The calibration of NIR reflectance spectrometer for the determination of diverse compositional parameters

Barton, Franklin E.; Cavanagh, G. C.

doi:10.1007/bf02542528

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…The program REG70 was added to the NIRS Network software selections in 1987 and is a population sampling approach to calibration which is essentially wavelength independent. Additional information on this program has been presented by Barton and Cavanagh ( 1988). The PLS/PCR software was developed by J.S.…”

Section: Methodsmentioning

confidence: 99%

Botanical Composition of Tropical Grass‐Legume Pastures Estimated with Near‐Infrared Reflectance Spectroscopy

et al. 1991

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Quantifying pasture composition requires either laborious or subjective approaches. Evaluations of near‐infrared reflectance spectroscopy (NIRS) to determine botanical composition of mixed pasture swards have shown potential. In this study, characterization of botanical composition of pastures comprised primarily of bahiagrass (Paspalum notatum Flugge), aeschynomene (Aeschynomene americana L.) and phasey bean [Macroptilium lathyriodes (L.) Urb.] by NIRS was evaluated. Three approaches (hand‐composited samples, single‐component samples, and actual pasture samples) were compared for equation development. Theoretical potential of NIRS is illustrated by high coefficients of determination (0.98–0.99) and low standard errors (1.4–2.9%) of equations for the above species from hand‐composited samples. Equations developed from the three approaches were evaluated for estimation of the botanical composition of a separate group of pasture samples. Equations developed from hand‐composited samples from a single source of each component were not acceptable for estimating composition of pasture samples despite the excellent calibration statistics. Single‐component samples approached adequate results only for composite total grass and total legume groups, even though the pasture sample composition appeared to be well represented in the calibration sample set. Equations from pasture samples provided useful estimates of sample means, although some individual samples were poorly estimated. Thus, botanical composition of these pastures may be estimated using equations from actual pasture samples, and estimates of total grass and total legume may be obtained from use of single‐component samples, which provides further labor reductions. A comparison of original software and updated software packages CAL, BEST, REG70, and partial least squares principal component regression showed none to be consistently superior.

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Section: Methodsmentioning

confidence: 99%

Botanical Composition of Tropical Grass‐Legume Pastures Estimated with Near‐Infrared Reflectance Spectroscopy

et al. 1991

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“…The development of Near Infrared Reflectance (NIR) and Near Infrared Transmittance (NIT) has accelerated the rate of numerous chemical analyses from hours to seconds. The infrared data correlates well with standard proximate analyses (Barton and Cavanagh, 1988).…”

Section: Product Yield Predictors and Determinantssupporting

confidence: 62%

Rapidly measured predictors for the dry milling performance of corn

Fradgley

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Near‐infrared reflectance measurement of moisture, protein and oil content of ground crambe seed

Hartwig

Hurburgh

1990

J Americ Oil Chem Soc

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435Crambe moisture, protein and oil percentages were predicted by fixed-filter near-infrared reflectance with standard errors of prediction (SEP) of 0.26, 0.59 and 0.86 percentage points, respectively. Crambe had a large range of protein and oil percentages, 17.4%-25.0% and 17.7%-36.4% respectively. Calibration samples were selected on the basis of relative spectral data, with no advance knowledge of protein and oil content. This procedure selected samples representing the full range of constituent values, and resulted in calibrations that had lower SEP's than standard errors of calibration.is regarded as a promising new industrial oil seed. Crambe oil is about 55-60% erucic acid (cis-13docosenoic) (1). When treated with ozone, erucic acid gives brassylic and pelargonic acids. Brassylic acid can be used for polyesters, plasticizers, alkyd resins, lubricants, rubber additives and surface-active agents. Pelargonic acid is used for plasticizers, alkyd resins, vinyl stabilizers, hydrotropic salts, pharmaceuticals, synthetic flavors and odors, flotation agents and insect repellents (2).The oil-free, hull-free meal contains 46%-58% crude protein with a good amino-acid balance for feed. The meal will contain thioglucosides, which places some restrictions on its use as a feedstuff.Agronomic research in crambe breeding gave rise to a need for nutrient analysis of crambe seed. Previous success with soybeans (3) and other oilseeds (4) suggested that near-infrared reflectance (NIR) could be used in lieu of wet chemical methods. However, crambe seed is light and small, conditions certain to complicate grinding.The multiple-linear-regression calibration of fixedwavelength near-infrared reflectance analyzers has been reported extensively in the literature. Little has been written about the actual selection process of calibration and prediction samples. Typically, a number of samples are collected and randomly divided into two groups--one for calibration and one for prediction (5,6), or samples are compared against those of previously known chemical composition (4,7). Barton and Cavanagh {8) used two stepwise linear-regression analysis programs, which allowed every i th sample to be saved for prediction verification.Selection of calibration samples by spectra alone has been reported for near-infrared monochramotors (9) but not for fixed-filter units.The range of protein and oil values for crambe was unknown. Instead of running wet chemistry on all our available samples, we wanted to select a spectrally representative calibration set by using only reflectance *To whom correspondence should be addressed.

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The calibration of NIR reflectance spectrometer for the determination of diverse compositional parameters

Cited by 6 publications

References 9 publications

Botanical Composition of Tropical Grass‐Legume Pastures Estimated with Near‐Infrared Reflectance Spectroscopy

Botanical Composition of Tropical Grass‐Legume Pastures Estimated with Near‐Infrared Reflectance Spectroscopy

Rapidly measured predictors for the dry milling performance of corn

Near‐infrared reflectance measurement of moisture, protein and oil content of ground crambe seed

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