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中國精品科技期刊2020

基于徑向基人工神經網絡遺傳算法的長壽老人源益生菌微膠囊工藝探索及其特性測評

李銳定 王文萱 張雨荷 周樊 鄭文軒 張欽任 孟寧 時鳳翠 李全陽

李銳定,王文萱,張雨荷,等. 基于徑向基人工神經網絡遺傳算法的長壽老人源益生菌微膠囊工藝探索及其特性測評[J]. 食品工業科技,2022,43(16):119?129. doi:  10.13386/j.issn1002-0306.2021110155
引用本文: 李銳定,王文萱,張雨荷,等. 基于徑向基人工神經網絡遺傳算法的長壽老人源益生菌微膠囊工藝探索及其特性測評[J]. 食品工業科技,2022,43(16):119?129. doi:  10.13386/j.issn1002-0306.2021110155
LI Ruiding, WANG Wenxuan, ZHANG Yuhe, et al. Exploration and Performance Evaluation of Probiotic Microcapsule Technology for Longevity Elderly Based on RBF-GA[J]. Science and Technology of Food Industry, 2022, 43(16): 119?129. (in Chinese with English abstract). doi:  10.13386/j.issn1002-0306.2021110155
Citation: LI Ruiding, WANG Wenxuan, ZHANG Yuhe, et al. Exploration and Performance Evaluation of Probiotic Microcapsule Technology for Longevity Elderly Based on RBF-GA[J]. Science and Technology of Food Industry, 2022, 43(16): 119?129. (in Chinese with English abstract). doi:  10.13386/j.issn1002-0306.2021110155

基于徑向基人工神經網絡遺傳算法的長壽老人源益生菌微膠囊工藝探索及其特性測評

doi: 10.13386/j.issn1002-0306.2021110155
基金項目: 廣西研究生教育創新計劃資助項目(YCSW2021014);國家自然科學基金(31871802)。
詳細信息
    作者簡介:

    李銳定(1997?),男,碩士研究生,研究方向:發酵工程,E-mail:2016301014@st.gxu.edu.cn

    通訊作者:

    李全陽(1964?),男,博士,教授,研究方向:膳食營養與健康長壽,E-mail:liquanyang@gxu.edu.cn

  • 中圖分類號: TS201.3

Exploration and Performance Evaluation of Probiotic Microcapsule Technology for Longevity Elderly Based on RBF-GA

  • 摘要: 益生菌產品由于在儲存或在胃腸道中活性會產生一定程度的降低,導致它們潛在的益生特性不能很好地發揮,因此,本研究對源自廣西巴馬百歲老人的發酵乳桿菌LTP1332進行了微膠囊化研究。在海藻酸鈉(Sodium alginate,SA)和氯化鈣為主要壁材的基礎上,引入明膠(Gelatin,GEL)和殼聚糖(Chitosan,CS)進行復合,通過單因素實驗尋找影響包埋率的關鍵因素,利用響應面設計(Box-Behnken Design,BBD)構建徑向人工神經網絡模型(Radial Basis Function,RBF),并借助遺傳算法(Genetic Algorithms,GA)對其包埋工藝進行尋優。利用SEM掃描電鏡等方法對優化后的膠囊進行表征,并進行體外模擬消化試驗。結果表明,微膠囊的最佳制備工藝參數為:2.210%海藻酸鈉,4.451% CaCl2,0.1%明膠,13.529 min固化時間,在該條件下所制樣品的平均包埋率為95.08%±0.25%。優化工藝條件下的CS-GEL-SA-微膠囊經模擬胃液處理120 min后,菌體存活率仍可達22.48%±0.78%;在模擬腸液消化90 min時,菌體釋放率即可達到最大值,具有較好的腸溶性。綜上,該工藝條件下制備的長壽老人源益生菌微膠囊具有較好的護菌效果和開發應用前景。
  • 圖  1  RBF神經網絡實現過程

    Figure  1.  Implementation process of RBF neural network

    圖  2  RBF-GA尋優的基本流程

    Figure  2.  Basic flow of RBF-GA

    圖  3  固化劑濃度對LTP1332微膠囊包埋率的影響

    Figure  3.  Effect of curing agent concentration on the encapsulation rate of LTP1332 microcapsules

    注:不同小寫字母表示各組間差異顯著(P<0.05);圖4~圖5、圖15同。

    圖  4  固化劑時間對LTP1332微膠囊包埋率的影響

    Figure  4.  Effect of curing time on the encapsulation rate of LTP1332 microcapsules

    圖  5  海藻酸鈉濃度對LTP1332微膠囊包埋率的影響

    Figure  5.  Effect of sodium alginate concentration on the encapsulation rate of LTP1332 microcapsules

    圖  6  明膠濃度對LTP1332微膠囊包埋率的影響

    Figure  6.  Effect of gelatin concentration on the encapsulation rate of LTP1332 microcapsules

    注:“**”表示差異極顯著(P<0.01)。

    圖  7  各因素交互作用對包埋率的響應曲面

    Figure  7.  Response surface of interaction of various factors to embedding rate

    注:A:固化時間與固化劑濃度;B:海藻酸鈉濃度與固化時間;C:海藻酸鈉濃度與固化劑濃度。

    圖  8  RBF模型的訓練樣本和測試樣本中的實際值和預測值對比

    Figure  8.  Comparison of actual and predicted values in training samples and test samples of RBF model

    圖  9  RBF-GA模型60次迭代尋優結果

    Figure  9.  Result of optimization with 60 iterations of the RBF-GA model

    圖  10  對比包埋率的實測值和預測值

    Figure  10.  Comparison of measured value and predicted value of embedding rate

    注:A:BBD模型;B:RBF-GA模型。

    圖  11  不同模型RMSE和MAE比較

    Figure  11.  Comparison of RMSE and MAE of different models

    圖  12  發酵乳桿菌LTP1332微膠囊效果圖

    Figure  12.  Effect diagram of Lactobacillus fermentum LTP1332 microcapsule

    注:A:濕膠囊;B:干膠囊。

    圖  13  發酵乳桿菌LTP1332微膠囊掃描電鏡圖

    Figure  13.  SEM of the Lactobacillus fermentum LTP1332 microcapsule

    注:A:280倍,300 μm;B:830倍,100 μm;C:2600倍,20 μm;D:4700倍,10 μm。

    圖  14  胃液耐受性模擬評估試驗結果

    Figure  14.  Gastric fluid tolerance simulation assessment test results

    注:同一時間點不同小寫字母表示均值差異顯著(P<0.05)。

    圖  15  腸液釋放性模擬評估試驗結果

    Figure  15.  Results of the simulated evaluation test for the release of intestinal fluid

    注:A:活菌數變化;B:存活率變化。

    表  1  響應面因素與水平編碼設計

    Table  1.   Factors and levels used in Box-Behnken design

    因素水平
    ?101
    A固化劑濃度(%)3.33.94.5
    B固化時間(min)101520
    C海藻酸鈉濃度(%)1.522.5
    下載: 導出CSV

    表  2  響應面試驗設計方案及結果

    Table  2.   Design scheme and results of response surface test

    實驗號ABCY包埋率(%)
    100097.64
    210195.45
    3?1?1095.48
    40?1?192.67
    510?194.33
    6?11095.00
    701?193.01
    800098.36
    900097.13
    10?10?191.94
    1100098.09
    120?1196.94
    1311093.72
    1401194.67
    1500098.38
    16?10197.07
    171?1094.80
    下載: 導出CSV

    表  3  以包埋率為響應值的回歸模型及方差分析

    Table  3.   Regression model and analysis of variance with embedding rate as response value

    方差來源平方和自由度均方FP顯著性
    模型65.6397.2924.330.0002**
    A0.1810.180.590.4673NS
    B1.5211.525.080.0589NS
    C18.54118.5461.870.0001**
    AB0.0910.090.300.6007NS
    AC4.0214.0213.410.0080**
    BC1.7011.705.680.0486*
    A28.2218.2227.440.0012**
    B213.23113.2344.130.0003**
    C214.02114.0246.790.0002**
    失擬項0.9630.321.130.4377NS
    R20.9690R2adj0.9292CV0.57
    注:“*”表示差異顯著(P<0.05),“**”表示差異極顯著(P<0.01),NS表示差異不顯著(P>0.05)。
    下載: 導出CSV

    表  4  BBD和RBF-GA模型的預測值

    Table  4.   Predicted values of BBD and RBF-GA models

    實驗號實測值(%)BBD預測值(%)RBF-GA預測值(%)
    197.6497.9297.92
    295.4595.0795.45
    395.4895.1995.48
    492.6792.5892.67
    594.3394.0394.33
    695.0094.6195.00
    793.0193.0293.01
    898.3697.9297.92
    997.1397.9297.92
    1091.9492.3291.94
    1198.0997.9297.92
    1296.9496.9396.94
    1393.7294.0293.72
    1494.6794.7694.67
    1598.3897.9297.92
    1697.0797.3797.07
    1794.8095.2094.80
    下載: 導出CSV

    表  5  RBF-GA模型與響應面優化模型下的預測值和實際值比較

    Table  5.   Comparison of optimization results of RBF-GA and BBD for protectants

    優化模型最優條件預測包埋率(%)實測平均包埋率(%)
    固化劑濃度(%)固化時間(min)海藻酸鈉濃度(%)
    RBF-GA4.45113.5292.21094.91995.08±0.25
    BBD3.40119.3241.69693.72094.32±0.42
    下載: 導出CSV
    人妻AV无码系列一区二区三区
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  • 收稿日期:  2021-11-15
  • 網絡出版日期:  2022-08-11
  • 刊出日期:  2022-08-11

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