Wavelength variable selection was performed using six algorithms: variable importance in projection (VIP), genetic algorithm (GA), sparse partial least squares regression (s-PLS), interval partial least squares regression (i-PLS), recursive partial least squares regression (r-PLS), and nonlinear partial least squares regression (n-PLS) [8 (link)]. These algorithms were used to select the most responsive wavelengths within the range of 350 to 2500 nm for assessing biophysical parameters in variegated leaves. For this purpose, MATLAB 2022a software (MathWorks, Inc., Natick, MA, USA) and PLS_Toolbox (Engenvector Research, Inc., Manson, WA, USA) were utilized for data analysis. The performance of each algorithm was evaluated based on its ability to discriminate between wavelengths and select the most responsive ones for the generated models based on weight (g leaf−1), leaf area (m2), specific leaf area (cm2 g−1), and leaf thickness (mm) (Figure S2 ).
Matlab 2022a software
Manufactured by MathWorks
Sourced in United States
MATLAB 2022a is a high-performance numerical computing software that provides a powerful programming environment for technical and scientific computing. It offers a wide range of functions and tools for data analysis, algorithm development, and visualization, making it a versatile tool for researchers, engineers, and scientists.
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Lab products found in correlation
4 protocols using matlab 2022a software
1
Wavelength Selection for Biophysical Leaf Analysis
2
Measuring Myelin Water Fraction in MS Lesions
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Elastix registration software is utilized for co-registration between STAIR-FSE and PD-FSE images (40 (link)). Regions of interest (ROIs) were manually drawn on the MS lesions using images captured by the STAIR-FSE technique in MS patients, as well as on eight non-lesion white matter regions in both the healthy volunteers and the MS patients. The lesions were selected for analysis when they met the classical definition for MS lesion appearance: hyperintense in T2, oval shape or patchy, high predilection for periventricular white matter, and perpendicular to the ependymal surface (i.e., Dawson’s fingers). Any other non-MS lesions were avoided while drawing ROIs. The MS lesions with sizes ranging from 20 to 300 mm2 were included for aMWF measurement. The non-lesion regions consisted of the left and right centrum semiovale, subcortical white matter, periventricular regions, splenium, and genu of the corpus callosum. The sizes of the ROIs for NWM and NAWM areas were ~300 mm2. The signal to noise (SNR) was calculated according to the following formula: SNR = (NAWM signal)/noise standard deviation (SD), and the contrast to noise ratio (CNR) for MS lesions was calculated according to the following formula: CNR = (lesion signal − NAWM signal)/Noise SD. Drawing of ROIs and calculation of aMWF were performed using MATLAB 2022a software (MathWorks Inc., Natick, MA, USA).
3
Advanced Wavelength Selection Algorithms
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To accurately discern the most relevant wavelengths for our investigations of the Hibiscus and Geranium plants, a suite of advanced algorithms was utilised. It incorporates techniques such as partial least squares (PLS), variable importance in projection (VIP), interval PLS-VIP (iPLS-VIP), genetic algorithms (GA), random forests (RF), and competitive adaptive reweighted sampling (CARS). Data analysis was performed with precision using multiple software platforms. The R software package version 4.2.2 Corrplot R-Core Team 2021 and the Python programming language version 3.11.5 (Python Software Foundation, Wilmington, DE, USA) formed the foundation of our analytical framework. In Python, RF procedures were facilitated by the scikit-learn library, whereas the DEAP library underpinned our GA evaluations. In the R environment, the PLS package was paramount for the PLS-focused analyses. Additionally, for iPLS analyses, MATLAB 2022a software version 9.12 (MathWorks, Inc., Natick, MA, USA) was used and seamlessly integrated with PLS_Toolbox (Eigenvector Research, Inc., Manson, WA, USA). The relative contribution of each wavelength was determined by identifying the most responsive wavelengths. This was based on the maximum and minimum values selected by the wavelength selection algorithms.
4
Advanced Wavelength Selection Algorithms
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To accurately discern the most relevant wavelengths for our investigations of the Hibiscus and Geranium plants, a suite of advanced algorithms was utilised. It incorporates techniques such as partial least squares (PLS), variable importance in projection (VIP), interval PLS-VIP (iPLS-VIP), genetic algorithms (GA), random forests (RF), and competitive adaptive reweighted sampling (CARS). Data analysis was performed with precision using multiple software platforms. The R software package version 4.2.2 Corrplot R-Core Team 2021 and the Python programming language version 3.11.5 (Python Software Foundation, Wilmington, DE, USA) formed the foundation of our analytical framework. In Python, RF procedures were facilitated by the scikit-learn library, whereas the DEAP library underpinned our GA evaluations. In the R environment, the PLS package was paramount for the PLS-focused analyses. Additionally, for iPLS analyses, MATLAB 2022a software version 9.12 (MathWorks, Inc., Natick, MA, USA) was used and seamlessly integrated with PLS_Toolbox (Eigenvector Research, Inc., Manson, WA, USA). The relative contribution of each wavelength was determined by identifying the most responsive wavelengths. This was based on the maximum and minimum values selected by the wavelength selection algorithms.
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