Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025/r/gumushane/14138
  Comprehensive Analysis of the Antioxidant and Enzyme Inhibitory Properties and Phenolic Compounds of Phlomis armeniaca: Potential Applicant in Neurodegenerative Diseases

  Menevseoglu, Ahmed; Yılmaz, Fırat; Topal, Meryem; Topal, Fevzi

  This study investigates the antioxidant and enzyme inhibitory properties of Phlomis armeniaca, a perennial plant native to the eastern and southeastern regions of Turkey. Ethanol extracts of the plant were analyzed using various bioanalytical methods, including Fe3⁺-Fe2⁺ reducing power, Cupric Reducing Antioxidant Capacity, 1,1-diphenyl-2-picryl-hydrazyl (DPPH), and 2,2-azinobis 3-ethylbenzthiazoline-6-sulfonic acid (ABTS) radical scavenging activities, as well as total phenolic and flavonoid content assessments. The results showed that P. armeniaca is rich in phenolic (38.75 ± 0.59 µg gallic acid equivalents/mg extract) and flavonoid compounds (39.34 ± 4.08 µg quercetin equivalents/mg extract). The plant demonstrated significant inhibitory effects on acetylcholinesterase (half-maximal inhibitory concentration [IC50]: 45.00 ± 0.48 mg/mL) and butyrylcholinesterase (IC50: 84.51 ± 0.84 mg/mL) enzymes, indicating its potential use in treating neurodegenerative diseases such as Alzheimer's. The study also highlighted P. armeniaca’s high chlorogenic acid, salicylic acid, 4-dihydroxybenzoic acid, and quinic acid contents, supporting its antioxidant capacity. These findings suggest that P. armeniaca could be a valuable natural resource for developing therapeutic agents to manage oxidative stress and related conditions, warranting further pharmacological exploration.

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• MakaleAçık Erişim2022/r/gumushane/17169
  Authentication of hazelnut oil by portable ft-nir, ft-mir and raman spectrometers combined with multivariate data analysis

  Aykas, Didem Peren; Menevseoglu, Ahmed

  This research studied the authentication of hazelnut oil by portable FT-NIR, FT-MIR, and Raman spectrometers. Hazelnut oils were adulterated with vegetable oils at various concentrations (0-25%) (w/w). Collected spectra were analyzed using Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) to generate classification models to authenticate pure hazelnut oil and Partial Least Squares Regression (PLSR) to predict the fatty acids and adulterant levels. For confirmation, oil’s fatty acid profile was determined by gas chromatography. In all three instruments, SIMCA provided distinct clusters for pure and adulterated samples with interclass distance (ICD)>3. All instruments showed excellent performance in predicting fatty acids and adulteration levels with rᵥₐₗ>0.93 and standard error prediction (SEP)<1.75%. Specifically, the FT-MIR unit provided the best performances. Still, all the units can be used as an alternative to traditional methods. These units showed great potential for in-situ surveillance to detect hazelnut oil adulterations.

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• MakaleYayıncıda açık erişim2022/r/gumushane/11522
  Information theory and machine learning based authentication of flaxseed oil using portable and handheld vibrational spectroscopy sensors

  Aykas, Didem-Peren; Menevseoglu, Ahmed; Gunes, Nurhan

  Flaxseed oil is one of the popular edible oils, and its adulteration has become a concern because of its high commodity value. Therefore, there is a need to detect adulterated flaxseeds with a rapid, non-invasive, and simple method to keep the quality of flaxseed oil. This study aimed to develop a machine learning algorithm and information theory to detect adulterated flaxseed oils by vibrational spectroscopy units combined with chemometrics. Oil spectra were collected with portable/handheld vibrational spectroscopic units and analyzed using conditional entropy, support vector machines (SVM), and soft independent modeling of class analogy (SIMCA) to generate classification models to identify the pureness of oils and partial least square regression (PLSR) to predict the adulterant levels and fatty acid distributions. Conditional entropy showed that two or three wavenumbers/shifts could discriminate the oils using machine learning algorithms with an accuracy of over 95%. SIMCA models determined the oils with an interclass distance (ICD) over 1.1. Specifically, the Fourier transform mid-infrared (FT-MIR) unit provided the best classification of the oils with ICD over 4.9. All instruments showed good performance in predicting fatty acids and adulteration levels with rval≥0.89 and standard error prediction (SEP) ≤1.7%. Overall, these portable/handheld units showed great potential for rapid, simple, and non-invasive monitoring to identify adulterated flaxseed oils.

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• BildiriYalnız künye2021/r/gumushane/16803
  Evaluation of Vibrational Spectroscopy Techniques (NIR, MIR, Raman) Combined with Chemometrics in Detecting Adulteration of Essential Oils.

  Menevseoglu, Ahmed; Gumus, Cansu

  İncele
• MakaleAçık Erişim2021/r/gumushane/10418
  Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy

  Menevseoglu, Ahmed; Aykas, Didem P.; Hatta-Sakoda, Beatriz; Toledo-Herrera, Victor Hugo; Rodriguez-Saona, Luis E.

  Handheld Raman and portable FT-IR spectroscopy devices were evaluated for fast and non-invasive determination of methanol and ethanol levels in Peruvian Pisco. Commercial Peruvian Pisco (n = 171) samples were kindly provided by the UNALM Alliance for Research in Alcohol and its Derivatives (Lima, Peru) and supplemented by purchases at grocery and online stores. Pisco spectra were collected on handheld Raman spectrometers equipped with either a 1064 nm or a 785 nm excitation laser and a portable infrared unit operating in transmission mode. The alcohol levels were determined by GC-MS. Calibration models used partial least-squares regression (PLSR) to develop prediction algorithms. GC-MS data revealed that 10% of Pisco samples had ethanol levels lower than 38%, indicating possible water dilution. Methanol levels ranged from 10 to 130 mg/100 mL, well below the maximum levels allowed for fruit brandies. Handheld Raman equipped with a 1064 nm excitation laser gave the best results for determining ethanol (SEP = 1.2%; RPre = 0.95) and methanol (SEP = 1.8 mg/100 mL; RPre = 0.93). Randomly selected Pisco samples were spiked with methanol (75 to 2800 mg/100 mL), and their Raman spectra were collected through their genuine commercial bottles. The prediction models gave an excellent performance (SEP = 98 mg/100 mL; RPre = 0.97), allowing for the non-destructive and non-contact determination of methanol and ethanol concentrations without opening the bottles.

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• MakaleYalnız künye2021/r/gumushane/10387
  Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV-Vis spectroscopy

  Menevseoglu, Ahmed; Aykas, Didem Peren; Adal, Eda

  Pistachio is one of the most expensive nuts with having high economic importance in Turkey. It has become more prone to adulteration because of its high commodity value. Peanut with color additives and green pea are generally used to adulterate ground pistachio. Vibrational spectroscopy is a potential technique to detect adulterations in pistachio. The objective of this study was to generate a non-targeted method for portable FT-IR and UV-Vis spectrometers to authenticate pistachio and detect green pea and peanut adulterations. Pistachio granules were adulterated with green pea and peanut at different concentrations (5 to 40% w/w). Spectra were collected by a portable FT-IR spectrometer and by a conventional UV-Vis spectrometer and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate classification algorithms to authenticate pistachio, and Partial Least Square Regression (PLSR) to predict the concentrations of adulterants. SIMCA showed very distinct clusters for pure samples. Moreover, adulterated pistachio samples were discriminated by SIMCA even in low levels of adulteration (5%). Portable FTIR showed excellent performance (r(val) > 0.93) of predicting the adulterant levels with a standard error of prediction (SEP) 0.66% and 0.80% for green pea and peanut, respectively. Similarly, UV-VIS predicted (r(val) > 0.93) the adulterant levels with SEP 0.58% and 0.14% for green pea and peanut, respectively. The results supported that portable FT-IR, and UV-Vis units present great potential for real-time surveillance of green pea and peanut adulteration in pistachio.

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• MakaleYayıncıda açık erişim2021/r/gumushane/14914
  Evaluating the chemical and metal contamination of commercial raki, a grape-based alcoholic beverage from turkey

  Menevseoglu, Ahmed; Cabaroglu, Turgut

  Turkish Raki is one of the most consumed alcoholic beverages in Turkey. The chemical and metal contamination of alcoholic beverages is a threat to human health and lowers the quality of the product. Ethyl carbamate and furfural are carcinogenic to animals, and have been classified as Group 2A and 3 agents respectively by the International Agency for Research on Cancer. Factors, such as raw materials, process time and storage, affect metal concentrations in beverages. Limits for chemicals and metals in Raki have not yet been established by the Turkish agency. The present study aimed to evaluate the chemical and metal contamination of commercial Turkish Raki. Thirty-seven different types of Turkish Raki were purchased from local markets in Turkey. Ethyl carbamate (EC) and furfural (FR) levels were measured by Gas Chromatography-Mass Spectroscopy (GC-MS). Arsenic, copper, lead, and zinc levels were measured by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES). None of the Raki samples contained EC. Furfural was not detected in commercial Raki samples but only in illegal Raki samples. Ethanol and methanol levels complied with Turkish regulation. Our data shows that commercial Raki from Turkey was contaminated by very low amounts of arsenic (<LOD-0.02 mg/L), copper (0.05-0.41 mg/L), lead (< LOD- 0.03 mg/L) and zinc (0.01-0.39 mg/L).

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• MakaleYalnız künye2021/r/gumushane/14760
  A rapid method to detect green pea and peanut adulteration in pistachio by using portable FT-MIR and FT-NIR spectroscopy combined with chemometrics

  Aykas, Didem Peren; Menevseoglu, Ahmed

  In this research, the determination of green pea and peanut adulteration in pistachio by portable Fourier transform mid-infrared (FT-MIR) and Fourier transform near-infrared (FT-NIR) spectroscopy was studied. A rapid method to detect adulteration in pistachio is a necessity in the food industry because of its high commodity value and being one of the most consumed tree nuts. Pistachio nut is a target for economically motivated adulteration, and green pea, spinach, and peanut are the most seen adulterants in pistachio. Portable FT-MIR and FT-NIR spectrometers are simple, non-invasive, rapid, easy to transport, and cheaper alternatives to traditional methods, including chromatography (GC, HPLC), mass-spectroscopy (GC-MS, LC-MS) for the detection of pistachio adulteration. Powdered pistachio samples were adulterated with powdered green pea and peanut samples at different concentrations (5–40%). Spectra were collected with a portable FT-MIR and FT-NIR spectrometers and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate a classification model to authenticate pure pistachio, and Partial Least Square Regression (PLSR) to predict the levels of adulterants in pistachio. SIMCA provided, in both units, very distinct clusters for pure and adulterated samples with inter-class distance (ICD) over 3. Both units also showed superior performance in predicting adulterant levels with rVal>0.99, standard error of prediction (SEP) < 2.5%. Based on SIMCA and PLSR models, portable FT-NIR spectrometer provided more advantages over portable FT-MIR unit because the prediction with the FT-NIR spectrometer was more precise than the FT-MIR unit. Both portable FT-MIR and FT-NIR units can be used as alternative methods to traditional methods and showed great potential for real-time surveillance to detect green pea and peanut adulteration in pistachio.

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• MakaleAçık Erişim2021/r/gumushane/12356
  Non-destructive detection of sesame oil adulteration by portable FTNIR, FT-MIR, and Raman spectrometers combined with chemometrics

  Menevseoglu, Ahmed

  Edible oils are often adulterated with fixed oils because of their high quality and price. Sesame oil is prone to adulteration due to its high commodity value and popularity. Therefore, a rapid, simple, and non-invasive method to detect adulteration in sesame oil is necessary for quality control purposes. Handheld and portable FT-NIR, FT-MIR, and Raman spectrometers are easy to operate, non-destructive, rapid, and easy to transport for in-situ assessments as well as being cheaper alternatives to traditional instruments. This study aimed to evaluate three different vibrational spectroscopic techniques in detecting sesame oil adulteration with sunflower and canola oil. Sesame oils were adulterated with fixed oils at different concentrations (0 – 25%) (w/w). Spectra were collected with portable devices and analyzed using Soft Independent Modelling of Class Analogy (SIMCA) to generate a classification model to authenticate pure sesame oil and Partial Least Squares Regression (PLSR) to predict the levels of the adulterant. For confirmation, the fatty acid profile of the oils was determined by gas chromatography (GC). In all three instruments, SIMCA provided distinct clusters for pure sesame oils and adulterated samples with interclass distance (ICD) over 3. Furthermore, FT-NIR and FT-MIR showed excellent performance in predicting adulterant levels with rval>0.96. Specifically, the FT-MIR unit provided more precise classification and PLSR prediction models over FT-NIR and Raman units. Still, all the units can be used as an alternative method to traditional methods such as GC, GC-MS, etc. These units showed great potential for in-situ surveillance to detect sesame oil adulterations.

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• MakaleAçık Erişim2020/r/gumushane/14841
  In situ monitoring of sugar content in breakfast cereals using a novel ft-nir spectrometer

  Aykas, Didem Peren; Ball, Christopher; Menevseoglu, Ahmed; Rodriguez-Saona, Luis E.

  This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (Rpre ≥ 0.93) and low standard error of prediction (SEP ≤ 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.

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