基于中红外和远红外数据融合的油茶籽成熟度鉴别

Maturity Identification of Camellia Seeds Based on Mid-and Far-Infrared Data Fusion

针对油茶籽采收过程中缺乏判断其成熟度的依据,导致茶油的质量和产量不佳等问题,提出一种基于中红外和远红外光谱数据融合检测油茶籽成熟度的方法。采用傅里叶变换红外光谱仪测试了在不同成熟阶段,不同含油率油茶籽的中红外和远红外光谱数据,利用不同特征提取方法(主成分分析法、连续投影算法、无信息变量消除法)对原始光谱数据进行特征提取,再结合支持向量机算法(SVM)建立了油茶籽成熟度的鉴别模型。结果表明:在中红外波段范围内,采用连续投影算法结合遗传算法优化的SVM模型,获得最优的鉴别精度为93.33%;在远红外波段范围内,利用主成分分析法结合遗传算法优化的SVM模型,实现了96.67%的鉴别精度。将中红外波段数据与远红外波段进行数据融合,结合优化后的SVM算法能将鉴别精度提高到100%。该研究表明,红外光谱技术结合优化后的SVM模型可以实现对油茶籽含油率的精确鉴别,数据融合技术能够有效地增加光谱信息并且去除单一光谱的冗余信息。该结果可为油茶的最佳采摘时间提供参考,并可拓展应用到其他农林产品成熟度的鉴别中。

To solve the problems of poor quality and yield of camellia oil owing to the lack of basis for determining the maturity of camellia seeds during the process of harvesting,a method for detecting the maturity of camellia seeds based on mid-and farinfrared spectral data fusion was proposed herein.A Fourier transform infrared spectrometer was used to test the mid-and farinfrared spectroscopy data of camellia seeds with different oil contents at different maturity stages.Various feature extraction methods(principal component analysis,successive projection algorithm,and noninformation variable elimination method)were used to extract the original spectral data,and the methods were combined with a support vector machine algorithm(SVM)to develop a model for identifying the maturity of camellia seeds.The results show that the best discrimination accuracy in the midinfrared band is 93.33%when using the successive projection algorithm combined with the genetic algorithm to optimize the SVM model.In the farinfrared band,nine variables extracted using the principal component analysis are used as input variables,and when combined with the SVM model optimized by the genetic algorithm,the identification accuracy of 96.67%is attained.The identification model of camellia seed maturity is established using the SVM algorithm after parameter optimization.The experimental results show that the accuracy of intermediate data fusion combined with the optimized SVM algorithm can reach 100%.The results of this study show that when combined with an improved SVM model,the infrared spectroscopy can accurately determine the oil content of camellia seeds.Data fusion can effectively increase the spectral information and remove redundant information from a single spectrum.The results can provide a reference to determine the best picking time of camellia and can be extended to determine the maturity of other agricultural and forestry products.