Liu et al., 2008 - Google Patents
Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysisLiu et al., 2008
- Document ID
- 16970841482976430110
- Author
- Liu F
- He Y
- Wang L
- Publication year
- Publication venue
- Analytica chimica acta
External Links
Snippet
Near infrared (NIR) spectroscopy based on effective wavelengths (EWs) and chemometrics was proposed to discriminate the varieties of fruit vinegars including aloe, apple, lemon and peach vinegars. One hundred eighty samples (45 for each variety) were selected randomly …
- 235000013399 edible fruits 0 title abstract description 56
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
- G01N21/17—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
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- 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
- G01N33/02—Investigating or analysing materials by specific methods not covered by the preceding groups food
- G01N33/14—Investigating or analysing materials by specific methods not covered by the preceding groups food beverages
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Liu et al. | Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis | |
Liu et al. | Detection of organic acids and pH of fruit vinegars using near-infrared spectroscopy and multivariate calibration | |
Liu et al. | Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy | |
Yang et al. | Deep learning for vibrational spectral analysis: Recent progress and a practical guide | |
Liu et al. | Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar | |
Zhang et al. | Understanding the learning mechanism of convolutional neural networks in spectral analysis | |
Bao et al. | Measurement of soluble solid contents and pH of white vinegars using VIS/NIR spectroscopy and least squares support vector machine | |
Ji-yong et al. | Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine | |
Wang et al. | FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of artificial sweeteners | |
Shao et al. | Visible/near-infrared spectra for linear and nonlinear calibrations: a case to predict soluble solids contents and pH value in peach | |
Rossel | ParLeS: Software for chemometric analysis of spectroscopic data | |
Shi et al. | Improving performance: A collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice | |
Xie et al. | Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics | |
Shao et al. | Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice | |
Wu et al. | Short-wave near-infrared spectroscopy of milk powder for brand identification and component analysis | |
Shah et al. | A feature-based soft sensor for spectroscopic data analysis | |
Bian et al. | Ensemble calibration for the spectral quantitative analysis of complex samples | |
Jiang et al. | Rapid nondestructive detecting of wheat varieties and mixing ratio by combining hyperspectral imaging and ensemble learning | |
Lee et al. | Kernel-based calibration methods combined with multivariate feature selection to improve accuracy of near-infrared spectroscopic analysis | |
Chen et al. | Geographical origin identification of ginseng using near-infrared spectroscopy coupled with subspace-based ensemble classifiers | |
Shi et al. | Whale optimization algorithm-based multi-task convolutional neural network for predicting quality traits of multi-variety pears using near-infrared spectroscopy | |
Liu et al. | Variety identification of rice vinegars using visible and near infrared spectroscopy and multivariate calibrations | |
Bai et al. | Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network | |
Wang et al. | SVM classification method of waxy corn seeds with different vitality levels based on hyperspectral imaging | |
Dong et al. | Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics |