# Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
## 3 Results and Discussions
### Regression Model Results  
A neural network, with supervised training, was used for the regression model. The first computational step was the selection of influential variables using the MRMR algorithm. The algorithm ranked the variables (wavelengths) according to their influence in estimating the response data (absolute humidity %) based on the input data (wavelengths), assigning a value (score) between 0 and 0.6. Wavelengths with scores greater than 0.5 were chosen to be used. There were five wavelengths selected, visible in red in Figure 3. Specifically, the selected wavelengths were 565 nm, 790 nm, 890 nm, 945 nm, and 1465 nm. The first is contained in the visible band and specifically in the green-yellow band (565 nm), while the other four fall in the infrared band. [PERSON] et al. [25] utilized spectral data within the 400-1100 nm range, encompassing water absorption bands around 760 nm and 970 nm, for the simultaneous determination of crucial biochemical parameters such as chlorophyll and nitrogen. This approach effectively optimized the efficiency of plant physiological state assessment. In a separate study, the visible near-infrared (VIS-NIR) spectroscopy was employed for the rapid discrimination between organic and non-organic leafy vegetables. Within this context, the 1400-2500 nm wavelength range, primarily governed by plant water content absorption, was leveraged for determining structural characteristics and the overall composition of leafy vegetables [38]. Further spectroscopic analysis has identified specific wavelengths and spectral regions critical for the non-invasive assessment of physiological parameters in leafy plants. In the visible range, the region around 565 nm (assimilated to the ranges 506-524 nm and 566-577 nm) has a significant correlation with water content, suggesting an interconnection with chlorophyll content and indicating the influence of hydration status on plant pigmentation. Moving into the near-infrared (NIR), the 790 nm wavelength (assimilated to the 765-784 nm region) is of particular interest because light absorption in this area is  
Figure 3: Score of the 261 wavelengths according to the MRMR algorithm. predominantly determined by the structural characteristics and intrinsic content of leaf vegetables, showing a strong correlation with water content. Similarly, at 890 nm, within the 780-1400 nm range, light transmission and reflection are primarily modulated by the structural properties of the leaf; although not a primary band of water absorption, its relevance to the classification of leaf spectra in the 550-910 nm band suggests a contribution to the discrimination of physiological states related to hydration and structural integrity. Continuing in the NIR, the 945 nm wavelength (near the 950-957 nm and 950-970 nm regions) showed superior correlation with water content and is recognized as a reliable indicator of plant water status, with absorption at about 960 nm influenced by the molecular structure of plant contents. Finally, at 1465 nm, very close to 1450 nm, there is one of the most pronounced water absorption characteristics influenced by molecular structural aspects, including O-H bond combinations [23; 24; 25; 38; 39]. The neural network was subsequently trained using data from the training set. This training leveraged the absolute leaf moisture data associated with their corresponding reflectance spectra, specifically employing the five selected influential wavelengths. Figure 4 details the training parameters utilized for this neural network. As shown, the training phase required 82 computational cycles in which the operational parameters of the network were set to prepare the network for the regression of the validation data. Specifically, the number of intermediate layers was determined to be three following the data processing. These three intermediate layers are populated by a set of interconnected nodes, each housing mathematical functions, with connections extending to nodes in subsequent layers.