The application of near-infrared spectroscopy in forestry

doi:10.12302/j.issn.1000-2006.202203001

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3): 237-246.doi: 10.12302/j.issn.1000-2006.202203001

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 E-mail:wangjue72999@163.com;wyd11111@126.com

Abstract

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

CLC Number:

S759.3

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, 2023, 47(3): 237-246.

Figures/Tables 3

Table 1

Fig.1

Table 2

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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]

The application of near-infrared spectroscopy in forestry

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