FT-NIR光谱法定量分析烟草薄片中5种化学成分

FT-NIR Spectroscopic Determination of Five Key Chemical Components in Tobacco Sheets

为了研究近红外光谱分析技术预测烟草薄片中的总糖、总氮、烟碱、钾和氯含量的可行性,本文以250个具有代表性的烟草薄片样品的近红外漫反射光谱数据和它们相对应化学测定数据为基础,通过偏最小二乘回归法(PLS)建立了以上5种成分的近红外分析模型,并对模型的预测效果进行了评价。结果表明:近红外预测值与化学测定值之间具有很好的相关性,各模型均具有较好地预测准确性。总糖实际预测的平均相对误差为2.89%,总氮、烟碱、钾和氯模型实际预测的平均误差分别为0.10%,0.07%,0.19%和0.09%。各模型的预测重现性,即相对标准偏差(RSD)均小于4%。因此,可以用近红外光谱法快速、简便和大批量地分析烟草薄片中的以上5种化学成分。

In order to test the prediction feasibility of total sugar, total nitrogen, nicotine, potassium and chlorine content in tobacco sheet, predictive models were established by partial least square （PLS） technique, based on the data of NIR diffuse reflective spectrum and determined chemical results of 250 tobacco sheet samples. Establishing parameters and the predictive effects of the models were evaluated in this study as well. Results showed that there was a remarkable correlation between predictive and chemical determinative values and all models present a good predictive veracity. The actual predictive mean relative error was 2.89% for total sugar model, and mean errors were 0. 10%, 0.07%, 0. 19% and 0.09% for total nitrogen, nicotine, potassium and chlorine model respectively. The relative standard deviations （RSD） were less than 4% for all the models. Accordingly, the analysis of total sugar, total nitrogen, nicotine, potassium and chlorine could be substituted by NIR spectroscopy, and could meet the need of rapid, convenient and mass analysis of the tobacco sheet samples.