奥林达夏橙叶片锌含量可见近红外光谱监测模型研究

以奥林达夏橙叶片粉末干样为对象,利用化学分析与可见近红外光谱技术相结合的方法,通过样品原始光谱的二阶微分及消噪(Noise)处理,运用偏最小二乘法(PLS)与交叉验证方法建立的Zn含量数学模型,其中使用Zn含量特征光谱400～500nm和1201～1300nm的组合波段建模,具有较好的预测能力,校正建模和预测模型的相关系数分别为0.9975和0.9920,交互验证预测均方根误差为0.5868。因此,利用可见近红外光谱技术,运用PLS及交叉验证方法,建立叶片Zn含量与特征波段的光谱校正模型,能快速定量检测柑桔叶片Zn含量。

Research on zinc content in leaf of Olinda Valencia orange using visible near infrared spectroscopy model

Olinda valencia orange leaves dry powder-like were taken as sample, and chemical analysis combined with technology of visible near-infrared spectroscopy (Vis/NIRS) was used, through the treatment process of second derivative spectrum of samples of the original spectrum and denoising (Noise). Meanwhile, method of partial least squares (PLS) and cross-validation were used to establish maths model of Zn concentration which applying band combination composited by 400-500 and 1201-1300 nm of characteristic wavelength band. The coefficient of establishing models is 0.9975, while the coefficient of correlation coefficient of prediction is 0.9920. The root mean square error of prediction (RMSEP) of cross-validation is 0.5868. Therefore, the means using visible near-infrared spectroscopy (Vis/NIRS) and the methods of cross-validation and PLS to establish the spectral correction model reflecting the Zn content in leaves and characteristic wavelength bands can detect the Zn content in citrus leaves quantitatively and quickly.