本試驗擬以褐藻酸鈉作為微膠囊之囊壁材質,包覆原生菌元(Lactobacillus acidophilus、Lb. casei、Bifidobacterium longum及B. bifidum),並添加益菌質 (肽類、果寡糖、異麥芽寡糖) 於囊壁中,期望藉由反應曲面法 (Response Surface Methodology, RSM) 及最佳化方法探討褐藻酸鈉及益菌質之濃度對微膠囊原生菌之包覆菌數及耐酸性的影響,並尋求最佳囊壁材質之組合。試驗中以褐藻酸鈉、肽類、果寡糖及異麥芽寡糖之添加濃度為變因 (factor),而微膠囊之包覆原生菌數及經模擬胃液處理後存活菌數則為反應性狀 (response),以反應曲面法中Box Behnken Design之四因子三階次試驗設計,共得30個實驗組,因組數太多而分為3個區集 (block) 進行,實驗結果以Design-Expert軟體建立最適方程式,再經由遺傳演算法 (Genetic algorithm, GA)、序列二次規劃法(Sequential quadratic programming, SQP) 與反應曲面法中之陡升法 (Steepest ascent method) 尋求褐藻酸鈉及益菌質之最佳添加比例。 結果顯示,在貯存0週時褐藻酸鈉及異麥芽寡醣之最佳添加量隨肽類及果寡醣用量增加而減少,推測肽類及果寡醣有助微膠囊成形而降低褐藻酸鈉之需要量,而在貯存1、2週方面,褐藻酸鈉及異麥芽寡醣之推薦添加量則逐漸增加。以遺傳演算法及序列二次規劃法獲得之褐藻酸鈉及益菌質推薦添加量,於貯存第0、1、2週均相同,肽類及果寡醣均推薦添加3%,褐藻酸鈉及異麥芽寡醣之推薦添加量在貯存0週時分別為1%及0%、貯存1週時為3%及0%、貯存2週時為3%及3%;而陡升法之搜尋結果則與SQP及GA不同。將上述三種最適化方法之最適化推薦組以實驗進行驗證,結果顯示在未經過模擬胃液測試的菌數預測值,與實際實驗值間均無顯著差異 (P>0.05),表示三種最適化方法在此部分均達到最適化的效果。若經過模擬胃液測試,則只有遺傳演算法與序列二次規劃法對貯存0週之雙叉乳桿菌部分及陡升法對貯存1週之乳酸桿菌、雙叉乳桿菌部分的預測值,與實際實驗值之間沒有顯著差異,其餘均有顯著差異;但即使經過模擬胃液處理,各驗證組之存活菌數仍能維持7~8 log CFU/ml,其中貯存2週並經模擬胃液處理之雙叉乳桿菌存活菌數,甚至出現實際實驗值高於預測值之現象。 將最適化推薦組經12週貯存試驗後發現,原生菌微膠囊以蒸餾水貯存時,最適化推薦組之存活菌數下降程度顯著低於無添加益菌質之對照組,而經過模擬胃液及膽鹽測試後,亦顯示益菌質可能對微膠囊化原生菌受模擬胃液及膽鹽傷害的情形有所改善,且以冷凍乾燥貯存之微膠囊也具有相同情形。觀察顯微構造則發現,褐藻酸鈉濃度較高時,微膠囊顆粒較大而圓,表面構造較細緻,內部孔洞較小而少,整體結構較緻密。 綜觀上述結果,隨著貯存時間增加,增加褐藻酸鈉及益菌質的濃度有助於提升微膠囊結構之穩定、所包覆原生菌之存活菌數,以及原生菌對模擬腸胃道環境之耐受性。
The purpose of this research was to create a new probiotic microcapsule by using prebiotics and to attempt to apply modern optimization techniques to obtain optimal processing conditions and performance of the survival rate of probiotics. The prebiotics (peptides, fructooligosaccharides, or isomaltooligosaccharides) were incorporated with calcium alginate as wall materials to microencapsulate four probiotics (Lactobacillus acidophilus, L. casei, Bifidobacterium bifidum and B. longum). The proportion of prebiotics and calcium alginate was optimized using a response surface methodology (RSM) to build surface model first, sequential quadratic programming (SQP) and genetic algorithm (GA) were consequently used to optimize the model to evaluate the survival of microencapsulated probiotics under simulated gastro-intestinal conditions and during storage. Optimization results indicated both GA and SQP could be used to determine the optimal combinations of wall materials for probiotic microcapsules. An optimal survival rate for micro- encapsulated probiotics was obtained at the 61 function evaluations during the SQP calculation approximately, while the GA converged to the same optimal value at 1500 function evaluations. Comparing the optimization results, this study showed that SQP was more efficient than GA in finding the optimal survival rate. The final responses in a practical case provided a result that was close to the predicted values with no apparent significant difference between both sides (P>0.05). The storage results demonstrated that addition of prebiotics in the wall materials of probiotic microcapsules provided a better protection for probiotics. Raising the proportion of prebiotics in wall materials could increase the survival of microencapsulated probiotics in the simulated gastro-intestinal tract conditions. The probiotic counts still maintained 106-107 CFU/ml for one-month microcapsules treated in simulated gastro-intestinal tests. According to above results, incorporation of prebiotics with calcium alginate as wall materials could improve the survivability of probiotics during encapsulation, simulated gastro-intestinal conditions and storage. The current study also suggested that the two-stage effort of obtaining a surface model using RSM, and optimizing this model using SQP and GA techniques has been demonstrated to represent an effective approach. The SQP and GA all produced the optimal conditions with the SQP being the most efficient.