σ-Amylase inactivation during corn starch hydrolysis process

Apar, Dilek Kılıç; Özbek, Belma

doi:10.1016/j.procbio.2003.09.014

Cited by 40 publications

(17 citation statements)

References 16 publications

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“…Therefore, to save energy and time in the process of bioethanol production from watermelon waste, it would be better to reduce the length of the fermentation process from 35 to 30 hr. These results are consistent with the results obtained by Salehi, Taghizadeh‐Alisaraei, Shahidi, and Jahanbakhshi (), Salehi, Taghizadeh‐Alisaraei, Jahanbakhshi, and Shahidi (), and Apar and Özbek (). Moreover, changes in the amount of the alcohol produced at all the different agitator speeds, shows an ascending trend.…”

Section: Resultssupporting

confidence: 92%

Processing watermelon waste using Saccharomyces cerevisiae yeast and the fermentation method for bioethanol production

Jahanbakhshi

Salehi

2019

J Food Process Engineering

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Bioethanol is a source of energy for the future when petroleum is going to finish. The aim of this study is to assess bioethanol production from watermelon waste using Saccharomyces cerevisiae (S. cerevisiae) yeast and fermentation method. The test was replicated three times in 35 hr at three different fermenter agitator speeds and three levels of yeast. The results showed that about 35.5 g of bioethanol was obtained from each kilogram of watermelon. Investigating different variables shows that the fermenter agitator speed of 120 rpm and the yeast amount of 5 g could lead to the best yield in the process of fermenting watermelon waste for the purpose of producing bioethanol. The results of the evaluating the artificial neural network (ANN) model and adaptive neuro‐fuzzy inference system (ANFIS) in predicting bioethanol production from watermelon waste with the highest coefficient of determination (R2) were 0.9895 and 0.9993, respectively. These results indicate that ANNs and ANFIS are effective in predicting bioethanol production from watermelon waste. Practical Applications Advanced, green, clean, and sustainable processing technologies to reduce watermelon waste. Fermentation of agricultural waste in the shortest time (30 hr). Investigating important factors in waste fermentation process. The performance of ANN and ANFIS network in predicting bioethanol production.

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Section: Resultssupporting

confidence: 92%

Processing watermelon waste using Saccharomyces cerevisiae yeast and the fermentation method for bioethanol production

Jahanbakhshi

Salehi

2019

J Food Process Engineering

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“…As explained by Textor et al. (1998) and Apar and Ozbek (2004), this may be due to some sort of enzyme–enzyme interaction that reduces the ability of the active sites to bind effectively to the lactose particles. Also, the hydrolysis products that increased as a result of increased enzyme concentration may have stabilising effect on enzyme.…”

Section: Resultsmentioning

confidence: 99%

A Modelling study on skimmed milk lactose hydrolysis and β‐galactosidase stability using three reactor types

Akgül

Demırhan

Özbek

2012

Int J of Dairy Tech

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The hydrolysis of lactose in skimmed milk and β‐galactosidase inactivation studies were carried out in three different devices – bioreactor, sonicator and homogeniser – to evaluate the performance of such reactors that have different operational systems. The experiments were carried out using β‐galactosidase produced from Kluyveromyces marxianus lactis. At the optimum process conditions obtained from the experiments performed in bioreactor, sonicator and homogeniser, 89%, 90% and 54% of lactose were hydrolysed and the enzyme lost its activity by 14%, 13% and 24%, respectively, at the end of the processing time of 30 min. The commercial milk lactose content (1 g/L lactose) was reached at 60 min for bioreactor and sonicator. After evaluation of the data, it was found that the kinetics of hydrolysis and enzyme inactivation could be represented by a first‐order kinetic model and a single‐step non‐first‐order enzyme inactivation kinetic model, respectively, for all process conditions applied. The activation energy for hydrolysis reaction and the enzymatic inactivation energy values were also calculated.

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“…The target parameters were temperature (T), pH, enzyme dosage (enzyme to starch ratio; E o ) and reaction time (t rxn ), which are considered the most important factors influencing enzymatic starch hydrolysis [30,31]. The starch conversion (Y) was used as the response since it is a direct indicator of the reducing sugar content in the substrate.…”

Section: Enzymatic Hydrolysis Of Starchmentioning

confidence: 99%

Feasibility study on fermentative conversion of raw and hydrolyzed starch to hydrogen using anaerobic mixed microflora

Wang

Chang

2007

International Journal of Hydrogen Energy

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σ-Amylase inactivation during corn starch hydrolysis process

Cited by 40 publications

References 16 publications

Processing watermelon waste using Saccharomyces cerevisiae yeast and the fermentation method for bioethanol production

Processing watermelon waste using Saccharomyces cerevisiae yeast and the fermentation method for bioethanol production

A Modelling study on skimmed milk lactose hydrolysis and β‐galactosidase stability using three reactor types

Feasibility study on fermentative conversion of raw and hydrolyzed starch to hydrogen using anaerobic mixed microflora

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