Browsing by Subject "Packaging materials"
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Item Open Access Antibacterial electrospun zein nanofibrous web encapsulating thymol/cyclodextrin-inclusion complex for food packaging(Elsevier, 2017-10) Aytac Z.; Ipek, S.; Durgun, Engin; Tekinay, T.; Uyar, TamerThymol (THY)/γ-Cyclodextrin(γ-CD) inclusion complex (IC) encapsulated electrospun zein nanofibrous webs (zein-THY/γ-CD-IC-NF) were fabricated as a food packaging material. The formation of THY/γ-CD-IC (1:1 and 2:1) was proved by experimental (X-ray diffraction (XRD), thermal gravimetric analysis (TGA), 1H NMR) and computational techniques. THY/γ-CD-IC (2:1) exhibited higher preservation rate and stability than THY/γ-CD-IC (1:1). It is worth mentioning that zein-THY/γ-CD-IC-NF (2:1) preserved much more THY as observed in TGA and stability of THY/γ-CD-IC (2:1) was higher, as shown by a modelling study. Therefore, much more THY was released from zein-THY/γ-CD-IC-NF (2:1) than zein-THY-NF and zein-THY/γ-CD-IC-NF (1:1). Similarly, antibacterial activity of zein-THY/γ-CD-IC-NF (2:1) was higher than zein-THY-NF and zein-THY/γ-CD-IC-NF (1:1). It was demonstrated that zein-THY/γ-CD-IC-NF (2:1) was most effective in inhibiting the growth of bacteria on meat samples. These webs show potential application as an antibacterial food packaging material.Item Open Access Antioxidant α-tocopherol/γ-cyclodextrin–inclusion complex encapsulated poly(lactic acid) electrospun nanofibrous web for food packaging(John Wiley and Sons Inc., 2017-01) Aytac, Z.; Keskin, N. O. S.; Tekinay, T.; Uyar, Tamerα-Tocopherol (α-TC) and α-TC/cyclodextrin (CD)–inclusion complex (IC) incorporated electrospun poly(lactic acid) (PLA) nanofibers (NF) were developed via electrospinning (PLA/α-TC–NF and PLA/α-TC/γ-CD–IC–NF). The release of α-TC into 95% ethanol (fatty food simulant) was much greater from PLA/α-TC/γ-CD–IC–NF than from PLA/α-TC–NF because of the solubility increase in α-TC; this was confirmed by a phase-solubility diagram. 2,2-Diphenyl-1-picrylhydrazyl radical-scavenging assay shows that PLA/α-TC–NF and PLA/α-TC/γ-CD–IC–NF had 97% antioxidant activities; this value was expected to be high enough to inhibit lipid oxidation. PLA/α-TC–NF and PLA/α-TC/γ-CD–IC–NF were tested directly on beef with the thiobarbituric acid reactive substance (TBARS) method, and the nanofibers displayed a lower TBARS content than the unpackaged meat sample. Thus, active packaging significantly enhanced the oxidative stability of the meat samples at 4 °C. In conclusion, PLA/α-TC/γ-CD–IC–NF was shown to be promising as an active food-packaging material for prolonging the shelf life of foods.Item Open Access Extraction of target features using infrared intensity signals(IEEE, 2005-09) Aytaç, Tayfun; Barshan, BillurWe propose the use of angular intensity signals obtained with low-cost infrared (IR) sensors and present an algorithm to simultaneously extract the geometry and surface properties of commonly encountered features or targets in indoor environments. The method is verified experimentally with planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1°, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99% and 81%, respectively, which shows that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple IR sensors, when coupled with appropriate signal processing, can be used to extract substantially more information than such devices are commonly employed for.Item Open Access Target classification with simple infrared sensors using artificial neural networks(IEEE, 2008-10) Aytaç, T.; Barshan, BillurThis study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90° edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for. © 2008 IEEE.