The use of near infrared spectroscopy to predict foliar nutrient levels of hydroponically grown teak seedlings

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2021-07-08 DOI:10.1177/09670335211025649
W. A. Whittier, G. Hodge, Juan López, C. Saravitz, J. Acosta
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引用次数: 2

Abstract

Due to a combination of durability, strength, and aesthetically pleasing color, teak (Tectona grandis L.f.) is globally regarded as a premier timber species. High value, in combination with comprehensive harvesting restrictions from natural populations, has resulted in extensive teak plantation establishment throughout the tropics and subtropics. Plantations directly depend on the production of healthy seedlings. In order to assist growers in efficiently diagnosing teak seedling nutrient issues, a hydroponic nutrient study was conducted at North Carolina State University. The ability to accurately diagnose nutrient disorders prior to the onset of visual symptoms through the use of near infrared (NIR) technology will allow growers to potentially remedy seedling issues before irreversible damage is done. This research utilized two different near infrared (NIR) spectrometers to develop predictive foliar nutrient models for 13 nutrients and then compared the accuracy of the models between the devices. Destructive leaf sampling and laboratory grade NIR spectroscopy scanning was compared to nondestructive sampling coupled with a handheld NIR device used in a greenhouse. Using traditional wet lab foliar analysis results for calibration, nutrient prediction models for nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), sulfur (S), copper (Cu), molybdenum (Mo), magnesium (Mg), boron (B), calcium (Ca), manganese (Mn), iron (Fe), sodium (Na), and zinc (Z) were developed using both NIR devices. Models developed using both techniques were good for N, P, and K (R2 > 0.80), while the B model was adequate only with the destructive sampling procedure. Models for the remaining nutrients were not suitable. Although destructive sampling and desktop scanning procedure generally produced models with higher correlations they required work and time for sample preparation that might reduce the value of this NIR approach. The results suggest that both destructive and nondestructive sampling NIR calibrations can be useful to monitor macro nutrient status of teak plants grown in a nursery environment.
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利用近红外光谱预测水培柚木幼苗叶片营养水平
由于其耐用性、强度和美观的颜色,柚木(Tectona grandis l.f.)在全球范围内被认为是一种重要的木材品种。高价值,加上自然种群的全面采伐限制,导致整个热带和亚热带地区建立了广泛的柚木种植园。种植园直接依赖于健康幼苗的生产。为了帮助种植者有效地诊断柚木幼苗的营养问题,在北卡罗来纳州立大学进行了一项水培营养研究。通过使用近红外(NIR)技术,在视觉症状出现之前准确诊断营养失调的能力将使种植者能够在造成不可逆转的损害之前潜在地补救幼苗问题。本研究利用两种不同的近红外(NIR)光谱仪建立了13种营养物质的叶片营养预测模型,然后比较了两种设备之间模型的准确性。将破坏性叶片取样和实验室级近红外光谱扫描与温室中使用的手持近红外装置的非破坏性取样进行了比较。利用传统湿法实验室叶片分析结果进行校准,利用两种近红外装置建立了氮(N)、磷(P)、钾(K)、钙(Ca)、硫(S)、铜(Cu)、钼(Mo)、镁(Mg)、硼(B)、钙(Ca)、锰(Mn)、铁(Fe)、钠(Na)和锌(Z)的养分预测模型。使用这两种技术开发的模型对N、P和K都很好(R2 > 0.80),而B模型仅适用于破坏性采样程序。剩余营养物质的模型不合适。虽然破坏性取样和桌面扫描程序通常产生具有较高相关性的模型,但它们需要工作和时间来制备样品,这可能会降低这种近红外方法的价值。结果表明,破坏性和非破坏性采样近红外校准都可以用于监测苗圃环境中柚木植物的宏观营养状况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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