Liu Haowen, Chen Fengnong, Zhang Leilei, Meng Detong, Sun Hongwei
College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018 Zhejiang, China.
College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018 Zhejiang, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Dec 15;343:126571. doi: 10.1016/j.saa.2025.126571. Epub 2025 Jun 13.
Moisture significantly affects tea plants' growth and quality. Traditional methods of leaf moisture detection are usually destructive to samples, slow and labour-intensive. In this study, visible-near infrared (VIS-NIR) spectroscopy was used to detect the moisture content of tea leaves quickly and accurately in the spectral range of 500-870 nm. The experimental materials are "Longjing 43″, which are divided into two batches. The first batch consists of 135 tea samples collected in April 2022, and the second batch includes 349 tea samples collected in April 2024.The FD + SNV + CARS + ε-SVR model had the best prediction effect on the moisture content of tea leaf in 2024, with the prediction effects of R, R, RMSEC, RMSEP and RPD being 0.9676, 0.903, 0.0221, 0.04 and 2.3367, respectively. However, the prediction result R of the constructed model applied to the 2022 data was only 0.138. In order to improve the generalisation of the model, this study proposes stacking ensemble learning and instance-based transfer learning. In particular, the transfer learning model only needed 55 transfer samples, and the R was the highest at 0.851. Compared with the stacking ensemble, which required 60 samples, the R was the highest at 0.85, which realised the use of fewer samples to achieve a better prediction effect. These studies not only confirmed the potential of VIS-NIR spectroscopy to assess the moisture content of tea leaves but also investigated the transfer optimisation of the model, which was helpful to improve the generalisation ability of the model.
水分显著影响茶树的生长和品质。传统的叶片水分检测方法通常对样本具有破坏性，且速度慢、劳动强度大。在本研究中，利用可见-近红外（VIS-NIR）光谱在500-870nm光谱范围内快速准确地检测茶叶的水分含量。实验材料为“龙井43”，分为两批。第一批由2022年4月采集的135个茶叶样本组成，第二批包括2024年4月采集的349个茶叶样本。FD + SNV + CARS + ε-SVR模型对2024年茶叶水分含量的预测效果最佳，R、R、RMSEC、RMSEP和RPD的预测效果分别为0.9676、0.903、0.0221、0.04和2.3367。然而，将构建的模型应用于2022年数据的预测结果R仅为0.138。为了提高模型的泛化能力，本研究提出了堆叠集成学习和基于实例的迁移学习。特别是迁移学习模型仅需要55个迁移样本，R最高为0.851。与需要60个样本的堆叠集成相比，R最高为0.85，实现了用更少的样本达到更好的预测效果。这些研究不仅证实了VIS-NIR光谱在评估茶叶水分含量方面的潜力，还研究了模型的迁移优化，有助于提高模型的泛化能力。
