Nothing Special   »   [go: up one dir, main page]

Esbensen et al., 2018 - Google Patents

Theory of Sampling (TOS): a necessary and sufficient guarantee for reliable multivariate data analysis in pharmaceutical manufacturing

Esbensen et al., 2018

Document ID
3845373579926165066
Author
Esbensen K
Romañach R
Román-Ospino A
Publication year
Publication venue
Multivariate Analysis in the Pharmaceutical Industry

External Links

Snippet

Process monitoring and control in technology and industry is incomplete without full understanding of all sources of variation, and pharmaceutical manufacturing is no exception. The power of multivariate data modeling and error treatment is not optimal when sampling …
Continue reading at www.sciencedirect.com (other versions)

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
    • G01N21/359—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
    • 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
    • G01N21/88—Investigating the presence of flaws or contamination
    • 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
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection

Similar Documents

Publication Publication Date Title
Singh et al. Implementation of an advanced hybrid MPC–PID control system using PAT tools into a direct compaction continuous pharmaceutical tablet manufacturing pilot plant
Esbensen et al. Theory of Sampling (TOS): a necessary and sufficient guarantee for reliable multivariate data analysis in pharmaceutical manufacturing
Colón et al. Near infrared method development for a continuous manufacturing blending process
El-Hagrasy et al. A process analytical technology approach to near-infrared process control of pharmaceutical powder blending. Part I: D-optimal design for characterization of powder mixing and preliminary spectral data evaluation
Kandpal et al. In-process control assay of pharmaceutical microtablets using hyperspectral imaging coupled with multivariate analysis
Harms et al. Characterization of near-infrared and Raman spectroscopy for in-line monitoring of a low-drug load formulation in a continuous manufacturing process
Realpe et al. Image processing and analysis for determination of concentrations of powder mixtures
US10916334B2 (en) Sample analysis system
Melenteva et al. Building global models for fat and total protein content in raw milk based on historical spectroscopic data in the visible and short-wave near infrared range
Sierra-Vega et al. In line monitoring of the powder flow behavior and drug content in a Fette 3090 feed frame at different operating conditions using Near Infrared spectroscopy
Lee et al. Near infrared spectroscopy for rapid and in-line detection of particle size distribution variability in lactose during mixing
Wagner et al. Theory of sampling: four critical success factors before analysis
Courand et al. Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring
Ferreira et al. Multivariate analysis in the pharmaceutical industry
Skibsted et al. Examples of NIR based real time release in tablet manufacturing
Beke et al. Digital twin of low dosage continuous powder blending–Artificial neural networks and residence time distribution models
Wold et al. The chemometric analysis of point and dynamic data in pharmaceutical and biotech production (PAT)—some objectives and approaches
Kucheryavskiy et al. Monitoring of whey quality with NIR spectroscopy—a feasibility study
Haware et al. Data fusion of Fourier transform infrared spectra and powder X-ray diffraction patterns for pharmaceutical mixtures
García-Muñoz et al. Coating uniformity assessment for colored immediate release tablets using multivariate image analysis
Yekpe et al. Developing a quality by design approach to model tablet dissolution testing: an industrial case study
Esbensen et al. 19 A Framework for Representative Sampling for NIR Analysis–Theory of Sampling (TOS)
Gruszka et al. Chemometric approach to discrimination and determination of binary mixtures of silver ions and nanoparticles in consumer products by graphite furnace atomic absorption spectrometry
Ficzere et al. Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision
Asachi et al. Multi-sensors data fusion for monitoring of powdered and granule products: Current status and future perspectives