## 4 Discussion
### Comparison with Other Studies  
In comparison to our study on fresh tea leaves--where FOD-CARS-PLSR over 350-2500 nm yielded R\({}^{2}\) = 0.80 for AA and R\({}^{2}\) = 0.42 for polyphenols--other investigations report higher accuracy under different conditions. For example, [PERSON] et al. (2023) [36] also measured fresh leaves across the same spectral range but used first-order derivatives, achieving R\({}^{2}\) = 0.79 for AA and only 0.28 for polyphenols. In contrast, [PERSON] et al. (2013) [37] applied CARS-PLSR to raw spectra of piled dried leaves and reached R\({}^{2}\) = 0.89 for polyphenols and 0.81 for AA, while [PERSON] et al. (1999) [9] employed NIRS on dried leaf extracts (350-2500 nm) to obtain R\({}^{2}\) values above 0.85 for key catechins. [PERSON] et al. (2017) [38] focused on leaf powder in the 900-1701.5 nm region with SNV preprocessing, reporting an R\({}^{2}\) between 0.91 and 0.95 for polyphenols, and [PERSON] et al. (2010) [39] achieved a cross-validation R\({}^{2}\) of 0.97 for polyphenols and 0.99 for AA on tea powder over 1000-2500 nm. More recently, [PERSON] et al. (2023) [40] analyzed black tea extracts by FT-NIR (4000-10,000 cm\({}^{-1}\)) and obtained R\({}^{2}\) = 0.91 for polyphenols using a Si-CARS-PLS model. These higher R\({}^{2}\) values from dried leaves and powders likely reflect reduced water interference and structural heterogeneity, while narrower spectral windows can miss important overtone bands. Our fractional-order derivative approach, although more sensitive to surface moisture variation in fresh leaves, offers finer tuning between amplifying subtle absorption features and suppressing noise, highlighting its potential for applications where maintaining sample integrity is critical.  
These performance differences between AA and TP prediction models may be attributed to multiple factors. First, the choice of spectral technology and the variety of tea and data processing methods (e.g., PLSR, Support Vector Machines, and deep learning) may all have an influence on model performance. Advanced techniques are capable of capturing more complex spectral-chemical relationships, thereby enhancing prediction accuracy. Second, sample properties and diversity play a critical role. Different varieties of tea and sample forms (e.g., powder, dried leaf, and fresh leaf) may exhibit unique spectral characteristics that affect the generalizability and accuracy of the model. Moreover, variations in sample collection, preparation, and measurement can also introduce biases that contribute to the differences observed across studies.  
While our study focuses on tea fresh leaves, the proposed combination of fractional-order derivatives and Competitive Adaptive Reweighted Sampling with PLSR is broadly applicable. By adjusting the fractional order and sampling scheme, researchers can employ this approach to quantify key quality indicators in fruits, vegetables, grains, or even non-plant materials (e.g., soil organic matter and water pollutants). Such adaptability makes the FOD-CARS-PLSR framework a versatile asset for diverse biochemical sensing tasks in precision agriculture, food safety, and environmental monitoring.  
This study does have some limitations that could be improved in future studies, including a limited sample size and constrained indicators (AAs and TPs). Importantly, it was also limited to the fresh leaf scale, excluding canopy-level data, which could enhance broader applications in tea production.