Feng et al., 2023 - Google Patents
Practical considerations for using the neospectra-scanner handheld near-infrared reflectance spectrometer to predict the nutritive value of undried ensiled forageFeng et al., 2023
View HTML- Document ID
- 328447940387182245
- Author
- Feng X
- Cherney J
- Cherney D
- Digman M
- Publication year
- Publication venue
- Sensors
External Links
Snippet
Prediction models of different types of forage were developed using a dataset of near- infrared reflectance spectra collected by three handheld NeoSpectra-Scanners and laboratory reference values for neutral detergent fiber (NDF), in vitro digestibility (IVTD) …
- 239000004459 forage 0 title abstract description 57
Classifications
-
- 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
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- 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
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
-
- 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
-
- 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
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- 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
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
-
- 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
- G06Q10/00—Administration; Management
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tahmasbian et al. | Comparison of hyperspectral imaging and near-infrared spectroscopy to determine nitrogen and carbon concentrations in wheat | |
Baath et al. | Predicting forage quality of warm-season legumes by near infrared spectroscopy coupled with machine learning techniques | |
Ye et al. | Application of near-infrared spectroscopy and hyperspectral imaging combined with machine learning algorithms for quality inspection of grape: a review | |
Feng et al. | Combining Fourier transform mid-infrared spectroscopy with chemometric methods to detect adulterations in milk powder | |
Lim et al. | Application of near infrared reflectance spectroscopy for rapid and non-destructive discrimination of hulled barley, naked barley, and wheat contaminated with Fusarium | |
Feng et al. | Practical considerations for using the neospectra-scanner handheld near-infrared reflectance spectrometer to predict the nutritive value of undried ensiled forage | |
Beć et al. | Advances in near-infrared spectroscopy and related computational methods | |
Lei et al. | Molecular structural changes in alfalfa detected by ATR-FTIR spectroscopy in response to silencing of TT8 and HB12 genes | |
Goi et al. | Application of a handheld near-infrared spectrometer to predict gelatinized starch, fiber fractions, and mineral content of ground and intact extruded dry dog food | |
Digman et al. | The relative performance of a benchtop scanning monochromator and handheld Fourier transform near-infrared reflectance spectrometer in predicting forage nutritive value | |
Tan et al. | Combining vis-NIR and NIR spectral imaging techniques with data fusion for rapid and nondestructive multi-quality detection of cherry tomatoes | |
Zhang et al. | Algorithm of stability-analysis-based feature selection for NIR calibration transfer | |
Yu et al. | Multiscale deepspectra network: detection of pyrethroid pesticide residues on the Hami melon | |
Zhao et al. | Single-and multiple-adulterants determinations of goat milk powder by NIR spectroscopy combined with chemometric algorithms | |
Sitorus et al. | Exploring deep learning to predict coconut milk adulteration using FT-NIR and micro-NIR spectroscopy | |
Lim et al. | Classification of Fusarium-infected Korean hulled barley using near-infrared reflectance spectroscopy and partial least squares discriminant analysis | |
Bowler et al. | Domain adaptation for in-line allergen classification of agri-food powders using near-infrared spectroscopy | |
Abincha et al. | Portable spectroscopy calibration with inexpensive and simple sampling reference alternatives for dry matter and total carotenoid contents in cassava roots | |
Jiang et al. | Development of electronic nose and near infrared spectroscopy analysis techniques to monitor the critical time in SSF process of feed protein | |
Nadimi et al. | Recent applications of near-infrared spectroscopy in food quality analysis | |
Han et al. | Detection of spray-dried porcine plasma (SDPP) based on electronic nose and near-infrared spectroscopy data | |
Alemu et al. | Optimizing near infrared reflectance spectroscopy to predict nutritional quality of chickpea straw for livestock feeding | |
Wu et al. | Several Feature Extraction Methods Combined with Near-Infrared Spectroscopy for Identifying the Geographical Origins of Milk | |
Tassone et al. | Laboratory analyses used to define the nutritional parameters and quality indexes of some unusual forages | |
Sheng et al. | Near-infrared spectroscopy and mode cloning (NIR-MC) for in-situ analysis of crude protein in bamboo |