畜牧与饲料科学 ›› 2025, Vol. 46 ›› Issue (3): 48-57.doi: 10.12160/j.issn.1672-5190.2025.03.007

• 动物营养与饲料科学 • 上一篇    下一篇

低成本发酵制备饲用酿酒酵母SCL6的工艺优化及生物量提升研究

刘佩伶, 陈颖禧, 刘琳, 崔金明   

  1. 广东海大集团股份有限公司,广东 广州 511400
  • 收稿日期:2025-02-07 出版日期:2025-05-30 发布日期:2025-09-02
  • 通讯作者: 崔金明(1980—),男,高级工程师,博士,主要从事饲料研究工作。
  • 作者简介:刘佩伶(1998—),女,硕士,主要从事饲料研究工作。
  • 基金资助:
    广州市2024年度农村科技特派员专题(2024E04J1246)

Study on the Process Optimization and Biomass Enhancement of Low-cost Fermentation for the Production of Feed-grade Saccharomyces cerevisiae SCL6

LIU Peiling, CHEN Yingxi, LIU Lin, CUI Jinming   

  1. Guangdong Haid Group Co., Ltd., Guangzhou 511400, China
  • Received:2025-02-07 Online:2025-05-30 Published:2025-09-02

摘要: [目的]解决饲料级酿酒酵母生产成本高、生物量低的产业化难题。[方法]以本土低活性酿酒酵母SCL6为研究对象,开展基于YPD培养基的发酵工艺优化研究。采用两阶段工艺优化策略:首先通过单因素试验筛选关键培养基组分(碳源、氮源、无机盐),采用Plackett-Burman试验筛选对发酵过程影响显著的关键因素;随后运用Box-Behnken中心复合设计结合响应面法建立多因素交互作用模型,采用Design-Expert软件进行参数优化与模型验证。采用OD600 nm值结合湿重测量法检测生物量,通过方差分析确定有显著影响的关键因素,最终在3 L发酵罐中实施工艺验证,通过动态补料与参数调控实现过程优化。[结果]①因子显著性分析表明:碳源类型、培养温度和初始pH值对生物量积累具有极显著(P<0.01)影响,构成主要限制因子;氮源组成、无机盐浓度及接种量对生物量的影响未达显著(P>0.05)水平,属于非主要影响因子。②通过模型优化获得最佳工艺参数:碳源采用麦芽糖(50.46 g/L),复合氮源为玉米浆(20 g/L),微量元素配比为七水硫酸镁(4 g/L)、无水硫酸锌(0.1 g/L)、无水磷酸二氢钾(2 g/L),初始pH值为6.10,培养温度实施动态调控(29.52 ℃)。③工艺验证显示:在优化条件下进行摇瓶培养,SCL6菌株的OD600 nm 值为46.6,在3 L发酵罐中,SCL6菌株的OD600 nm值达到106.8,分别较初始摇瓶培养的OD600 nm值(20)提高了1.33倍和4.34倍。上罐发酵的酵母活菌数达到2.1×109 CFU/mL,符合GB/T 22547—2008饲料添加剂标准要求。④经济性评估表明:优化后工艺的吨培养液生产成本降低35.8%,其中碳源成本节约29.9%,能耗降低42.5%,有效解决了传统工艺中葡萄糖利用效率低、培养温度控制能耗高等问题。[结论]本研究建立的基于响应面法的两阶段发酵优化工艺,显著提高了本土酿酒酵母SCL6的工业化培养效率。通过精准识别关键限制因子并建立动态调控模型,实现了菌体生物量的指数级提升与生产成本的显著下降。

关键词: 酿酒酵母, 生物量, 响应面法, 饲料添加剂, 培养条件, 工艺优化

Abstract: [Objective] To address the industrial challenges of high production costs and low biomass in feed-grade Saccharomyces cerevisiae. [Methods] The indigenous low-activity strain SCL6 was selected as the research subject, and fermentation process optimization was conducted based on YPD medium. A two-stage optimization strategy was implemented. First, key medium components (carbon source, nitrogen source, inorganic salts) were screened through single-factor experiments, and the key factors affecting the fermentation process were screened by Plackett-Burman test. Subsequently, a Box-Behnken central composite design combined with response surface methodology (RSM) was employed to construct a mathematical model for multi-factor interactions. Parameter optimization and model validation were performed using Design-Expert software. Biomass was quantified via OD600 measurements coupled with wet weight determination. Key influential factors were identified via analysis of variance (ANOVA). Finally, the optimized process was conducted in a 3 L fermenter with dynamic feeding and parameter control. [Results] ①Factor significance analysis:carbon source type, cultivation temperature and initial pH exhibited highly significant (P<0.01) impacts on biomass accumulation, identifying them as primary limiting factors. Nitrogen composition, inorganic salt concentration and inoculum size showed no significant (P>0.05) effects. ②Optimized parameters:maltose (50.46 g/L) as carbon source, corn steep liquor (20 g/L) as complex nitrogen source, trace elements (MgSO4·7H2O 4 g/L, ZnSO4 0.1 g/L, KH2PO4 2 g/L), initial pH 6.10, and dynamic temperature control (29.52 ℃). ③Process validation:in 3 L fermenters, SCL6 achieved an OD600 nm of 106.8, cell wet weight of 150.2 g/L, representing 2.33-fold and 5.34-fold improvements over optimized flask culture (OD600 nm=46.6) and original flask culture (OD600 nm=20), respectively. The viable cell count reached 2.1×109 CFU/mL, complying with GB/T 22547—2008 feed additive standards. ④Economic evaluation:the optimized process reduced production costs by 35.8% per ton of culture, including 29.9% savings in carbon source expenditure and 42.5% reduction in energy consumption, effectively resolving inefficiencies in glucose utilization and excessive energy demand for temperature control in conventional processes. [Conclusion] The two-stage fermentation optimization process based on the response surface methodology established in this study has significantly improved the industrial cultivation efficiency of the indigenous Saccharomyces cerevisiae SCL6. By accurately identifying the key limiting factors and establishing a dynamic regulation model, an exponential increase in the biomass of the yeast cells and a significant reduction in the production cost have been achieved.

Key words: Saccharomyces cerevisiae, biomass, response surface methodology, feed additive, cultivation conditions, process optimization

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