Multivariate data analysis of near-infrared spectra of cultivation medium powders for mammalian cells

The pyrometallurgical processes for primary copper production have only off-line and time-demanding analytical techniques to characterize the in and out streams of the smelting and converting steps. Since these processes are highly exothermic, relevant process information could potentially be obtained from the visible and near-infrared radiation emitted to the environment. In this work, we apply spectral sensing and multivariate data analysis methodologies to identify and classify copper and iron sulfide minerals present in the blend from spectra measured during their combustion in a laboratory drop-tube setup, in which chemical reactions that take place in flash smelting furnaces can be reproduced. Controlled combustion experiments were conducted with two industrial concentrates and with high-grade mineral species as well, with a focus on pyrite and chalcopyrite. Exploratory analysis by means of Principal Component Analysis (PCA) applied on the spectral data depicted high correlation features among species with similar elemental compositions. Classification algorithms were tested on the spectral data, and a classification accuracy of 95.3% with a support vector machine (SVM) algorithm with a Gaussian kernel was achieved. The results obtained by the described procedures are shown to be very promising as a first step in the development of a predictive and analytical tool in search of fitting the current need for real-time control of pyrometallurgical processes.

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Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

Applied Sciences ◽

10.3390/app7010090 ◽

2017 ◽

Vol 7 (1) ◽

pp. 90 ◽

Cited By ~ 12

Author(s):

Xin Zhao ◽

Wei Wang ◽

Xuan Chu ◽

Chunyang Li ◽

Daniel Kimuli

Keyword(s):

Data Analysis ◽

Early Detection ◽

Hyperspectral Imaging ◽

Multivariate Data Analysis ◽

Near Infrared ◽

Aspergillus Parasiticus ◽

Multivariate Data ◽

Maize Kernels

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Fungal decay of spruce and beech wood assessed by near-infrared spectroscopy in combination with uni- and multivariate data analysis

Holzforschung ◽

10.1515/hf.2007.098 ◽

2007 ◽

Vol 61 (6) ◽

pp. 680-687 ◽

Cited By ~ 26

Author(s):

Karin Fackler ◽

Manfred Schwanninger ◽

Cornelia Gradinger ◽

Ewald Srebotnik ◽

Barbara Hinterstoisser ◽

...

Keyword(s):

Data Analysis ◽

Multivariate Data Analysis ◽

Near Infrared ◽

Lignin Content ◽

Beech Wood ◽

Multivariate Data ◽

Principal Component ◽

Brown Rot ◽

White Rot ◽

Least Squares Regression

Abstract Wood is colonised and degraded by a variety of micro-organisms, the most efficient ones are wood-rotting basidiomycetes. Microbial decay processes cause damage to wooden constructions, but also have great potential as biotechnological tools to change the properties of wood surfaces and of sound wood. Standard methods to evaluate changes in infected wood, e.g., EN350-1 1994, are time-consuming. Rapid FT-NIR spectroscopic methods are also suitable for this purpose. In this paper, degradation experiments on surfaces of spruce (Picea abies L. Karst) and beech (Fagus silvatica L.) were carried out with white rot basidiomycetes or the ascomycete Hypoxylon fragiforme. Experiments with brown rot or soft rot caused by Chaetomium globosum were also performed. FT-NIR spectra collected from the degraded wood were subjected to principal component analysis. The lignin content and mass loss of the specimens were estimated based on univariate or multivariate data analysis (partial least squares regression).

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