This work first describes a simple approach for the untargeted profiling of volatile compounds for distinguishing between white duck down (WDD) and white goose down (WGD) based on resolution-optimized GC-IMS combined ...This work first describes a simple approach for the untargeted profiling of volatile compounds for distinguishing between white duck down (WDD) and white goose down (WGD) based on resolution-optimized GC-IMS combined with optimized chemometric techniques, namely PCA. The detection method for down samples was established by using GC-IMS. Meanwhile, the reason of unpleasant odors caused by WDD was explained on the basis of the characteristic volatile compounds identification. GC-IMS fingerprinting can be considered a revolutionary approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. A total of 22 compounds were successfully separated and identified through GC-IMS method, and the significant differences in volatile compounds were observed in three parts of WDD and WGD samples. The most characteristic volatile compounds of WGD belong to aldehydes, whereas carboxylic acids from WDD were detected generated by autoxidation reaction. Meanwhile, the main reason of unpleasant odor generation was possibly attributed to the high concentration of volatile carboxylic acids of WDD. Therefore, the constructed model presents a simple and efficient method of analysis and serves as a basis for down processing and quality control.展开更多
Data evaluation strategies for the novel coupled MCC-IMS sensory system are developed. Mayor attention to the plausibility of applied procedures and the feasibility of automation was paid. Three stages of extraction l...Data evaluation strategies for the novel coupled MCC-IMS sensory system are developed. Mayor attention to the plausibility of applied procedures and the feasibility of automation was paid. Three stages of extraction levels with increasing data reduction are presented for several fields of application. According to suitable extraction levels, real data were tested on various structures of artificial neural networks (ANN) with the result, that the computational levels must still be chosen by expertise, but subsequent processing and training can be fully automated. For the training of larger net- works a method of automated generation of secondary training data is presented which exceeds the quality of previous noise models by far. It is concluded that the combination of MCC-IMS as measuring instrument and ANNs as evalua- tion technique have high potential for industrial use in process monitoring.展开更多
文摘This work first describes a simple approach for the untargeted profiling of volatile compounds for distinguishing between white duck down (WDD) and white goose down (WGD) based on resolution-optimized GC-IMS combined with optimized chemometric techniques, namely PCA. The detection method for down samples was established by using GC-IMS. Meanwhile, the reason of unpleasant odors caused by WDD was explained on the basis of the characteristic volatile compounds identification. GC-IMS fingerprinting can be considered a revolutionary approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. A total of 22 compounds were successfully separated and identified through GC-IMS method, and the significant differences in volatile compounds were observed in three parts of WDD and WGD samples. The most characteristic volatile compounds of WGD belong to aldehydes, whereas carboxylic acids from WDD were detected generated by autoxidation reaction. Meanwhile, the main reason of unpleasant odor generation was possibly attributed to the high concentration of volatile carboxylic acids of WDD. Therefore, the constructed model presents a simple and efficient method of analysis and serves as a basis for down processing and quality control.
文摘Data evaluation strategies for the novel coupled MCC-IMS sensory system are developed. Mayor attention to the plausibility of applied procedures and the feasibility of automation was paid. Three stages of extraction levels with increasing data reduction are presented for several fields of application. According to suitable extraction levels, real data were tested on various structures of artificial neural networks (ANN) with the result, that the computational levels must still be chosen by expertise, but subsequent processing and training can be fully automated. For the training of larger net- works a method of automated generation of secondary training data is presented which exceeds the quality of previous noise models by far. It is concluded that the combination of MCC-IMS as measuring instrument and ANNs as evalua- tion technique have high potential for industrial use in process monitoring.
文摘以“红阳”猕猴桃为试材,通过气相迁移离子色谱(gas chromatography-ion mobility spectrometry,GC-IMS)技术分析比较灭菌后果汁(YL)、发酵对照组(CK)、植物乳杆菌发酵果汁(PL)挥发性风味物质组成,并采用偏最小二乘判别分析(Partial least squares-discrimination analysis,PLS-DA)筛选不同处理样品的差异物质,探讨乳酸菌发酵对“红阳”猕猴桃汁挥发性风味物质组成的影响。结果表明,该研究所选用菌株(Lactobacillus plantanum B-1)应用于猕猴桃果汁发酵在24 h内菌落数由6.21 lg CFU/mL快速上升至8.60 lg CFU/mL,总酸由原来的0.60%增加至1.33%,发酵性能良好。GC-IMS结合PLS-DA分析结果表明,植物乳杆菌发酵可提高(E)-2-己烯醛、己醇、芳樟醇、乙酸乙酯、丁酸乙酯,丙酮、4-庚酮、2-壬酮等含量,增加果汁果香味,减少1,8-桉叶素、(E)-2-己烯醇、己醛等猕猴桃特征风味物质的损失,抑制乙醇、糠醛等对风味产生不良影响的物质生成,从而提升猕猴桃果汁风味品质。该研究为植物乳杆菌在猕猴桃果汁产品中应用提供了理论支撑。
