香根草叶片铅含量的近红外光谱快速检测

提出了一种应用近红外光谱技术快速检测香根草叶内重金属铅含量的方法,采用多种预处理方法建立偏最小二乘法(PLS)模型并对建模效果对比分析,得出最优预处理方法。结合不同波段选择方法优化PLS模型参数,建立了香根草叶内重金属铅含量定量分析模型,预测决定系数R2为0.87,预测均方根误差RMSEP为0.18。研究结果表明,利用近红外光谱技术快速定量检测香根草叶内重金属铅含量具有可行性。

Fast Detection of Heavy Metal Lead (Pb) in Vetiver Grass Leaves Using Near Infrared Spectroscopy

Heavy mental ionsin plants always have complex chelation with the organic molecular groups that have the near-infrared spectral (NIRS) absorptions. Therefore heavy mental ions in plants can be indirectly detected by using INRS technique basing on the chelation. An application of near infrared spectral technology fast detecting heavy metal lead (Pb) in vetiver grass leaves was analyzed. Combined with partial least squares (PLS), different preprocessing methods including smoothness, standard normal variate, baseline correction, multiplicative scatter correction, first derivative and second derivative were compared, model parameters were optimized by different wavelength selection methods including genetic algorithm, interval partial least square and successive projections algorithm, established the fast detection models of heavy metal Pb in vetiver grass leaves. The results showed that the external validation determination coefficient (R2) and root mean square error of prediction (RMSEP) was 0.87 and 0.18 separately. The study shows that the fast detection of heavy metal Pb in vetiver grass leaves using INRS technique is feasible.