Chen et al., 2020 - Google Patents
Untargeted identification of adulterated Sanqi powder by near-infrared spectroscopy and one-class modelChen et al., 2020
- Document ID
- 7397862994484522353
- Author
- Chen H
- Tan C
- Li H
- Publication year
- Publication venue
- Journal of Food Composition and Analysis
External Links
Snippet
Sanqi is a widely used traditional Chinese medicines (TCM) for its outstanding efficacy. In Chinese market, Sanqi powder is the goal of counterfeiting for a long time. Investigation of Sanqi authenticity is very important in both economic and public health terms. The present …
- 239000008678 sanqi 0 title abstract description 52
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
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
- G01N2021/653—Coherent methods [CARS]
-
- 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
- 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
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
- G01N21/658—Raman scattering enhancement Raman, e.g. surface plasmons
-
- 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
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
- G01J3/28—Investigating the spectrum
-
- 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
- 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
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/62—Detectors specially adapted therefor
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Chen et al. | Untargeted identification of adulterated Sanqi powder by near-infrared spectroscopy and one-class model | |
Chen et al. | Quantifying several adulterants of notoginseng powder by near-infrared spectroscopy and multivariate calibration | |
Ren et al. | Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality | |
Li et al. | Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis | |
Chu et al. | Development of noninvasive classification methods for different roasting degrees of coffee beans using hyperspectral imaging | |
Chen et al. | Fast discrimination of the geographical origins of notoginseng by near-infrared spectroscopy and chemometrics | |
Pan et al. | Rapid On-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy | |
Long et al. | Fast identification of the geographical origin of Gastrodia elata using excitation-emission matrix fluorescence and chemometric methods | |
Yang et al. | Rapid discrimination of adulteration in Radix Astragali combining diffuse reflectance mid-infrared Fourier transform spectroscopy with chemometrics | |
Xia et al. | Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near‐Infrared Hyperspectral Imaging Technology | |
Sun et al. | Identification of genuine and adulterated pinellia ternata by mid-infrared (MIR) and near-infrared (NIR) spectroscopy with partial least squares-discriminant analysis (PLS-DA) | |
Yu et al. | Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy | |
Fu et al. | Challenges of large-class-number classification (LCNC): a novel ensemble strategy (ES) and its application to discriminating the geographical origins of 25 green teas | |
Qi et al. | An integrated spectroscopic strategy to trace the geographical origins of emblic medicines: Application for the quality assessment of natural medicines | |
Chen et al. | Discrimination between wild-grown and cultivated Gastrodia elata by near-infrared spectroscopy and chemometrics | |
Giebelhaus et al. | Detection of common adulterants in olive oils by bench top 60 MHz 1H NMR with partial least squares regression | |
Biancolillo et al. | ATR-FTIR-based rapid solution for the discrimination of lentils from different origins, with a special focus on PGI and Slow Food typical varieties | |
Zhu et al. | Identification of peanut oil origins based on Raman spectroscopy combined with multivariate data analysis methods | |
Chen et al. | Geographical origin identification of ginseng using near-infrared spectroscopy coupled with subspace-based ensemble classifiers | |
Li et al. | Data fusion of multiple‐information strategy based on Fourier transform near infrared spectroscopy and Fourier‐transform mid infrared for geographical traceability of Wolfiporia cocos combined with chemometrics | |
Qin et al. | Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae | |
Jin et al. | Rapid discrimination of Anji Baicha origin using field-portable spectroradiometer | |
Jang et al. | A weighted twin support vector machine as a potential discriminant analysis tool and evaluation of its performance for near-infrared spectroscopic discrimination of the geographical origins of diverse agricultural products | |
Bai et al. | Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network | |
Zhao et al. | Accurate and non-destructive identification of origins for lily using near-infrared hyperspectral imaging combined with machine learning |