Handbook of Near-Infrared Analysis

doi:10.1201/9781420007374

Cited by 272 publications

(20 citation statements)

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“…It is a spectroscopic method which uses radiation in the NIR region of the electromagnetic spectrum (750-2500 nm) [82][83][84][85][86][87][88][89][90][91]. There are mainly three bands in NIR range i.e.…”

Section: Near-infrared Spectroscopymentioning

confidence: 99%

Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy

Yadav

Rani

Singh

et al. 2015

Biomedical Signal Processing and Control

301

144

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Section: Near-infrared Spectroscopymentioning

confidence: 99%

Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy

Yadav

Rani

Singh

et al. 2015

Biomedical Signal Processing and Control

301

144

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“…were in the NIR regions associated with C = O and O-H overtones in carboxylic acid or water, while for the must and wine absorption spectra ( Figures 3 and 4) the observed peaks (1784, 1927, 2090 and 2270 nm) were in the regions associated with C-H and O-H stretching of starch and alcohol (Boido et al, 2013;Burns & Ciurczak, 2007).…”

Section: Discussionmentioning

confidence: 94%

“…The C-H stretch first overtone associated with aromatic compounds can be seen between 1680 and 1690 nm, while O-H stretching associated with glucose, cellulose, water, and alcohol can be observed at approximately 1930, 2090, 2270, and 2330 nm. Lastly, the region between 1900 and 1910 nm corresponds to C=O stretching associated with carboxylic acids and water (Boido et al, 2013;Burns & Ciurczak, 2007;Gonzalez Viejo et al, 2018). Thus, this region was found to be effective for assessing levels of smoke contamination.…”

Section: Discussionmentioning

confidence: 95%

Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms

Summerson

Viejo

Torrico³

et al. 2020

OENO One

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The number and intensity of wildfires are increasing worldwide, thereby raising the risk of smoke contamination of grapevine berries and the development of smoke taint in wine. This study aimed to develop five artificial neural network (ANN) models from berry, must, and wine samples obtained from grapevines exposed to different levels of smoke: (i) Control (C), i.e., no misting or smoke exposure; (ii) Control with misting (CM), i.e., in-canopy misting, but no smoke exposure; (iii) low-density smoke treatment (LS); (iv) high-density smoke treatment (HS) and (v) a high-density smoke treatment with misting (HSM). Models 1, 2, and 3 were developed using the absorbance values of near-infrared (NIR) berry spectra taken one day after smoke exposure to predict levels of 10 volatile phenols (VP) and 18 glycoconjugates in grapes at either one day after smoke exposure (Model 1: R = 0.98; R2 = 0.97; b = 1) or at harvest (Model 2: R = 0.98; R2 = 0.97; b = 0.97), as well as six VP and 17 glycoconjugates in the final wine (Model 3: R = 0.98; R2 = 0.95; b = 0.99). Models 4 and 5 were developed to predict the levels of six VP and 17 glycoconjugates in wine. Model 4 used must NIR absorbance spectra as inputs (R = 0.99; R2 = 0.99; b = 1.00), while Model 5 used wine NIR absorbance spectra (R = 0.99; R2 = 0.97; b = 0.97). All five models displayed high accuracies and could be used by grape growers and winemakers to non-destructively assess at near real-time the levels of smoke-related compounds in grapes and/or wine in order to make timely decisions about grape harvest and smoke taint mitigation techniques in the winemaking process.

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“…Comparing the NIR absorption bands with the measurement result of the NIR reflectance curves in Figure 3, it can be recognized that the curve shapes in R-1 and R-2 are more stable than those in other ranges, because the wave spectra in higher-order (i.e., second and third) overtones tend to be less complicated due to the concept of the local modes. The main idea of the local mode model is to treat a molecule as if it were made up of a set of equivalent diatomic oscillators [30]. The absorption bands in the spectrum can, thus, be interpreted as if they are originated from the local modes that makes the spectra in R-1 and R-2 appear more stable compared to other regions [30].…”

Section: F Effect Of Nir Wavelength To the Shape Of The Reflection Cmentioning

confidence: 99%

Study of Particle Reduction Method and Particle Size Effect on TiO<sub>2</sub> as Near-infrared Reflector Coating in Cotton Fabric

Hamzens

Kurniawan

Adhika

et al. 2021

e-J. Surf. Sci. Nanotechnol.

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A near-infrared (NIR) reflector in cotton clothes may prevents several dermatological problems. TiO 2 is commonly used for the NIR reflector coating in cotton. The NIR reflectance value is expected to increase if smaller TiO 2 particles were used. A simple mechanical process of ultrasonic wet-milling to reduce the TiO 2 particle size is proposed in this study. The reduction process utilizes ultrasonic cavitation by an ultrasonic homogenizer has succeeded in reducing the particle size from 170 to 80 nm as indicated by particle size measurement results and as supported by transmission electron microscopy results. The wet-milling particles of 80 nm TiO 2 , along with 170 and 280 nm TiO 2 , were used to coat cotton fabrics. Optimization of the coating process was performed using citric acid and chitosan as binder agents. The effect of citric acid and chitosan was studied through NIR spectroscopy, scanning electron microscopy, and X-ray fluorescence. From the NIR spectroscopy characterization results, it is found that the fabrics which are coated with the smallest TiO 2 particle size (80 nm) indeed show highest NIR reflectance among others.

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Handbook of Near-Infrared Analysis

Cited by 272 publications

References 0 publications

Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy

Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy

Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms

Study of Particle Reduction Method and Particle Size Effect on TiO<sub>2</sub> as Near-infrared Reflector Coating in Cotton Fabric

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