Spectral Interpretation

2021 ◽  
pp. 45-67
Author(s):  
Jerome Workman
Keyword(s):  
Spectral Interpretation

scholarly journals SHRIMP PASTE “TERASI” VOLATILE COMPOUNDS FROM NORTHERN COAST OF CENTRAL JAVA

2016 ◽  
Vol 78 (4-2) ◽  
Author(s):  
Retno Murwani ◽  
Hera Surya Adhi Putra ◽  
Henggar Widiyanto ◽  
Agus Trianto ◽  
Ambariyanto Ambariyanto
Keyword(s):  
Volatile Compounds ◽  
Northern Coast ◽  
Mass Spectral ◽  
Central Java ◽  
Spectral Interpretation

A study was conducted to identify the composition of volatile compounds from traditional fermented shrimp called ‘terasi’. Terasi samples were collected from six regions of northern coast of Central Java, Indonesia namely Pekalongan, Batang, Kendal, Demak, Jepara, and Pati. Mass spectral interpretation showed that terasi from these regions could be identified to contain a total of 102 volatile compounds. Terasi from Pekalongan, Batang, Kendal, Demak, Jepara, and Pati, each contained nine, 21, 10, 29, 12, and 21 volatile compounds respectively. There were four similar volatile compounds from Demak, Jepara, and Pati samples, and two distinctive off odor in all six regions. 

scholarly journals PHOTOGRAMMETRIC PROCESSING OF OSIRIS-REX IMAGES OF ASTEROID (101955) BENNU

2020 ◽  
Vol V-3-2020 ◽  
pp. 587-594
Author(s):  
K. L. Edmundson ◽  
K. J. Becker ◽  
T. L. Becker ◽  
C. A. Bennett ◽  
D. N. DellaGiustina ◽  
...  
Keyword(s):  
Control Process ◽  
Laser Altimeter ◽  
Shape Models ◽  
Spectral Interpretation ◽  
Global Mapping ◽  
Resource Identification ◽  
Imaging Sensors ◽  
Future Work ◽  
Selection Of ◽  
Reconnaissance Surveys

Abstract. The principal objective of the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission is to retrieve a sample of the asteroid (101955) Bennu and return it to Earth. OSIRIS-REx arrived at Bennu in December 2018. Images of the asteroid by the OSIRIS-REx Camera Suite (OCAMS) were photogrammetrically controlled to produce a global basemap and site-specific image mosaics essential to the selection of a primary and backup sample site, which were announced in December 2019. In the control process, OCAMS images were registered to shape models created from OSIRIS-REx Laser Altimeter (OLA) data and from the process of stereophotoclinometry. This paper summarizes the photogrammetric control to date of images collected at Bennu. We briefly review the mission and the OCAMS imaging sensors. We then describe the photogrammetric control process for the global mapping campaign and targeted reconnaissance surveys of candidate sample sites. Finally, we discuss ongoing and future work.

scholarly journals Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

2021 ◽  
Author(s):  
Abigail Enders ◽  
Nicole North ◽  
Chase Fensore ◽  
Juan Velez-Alvarez ◽  
Heather Allen
Keyword(s):  
Machine Learning ◽  
Gas Phase ◽  
Functional Groups ◽  
Functional Group ◽  
Spectroscopic Method ◽  
Group Identification ◽  
Spectroscopic Technique ◽  
Ftir Spectra ◽  
Spectral Interpretation ◽  
Time Required

<p>Fourier Transform Infrared Spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using Convolutional Neural Networks (CNNs) to identify the presence of functional groups in gas phase FTIR spectra. The ML models will reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas phase organic molecules within the NIST spectral database and transform the data into images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that inference in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.</p>

TEAM-BASED LEARNING STRATEGY TO IMPROVE MASS SPECTRAL INTERPRETATION ON POST-GRADUATE STUDENTS

2020 ◽  
Author(s):  
Ane Arrizabalaga-Larrañaga ◽  
Guillem Campmajó ◽  
Juan Francisco Ayala-Cabrera ◽  
Raquel Seró ◽  
Francisco Javier Santos ◽  
...  
Keyword(s):  
Graduate Students ◽  
Learning Strategy ◽  
Post Graduate ◽  
Mass Spectral ◽  
Spectral Interpretation ◽  
Team Based Learning
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