Chemotherapy drugs are commonly used in cancer treatment, despite their numerous negative side effects. Today, there are studies to reduce the side effects of these drugs and to increase their benefits by combining them with natural substances. Our study investigated the antioxidant and antiradical activities of Ellagic acid (EA), a natural substance, DOC and MIX, commonly used chemotherapeutics, as well as their combinations (EA+DOC, EA+MIX). For this purpose, the methods of DPPH• and ABTS•+ scavenging activity, Fe3+-Fe2+ and Cu2+-Cu1+ reduction capacity were used. After the study, the interactions were analysed with the combination index using Compusyn software. Concentrations with maximum synergy in combinations have been identified. For this purpose, firstly, the combination concentrations used in the antioxidant activity methods and the absorbance values were entered into the Compusyn programme. When DPPH• and ABTS•+ scavenging activity results were entered into the programme, more effective synergistic effect was observed for EA+DOC at lower concentrations (7:7µg/mL), (0.125:1µg/mL) respectively. For EA+MIX, no synergistic effect was observed with the application of DPPH scavenging activity results to the programme, while for ABTS•+ scavenging activity, more effective synergistic effect was observed at higher concentrations within the applied doses (0.750:6µg/ml). When Fe3+-Fe2+ and Cu2+-Cu1+ reduction results were applied to the programme; synergistic effect was observed at higher concentrations (17:17µg/mL) for both EA+DOC and EA+MIX and even strong synergistic effect was observed in many of them. The obtained results provide guidance for the use of these combinations in further studies, such as anticancer and enzyme studies.

Erzincan Tulum Cheese (ETC) holds a significant place among the most popular cheeses in Türkiye. It has been awarded Protected Geographical Indication status, which restricts the allowable milk species, its production area, and specific sheep breed used in its production. Mineral content and antioxidant activity of ETC were aimed to be predicted using conventional FT-NIR and a portable FT-MIR spectrometer combined with partial least square regression (PLSR) and machine learning algorithms based on conditional entropy. Seventy ETC samples were analyzed for their mineral (Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, and P) content using ICP-MS. The samples' antioxidant activity was measured using the DPPH•+ scavenging activity method. PLSR combined with FT-NIR spectral data correlated with antioxidant activity (r=0.89) and minerals (as low as r=0.83) except for Cr and Fe. FT-MIR data provided a good correlation for minerals (as low as r=0.82) except for Cr and Mn and a moderate correlation with antioxidant activity (r=0.64). Information theory was applied to select wavenumbers used in machine learning algorithms, and better results were obtained compared to PLSR. Overall, FT-NIR and FT-MIR spectroscopy provided rapid (~ 1 min), non-destructive, sensitive, and reliable output for mineral and antioxidant activity predictions in commercial cheese samples.