Detection of Camellia oleifera anthracnose based on THz combined with FT-NIR

Abstract

Camellia oleifera anthracnose is a prevalent and highly destructive disease in Camellia oleifera industry, seriously restricting its development. In light of the current problems of complex and inefficient detection of Camellia oleifera anthracnose, data fusion technology has been widely employed across various fields. Extensive research has demonstrated that data fusion enhances the detection accuracy and classification accuracy of the model. Consequently, a data fusion method combining terahertz spectroscopy (THz) and Fourier transform near-infrared (FT-NIR) spectroscopy was proposed to detect the level of Camellia oleifera anthracnose. Partial least squares discriminant analysis (PLS-DA) was utilized to establish the single-spectrum and fusion-spectrum anthracnose classification models of Camellia oleifera, enabling rapid, efficient, non-destructive, and highly precise determination of Camellia oleifera anthracnose. The single spectral model, THz-PLS-DA, exhibited a misjudgment rate of 7.5 % in the modeling set, and 17.5 % in the prediction set. In contrast, the low-level fusion model, THz-FT-NIR-PLS-DA, exhibited a misjudgment rate of 3.25 % in the modeling set and 0 in the prediction set. The intermediate fusion model, (THz-VIP)-(FT-NIR-VIP)-PLS-DA, showcased a modeling set misjudgment rate of 0.75 % and a prediction set misjudgment rate of 0. Importantly, the intermediate-level model exhibited superior qualitative analysis accuracy and stability compared to both the single model and the low-level fusion model. THz spectroscopy combined with FT-NIR spectroscopy can be used to nondestructively, rapidly and accurately differentiate healthy Camellia oleifera from different grades of anthracnose Camellia leaves.