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    Application of multivariate machine learning methods to investigate organic compound content of different pepper spices
    (Elsevier, 2023) Durmuş, Yusuf; Atasoy, Ahmet Ferit
    The aim of this study was to uncover all aspects and extract comprehensive and valuable information from the data obtained from different pepper varieties using machine learning (ML) methods. The red pepper (RP), fabricated isot (FI), and customary isot (CI) spices were stored for 12 months and the variations in the organic compound content were monitored every 3 months. The data set has been subjected to a supervised ML method Random Forest (RF), unsupervised ML methods principal component analysis (PCA), t-Distributed stochastic neighbor embedding (t-SNE), and hierarchical cluster analysis (HCA). The classification accuracy yielded by the RF model was 100%. RF model showed that terpenoids, acids, and alkanes were ineffective in identifying the differences between pepper spices, but glucose, succinic acid, citric acid, and fructose were primarily responsible for the variations between pepper spices. FI peppers differed significantly from other pepper spices in terms of their chemical compositions. Although most organic compounds exhibited positive correlations; furan-fructose, furan-glucose, furan-citric acid, and glucose-malic acid showed negative correlations. RP peppers were mostly stable for the first 6 months of storage, but after this month, due to changes in malic acid, aldehyde, glucose, and fructose, they displayed similar properties as CI. The organic compound content of CI peppers rapidly changed in the first 3 months of storage and stayed almost stable for the remaining 9 months. Various ML methods were effectively employed in this study to examine the changes that different pepper spices exhibited in association with storage.
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    Effects of tea origin, type, concentration and brewing tıme on essential and trace elements in tea infusion and daily intake by human
    (Gheorghe Asachi Technical University of Iasi, Romania, 2024) Atasoy, Ayşe Dilek; Durmuş, Yusuf; Atasoy, Ahmet Ferit
    The economic and social significance of tea is easily understood from the fact that approximately 20 billion cups of brewed tea are consumed daily worldwide. Türkiye and Sri Lanka are major tea producers, following China, India, and Kenya. The objective of this study was to determine the influence of origin (Turkish and Ceylon), type (black and green), and concentration (1%, 2%, and 3%), as well as brewing time (2, 5, 10, 20, 30, 45, and 60 minutes) on the trace elements (Al, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, and Zn) and daily intake in tea infusion. The concentrations of Al, Fe, Mn, and Ni in Turkish tea infusions were higher than those in Ceylon tea infusions, while the Cu content of Turkish tea infusions was lower than that of Ceylon tea infusions. Moreover, the infusion made from Turkish tea had similar Zn, Cd, Cr, Hg, and Pb value to its Ceylon counterpart. The average Al, Cu, Fe, Mn, Ni and Zn content of black and green tea infusions were 3.584±0.217, 4.188±0.229 mg L-1; 0.040±0.000, 0.051±0.003 mg L-1; 0.090±0.008, 0.119±0.006 mg L-1; 2.626±0.277, 3.206±0.229 mg L-1; 0.040±0.000, 0.040±0.000 mg L-1; 0.161±0.007, 0.176±0.008, respectively. Pb was only extracted in 3% concentration of Ceylon green tea infusion with 45 and 60 min (0.063 mg L-1). Cu and Fe concentration of black tea infusions was lower than green counterpart. Tea concentration did not affect Cd, Cr, and Hg amount. In general, the longer brewing time and high tea concentration were found to have a higher toxic metal content in tea infusion. Except for Mn and Pb, human daily intake rates of trace elements did not exceed the limits.
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    Mathematical optimization of multilinear and artificial neural network regressions for mineral composition of different tea types infusions
    (Nature Research, 2024) Durmuş, Yusuf; Atasoy, Ayşe Dilek; Atasoy, Ahmet Ferit
    The objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish (GT), were used to produce teas at concentrations of 1, 2, and 3%, respectively. These teas were produced using 7 different steeping times: 2, 5, 10, 20, 30, 45, and 60 min. It was also aimed to optimize the regression equations utilizing these factors to identify parameters conducive to maximizing Zn, K, Cu, Mg, Ca, Na, and Fe levels; minimizing Al content, and maintaining Mn level at 5.3 mg/L. The optimal conditions for achieving a Mn content of 5.3 mg/L in black Turkish tea entailed steeping at a concentration of 1.94% for 11.4 min. Variations in K and Mg levels across teas were inconsistent with those observed for other minerals, whereas variations in Al, Cu, Fe, Mn, Na, and Zn levels exhibited a close relationship. Overall, mineral levels in tea can be predicted through regression analysis, and by mathematically optimizing the resultant equations, the requisite conditions for tea production can be determined to achieve maximum, minimum, or target mineral values.