Near Infra Red Spectroscopy

Eldin, Ahmed Badr

doi:10.5772/24208

Cited by 16 publications

(15 citation statements)

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“…Unlike methods such as HPLC or infrared (IR) spectroscopy, these specific differences in the spectra cannot be assigned to a certain compound. In this case, it is more like a group of compounds that, at certain wavelength ranges, have CH 3 , CH 2 , OH or other overtones that can be correlated to the specific UV‐vis and NIR regions 35 . Although, in most cases, chemometric analysis is firstly done on the whole spectra, in this case, the best results were obtained with the combination of three spectral ranges; 410–900, 904–928, and 1399–1699 nm.…”

Section: Resultsmentioning

confidence: 99%

Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Valinger

Jurina

Šain

et al. 2020

Phytochemical Analysis

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Objectives The aim of this study was to develop artificial neural network (ANNs) models for prediction of physical (total dissolved solids, extraction yield) and chemical (total polyphenolic content, antioxidant activity) properties of industrial hemp extracts, prepared by two different extraction methods (solid‐liquid extraction and microwave‐assisted extraction) based on combined UV‐VIS‐NIR spectra. Spectral data were gathered for 46 samples per extraction method. Results The PCA analysis ensured efficient separation of the samples based on the amount of ethanol in extraction solvent using NIR spectra for both conventional and microwave‐assisted extraction. Conclusions Results showed that reliable ANN models (R2>0.7000) for describing physical, chemical, and simultaneously physical and chemical characteristics can be developed based on combined UV‐VIS‐NIR spectra of industrial hemp extracts without spectra pre‐processing.

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Section: Resultsmentioning

confidence: 99%

Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Valinger

Jurina

Šain

et al. 2020

Phytochemical Analysis

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“…C-H bonds are basic chemical building blocks of life and could be responsible for numerous structures within the P. falciparum parasite. Four wavelengths of interest represent lipids [49–54]. Lipids have been found in P. falciparum and have been shown to play several roles such as toxicity activity [68], gametocytogenesis [69], and development [70–73].…”

Section: Discussionmentioning

confidence: 99%

“…Summary of peaks identified for P. falciparum their respective functional group relative to literature[49][50][51][52][53][54].…”

mentioning

confidence: 99%

Identification of visible and near-infrared signature peaks for arboviruses andPlasmodium

Goh,

Soares Magalhães,

Ciocchetta

et al. 2023

Preprint

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Arbovirus and malaria infections affect more than half of the world's population causing major financial and physical burden. Current diagnostic tools such as microscopy, molecular and serological techniques are technically demanding, costly, or time consuming. Near-infrared spectroscopy has recently been demonstrated as a potential diagnostic tool for malaria and arbovirus and as a screening tool for disease vectors. However, pathogen specific infrared peaks that allow detection of these infections are yet to be described. In this study, we identified unique NIRS peaks from existing laboratory strains of four major arboviruses including Barmah Forest virus (BFV), Dengue virus (DENV), Ross River virus (RRV), Sindbis virus (SINV) and Plasmodium falciparum. Secondly, to determine the diagnostic ability of these peaks, we developed machine learning algorithms using Artificial Neural network (ANN) to differentiate arboviruses from media in which they are grown. Signature peaks for BFV were identified within the visible region at 410, 430, 562 and 588nm and the NIR region at, 946, 958, 1130, 1154 and 1780 nm. DENV related peaks were seen at 410nm within the visible region and 1130 nm within the NIR region. Signature peaks for RRV were observed within the visible region at 410 and 430 nm and within the NIR region at 1130 and 1780 nm, while SINV had a prominent peak at 410 nm within the visible region. Peaks at 514, 528, 547, 561, 582, and 595nm and peaks at 1388, 1432, 1681, 1700, 1721, 1882, 1905, 2245, 2278, 2300 nm were unique for P. falciparum. NIRS predictive sensitivity defined as the ability to predict an arbovirus as an infection was 90% (n = 20) for BFV, 100% (n =10) for RRV and 97.5% (n= 40) for DENV, while infection specificity defined as the ability to predict media as not-infected was 100% (n= 10). Our findings indicate that spectral signatures obtained by NIRS are potential biomarkers for diagnosis arboviruses and malaria.

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“…Chemical bonds between light atoms in molecules such as C, N, O, and H with primary absorption in the infrared (IR) region have strong vibrational overtones and combination bands that absorb light in the near-infrared (NIR) region (780-2500 nm) of electro-magnetic radiation. The NIRS region in the electromagnetic radiation has a linear relationship between the absorbance and concentration (i.e., the Beer-Lambert-Bouguerre relationship), making it an important analytical tool [38]. The Beer-Lambert-Bouguerre relationship is a main rule in spectrophotometric analysis that gives a great opportunity to find the concentration of a substance by measuring the absorbance of its solution [38].…”

Section: Overview Of Near-infrared Reflectance Spectroscopy (Nirs)mentioning

confidence: 99%

“…The NIRS region in the electromagnetic radiation has a linear relationship between the absorbance and concentration (i.e., the Beer-Lambert-Bouguerre relationship), making it an important analytical tool [38]. The Beer-Lambert-Bouguerre relationship is a main rule in spectrophotometric analysis that gives a great opportunity to find the concentration of a substance by measuring the absorbance of its solution [38].…”

Section: Overview Of Near-infrared Reflectance Spectroscopy (Nirs)mentioning

confidence: 99%

A Review of the Use of Near-Infrared Hyperspectral Imaging (NIR-HSI) Techniques for the Non-Destructive Quality Assessment of Root and Tuber Crops

et al. 2023

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Hyperspectral imaging (HSI) is one of the most often used techniques for rapid quality evaluation for various applications. It is a non-destructive technique that effectively evaluates the quality attributes of root and tuber crops, including yam and cassava, and their food products. Hyperspectral imaging technology, which combines spectroscopy and imaging principles, has an advantage over conventional spectroscopy due to its ability to simultaneously evaluate the physical characteristics and chemical components of various food products and specify their spatial distributions. HSI has demonstrated significant potential for obtaining quick information regarding the chemical composition of the root and tuber, such as starch, protein, dry matter, amylose, and soluble sugars, as well as physical characteristics such as textural properties and water binding capacity. This review highlights the principles of near-infrared hyperspectral imaging (NIR-HSI) techniques combined with relevant image processing tools. It then provides cases of its application in determining crucial biochemical quality traits and textural attributes of roots and tuber crops, focusing on cassava and yam. The need for more information on using NIR-HSI in the quality evaluation of yam and cassava was underscored. It also presents the challenges and prospects of this technology.

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Near Infra Red Spectroscopy

Cited by 16 publications

References 0 publications

Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Identification of visible and near-infrared signature peaks for arboviruses andPlasmodium

A Review of the Use of Near-Infrared Hyperspectral Imaging (NIR-HSI) Techniques for the Non-Destructive Quality Assessment of Root and Tuber Crops

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