FTIR spectroscopy with machine learning: A new approach to animal DNA polymorphism screening

Rios, Thaynádia Gomes; Larios, Gustavo; Marangoni, Bruno; Oliveira, Sílvio C. de; Cena, Cícero; Ramos, Carlos Alberto do Nascimento

doi:10.1016/j.saa.2021.120036

Cited by 15 publications

(10 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Pacher,

Franca,

Lacerda

et al. 2024

ACS Infect. Dis.

Self Cite

View full text Add to dashboard Cite

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800−700 cm −1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: ■ Results and Discussionmentioning

confidence: 99%

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Pacher,

Franca,

Lacerda

et al. 2024

ACS Infect. Dis.

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

Beagle: a near-edge X-ray absorption fine-structure spectroscopy data processing solution for beamline experiments at Pohang Accelerator Laboratory

Park,

Lee,

Jeong

et al. 2024

J Synchrotron Radiat

View full text Add to dashboard Cite

Near-edge X-ray absorption fine-structure (NEXAFS) spectroscopy is a powerful tool for identifying chemical bonding states at synchrotron radiation facilities. Advances in new materials require researchers in both academia and industry to measure tens to hundreds of samples during the available beam time on a synchrotron beamline, which is typically allocated to users. Automated measurement methods, along with analysis software, have been developed for beamlines. Automated measurements facilitate high-throughput experiments and accumulate vast amounts of measured spectral data. The analysis software supports various functions for analyzing the experimental data; however, these analysis methods are complicated, and learning them can be time-consuming. To process large amounts of spectral data, a new analysis software, dedicated to NEXAFS spectroscopy, that is easy to use and can provide results in a short time is desired. Herein, the development of Beagle is described, software calculating molecular orientation from NEXAFS spectroscopy data that can report results in a short time comparable with that required to measure one sample at the beamline. It was designed to progress in a single sequence from data loading to the printing of the results with a `click of a button'. The functions of the software include recognizing the dataset, correcting the background, normalizing the plot, calculating the electron yield and determining the molecular orientation. The analysis results can be saved as {\tt{.txt}} files (spectral data), {\tt{.pdf}} files (graphic images) and Origin files (spectral data and graphic images).

show abstract

“…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%

Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films

Chávez‐Ángel

Sandell

et al. 2023

Polymers

View full text Add to dashboard Cite

The thermal imaging of surfaces with microscale spatial resolution over micro-sized areas remains a challenging and time-consuming task. Surface thermal imaging is a very important characterization tool in mechanical engineering, microelectronics, chemical process engineering, optics, microfluidics, and biochemistry processing, among others. Within the realm of electronic circuits, this technique has significant potential for investigating hot spots, power densities, and monitoring heat distributions in complementary metal–oxide–semiconductor (CMOS) platforms. We present a new technique for remote non-invasive, contactless thermal field mapping using synchrotron radiation-based Fourier-transform infrared microspectroscopy. We demonstrate a spatial resolution better than 10 um over areas on the order of 12 000 um2 measured in a polymeric thin film on top of CaF2 substrates. Thermal images were obtained from infrared spectra of poly(methyl methacrylate) thin films heated with a wire. The temperature dependence of the collected infrared spectra was analyzed via linear regression and machine learning algorithms, namely random forest and k-nearest neighbor algorithms. This approach speeds up signal analysis and allows for the generation of hyperspectral temperature maps. The results here highlight the potential of infrared absorbance to serve as a remote method for the quantitative determination of heat distribution, thermal properties, and the existence of hot spots, with implications in CMOS technologies and other electronic devices.

show abstract

FTIR spectroscopy with machine learning: A new approach to animal DNA polymorphism screening

Cited by 15 publications

References 29 publications

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Beagle: a near-edge X-ray absorption fine-structure spectroscopy data processing solution for beamline experiments at Pohang Accelerator Laboratory

Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films

Contact Info

Product

Resources

About