Bayesian Hierarchical Modeling for Online Process Monitoring and Quality Control, with Application to Real Time Image Data

2021 ◽  
pp. 107446
Author(s):  
Andrew J. Radcliffe ◽  
Gintaras V. Reklaitis
Keyword(s):  
Quality Control ◽  
Process Monitoring ◽  
Hierarchical Modeling ◽  
Image Data ◽  
Bayesian Hierarchical Modeling ◽  
Bayesian Hierarchical ◽  
Real Time Image ◽  
Online Process Monitoring ◽  
Time Image ◽  
Online Process

scholarly journals Application of Near-Infrared Spectroscopy Analysis Technology to Total Nucleosides Quality Control in the Fermented Cordyceps Powder Production Process

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Tiannv Shi ◽  
Yongmei Guan ◽  
Lihua Chen ◽  
Shiyu Huang ◽  
Weifeng Zhu ◽  
...  
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Production Process ◽  
Process Monitoring ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Powder Preparation ◽  
Spectroscopy Analysis ◽  
Online Process Monitoring ◽  
Online Process

Product quality control is a prerequisite for ensuring safety, effectiveness, and stability. However, because of the different strain species and fermentation processes, there was a significant difference in quality. As a result, they should be clearly distinguished in clinical use. Among them, the fermentation process is critical to achieving consistent product quality. This study aims to introduce near-infrared spectroscopy analysis technology into the production process of fermented Cordyceps powder, including strain culture, strain passage, strain fermentation, strain filtration, strain drying, strain pulverizing, and strain mixing. First, high performance liquid chromatography (HPLC) was used to measure the total nucleosides content in the production process of 30 batches of fermented Cordyceps powder, including uracil, uridine, adenine, guanosine, adenosine, and the process stability and interbatch consistency were analyzed with traditional Chinese medicine (TCM) fingerprinting, followed by the near-infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR) to establish a quantitative analysis model of total nucleosides for online process monitoring of fermented Cordyceps powder preparation products. The model parameters indicate that the established model with good robustness and high measurement precision. It further clarifies that the model can be used for online process monitoring of fermented Cordyceps powder preparation products.

scholarly journals Variance prior specification for a basket trial design using Bayesian hierarchical modeling

2018 ◽  
Vol 16 (2) ◽  
pp. 142-153 ◽  
Author(s):  
Kristen M Cunanan ◽  
Alexia Iasonos ◽  
Ronglai Shen ◽  
Mithat Gönen
Keyword(s):  
Standard Deviation ◽  
Prior Distribution ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Bayesian Hierarchical ◽  
Inverse Gamma ◽  
Wide Range ◽  
Basket Trial ◽  
Basket Trials ◽  
Variance Parameter

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. Methods: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. Results: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero [Formula: see text], this can lead to unacceptably high false-positive rates [Formula: see text] in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. Conclusion: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

Improving Real-Time Image-Data Quality With A Telemetry Model

2012 ◽  
Vol 27 (03) ◽  
pp. 383-392 ◽  
Author(s):  
Andreas Hartmann ◽  
Oleg Akimov ◽  
Stephen Morris ◽  
Christian Fulda
Keyword(s):  
Data Quality ◽  
Real Time ◽  
Image Data ◽  
Real Time Image ◽  
Time Image

scholarly journals Online process monitoring of a batch distillation by medium field NMR spectroscopy

2021 ◽  
Author(s):  
Anne Friebel ◽  
Erik von Harbou ◽  
Kerstin Münnemann ◽  
Hans Hasse
Keyword(s):  
Process Monitoring ◽  
Binary Systems ◽  
Spectroscopic Analysis ◽  
Ternary Systems ◽  
Ethyl Formate ◽  
Batch Distillation ◽  
Non Invasive ◽  
Robust Measurements ◽  
Online Process Monitoring ◽  
Online Process

Medium field NMR spectrometers are attractive for online process monitoring. Therefore, in the present work, a single-stage laboratory batch distillation still was coupled online with a medium field NMR spectrometer. This enables quantitative non-invasive measurements without calibration. The technique was used for studying isobaric and isothermal residue curves in two ternary systems: (dimethyl sulfoxide + acetonitrile + ethyl formate) and (ethyl acetate + acetone + diethyl ether) and boiling curves and high-boiling azeotropes in two binary systems: (acetic acid + pyridine) and (methanol + diethylamine). The results of the online NMR spectroscopic analysis were compared to results from offline analysis as well as to results from thermodynamic modeling using NRTL parameters that were parametrized with literature data. The new method for online process monitoring gives reliable results and is well-suited for fast and robust measurements of residue curves.

A bank-account-information-based credit scoring method with Bayesian hierarchical modeling

2021 ◽  
pp. 2150036
Author(s):  
Suguru Yamanaka ◽  
Rei Yamamoto
Keyword(s):  
Hierarchical Modeling ◽  
Credit Scoring ◽  
Information Criterion ◽  
Bayesian Hierarchical Modeling ◽  
Scoring Method ◽  
Bank Account ◽  
Bayesian Hierarchical ◽  
Account Information ◽  
Activity Information ◽  
Financial Technology

Recent interest in financial technology (fintech) lending business has caused increasing challenges of credit scoring models using bank account activity information. Our work aims to develop a new credit scoring method based on bank account activity information. This method incorporates borrower firms’ segment-level heterogeneity, such as a segment of sales size and firm age. We employ Bayesian hierarchical modeling, which mitigates data sparsity issue due to data segmentation. We describe our modeling procedures, including data handling and variable selection. Empirical results show that our model outperforms the traditional logistic model for credit scoring in information criterion. Our model realizes advanced credit scoring based on bank account activity information in fintech lending businesses, taking segment-specific features into credit risk assessment.

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