玫瑰油化学成分分析和电子香味分析

为区分不同档次的玫瑰油,利用GC/MS法对高、中档玫瑰油进行了化学成分的定性和定量分析,分别鉴定出63和72种化合物。并对这2种玫瑰油和1种待测样品进行了电子香味扫描,确定电子鼻在玫瑰油档次辨别中的作用。     

Novel e-nose for the discrimination of volatile organic biomarkers with an array of carbon nanotubes (CNT) conductive polymer nanocomposites (CPC) sensors

We report the original design of a new type of electronic nose (e-nose) consisting of only five sensors made of hierarchically structured conductive polymer nanocomposites (CPC). Each sensor benefits from both the exceptional electrical properties of carbon nanotubes (CNT) used to build the conductive architecture and the spray layer by layer (sLbL) assembly technique, which provides the transducers with a highly specific 3D surface structure. Excellent sensitivity and selectivity were obtained by optimizing the amount of CNT with five different polymer matrices: poly(caprolactone) (PCL), poly(lactic acid) (PLA), poly(carbonate) (PC), poly(methyl methacrylate) (PMMA) and a biobased polyester (BPR). The ability of the resulting e-nose to detect nine organic solvent vapours (isopropanol, tetrahydrofuran, dichloromethane, n-heptane, cyclohexane, methanol, ethanol, water and toluene), as well as biomarkers for lung cancer detection in breath analysis, has been demonstrated. Principal component analysis (PCA) proved to be an excellent pattern recognition tool to separate vapour clusters.     

Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools

Electronic nose (E-nose) technique was attempted to discriminate green tea quality instead of human panel test in this work. Four grades of green tea, which were classified by the human panel test, were attempted in the experiment. First, the E-nose system with eight metal oxide semiconductors gas sensors array was developed for data acquisition; then, the characteristic variables were extracted from the responses of the sensors; next, the principal components (PCs), as the input of the discrimination model, were extracted by principal component analysis (PCA); finally, three different linear or nonlinear classification tools, which were K-nearest neighbors (KNN), artificial neural network (ANN) and support vector machine (SVM), were compared in developing the discrimination model. The number of PCs and other model parameters were optimized by cross-validation. Experimental results showed that the performance of SVM model was superior to other models. The optimum SVM model was achieved when 4 PCs were included. The back discrimination rate was equal to 100% in the training set, and predictive discrimination rate was equal to 95% in the prediction set, respectively. The overall results demonstrated that E-nose technique with SVM classification tool could be successfully used in discrimination of green tea's quality, and SVM algorithm shows its superiority in solution to classification of green tea's quality using E-nose data.     

Electronic nose and GC–MS analysis of volatile compounds in Tuber magnatum Pico: Evaluation of different storage conditions

The characteristic aromatic composition of white truffles (Tuber magnatum Pico) determines its culinary and commercial value. However modifications of truffle organoleptic proprieties occur during preservation. A study of headspace of white truffles by using Electronic nose (E-nose), gas chromatography–mass spectrometry (GC–MS) and sensory analyses was performed. Truffles were stored at different conditions for 7 days: +4 and +8 °C wrapped in blotting paper or covered by rice or none of the above. Headspace E-nose measurements and sensory analyses were performed each day. Statistical multivariate analysis of the data showed the capability of E-nose to predict sensorial analysis scores and to monitor aroma profile changes during storage. Truffle’s volatile molecules were also extracted by headspace solid phase microextraction technique and separated and identified by GC–MS. Partial Components Analysis of data was performed. E-nose and GC–MS results were in agreement and showed that truffle storage in paper at +8 °C seemed to be the best storage condition.     

Analysis of the influence of the type of closure in the organoleptic characteristics of a red wine by using an electronic panel

An electronic panel formed by an electronic nose, an electronic tongue and an electronic eye has been successfully used to evaluate the organoleptic characteristics of red wines vinified using different extraction techniques and micro-oxygenation methods and bottled using closures of different oxygen transmission rates (OTR).     

基于电子鼻、HS-GC-IMS、HS-SPME-GC-MS技术联用分析不同发酵年份老香黄挥发性成分差异

基于电子鼻、顶空气相离子迁移谱（HS-GC-IMS）和顶空固相微萃取-气相色谱-质谱技术联用（HS-SPME-GC-MS）分析不同发酵年份老香黄挥发性成分变化,并结合正交偏最小二乘判别分析（OPLS-DA）法区分不同发酵年份老香黄。电子鼻主成分分析能明显区分发酵与未发酵的老香黄,两者风味差异较大,老香黄发酵3年和4年的风味成分最为相似,而其余发酵年份风味存在较大差异。HS-GC-IMS定性检测出39种挥发性成分,包括萜烯类、醇类、醛类、酯类、酮类、酚类、酸类、杂环化合物和其它共9类。HS-SPME-GC-MS则一共鉴别出50种挥发性成分,包括萜烯类、醇类、醛类、酚类、酯类、醚类、杂环化合物和其它共8类。α-蒎烯、β-蒎烯、月桂烯、萜品油烯、柠檬烯、异松油烯、1-石竹烯、巴伦西亚橘烯、芳樟醇、α-松油醇、糠醛、麦芽酚、茴香脑、2,4-二甲基苯乙烯为14种共有挥发性成分,经OPLS-DA模型筛选出8种标志性挥发性化合物（VIP>1)。综上,未发酵和发酵1~5年的老香黄风味存在较大差异,筛选出的8种挥发性成分为区别不同年份老香黄提供一定依据。     

A high precise E-nose for daily indoor air quality monitoring in living environment

E-nose, whose major components include a sensor array and a pattern recognition algorithm, is considered to be a potential way to balance the trade-off between cost and accuracy for daily indoor air quality monitoring in living environment. In this paper, we presented a high precise E-nose for such application. QS-01 from FIS, TGS2600 and TGS2602 from FIGARO, temperature and humidity sensor SHT10 are selected to compose the sensor array. Back Propagation (BP) nueral network, the typical machine learning algorithm is used to be the pattern recognition algorithm of the E-nose. The performance comparison between the proposed E-nose and other E-nose solutions shows the improvement.