A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea

To investigate different contents of pu-erh tea polyphenol affected by abiotic stress, this research determined the contents of tea polyphenol in teas produced by Yuecheng, a Xishuangbanna-based tea producer in Yunnan Province. The study drew a preliminary conclusion that eight factors, namely, altitude, nickel, available cadmium, organic matter, N, P, K, and alkaline hydrolysis nitrogen, had a considerable influence on tea polyphenol content with a combined analysis of specific altitudes and soil composition. The nomogram model constructed with three variables, altitude, organic matter, and P, screened by LASSO regression showed that the AUC of the training group and the validation group were respectively 0.839 and 0.750, and calibration curves were consistent. A visualized prediction system for the content of pu-erh tea polyphenol based on the nomogram model was developed and its accuracy rate, supported by measured data, reached 80.95%. This research explored the change of tea polyphenol content under abiotic stress, laying a solid foundation for further predictions for and studies on the quality of pu-erh tea and providing some theoretical scientific basis.

[1]  Wei Huang,et al.  Edge Device Detection of Tea Leaves with One Bud and Two Leaves Based on ShuffleNetv2-YOLOv5-Lite-E , 2023, Agronomy.

[2]  B. Bu,et al.  Dynamic nomogram for predicting generalized conversion in adult-onset ocular myasthenia gravis , 2022, Neurological Sciences.

[3]  Bing Gao,et al.  Risk stratification system and visualized dynamic nomogram constructed for predicting diagnosis and prognosis in rare male breast cancer patients with bone metastases , 2022, Frontiers in Endocrinology.

[4]  Chao Tu,et al.  Identification of cuproptosis-related lncRNA prognostic signature for osteosarcoma , 2022, Frontiers in Endocrinology.

[5]  Q. Cai,et al.  Construction and Validation of Mortality Risk Nomograph Model for Severe/Critical Patients with COVID-19 , 2022, Diagnostics.

[6]  Qianying Dai,et al.  Prediction Model for Tea Polyphenol Content with Deep Features Extracted Using 1D and 2D Convolutional Neural Network , 2022, Agriculture.

[7]  Jianyu Zou,et al.  Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage , 2022, Frontiers in Neuroscience.

[8]  J. Zhang,et al.  Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging , 2022, Foods.

[9]  Ming Yang,et al.  Establishment of a Nomogram-Based Prognostic Model (LASSO-COX Regression) for Predicting Progression-Free Survival of Primary Non-Small Cell Lung Cancer Patients Treated with Adjuvant Chinese Herbal Medicines Therapy: A Retrospective Study of Case Series , 2022, Frontiers in Oncology.

[10]  Qinghua Zhang,et al.  Establishment and Validation of a Non-invasive Diagnostic Nomogram to Identify Heart Failure in Patients With Coronary Heart Disease , 2022, Frontiers in Cardiovascular Medicine.

[11]  Yanan Liu,et al.  Tea Polyphenols Prevent and Intervene in COVID-19 through Intestinal Microbiota , 2022, Foods.

[12]  S. Wicha,et al.  A dosing nomograph for cerebrospinal fluid penetration of meropenem applied by continuous infusion in patients with nosocomial ventriculitis. , 2022, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[13]  Nuofu Zhang,et al.  Development and validation of a simple clinical nomogram for predicting obstructive sleep apnea , 2022, Journal of sleep research.

[14]  Horacio Pérez-Sánchez,et al.  Accelerating Big Data Analysis through LASSO-Random Forest Algorithm in QSAR Studies , 2022, Bioinform..

[15]  A. Fassihi,et al.  Accelerating Big Data Quantitative Structure-Activity Prediction through LASSO-Random Forest Algorithm. , 2021, Bioinformatics.

[16]  Jianbo Xiao,et al.  Investigation and dynamic profiling of oligopeptides, free amino acids and derivatives during Pu-erh tea fermentation by ultra-high performance liquid chromatography tandem mass spectrometry. , 2021, Food chemistry.

[17]  L. Tian,et al.  Nomogram based on nutritional and inflammatory indicators for survival prediction of small cell carcinoma of the esophagus. , 2020, Nutrition.

[18]  Jianjun He,et al.  Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors , 2020, Annals of Surgical Oncology.

[19]  Y. Liu,et al.  NIR hyperspectral imaging coupled with chemometrics for nondestructive assessment of phosphorus and potassium contents in tea leaves , 2020 .

[20]  Lanjuan Li,et al.  A Predictive Nomogram for Predicting Improved Clinical Outcome Probability in Patients with COVID-19 in Zhejiang Province, China , 2020, Engineering.

[21]  Shuang Xia,et al.  Development and validation of machine learning prediction model based on computed tomography angiography–derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study , 2020, European Radiology.

[22]  Xiaoyan Yin,et al.  Abstract WP123: Risk Factors and Nomogram to Predict Intracranial Hemorrhage in Stroke Patients Undergoing Thrombolysis , 2020, Stroke.

[23]  Y. Duan,et al.  Antioxidant mechanism of tea polyphenols and its impact on health benefits , 2020, Animal nutrition.

[24]  Weidong Dai,et al.  Nontargeted metabolomics predicts the storage duration of white teas with 8-C N-ethyl-2-pyrrolidinone-substituted flavan-3-ols as marker compounds. , 2019, Food research international.

[25]  B. Mohanty,et al.  A Nomograph to Incorporate Geophysical Heterogeneity in Soil Moisture Downscaling , 2019, Water Resources Research.

[26]  Yue Yu,et al.  Adapting & testing use of USLE K factor for agricultural soils in China , 2019, Agriculture, Ecosystems & Environment.