Development of Near Infrared Reflectance Spectroscopy Calibrations to Estimate Legume Content of Multispecies Legume–Grass Mixtures

Locher, F.; Heuwinkel, H.; Gutser, R.; Schmidhalter, Urs

doi:10.2134/agronj2005.0011

Cited by 23 publications

(33 citation statements)

References 16 publications

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“…In this study, 132 sample spectra were set aside as external, independent validation set, which was not included in either of the calibration and test validation, but contained, at least in part, some of the original pure root material. Most studies have shown that crossvalidation provides slightly superior predictive performance to external test validation (Locher et al 2005a;Martens and Dardenne 1998;Moron and Cozzolino 2004). However, in the present study, Model 1, which was based on cross-validation for the whole dataset, was not superior with respect to the RMSECV or RMSEP and r 2 when compared to Model 2 and Model 3, which were developed through validation against external test sets following calibration.…”

Section: Model Development and Validationcontrasting

confidence: 72%

“…Near infrared reflectance spectroscopy (NIRS) is a rapid non-destructive technique with the capacity to distinguish and quantify botanical composition in mixtures (Locher et al 2005a;Pitman et al 1991;Wachendorf et al 1999). The fact that near-infrared radiation is absorbed mainly by C-H, N-H and O-H bonds, of which organic compounds are composed, makes it particularly suitable to determine the composition of organic mixtures (Foley et al 1998;Richardson and Reeves 2005).…”

Section: Introductionmentioning

confidence: 99%

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Use of near-infrared reflectance spectroscopy to predict species composition in tree fine-root mixtures

Bauhus

2010

Plant Soil

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Assessment of belowground interactions in mixed forests has been largely constrained by the ability to distinguish fine roots of different species. Here, we explored near infrared reflectance spectroscopy (NIRS) to predict the proportion of woody fine roots in mixed samples and analyzed whether the prediction quality of NIRS models is related to the complexity of the fine-root mixture. For model calibration and validation purposes, 11 series of artificial mixed species samples containing known amounts of fine roots of up to four temperate tree species and non-woody plants were prepared. Three types of models with different calibration/validation approaches were developed and tested against external independent data for additional validation. With these models the proportion of each species in root mixtures was predicted accurately with low standard error of prediction (RMSECV/RMSEP <6.5%) and high coefficient of determination (r 2 >0.93) for all fine-root mixtures. In addition, NIRS models also provided satisfactory estimates for samples with low (<15%) or no content of particular components. The predictive power of the NIRS models did not decrease substantially with increasing complexity of the root samples. The approach presented here is a promising alternative to hand sorting of fine roots, which may be influenced substantially by operator variation, and it will facilitate investigating belowground interactions between woody species.

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Section: Model Development and Validationcontrasting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Use of near-infrared reflectance spectroscopy to predict species composition in tree fine-root mixtures

Bauhus

2010

Plant Soil

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“…However, a handful of recent studies including this one demonstrate that NIRS can be applied broadly, and can include a number of different plant species in a single equation that predicts nutritional attributes(da Costa and Volery 2005;Lawler et al 2006;Locher et al 2005;Woolnough and Foley 2002),…”

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confidence: 99%

Rapid Assessment of the Nutritional Value of Foods Eaten by Mountain Gorillas: Applying Near-Infrared Reflectance Spectroscopy to Primatology

et al. 2009

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The increasing recognition of the considerable intraspecific spatial and temporal variability in the nutritional contents of primate foods has necessitated development of fast and cost-effective analytical methods. Used widely for agricultural products, near-infrared reflectance spectroscopy (NIRS) is a quick, inexpensive means of assessing nutritional chemistry. The general principle of NIRS is that when the sample is irradiated with near-infrared light, the reflectance Int J Primatol (2009) 30:729-742 spectrum is characteristic of the mixture of chemical bonds present in the sample. These spectra, when calibrated against reference values-determined via traditional nutritional analysis-to develop regression equations, can be used to estimate nutritional values of similar samples without doing traditional nutritional analysis. We validated the use of NIRS for estimating the nutritional attributes of African herbs and trees, which were foods eaten by mountain gorillas (Gorilla beringei) collected as part of a larger study on gorilla nutritional ecology. We determined the near-infrared spectra (1100-2400 nm) of 241 dried samples of 13 species of tropical herbs and trees that formed the staple diet of the gorillas. We used modified partial least-squares regression to develop calibration equations that could predict nutritional attributes of gorilla foods, and we performed an independent validation of the calibrations. The equations had robust predictive power similar to those used in agricultural and ecology, and we found no differences between samples measured via NIRS and traditional nutritional analysis. Our analysis indicates that NIRS offers a rapid and cost-effective means of analysis of tropical leaves and herbs, and has the potential to transform primate feeding ecology studies by allowing us to evaluate the importance of intraspecific variation in nutritional value.

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“…Grass species are likely to have similar biochemical composition, leading to similar spectral signatures. Locher et al (2005a) developed a NIRS calibration that predicted legume content based on artificial sample mixtures containing a range of legumes and grasses from one farm in Germany. Locher et al (2005a) developed a NIRS calibration that predicted legume content based on artificial sample mixtures containing a range of legumes and grasses from one farm in Germany.…”

mentioning

confidence: 99%

“…Legume proportion in mixture with multiple grass species was predicted with high accuracy (Chataigner et al, 2010). Different calibration strategies did not lead to relevant differences, and sample preparation was less important for prediction accuracy than the precision of the reference data used (Locher et al, 2005a). Different calibration strategies did not lead to relevant differences, and sample preparation was less important for prediction accuracy than the precision of the reference data used (Locher et al, 2005a).…”

mentioning

confidence: 99%

Botanical Composition Prediction of Alfalfa–Grass Mixtures using NIRS: Developing a Robust Calibration

Karayilanli

Cherney

Sirois³

et al. 2016

Crop Science

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Botanical composition of alfalfa (Medicago sativa L.)–grass fresh and ensiled mixtures is a key parameter for assessing forage and diet quality as well as for managing mixed stands. Previous attempts to validate near‐infrared reflectance spectroscopy (NIRS) equations for estimating botanical composition have had mixed results. This study was conducted to develop a robust NIRS method to estimate botanical composition of binary alfalfa–grass mixtures. Alfalfa–grass samples were collected across New York State over four growing seasons, hand separated, and a subset were ensiled separately. Dry samples were coarsely ground, mixed in known proportions, and reground for analysis by NIRS at Dairy One Forage Laboratory, Ithaca, NY. Samples were mixed to range from 0 to 100% alfalfa for NIRS calibration, with a total of 741 individual samples from 3 yr used for calibration of three NIRS instruments and samples from a fourth year used for validation. Grass composition was predicted with good precision and accuracy showing biases of 2.49 and standard errors of prediction (SEP) of 5.06, with R2 of 0.972, using the equation developed across multiple instruments. With selection of a robust set of calibration samples over many environments, NIRS can be used to determine the botanical composition of fresh‐dried or ensiled‐dried alfalfa–grass samples, and replicate scans from multiple instruments can be combined to develop a single calibration that will perform with equal efficiency across different instruments.

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Development of Near Infrared Reflectance Spectroscopy Calibrations to Estimate Legume Content of Multispecies Legume–Grass Mixtures

Cited by 23 publications

References 16 publications

Use of near-infrared reflectance spectroscopy to predict species composition in tree fine-root mixtures

Use of near-infrared reflectance spectroscopy to predict species composition in tree fine-root mixtures

Rapid Assessment of the Nutritional Value of Foods Eaten by Mountain Gorillas: Applying Near-Infrared Reflectance Spectroscopy to Primatology

Botanical Composition Prediction of Alfalfa–Grass Mixtures using NIRS: Developing a Robust Calibration

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