Forestry bark residues are an underutilised resource that could be exploited as a biorefinery feedstock to produce sustainable platform chemicals. However, variability in bark quality will significantly impact biorefinery processes, yields, and product quality. Therefore, simple, quick, and cost-effective analytical approaches for characterising incoming bark are required. A range of bark samples were analysed to determine hot water extract yield, gross calorific value, ash content, and properties of the hot water extract (total phenolic content, monomer concentration, and molecular weight). Near infrared and mid infrared spectra of the ground barks were used to develop calibration models using partial least squares regression. Calibration models for gross calorific value and total phenolics demonstrated good predictive performance for both techniques based on their coefficient of determination (R > 0.82) and root mean square error of cross-validation (RMSECV). Hot water extract yield and molecular weight were not well predicted by either technique (R < 0.69). Although both techniques performed similarly, near infrared spectroscopy may be more practical in an industrial setting. Near infrared spectroscopy combined with partial least squares regression may be a useful approach for incoming bark quality management in a bark biorefinery operation.

中文翻译：

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近红外和中红外光谱预测与生物精炼厂相关的辐射松树皮化学特性的比较


林业树皮残留物是一种未得到充分利用的资源，可作为生物炼制原料来生产可持续的平台化学品。然而，树皮质量的变化将显着影响生物精炼工艺、产量和产品质量。因此，需要简单、快速且经济高效的分析方法来表征传入的树皮。对一系列树皮样品进行了分析，以确定热水提取物的产率、总热值、灰分含量和热水提取物的特性（总酚含量、单体浓度和分子量）。地面树皮的近红外和中红外光谱用于使用偏最小二乘回归开发校准模型。根据确定系数 (R > 0.82) 和交叉验证均方根误差 (RMSECV)，总热值和总酚的校准模型证明了这两种技术具有良好的预测性能。这两种技术都不能很好地预测热水提取物产量和分子量（R < 0.69）。尽管两种技术的性能相似，但近红外光谱在工业环境中可能更实用。近红外光谱与偏最小二乘回归相结合可能是树皮生物精炼操作中引入的树皮质量管理的有用方法。