Yazar "Dogan, Cemhan" seçeneğine göre listele

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    Centrifugally spun micro-nanofibers based on lemon peel oil/gelatin as novel edible active food packaging: Fabrication, characterization, and application to prevent foodborne pathogens E. coli and S. aureus in cheese
    (Elsevier Sci Ltd, 2022) Dogan, Nurcan; Dogan, Cemhan; Eticha, Andinet Kumella; Gungor, Melike; Akgul, Yasin
    This study aimed to develop innovative micro-nano fibers loaded with lemon peel essential oil for food packaging via centrifugal spinning. Lemon peel essential oil (LPO) was extracted from dried lemon peel by hydrodistillation. The major one of the 16 components of the extract detected by GS-MS was limonene (60.4%). Gelatin fibrous mats loaded with three different concentrations of LPO were fabricated with centrifugal spinning and then crosslinked. Micro-nanofibers were characterized in encapsulation efficiency, morphological, chemical, thermal, hydrophobicity and microbiological aspects. The in-vitro evaluation showed that the effect of fibers on grampositive Staphylococcus aureus (S. aureus) ATCC 29213, especially due to the antimicrobial activity of limonene, was greater than that of gram-negative Escherichia coli (E.coli) ATCC 35218. This effect was also consistent with the in-situ evaluation in which micro-nano fibers were applied to contaminated cheeses. Moreover, LPOloaded gelatin centrifugal spun positively affected the shelf life by suppressing the growth of aerobic mesophilic bacteria and yeast molds that cause spoilage in cheese.
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    Determination of margarine adulteration in butter by machine learning on melting video
    (Springer, 2023) Sehirli, Eftal; Dogan, Cemhan; Dogan, Nurcan
    Butter is a product that is often vulnerable to adulteration with cheaper ingredients such as margarine. In this study, butter was artificially adulterated with margarine at different rates to create different levels of adulteration. Then, the melting was captured using video footage, and image processing and machine learning (ML) were used to automatically detect the level of adulteration in the butter. To create the final numerical dataset for ML models, a total of 30,000 images were collected from the video, with equal numbers of images for each class. The images were divided into five classes using an algorithm that detected region of interest (ROI) in the adulterated butter images. Two types of numerical datasets were created: single frame-based and first-middle-last (FML) frame-based. Seven different ML models (decision tree (DT), linear discriminant analysis (LDA), Naive Bayes (NB), support vector machines (SVM), k-nearest neighbor (KNN), random forest (RF) and artificial neural networks (ANN) were trained and tested on the datasets. To improve accuracy and efficiency, 10-fold cross-validation was applied to the ML models. The ML models achieved high accuracy in classifying the loaded butter videos. KNN, RF, and ANN had the highest accuracy (99.9%), followed by SVM (99.7%) and DT (99.4%) on the single frame-based dataset. NB had the lowest accuracy (87.1%). On the FML frame-based dataset, DT had the highest accuracy (99.9%) while SVM had the lowest accuracy (73.3%). Overall, the method used in this study was successful in classifying butter adulteration with high accuracy using image processing and ML techniques.
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    Non-targeted approach to detect pistachio authenticity based on digital image processing and hybrid machine learning model
    (Springer, 2023) Dogan, Cemhan; Sehirli, Eftal; Dogan, Nurcan; Buran, Ilkay
    In this study, we propose a new method for detecting green pea adulteration in pistachio based on digital image and machine learning (ML). An algorithm was built using digital image processing techniques to detect region of interest (ROI) on adulterated pistachio images and a hybrid ML to classify the level of adulteration as class 1 (%0), class 2 (%10), class 3 (%20), class 4 (%30), class 5 (%40), and class 6 (%50) in a fully automated way. A dataset with size of 1254 x 15 were created. Training set and test set with the rate of 80% and 20% based on fivefold cross validation were created. Decision tree, random forest (RF), k-nearest neighboring, support vector machines, naive bayes and artificial neural network (ANN) are performed and compared to classify the level of adulteration in two steps as direct and binary classification. ANN has achieved the highest results as 93.65% of accuracy and 0.87 of Matthews correlation coefficient (MCC) based on direct classification to separate class1, class 2, class 5, and class 6 from class 3 and class 4. RF has achieved the highest results as 89.56% of accuracy and 0.79 of MCC based on binary classification to separate class3 from class 4. As a result of this, a hybrid ML model including ANN and RF in the form of a tree structure to classify the level of pistachio adulterated images was built in this study.
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    Novel active food packaging based on centrifugally spun nanofibers containing lavender essential oil: Rapid fabrication, characterization, and application to preserve of minced lamb meat
    (Elsevier, 2022) Dogan, Cemhan; Dogan, Nurcan; Gungor, Melike; Eticha, Andinet Kumella; Akgul, Yasin
    This study aimed to develop novel nano fibers loaded with lavender essential oil (LEO) for food packaging via centrifugal spinning technique. LEO was extracted from dried lavender flowers by hydrodistillation. The dominant two of the 16 components of the extract identified by GS-MS were linalool (34.37 %) and linalyl acetate (28.82 %). PVP nanofiber mats loaded with three different concentrations of LEO were fabricated with centrifugal spinning and subsequently crosslinked. Nanofibers were characterized in encapsulation efficiency, morphological, mechanical, chemical, thermal, hydrophobicity and bioactivity aspects. The in-vitro antioxidant effect of nanofiber mats, which increased with the loaded LEO concentration, were determined by DPPH and ABTS methods. This effect was also consistent with the in-situ assessment where nanofibers were applied to minced lamb meat. Moreover, LEO-loaded PVP centrifugal spun positively affected the shelf life by suppressing the growth of aerobic mesophilic bacteria, psychotropic bacteria and, yeast molds that cause spoilage in meat.
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    A Novel Approach to Crosslink Gelatin Nanofibers Through Neutralization-Induced Maillard Reaction
    (Springer, 2024) Ahmed, Salih Birhanu; Dogan, Nurcan; Dogan, Cemhan; Akgul, Yasin
    Improved mechanical strength and stability are key to expanding the use of biopolymers in a range of applications such as food packaging, tissue engineering, and wound healing. This can be achieved through crosslinking, a process that introduces chemical bonds between polymer chains in a nanofiber structure. In this particular investigation, gelatin nanofibers were produced using electrically assisted solution blow spinning. The study aimed to introduce and compare the effectiveness of the innovative neutralization-triggered Maillard crosslinking method with thermal crosslinking and classical Maillard crosslinking techniques. Several aspects, including mechanical properties, thermal stability, hydrophobicity, antibacterial and antioxidant activities, amino acid profile, as well as physical properties like FTIR spectra, SEM, TGA, water contact angle, and air permeability of the nanofiber webs, were examined. The outcomes revealed that the samples crosslinked via the novel method exhibited the highest hydrophobicity (with a water contact angle of 103.38 & DEG;), a more rigid network structure with a tensile strength of 0.887 MPa, and 8.4 mm inhibition zones against E. coli and S. aureus. Overall, this research introduces a promising technique for modifying gelatin for food packaging, bioactive delivery, and biomedical applications.
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    Rapid fabrication of micro-nanofibers from grapevine leaf extract and gelatine via electroblowing: A novel approach for edible active food packaging
    (Elsevier, 2023) Dayisoylu, Kenan Sinan; Akboga, Zisan; Dogan, Cemhan; Kaya, Elife; Akgul, Yasin; Dogan, Nurcan; Eticha, Andinet Kumella
    The objective of this study was to develop novel micro-nanofibers for food packaging using grapevine extract (GLP) and gelatine using electroblowing technique. The identified components of GLP were dominated by the flavone group phenolics, as analyzed by LC-MS/MS. SBS was used to fabricate gelatine micro-nanofiber mats loaded with three different concentrations of GLP, which were subsequently cross-linked. The micro-nanofibers were characterized by their morphology, chemistry, thermal properties, and bioactivity. The in-vitro antioxidant and antimicrobial effects of the nanofiber mats were determined using various methods, which showed an in-crease in effectiveness with increasing GLP concentration. The in-situ assessment, where the nanofibers were applied to cheese, also showed a consistent improvement in shelf life with the use of GLP-loaded gelatin electroblown fibers.
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    Tailoring of Gelatin-Chitosan Nanofibers Functionalized with Eucalyptus Essential Oil via Electroblowing for Potential Food Packaging and Wound Dressing Applications
    (Korean Fiber Soc, 2024) Elomar, Zeyne; Eticha, Andinet Kumella; Dogan, Nurcan; Akgul, Yasin; Dogan, Cemhan
    In recent years, new approaches to fabricating nanofiber networks for potential applications in wound dressing and food packaging have been in the spotlight. This study aimed to produce functional webs based on gelatin, chitosan, and eucalyptus essential oil using the electro-blowing method instead of traditional spinning methods such as electrospinning. The resultant nanofiber webs exhibit promising morphological characteristics, including reduced fiber diameters, enhanced air permeability, and improved thermal stability. The integration of chitosan and eucalyptus essential oil overcomes limitations associated with gelatin, offering enhanced mechanical properties, antibacterial efficacy, and potential attributes for wound healing and food packaging. The combination of gelatin and chitosan contributes to biodegradability and biocompatibility, crucial for developing materials compatible with the natural environment. The addition of eucalyptus essential oil provides an additional layer of antimicrobial protection, aligning with sustainability goals in wound care and active food packaging. A comprehensive analysis encompassing SEM morphologies, fiber diameters, air permeability, FTIR spectra, TGA thermograms, and contact angle measurements establishes a thorough understanding of the fabricated nanofiber webs' characteristics. Despite the favorable properties exhibited by the developed nanofiber webs for wound healing and food packaging applications, the incorporation of eucalyptus essential oil resulted in a reduction in tensile strength and elongation ratios. This observation highlights the necessity for further optimization and fine-tuning of the formulation to strike a balance between antimicrobial benefits and mechanical properties. Distinguished by its unique combination of gelatin, chitosan, and eucalyptus essential oil, this research contributes to the advancement of nanofiber technology, expanding knowledge in the field and paving the way for the development of advanced materials with enhanced therapeutic properties for wound healing and food packaging.