Raman Spectroscopic Barcode Use for Differentiation of Vegetable Oils and Determination of Their Major Fatty Acid Composition

In this study, differentiation of vegetable oils and determination of their major fatty acid (FA) composition were performed using Raman spectral barcoding approach. Samples from seven different sources (sunflower, corn, olive, canola, mustard, soybean and palm) were analyzed using Raman spectroscopy. Second derivative of the spectral data was utilized to generate unique barcodes of oils. Chemometric analyses, namely, principal component analysis (PCA) and partial least square (PLS) methods were used for data analysis. PCA was applied for classification of the samples according to the differences in their levels arising from their barcode data. A successful differentiation based on second derivative barcodes of Raman spectra (2D‐BRS) of vegetable oils was obtained. In addition, PLS method was applied on 2D‐BRS in order to determine the major FA composition of these samples. Coefficient of determination values for palmitic, stearic, oleic, linoleic, α‐linolenic, cis‐11 eicosenoic, erucic and nervonic acids were in the range of 0.970–0.989. Limit of detection and limit of quantification values were found to be satisfactory (0.09–8.09 and 0.30–26.95 % in oil) for these fatty acids . Advantages of both chemometric analysis and spectral barcoding approach have been utilized in the present study. Taking the second derivative of the Raman spectra has minimized background variability and sensitivity to intensity fluctuations. Spectral conversion to the barcodes has further increased the quality of information obtained from Raman spectra and also made it possible to improve the visualization of the data. Converting Raman spectra of oils into barcodes enables simpler presentation of the valuable information, and still allows further analysis such as classification of vegetable oils and prediction of their major fatty acids with high accuracy.     

Chemometric approach to evaluate trace metal concentrations in some spices and herbs

Herbs (mint, thyme and rosemary) and spices (black pepper, chili pepper, cinnamon, cumin, sweet red pepper and turmeric) were analysed using atomic spectrometry and then subjected to chemometric evaluation in an attempt to classify them using their trace metallic analyte concentrations (As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr and Zn). Trace metals in acid digests of these materials were determined using both inductively coupled plasma-atomic emission spectrometry and inductively coupled plasma-mass spectrometry. The chemometric techniques of principal component analysis (PCA), linear discriminant analysis (LDA) and cluster analysis (CA) were used for the classification studies. These herbs and spices were classified into five groups by PCA and CA. When the results of these techniques were compared with those from LDA, it was found that all group members determined by PCA and CA are in the predicted group that 100.0% of original grouped cases correctly classified by LDA.     

Synthesis and surface properties of polydimethylsiloxane‐based block copolymers: poly[dimethylsiloxane‐block‐ (ethyl methacrylate)] and poly[dimethylsiloxane‐block‐(hydroxyethyl methacrylate)]

A polydimethylsiloxane (PDMS) macroazoinitiator was synthesized from bis(hydroxyalkyl)‐terminated PDMS and 4,4′‐azobis‐4‐cyanopentanoic acid by a condensation reaction. The bifunctional macroinitiator was used for the block copolymerization of ethyl methacrylate (EMA) and 2‐(trimethylsilyloxy)ethyl methacrylate (TMSHEMA) monomers. The poly(DMS‐block‐EMA) and poly(DMS‐block‐TMSHEMA) copolymers thus obtained were characterized using Fourier transform infrared and ¹H NMR spectroscopy and differential scanning calorimetry. After the deprotection of trimethylsilyl groups, poly(DMS‐block‐HEMA) and poly(DMS‐block‐EMA) copolymer film surfaces were analysed using scanning electron microscopy and X‐ray photoelectron spectroscopy. The effects of the PDMS concentration in the copolymers on both air and glass sides of films were examined. The PDMS segments oriented and moved to the glass side in poly(DMS‐block‐EMA) copolymer film while orientation to the air side became evident with increasing DMS content in poly(DMS‐block‐HEMA) copolymer film. The block copolymerization technique described here is a versatile and economic method and is also applicable to a wide range of monomers. The copolymers obtained have phase‐separated morphologies and the effects of DMS segments on copolymer film surfaces are different at the glass and air sides. Copyright © 2010 Society of Chemical Industry     

Effect of different denture adhesives on retention of complete dentures: an in vivo study

The increase in prevalence of tooth loss with the effect of population aging produces the growing need for complete dentures. The success and acceptance of complete dentures by the patient depends on sufficient retention and stability. Therefore, denture adhesives are regularly used by denture wearers to improve the function of complete denture. We evaluated the effect of three different denture adhesives (Corega, Protefix, Fittydent) on the retention of maxillary complete denture (MCD) using with digital dynamometer (DD). For this purpose, denture adhesives were applied on MCDs of 30 participants. After chewing procedure, the force was applied at 45° to the palatal surface of denture by DD. Dislodgement force was recorded by means of Newton. There were four measurements on each patient including; group of control: Group C; Group CR: Corega; Group F: Fittydent; Group P: Protefix. The result of the study was statistically evaluated by using analysis of variance (ANOVA) and Tukey HSD test. Statistics of ANOVA showed a significant difference among all the four groups (p = 0.00, <0.05). Tukey HSD test indicated that there was a statistical difference between Group F and the other groups, but there was not a significant difference between the other groups. The highest adhesive strength value was observed in group F, the lowest in group C. Use of denture adhesives improved the retentive strength of complete denture.     

Excitation energy-dependent nature of Raman scattering spectrum in GaInNAs/GaAs quantum well structures

The excitation energy-dependent nature of Raman scattering spectrum, vibration, electronic or both, has been studied using different excitation sources on as-grown and annealed n- and p-type modulation-doped Ga_1 − xIn_xN_yAs_1 − y/GaAs quantum well structures. The samples were grown by molecular beam technique with different N concentrations (y = 0%, 0.9%, 1.2%, 1.7%) at the same In concentration of 32%. Micro-Raman measurements have been carried out using 532 and 758 nm lines of diode lasers, and the 1064 nm line of the Nd-YAG laser has been used for Fourier transform-Raman scattering measurements. Raman scattering measurements with different excitation sources have revealed that the excitation energy is the decisive mechanism on the nature of the Raman scattering spectrum. When the excitation energy is close to the electronic band gap energy of any constituent semiconductor materials in the sample, electronic transition dominates the spectrum, leading to a very broad peak. In the condition that the excitation energy is much higher than the band gap energy, only vibrational modes contribute to the Raman scattering spectrum of the samples. Line shapes of the Raman scattering spectrum with the 785 and 1064 nm lines of lasers have been observed to be very broad peaks, whose absolute peak energy values are in good agreement with the ones obtained from photoluminescence measurements. On the other hand, Raman scattering spectrum with the 532 nm line has exhibited only vibrational modes. As a complementary tool of Raman scattering measurements with the excitation source of 532 nm, which shows weak vibrational transitions, attenuated total reflectance infrared spectroscopy has been also carried out. The results exhibited that the nature of the Raman scattering spectrum is strongly excitation energy-dependent, and with suitable excitation energy, electronic and/or vibrational transitions can be investigated.     

An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks

Abstract: In this study a wavelet‐based neural network model, employing the multilayer perceptron, is presented for the detection of electrocardiographic changes in patients with partial epilepsy. Decision making is performed in two stages: feature extraction using the wavelet transform, and multilayer perceptron neural networks (MLPNNs) trained with the backpropagation, delta‐bar‐delta, extended delta‐bar‐delta and quick propagation algorithms as classifiers. The classification results, the values of statistical parameters and performance evaluation parameters of the MLPNNs trained with different algorithms are compared. Two types of electrocardiogram beats (normal and partial epilepsy) obtained from the MIT‐BIH database were classified with accuracy varying from 90.00% to 97.50% by the MLPNNs trained with different algorithms.     

Comparison of different classification algorithms in clinical decision-making

Abstract: This paper gives an integrated view of implementing automated diagnostic systems for clinical decision-making. Because of the importance of making the right decision, better classification procedures are necessary for clinical decisions. The major objective of the paper is to be a guide for readers who want to develop an automated decision support system for clinical practice. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and benchmarked for their performance. The performance of the classification algorithms is illustrated on two data sets: the Pima Indians diabetes and the Wisconsin breast cancer. The present research demonstrates that the support vector machines achieved diagnostic accuracies which were higher than those of other automated diagnostic systems.     

Investigating Neutronic Parameters of a Thorium Fusion Breeder with Recurrent Neural Networks

Artificial neural networks (ANNs) have recently been introduced to the nuclear engineering applications as a fast and flexible vehicle to modeling, simulation and optimization. In this paper, a new approach based on recurrent neural networks (RNNs) was presented for the neutronic parameters of a thorium fusion breeder. The results of the RNNs implemented for the tritium breeding ratio computation, energy multiplication factor and net ²³³U production in a thorium fusion breeder and the results available in the literature obtained by using Scale 4.3 were compared. The drawn conclusions confirmed that the proposed RNNs could provide an accurate computation of the tritium breeding ratio computation, the energy multiplication factor and the net ²³³U production of the thorium fusion breeder.     

A direct borohydride-peroxide fuel cell-LiPO hybrid motorcycle prototype: Investigation of short-term behavior of DBPFC stack – I

In the present study, a safer and more performance 270?W Direct Borohydride/Peroxide Fuel Cell (DBPFC) Stack has been constructed for an electrical hybrid motorbike application. Performance tests were carried out with single cell and 5–10–25?cell stacks. Performance loss has been not observed while stacking DBPFC because of the Independent Cell Liquid Distribution Network (ICLDN) system and special bipolar plate design. The power densities have been approximately 120?mWcm^?2 for a single DBPFC and 25-cell DBPFC stack without any stacking loss. Additionally, the stack temperature has been controlled by keeping the oxidant concentration low, and it has been maintained at approximately 52?°C without using a cooling system. The short-term performances of the 25-cell DBPFC stack have been tested over 25?min and 50?min, which showed that the performance and stack security of the DBPFC are highly related to the oxidant properties, such as the concentration, temperature and feed type.