近红外光谱技术在林业领域的应用

doi:10.12302/j.issn.1000-2006.202203001

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南京林业大学学报（自然科学版） ›› 2023, Vol. 47 ›› Issue (3): 237-246.doi: 10.12302/j.issn.1000-2006.202203001

• 综合述评 • 上一篇

近红外光谱技术在林业领域的应用

王珏¹(), 李彦杰², 陈益存², 高暝², 赵耘霄², 吴立文², 黄世清³, 张永志³, 朱康烁³, 汪阳东¹^,²^,^*()

1.浙江农林大学林业与生物技术学院,浙江杭州 311300
2.中国林业科学研究院亚热带林业研究所, 浙江杭州 311400
3.安吉县龙山林场,浙江安吉 313306

收稿日期:2022-03-01 修回日期:2022-06-19 出版日期:2023-05-30 发布日期:2023-05-25
通讯作者: 汪阳东
基金资助:
国家科技基础资源调查专项项目(2019FY100803);贵州省科技计划项目(黔科合支撑[2020]1Y136号)

The application of near-infrared spectroscopy in forestry

WANG Jue¹(), LI Yanjie², CHEN Yicun², GAO Ming², ZHAO Yunxiao², WU Liwen², HUANG Shiqing³, ZHANG Yongzhi³, ZHU Kangshuo³, WANG Yangdong¹^,²^,^*()

1. College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, China
2. Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China
3. Anji Longshan Forest Farm, Anji 313306, China

Received:2022-03-01 Revised:2022-06-19 Online:2023-05-30 Published:2023-05-25
Contact: WANG Yangdong

摘要/Abstract

摘要：

近红外光谱技术是一种快速、准确、无损、高通量、低成本的分析技术,广泛应用于林业领域。在木材性质检测方面,近红外光谱技术对于木材力学特征及化学成分含量等检测都有较高的准确性;在经济林产品品质分析方面,则主要用于反映林业产品的化学组分及含量、口感及硬度等直接和间接性状,对经济林产品品质及林木遗传育种的研究有较好应用前景;在林木分类鉴别应用方面,近红外光谱能够鉴别不同树种及种源,乃至反映树龄信息,在多物种模型中应用效果更好;近红外光谱还能够有效区分多物种的正常植物体与染病植物体,可在林木病虫害研究及生物检疫方面发挥作用。此外,近红外光谱还能应用于预测森林凋落物分解速率、预测土壤成分含量等。笔者分析了近红外光谱在林业科研和生产实际应用中的影响因素,包括样品状态、样本集特征等内在因素,以及预处理、波长选择、建模方式、硬件条件等外在因素对最终模型稳定性和准确度产生的影响。总结认为,近红外光谱技术引入林业研究后大大提高了林业样品检测效率,实现了绿色无损的高通量检测,对林地现场快速测量及林木遗传育种有极好的适应性,对促进林业产业的发展意义重大。

关键词: 近红外光谱, 林业, 经济林, 快速预测, 无损检测

Abstract:

To obtain reliable forestry data, a large number of samples need to be tested at each stage; however, larger sample sizes can result in increased time, costs, and manpower. Therefore, a fast and efficient detection method to reduce costs and increase forestry study efficiency is needed. Near-infrared spectroscopy (NIR) is a fast, accurate, non-destructive, high-throughput and low-cost analytical technique, which is gradually gaining popularity for use in forestry studies and its potential for the development. The application of NIR spectroscopy for wood property detection can detect wood mechanical characteristics and chemical composition contents with high accuracy. For analyses of economic forestry product quality, NIR spectroscopy is primarily used to reflect indirect and direct traits such as texture, hardness, chemical composition, and content of forestry products; thus, NIR spectroscopy shows good prospects for use in studies of economic forestry product quality and even forest/tree genetic breeding. In addition, NIR spectroscopy can be used in forest tree classification to identify different tree species and species origins, including age information, with better results obtained in multi-species models. Moreover, NIR spectroscopy is useful in the study of forest pests and diseases, as it can effectively distinguish between the normal and diseased plant bodies of multiple species, can be used in bio-quarantine and to predict forest foliage decomposition rates of forest foliage and soil composition. Based on the multiple cross-disciplinary applications of this method, this paper analyzed the factors that influence NIR spectroscopy in practical applications. Intrinsic factors such as sample state and sample set characteristics, and external factors such as pre-processing, wavelength selection, modeling methods, and hardware conditions all have an impact on the stability and accuracy of the final model. It is clear that the introduction of NIR spectroscopy into forestry research has greatly improved the efficiency of forestry sample detection, achieved green and non-destructive high-throughput detection, and has excellent adaptability to rapid measurement of forest land sites and forest genetic breeding; thus, NIR spectroscopy plays a significant role in promoting forestry development.

Key words: near-infrared spectroscopy, forestry, economic forest, rapid prediction, non-destructive testing

中图分类号:

S759.3

王珏,李彦杰,陈益存,等. 近红外光谱技术在林业领域的应用[J]. 南京林业大学学报（自然科学版）, 2023, 47(3): 237-246.

WANG Jue, LI Yanjie, CHEN Yicun, GAO Ming, ZHAO Yunxiao, WU Liwen, HUANG Shiqing, ZHANG Yongzhi, ZHU Kangshuo, WANG Yangdong. The application of near-infrared spectroscopy in forestry[J].Journal of Nanjing Forestry University (Natural Science Edition), 2023, 47(3): 237-246.DOI: 10.12302/j.issn.1000-2006.202203001.

图/表 3

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对比项目 comparison item	近红外光谱 near infrared spectroscopy	红外光谱 infrared spectroscopy	拉曼光谱 raman spectroscopy
波长范围 wavelength range	780~2 526 mm	2 500~25 000 mm	2 500~200 000 mm
激发形式excitation mechanism	吸收	吸收	非弹性光子散射
采样模式 acquisition mode	漫反射,透射	漫反射,透射,衰减全反射	散射
采样深度sampling depth	深	浅	视样品透光度及波长而定
仪器复杂度instrument complexity	低	中	高
化学特异性chemical specificity	低	高	高
样本要求sample requirements	宽泛,要求少	不含游离水	均一,不含游离水
主要缺点main problems	灵敏度较低,光谱重合, 解释困难	大气信号干扰, 不适用于潮湿样品	自发荧光干扰, 激光可能破坏分子结构

样品 sample	检测内容 test content	预处理/建模方法 pretreatment / modeling method	文献编号 reference No.
	综纤维素、木质素	Savitzky-Golay平滑、二阶导数等+BPNN	[18]
日本柳杉Cryptomeria japonica	微纤丝角	一阶导数+PLSR	[23]
	种源	Savitzky-Golay平滑、SNV、MSC+PCA	[33]
桉木 Eucalyptus bosistoana	心材内含物	二阶导数、一阶导数、SNV+PLSR	[19]
落叶松 Larix gmelinii	木材密度	径向基函数+SVR	[20]
	树种鉴别	一阶导数、二阶导数+PCR、Fisher判别	[35]
杉木 Cunninghamia lanceolata	密度	PCA+多元线性回归/(GWO-)SVR	[21]
蒙古栎 Quercus mongolica	机械性能	Savitzky-Golay卷积平滑、MSC、一阶导数等+ (CLE-)PLSR	[22]
咖啡 Coffea arabica	pH、酸度	一阶导数、SNV+PLSR	[26]
甜柿 Diospyros kaki	果皮强度、脆性	MSC、一阶微分+改进PLSR	[27]
	果肉平均硬度	SNV、一阶微分等+改进PLSR
辣木 Moringa oleifera	蛋白质	MSC、BC、PCA+PLSR	[28]
	矿物质	MSC、BC、Savitzky-Golay卷积平滑等+PLSR
油桐Vernicia fordii 油茶Camellia oleifera 核桃Juglans regia	含油率	均值中心化、一阶导数、二阶导数等+PLSR/RBFNN	[29]
橄榄 Olea europaea	水、油、油酸、亚油酸	二阶导数+PLSR	[30]
红松Pinus koraiensis	种子活性、虫害	正交信号校正+PLSR	[31]
马尾松Pinus massoniana 樟子松P. sylvestris var. mongolica	树种鉴别	一阶导数、二阶导数+PCR、Fisher判别	[35]
欧洲赤松P. sylvestris	树龄、早晚材	PCA+PLS-DA	[37]
橡胶Hevea brasiliensis	病叶与正常叶	MSC+PCA	[38]
柑橘Citrus sinensis	黄龙病	PCA+MLPNN	[39]

近红外光谱技术在林业领域的应用

The application of near-infrared spectroscopy in forestry

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