Li et al., 2023 - Google Patents
Hyperspectral imaging with machine learning approaches for assessing soluble solids content of tribute citruLi et al., 2023
View HTML- Document ID
- 13321835786299854941
- Author
- Li C
- He M
- Cai Z
- Qi H
- Zhang J
- Zhang C
- Publication year
- Publication venue
- Foods
External Links
Snippet
Tribute Citru is a natural citrus hybrid with plenty of vitamins and nutrients. Fruits' soluble solids content (SSC) is a critical quality index. This study used hyperspectral imaging at two spectral ranges (400–1000 nm and 900–1700 nm) to determine SSC in Tribute Citru. Partial …
- 238000000701 chemical imaging 0 title abstract description 39
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
- G01N21/3577—Investigating 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
-
- 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
- G01N21/84—Systems specially adapted for particular applications
-
- 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
- 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
- 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
- G06Q50/01—Social networking
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | Classification of frozen corn seeds using hyperspectral VIS/NIR reflectance imaging | |
Jiang et al. | Determination of adulteration content in extra virgin olive oil using FT-NIR spectroscopy combined with the BOSS–PLS algorithm | |
Weng et al. | Non-destructive detection of strawberry quality using multi-features of hyperspectral imaging and multivariate methods | |
Zhang et al. | Leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors | |
Sarkar et al. | A comparative study of PLSR and SVM-R with various preprocessing techniques for the quantitative determination of soluble solids content of hardy kiwi fruit by a portable Vis/NIR spectrometer | |
Roberts et al. | A short update on the advantages, applications and limitations of hyperspectral and chemical imaging in food authentication | |
Yang et al. | Estimation method of soluble solid content in peach based on deep features of hyperspectral imagery | |
Zhang et al. | Feasibility of the detection of carrageenan adulteration in chicken meat using visible/near-infrared (vis/nir) hyperspectral imaging | |
Huang et al. | Identification of apple varieties using a multichannel hyperspectral imaging system | |
Zhang et al. | NIR hyperspectral imaging technology combined with multivariate methods to study the residues of different concentrations of omethoate on wheat grain surface | |
Zhang et al. | A comprehensive peach fruit quality evaluation method for grading and consumption | |
Liu et al. | Research on the prediction of green plum acidity based on improved XGBoost | |
Li et al. | Hyperspectral imaging with machine learning approaches for assessing soluble solids content of tribute citru | |
Hasanzadeh et al. | Non-destructive detection of fruit quality parameters using hyperspectral imaging, multiple regression analysis and artificial intelligence | |
Xu et al. | Rapid nondestructive detection of water content and granulation in postharvest “shatian” pomelo using visible/near-infrared spectroscopy | |
Gomes et al. | Prediction of sugar content in port wine vintage grapes using machine learning and hyperspectral imaging | |
Wu et al. | Nondestructive analysis of internal quality in pears with a self-made near-infrared spectrum detector combined with multivariate data processing | |
Kandpal et al. | Development of a low-cost multi-waveband LED illumination imaging technique for rapid evaluation of fresh meat quality | |
Hasanzadeh et al. | Non-destructive measurement of quality parameters of apple fruit by using visible/near-infrared spectroscopy and multivariate regression analysis | |
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. | Online detection of watercore apples by Vis/NIR full-transmittance spectroscopy coupled with ANOVA method | |
Wang et al. | Non-destructive detection of pH value of kiwifruit based on hyperspectral fluorescence imaging technology | |
Jiang et al. | Variety identification of Chinese Walnuts using hyperspectral imaging combined with chemometrics | |
Xu et al. | An accuracy improvement method based on multi-source information fusion and deep learning for TSSC and water content nondestructive detection in “luogang” orange | |
Liu et al. | Effects of orientations and regions on performance of online soluble solids content prediction models based on near-infrared spectroscopy for peaches |