炭疽病是油茶最常见的病害之一,因此对油茶炭疽病进行快速检测具有重要意义。提出了一种检测油茶叶片炭疽病的新方法,采用激光诱导击穿光谱检测技术,快速无损诊断正常和感染炭疽病油茶叶片中的Mn元素含量,根据火焰原子吸收光谱法对样品中Mn元素的真实含量进行了分析。分别采用平滑、去噪、归一化、基线校正、一阶求导降噪、二阶求导降噪对光谱数据进行预处理,使用偏最小二乘方法(PLS)建立定量模型,采用间隔偏最小二乘法(iPLS)对光谱数据进行波段筛选。最终结合7点平滑和一阶导数降噪进行预处理,根据iPLS建立定量模型。实验结果表明,平分为24个子区间时,第6个子区间的建模效果最佳,建模相关系数为0.9076,建模均方根误差为0.2090 μg/mg,预测相关系数为0.8947,预测均方根误差为0.2100 μg/mg。

Anthracnose is one of the most common diseases of camellia oleifera, so it is of great significance to quickly detect it. In this paper, a new method for the detection of anthracnose in camellia anthracnose leaves is proposed. First, laser induced breakdown spectroscopy is used to quickly and non-destructively diagnose the Mn element content in the leaves of normal and infected anthracnose camellia oleifera leaves. The true content of Mn element in the samples is analyzed by flame atomic absorption spectrometry. Second, different pretreatment methods, such as smoothing, denoising, normalization, baseline correction, first-order derivation noise reduction, and second-order derivation noise reduction, are used to preprocess the spectral data. Partial least squares (PLS) method is used to establish a quantitative model. Interval partial least-squares regression (iPLS) method is utilized to filter the spectral data. Finally, combined with 7-point smoothing and first derivative noise reduction, the quantitative model is established based on iPLS. The results show that the modeling effect of the sixth subinterval is the best when it is divided into 24 subintervals, the modeling correlation coefficient is 0.9076, the modeling root mean square error is 0.2090 μg/mg, the prediction correlation coefficient is 0.8947, and the prediction root mean square error is 0.2100 μg/mg.