Sheng et al., 2022 - Google Patents
Near-infrared spectroscopy and mode cloning (NIR-MC) for in-situ analysis of crude protein in bambooSheng et al., 2022
View HTML- Document ID
- 9076244039465056051
- Author
- Sheng Q
- Santos-Rivera M
- Ouyang X
- Kouba A
- Vance C
- Publication year
- Publication venue
- Remote Sensing
External Links
Snippet
This study develops Near-Infrared Spectroscopy (NIRS) and Mode-Cloning (MC) for the rapid assessment of the nutritional quality of bamboo leaves, the primary diet of giant pandas (Ailuropoda melanoleuca) and red pandas (Ailurus fulgens). To test the NIR-MC …
- 235000017166 Bambusa arundinacea 0 title abstract description 71
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
- G01N21/359—Investigating 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tao et al. | Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images | |
Tahmasbian et al. | Comparison of hyperspectral imaging and near-infrared spectroscopy to determine nitrogen and carbon concentrations in wheat | |
Howarth et al. | Genotype and environment affect the grain quality and yield of winter oats (Avena sativa L.) | |
Jiang | Comparison and application of non-destructive NIR evaluations of seed protein and oil content in soybean breeding | |
Roberts et al. | A short update on the advantages, applications and limitations of hyperspectral and chemical imaging in food authentication | |
Baath et al. | Predicting forage quality of warm-season legumes by near infrared spectroscopy coupled with machine learning techniques | |
Smith et al. | Machine learning algorithms to predict forage nutritive value of in situ perennial ryegrass plants using hyperspectral canopy reflectance data | |
Soulat et al. | Comparison of the potential abilities of three spectroscopy methods: Near-infrared, mid-infrared, and molecular fluorescence, to predict carotenoid, vitamin and fatty acid contents in cow milk | |
Lugassi et al. | Estimating pasture quality of fresh vegetation based on spectral slope of mixed data of dry and fresh vegetation—Method development | |
Smith et al. | Field spectroscopy to determine nutritive value parameters of individual ryegrass plants | |
Parrini et al. | Can grassland chemical quality be quantified using transform near-infrared spectroscopy? | |
Tan et al. | Combining vis-NIR and NIR spectral imaging techniques with data fusion for rapid and nondestructive multi-quality detection of cherry tomatoes | |
Feng et al. | Practical considerations for using the neospectra-scanner handheld near-infrared reflectance spectrometer to predict the nutritive value of undried ensiled forage | |
Punalekar et al. | Assessing suitability of Sentinel-2 bands for monitoring of nutrient concentration of pastures with a range of species compositions | |
Digman et al. | The relative performance of a benchtop scanning monochromator and handheld Fourier transform near-infrared reflectance spectrometer in predicting forage nutritive value | |
Liu et al. | Effects of orientations and regions on performance of online soluble solids content prediction models based on near-infrared spectroscopy for peaches | |
Wang et al. | Development of a general prediction model of moisture content in maize seeds based on LW-NIR hyperspectral imaging | |
de Oliveira Carneiro et al. | Characterizing and predicting the quality of milled rice grains using machine learning models | |
Guo et al. | Measurements of chemical compositions in corn stover and wheat straw by near-infrared reflectance spectroscopy | |
Sheng et al. | Near-infrared spectroscopy and mode cloning (NIR-MC) for in-situ analysis of crude protein in bamboo | |
Morgan et al. | The application of NIRS to determine animal physiological traits for wildlife management and conservation | |
Nankar et al. | Compositional analyses reveal relationships among components of blue maize grains | |
Zhang et al. | Non-destructive hyperspectral imaging for rapid determination of catalase activity and ageing visualization of wheat stored for different durations | |
Maduro Dias et al. | Near-Infrared Spectroscopy Integration in the Regular Monitorization of Pasture Nutritional Properties and Gas Production | |
Wang et al. | Improved model for starch prediction in potato by the fusion of near-infrared spectral and textural data |