基于近红外光谱技术建立橡胶草根部菊糖和总糖含量快速检测方法

A rapid determination method for inulin and total sugar content in araxacum kok-saghyz Rodin based on near-infrared spectroscopy

  • 摘要: 【目的】 基于近红外光谱技术建立橡胶草根部菊糖和总糖含量绿色、低成本的快速检测技术。 【方法】 以103个橡胶草干根粉末样品作为研究对象,使用福斯公司的FOSS NIRSTM DS2500F SR近红外光谱仪(850~2500nm)采集样品光谱,使用酶标仪法检测样品根部的总糖、还原糖含量,并使用总糖含量减去还原糖含量计算出菊糖含量,采用SPXY算法划分训练集和验证集。用移动窗口平滑(MWS)、标准化(SNV)、多元散射校正(MSC)和一阶导数(FD)算法对原始光谱进行预处理,使用竞争性自适应重加权采样法(CARS)筛选波长,建立橡胶草根部菊糖和总糖含量的PLS预测模型。 【结果】 检测样品集总糖、还原糖和菊糖含量的区间分别为9.79%~51.85%、3.11%~8.98%和6.41%~45.35%,变异系数分别为34.18%、32.61%和39.46%。使用SPXY算法将校正集和验证集按照4∶1的比例划分,总糖和菊糖的校正集、验证集的个数均为82和21个。使用划分后的样品集进行模型建立,最优的菊糖含量PLS预测模型使用的光谱预处理方法为MWS-SNV-CARS,其验证集相关系数Rv达到了0.942,验证集均方根误差RMSEv为2.515,验证集相对分析误差RPDv为2.977;最优的总糖含量PLS预测模型使用的光谱预处理方为MWS-SNV-CARS,其Rv达到了0.949,RMSEv为2.490,RPDv为3.175。2个模型的RPDv均大于2.5,性能较佳。 【结论】 使用近红外光谱技术能够实现对橡胶草根部菊糖和总糖的绿色、低成本的快速定量检测。

     

    Abstract: 【Objective】 This study aims to develop a green, low-cost, rapid detection technique for inulin and total sugar content in the roots of Taraxacum kok-saghyz Rodin(TKS) using near-infrared (NIR) spectroscopy, thereby advancing research in cultivation and breeding of TKS. 【Methods】 This study involved 103 dry root powder samples of TKS (Rubber Grass). Spectra were collected using the FOSS NIRSTM DS2500F SR near-infrared spectrometer (wavelength range 850-2500 nm) by Foss Company. Enzyme labeling method was employed to measure the total sugar and reducing sugar content in the root samples and the inulin content was calculated by subtracting the reducing sugar content from the total sugar content. The SPXY algorithm was utilized to divide the samples into training and validation sets. Pretreatments on the raw spectral data included Moving Window Smoothing (MWS), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and First Derivative (FD). Competitive Adaptive Reweighted Sampling (CARS) was applied for wavelength selection, followed by establishing Partial Least Squares (PLS) prediction models for inulin and total sugar content in TKS roots. 【Results】 The content range of total sugar, reducing sugar, and inulin in the sample set was 9.79%-51.85%, 3.11%-8.98%, and 6.41%-45.35%, respectively, with variation coefficients of 34.18%, 32.61%, and 39.46%. The samples were divided into calibration and validation sets at a ratio of 4∶1 using the SPXY algorithm, with each set containing 82 and 21 samples for both total sugar and inulin. The optimal PLS prediction model for inulin content utilized MWS-SNV-CARS preprocessing, achieving a validation set correlation coefficient (Rv) of 0.942, a root mean square error (RMSEv) of 2.515, and a relative performance deviation (RPDv) of 2.977. For total sugar, the best PLS model also employed MWS-SNV-CARS preprocessing, reaching an Rv of 0.949, RMSEv of 2.490, and RPDv of 3.175. Both models exhibited RPDv values exceeding 2.5, indicating good performance. 【Conclusion】 NIR spectroscopy proves to be an effective method for the green, low-cost, and rapid quantitative determination of inulin and total sugar contents in the roots of TKS.

     

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