Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

Browse

Filters

2021 - 20232

Settings

COAR Access Rights: search.filters.coarAccessRights.metadata only accessSubject: search.filters.subject.Principal component analysis (PCA)

Search Results

Now showing 1 - 2 of 2
  • 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: 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.