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.     

Front face fluorescence spectroscopy as a tool for the assessment of egg freshness during storage at a temperature of 12.2 degrees C and 87% relative humidity

The objective of this study was to investigate intrinsic fluorophores of thick albumen and egg yolk in order to assess egg freshness during storage at a temperature of 12.2 °C and 87% relative humidity (RH). A total of 126 intact brown-shelled eggs of the same flock (29 weeks of age) were stored for 1, 6, 8, 12, 15, 20, 22, 26, 29, 33, 40, 47 and 55 days. The emission fluorescence spectra of aromatic amino acids and nucleic acids (AAA + NA) (excitation: 250 nm; emission: 280-450 nm), fluorescent Maillard reaction products (FMRP) (excitation: 360 nm; emission: 380-580 nm) and the excitation spectra of vitamin A (emission: 410 nm; excitation: 270-350 nm) were scanned on thick albumen and egg yolk. Among the intrinsic fluorophores, only the principal component analysis (PCA) applied on the vitamin A fluorescence spectra allowed a good identification of eggs as a function of their storage time. Factorial discriminant analysis (FDA) was then applied on the first five principal components (PCs) of the PCA applied on each spectral data set. Regarding AAA + NA recorded on thick albumen, correct classification of 69.4% and 63.9% was observed for the calibration and validation sets, respectively. Quite similar results were obtained on AAA + NA scanned on egg yolks. The best results were obtained with vitamin A fluorescence spectra since 97.7% and 85.7% of the calibration and validation sets was obtained, respectively. These results showed that vitamin A fluorescence spectra provide useful fingerprints, mainly allow the identification of eggs during storage and could be considered as a powerful intrinsic probe for the evaluation of egg freshness.     

The Study on the Thermal Stability of CLA in Egg Yolk

Conjugated linoleic acids (CLA) have been a subject of extensive investigation for their anticarcinogenic, hypolipidemic, antiatherosclerotic and immune-enhancing activities. Stability of CLA in foods has not received much attention by both academics and industry. Although CLA has shown many beneficial effects, its decomposition must be prevented when CLA in foods is processed, stored and transported. However, no study to date has addressed the stability of CLA in foods. The present study was carried out further to examine the stability of CLA in egg yolk during the storage and frying, using gas liquid chromatography (GLC) and silver ion high-performance liquid chromatography (Agt-HPLC). The eggs, containing 4.0% CLA per gramme of egg yolk, were 40s. Either storage for 6 months or frying for 40 s did not significantly change the composition of CLA in egg yolk. However, the degradation of CLA was statistically significant when the CLA components of egg yolk protected CLA from degradation. It is concluded that CLA is well preserved in egg before it is consumed.     

NMR-based differentiation of conventionally from organically produced chicken eggs in Germany

Both the German and European organic food markets are growing fast, and there is also a rising demand for organic chicken eggs. Consumers are willing to pay higher prices for organic eggs produced in an animal-appropriate environment considering animal welfare. Strict labelling requirements do not prevent chicken eggs from being a subject of food fraud. Conventionally produced (barn/free-range) eggs can easily be mislabeled as organic eggs. Especially because the demand for organically produced chicken eggs is likely to exceed supply in the future, mislabeling appears to be a realistic scenario. Therefore, there is a need for analytical methods that are suitable to classify eggs as being either conventionally or organically produced. Nuclear magnetic resonance (NMR) spectroscopy in combination with multivariate data analysis is a suitable tool to screen eggs according to the different systems of husbandry. Sample preparation is based on a fat extraction method, which was optimised for application to freeze-dried egg yolk. Samples were analysed using typical q-NMR parameters. A nontargeted approach was used for the analysis of the ¹H NMR data. Principal component analysis (PCA) was applied followed by a linear discriminant analysis (PCA-LDA) and Monte Carlo cross-validation. In total, 344 chicken eggs (214 barn/free-range eggs and 130 eggs from organic farms), most of them originating from Germany, were used to build and validate the prediction model. The results showed that the prediction model allowed for the correct classification of about 93% of the organic eggs.     

基于内源性致香物质和化学计量学的烟草感官评价研究

采用主成分分析法结合遗传算法和神经网络,建立了基于烟草内源性致香物质的感官质量评价预测模型。利用气相色谱-质谱(GC-MS)技术对超临界萃取-分子蒸馏所得烟草精油中的内源性致香组分进行定性定量分析,汇总各类致香指标后,对其进行主成分分析;以提取所得5个主成分的得分作为输入变量,感官评吸分数作为输出变量,分别使用标准BP神经网络和遗传算法(GA)优化的BP神经网络建立预测模型。对比实验结果表明,GA优化后的模型预测效果更优,其预测值与实验值间的相关系数为0.96,预测均方根误差为1.81,说明GA-BP模型具有更好的拟合能力和预测能力,该模型能有效地预测烟草精油的感官品质。     

Recognition of the bouquet of Chinese spirits by artificial neural network analysis

Forty-six samples of Chinese spirits, whose bouquets were determined by sensory evaluations, and 17 compounds characteristic of the flavors determined by gas chromatography/gas chromatography-mass spectrometry (GC/GC-MS), were subjected to neural network analysis and their corresponding factor scores developed. To make the bouquet recognition more efficient, an improved artificial back-propagation neural network (BPNN) was applied. In each kind of data, the BPNN was trained repeatedly until the error rate was less than the predetermined threshold error; then the trained network was applied to the test set that was not involved in the training process to establish the validity of the network, and a correct prediction rate of 100% was obtained. The BPNN provided a correlation between the data offered from sensory evaluations and the data of chemical compositions determined by instrumental analysis. The BPNN approach is feasible regardless of whether the crude data or the factor scores are used; however, recognition results were better with the latter than with the former. In a comparison of all the results obtained by BPNN, cluster analysis, and discriminant analysis, the method of artificial neural network analysis appeared to be the optimal technique for recognizing the bouquet of Chinese spirits.     

Comparative analysis of lipophilic compounds in eggs of organically raised ISA Brown and Araucana hens

Hens?? eggs represent a rich source of important nutrients, including lipids and carotenoids. The lipid composition of hens?? eggs is influenced by genetic factors, age, and diet. The aim of this study was to compare the fatty acids, cholesterol, and carotenoids content of the egg yolk of ISA Brown and Araucana hens grown in free-range housing systems. Fatty acids and cholesterol were analysed by GC-FID and GC-MS and carotenoids were quantified by RP-HPLC-PDA. The Araucana egg yolk has a higher lipid content and higher egg-to-albumen ratio than the ISA Brown yolk, while the total cholesterol, carotenoids content and profile are not significantly different. The lipids of the Araucana egg yolk have a higher content of mono-unsaturated fatty acids (MUFAs), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) and a better n-6/n-3 ratio than the ISA Brown egg yolk lipids. The major carotenoids were lutein and zeaxanthin, which account for more than 83 % in egg yolk. Eggs of both breeds, when raised organically, represent very good sources of highly bio-available lutein and zeaxanthin, pigments which are related to lower risk of age-related macular degeneration. We report for the first time on the fatty acids composition in lipid fractions and the profile and content of carotenoids of the Araucana egg yolk.     

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.     

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.     

Characterization and determination of piperine and piperine isomers in eggs

A new analytical method for the determination of piperine and its isomers in egg yolk and albumen is described here. All four isomers were separated by HPLC and detected using UV, DAD and electrochemical detection. The absolute detection limit (UV detection, S/ N=3) of a standard solution of piperine was 370 pg piperine. The correlation coefficients for the linear calibration graphs (concentration range: c=100 ng-10 micro g piperine isomer/mL) are generally better than 0.996. The piperine isomers were characterized and identified by spectroscopy (MS, (1)H-NMR, FT-IR). The method was successfully applied to the determination of piperine deposits in eggs (egg yolk and albumen) after feeding hens with piperine-spiked feed. The detection limit for piperine (24.8(+/-0.2) ng/g egg yolk and 37.9(+/-4.9) ng/g albumen) and the recoveries (70.3(+/-7.7)% (egg yolk) and 75.7(+/-1.9)% (albumen)) of piperine were determined.     

Calibration transfer between electronic nose systems for rapid In situ measurement of pulp and paper industry emissions

Electronic nose systems when deployed in network mesh can effectively provide a low budget and onsite solution for the industrial obnoxious gaseous measurement. For accurate and identical prediction capability by all the electronic nose systems, a reliable calibration transfer model needs to be implemented in order to overcome the inherent sensor array variability. In this work, robust regression (RR) is used for calibration transfer between two electronic nose systems using a Box–Behnken (BB) design. Out of the two electronic nose systems, one was trained using industrial gas samples by four artificial neural network models, for the measurement of obnoxious odours emitted from pulp and paper industries. The emissions constitute mainly of hydrogen sulphide (H₂S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl disulphide (DMDS) in different proportions. A Box–Behnken design consisting of 27 experiment sets based on synthetic gas combinations of H₂S, MM, DMS and DMDS, were conducted for calibration transfer between two identical electronic nose systems. Identical sensors on both the systems were mapped and the prediction models developed using ANN were then transferred to the second system using BB–RR methodology. The results showed successful transmission of prediction models developed for one system to other system, with the mean absolute error between the actual and predicted concentration of analytes in mg L⁻¹ after calibration transfer (on second system) being 0.076, 0.1801, 0.0329, 0.427 for DMS, DMDS, MM, H₂S respectively.     

Flubendazole residues in eggs after oral administration to laying hens: determination with reversed phase liquid chromatography.

Flubendazole residues in eggs were experimentally induced by providing groups of 8 laying hens feed with approximately 3, 10 and 30 mg kg-1 flubendazole for 21 days. Eggs were sampled during this period and one week after the administration. Samples of both whole egg and egg white/yolk were analysed separately. Flubendazole analysis was performed by reversed phase HPLC and UV detection at 250 nm (eggs) or 320 nm (feed). The limit of detection (LOD) for flubendazole in feed was 0.3 mg kg-1 and in whole egg 0.012 mg kg-1. Both the hydrolysed and reduced metabolites of flubendazole were also determined quantitatively. The eggs of control hens housed in the same room during the study period did not contain any detectable flubendazole or metabolite residue. The eggs from the lowest dosed group (3 mg kg-1 feed) did contain residues, but most of them were only slightly higher than the LOD. Residues in eggs collected from the laying hens that obtained feed with 10 and 30 mg kg-1 flubendazole reached a plateau level after some 10 days and there was a good dose response relation between levels in feed and those in eggs. The residues of parent compound and metabolites almost exclusively occurred in yolk, the metabolites accounting for some 60-65% of the total residue. The residues of the parent compound and its metabolites declined below 100 micrograms kg-1 5 days after the administration of dosed feed had ended.     

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.     

An effective method for accurate prediction of the first hyperpolarizability of alkalides

The proper theoretical calculation method for nonlinear optical (NLO) properties is a key factor to design the excellent NLO materials. Yet it is a difficult task to obatin the accurate NLO property of large scale molecule. In present work, an effective intelligent computing method, as called extreme learning machine-neural network (ELM-NN), is proposed to predict accurately the first hyperpolarizability (β(0)) of alkalides from low-accuracy first hyperpolarizability. Compared with neural network (NN) and genetic algorithm neural network (GANN), the root-mean-square deviations of the predicted values obtained by ELM-NN, GANN, and NN with their MP2 counterpart are 0.02, 0.08, and 0.17 a.u., respectively. It suggests that the predicted values obtained by ELM-NN are more accurate than those calculated by NN and GANN methods. Another excellent point of ELM-NN is the ability to obtain the high accuracy level calculated values with less computing cost. Experimental results show that the computing time of MP2 is 2.4-4 times of the computing time of ELM-NN. Thus, the proposed method is a potentially powerful tool in computational chemistry, and it may predict β(0) of the large scale molecules, which is difficult to obtain by high-accuracy theoretical method due to dramatic increasing computational cost.     

Supplemental Feeding of Laying Hens with Wood Vinegar to Decrease the Ratio of n-6 to n-3 Fatty Acids in Eggs

A balanced ratio of fatty acids n-6 to n-3 in chicken eggs is important for health and to help prevent and manage obesity and other diseases. Traditionally, fish oil or flax seed has been utilized as feed additives to decrease the ratio of n-6 to n-3(n-6:n-3) fatty acids in eggs. The hull of spina date seed(HSDS) is a common agricultural waste product in China, from which wood vinegar(HSDSWV) may be derived. This study evaluated HSDSWV as a supplement in hen feeds to improve the quality of eggs and decrease the ratio of fatty acids n-6:n-3. HSDSWV was obtained via carbonization, and refined. Six concentrations(nil to 0.5%) of HSDSWV were prepared and fed to 6 hen groups, respectively, for 50 d. The fatty acids of the hen's egg yolks were analyzed by gas chromatography/electron ionization-mass spectrometry(GC/EI-MS) in the selected ion monitoring(SIM) mode. The 0.2% HSDSWV resulted in the best egg yolk quality, with a lower percentage of linoleic acid(C18:2n6) and higher percentages of cis-5,8,11,14,17-eicosapentaenoic acid(C20:5n3) and cis-4,7,10,13,16,19-docosahexaenoic acid(C22:6n3), and thus a lower n-6:n-3 ratio compared with the other HSDSWV concentrations. In addition, the eggs contained higher levels of yolk fat and egg yolk than the controls did. In conclusion, to modify the fatty acid composition of hens' eggs and obtain a balanced ratio of n-6:n-3, 0.2% HSDSWV may be considered suitable as a dietary supplement in hens' feed.     

Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin

A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.     

Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

Partial least squares(PLS),back-propagation neural network(BPNN)and radial basis function neural network(RBFNN)were respectively used for estalishing quantative analysis models with near infrared(NIR)diffuse reflectance spectra for determining the contents of rifampincin(RMP),isoniazid(INH)and pyrazinamide(PZA)in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform(FFT)and standard normal variate(SNV)transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis(PCA)method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models' parameters were chosen by comparing the root mean square error of cross-validation(RMSECV)values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.     

Preliminary chemometric study of minerals and trace elements in Spanish infant formulae

The concentrations of minerals (Na, K, P, Ca and Mg) and trace elements (Fe, Zn, Cu, Mn, Se, Al, Cd and Pb) in a total of 105 different infant formulae (starter, follow-up, premature, specialised and soya formulae) marketed in Spain were determined by atomic spectrometry (flame and electrothermal) and inductively coupled plasma emission spectroscopy after acid-microwave decomposition. On the basis of the elements distribution, a preliminary chemometric study with the use of pattern recognition methods was carried out. Hierarchical cluster analysis (HCA), principal component analysis (PCA), as unsupervised exploratory techniques, and linear discriminant analysis (LDA), were applied to characterise, classify and distinguish the different types of infant formulae. The HCA results showed that mineral and trace element content data support adequate information to obtain the infant formula differentiation. PCA permitted the reduction of 13 variables to four principal components accounting for 61.9% of the total variability. This four-factor model interprets reasonably well the correlations of these studied elements. The obtained element associations may be attributed to the composition of matrix ingredients, the contamination during elaboration, the additives and mineral supplements added and the present tendency of standardization in the manufacture of infant formulae. The application of LDA gave a 77.1% of infant formulae correctly assigned with three clearly differentiated and two overlapped groups. The use of discriminant functions, as a complementary tool, to distinguish the different types depending on protein matrix of infant formula, is also discussed. This survey shows that HCA, PCA and LDA techniques appear useful tools for the characterisation and classification of infant formulae using their elemental profile.     

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.     

Classification of white wine aromas with an electronic nose

This paper reports the use of a tin dioxide multisensor array based electronic nose for recognition of 29 typical aromas in white wine. Headspace technique has been used to extract aroma of the wine. Multivariate analysis, including principal component analysis (PCA) as well as probabilistic neural networks (PNNs), has been used to identify the main aroma added to the wine. The results showed that in spite of the strong influence of ethanol and other majority compounds of wine, the system could discriminate correctly the aromatic compounds added to the wine with a minimum accuracy of 97.2%.