Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

Multifunctional carbon-coated magnetic sensing graphene oxide-cyclodextrin nanohybrid for potential cancer theranosis

Journal of Nanoparticle Research - Over the last years, an environmentally friendly and economically efficient way of nanoparticle production has been found in the biosynthesis of metal...     

Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining

This paper presents a hybridization model of support vector machine (SVM) and grey relational analysis (GRA) in predicting surface roughness value of abrasive water jet (AWJ) machining process. The influential factors of five process parameters in AWJ, namely traverse speed, water jet pressure, standoff distance, abrasive grit size and abrasive flow rate, need to be analyzed using GRA approach. Then, the irrelevance factors of process parameters are eliminated. There is a need of determining the influential factors of process parameters to the surface roughness as to develop a robust prediction model. GRA acts as feature selection method in preprocessing process of hybrid grey relational-support vector machine (GR-SVM) prediction model. Efficiency of the proposed model is demonstrated. GR-SVM presents more accurate result than conventional SVM as it removes the redundant features and irrelevant element from the experimental datasets.     

Preliminary Evaluation of Supercritical Carbon Dioxide Extracted Dabai Pulp Oleoresin as a New Alternative Fat

There has been growing interest among food scientists in producing a toxin-free fat as an end product with varying physical or nutritional properties of interest to the food industry. Oleoresin is a rich source of bioactive compounds which consumers can easily add to a large variety of food. Dabai (Canarium odontophyllum) pulp oleoresin (DPL) was extracted using supercritical carbon dioxide (SC-CO₂) extraction, a green extraction technology. This study investigates the quality of SC-CO₂ extracted DPL in discovering its potential as a new alternative fat. The extraction experiment was carried out at a pressure of 40 MPa and a temperature of 40 °C. DPL is a saturated fatty acid (SFA)-rich fat due to its high SFA composition (47.72 ± 0.01%). In addition, the low content of peroxide value (PV) (5.60 ± 0.09 mEq/kg) and free fatty acids (FFA) (3.40 ± 0.03%) indicate the quality and stability of DPL for various applications besides food consumption. DPL also has a low slip melting point (SMP) (20.20 ± 0.03 °C), and HPLC-FID revealed that DPL contained 0.13 ± 0.02 mg/100 g of vitamin E (α-tocopherol), indicating its potential application as a solid fat with a bioactive compound. This present work demonstrates the possible prospect of DPL in the formulation of end products for food industries.     

Preparation of multiwall carbon nanotubes (MWCNTs) stabilised by highly branched hydrocarbon surfactants and dispersed in natural rubber latex nanocomposites

The performance of single-, double- and triple-chain anionic sulphosuccinate surfactants for dispersing multiwall carbon nanotubes (MWNCTs) in natural rubber latex (NR-latex) was studied using a range of techniques, including field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), thermogravimetric analysis (TGA) and Raman spectroscopy. The conductivities of the nanocomposites were also investigated using four-point probe measurements. Here, MWCNTs were efficiently dispersed in NR-latex with the aid of hyperbranched tri-chain sulphosuccinate anionic surfactants, specifically sodium 1,4-bis(neopentyloxy)-3-(neopentyloxycarbonyl)-1,4-dioxobutane-2-sulphonate (TC14). This paper highlights that TC14 performs much better than that of the commercially available surfactant sodium dodecyl sulphate (SDS), demonstrating how careful consideration of surfactant architecture leads to improved dispersibility of MWCNTs in NR-latex. The results should be of significant interest for improving nanowiring applications suitable for aerospace-based technology.     

Antioxidative properties of defatted dabai pulp and peel prepared by solid phase extraction

Solid phase extraction (SPE) using Sep-Pak^? cartridges is one of the techniques used for fractionation of antioxidant compounds in waste of dabai oil extraction (defatted dabai parts). The aim of this study was to determine the phenolic compounds and antioxidant capacity in crude extracts and several SPE fractions from methanolic extract of defatted dabai pulp and peel. Based on SPE, Sep-Pak^? cyanopropyl and C₁₈ cartridges were used to fractionate the antioxidant-rich crude extracts into water and methanolic fractions. Analyzed using LC-MS, flavonoids, anthocyanins, saponin derivatives and other unknown antioxidative compounds were detected in the defatted dabai crude extracts and their SPE fractions. Anthocyanins were the major phenolic compounds identified in the defatted dabai peel and detected in most of the SPE fractions. Methanolic fractions of defatted dabai parts embraced higher total phenolics and antioxidant capacity than water fractions. This finding also revealed the crude extracts of defatted dabai peel have the most significant antioxidant properties compared to the methanolic and water fractions studied. The crude extract of defatted dabai parts remain as the most potent antioxidant as it contains mixture of flavonoids, anthocyanins and other potential antioxidants.     

Metabolic profiling of Lactococcus lactis under different culture conditions

Gas chromatography mass spectrometry (GC-MS) and headspace gas chromatography mass spectrometry (HS/GC-MS) were used to study metabolites produced by Lactococcus lactis subsp. cremoris MG1363 grown at a temperature of 30 °C with and without agitation at 150 rpm, and at 37 °C without agitation. It was observed that L. lactis produced more organic acids under agitation. Primary alcohols, aldehydes, ketones and polyols were identified as the corresponding trimethylsilyl (TMS) derivatives, whereas amino acids and organic acids, including fatty acids, were detected through methyl chloroformate derivatization. HS analysis indicated that branched-chain methyl aldehydes, including 2-methylbutanal, 3-methylbutanal, and 2-methylpropanal are degdradation products of isoleucine, leucine or valine. Multivariate analysis (MVA) using partial least squares discriminant analysis (PLS-DA) revealed the major differences between treatments were due to changes of amino acids and fermentation products.     

Essential Oil Composition of Pavetta siamica

Chemistry of Natural Compounds -