Evaluation of changes in lignocellulosic content of different substrates during the production process of king oyster mushroom (pleurotus eryngii)
2025
0 görüntülenme
0 i̇ndirme
Danışman: Prof. Dr. Ahmet Kazankaya ; Doç. Dr. Funda Atila
Özet (EN)
In this thesis, the bioconversion efficiency of Pleurotus eryngii grown on four lignocellulosic substrates (lentil straw (LS), peanut straw (PS), peanut shell (PH) and poplar sawdust (POS)) was evaluated using Fourier Transform Infrared (FTIR) spectroscopy to analyze substrate degradation dynamics. On different substrates, the spawning period ranged from 20.2-23.7 days, primordia formation took 41.8-47.2 days and first harvest occurred in 51.5-59.9 days, yield and biological efficiency ranged from 155.6-301.8 g/kg and 45.76-86.23%. The study confirmed the agreement between FTIR spectroscopy and chemical-lignocellulosic analysis. While the initial spectra revealed substrate-specific structural features, the post-harvest profiles converged, showing partial lignin (1600, 1510 cm-1) and cellulose (1158, 1050, 896 cm-1) degradation as well as fungal biomass accumulation (protein/polysaccharide bands at 1650-1540 cm-1). The LS substrate, characterized by low lignin (LS: 8.57%) and high carbohydrate content, exhibited rapid decreases in cellulose/hemicellulose bands, which was associated with improved mycelial colonization, shorter cultivation cycles (20.2 days spawning run) and high biological efficiency (BE) (86.23%). In contrast, lignin-rich substrates (PH: 22.43%; POS: 22.70%) showed limited spectral shifts, inefficient polysaccharide degradation and long spawning time (25.3 and 23.7 days), resulting in reduced BE (55.53% and 45.76%, respectively). The differential degradation of lignocellulosic components underlined substrate-specific enzymatic activity, with lignin recalcitrance inhibiting cellulose accessibility at PH and POS. These findings establish a direct link between substrate composition, FTIR-derived degradation patterns and fungal productivity. Furthermore, the nutrient content of P. eryngii mushrooms grown on different substrates was revealed by determining their FTIR spectra. The POS substrate shows the richest and most balanced FTIR profile. Mushrooms growing on this substrate exhibited high content of protein, fat and phenolic compounds. The FTIR absorptions of fungi grown on PS substrate have the lowest intensities, probably due to limited nutrients and low bioactivity. Mushrooms grown on LS substrate exhibited a profile close to that of the POS substrate. The study highlights that FTIR spectroscopy is a cost-effective tool to assess the biodegradability of the substrate and guides the selection of sustainable agricultural residues for mushroom cultivation. According to the results of the study, LS substrate showed promising results in terms of mushroom yield and quality for use in P. eryngii production in our country. In addition, FTIR spectroscopy was found to be a method that can be successfully used in mushroom cultivation both to determine lignocellulosic changes and to reveal the nutrient profiles of mushrooms.
Yazar
Burak Salmanoğlu
Bu Yayına Nasıl Atıf Yapılır
Burak Salmanoğlu (Master Thesis). Evaluation of changes in lignocellulosic content of different substrates during the production process of king oyster mushroom (pleurotus eryngii), 2025, Kırşehir Ahi Evran University.
Anahtar Kelimeler
Lisans
Tüm Hakları Saklıdır
Bu eser belirtilen lisans koşulları altında paylaşılmaktadır.
Kırşehir Ahi Evran University tezlerinden daha fazlası
- In teaching of mathematical concept, the effect of storyline method on attitude and success(2013)
- Jean-Jacques Rousseau's thoughts on life science and education in his Work "emile"(2019)
- The effect of pre-breeding weights and placental characteristics on birth weight in Karayaka sheeps(2020)
- H. 1301 (M. 1884) in light of the yearbook dated hejaz province during the reign of Abdulhamid II (History and geography, social-cultural, economic, administrative-military structure)(2020)
- Proteinurin in diabetic patients effect on mortality(2022)
- Wind energy forecasting methods: A case study of the long short term memory model (LSTM)(2024)
