Characterization of Turkish Virgin Olive Oils Produced from Early Harvest Olives

Olives were collected from various districts of Turkey (North and South Aegean sub-region, Bursa-Akhisar, South East Anatolia region) harvested over seven (2001–2007) seasons. The aim of this study was to characterize the chemical profiles of the oils derived from single variety Turkish olives including Ayvalik, Memecik, Gemlik, Erkence, Nizip Yaglik and Uslu. The olive oils were extracted by super press and three phase centrifugation from early harvest olives. Chosen quality indices included free fatty acid content (FFA), peroxide value (PV) and spectrophotometric characteristics in the ultraviolet (UV) region. According to the FFA results, 46% (11 out of 24 samples) were classified as extra virgin olive oils; whereas using the results of PV and UV, over 83% (over 19 of the 24 samples) had the extra virgin olive oil classification. Other measured parameters included oil stability (oxidative stability, chlorophyll pigment, pheophytin-α), cis–trans fatty acid composition and color index. Oxidative stability among oils differed whereas the cis–trans fatty acid values were within the national and international averages. Through the application of two multivariate statistical methods, Principal component and hierarchical analyses, early harvest virgin olive oil samples were classified according to the geographical locations categorized in terms of fatty acid profiles. Such statistical clustering gave rise to defined groups. These data provide evidence of the variation in virgin olive oil quality, especially early harvest and cis–trans isomers of fatty acid profiles from the diverse agronomic conditions in the olive growing regions of Turkey.     

Multivariate Portmanteau Test For Autoregressive Models with Uncorrelated but Nonindependent Errors

Abstract. We study the asymptotic behaviour of the least squares estimator, of the residual autocorrelations and of the Ljung–Box (or Box–Pierce) portmanteau test statistic for multiple autoregressive time series models with nonindependent innovations. Under mild assumptions, it is shown that the asymptotic distribution of the portmanteau tests is that of a weighted sum of independent chi‐squared random variables. When the innovations exhibit conditional heteroscedasticity or other forms of dependence, this asymptotic distribution can be quite different from that of models with independent and identically distributed innovations. Consequently, the usual chi‐squared distribution does not provide an adequate approximation to the distribution of the Box–Pierce goodness‐of‐fit portmanteau test in the presence of nonindependent innovations. Hence we propose a method to adjust the critical values of the portmanteau tests. Monte carlo experiments illustrate the finite sample performance of the modified portmanteau test.     

3D modeling of residual stresses induced in finish turning of an AISI304L stainless steel

This paper addresses the development of a new methodology predicting residual stresses induced in finish turning of a AISI304L stainless steel. A hybrid approach combining experimental results and a numerical model is applied. The model simulates the residual stresses generation by applying equivalent thermo-mechanical loadings onto the machined surface without modeling the chip removal process, which enables rapid calculation. The shape and the intensity of equivalent thermo-mechanical loadings are identified through experimental measurements. Friction tests enable to model the thermal and mechanical loadings along the tool-workmaterial interface. Orthogonal cutting tests provide thermal and mechanical loadings below the primary and third shear zone. This model has already been presented in several papers, but only in a 2D configuration. The objective of this paper is to transfer this hybrid approach into a 3D configuration, which is closer to a concrete longitudinal turning operation. Based on this new model, the paper aims at investigating the interactions between each revolution. It is shown that around five revolutions are necessary to reach a steady state. Finally numerical results are compared with experimental measurements obtained by X-Ray diffraction. It is shown that residual stresses cannot be considered as homogeneous over the surface due to tool's feed. Additionally, the X-Ray beam is much too large to be able to quantify this heterogeneity. Based on average numerical values coherent with average values obtained by X-Ray diffraction, it is shown that the numerical model provides consistent results compared to experimental measurements for a large range of cutting speed and feed.     

Supplier selection under risk of delivery failure: a decision-support model considering managers’ risk sensitivity

This paper studies the problem of supplier selection and order allocation in a retail supply chain (comprising suppliers, a central purchasing unit and outlets) under disruption risk. The final demand is deterministic. Suppliers are located in different geographic areas, and supplies are subject to a positive probability of disruption. Different capacity and failure probabilities for each supplier are considered. Our analysis focuses on the insurance versus profitability trade-off faced by a supply manager who buys from suppliers for the outlets. Instead of determining optimal decisions given an objective function and the risk sensitivity of the decision-maker, we use a mixed integer linear programming approach to provide decision-making support that shows a supply manager the ‘elasticity of (expected) losses versus (expected) profits’. Under this model, and depending on the profit-and-loss targets, a supply manager of known risk sensitivity (i.e. risk aversion and loss aversion) can make better decisions when choosing suppliers. Moreover, taking into account, the impact of the share of fixed costs that must be covered by the operation, we consider the net values of expected profit and loss. We discuss the potential influence of the level of the firm’s fixed costs on the supply strategy. In particular, we show how the minimum value of the gross margin needed for the strategy’s profitability affects that strategy. A numerical application is conducted to illustrate the contribution of our decision-making support mechanism, and several managerial insights are obtained.     

A study on non‐invasive detection of blood glucose concentration from human palm perspiration by using artificial neural networks

Abstract: In this paper the relationship between blood glucose concentration and palm perspiration rate is studied as a non‐invasive method. A glucose concentration range from 83 mg/dl to 116.5 mg/dl is examined. An artificial neural network (ANN) trained by the Levenberg–Marquardt algorithm is developed to detect the performance indices based on the one‐ and two‐input variables. A data set for 72 volunteers is used for this study. Data of 36 volunteers are used for training the ANN and data of 36 volunteers were reserved for testing. Results of the study are acceptable with an error of 8.38% for the Elman neural network and 8.77% for the multilayer neural network. Therefore, the palm perspiration rate may be used as a good indicator for detecting glucose concentration in blood. This non‐invasive method has advantages such as time saving, cost etc. over other methods and it is painless. The results of clinical experiments, follow‐up methods and other applications are presented.     

A Universal Interfacial Strategy Enabling Ultra-Robust Gel Hybrids for Extreme Epidermal Bio-Monitoring

A seamless and tough interface to integrate incompatible/immiscible soft materials is highly desired for flexible/wearable electronics and many soft devices with multi-layer structures. Here, a surfactant-mediated interfacial chemistry is introduced to achieve seamless and tough interfaces in soft multi-layer structures, with an ultra-high interfacial toughness up to ≈1300 J m⁻² for the architectural gel hybrid (AGH). The reversible noncovalent interfacial interactions efficiently dissipate energy at the interface, thereby providing excellent durability. The interfacial toughness only decreases by ≈6.9% after 10 000 tensile cycles. This strategy can be universally applied to hybrid systems with various interfaces between an interior hydrogel (PAA, PVA, PAAm, and gelatin) and an exterior hydrophobic soft matter (ionogel, lipogel and elastomer). The AGH-based mechano-sensor presents high robustness and stability in a wide range of conditions, including open air, underwater, and various solvents and temperatures. Epidermal bio-monitoring, tactile trajectory, and facial expression recognition are demonstrated using the AGH sensors in various environments. A rich set of electrophysiological signals of high quality are acquired.     

An Enhanced Graphical Authentication Scheme Using Multiple-Image Steganography

Most remote systems require user authentication to access resources. Text-based passwords are still widely used as a standard method of user authentication. Although conventional text-based passwords are rather hard to remember, users often write their passwords down in order to compromise security. One of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by users. Graphical user authentication methods have recently been proposed to verify the user identity. However, the fundamental limitation of a graphical password is that it must have a colorful and rich image to provide an adequate password space to maintain security, and when the user clicks and inputs a password between two possible grids, the fault tolerance is adjusted to avoid this situation. This paper proposes an enhanced graphical authentication scheme, which comprises benefits over both recognition and recall-based graphical techniques besides image steganography. The combination of graphical authentication and steganography technologies reduces the amount of sensitive data shared between users and service providers and improves the security of user accounts. To evaluate the effectiveness of the proposed scheme, peak signal-to-noise ratio and mean squared error parameters have been used.     

Production of bioactive peptides from tomato seed isolate by Lactobacillus plantarum fermentation and enhancement of antioxidant activity

Tomato seed contains proteins of high nutritional value and nutraceutical properties, which can be recovered for application as food additives. In this study, we investigated the use of a Lactobacillus plantarum strain to obtain high-added-value peptides from the fermentation process using tomato seed meal extract as the substrate. Potentially tomato seed meal extract have antioxidant activity which is correlated to the amino acid structures, compositions and sequences. After 24 h of fermentation, the radical scavenging activity of the isolated extract was increased by 87%. The increase of antioxidant activity is potentially attributed to the production of different bioactive peptides bio transformed during fermentation. L. plantarum growth on tomato seed meal extract as substrate reduced content of crude and soluble proteins by 18.44% and 68.99%, respectively, after 24 h of fermentation. Gel filtration chromatography showed a depolymerization of high molecular weight of polymers. HPLC analysis showed a significant decrease in the concentration of total amino acids, especially glutamic acid and aspartic acid. FTIR results showed that the fermentation favors the production of new amides and aromatic compounds. The production of protease by L. plantarum was investigated and results showed that highest activity (401.45 U/ml) were obtained after 20 h of fermentation. Results confirmed that L. plantarum could degrade and convert tomato seed proteins into bioactive peptides that contributed positively to the improvement of antioxidant activity of the protein isolate.