scholarly journals Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2028
Author(s):  
Alexander Miller ◽  
Jacqueline Huvanandana ◽  
Peter Jones ◽  
Heather Jeffery ◽  
Angela Carberry ◽  
...  
Keyword(s):  
Young Children ◽  
South African ◽  
Fat Mass ◽  
Near Infrared ◽  
Reference Method ◽  
Growth Monitoring ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Body Composition Assessment ◽  
Infants And Young Children

Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3–24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3–24 months (all subjects), 0–6 months, and 7–24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings.

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

scholarly journals Climate predicts both visible and near‐infrared reflectance in butterflies

2021 ◽  
Author(s):  
Changku Kang ◽  
Sehyeok Im ◽  
Won Young Lee ◽  
Yunji Choi ◽  
Devi Stuart‐Fox ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

scholarly journals One-Pot Hydrothermal Synthesis of Victoria Green (Ca3Cr2Si3O12) Nanoparticles in Alkaline Fluids and Its Colour Hue Characterisation

Nanomaterials ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 521
Author(s):  
Juan Carlos Rendón-Angeles ◽  
Zully Matamoros-Veloza ◽  
Jose Luis Rodríguez-Galicia ◽  
Gimyeong Seong ◽  
Kazumichi Yanagisawa ◽  
...  
Keyword(s):  
Near Infrared ◽  
Single Step ◽  
Spectral Space ◽  
Infrared Reflectance ◽  
Hydrothermal Preparation ◽  
Near Infrared Reflectance ◽  
One Pot ◽  
Factors Affecting ◽  
B Values ◽  
Alkaline Conditions

One-pot hydrothermal preparation of Ca3Cr2Si3O12 uvarovite nanoparticles under alkaline conditions was investigated for the first time. The experimental parameters selected for the study considered the concentration of the KOH solvent solution (0.01 to 5.0 M), the agitation of the autoclave (50 rpm), and the nominal content of Si4+ (2.2–3.0 mole). Fine uvarovite particles were synthesised at 200 °C after a 3 h interval in a highly concentrated 5.0 M KOH solution. The crystallisation of single-phase Ca3Cr2Si3O12 particles proceeded free of by-products via a one-pot process involving a single-step reaction. KOH solutions below 2.5 M and water hindered the crystallisation of the Ca3Cr2Si3O12 particles. The hydrothermal treatments carried out with stirring (50 rpm) and non-stirring triggered the crystallisation of irregular anhedral particles with average sizes of 8.05 and 12.25 nm, respectively. These particles spontaneously assembled into popcorn-shaped agglomerates with sizes varying from 66 to 156 nm. All the powders prepared by the present method exhibited CIE-L*a*b* values that correspond to the Victoria green colour spectral space and have a high near infrared reflectance property. The particle size and structural crystallinity are factors affecting the Victoria pigment optical properties, such as CIE-L*a*b* values, green tonality, and near-infrared reflectance.

scholarly journals Methods to Assess Fat Mass in Infants and Young Children: A Comparative Study Using Skinfold Thickness and Air-Displacement Plethysmography

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 75
Author(s):  
Stefanie M. P. Kouwenhoven ◽  
Nadja Antl ◽  
Jos W. R. Twisk ◽  
Berthold V. Koletzko ◽  
Martijn J. J. Finken ◽  
...  
Keyword(s):  
Young Children ◽  
Fat Mass ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Correlation Coefficients ◽  
Skinfold Thickness ◽  
Limited Information ◽  
Air Displacement Plethysmography ◽  
Mixed Model Analysis ◽  
Infants And Young Children

Background: Traditionally, fat mass is estimated using anthropometric models. Air-displacement plethysmography (ADP) is a relatively new technique for determining fat mass. There is limited information on the agreement between these methods in infants and young children. Therefore we aimed to longitudinally compare fat mass percentage values predicted from skinfold thicknesses (SFTs) and ADP in healthy infants and young children. Methods: Anthropometry and body composition were determined at the ages of 1, 4, and 6 months and 2 years. We quantified the agreement between the two methods using the Bland–Altman procedure, linear mixed-model analysis, and intra-class correlation coefficients (ICC). Results: During the first 6 months of life, fat mass% predicted with SFT was significantly different from that measured with ADP in healthy, term-born infants (n = 245). ICCs ranged from 0.33 (at 2 years of age) and 0.47 (at 4 months of age). Although the mean difference (bias) between the methods was low, the Bland–Altman plots showed proportional differences at all ages with wide limits of agreement. Conclusions: There is poor agreement between ADP and SFTs for estimating fat mass in infancy or early childhood. The amount of body fat was found to influence the agreement between the methods.

Sign in / Sign up

Export Citation Format

Share Document