Saccharification Fermentation and Process Integration

Sharma, Deepansh; Saini, Anita

doi:10.1007/978-981-15-4573-3_4

Cited by 6 publications

(2 citation statements)

References 110 publications

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“…This microorganism exhibits ethanol tolerance, higher glucose uptake, and efficient ethanol production capabilities [149,150]. It utilizes the Entner-Doudoroff pathway, employing enzymes such as pyruvate decarboxylase and alcohol dehydrogenase to convert glucose into ethanol [151]. Z. mobilis has a reported higher ethanol yield (97.0%) and productivity due to the production of less biomass and the maintenance of a higher rate of glucose metabolism through its ED pathway in contrast to S. cerevisiae, which achieves ethanol yields of only 90.0-93.0% [152].…”

Section: Fermentationmentioning

confidence: 99%

Comparative Review on the Production and Purification of Bioethanol from Biomass: A Focus on Corn

Assaf,

Mortada,

Rezzoug

et al. 2024

Processes

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In the contemporary era, conventional energy sources like oil, coal, and natural gas overwhelmingly contribute 89.6% to global CO2 emissions, intensifying environmental challenges. Recognizing the urgency of addressing climate concerns, a pivotal shift towards renewable energy, encompassing solar, wind, and biofuels, is crucial for bolstering environmental sustainability. Bioethanol, a globally predominant biofuel, offers a versatile solution, replacing gasoline or integrating into gasoline–ethanol blends while serving as a fundamental building block for various valuable compounds. This review investigates the dynamic landscape of biomass generations, drawing insightful comparisons between the first, second, third, and fourth generations. Amid the drive for sustainability, the deliberate focus on the initial generation of biomass, particularly corn, in bioethanol production is grounded in the current dependence on edible crops. The established utilization of first-generation biomass, exemplified by corn, underscores the necessity for a comprehensive examination of its advantages and challenges, allowing for a nuanced exploration of existing infrastructure and practices. To produce bioethanol from corn feedstock, various milling methods can be employed. Thus, this paper delves into a comparative assessment of dry-milling and wet-milling processes scrutinizing their efficiency, environmental impact, and economic feasibility.

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Section: Fermentationmentioning

confidence: 99%

Comparative Review on the Production and Purification of Bioethanol from Biomass: A Focus on Corn

Assaf,

Mortada,

Rezzoug

et al. 2024

Processes

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show abstract

“…Saccharification is the process by which complex carbohydrates (such as starch and cellulose) are enzymatically broken down into simple sugars. (such as glucose) 1 . Wheat and other raw materials undergo saccharification during fermentation, and the products thereof contribute to the flavor and properties of the resulting fermentation product during the brewing process 2,3 .…”

Section: Introductionmentioning

confidence: 99%

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Huang,

Tian,

Yang

et al. 2024

J Sci Food Agric

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BACKGROUNDWheat is one of the key ingredients used to make Chinese liquor, and its saccharification power and protein content directly affect the quality of the liquor. In pursuit of a non‐destructive assessment of wheat components and the optimization of raw material proportions in liquor, this study introduces a precise predictive model that integrates hyperspectral imaging (HSI) with stacked ensemble learning (SEL).RESULTSThis study extracted hyperspectral information from 14 different varieties of wheat and employed various algorithms for preprocessing. It was observed that multiplicative scatter correction (MSC) emerged as the most effective spectral preprocessing method. The feature wavelengths were extracted from the preprocessed spectral data using three different feature extraction methods. Then, single models (support vector machine (SVM), backpropagation neural network (BPNN), random forest (RF), and gradient boosting tree (XGBoost)) and a SEL model were developed to compare the prediction accuracies of the SEL model and the single models based on the full‐band spectral data and the characteristic wavelengths. The findings indicate that the MSC–competitive adaptive reweighted sampling–SEL model demonstrated the highest prediction accuracy, with Rp2 (test set‐determined coefficient) values of 0.9308 and 0.9939 for predicting the saccharification power and protein content and root mean square error of the test set values of 0.0081 U and 0.0116 g kg−1, respectively.CONCLUSIONThe predictive model established in this study, integrating HSI and SEL models, accurately detected wheat saccharification power and protein content. This validation underscores the practical potential of the SEL model and holds significant importance for non‐destructive component analysis of raw materials used in liquor. © 2024 Society of Chemical Industry.

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Fermentation of Rice Straw Hydrolyzates for Bioethanol Production and Increasing its Yield by Applying Random Physical and Chemical Mutagenesis

Ningthoujam,

Jangid,

Yadav

et al. 2024

Waste Biomass Valor

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Saccharification Fermentation and Process Integration

Cited by 6 publications

References 110 publications

Comparative Review on the Production and Purification of Bioethanol from Biomass: A Focus on Corn

Comparative Review on the Production and Purification of Bioethanol from Biomass: A Focus on Corn

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Fermentation of Rice Straw Hydrolyzates for Bioethanol Production and Increasing its Yield by Applying Random Physical and Chemical Mutagenesis

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