## 2 Materials and Methods
### Feature Selection, Modeling, and Performance Evaluation  
Five feature selection methods were employed to identify relevant features for predicting ChIP parameters: boruta (BO), genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and stepwise selection. Initially, a correlation analysis was used to filter wavelengths, retaining only those that exhibited significant associations with the target variables in each FOD spectral dataset. The BO algorithm is a feature selection method based on random forests, which identifies key features by assessing their importance in relation to randomly generated shadow features. In this study, the \"Boruta\" package was used, with all parameter settings following the default values [34]. The GA simulates natural evolution to search for the optimal feature subset by iterating through populations of feature combinations using crossover, mutation, and fitness evaluation. In this study, the \"GA\" package was used, with the population size set to 30, the mutation probability for a parent chromosome set to 0.2, and the maximum number of iterations before termination set to 50 [35]. LASSO is a regularization technique that simultaneously performs feature selection and model fitting by applying an L1 penalty, which shrinks the coefficients of less key features to zero. In this study, the penalty parameter was set to 1, and model performance was evaluated using the mean squared error with the \"glmnet\" package [36]. RFE and stepwise selection are both iterative feature selection methods that refine the set of predictive variables based on model performance. In this study, RFE, implemented using the \"caret\" package, evaluated different feature subset sizes with a step size of 5 [37]. The stepwise selection method followed a forward selection approach, adding variables based on the Akaike information criterion (AIC), using the \"MASS\" package [38]. Forward selection was chosen because it prioritizes the most informative variables early in the process while reducing computational complexity and minimizing the risk of overfitting [39]. Each feature selection method was executed using parallel computing to enhance efficiency, with each method running for 10 iterations. Features that appeared in more than 70% of the iterations were retained. To address potential multicollinearity among the selected features, the variance inflation factor (VIF) method was applied with a threshold of 10, ensuring that the final dataset remained both informative and robust. To develop data-driven predictive models for ChIP parameters, PLSR was applied using features selected by the aforementioned feature selection methods. The modeling process was conducted separately for each FOD spectral dataset. The dataset was first divided into training and test sets in a stratified manner to ensure that all observations were represented in both subsets, with 70% allocated to the training set (336 samples) and 30% to the test set (141 samples). To optimize model performance, Bayesian optimization was employed to determine the optimal number of components for PLSR. The selection criterion was based on minimizing the cross-validation root mean squared error (RMSE), starting  
Figure 1: Distribution and properties of six chlorophyll fluorescence (ChlF) parameters in the dataset: maximum quantum yield of PSII (PSIImax), non-photochemical quenching (NPQ), the fraction of open reaction centers in photosystem II (qL), the quantum yield of photochemistry (\(\Phi\)P), the quantum yield of non-regulated energy dissipation (\(\Phi\)N), and the quantum yield of fluorescence emission (\(\Phi\)F). The violin shapes represent the distribution density of the parameter values, with the width indicating the frequency of values at different levels. The box plots show the interquartile range, with the white line inside each box marking the median. The red dot in each plot represents the mean value of the parameter.