###### Abstract  
The efficient and non-destructive evaluation of Yongchuan Xiuya tea quality represents a key advancement in the tea industry. Near-infrared spectroscopy (NIRS), a non-invasive analytical technology, allows for the acquisition of spectral data while preserving sample integrity. Through preprocessing the spectral data and employing the synergy interval partial least squares (siPLS) method to identify characteristic spectral regions, principal component analysis (PCA) is applied, followed by the development of a Jordan-Elman artificial neural network prediction model (ANN) for tea quality assessment. The optimal spectral preprocessing approach identified in this study is a combination of multiplicative scatter correction and second derivative processing. Key spectral intervals include 4377.6 cm\({}^{-1}\)-4751.7 cm\({}^{-1}\), 4755.6 cm\({}^{-1}\)-5129.7 cm\({}^{-1}\), 6262.7 cm\({}^{-1}\)-6633.9 cm\({}^{-1}\), and 7386 cm\({}^{-1}\)-7756.3 cm\({}^{-1}\), with the first three principal components achieving a cumulative contribution rate of 99.05%. Utilizing a tanh activation function, the model exhibited strong predictive performance: an Rp\({}^{2}\) of 0.980 and RMSEP of 0.341 for prediction set samples, and an Rp\({}^{2}\) of 0.978 with RMSEP of 0.373 for unknown samples. These findings demonstrate the potential of integrating NIRS with Jordan-Elman neural networks for rapid and accurate Yongchuan Xiuya tea quality evaluation, establishing a solid technological foundation for the application of NIRS in tea quality assessment. 2024 2020 2  
Tea quality is traditionally assessed using the sensory evaluation method [4], where trained tea experts score five key attributes: appearance, liquor color, aroma, taste, and infused leaf quality. The appearance is evaluated based on the tea leaf's shape, tenderness, color, integrity, and cleanliness. The liquor color is examined for its type, hue, brightness, and clarity. The aroma is assessed by its type, intensity, purity, and persistence. The taste is evaluated based on its strength, body, purity, astringency, and freshness. The infused leaf is judged on its tenderness, color, brightness, and uniformity. The aggregate score of these factors is then used to determine the overall quality of the tea [5; 6; 7; 8; 9]. Although this method is widely recognized for its authority and reliability, it demands highly skilled evaluators and is prone to subjective influences, including personal preferences, physical condition, and environmental factors. As a result, the sensory evaluation method often lacks consistency and fails to meet the need for rapid tea quality assessment. In contrast, the wet chemistry method assesses tea quality by accurately quantifying the intrinsic chemical components of teas across different quality grades [10; 11] using advanced analytical instruments such as high-performance liquid chromatography (HPLC) to detect catechins [12], gas chromatography (GC) to determine aroma [13], high-performance liquid chromatography-mass spectrometry (HPLC-MS) to detect metabolites [14], and gas chromatography-mass spectrometry (GC-MS) to determine volatile components [15]. While this approach is objective, precise, and impartial, it requires complex and time-consuming sample preparation, extensive labor, and large volumes of chemical reagents, raising environmental concerns. Furthermore, its high costs and inability to provide real-time or non-destructive evaluation significantly limit its practicality. This highlights the urgent need for a novel, efficient, and objective method to enable the evaluation of YCXY tea rapidly and non-destructively. Near-infrared spectroscopy (NIRS), with a wavelength range of 780 nm-2526 nm, primarily detects the chemical bond information of X-H groups in samples [16]. Through the establishment of predictive models, NIRS enables rapid, non-destructive, and digital analysis of specific components. It has found broad applications across industries such as agriculture, petrochemicals, textiles, and pharmaceuticals [17; 18; 19].