Lu Hui-Shan, Fu Xia-Ping, Xie Li-Juan, Ying Yi-Bin
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2007 Sep;27(9):1727-30.
Visible/Near-infrared (Vis/NIR) spectroscopy has become a very popular technique for the non-invasive assessment of intact fruit. The feasibility of using Vis/NIR spectroscopic technology for rapid quantifying soluble solids content (SSC) of citrus fruit was investigated by means of spectral transmittance mode. A total of 110 citrus fruit samples were used to develop the calibration and prediction models. The relationship between actual SSC and Vis/NIRS spectra of citrus fruit samples was analyzed via pricipal component regression (PCR) and partial least squares (PLS) regression method using TQ 6.2 spectral analysis software. Models based on the different spectral pre-processing methods were compared in the present research. Performance of different models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r2) of prediction set of samples. The best predictive models feature a RMSEP of 0.538% and correlation coefficient (r2) of 0.801 for SSC. The results show that the Vis/NIR transmittance technique is a feasible, accurate and fast method for non-invasive estimation of citrus fruit SSC.
可见/近红外(Vis/NIR)光谱技术已成为用于完整水果无创评估的一种非常流行的技术。通过光谱透射模式研究了使用Vis/NIR光谱技术快速定量柑橘类水果可溶性固形物含量(SSC)的可行性。总共使用了110个柑橘类水果样本建立校准和预测模型。使用TQ 6.2光谱分析软件,通过主成分回归(PCR)和偏最小二乘法(PLS)回归方法分析了柑橘类水果样本实际SSC与Vis/NIRS光谱之间的关系。本研究比较了基于不同光谱预处理方法的模型。根据预测样本集的预测均方根误差(RMSEP)和相关系数(r2)评估不同模型的性能。对于SSC,最佳预测模型的RMSEP为0.538%,相关系数(r2)为0.801。结果表明,Vis/NIR透射技术是一种用于无创估计柑橘类水果SSC的可行、准确且快速的方法。