2021
DOI: 10.1016/j.saa.2021.120036
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FTIR spectroscopy with machine learning: A new approach to animal DNA polymorphism screening

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Cited by 16 publications
(10 citation statements)
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“…Vibrational bands from N–H and C–H stretching assigned to lipids, proteins, and fatty acid molecules were evidenced in the 2858–2962 cm –1 range. The wide vibrational band at around 3295 cm –1 is attributed to the superposition of N–H and O–H molecular modes between proteins and water molecules. , …”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Vibrational bands from N–H and C–H stretching assigned to lipids, proteins, and fatty acid molecules were evidenced in the 2858–2962 cm –1 range. The wide vibrational band at around 3295 cm –1 is attributed to the superposition of N–H and O–H molecular modes between proteins and water molecules. , …”
Section: Resultsmentioning
confidence: 99%
“…The wide vibrational band at around 3295 cm −1 is attributed to the superposition of N−H and O−H molecular modes between proteins and water molecules. 17,21 The FTIR-normalized spectra potential for group separation was analyzed by principal component analysis (PCA). First, we used the PC1 × PC2 score plot to investigate the clustering tendency and group separation for the three sample sets� blood serum, RHFP, and PLN�to improve the cluster formation.…”
Section: ■ Results and Discussionmentioning
confidence: 99%
“…The future developmental plans for Beagle include the following: (i) incorporating artificial intelligence (AI) for faster and more accurate analysis, (ii) enhancing the capability to load more data structures from other beamlines or spectroscopies, and (iii) porting the tool to an open-source programming environment for compatibility with various computer operating systems. Recently, the scope of AI application was expanded to spectral analysis, which conducts data noise reduction (Kim et al, 2021), plot trend recognition (Rios et al, 2021), background correction (Valensise et al, 2020) and peak position determination (Ghosh et al, 2019). Using these AI modules or libraries, molecular orientation can be obtained faster and more accurately.…”
Section: Further Plans For Beaglementioning
confidence: 99%
“…Thus, we incorporate a simple and fast approach to measure temperature via a linear regression (LR) approach. In addition, we also propose the use of machine learning (ML) algorithms to fully exploit FTIR spectra and apply them to extract temperature information from single peaks or even from a continuous spectral range [ 37 , 38 ]. The development of software, faster computers, and ML enable the application of more general approaches for predicting temperature via measured infrared spectra.…”
Section: Introductionmentioning
confidence: 99%