
The application of near-infrared spectroscopy in forestry
WANG Jue, LI Yanjie, CHEN Yicun, GAO Ming, ZHAO Yunxiao, WU Liwen, HUANG Shiqing, ZHANG Yongzhi, ZHU Kangshuo, WANG Yangdong
JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3) : 237-246.
The application of near-infrared spectroscopy in forestry
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.
near-infrared spectroscopy / forestry / economic forest / rapid prediction / non-destructive testing
[1] |
|
[2] |
ASTM. Standard practice for near infrared qualitative analysis[S]. West Conshohocken, PA: ASTM International, 2016.
|
[3] |
|
[4] |
|
[5] |
|
[6] |
陆婉珍. 现代近红外光谱分析技术[M]. 北京: 中国石化出版社, 2007.
|
[7] |
|
[8] |
|
[9] |
|
[10] |
杨忠, 江泽慧, 费本华. 近红外光谱技术及其在木材科学中的应用[J]. 林业科学, 2005, 41(4): 177-183.
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
莫军前, 张文博. 基于近红外光谱技术的热处理竹材物理力学性能[J]. 林业工程学报, 2019, 4(1):32-38.
|
[19] |
丁丽, 相玉红, 黄安民, 等. BP神经网络与近红外光谱定量预测杉木中的综纤维素、木质素、微纤丝角[J]. 光谱学与光谱分析`, 2009(7): 1784-1787.
|
[20] |
刘镇波, 孙凤亮,
|
[21] |
胡云超, 王红鸿, 熊智新, 等. 基于SWSRA-UVE算法的纸浆材综纤维素近红外预测模型共享研究[J]. 林业工程学报, 2023, 8(2):101-108.
|
[22] |
|
[23] |
|
[24] |
谭念, 王学顺, 黄安民, 等. 基于灰狼算法SVM的NIR杉木密度预测[J]. 林业科学, 2018, 54(12): 137-141.
|
[25] |
|
[26] |
|
[27] |
王钰. 2018年我国林业产业总产值7.33万亿元[EB/OL].
|
[28] |
胡逸磊, 姜洪喆, 周宏平, 等. 水果成熟度近红外光谱及高光谱成像无损检测研究进展[J]. 食品工业科技, 2021, 42(20):377-383.
|
[29] |
|
[30] |
王丹, 鲁晓翔, 张鹏, 等. 可见/近红外漫反射光谱无损检测甜柿果实硬度[J]. 食品发酵工业, 2013, 39(5): 180-184.
|
[31] |
李玉荣, 刘英, 汪力. 多信息融合的马尾松苗木静态指标检测方法[J]. 林业工程学报, 2019, 4(5):129-133.
|
[32] |
|
[33] |
李水芳, 付红军, 马强, 等. 构建三种木本油料植物种子含油率NIR通用模型的可行性研究[J]. 林产化学与工业, 2017, 37(4): 137-142.
|
[34] |
|
[35] |
|
[36] |
|
[37] |
|
[38] |
骆立, 徐兆军, 王晓羽. 基于支持向量机的木材树种识别模型[J]. 林业工程学报, 2022, 7(4):122-127.
|
[39] |
汪紫阳, 尹世逵, 李颖, 等. 基于可见/近红外光谱识别东北地区常见木材[J]. 浙江农林大学学报, 2019, 36(1): 162-169.
|
[40] |
杨金勇, 李学春, 黄安民, 等. 基于主成分分析与 Fisher 判别的 NIR 木材识别[J]. 东北林业大学学报, 2013, 41(12): 132-134.
|
[41] |
杨忠, 江泽慧, 吕斌. 红木的近红外光谱分析[J]. 光谱学与光谱分析, 2012, 32(9): 2405-2408.
|
[42] |
|
[43] |
LUCAS DOMINGOS DA SILVA A,
|
[44] |
|
[45] |
王震, 张晓丽, 安树杰. 松材线虫病危害的马尾松林木光谱特征分析[J]. 遥感技术与应用, 2007(3): 367-370.
|
[46] |
黄明祥, 龚建华, 李顺, 等. 松材线虫病害高光谱时序与敏感特征研究[J]. 遥感技术与应用, 2012, 27(6): 954-960.
|
[47] |
马跃, 吕全, 赵相涛, 等. 接种不同浓度松材线虫的黑松光谱学特征分析[J]. 山东农业科学, 2012, 44(11): 1216.
|
[48] |
|
[49] |
|
[50] |
|
[51] |
|
[52] |
|
[53] |
江泽慧, 黄安民. 木材中的水分及其近红外光谱分析[J]. 光谱学与光谱分析, 2006(8): 1464-1468.
|
[54] |
|
[55] |
|
[56] |
赵荣军, 邢新婷, 吕建雄, 等. 粗皮桉木材力学性质的近红外光谱方法预测[J]. 林业科学, 2012, 48(10): 106-111.
|
[57] |
余雁, 江泽慧, 王戈, 等. 采谱方式对竹材气干密度近红外预测模型精度的影响[J]. 北京林业大学学报, 2007, 29(4): 80-83.
|
[58] |
|
[59] |
|
[60] |
高升, 王巧华, 付丹丹, 等. 红提糖度和硬度的高光谱成像无损检测[J]. 光学学报, 2019, 39(10): 1030004.
|
[61] |
褚小立, 袁洪福, 陆婉珍. 近红外分析中光谱预处理及波长选择方法进展与应用[J]. 化学进展, 2004, 16(4): 528.
|
[62] |
|
[63] |
|
[64] |
|
[65] |
袁天军, 王家俊, 者为, 等. 近红外光谱法的应用及相关标准综述[J]. 中国农学通报, 2013, 29(20): 190-196.
|
/
〈 |
|
〉 |