Purpose of Review

Forest and wood products are often characterized by a uniformity of quality attributes, which necessitates the development of rapid and non-destructive quality evaluation methods to ensure their optimal quality. Near-infrared spectroscopy (NIRS) represents a highly suitable approach for the characterization of organic compounds, and is generally combined with sophisticated multivariate analysis methods. This review article presents a range of scientific and technical reports showcasing the successful use of NIRS for evaluating forest and wood products, mainly published within the past 5 years.

Recent Findings

Continuous advancements in spectral imaging techniques and the integration of big-data analytics have greatly enhanced the capabilities of NIR instrumentation, enabling its widespread application across diverse fields. Although NIR spectral imaging methods do have some limitations when it comes to online grading, they can still be used to test small quantities of samples at a batch level. Moreover, the ever-increasing use of handheld devices has made NIRS easily accessible.

Summary

We aim to provide a summary of new research in basic spectroscopic research, integrating the improvements of spectral imaging methods and big-data analytics. Furthermore, low-cost and portable devices have been produced, enabling remote analysis and further expanding the scope of NIRS applications. Looking forward, we anticipate that continued advancements in this field will enable even wider applications of NIRS for online or at-line quality monitoring in diverse fields.

中文翻译：

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近红外光谱在林业和木制品中的应用

审查目的

森林和木制品通常具有质量属性的一致性，这需要开发快速、无损的质量评估方法以确保其最佳质量。近红外光谱 (NIRS) 是一种非常适合有机化合物表征的方法，通常与复杂的多元分析方法相结合。这篇综述文章介绍了一系列科学和技术报告，展示了近红外光谱技术在评估森林和木制品方面的成功应用，这些报告主要在过去 5 年内发表。

最近的发现

光谱成像技术的不断进步和大数据分析的集成极大地增强了近红外仪器的能力，使其能够在各个领域得到广泛应用。尽管近红外光谱成像方法在在线分级方面确实存在一些局限性，但它们仍然可以用于批量测试少量样品。此外，手持设备的使用不断增加，使得 NIRS 变得更容易使用。

概括

我们的目标是总结基础光谱研究的新研究，整合光谱成像方法和大数据分析的改进。此外，低成本和便携式设备的出现，使得远程分析成为可能，并进一步扩大了近红外光谱的应用范围。展望未来，我们预计该领域的持续进步将使 NIRS 在不同领域的在线或在线质量监测中得到更广泛的应用。