Pls toolbox

Micro-XRF and HSI derived data were analyzed adopting standard chemometric methods [45 ,46 ] by the PLS_Toolbox (Version 8.9 Eigenvector Research, Inc., Manson, WA, USA) [47 ] running inside Matlab 2020b (The Mathworks, Inc., Natick, MA, USA). In more detail, micro-XRF and HSI data analysis was performed as follows: the raw spectra were pre-processed to reduce unwanted effects and highlight useful information. PCA was then applied for exploratory purposes. Finally, PLS-DA was utilized to create a classification model from HSI data.

Data of all measurements presented in the tables are the mean of three replicates ± standard deviation. Tukey’s test was used to detect the significance of differences (p ≤ 0.05) between mean values. Statistical analyses were performed using the NCSS program (www.ncss.com) [44 (link)]. Principal component analysis was performed using the PLS_ToolBox software package for MATLAB (Version 7.12.0; (Eigenvector Research, Inc., Wenatchee, WA, USA) [44 (link)].

Multivariate data analysis (MVA) was performed with “Aspen Unscrambler^TM, version 10.5.1” (Aspen Technology Inc., Bedford, MA, USA). The UV spectra were pretreated prior to the multivariate data analysis in the following way: Linear baseline correction followed by a Savitzky–Golay smoothing (8 points, symmetric, 2nd polynomial order). The principal component analysis (PCA) models were calculated with mean centering, cross-validation, and the NIPALS algorithm. A partial least squares regression (PLS-R) model for the sugar concentrations was created with mean centering, segmented cross-validation according to the sample type, and the Kernel-algorithm. All cotton samples of each concentration were used to develop the PLS-R model. The PLS-R model was tested by predicting the honeydew content on the real cotton samples. Three different areas from these real cotton samples were also investigated by PCA with the aforementioned settings. To show the quality of the model, PCA was combined with quadratic discriminant analysis (QDA, 4 PCs). A fourth area was predicted by the PCA-QDA model.
MATLAB (MATLAB 9.2.0, Mathworks, MA, USA) and PLS_Toolbox (PLS Toolbox 8.5.1, Eigenvector Research, Inc., Wenatchee, WA, USA) were used for presenting the data.

Prior to multivariable analysis, collected p-J-resolved NMR spectra were segmented into chemical shift bins between 0.2 and 10.0 ppm corresponding to the bin width of 0.005 ppm (3.0 Hz) using customized-developed ProMetab software (version 3.3) [24 (link), 25 ] in MATLAB (The Mathworks, Natick, MA). After removal of internal standards (TMSP and TMS) and water resonance, the peak areas within each bin were integrated and normalized to total integrated area. The normalized data were further log transformed, and the variables were mean-centered prior to multivariate analysis [26 (link), 27 (link)]. The acquired data sets were further analyzed to inspect the variation among controls and two naphthalene treatments groups with multivariate, principle component analysis (PCA) using PLS_Toolbox (Version 3.5; Eigenvector Research, Manson, WA) encrypted in the MATLAB. PCA is commonly used to decompose a multivariate dataset and account for a maximum amount of the variance. PCA scores plots summarize the metabolome similarity among samples into a set of principle components. A loading plot shows the influence (weighted factor) of the individual variable (metabolite) in the model.

Six algorithms were employed for variable selection: 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 utilized to identify and select the most responsive wavelengths within the 350 to 2500 nm range for evaluating the phenotypic parameters of tobacco plants. Data analysis was conducted using MATLAB 2022a software (MathWorks, Inc., Natick, MA, USA) and PLS_Toolbox (Eigenvector Research, Inc., Manson, WA, USA). The efficacy of each algorithm was assessed based on its capacity to differentiate between wavelengths and select the most responsive ones for generating phenotypic classification models related to height (cm), leaf area (m²), yield energetic (m³), and biomass (g).

AFM–IR measurements were performed with a NanoIR2 system (Anasys Instruments Inc., Santa Barbara, USA). The IR source was an optical parametric oscillator (OPO) laser, producing a 10 ns pulse at a 1 kHz repetition rate. A silicon cantilever (AppNano, Mountain View, CA 94043, USA) was used with nominal radius of 10 nm and a nominal spring constant of 0.5 N/m. The system was purged with N₂ to control humidity. For each strain, 3 biological replicates were studied, with 6 single cells investigated for each biological replicate (n_{single_strain} = 18, n_total = 90). All single spectra were collected in the range of 1800–900 cm⁻¹ with a spectral resolution of 8 cm⁻¹ and IR maps at fixed wavenumber values, to investigate the distribution of selected components (specific wavenumber values are given in Results). Simultaneously to each IR map, the AFM height and deflection images were acquired. The maps were subsequently combined in MatLab (Mathworks, Natick, USA), PLS_toolbox (Eigenvector research, Manson, USA) and analyzed using k-means clustering to identify the presence and location of LBs. Following this analysis, single spectra were recorded from cytoplasm and LBs. All presented single spectra were normalized using the Standard Normal Variate (SNV) method and smoothed using the Savitzky-Golay algorithm with 13 smoothing points.