基于XRF和Vis-NIR光谱数据融合的土壤镉含量定量分析法

根据鄱阳湖南矶山区域土壤的X荧光光谱和可见近红外光谱特征,建立了3种数据融合(等权融合、累加融合、外积融合)的最小二乘向量机定量分析模型。结果表明,等权融合和外积融合模型精度和稳定性均优于单一光谱定量分析模型。其中外积融合模型性能最佳,其决定系数(R2)为0.85,校正均方根误差(RMSEC)为009,预测均方根误差(RMSEP)为006,相对分析误差(RPD)为2.41,满足实际土壤中Cd的检测需求。该方法准确可靠,可为我国土壤重金属分类分级方法研究提供参考。     

Rapid discrimination of slimming capsules based on illegal additives by electronic nose and flash gas chromatography

The discrimination of counterfeit and/or illegally manufactured medicines is an important task in the pharmaceutical industry for pharmaceutical safety. In this study, 22 slimming capsule samples with illegally added sibutramine and phenolphthalein were analyzed by electronic nose and flash gas chromatography. To reveal the difference among the different classes of samples, principal component analysis and linear discriminant analysis were employed to analyze the data acquired from electronic nose and flash gas chromatography, respectively. The samples without illegal additives can be discriminated from the ones with illegal additives by using electronic nose or flash gas chromatography data individually. To improve the performance of classification, a data fusion strategy was applied to integrate the data from electronic nose and flash gas chromatography data into a single model. The results show that the samples with phenolphthalein, sibutramine and both can be classified well by using fused data.     

Determination of indoor air quality of a phytosanitary plant

Effective fermentation monitoring is a growing need due to the rapid pace of change in the wine industry, which calls for fast methods providing real time information in order to assure the quality of the final product. The objective of this work is to investigate the potential of non-destructive techniques associated with chemometric data analysis, to monitor time-related changes that occur during red wine fermentation. Eight micro-fermentation trials conducted in the Valtellina region (Northern Italy) during the 2009 vintage, were monitored by a FT-NIR and a FT-IR spectrometer and by an electronic nose and tongue. The spectroscopic technique was used to investigate molecular changes, while electronic nose and electronic tongue evaluated the evolution of the aroma and taste profile during the must-wine fermentation. Must-wine samples were also analysed by traditional chemical methods in order to determine sugars (glucose and fructose) consumption and alcohol (ethanol and glycerol) production. Principal Component Analysis was applied to spectral, electronic nose and electronic tongue data, as an exploratory tool, to uncover molecular, aroma and taste modifications during the fermentation process. Furthermore, the chemical data and the PC1 scores from spectral, electronic nose and electronic tongue data were modelled as a function of time to identify critical points during fermentation. The results showed that NIR and MIR spectroscopies are useful to investigate molecular changes involved in wine fermentation while electronic nose and electronic tongue can be applied to detect the evolution of taste and aroma profile. Moreover, as demonstrated through the modeling of NIR, MIR, electronic nose and electronic tongue data, these non destructive methods are suitable for the monitoring of must-wine fermentation giving crucial information about the quality of the final product in agreement with chemical parameters. Although in this study the measurements were carried out in off-line mode, in future these non destructive techniques could be valid and simple tools, able to provide in-time information about the fermentation process and to assure the quality of wine.     

Prediction of soluble solids content, firmness and pH of pear by signals of electronic nose sensors

The objective of this study was to investigate the predictability of an electronic nose for fruit quality indices. Responses signal of sensor array in electronic nose were employed to establish quality indices model for “xueqing” pear. The relationships were established between signal of electronic nose and the quality indices of fruit (firmness, soluble solids content (SSC) and pH) by multiple linear regressions (MLR) and artificial neural network (ANN). The prediction models for firmness and soluble solids content indicated a good prediction performance. The SSC model by ANN had a standard error of prediction (SEP) of 0.41 and correlation coefficient 0.93 between predicted and measured values, the model by ANN for the penetrating force (CF) had a 3.12 SEP and 0.94 coefficient, respectively. The results imply that it is possible to predict “xueqing” pear quality characteristics from signal of E-nose.     

Bio-inspired Sensor Fusion for Quality Assessment of Harumanis Mangoes

In recent years, there have been a number of reported studies on the use of non-destructive technique to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose (e-nose), acoustics, CCD, IR sensor or by other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using data fusion of the electronic nose (e-nose) and acoustic sensor and combine with CCD and IR sensor. A Fourier-based shape separation method was developed from CCD camera images to grade mango by its shape and able to correctly classify 100%. Colour intensity from infrared image was used to distinguish and classify the level of maturity and ripeness of the fruits. The finding shows 92% correct classification of maturity levels by using infrared vision Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved.     

Comparison of indices and multivariate models to non-destructively predict the fruit chlorophyll by means of visible spectrometry in apple fruit

Non-destructive, rapid, instrumental tools in fruit production are required for predicting the optimum harvest window and monitoring fruit quality during shelf life. The degree of chlorophyll degradation is a sensitive indicator for fruit maturation and ageing. Adequate indices for chlorophyll prediction by means of non-destructive spectral analysis have been studied in the fields of photosynthesis research and remote sensing developments. However, an evaluation of these indices and multivariate linear regression models does not exist so far for predicting the fruit chlorophyll content.Spectral transmittance recordings in the visible wavelength range were carried out on apple fruit Malus domestica Borkh. ‘Elstar’ (n=99) and ‘Jonagold’ (n=117). The fruit chlorophyll a content of ‘Elstar’ apples was measured wet-chemically and predicted by means of the specific indices: NDVI, Tr₆₉₈/Tr₇₆₀, red-edge, TrII on Tr′(λ), TrII, and RVSI with the correlation coefficients of determination R²=0.84, 0.81, 0.75, 0.81, 0.15, and 0.67, respectively. Partial least-squares (PLS) calibration models were built using calibrated spectra (630-730 nm), first derivative of spectra and second derivative of spectra yielding multivariate correlation coefficients of determination R²=0.81, 0.86, and 0.92, respectively. Similar results were found for ‘Jonagold’ apple fruit. Linear regression of indices and PLS calibration models were empirically tested on the chlorophyll a content of apple fruit measuring the same cultivars grown in a different seasons and growing locations. In the case of ‘Elstar’ apple fruit, correlation coefficients of fruit chlorophyll a content analysed wet-chemically as well as fruit maturation measured as calendar weeks were similar to those in the calibration experiment. Application of linear regression equations of indices and PLS models on spectra of ‘Jonagold’ apple fruit led to less accurate results for those methods, which use wavelengths above 720 nm as indicative range.     

Detecting spoiled fruit in the house of the future

An electronic nose based on acoustic wave sensors has been developed to detect spoilt fruit. Different varieties of fruits, edible and rotten, were analysed. Starting from six sensors, the minimum number of sensors capable of discriminating between spoiled and unspoiled fruit was found. The discrimination capability of the sensor array was studied separately for each fruit variety, as well as for the whole set. Mathematical models were built to classify the fruits within a fruit variety, in an objective and clear way. The models were able to distinguish between edible and rotten fruits with 100% success for New Hall oranges, Golden apples, Kiwis and William pears, and with 97.2% of success for the Starking apples. Without forming fruit variety subsets, discrimination between edible and rotten fruit was achieved with 95% success.     

Enhancing Classification Performance of Multisensory Data through Extraction and Selection of Features

Linear discriminant analysis (LDA) has been widely used in the classification of multi sensor data fusion. This paper discusses the performance of LDA when the classifications were performed based on feature extraction and feature selection methods. Comparisons were also made based on single sensor modality. These strategies were studied using a honey dataset along with two types of sugar concentration collected from two types of sensors namely electronic nose (e-nose) and electronic tongue (e-tongue). Assessment of error rate was achieved using the leave-one-out procedure.     

Influence of the differentiation system on the analytical parameters for the spectrophotometric determination of clonazepam in urine

Electronic and mathematical differentiation of the ultraviolet visible spectra were compared from the study of the analytical parameters of the direct determination of clonazepam in urine samples. Third-order derivative spectra were used. The mathematical differentiation provides higher sensitivity, a lower limit of detection, and better accuracy and precision than the electronic one. The use of a diode array instrument provides lower sensitivity than that found using a conventional spectrometer; however, the limit of detection obtained in both cases is of the same order.     

多源光谱信息融合在水质分析中的应用

为解决现有单一光谱法用于水质有机污染综合指标分析精度较低的问题,提出一种基于多源光谱信息融合的水质分析新方法。本方法采用改进的参考独立成分分析方法分别提取紫外吸收光谱和三维荧光光谱对检测水样的多种有效信息特征,去除与水质分析无关的干扰信号;然后采用最小二乘支持向量机进行特征信息融合建模。采用总有机碳指标覆盖范围在3.4~125.3 mg/L内的32个城市地表水和生活污水样本为研究对象,对其紫外光谱、荧光光谱数据进行了信息融合分析实验。结果表明:采用融合分析方法后,对总有机碳指标的分析误差均方根比单一紫外光谱分析法和单一荧光光谱分析法分别下降36.1%和34.7%。     

Library of FT-Raman spectra of pigments, minerals, pigment media and varnishes, and supplement to existing library of Raman spectra of pigments with visible excitation.

Sixty pigments, minerals and media have been analysed by Fourier-transform Raman (FT-Raman) microscopy in order to assemble a database of reference FT-Raman spectra for scientists working at the Arts-Science interface. An earlier library of Raman spectra compiled using visible excitation has been extended by the addition of 22 further reference spectra obtained with 780.0, 647.1, 632.8 and/or 514.5 nm excitation. The relative merits of 1064 nm and visible excitation are discussed.     

A theoretical spectroscopy investigation of oxosumanenes

Using Density Functional Theory approaches, we investigate the structure and spectroscopic signatures of sumanene, monooxosumanene and trioxosumanene, three synthesised bowl-shaped compounds. The simulated properties include geometries, charges, polarisabilities, infrared and UV/visible spectra as well as complexation energies. Refined approaches have been considered (anharmonic frequencies, state-specific solvent model, dispersion-corrected DFT…) and a valuable agreement with experimental reference values is reached for most properties. The evolution of the electronic features of sumanene upon oxidation has been rationalised.     

Differentiation of red wines using an electronic nose based on surface acoustic wave devices

An electronic nose, utilizing the principle of surface acoustic waves (SAW), was used to differentiate among different wines of the same variety of grapes which come from the same cellar. The electronic nose is based on eight surface acoustic wave sensors, one is a reference sensor and the others are coated by different polymers by spray coating technique. Data analysis was performed by two pattern recognition methods; principal component analysis (PCA) and probabilistic neuronal network (PNN). The results showed that electronic nose was able to identify the tested wines.     

A comparison of neutron-activation analysis and hot extraction analysis of the oxygen content of steel

A system developed for the fast neutron-activation analysis of the oxygen content of metals has been tested comparatively with the conventional vacuum fusion and carrier-gas fusion techniques. The results of these tests indicate that neutron-activation analysis is much faster (the total analysis takes only 2 min or less), and more reliable than vacuum fusion and carrier-gas fusion methods because all oxygen present is analysed. Samples can be much larger than the 0.2-3 g commonly used for the fusion methods. Furthermore, the analysis is non-destructive-the same samples can be re-analysed as often as desired. The fast neutron-analysis system includes a 14-MeV neutron generator producing 10(11) neutrons/sec, a dual-tube pneumatic transfer system, a 5 x 5 inch NaI(T1)crystal, a single-channel analyser, two scalers, and timers and switch-gear. A sample, in a polyethylene bottle, and a Lucite reference are irradiated simultaneously, after which the sample is returned to a detector for counting the (16)N gammas from the (16)O(n,p)(16) N reaction. The reference is then counted in a second detector; the ratio of the sample counts to the reference counts is proportional to the oxygen content of the sample. Samples with oxygen contents from 0.002 to 0.1 % of oxygen have been analysed by neutron activation, then cut in several pieces for hot extraction analysis of the total sample.     

Density Functional Theory Study on Organic Dye Sensitizers Containing Bis-dimethylfluorenyl Amino Benzofuran

The geometries, electronic structures, polarizabilities and hyperpolarizabilities, as well as the UV-Vis spectra of the two organic dye sensitizers containing bis-dimethylfluorenyl amino benzofuran were studied via density functional theory （DFT） and time-dependent DFT. The features of electronic absorption spectra were assigned on account of the agreement between the experiment and the calculations. The absorption bands in visible region are related to photoinduced electron transfer processes, and the dimethylfluorenyl amino benzo[b]furan groups are major chromophore that contributed to the sensitization of photo-to-current conversion. The role of vinylene group in geometry, electronic structure and spectra property is analyzed according to the comparative study of the dyes.     

Application of principal component analysis and discriminant analysis using latent structure regression to the identification of highly volatile organic compounds in gas mixtures based on piezoelectric quartz microweighing parameters

The applicability of the new parameters of piezoelectric quartz microweighing and principal component and discriminant analysis with the use of latent structure regression to the treatment of the output data of an array of piezoelectric sensors in the identification of individual highly volatile compounds in model three-component gas mixtures is discussed. The parameters proposed and the methods of chemometrics were used in the treatment of the multidimensional data of an electronic nose for detecting individual aromaforming compounds and evaluating changes in the aroma of food systems with functional additives.     

Combining information from headspace mass spectrometry and visible spectroscopy in the classification of the Ligurian olive oils

An electronic nose and an UV-Vis spectrophotometer, in combination with multivariate analysis, have been used to verify the geographical origin of extra virgin olive oils. Forty-six oil samples from three different areas of Liguria were included in this analysis.Initially, the data obtained from the two instruments were analysed separately. Then, the potential of the synergy between these two technologies for testing food authenticity and quality was investigated.Application of Linear Discriminant Analysis, after feature selection, was sufficient to differentiate the three geographical denominations of Liguria (“Riviera dei Fiori”, “Riviera del Ponente Savonese” and “Riviera di Levante”), obtaining 100% success in classification and close to 100% in prediction. The models built using SIMCA as a class-modelling tool, were not so effective, but confirmed that the results improve using the synergy between different analytical techniques.This paper shows that objective instrumental data related to two important organoleptic features such as oil colour and aroma, supply complementary information.     

Fourier-transform infrared and optical absorption spectra of 4-tricyanovinyl-N,N-diethylaniline thin films

Thin films of 4-tricyanovinyl-N,N-diethylaniline (TCVA) were prepared for the first time using thermal evaporation technique. The molecular structure and electronic transitions of TCVA films were investigated by Fourier-transform infrared (FTIR) and ultraviolet-visible (UV-vis) spectra. The observed vibrational wavenumbers in FTIR spectra were analysed and assigned to different normal modes of the molecule. UV-vis electronic absorption spectral measurements of TCVA films were analysed to obtain the electronic transitions and optical band gap (E(g)). Other important optical parameters such as molar extinction coefficient (varepsilon(molar)), the oscillator strength (f), and the electric dipole strength (q(2)) were also reported.     

Rapid honey characterization and botanical classification by an electronic tongue

In this paper a commercial electronic tongue (αAstree, Alpha M.O.S.) was applied for botanical classification and physicochemical characterization of honey samples. The electronic tongue was comprised of seven potentiometric sensors coupled with an Ag/AgCl reference electrode. Botanical classification was performed by PCA, CCA and ANN modeling on 12 samples of acacia, chestnut and honeydew honey. The physicochemical characterization of honey was obtained by ANN modeling and the parameters included were electrical conductivity, acidity, water content, invert sugar and total sugar. The initial reference values for the physicochemical parameters observed were determined by traditional methods. Botanical classification of honey samples obtained by ANN was 100% accurate while the highest correlation between observed and predicted values was obtained for electrical conductivity (0.999), followed by acidity (0.997), water content (0.994), invert sugar content (0.988) and total sugar content (0.979).All developed ANN models for rapid honey characterization and botanical classification performed excellently showing the potential of the electronic tongue as a tool in rapid honey analysis and characterization. The advantage of using such a technique is a simple sample preparation procedure, there are no chemicals involved and there are no additional costs except the initial measurements required for ANN model development.     

尾式甘氨酸四苯基卟啉锌与吡啶类化合物轴配反应光谱和热力学

取代四苯基卟啉锌配合物与咪唑类和吡啶类等含氮化合物轴向配位反应的研究在模拟生物体中锌酶的作用机理方面具有重要意义。在现有的文献中 ,人们的研究主要集中在四苯基卟啉的苯环上连有如卤素、甲氧基、甲基等较小取代基的卟啉锌上[1～3] ,对于结构不对称且苯环上连有氨基酸的四苯基卟啉锌与含氮化合物轴向配位反应的研究尚不多见。有关尾式氨基酸四苯基卟啉锌配合物的合成和性质我们进行过较系统的研究[4～6] 。本文以 5 ( 4 甘氨酸丁氧苯基 ) 1 0 ,1 5 ,2 0 三苯基卟啉锌 (ＺｎＰ)为锌酶的模型化合物 ,用热力学方法研究了它与吡啶 (…