胡瑞萍, 丁贤, 李俊伟, 段亚飞, 李育仁, 伍文超, 徐宁. 多指标综合加权分析法优化固态发酵豆粕工艺[J]. 农业工程学报, 2019, 35(12): 304-312. DOI: 10.11975/j.issn.1002-6819.2019.12.037
    引用本文: 胡瑞萍, 丁贤, 李俊伟, 段亚飞, 李育仁, 伍文超, 徐宁. 多指标综合加权分析法优化固态发酵豆粕工艺[J]. 农业工程学报, 2019, 35(12): 304-312. DOI: 10.11975/j.issn.1002-6819.2019.12.037
    Hu Ruiping, Ding Xian, Li Junwei, Duan Yafei, Li Yuren, Wu Wenchao, Xu Ning. Optimization of solid state fermentation of soybean meal by multi-index comprehensive weighted score evaluation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(12): 304-312. DOI: 10.11975/j.issn.1002-6819.2019.12.037
    Citation: Hu Ruiping, Ding Xian, Li Junwei, Duan Yafei, Li Yuren, Wu Wenchao, Xu Ning. Optimization of solid state fermentation of soybean meal by multi-index comprehensive weighted score evaluation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(12): 304-312. DOI: 10.11975/j.issn.1002-6819.2019.12.037

    多指标综合加权分析法优化固态发酵豆粕工艺

    Optimization of solid state fermentation of soybean meal by multi-index comprehensive weighted score evaluation

    • 摘要: 运用正交设计L9(34)与多指标综合加权分析方法优化3种益生复合菌发酵豆粕的工艺,采用接种量(A)、环境温度(B)、料水比(C)、发酵菌种(D)为处理因素,以还原糖、乳酸、氨基酸含量等作为综合评价指标,利用Minitab 17软件以及多指标试验公式法进行加权数据处理,优化豆粕的固体发酵工艺参数。结果显示,1)经过加权分析,中性蛋白酶权重系数最高,其次为还原糖(1.719)和乳酸(1.590),粗脂肪权重系数最低。说明本试验中性蛋白酶、还原糖以及乳酸对发酵豆粕的品质影响较大。2)通过综合评分公式对9个处理组数据分析得到:T9组综合评分最高(0.986 3),T1组(0.965 4)和T8组(0.962 6)次之,表明在T9组合(A3B3C2D1)条件下发酵豆粕,所选8项考察指标综合水平达到最高。因此拟选T9组合为最佳发酵工艺组合;3)均值回应表显示影响豆粕发酵工艺的因素依次为:发酵菌种>料水比>环境温度>接种量,其中发酵菌种和料水比为显著影响因素(P<0.05),而接种量和环境温度对试验结果影响不显著。从资源节约以及生产实际角度考虑,将T9组发酵工艺A3B3C2D1优化为A1B2C2D1,即接种质量分数1%,环境温度30℃,料水比质量为2∶1,发酵菌种配比为1∶1∶1。4)Minitab17软件对优选工艺A1B2C2D1进行预测,结果显示优选工艺综合评分高于拟选工艺A3B3C2D1综合评分(0.986 3)。验证试验得出各指标产出与预期结果相符,表明该优化工艺合理、可行,各指标产出率较高,为豆粕发酵工艺的确定提供了参考依据。

       

      Abstract: Abstract: Three kinds of probiotics (Bacillus subtilis NHS1, Lactic acid bacteria NHS03 and Marine red yeast NHS05) were used to optimize the solid state fermentation of soybean meal process by orthogonal design method L9 (34) combined with the comprehensive weighted evaluation method. The effects of inoculum size (A), environmental temperature (B), water feed ratio (C) and fermentation strains (D) on the protein content, lactic acid content, reducing sugar content and some other indicators were detected. Taguchi design and analysis were carried out by using Minitab 17, and weighted data processing was conducted by using multi-index experimental formula method. The results as follows: 1) Based on the weighted analysis, the weight coefficient of neutral protease was the greatest, reaching 1.773, followed by reducing sugar (1.719), lactic acid (1.590) and crude fat (the lowest). The results showed that neutral protease, reducing sugar and lactic acid had great influence on the quality of fermented soybean meal. 2) The date of the processing groups were analyzed according to the indicators comprehensive score formula Pi=Σ(Fj×Dij)/ΣF, and T9 had the greaest overall score, followed by T1 group and T8 group. The results indicated that the comprehensive level of 8 selected indeices reached the highest in the fermentation process of soybean meal in the condition of T9 combination (A3B3C2D1). Therefore, T9 was chosen as the optimal fermentation process. 3) The order of the importance of fermentation parameters for soybean meal based on the mean response table was fermentation strains > water feed ratio > environmental temperature > inoculum size. Fermentation strains and the ratio of water to feed were the main impact factors (P<0.05), while the mean response table for collogation score of the orthogonal test were not significant affected by environmental temperature and inoculum size. Considering the resource saving and production practice, the inoculation quantity of probiotics should be less collection and the environmental temperature should be lower (30℃). Therefore, the fermentation process A3B3C2D1 was optimized to be A1B2C2D1 in the present study, that means: the inoculum size of probiotics was 1%, environmental temperature was 30℃, the ratio of material to water was 2:1, and the optimal combination of Bacillus subtilis (NHS1), Lactic acid bacteria (NHS03) and Marine red yeast (NHS05) was 1:1:1. 4) Minitab17 was used to predict the optimal process A1B2C2D1, and the predicted results showed that the mean value was 0.993 3, and it was higher than the comprehensive score of the combination of A3B3C2D1 (0.986 3). Through experimental verification, the output of each impact indicator was consistent with the expected results, and the comprehensive score is 0.992 0, which was consistent with the predicted results. Moreover, all the technical indexes of fermented soybean meal were superior to the industry standard. The content of antinutrient factors were reduced greatly in the present study, and the content of amino acid showed an increasing trendency. The optimized process is stable, reasonable and efficient based on the above results, and it can be recommended for the fermentation process of soybean meal.

       

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