scholarly journals SmartSpectrometer—Embedded Optical Spectroscopy for Applications in Agriculture and Industry

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4476
Author(s):  
Julius Krause ◽  
Heinrich Grüger ◽  
Lucie Gebauer ◽  
Xiaorong Zheng ◽  
Jens Knobbe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Real Time ◽  
Spectral Data ◽  
Optical Spectroscopy ◽  
Multivariate Data Analysis ◽  
Multivariate Data

The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its interface is presented. The advantages of the SmartSpectrometer are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field.

scholarly journals Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 1017 ◽  
Author(s):  
Walter Díaz ◽  
Carlos Toro ◽  
Eduardo Balladares ◽  
Victor Parra ◽  
Pablo Coelho ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Iron Sulfide ◽  
Multivariate Data ◽  
Copper Production ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Drop Tube

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.

Attribution of fentanyl analogue synthesis routes by multivariate data analysis of orthogonal mass spectral data

Talanta ◽  
2019 ◽  
Vol 203 ◽  
pp. 122-130 ◽  
Author(s):  
Lina Mörén ◽  
Johanna Qvarnström ◽  
Magnus Engqvist ◽  
Robin Afshin-Sander ◽  
Xiongyu Wu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
Synthesis Routes

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

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