WO2023090015A1 - 情報処理装置、情報処理装置の作動方法、情報処理装置の作動プログラム、校正済み状態予測モデルの生成方法、並びに校正済み状態予測モデル - Google Patents

情報処理装置、情報処理装置の作動方法、情報処理装置の作動プログラム、校正済み状態予測モデルの生成方法、並びに校正済み状態予測モデル Download PDF

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WO2023090015A1
WO2023090015A1 PCT/JP2022/038480 JP2022038480W WO2023090015A1 WO 2023090015 A1 WO2023090015 A1 WO 2023090015A1 JP 2022038480 W JP2022038480 W JP 2022038480W WO 2023090015 A1 WO2023090015 A1 WO 2023090015A1
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component
target
state
related information
measurement data
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French (fr)
Japanese (ja)
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惟 杉田
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Fujifilm Corp
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Fujifilm Corp
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Priority to EP22895301.4A priority Critical patent/EP4439053A4/en
Priority to JP2023561463A priority patent/JPWO2023090015A1/ja
Priority to CN202280077216.0A priority patent/CN118284802A/zh
Publication of WO2023090015A1 publication Critical patent/WO2023090015A1/ja
Priority to US18/662,934 priority patent/US20240296917A1/en
Anticipated expiration legal-status Critical
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M1/00—Apparatus for enzymology or microbiology
    • C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M3/00—Tissue, human, animal or plant cell, or virus culture apparatus
    • 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 sub-millimetre waves, infrared, visible or ultraviolet 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
    • 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 groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods
    • G01N2201/1296—Using chemometrical methods using neural networks

Definitions

  • the calibrated state prediction model is preferably a machine learning model learned using calibrated data as teacher data.
  • the Raman spectrometer 40 is composed of a probe 41 and an analyzer 42.
  • the tip of the probe 41 is immersed in the target first purified liquid 28T.
  • the probe 41 emits excitation light from an emission port at the tip, and receives Raman scattered light generated by the interaction between the excitation light and the target first purified liquid 28T at the light receiving part provided at the tip.
  • the probe 41 outputs the received Raman scattered light to the analyzer 42 .
  • a laser beam is used as the excitation light
  • the output of the laser beam is 200 mW
  • the center wavelength is 785 nm
  • the irradiation time is 1 second.
  • the Raman spectrometer 40 is not limited to a type in which a probe 41 having a light receiving portion is immersed in a liquid, and may be a type in which a flow cell having a light receiving portion is installed in a channel.
  • the display control unit 80 controls the display of various screens on the display 50. For example, the display control unit 80 causes the display 50 to display an input screen for the target component-related information 47T. Further, the display control unit 80 causes the display 50 to display a notification screen for informing the operator of the density predicted value 85 from the prediction unit 79 .
  • the learning device inputs the preprocessed spectral measurement data 46P of the teacher data 111 to the first model 95, and from the first model 95 A temporary density prediction value 85TL for learning is output.
  • the learning provisional concentration predicted value 85TL is an example of the “learning state prediction result” according to the technology of the present disclosure.
  • the target component related information 47T is read from the storage 60 by the RW control unit 77 and output to the prediction unit 79.
  • the prediction unit 79 As shown in FIG. 9, first, the preprocessed target spectrum measurement data 46TP is input to the first model 95, and the first model 95 outputs a provisional concentration prediction value 85T (step ST420). Subsequently, the provisional concentration predicted value 85T and the target component related information 47T are input to the second model 96, and the concentration predicted value 85 is output from the second model 96 (step ST430). The density predicted value 85 is output from the prediction section 79 to the display control section 80 .
  • One processing unit may be configured with one of these various processors, or a combination of two or more processors of the same or different type (for example, a combination of a plurality of FPGAs and/or a CPU and combination with FPGA). Also, a plurality of processing units may be configured by one processor.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Biomedical Technology (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Biochemistry (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Analytical Chemistry (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Organic Chemistry (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Immunology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Pathology (AREA)
  • Biotechnology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Databases & Information Systems (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
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PCT/JP2022/038480 2021-11-22 2022-10-14 情報処理装置、情報処理装置の作動方法、情報処理装置の作動プログラム、校正済み状態予測モデルの生成方法、並びに校正済み状態予測モデル Ceased WO2023090015A1 (ja)

Priority Applications (4)

Application Number Priority Date Filing Date Title
EP22895301.4A EP4439053A4 (en) 2021-11-22 2022-10-14 INFORMATION PROCESSING DEVICE, METHOD FOR OPERATING THE INFORMATION PROCESSING DEVICE, OPERATING PROGRAM FOR INFORMATION PROCESSING DEVICE, METHOD FOR GENERATING
JP2023561463A JPWO2023090015A1 (https=) 2021-11-22 2022-10-14
CN202280077216.0A CN118284802A (zh) 2021-11-22 2022-10-14 信息处理装置、信息处理装置的工作方法、信息处理装置的工作程序、校准完毕状态预测模型的生成方法以及校准完毕状态预测模型
US18/662,934 US20240296917A1 (en) 2021-11-22 2024-05-13 Information processing apparatus, operation method of information processing apparatus, operation program of information processing apparatus, generation method of calibrated state predictive model, and calibrated state predictive model

Applications Claiming Priority (2)

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JP2021189571 2021-11-22
JP2021-189571 2021-11-22

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US18/662,934 Continuation US20240296917A1 (en) 2021-11-22 2024-05-13 Information processing apparatus, operation method of information processing apparatus, operation program of information processing apparatus, generation method of calibrated state predictive model, and calibrated state predictive model

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WO2023090015A1 true WO2023090015A1 (ja) 2023-05-25

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US (1) US20240296917A1 (https=)
EP (1) EP4439053A4 (https=)
JP (1) JPWO2023090015A1 (https=)
CN (1) CN118284802A (https=)
WO (1) WO2023090015A1 (https=)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025052895A1 (ja) * 2023-09-05 2025-03-13 富士フイルム株式会社 情報処理装置、情報処理装置の作動方法、および情報処理装置の作動プログラム
WO2025070168A1 (ja) * 2023-09-29 2025-04-03 富士フイルム株式会社 プローブ
WO2025142150A1 (ja) * 2023-12-25 2025-07-03 富士フイルム株式会社 情報処理装置、情報処理装置の作動方法、および情報処理装置の作動プログラム
CN121479712A (zh) * 2026-01-09 2026-02-06 山东科技大学 一种基于RLS的NOx浓度预测集成模型在线更新方法

Citations (3)

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JP2016128822A (ja) 2010-09-17 2016-07-14 アッヴィ・インコーポレイテッド バイオプロセス操作用のラマン分光法
JP2019522802A (ja) * 2016-04-04 2019-08-15 ベーリンガー インゲルハイム エルツェーファウ ゲゼルシャフト ミット ベシュレンクテル ハフツング ウント コンパニー コマンディトゲゼルシャフト 製剤精製のリアルタイムモニタリング
WO2021215179A1 (ja) * 2020-04-21 2021-10-28 富士フイルム株式会社 培養状態の推定方法、情報処理装置及びプログラム

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CN119264211A (zh) * 2018-08-27 2025-01-07 瑞泽恩制药公司 拉曼光谱在下游纯化中的应用
MX2021004510A (es) * 2018-10-23 2021-06-08 Amgen Inc Calibracion automatica y mantenimiento automatico de modelos espectroscopicos de raman para predicciones en tiempo real.
CN113196053A (zh) * 2018-12-20 2021-07-30 佳能株式会社 信息处理装置、信息处理装置的控制方法及程序

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JP2016128822A (ja) 2010-09-17 2016-07-14 アッヴィ・インコーポレイテッド バイオプロセス操作用のラマン分光法
JP2019522802A (ja) * 2016-04-04 2019-08-15 ベーリンガー インゲルハイム エルツェーファウ ゲゼルシャフト ミット ベシュレンクテル ハフツング ウント コンパニー コマンディトゲゼルシャフト 製剤精製のリアルタイムモニタリング
WO2021215179A1 (ja) * 2020-04-21 2021-10-28 富士フイルム株式会社 培養状態の推定方法、情報処理装置及びプログラム

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Title
See also references of EP4439053A4

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025052895A1 (ja) * 2023-09-05 2025-03-13 富士フイルム株式会社 情報処理装置、情報処理装置の作動方法、および情報処理装置の作動プログラム
WO2025070168A1 (ja) * 2023-09-29 2025-04-03 富士フイルム株式会社 プローブ
WO2025142150A1 (ja) * 2023-12-25 2025-07-03 富士フイルム株式会社 情報処理装置、情報処理装置の作動方法、および情報処理装置の作動プログラム
CN121479712A (zh) * 2026-01-09 2026-02-06 山东科技大学 一种基于RLS的NOx浓度预测集成模型在线更新方法
CN121479712B (zh) * 2026-01-09 2026-04-03 山东科技大学 一种基于RLS的NOx浓度预测集成模型在线更新方法

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JPWO2023090015A1 (https=) 2023-05-25
CN118284802A (zh) 2024-07-02
EP4439053A1 (en) 2024-10-02
US20240296917A1 (en) 2024-09-05
EP4439053A4 (en) 2025-03-12

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