# Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
## 1 Introduction
Hyperspectral spectroscopy was used in the study of vegetation in ice-free areas of Antarctica to assess the spectral separability of different taxa. After data processing with PCA and LDA, the visible and near-UV regions (380-700 nm) were identified as the most effective for distinguishing vegetation classes. The results revealed distinct spectral signatures among taxa, confirming the potential of hyperspectral spectroscopy for environmental monitoring in Antarctic ecosystems [20]. Other studies make use of spectral indices that take advantage of the near-infrared (NIR) or shortwave near-infrared (SWIR) band to evaluate leaf water content, using the NIR band as a reference to normalize the effect of leaf structural variability [21]. As highlighted by [PERSON] et al., a portable, low-cost multispectral optical sensor system has been developed for the non-invasive determination of nitrogen and water content in the leaves of various crops. This system acquires data in the visible and near-infrared bands and employs machine learning regression models. Specifically, Gaussian process regression (GPR) demonstrated high reliability, with an R\({}^{2}\) value exceeding 0.90 in estimating leaf water content, underscoring the promising feasibility of this approach for more efficient irrigation management and crop health monitoring [22]. Another recent study explored the application of visible and near-infrared (VIS/NIR) spectroscopy for the non-destructive determination of leaf water content in _Miscanthus_. VIS/NIR spectroscopy proved to be an effective method for this non-destructive determination. The research involved acquiring reflectance spectra in the 400-2500 nm range and applying various multivariate regression models. These models demonstrated significantly superior precision in estimating water content, even when utilizing only 75 sensitive wavelengths [16]. Currently, both artificial vision and spectroscopic technology are widely used in the field of agricultural product detection due to their non-invasive, fast, and reliable characteristics [15]. Hyperspectral imaging has rapidly risen to prominence in recent years, revolutionizing the way to obtain and analyze complex data. The research conducted by [PERSON] et al. specifically focuses on the detection of water content in lettuce using a portable hyperspectral imaging system with spectral range from 391.65 to 1018 nm, resulting in the selection of characteristic bands and the construction of a robust predictive model [23]. In a separate study, MIVIS hyperspectral imaging was applied to evaluate the water content of poplar plantations at leaf and canopy scales. This involved collecting data in water-sensitive spectral bands and implementing both empirical models derived from hyperspectral indices and the inversion of radiative transfer models [18]. Another study discusses hyperspectral field imaging and functional linear regression (FLR) for the rapid and nondestructive estimation of water content in grapevine (_Vitis vinifera_ L.) leaves. This approach successfully identified optimal spectral bands (_Z3_ at 1465 nm and Z4 at 2035 nm), providing a precise tool for irrigation management in vine-yards and effectively overcoming the limitations of traditional destructive methods [24]. As reported in the study of [PERSON] et al., hyperspectral imaging has been used to assess the freshness of leafy vegetables, including spinach and pakchoi. As the water content in plant tissues consistently decreases during storage, leading to deterioration and changes in freshness, hyperspectral imaging emerges as a significant method for rapid and accurate freshness evaluation without exacerbating water loss during the detection process [25]. Nowadays, in the context of food analysis, detectors sensitive to the visible-near-infrared (Vis-NIR) and near-infrared (NIR) regions are commonly used due to their ability to provide detailed information about product composition [26], to detect fraud [27], and even the ripening stage of fruit [28].