SG10201900755WA - Methods and system for processing lipid contents of at least one oil sample and simulating at least one training sample, and for predicting a blending formula, amongst others - Google Patents

Methods and system for processing lipid contents of at least one oil sample and simulating at least one training sample, and for predicting a blending formula, amongst others

Info

Publication number
SG10201900755WA
SG10201900755WA SG10201900755WA SG10201900755WA SG10201900755WA SG 10201900755W A SG10201900755W A SG 10201900755WA SG 10201900755W A SG10201900755W A SG 10201900755WA SG 10201900755W A SG10201900755W A SG 10201900755WA SG 10201900755W A SG10201900755W A SG 10201900755WA
Authority
SG
Singapore
Prior art keywords
amongst
simulating
predicting
methods
sample
Prior art date
Application number
SG10201900755WA
Inventor
Junliang Kevin Lim
Original Assignee
Wilmar International Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wilmar International Ltd filed Critical Wilmar International Ltd
Priority to SG10201900755WA priority Critical patent/SG10201900755WA/en
Priority to EP20151355.3A priority patent/EP3686596A3/en
Priority to PCT/SG2020/050030 priority patent/WO2020159438A1/en
Priority to CN202010076889.7A priority patent/CN111487384B/en
Publication of SG10201900755WA publication Critical patent/SG10201900755WA/en

Links

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02Food
    • G01N33/03Edible oils or edible fats
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30Prediction of properties of chemical compounds, compositions or mixtures
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02Column chromatography
    • G01N30/86Signal analysis
    • G01N30/8693Models, e.g. prediction of retention times, method development and validation
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02Column chromatography
    • G01N30/86Signal analysis
    • G01N30/8696Details of Software
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computing arrangements based on specific mathematical models
    • G06N7/01Probabilistic graphical models, e.g. probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/084Backpropagation, e.g. using gradient descent
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70Machine learning, data mining or chemometrics

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Computing Systems (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Analytical Chemistry (AREA)
  • General Engineering & Computer Science (AREA)
  • Biochemistry (AREA)
  • Mathematical Physics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Food Science & Technology (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Algebra (AREA)
  • Medicinal Chemistry (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Edible Oils And Fats (AREA)
SG10201900755WA 2019-01-28 2019-01-28 Methods and system for processing lipid contents of at least one oil sample and simulating at least one training sample, and for predicting a blending formula, amongst others SG10201900755WA (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
SG10201900755WA SG10201900755WA (en) 2019-01-28 2019-01-28 Methods and system for processing lipid contents of at least one oil sample and simulating at least one training sample, and for predicting a blending formula, amongst others
EP20151355.3A EP3686596A3 (en) 2019-01-28 2020-01-13 Methods and system for processing lipid contents of at least one oil sample, simulating at least one training sample, and for predicting a blending formula
PCT/SG2020/050030 WO2020159438A1 (en) 2019-01-28 2020-01-22 Methods and system for processing lipid contents of at least one oil sample and simulating at least one training sample, and for predicting a blending formula, amongst others
CN202010076889.7A CN111487384B (en) 2019-01-28 2020-01-23 Method and system for processing lipid content simulation training samples and predicting blend formulations

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
SG10201900755WA SG10201900755WA (en) 2019-01-28 2019-01-28 Methods and system for processing lipid contents of at least one oil sample and simulating at least one training sample, and for predicting a blending formula, amongst others

Publications (1)

Publication Number Publication Date
SG10201900755WA true SG10201900755WA (en) 2020-08-28

Family

ID=69411210

Family Applications (1)

Application Number Title Priority Date Filing Date
SG10201900755WA SG10201900755WA (en) 2019-01-28 2019-01-28 Methods and system for processing lipid contents of at least one oil sample and simulating at least one training sample, and for predicting a blending formula, amongst others

Country Status (4)

Country Link
EP (1) EP3686596A3 (en)
CN (1) CN111487384B (en)
SG (1) SG10201900755WA (en)
WO (1) WO2020159438A1 (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113406249B (en) * 2021-06-16 2022-04-05 江南大学 Method for predicting adulterated oil types in tea oil
CN115985408B (en) * 2023-02-08 2023-10-13 嘉兴红点应用科技有限公司 Processing method and system of industrial yarn oiling agent
CN116990409A (en) * 2023-07-17 2023-11-03 中国科学院兰州化学物理研究所 Extra-high virgin olive oil identification method based on squalene and sterol composition

Family Cites Families (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5699270A (en) * 1995-06-23 1997-12-16 Exxon Research And Engineering Company Method for preparing lubrication oils (LAW232)
EP1382012B1 (en) * 2002-03-26 2007-01-17 Council of Scientific and Industrial Research Improved performance of artificial neural network models in the presence of instrumental noise and measurement errors
US7462490B2 (en) * 2003-10-31 2008-12-09 Chevron Oronite Company Llc Combinatorial lubricating oil composition libraries
US8407012B2 (en) * 2008-07-03 2013-03-26 Cold Spring Harbor Laboratory Methods and systems of DNA sequencing
US20100305872A1 (en) * 2009-05-31 2010-12-02 University Of Kuwait Apparatus and Method for Measuring the Properties of Petroleum Factions and Pure Hydrocarbon Liquids by Light Refraction
US9846147B2 (en) * 2012-01-06 2017-12-19 Bharat Petroleum Corporation Ltd. Prediction of refining characteristics of oil
CN103344661B (en) * 2013-07-05 2016-02-24 上海适济生物科技有限公司 A kind of method using hydrogen nuclear magnetic resonance method to identify adulterated oil and waste oil
JP2015052663A (en) * 2013-09-06 2015-03-19 キヤノン株式会社 Image processing method, image processing device, image-capturing device, and program
CN104090044B (en) * 2014-07-16 2016-03-23 中国农业科学院油料作物研究所 A kind of for analyzing the method for fatty acid composition and the edible vegetable oil distinguishing method between true and false based on fatty acid composition in edible vegetable oil
CN105353044B (en) * 2014-08-18 2018-09-14 丰益(上海)生物技术研发中心有限公司 The detection method of fat content
CN104597193A (en) * 2014-12-31 2015-05-06 中国农业科学院油料作物研究所 Peanut oil adulteration qualitative identification method
WO2016141451A1 (en) * 2015-03-11 2016-09-15 Hormoz Azizian Method and technique for verification of olive oil composition
CN105241861B (en) * 2015-09-18 2018-05-08 中国人民解放军第二军医大学 It is a kind of quickly to detect ten kinds of methods for mixing pseudo- component in lipid-loweringing class Chinese patent drug at the same time
US10519770B2 (en) * 2016-03-22 2019-12-31 Halliburton Energy Services, Inc. Calibration module for pooled optical sensors in downhole fluid analysis
CN107543838B (en) * 2017-09-20 2019-07-26 北京市食品安全监控和风险评估中心(北京市食品检验所) A kind of adulterated magnetic resonance detection method for planting butter cream in dilute cream
CN107727590A (en) * 2017-09-27 2018-02-23 天津工业大学 A kind of quantitative analysis method of polynary ready-mixed oil quick nondestructive
CN107703065A (en) * 2017-09-27 2018-02-16 天津工业大学 It is a kind of to be used for the versatility model that soybean oil content is predicted in ready-mixed oil
CN109002686B (en) * 2018-04-26 2022-04-08 浙江工业大学 Multi-grade chemical process soft measurement modeling method capable of automatically generating samples
SG10201811511RA (en) * 2018-12-21 2020-07-29 Wilmar International Ltd Method and system for predicting oil adulteration of an edible oil sample

Also Published As

Publication number Publication date
CN111487384B (en) 2023-05-02
EP3686596A2 (en) 2020-07-29
CN111487384A (en) 2020-08-04
WO2020159438A1 (en) 2020-08-06
EP3686596A3 (en) 2020-10-14

Similar Documents

Publication Publication Date Title
SG10201900755WA (en) Methods and system for processing lipid contents of at least one oil sample and simulating at least one training sample, and for predicting a blending formula, amongst others
EP3635366A4 (en) Novel universal testing system for quantitative analysis
IL258309A (en) Method, apparatus, and computer program product for analyzing biological data
EP3698331A4 (en) Apparatus and method for sharing a virtual reality environment
EP3762730A4 (en) Machine learning and molecular simulation based methods for enhancing binding and activity prediction
IL291252A (en) Method for identifying responders to smarca2/4 degraders
EP3514549A4 (en) Automated analyzer, automated analysis system, and method for displaying reagent list
MX2018009505A (en) Method for determining helicobacter pylori.
SG11202100897SA (en) Unbalanced sample data preprocessing method and device, and computer device
BR112017024765A2 (en) apparatus for determining a depth map for an image, method for determining a depth map for an image, and computer program product
EP3571994A4 (en) Method for developing and extracting biological trace evidence
TWI800530B (en) Method and nontransitory computer program product for evaluating an object
GB2622318B (en) System and method for performing object analysis
EP4010680A4 (en) Apparatus and methods for ultramicrotome specimen preparation
EP3251029A4 (en) Analysis engine and method for analyzing pre-generated data reports
EP3460454A4 (en) Biosensor and method for analyzing specimen by using same
EP3748018A4 (en) Method for diagnosing depression via bacterial metagenomic analysis
EP3721211A4 (en) System for analyzing a test sample and method therefor
HUE065371T2 (en) Virtual reality simulator and method for small laboratory animals
SG11202103841XA (en) Method for identifying an unknown biological sample from multiple attributes
EP3540757B8 (en) Mass analysis apparatus and mass analysis method
EP3617319A4 (en) Method for determining presence or absence of phytopathogenic microbe in test sample
IL312298A (en) Method for mixing cell-based meat
EP4247965A4 (en) Methods and assays for analyzing secretome-containing compositions
EP3575405A4 (en) Method for determining whether or not test sample contains phytopathogenic fungus