VOCs分子的半导体型传感器识别检测研究进展

具有体积小、功耗低、灵敏度高、硅工艺兼容性好等优点的金属氧化物半导体(MOS)气体传感器现已广泛地应用于军事、科研和国民经济的各个领域。然而MOS传感器的低选择性阻碍了其在物联网(IoT)时代的应用前景。为此,本文综述了解决MOS传感器选择性的研究进展,主要介绍了敏感材料性能提升、电子鼻和热调制三种改善MOS传感器选择性的技术方法,阐述了三种方法目前所存在的问题及其未来的发展趋势。同时,本文还对比介绍了机器嗅觉领域主流的主成分分析(PCA)、线性判别分析(LDA)和神经网络(NN)模式识别/机器学习算法。最后,本综述展望了具有数据降维、特征提取和鲁棒性识别分类性能的卷积神经网络(CNN)深度学习算法在气体识别领域的应用前景。基于敏感材料性能的提升、多种调制手段与阵列技术的结合以及人工智能(AI)领域深度学习算法的最新进展,将会极大地增强非选择性MOS传感器的挥发性有机化合物(VOCs)分子识别能力。     

Recognition of benzene, toluene and xylene using TGS array integrated with linear and non-linear classifier

Three volatile organic compounds (VOCs): benzene, toluene and xylene were measured with an array of six Taguchi gas sensors in the air with variable humidity content. The recognition of single compounds was performed, based on measurement results. The principal component analysis (PCA) pointed at humidity as the main classification factor in the measurement data set. The linear discriminant analysis (LDA) was applied to overcome this drawback and enforce classification with respect to benzene, toluene or xylene. It was shown that discriminant function analysis (DFA), which is an LDA method allowed for 100% success rate in test samples recognition of benzene. It did not allow for accurate recognition of test samples of toluene or xylene. Following, the non-linear classifier, radial basis function neural network (RBFNN) was applied. A specific configuration of input ‘s was found, which provided for successful recognition of each single compound: benzene, toluene or xylene in air with variable humidity content.     

Rational design of a minimal size sensor array for metal ion detection

The focus of this study was to demonstrate that, in the luminescent sensors, the signal transduction may possibly be the most important part in the sensing process. Rational design of fluorescent sensor arrays for cations utilizing extended conjugated chromophores attached to 8-hydroxyquinoline is reported. All of the optical sensors utilized in the arrays comprise the same 8-hydroxyquinoline (8-HQ) receptor and various conjugated chromophores to yield a different response to various metal cations. This is because the conjugated chromophores attached to the receptor are partially quenched in their resting state, and upon the cation coordination by the 8-HQ, the resulting metalloquinolinolate complex displays a change in fluorescence. A delicate balance of conjugation, fluorescence enhancement, energy transfer, and a heavy metal quenching effect results in a fingerprint-like pattern of responses for each sensor-cation complex. Principal component analysis (PCA) and linear discriminant analysis (LDA) are used to demonstrate the contribution of individual sensors within the array, information that may be used to design sensor arrays with the smallest number of sensor elements. This approach allows discriminating between 10 cations by as few as two or even one sensor element. Examples of arrays comprising various numbers of sensor elements and their utility in qualitative identification of Ca(2+), Mg(2+), Cd(2+), Hg(2+), Co(2+), Zn(2+), Cu(2+), Ni(2+), Al(3+), and Ga(3+) ions are presented. A two-member array was found to identify 11 analytes with 100% accuracy. Also the best two of the sensors were tested alone and both were found to be able to discriminate among the samples with 99% and 96% accuracy, respectively. To illustrate the utility of this approach to a real-world application, identification of enhanced soft drinks based on their Ca(2+), Mg(2+), and Zn(2+) cation content was performed. The same approach to reducing array elements was used to construct three- and two-member arrays capable of identifying these complex analytes with 100% accuracy.     

Selection of a piezoelectric sensor array for detecting volatile organic substances in water

Sensitive coatings for piezoelectric sensors used to assess the presence of anthropogenic volatile organic substances in the equilibrium gas phase over natural water have been selected using an array of measuring elements, and the data have been processed using principal component analysis. Groups of piezoelectric sensors with similar characteristics for substance identification have been determined based on the correlation between piezoelectric sensor responses when vapors of organic substances were detected. The stepby-step optimization of the piezoelectric sensor array has been carried out in order to identify the greatest number of organic compounds (vapors) in the sample. Volatile organic compounds can be identified in their aqueous solutions and natural water using the optimized piezoelectric sensor array.     

交叉响应传感器鉴别β受体激动剂及其混合体系

开发了一种鉴别β受体激动剂的新型阵列传感器。该传感器由8种传感物质构成,使用96孔板酶标仪采集响应数据,结合主成分分析(PCA)、分层聚类分析(HCA)、判别分析(LDA)等模式识别方法进行数据处理,对5类β受体激动剂及其混合物进行检测。PCA结果表明,该传感器主要是基于空间结构以及氢键作用实现对β受体激动剂的识别;HCA结果显示,93个分析样本归类正确;LDA结果显示,该传感器对于β受体激动剂识别的准确率达98.9%。本方法在β受体激动剂的检测中有潜在应用价值。     

Antioxidant activity of crude extracts and essential oils from flower buds and leaves of Cistus creticus and Cistus salviifolius

Volatile oils from flowers and leaves of C. creticus L. and C. salviifolius L. were extracted by two extraction methods; namely, hydrodistillation and solid-phase micro-extraction (SPME). The chemical composition of essential oils was analyzed by GC and GC–MS. The volatile extracted from leaves and flowers of C. criticus using SPME was dominated by monoterpenes and sesquiterpenes hydrocarbon with α-pinene, camphene and α-cubebene as major components. In hydrodistillation, the oil extracted from leaves was dominated by oxygenated diterpenes and diterpenes hydrocarbon with manoyl oxide and sclarene as major components, whereas, the oil extracted from flowers was dominated by oxygenated diterpenes and diterpenes hydrocarbon with manoyl oxide and abietatriene as major components. The volatile from flowers and leaves of C. salviifolius obtained by SPME were dominated by monoterpenes and sesquiterpenes with δ-3-carene, α-pinene, β-pinene, and E-caryophyllene as major constituents. On the other hand, the oils from flowers and leaves of C. salviifolius obtained by hydrodistillation were dominated by oxygenated diterpenes, diterpenes hydrocarbon and esters with dehydro abietol, abietol, manoyl oxide and methyl octadecenoate as major components. In the leaves, the major components of the oil were manoyl oxide, E-ethyl cinnamate, and Z-ethyl cinnamate. These oils showed weak antioxidant activity when compared to the positive controls α-tocopherol, ascorbic acid, and EDTA, while the crude extracts aq. MeOH, butanol, and water showed good antioxidant activity. Discriminating between the studied plants based on the extraction method was also possible upon applying Principle component analysis (PCA) to the obtained GC–MS data.     

Determination of hydrocarbons in water by evanescent wave absorption spectroscopy in the near-infrared region

An home-made EFA (Evanescent Field Absorbance)-sensor has been tested for the determination of hydrocarbons in water. The investigations have been performed both with crude oil emulsions and petrol solutions. Cuvette and evanescent wave spectra of crude oil and petrol in the near-infrared region are presented and discussed. The concentration of aromatic compounds in crude oil can be determined semiquantitatively by the standard addition method. The sorption behaviour of the hydrocarbons in the cladding of the fiberoptic sensor has been investigated and a correlation between the sensor signal and the concentration of the aqueous hydrocarbon emulsion/solution could be shown. The desorption of the enriched molecules after the measurements is also presented. The petrol molecules evaporate in ambient air so that the sensor is easily regenerated. In case of oil measurements the hydrocarbon molecules cannot be removed by rinsing the sensor with clear water or by evaporating them in ambient air. It has to be regenerated by washing it with a high volatile solvent instead.     

吸附丝富集化探样品中烃类的色谱—质谱检测

通过吸附丝富集样品中的游离烃，热解析后直接进行色谱－质谱分析，该法适用于土、岩石、水及油气样品，操作简便，灵敏度高，可检测至Ｃ１６的烃类，是油敢化探中行之有效的一种新方法。     

Systematic study of beta-asarone-rich volatile oil from Acori graminei rhizoma by off-line supercritical CO2 extraction-gas chromatography-mass spectrometry

Supercritical CO2 extraction (SCE) technology was used to extract a volatile oil, rich in beta-asarone, from Acori graminei rhizoma (AGR). The effect of different extraction and fractionation parameters on oil yield and selectivity towards beta-asarone was investigated by SCE using commercial AGR samples. The optimal conditions (P(e)/T(e) = 10 MPa/45 degrees C; P(f1)/T(f1) = 8 MPa/-10 degrees C; P(f2)/T(f2 )= 2 MPa/10 degrees C) gave a good oil yield and selectivity for beta-asarone. The extracts were also analyzed by GC-MS and compared with the volatile oil obtained by hydrodistillation, in which 39 main constituents including beta-asarone were found. Different cultivated AGR samples obtained from three areas of China were evaluated in terms of their volatile oil compositions obtained by extraction of commercial AGR samples under optimal conditions; the extract of the Guangdong (GD) sample showed a high beta-asarone content.     

Comparing the effect of sub-critical water extraction with conventional extraction methods on the chemical composition of Lavandula stoechas

The volatile extract composition of Lavandula stoechas flowers obtained by hydrodistillation (HD), subcrtical water extraction (SbCWE) and organic solvent extraction under ultrasonic irradiation (USE) were estimated by gas chromatography-mass spectrometry (GC-MS). One hundred and twenty four components were detected in SbCWE extracts while 94 and 65 signals were gained from HD and USE extracts, respectively. Most of the constituents were identified. The major compounds in all three extracts were fenchon, camphor, myrtenyl acetate, myrtenol and 1,8-cineol, but they differ in quantitatively. The total monoterpene hydrocarbons are higher in HD and USE extracts than those of SbCWE extract. However, SbCWE extract had higher concentration of light oxygenated compounds which contributes to the fragrance of the oil in a major extension. Heavy-oxygenated compounds was also in higher abundance in SbCWE extract (9.90%) than those of HD and USE extracts (3.19 and 4.78%, respectively). Effect of temperature on the extraction yield of SbCWE was investigated and while oil yield was increasing with an increase in temperature, a decrease in the extraction ability of sub-critical water toward the more polar compounds such as, 1,8-cineol, camphor and fenchon, was observed. Kinetic studies shown that SbCWE is clearly quicker than conventional alternatives. Most of components of volatile compounds were extracted at 15min.     

RELEASE OF VOLATILES FROM CUBIC PHASES: MONITORING BY GAS SENSORS

Structured fluids such as emulsions and liquid crystalline mesophases can be used to control aroma release

This study shows that the use of a gas sensor array coupled with pattern recognition analysis can be particularly useful in studying volatile profiles

A mixture of 8 carefully selected volatile, hydrophilic and hydrophobic compounds was entrapped in 4 different matrices: water, lipids (sunflower oil, unsaturated monoglycerides), emulsion (20% wt water) and cubic phases (20 and 3% wt water)

A clear discrimination between the release pattern of the different matrices has been measured by the sensor array system. It has been shown that the cubic phase release patterns is not only controlled by its composition but also depended strongly on ihe lipid/water interfacial area

The combined use of structured fluids (structure-release relationship) and sensor arrays allows to control and to distinguish the release patterns of volatile.     

Application of a potentiometric electronic tongue as a classification tool in food analysis

This paper reports on the application of a potentiometric sensor array to the food analysis field, in order to distinguish simple tastes and to classify food samples. This array is formed by a set of non-specific all-solid-state potentiometric sensors and has been used in combination with principal component analysis (PCA) for the classification of food samples in batch and in flow injection mode. First attempt was to classify synthetic samples prepared with controlled variability. Once this ability is proven, satisfactory classification results are presented for commercial waters, orange-based drinks and tea samples. An interesting correlation is achieved between the natural juice content and its first calculated component, which allows for a very simple tool for screening purposes.     

Colorimetric Artificial Nose for Identification of Breath Volatile Organic Compounds of Patients with Lung Cancer

A novel and highly sensitive colorimetric sensor array was developed for the detection and identification of breath volatile organic compounds（VOCs） of patients with lung cancer.Employing dimeric metalloporphyrins,metallosalphen complexes,and chemically responsive dyes as the sensing elements,the developed sensor array of artificial nose shows a unique pattern of colorific changes upon its exposure to eight less-reactive VOCs and their mixture gas at a concentration of 735 nmol/L within 3 min.Potential of quantitative analysis of VOCs samples was proved.A good linear relationship of 490-3675 nmol/L was obtained for benzene vapor with a detection limit of 49 nmol/L（S/N=3）.Data analysis was carried out by Hierarchical cluster analysis（HCA） and principal component analysis（PCA）.Each category of breath VOCs clusters together in the PCA score plot.No errors in classification by HCA were observed in 45 trials.Additionaly,the colorimetric sensor array showed good reproducibility under the cyclic sensing experiments.These results demonstrate that the developed colorimetric artificial nose system is an excellent sensing platform for the identification and quantitative analysis of breath VOCs of patients with lung cancer.     

Detection of bacterial contaminated milk by means of a quartz crystal microbalance based electronic nose

Headspace analysis by means of sensor arrays has been successfully applied to a wide range of qualitative applications. In this study, a six element array of coated Quartz Crystal Microbalance (QCM) sensors was used for the headspace analysis of milk volatiles. The sensors were exposed to uncontaminated samples of milk and samples contaminated with Pseudomonas fragi (Ps. fragi) or Escherichia coli (E. coli). Principal component analysis (PCA) was used to analyse the sensor array responses. No discrimination between uncontaminated milk samples and those contaminated with Ps. fragi was observed. This can be explained by Ps. fragi being a poor fermenter of milk. However, encouraging results were found for the discrimination between the milk samples and those contaminated with E. coli.This revised version was published online in November 2005 with corrections to the Cover Date.     

基于液体阵列味觉仿生传感器鉴别白酒香型的新方法

通过模拟哺乳动物的味觉系统, 建立了交叉响应的液体阵列传感器, 为鉴别白酒香型提供了新方法. 选用7种染料和1种卟啉化合物作为传感单元, 构建液体阵列传感器, 集合8个传感单元的光谱响应信号构成分析物的指纹图谱, 达到识别的目的. 使用96孔板酶标仪采集响应数据, 结合主成分分析(PCA)、分层聚类分析(HCA)和判别分析(LDA)等模式识别方法进行数据处理, 对9种具有代表性的不同香型白酒样品进行了鉴别分析. PCA结果表明, 该方法对于白酒的检测主要基于酒体微量成分, 其中酸类物质对识别的贡献最大(贡献率达54.3%), 芳香类物质贡献率为18.6%; 同时, 仅用63.4%的数据信息量即可对白酒香型进行区分. HCA结果表明, 平行样均正确归类, 各白酒之间的相似程度在聚类图上得到体现. LDA结果表明, 该阵列对于9种白酒样品香型识别的准确率达到100%.     

Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils

An “electronic nose” has been used for the detection of adulterations of virgin olive oil. The system, comprising 12 metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, feature selection techniques were employed to choose a set of optimally discriminant variables. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and artificial neural networks (ANN) were applied. Excellent results were obtained in the differentiation of adulterated and non-adulterated olive oils and it was even possible to identify the type of oil used in the adulteration. Promising results were also obtained as regards quantification of the percentages of adulteration.     

三种丁香花精油的挥发性成分比较分析

为了解紫丁香、白丁香和红丁香花蕾精油样品挥发性成分的差异,采用气相色谱-质谱联用技术（GC-MS）分析了三种丁香花精油样品的挥发性成分,并对三者的挥发性成分进行了主成分分析和聚类分析。结果从紫丁香、白丁香和红丁香精油中分别解析出46、64和56种挥发性物质,包括烷烃类、酯类、醇类、酮类、烯烃类、酚类、有机酸等,其中共有的挥发性成分为9种。三种丁香花精油样品的挥发性成分比较分析显示,酯类是含量最高的挥发性成分,其次是烷烃类,并且这两类物质在不同品种的丁香花精油中含量不同。统计分析结果表明三种样品的挥发性成分之间没有明显差别。本研究为增加丁香花精油的应用范围提供了理论依据。     

压电晶体传感器阵列用于混合有机溶剂蒸气中官能团的识别

为模拟生物化学传感体系, 提出了可用于识别有机官能团的传感器阵列, 用作人工气味识别系统。该阵列由八个压电晶体传感器组成, 每个传感器涂以具有广谱响应性能的不同吸附活性材料, 阵列对常见小分子有机溶剂混合蒸气的响应频移数据采用逐步判别分析(SDA)处理, 选出五个供信能力最佳的判别变量, 以此构成的阵列用于小分子有机溶剂混合蒸气中醇羟基、羰基与其它官能团的识别, 并采用主成分分析(PCA)法降维投影, 在二维空间含相同官能团的物质聚为一类; 阵列可用于酒类、软饮料的识别。     

Preliminary study on application of mid infrared spectroscopy for the evaluation of the virgin olive oil "freshness"

The freshness of virgin olive oils (VOO) from typical cultivars of Garda regions was evaluated by attenuated total reflectance (ATR) and Fourier transform infrared (FTIR) spectroscopy, in combination with multivariate analysis. The olive oil freshness decreased during storage mainly because of oxidation processes. In this research, 91 virgin olive oils were packaged in glass bottles and stored either in the light or in the dark at room temperature for different periods. The oils were analysed, before and after storage, using both chemical methods and spectroscopic technique.Classification strategies investigated were partial least square discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and soft independent modelling of class analogy (SIMCA).The results show that ATR-MIR spectroscopy is an interesting technique compared with traditional chemical index in classifying olive oil samples stored in different conditions. In fact, the FTIR PCA results allowed a better discrimination among fresh and oxidized oils, than samples separation obtained by PCA applied to chemical data. Moreover, the results obtained by the different classification techniques (PLS-DA, LDA, SIMCA) evidenced the ability of FTIR spectra to evaluate the olive oil freshness. FTIR spectroscopy results are in agreement with classical methods. The spectroscopic technique could be applied for the prediction of VOOs freshness giving information related to chemical modifications. The great advantages of this technique, compared to chemical analysis, are related to rapidity, non-destructive characteristics and low cost per sample. In conclusion, ATR-MIR represents a reliable, cheap and fast classification tool able to assess the freshness of virgin olive oils.