Peng Xiaowei, Chen Hongzhang
Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080, People's Republic of China.
Bioresour Technol. 2008 Dec;99(18):8869-72. doi: 10.1016/j.biortech.2008.04.055. Epub 2008 Jun 4.
Calibration model using near-infrared reflectance spectroscopy (NIRS) for estimation of SCO content in solid-state fermented mass was established. The NIRS calibration model was derived by partial least-squares (PLS) regression and prediction of SCO contents of independent solid-state fermented mass samples fermented by different oleaginous fungi showed the model to be rapid and accurate, giving R(2)-value higher than 0.9552 and root mean standard error of prediction (RMSEP) value lower than 0.5772%. The established NIRS calibration model could be used to estimate the SCO contents of the solid-state fermented masses and will provide much convenience to the research of SCO production in solid-state fermentation.
建立了利用近红外反射光谱（NIRS）估算固态发酵物料中SCO含量的校准模型。通过偏最小二乘法（PLS）回归得出NIRS校准模型，对不同产油真菌发酵的独立固态发酵物料样品的SCO含量进行预测，结果表明该模型快速且准确，决定系数（R²）值高于0.9552，预测均方根误差（RMSEP）值低于0.5772%。所建立的NIRS校准模型可用于估算固态发酵物料中的SCO含量，将为固态发酵中SCO生产的研究提供很大便利。
