Materials and methods Second part of glycolysis experimental data. Experimental data of PGAM, ENO and PPDK activities and pathway flux (J obs) are obtained from plots of a previous study 8. The free online software WebPlotDigitizer (Version 4.1, https ://autom eris.io/WebPl otDig itize r/) is used to extract data from plots. These data are available in Tables S1 and S2. Artificial neural networks (ANNs). ANNs functioning mimics that of biological neurons, the networks consist of many layers allowing input reception and processing and output delivery. This technique can be used for solving classification or regression problems 18. To build the second part of glycolysis in ANNs, different types of software are employed: RStudio (Version 1.1.456), an open-source integrated development environment for R 19 and two packages: NeuralNet (Version 1.44.2) and Nnet (Version 7.3-12) 20,21. Complex pathway SImulator (COPASI) metabolic networks. A first metabolic network of the studied pathway was kindly provided by the authors of a previous study 8. This model is developed on GEPASI 22 , an old software for metabolic pathway modeling, replaced by COPASI since 2002. The second part of the glycolysis is also modeled by using the open source software called COPASI (Version 4.24) 23. This software is used for metabolic network design, analysis and optimization. The resulting metabolic networks are based on the use of enzyme properties (kinetic parameters and mechanism-based rate equations). Ethics approval and consent to participate. Not applicable. Methodology Black-white-and grey-box approach procedure. To conduct the present study, a specific methodol
Fast advancement of machine learning methods and constant growth of the areas of application open up new horizons for large data management and processing. Among the various types of data available for analysis, the Fourier Transform InfraRed (FTIR) spectroscopy spectra are very challenging datasets to consider. In this study, machine learning is used to analyze and predict a rheological parameter: firmness. Various statistics have been gathered including both chemistry (such as ethylene, titrable acidity or sugars) and spectra values to visualize and analyze a dataset of 731 biological samples. Two-dimensional (2D) and three-dimensional (3D) principal component analyses (PCA) are used to evaluate their ability to discriminate for one parameter: firmness. Partial least squared regression (PLSR) modeling has been carried out to predict the rheological parameter using either sixteen physicochemical parameters or only the infrared spectra. We show that (i) the spectra alone allows good discrimination of the samples based on rheology, (ii) 3D-PCA allows comprehensive and informative visualization of the data, and (iii) that the rheological parameters are predicted accurately using a regression method such as PLSR; instead of using chemical parameters which are laborious to obtain, Mid-FTIR spectra gathering all physicochemical information could be used for efficient prediction of firmness. As a conclusion, rheological and chemical parameters allow good discrimination of the samples according to their firmness. However, using only the IR spectra leads to better results. A good predictive model was built for the prediction of the firmness of the fruit, and we reached a coefficient of determination R2 value of 0.90. This method outperforms a model based on physicochemical descriptors only. Such an approach could be very helpful to technologists and farmers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.