[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

John et al., 2022 - Google Patents

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice

John et al., 2022

View HTML
Document ID
1282569514542631543
Author
John R
Bhardwaj R
Jeyaseelan C
Bollinedi H
Singh N
Harish G
Singh R
Nath D
Arya M
Sharma D
Singh S
John K J
Latha M
Rana J
Ahlawat S
Kumar A
Publication year
Publication venue
Frontiers in Nutrition

External Links

Snippet

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic …
Continue reading at www.frontiersin.org (HTML) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/359Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light using near infra-red light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/3577Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light for analysing liquids, e.g. polluted water
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/3563Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light for analysing solids; Preparation of samples therefor
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
    • G01N2021/3155Measuring in two spectral ranges, e.g. UV and visible
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/02Investigating or analysing materials by specific methods not covered by the preceding groups food

Similar Documents

Publication Publication Date Title
John et al. Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice
Cozzolino The ability of near infrared (NIR) spectroscopy to predict functional properties in foods: Challenges and opportunities
Mahesh et al. Comparison of partial least squares regression (PLSR) and principal components regression (PCR) methods for protein and hardness predictions using the near-infrared (NIR) hyperspectral images of bulk samples of Canadian wheat
López et al. Carbohydrate analysis by NIRS-Chemometrics
Arslan et al. Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr.)
Padhi et al. Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm
Hadiwijaya et al. Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy
Lebot et al. Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops
Sun et al. Determination of moisture content in barley seeds based on hyperspectral imaging technology
Wang et al. Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics
Torres et al. LOCAL regression applied to a citrus multispecies library to assess chemical quality parameters using near infrared spectroscopy
Johnson et al. Application of infrared spectroscopy for the prediction of nutritional content and quality assessment of faba bean (Vicia faba L.)
Peiris et al. Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration
Nkouaya Mbanjo et al. Predicting starch content in cassava fresh roots using near-infrared spectroscopy
Ishikawa et al. Development of calibration model to predict nitrogen content in single seeds of cowpea (Vigna unguiculata) using near infrared spectroscopy
Sun et al. Near infrared spectroscopy determination of chemical and sensory properties in tomato
Forte et al. Quality evaluation of fair-trade cocoa beans from different origins using portable near-infrared spectroscopy (NIRS)
Gracia et al. Quantification of betaglucans, lipid and protein contents in whole oat groats (Avena sativa L.) using near infrared reflectance spectroscopy
Liu et al. Rapid determination of total dietary fiber and minerals in Coix seed by near-infrared spectroscopy technology based on variable selection methods
Wang et al. Application of near-infrared spectroscopy for screening the potato flour content in Chinese steamed bread
Maraphum et al. Spatial mapping of Brix and moisture content in sugarcane stalk using hyperspectral imaging
Rahman et al. Mapping the pungency of green pepper using hyperspectral imaging
Shang et al. Authenticity discrimination and adulteration level detection of camellia seed oil via hyperspectral imaging technology
Yang et al. A model for the detection of β-glucan content in oat grain based on near infrared spectroscopy
Shen et al. Classification of fish meal produced in China and Peru by online near infrared spectroscopy with characteristic wavelength variables