Predictive modeling inClostridium acetobutylicumfermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

2016 ◽  
Author(s):  
Theresah N. K. Zu ◽  
Sanchao Liu ◽  
Katherine L. Germane ◽  
Matthew D. Servinsky ◽  
Elliot S. Gerlach ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Data Analysis ◽  
Real Time ◽  
Predictive Modeling ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Time Culture ◽  
Culture Monitoring

Classification of iron-based inks by means of micro-Raman spectroscopy and multivariate data analysis

2013 ◽  
Vol 44 (9) ◽  
pp. 1299-1305 ◽  
Author(s):  
Giovanna Piantanida ◽  
Eva Menart ◽  
Marina Bicchieri ◽  
Matija Strlič
Keyword(s):  
Raman Spectroscopy ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Micro Raman Spectroscopy ◽  
Iron Based

Raman microspectroscopy and multivariate data analysis: optical differentiation of aqueous d- and l-tryptophan solutions

2017 ◽  
Vol 19 (45) ◽  
pp. 30533-30539 ◽  
Author(s):  
H. Koch ◽  
S. Polepil ◽  
K. Eisen ◽  
S. Will
Keyword(s):  
Raman Spectroscopy ◽  
Data Analysis ◽  
Intermolecular Interactions ◽  
Measurement Technique ◽  
Multivariate Data Analysis ◽  
Optical Measurement ◽  
Multivariate Data ◽  
Raman Microspectroscopy

The combination of Raman spectroscopy as a sensitive and selective optical measurement technique and multivariate data analysis forms a powerful tool for probing intermolecular interactions between enantiomers.

Differentiation of aged fibers by Raman spectroscopy and multivariate data analysis

Talanta ◽  
2016 ◽  
Vol 154 ◽  
pp. 467-473 ◽  
Author(s):  
Federica Bianchi ◽  
Nicolò Riboni ◽  
Valentina Trolla ◽  
Giada Furlan ◽  
Giorgio Avantaggiato ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data

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.

Discrimination of haloarchaeal genera using Raman spectroscopy and robust methods for multivariate data analysis

2017 ◽  
Vol 48 (8) ◽  
pp. 1122-1126 ◽  
Author(s):  
Nicoleta Elena Dina ◽  
Anda Leş ◽  
Andreea Baricz ◽  
Tiberiu Szöke-Nagy ◽  
Nicolae Leopold ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Robust Methods
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