Foley William J, McIlwee Allen, Lawler Ivan, Aragones Lem, Woolnough Andrew P, Berding Nils
Division of Botany and Zoology, Australia National University, Canberra 0200, Australia (e-mail:
Department of Zoology and Tropical Ecology, James Cook University, Townsville 4811, Australia, , , , , , AU.
Oecologia. 1998 Sep;116(3):293-305. doi: 10.1007/s004420050591.
Many ecological studies rely heavily on chemical analysis of plant and animal tissues. Often, there is limited time and money to perform all the required analyses and this can result in less than ideal sampling schemes and poor levels of replication. Near infrared reflectance spectroscopy (NIRS) can relieve these constraints because it can provide quick, non-destructive and quantitative analyses of an enormous range of organic constituents of plant and animal tissues. Near infrared spectra depend on the number and type of C[Formula: see text]H, N[Formula: see text]H and O[Formula: see text]H bonds in the material being analyzed. The spectral features are then combined with reliable compositional or functional analyses of the material in a predictive statistical model. This model is then used to predict the composition of new or unknown samples. NIRS can be used to analyze some specific elements (indirectly - e.g., N as protein) or well-defined compounds (e.g., starch) or more complex, poorly defined attributes of substances (e.g., fiber, animal food intake) have also been successfully modeled with NIRS technology. The accuracy and precision of the reference values for the calibration data set in part determines the quality of the predictions made by NIRS. However, NIRS analyses are often more precise than standard laboratory assays. The use of NIRS is not restricted to the simple determination of quantities of known compounds, but can also be used to discriminate between complex mixtures and to identify important compounds affecting attributes of interest. Near infrared reflectance spectroscopy is widely accepted for compositional and functional analyses in agriculture and manufacturing but its utility has not yet been recognized by the majority of ecologists conducting similar analyses. This paper aims to stimulate interest in NIRS and to illustrate some of the enormous variety of uses to which it can be put. We emphasize that care must be taken in the calibration stage to prevent propagation of poor analytical work through NIRS, but, used properly, NIRS offers ecologists enormous analytical power.
许多生态研究严重依赖于对动植物组织的化学分析。通常,进行所有所需分析的时间和资金有限,这可能导致采样方案不尽理想且重复水平较低。近红外反射光谱法(NIRS)可以缓解这些限制,因为它能够对动植物组织中种类繁多的有机成分进行快速、无损且定量的分析。近红外光谱取决于被分析材料中C—H、N—H和O—H键的数量和类型。然后,将光谱特征与该材料可靠的成分或功能分析相结合,构建一个预测性统计模型。接着使用这个模型来预测新的或未知样品的成分。NIRS可用于分析某些特定元素(间接分析——例如,将氮作为蛋白质)或明确界定的化合物(例如,淀粉),对于物质更复杂、定义不明确的属性(例如,纤维、动物食物摄入量),也已成功地利用NIRS技术进行建模。校准数据集参考值的准确性和精密度在一定程度上决定了NIRS预测的质量。然而,NIRS分析通常比标准实验室检测更为精确。NIRS的应用并不局限于简单测定已知化合物的含量,还可用于区分复杂混合物以及识别影响感兴趣属性的重要化合物。近红外反射光谱法在农业和制造业的成分与功能分析中已被广泛接受,但大多数进行类似分析的生态学家尚未认识到其用途。本文旨在激发人们对NIRS的兴趣,并举例说明它的一些广泛用途。我们强调,在校准阶段必须谨慎操作,以防止不良分析工作通过NIRS传播,但如果使用得当,NIRS可为生态学家提供强大的分析能力。