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Sitorus A, Lapcharoensuk R. Development of automatic tuning for combined preprocessing and hyperparameters of machine learning and its application to NIR spectral data of coconut milk adulteration. Food Chem 2024; 457:140108. [PMID: 38905832 DOI: 10.1016/j.foodchem.2024.140108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 05/29/2024] [Accepted: 06/12/2024] [Indexed: 06/23/2024]
Abstract
This study proposed a novel approach to automatically select the preprocessing methods and hyperparameters of machine learning (ML) algorithms based on their best performance in cross-validation for near-infrared (NIR) spectroscopy data. The proposed method simultaneously incorporates single or multiple-preprocessing steps and tunes hyperparameters to determine the best model performance for FT-NIR and Micro-NIR spectral data of coconut milk adulteration with distilled water and mature coconut water in the range of 0%-50%. Computational experiments were conducted using nine single preprocessing types, three types of ML classifier (linear discriminant analysis (LDA), k-nearest neighbour (KNN), multilayer perceptron (MLP)) and three types of ML regressor (partial least squares (PLS), KNN, MLP). The proposed performance strategy effectively addressed and produced satisfactory outcomes for classification and regression challenges in coconut milk adulteration. Finally, the results demonstrated that the proposed approach can more accurately determine the best model, particularly for NIR spectroscopy of coconut milk adulteration.
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Affiliation(s)
- Agustami Sitorus
- Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand; National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia
| | - Ravipat Lapcharoensuk
- Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
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Ardila CM, González-Arroyave D, Zuluaga-Gómez M. Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach. World J Clin Cases 2024; 12:2023-2030. [PMID: 38680255 PMCID: PMC11045505 DOI: 10.12998/wjcc.v12.i12.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/16/2024] Open
Abstract
In this editorial, we comment on the article by Wang and Long, published in a recent issue of the World Journal of Clinical Cases. The article addresses the challenge of predicting intensive care unit-acquired weakness (ICUAW), a neuromuscular disorder affecting critically ill patients, by employing a novel processing strategy based on repeated machine learning. The editorial presents a dataset comprising clinical, demographic, and laboratory variables from intensive care unit (ICU) patients and employs a multilayer perceptron neural network model to predict ICUAW. The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW. This editorial contributes to the growing body of literature on predictive modeling in critical care, offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.
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Affiliation(s)
| | | | - Mateo Zuluaga-Gómez
- Department of Emergency, Universidad Pontificia Bolivariana, Medellín 0057, Colombia
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Ali H, Muthudoss P, Chauhan C, Kaliappan I, Kumar D, Paudel A, Ramasamy G. Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability. AAPS PharmSciTech 2023; 24:254. [PMID: 38062329 DOI: 10.1208/s12249-023-02697-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/01/2023] [Indexed: 12/18/2023] Open
Abstract
Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the model's performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications.
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Affiliation(s)
- Hussain Ali
- Christ (Deemed to Be University), Bangalore, 560029, Karnataka, India
| | - Prakash Muthudoss
- A2Z4.0 Research and Analytics Private Limited, Chennai, 600062, Tamilnadu, India
- NuAxon Bioscience Inc., Bloomington, Indiana, 47401-6301, USA
- School of Pharmaceutical Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Velan Nagar P.V. Vaithiyalingam Road Pallavaram 600117, Chennai, Tamilnadu, India
| | | | - Ilango Kaliappan
- School of Pharmacy, Hindustan Institute of Technology and Science (HITS), Padur, 603 103, Chennai, Tamilnadu, India
| | - Dinesh Kumar
- Department of Pharmaceutical Engineering & Technology, IIT (BHU), Varanasi, 221011, Uttar Pradesh, India
| | - Amrit Paudel
- Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010, Graz, Austria.
- Graz University of Technology, Institute of Process and Particle Engineering, Inffeldgasse 13/3, 8010, Graz, Austria.
| | - Gobi Ramasamy
- Christ (Deemed to Be University), Bangalore, 560029, Karnataka, India.
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Otsuka M, Hayashi Y, Miyazaki K, Mizu M, Okuno M, Sasaki T. Quality evaluation of white sugar crystals using the friability test and their non-destructive prediction using near-infrared spectroscopy. J Drug Deliv Sci Technol 2023. [DOI: 10.1016/j.jddst.2023.104390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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