近红外光谱在虫草菌丝体腺苷和多糖含量检测中的应用

目的 利用近红外光谱技术建立蝙蝠拟青霉菌丝体中腺苷和多糖含量的定量分析模型。方法 采用常规方法对蝙蝠蛾拟青霉菌丝体中腺苷和多糖含量进行测定, 利用近红外光谱技术建立测定菌丝体中腺苷和多糖含量的相关模型, 并通过蒙特卡罗偏最小二乘法(Monte Carlo Partial Least Square, MCPLS)和可移动窗口偏最小二乘法(Moving Window Partial Least Square, MWPLS)对模型进行优化。结果 该模型校正集预测值和真实值间的相关系数(Rc)分别为0.9400和0.8781, 预测均方根误差(Root Mean Square Error of Prediction Set, RMSEP)分别为0.5949和1.6617, 校正均方根误差(Root Mean Square Error of Calibration Set, RMSEC)分别为0.5844和1.5572。结论 该模型的稳健性、拟合度和预测能力均能达到令人满意的程度, 该方法可以推广应用到其他发酵产品的检测。

The application of near-infrared spectroscopy in detection of adenosine and polysaccharide of cordyceps mycelium

Objective Near-infrared spectroscopy was used to quantify the adenosine and polysaccharide content in Paecilamyes hepiali mycelia. Methods Quantification of adenosine and polysaccharide content were achieved by the conventional method. Models were established by near-infrared spectroscopy for detection of the contents of adenosine and polysaccharide. Monte carlo partial least square (MCPLS) and moving window partial least square (MWPLS) were applied to optimize the models. Results The related coefficient between actual and predictive values of calibration samples (Rc) were 0.9400 and 0.8781, the root mean square error of prediction set (RMSEP) were 0.5949 and 1.6617, and the root mean square error of calibration set (RMSEC) were 0.5844 and 1.5572, respectively. Conclusion The robustness, fitness and predictive capability of these models were satisfied, and it can be applied to other fermentation processes.