Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII

Dairy industry produces enormous amount of cheese whey compromising of major milk nutrients but remains unutilized all over the globe. The present study investigates the production of β-Cryptoxanthin (β-CRX) by Kocuria marina DAGII using cheese whey as substrate. Response surface methodology (RSM) and artificial neural network (ANN) was implemented to obtain the maximum β-CRX yield. Significant factors viz. yeast extract, peptone, cheese whey and initial pH were the input variables in both the optimizing studies and β-CRX yield and biomass were taken as output variables. The ANN topology of 4-9-2 was found to be optimum when trained with feed-forward back propagation algorithm. Experimental values of β-CRX yield (17.14 mg/L) and biomass (5.35 g/L) were compared and ANN predicted (16.99 mg/L and 5.33 g/L respectively) values were found to be more accurate compared to RSM predicted values (16.95 mg/L and 5.23 g/L respectively). Detailed kinetic analysis of cellular growth, substrate consu...

乳制品工业会产生大量干酪乳清，这类副产物富含主要乳源营养成分，但在全球范围内仍未得到充分利用。本研究以干酪乳清为底物，探究海洋库特氏菌（Kocuria marina）DAGII合成β-隐黄质（β-Cryptoxanthin, β-CRX）的过程。研究采用响应面法（Response Surface Methodology, RSM）与人工神经网络（Artificial Neural Network, ANN）以获取最高的β-隐黄质产量。本优化研究选取酵母提取物、蛋白胨、干酪乳清及初始pH这四个显著影响因素作为输入变量，以β-隐黄质产量与生物量作为输出变量。采用前馈反向传播算法（feed-forward back propagation algorithm）进行训练时，4-9-2结构的人工神经网络拓扑为最优拓扑。将β-隐黄质产量（17.14 mg/L）与生物量（5.35 g/L）的实验值，分别与人工神经网络预测值（依次为16.99 mg/L与5.33 g/L）及响应面法预测值（依次为16.95 mg/L与5.23 g/L）进行对比，结果显示人工神经网络的预测精度更优。针对细胞生长、底物消耗的详细动力学分析……