{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T02:15:39Z","timestamp":1769566539205,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFD2100605"],"award-info":[{"award-number":["2021YFD2100605"]}]},{"name":"National Key Research and Development Program of China","award":["62006008"],"award-info":[{"award-number":["62006008"]}]},{"name":"National Key Research and Development Program of China","award":["62203020"],"award-info":[{"award-number":["62203020"]}]},{"name":"National Key Research and Development Program of China","award":["KM201911417008"],"award-info":[{"award-number":["KM201911417008"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFD2100605"],"award-info":[{"award-number":["2021YFD2100605"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006008"],"award-info":[{"award-number":["62006008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62203020"],"award-info":[{"award-number":["62203020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KM201911417008"],"award-info":[{"award-number":["KM201911417008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China","award":["2021YFD2100605"],"award-info":[{"award-number":["2021YFD2100605"]}]},{"name":"General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China","award":["62006008"],"award-info":[{"award-number":["62006008"]}]},{"name":"General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China","award":["62203020"],"award-info":[{"award-number":["62203020"]}]},{"name":"General Program of Science and Technology Development Project of Beijing Municipal Education Commission of China","award":["KM201911417008"],"award-info":[{"award-number":["KM201911417008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700\u20131000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.<\/jats:p>","DOI":"10.3390\/s23020571","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T05:02:30Z","timestamp":1672808550000},"page":"571","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1669-3337","authenticated-orcid":false,"given":"Jinghua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1982-9017","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China"}]},{"given":"Wancheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Bureau of Ecology and Environment of Hanting District, No. 1507 Fenghua Road, Weifang 261100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9419-7083","authenticated-orcid":false,"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China"},{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Quancheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China"}]},{"given":"Lei","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3390\/nitrogen3010003","article-title":"Identifying Sustainable Nitrogen Management Practices for Tea Plantations","volume":"3","author":"Rebello","year":"2022","journal-title":"Nitrogen"},{"key":"ref_2","unstructured":"FAO (2018). 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