Lignin plays an important role in the formation of stone cells in pears. However, the accumulation of lignin had adverse impact on the flavor and quality of the fruit. A rapid and accurate method for measuring the lignin content of pears is therefore required. An improved variables selection method called ‘the bootstrapping soft shrinkage combined with frequency and regression coefficient of variables (FRCBOSS)’ was therefore developed based on ‘the bootstrapping soft shrinkage (BOSS)’technique, to identify the characteristic wavelengths of near-infrared (NIR) spectra for non-destructive and rapid analysis of lignin. Sub-models were generated by weighted bootstrap sampling (WBS) in the FRCBOSS method. For the BOSS method, the new weights of variables were determined only as the absolute values of regression coefficients of variables in each iteration. In contrast, the FRCBOSS method also considers the frequency of variables in variable space. Moreover, the FRCBOSS algorithm overcomes the disadvantage of BOSS in selecting variables, which could incorporate useful wavelengths that would otherwise be removed by the BOSS method. In addition, a range of different pre-treatment methods were used for comparison in the detection of lignin in the Snow pears. These include Savitzky–Golay Smoothing (SG), Normalization (NORM), Standard Normal Variate (SNV), and 1st Derivative (D1), as well as a combination of these methods and the different variables selection method (SiPLS, SiPLS-SPA, SiPLS-CARS, SiPLS-BOSS, and SiPLS-FRCBOSS). The number of variables selected by FRCBOSS was a little larger than that selected by BOSS. The partial least square regression (PLSR) model based on the 19 variables selected by SiPLS-FRCBOSS method had the best prediction ability, with a Rp value of prediction of 0.880 and a RMSEP value of 1.004%. We conclude that NIR diffuse reflectance spectroscopy technology combined with FRC-BOSS is an accurate and useful tool for the non-destructive and rapid determination of pear lignin contents.

中文翻译：

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使用 NIR 光谱和化学计量学快速无损分析木质素

木质素在梨中石细胞的形成中起重要作用。然而，木质素的积累对果实的风味和品质产生不利影响。因此需要一种快速准确的方法来测量梨的木质素含量。因此，基于“自举软收缩（BOSS）”技术开发了一种称为“自举软收缩结合变量频率和回归系数（FRCBOSS）”的改进变量选择方法，以识别近红外（NIR）的特征波长。 ) 对木质素进行无损和快速分析的光谱。子模型是通过 FRCBOSS 方法中的加权自举抽样 (WBS) 生成的。对于 BOSS 方法，变量的新权重仅确定为每次迭代中变量回归系数的绝对值。相比之下，FRCBOSS 方法还考虑了变量在变量空间中的频率。此外，FRCBOSS 算法克服了 BOSS 在选择变量方面的缺点，它可以合并有用的波长，否则 BOSS 方法会删除这些波长。此外，还使用了一系列不同的预处理方法来比较雪梨中木质素的检测。其中包括 Savitzky-Golay 平滑 (SG)、归一化 (NORM)、标准正态变量 (SNV) 和一阶导数 (D1)，以及这些方法与不同变量选择方法（SiPLS、SiPLS-SPA、 SiPLS-CARS、SiPLS-BOSS 和 SiPLS-FRCBOSS）。FRCBOSS 选择的变量数量比 BOSS 选择的要多一些。基于SiPLS-FRCBOSS方法选取的19个变量的偏最小二乘回归（PLSR）模型预测能力最好，预测的Rp值为 0.880，RMSEP 值为 1.004%。我们得出结论，NIR 漫反射光谱技术与 FRC-BOSS 相结合是一种准确且有用的工具，可用于无损和快速测定梨木质素含量。