Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Access type: Open accessSubject: search.filters.subject.Principal component analysis (PCA)

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Comparison of Palynological Method and Chromatographic Analysis Combined With Chemometrics To Identify Botanical Origin of Propolis
    (Springer, 2023) Güzelmeriç, Etil; Daştan, Tuğçe; Sen, Nisa Beril; Erdem, Özge; Özdemir, Durmuş; Yeşilada, Erdem
    There has been a growing trend in consumer’s preferences for food supplements containing propolis due to having a wide range of phenolic compounds to promote health. Honeybees’ used main plant source will determine propolis chemical composition thus its biological activity. Thus, determination of the propolis botanical source is highly important for its standardization and prediction of its pharmacological activity. There are two commonly applied methods to seek propolis botanical sources: chromatographic techniques and palynological analysis. In this study, high-performance thin-layer chromatography (HPTLC) and ultra-high-performance liquid chromatography combined with mass spectrometer (LC-MS/MS) applied comparatively with pollen analysis to propolis samples. The results of the chromatographic analyses were evaluated with principal component analysis (PCA) and hierarchical clustering analysis (HCA). Consequently, chromatographic techniques applied in this study were found to be superior to pollen analysis to identify the main plant source of propolis. Besides, HPTLC images revealed not only main botanical sources but also minor sources of propolis. Therefore, HPTLC fingerprinting combined with PCA and HCA resulted in grouping propolis samples according to geographical regions. This study may lead to pharmaceutical industries for the quality assurance of propolis while preparing standardized propolis formulations in the market with desired pharmacological properties. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Use of Magic Sandwich Echo and Fast Field Cycling Nmr Relaxometry on Honey Adulteration With Corn Syrup
    (John Wiley and Sons Inc., 2021) Berk, Berkay; Çavdaroğlu, Çağrı; Grunin, Leonid; Ardelean, Ioan; Kruk, Danuta; Mazı, Bekir G.; Öztop, Halil Mecit
    conventional time domain nuclear magnetic resonance (TD-NMR) pulse sequence: magic sandwich echo (MSE) was used to detect the adulteration of honey by glucose syrup (GS) and high fructose corn syrup (HFCS) accompanied with T-1 and T-2 relaxation times. Also, fast field cycling NMR (FFC-NMR) relaxometry and multivariate analysis were performed to investigate the adulteration. RESULTS Higher maltose in GS and changing glucose to water ratio of HFCS gave high correlation with the crystal content values. In HFCS adulteration, two separate populations of protons having different T-2 values were detected and T-1 times were also used to determine GS adulteration. Addition of GS increased T-1 while addition of HFCS increased T-2, significantly. CONCLUSION The results showed that it is possible to differentiate the unadulterated and adulterated honey samples by using TD-NMR relaxation times and crystal content values obtained by the MSE sequence. By FFC-NMR relaxometry, not only GS addition but also the amount of GS was examined. The multivariate analysis technique of principal component analysis was able to distinguish the types of adulterants.
  • Article
    Citation - WoS: 28
    Citation - Scopus: 33
    Rapid Detection of Green-Pea Adulteration in Pistachio Nuts Using Raman Spectroscopy and Chemometrics
    (John Wiley and Sons Inc., 2021) Taylan, Osman; Çebi, Nur; Yılmaz, Mustafa Tahsin; Sağdıç, Osman; Özdemir, Durmuş; Balubaid, Mohammed
    BACKGROUND Ground pistachio nut is prone to adulteration because of its high economic value and wide usage. Green pea is known as the main adulterant in frauds involving pistachio nuts. The present study developed a new, rapid, reliable and low-cost methodology by using a portable Raman spectrometer in combination with chemometrics for the detection of green pea in pistachio nuts. RESULTS Three different methods of Raman spectroscopy-based chemometrics analysis were developed for the determination of green-pea adulteration in pistachio nuts. The first method involved the development of hierarchical cluster analysis (HCA) and principal component analysis (PCA), which differentiated authentic pistachio nuts from green pea and green pea-adulterated samples. The best classification pattern was observed in the adulteration range of 20-80% (w/w). In addition to classification methods, partial least squares regression (PLSR) and genetic algorithm-based inverse least squares (GILS) were also used to develop multivariate calibration models to determine quantitatively the degree of green-pea adulteration in grounded pistachio nuts. The spectral range of 1790-283 cm(-1)was used in the case of multivariate data analysis. A green-pea adulteration level of 5-80% (w/w) was successfully identified by PLSR and GILS. The correlation coefficient of determination (R-2) was determined as 0.91 and 0.94 for the PLSR and GILS analyses, respectively. CONCLUSION A Raman spectrometer combined with chemometrics has a high capability with regard to the detection of adulteration in pistachio nuts, combined with low cost, strong reliability, a high level of accuracy, rapidity of analysis, and minimum sample preparation.
  • Article
    Citation - WoS: 56
    Citation - Scopus: 67
    Characterization of Concrete Matrix/Steel Fiber De-Bonding in an Sfrc Beam: Principal Component Analysis and K-Mean Algorithm for Clustering Ae Data
    (Elsevier, 2018) Tayfur, Sena; Alver, Ninel; Abdi, Saeed; Saatçi, Selçuk; Ghiami, Amir
    Steel fibers have been used in concrete structures to increase the tensile strength and ductility of concrete. Fibers bridging cracks reduce micro cracking and improve post-cracking strength in concrete. Propagation of damage in a fiber reinforced concrete member occurs by concrete matrix cracking and widening of these cracks, which is accompanied by de-bonding of steel fibers from the concrete matrix. Fiber de-bonding is the main factor affecting the post-peak behavior of these members. Therefore, distinguishing the matrix cracking and fiber de-bonding mechanisms is important in nondestructive structural health monitoring methods. This study is focused on characterizing steel fiber/matrix de-bonding events apart from concrete matrix cracking sources in acoustic emission (AE) method. Two reinforced concrete beams, one of which included steel fibers within the concrete matrix, were tested under three point bending and monitored by AE. Afterwards, Principal Component Analysis (PCA) was applied to AE data and the failure mechanisms were clustered for characterization of steel fiber/matrix de-bonding. Finally, different AE features of these clusters were evaluated and applicable AE parameter distributions, which are useful to clarify steel fiber de-bonding mechanisms, were revealed.