。对于支持向量机(SVM)和人工神经网络(ANN)回归模型,在加工和贮藏过程中,AV 和 POV 的预测性能良好,决定系数(Rs)分别大于 0.914 和 0.814。因此,电子鼻在评估中式香肠加工和贮藏过程中脂质氧化程度方面具有较好的可行性。