scholarly journals Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra

Sensors ◽  
2013 ◽  
Vol 13 (12) ◽  
pp. 16023-16050 ◽  
Author(s):  
Weijiao Huang ◽  
Jingfeng Huang ◽  
Xiuzhen Wang ◽  
Fumin Wang ◽  
Jingjing Shi
Keyword(s):  
Response Function ◽  
Near Infrared ◽  
Spectral Response ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Satellite Sensors

Determination of Moisture in Cheddar Cheese by Near Infrared Reflectance Spectroscopy

1988 ◽  
Vol 71 (3) ◽  
pp. 571-574 ◽  
Author(s):  
Randy L Wehling ◽  
Michelle M Pierce
Keyword(s):  
Near Infrared ◽  
Spectral Response ◽  
Linear Regression Analysis ◽  
Nir Spectroscopy ◽  
Cheddar Cheese ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Wide Range ◽  
Validation Set

Abstract Near infrared reflectance (NIR) spectroscopy was used to determine the moisture content of Cheddar cheese. Through multiple linear regression analysis, a 3-wavelength calibration was developed for use with a commercial filter monochromator instrument. For a validation set of 47 samples, the correlation coefficient squared (r2) between the NIR and oven moisture methods was 0.92, with a standard error of performance (SEP) of 0.38%. Sample temperature was found to significantly affect the spectral response; therefore, it was necessary to equilibrate all samples to a uniform temperature prior to NIR analysis. Aging may also affect the NIR characteristics of cheese, although it was possible to develop a successful calibration that encompassed a wide range of aging times

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.

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