Uncertainty and periodic behavior of process derived from online NIR

Paakkunainen, Maaret; Kohonen, Jarno; Рейникайнен, Сату-Пиа

doi:10.1016/j.aca.2008.11.037

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Unfortunately, the methodology has not been readily adapted to field-measurements or preprocessing operations, but is routinely used for quality control in the food [14], beverage [15], cosmetics [16], pharmaceutical [17], and feed and forage testing industries [18], and is routinely used for the rapid characterization of biomass feedstock materials in the laboratory [12], [13]. Combined standard deviations of less than 8% are anticipated for online processing [19]. An RSD of 1.3 to 5.7% for crude protein and ADF and NDF measurements [18] have been demonstrated for feed and forage testing.…”

Section: Feedstockmentioning

confidence: 99%

Evolution and Development of Effective Feedstock Specifications

Gresham¹,

Emerson²,

Hoover³

et al. 2013

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Section: Feedstockmentioning

confidence: 99%

Evolution and Development of Effective Feedstock Specifications

Gresham¹,

Emerson²,

Hoover³

et al. 2013

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“…With multivariate extension of univariate uncertainty determination, the sampling interval can be determined on the basis of score vectors. Despite the fact that multivariate methods have been found highly suitable in handling of spectral data and univariate sampling optimization is well founded , multivariate optimization of sampling frequency is only rarely carried out .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of variation in dynamic processes via online spectrometers

Kohonen

Alatalo

Рейникайнен

2012

Journal of Chemometrics

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Theory of sampling offers powerful tools for process optimization. An adequate sampling interval can be determined for spectral measurements when utilizing a multivariate extension of variography by applying score vectors as independent sources of uncertainty. The traditional way is to apply variographic analyses into single process variables independently. In the multivariate extension, those process variables are replaced with score vectors of principal component analysis. The combined uncertainty found this way depends not only on the variance in the spectra, but also, for example, on the number of utilized score vectors and the preprocessing method. This approach is illustrated with a crystallization process continuously followed with an attenuated total reflectance Fourier transform infrared instrument. The results show that the approach is highly applicable but should only be utilized as an indicative tool. Copyright © 2012 John Wiley & Sons, Ltd.

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