A-TEEM^TM, a new molecular fingerprinting technique: simultaneous absorbance-transmission and fluorescence excitation-emission matrix method

Abstract

We investigate the new simultaneous Absorbance-Transmission and fluorescence Excitation-Emission Matrix method for rapid and effective characterization of the varying components from a mixture. The Absorbance-Transmission and fluorescence Excitation-Emission Matrix method uniquely facilitates correction of fluorescence inner-filter effects to yield quantitative fluorescence spectral information that is largely independent of component concentration. This is significant because it allows one to effectively monitor quantitative component changes using multivariate methods and to generate and evaluate spectral libraries. 
 We present the use of this novel instrument in different fields: i.e. tracking changes in complex mixtures including natural water, wine as well as monitoring stability and aggregation of hormones for biotherapeutics.&#13.

Fluorescence characteristics of dissolved organic matter during anaerobic digestion of oil crop straw inoculated with rumen liquid

“…The accompanying Aqualog-coupled software was equipped with a built-in tool for normalization of water Raman scattering as well as correction of the IFE and Rayleigh masking effects. 21 The intensities were further converted to Raman units (RU) by dividing the measured intensities by the Raman peak intensity of ultrapure water at Ex 350 nm. The fully corrected EEM data (the final dimensions of the data array were: 40 samples × 125 excitations × 121 emissions) were further analysed using PARAFAC with the Solo + MIA 8.6.1 software package from Eigenvector Research, Inc (Manson, WA, USA).…”

Characterization of lysozyme PEGylation products using polarized excitation‐emission matrix spectroscopy

“…However, this could be addressed by using a slower scan speed and multiple acquisitions, but with a concomitant trade off in terms of acquisition time. It would be better to move from a scanning-based system to the use of chargecoupled device-based spectrometers (Quatela et al, 2018), which would reduce measurement time significantly and thus facilitate high SNR data acquisition. Furthermore, we have shown by the use of variable selection that the full EEM space may not be required, and by reducing the measurement to those excitation wavelengths containing the essential spectral information, a substantial decrease in the acquisition time is possible.…”

Chemometrics and Related Fields in Python

“…60 For example, singular value decomposition (SDV) 59 is calculated by using nympy.linalg.svd class. Parallel factor analysis (PARAFAC) 61,62 of dimensionality reduction for multi-dimensional array data is included in the scikit-tensor-py3 (sktensor) 63 library. Such multi-dimensional array data are obtained in many different types of instrumental analyses through hyphenated techniques, e.g., GC-MS, [64][65][66] LC-MS, [67][68][69] LC-Vis, 37,70 LC-IR, 71,72 TG-IR, 73,74 etc., as well as fluorescence excitation-emission matrix (EEM) spectroscopy, 75,76 hyperspectral imaging, 77,78 microscopic spectroscopy, 36,79,80 etc.…”