## 4 Conclusions  
Rapid and non-destructive detection of YCXY tea quality is a key trend, enabling real-time quality monitoring during product sales. In this study, NIRS, siPLS, PCA, and Jordan-Elman neural networks were integrated to develop a robust quality prediction model for YCXY (Rp\({}^{2}\) = 0.980, RMSEP = 0.341). This model enables accurate quality predictions within seconds without damaging the samples, offering a cutting-edge solution for reducing product sales costs. In addition, by utilizing the selected characteristic spectral intervals and removing irrelevant spectral information, future applications can focus on developing specialized near-infrared spectrometers tailored for YCXY tea quality assessment. This approach eliminates the need for full-spectrum NIRS detectors, significantly reducing instrument development costs and expediting their deployment. To further enhance the model's predictive accuracy, it is recommended to expand the database by including YCXY tea samples from different processing years. This will improve the model's adaptability and robustness, ensuring broader applicability and reliability.  
While promising, model development is not without limitations:  
(1) The available sample data are limited in both quantity and diversity, making it challenging to cover the full range of tea types, origins, seasons, and processing methods, which weakens the model's generalizability. (2) The predictive accuracy of near-infrared spectral models may decline when applied to complex samples, such as those affected by variations in processing techniques or storage conditions. (3) Inconsistencies in spectral data due to differences among devices (instrument variability) hinder the model's adaptability across diverse production settings and hardware systems.  
To overcome these limitations in future applications:  
(1) Combining near-infrared spectroscopy with visual inspection technologies can enable the development of multimodal models, improving prediction accuracy and providing a more comprehensive assessment of both the external and internal qualities of tea. (2) Leveraging deep learning algorithms can enhance computational efficiency, facilitating compatibility with embedded systems and promoting the development of portable near-infrared spectrometers for real-time application on production lines. (3) Establishing a large-scale near-infrared spectral database for tea quality will improve the model's training capabilities and scope. Cloud computing technologies can support large-scale data processing and model training, enabling remote and online quality assessment. (4) Standardizing near-infrared spectral instruments and developing calibration algorithms tailored to various devices will enhance measurement consistency, laying the foundation for the standardized development of tea quality assessment within the industry.  
Conceptualization, Y.Z. ([PERSON]), J.W. and X.W.; methodology, R.C. and H.L.; software, J.Y.; validation, Q.W.; writing--original draft preparation, Y.Z. ([PERSON]) and J.W.; writing--review and editing, Z.X. and Y.Z. ([PERSON]); funding, Z.X. and Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This study was supported by the municipal financial special project of Chongqing Academy (cqaas2023 sjczzd002) and Chongqing Modern Agricultural Industry Technology System (CQMAITS2024084).  
Not applicable.  
The data are available from the corresponding author.  
The authors declare no conflicts of interest.