Near-infrared spectroscopy

Kaye, Wilbur

doi:10.1016/0371-1951(55)80025-2

Cited by 15 publications

(14 citation statements)

References 215 publications

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“…Near-infrared reflectance spectroscopy (NIRS), a well-established indirect measurement method for plant constituents routinely applied in agriculture 17 , holds the potential to overcome these limitations. NIRS is based on the light absorption of organic bonds in molecules (such as C-H, N-H, O-H) in the visible and near infra-red spectrum of light 18,19 . The combined absorptions at different wavelengths hold information about the content of the nutrients or constituent of interest 20 .…”

Section: Introductionmentioning

confidence: 99%

Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content

Ancin‐Murguzur

Bison

Smis

et al. 2019

Sci Rep

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Near-infrared spectroscopy (NIRS) is a high-throughput technology with potential to infer nitrogen (N), phosphorus (P) and carbon (C) content of all vascular plants based on empirical calibrations with chemical analysis, but is currently limited to the sample populations upon which it is based. Here we provide a first step towards a global arctic-alpine NIRS model of foliar N, P and C content. We found calibration models to perform well (R 2 validation = 0.94 and RMSEP = 0.20% for N, R 2 validation = 0.76 and RMSEP = 0.05% for P and R 2 validation = 0.82 and RMSEP = 1.16% for C), integrating 97 species, nine functional groups, three levels of phenology, a range of habitats and two biogeographic regions (the Alps and Fennoscandia). Furthermore, when applied for predicting foliar N, P and C content in samples from a new biogeographic region (Svalbard), our arctic-alpine NIRS model performed well. The precision of the resulting NIRS method meet international requirements, indicating one NIRS measurement scan of a foliar sample will predict its N, P and C content with precision according to standard method performance. The modelling scripts for the prediction of foliar N, P and C content using NIRS along with the calibration models upon which the predictions are based are provided. The modelling scripts can be applied in other labs, and can easily be expanded with data from new biogeographic regions of interest, building the global arctic-alpine model.

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Section: Introductionmentioning

confidence: 99%

Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content

Ancin‐Murguzur

Bison

Smis

et al. 2019

Sci Rep

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“…To confirm that the above conclusions are correct, an F-test, at the 95% confidence level, according to eqn. (14), was used to compare RMSEP for the different calibration methods:…”

Section: Resultsmentioning

confidence: 99%

Pruning neural network for architecture optimization applied to near-infrared reflectance spectroscopic measurements. Determination of the nitrogen content in wheat leaves

Mello¹,

Poppi²,

Andrade³

et al. 1999

Analyst

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The pruning neural network, based on the algorithm called optimum brain surgeon, was used for network architecture optimization. This network pruning procedure was applied for estimating the nitrogen contents in wheat leaves, using near-infrared diffuse reflectance spectroscopy. The results obtained with pruning were compared with those obtained by using ordinary procedures with neural networks, partial least squares, polynomial partial least squares and neural networks/partial least squares methodologies. Comparison of the results with those obtained by the conventional Kjeldahl method showed that the results with pruning neural networks were as good as those with ordinary neural networks and with PLS/neural networks, but better than those with the other methodologies. Although the comparison was performed for one data set, the pruning procedure has the advantage of introducing an automatic architecture optimization, which is cumbersome when performed by the other neural network procedures used in this work, generating a simplified model with better generalization abilities.

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“…The absence of a discussion of diffuse reflection apparently is indicative of the fact that measurements in the diffuse reflection mode were not thought to be useful at that time. 7 Wilbur Kaye published two reviews 8,9 solely devoted to NIR technology. In his first review, 8 Kaye writes of applications, including band assignments for bromoform, chloroform, methylene bromide, benzene, carbon tetrachloride and m-toluidine.…”

Section: Reviewsmentioning

confidence: 99%

204 Years of near Infrared Technology: 1800–2003

McClure

2003

Journal of Near Infrared Spectroscopy

187

146

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Near infrared spectroscopy has come of age. Today, it takes its place along side other major analytical technologies. Though it has inherent problems, its advantages make it an appealing tool for solving all sorts of analytical problems, for all sorts of samples, found in numerous fields, including food, agriculture, chemical, pharmaceuticals, textiles, polymers, cosmetics and medical-just to mention a few. Healthy, strong and still growing, NIR spectroscopy is a source of thrills and chills for many, whether from new measurement technology, chemometrics or from understanding and being able to express "old" ideas in fresher, clearer ways. a Modern NIR instruments integrate numerous technologies for determining absorbances at specific wavelengths: Monochromator, Fourier Transform, Filters [including, narrow-band interference filters, tilting filters, Acousto Optical Tunable Filters (AOTF) and Liquid Crystal Tunable Filters (LCTF)], Hadamard, Diode Emitters and Diode Arrays. Software for developing calibrations, utilising some types of classical and/or modern multivariate methods, is available with every commercial instrument. In addition, software vendors, independent of manufacturers, have designed and are marketing advanced chemometric packages for carrying out a variety of multivariate tasks, including mathematical spectral pretreatment, calibration and prediction, plus the implementation of process control statistics. The plethora of instruments and software has provided a sound basis for developing all sorts of applications throughout numerous fields. As a result, there are today more than 25,000 citations in the NIR literature and, since 1960, Medline alone has logged 2341 citations with NIR implications for the medical community, all tributes to the strength and viability of this fascinating spectroscopy. This review is a look at the big picture of NIR spectroscopy. It covers the field of NIR from the beginning to present day-204 years (1800-2003

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Near-infrared spectroscopy

Cited by 15 publications

References 215 publications

Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content

Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content

Pruning neural network for architecture optimization applied to near-infrared reflectance spectroscopic measurements. Determination of the nitrogen content in wheat leaves

204 Years of near Infrared Technology: 1800–2003

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