Spectral Pretreatment and Wavelength Selection for Soluble Solids Content in Gannan Navel Orange
Spectral Pretreatment and Wavelength Selection for Soluble Solids Content in Gannan Navel Orange
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DOI：
10.1166/sl.2010.1216
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发表时间：
2010-02
期刊：
影响因子：
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通讯作者：
Xudong Sun;Hailiang Zhang;Zhiyuan Gong;Aiguo Ouyang;Jianmin Zhou;Yande Liu
中科院分区：
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作者：
Xudong Sun;Hailiang Zhang;Zhiyuan Gong;Aiguo Ouyang;Jianmin Zhou;Yande Liu
研究了利用可见光和近红外（Vis-NIR）光谱与光纤技术快速测量完整赣南脐橙中可溶性固形物含量（SSC）的可行性。基于不同的光谱预处理和波长选择方法，采用偏最小二乘法（PLS）和多元线性回归（MLR）方法建立了赣南脐橙SSC的Vis-NIR光谱与实验室测量之间的关系。在本研究中，基于乘性散射校正（MSC）的模型表现略好于其他光谱预处理的模型。在与全光谱模型的比较中，结合敏感波长选择的MLR方法和结合光谱区间区域的PLS方法可以获得更好的校准模型。 MLR模型的SECV和rcv分别为0.620Brix和0.85，而iPLS模型的SECV和rcv分别为0.580Brix和0.87。相同的预测结果（SEP = 0.64，rp = 0.85，RSE％= 4.87％）表明MLR方法结合敏感波长选择和带有光谱间隔区域的PLS方法可以用于优化校准模型。更重要的是，MLR和iPLS可能会导致未来的应用变得简单。
The feasibility of using visible and near-infrared (Vis-NIR) spectroscopy with the fiber optic technique for a rapid measurement of soluble solids content (SSC) in intact Gannan navel orange was investigated. The relationships between Vis-NIR spectra and laboratory measurements of SSC in Gannan navel orange were developed by using partial least squares (PLS) and multiple linear regression (MLR) methods, based on different spectral pretreatments and wavelength selection methods. In this study, the model based on multiplicative scatter correction (MSC) performed slightly better than those of other spectral pretreatments. In the comparison with the full spectrum model, the MLR method combined with sensitive wavelength selection and PLS method with spectral interval regions could obtain better calibration models. The SECV and rcv of the MLR model were 0.620Brix and 0.85, while those for iPLS model were 0.580Brix and 0.87. The same prediction results (SEP= 0.64, rp = 0.85, RSE%= 4.87%) showed that MLR method combined with sensitive wavelength selection and PLS method with spectral interval regions could be used for optimizing the calibration models. More importantly, MLR and iPLS may lead to simple future application.