{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:37:58Z","timestamp":1762299478359,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T00:00:00Z","timestamp":1600300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"undefined  &lt;span style=&quot;color:gray;font-size:10px;&quot;&gt;undefined&lt;\/span&gt;","award":["NJ2019-02"],"award-info":[{"award-number":["NJ2019-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic strength make them difficult to apply in a hydroponic system. To minimize these deficiencies, we combined the multivariate standard addition (MSAM) sampling technique with the deep kernel learning (DKL) model for a six ISEs array to increase the prediction accuracy and precision of eight ions, including NO3\u2212, NH4+, K+, Ca2+, Na+, Cl\u2212, H2PO4\u2212, and Mg2+. The enhanced data feature based on feature enrichment (FE) of the MSAM technique provided more useful information to DKL for improving the prediction reliability of the available ISE ions and enhanced the detection of unavailable ISE ions (phosphate and magnesium). The results showed that the combined MSAM\u2013feature enrichment (FE)\u2013DKL sensing structure for validating ten real hydroponic samples achieved low root mean square errors (RMSE) of 63.8, 8.3, 29.2, 18.5, 11.8, and 8.8 mg\u00b7L\u22121 with below 8% coefficients of variation (CVs) for predicting nitrate, ammonium, potassium, calcium, sodium, and chloride, respectively. Moreover, the prediction of phosphate and magnesium in the ranges of 5\u2013275 mg\u00b7L\u22121 and 10\u201380 mg\u00b7L\u22121 had RMSEs of 29.6 and 8.7 mg\u00b7L\u22121 respectively. The results prove that the proposed approach can be applied successfully to improve the accuracy and feasibility of ISEs in a closed hydroponic system.<\/jats:p>","DOI":"10.3390\/s20185314","type":"journal-article","created":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T08:29:43Z","timestamp":1600331383000},"page":"5314","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution"],"prefix":"10.3390","volume":"20","author":[{"given":"Vu","family":"Tuan","sequence":"first","affiliation":[{"name":"Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"},{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Faculty of Electrical and Electronic Engineering, Nam Dinh University of Technology Education, Nam Dinh 420000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4417-489X","authenticated-orcid":false,"given":"Abdul","family":"Khattak","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Departemnt of Horticulture, The University of Agriculture, Peshawar 25120, Pakistan"}]},{"given":"Hui","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Liquor Making Biological Technology and Application, Zigong 643000, China"},{"name":"School of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, China"}]},{"given":"Wanlin","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"},{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"given":"Minjuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"},{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1021\/jf203275m","article-title":"Hydroponic Cultivation Improves the Nutritional Quality of Soybean and Its Products","volume":"60","author":"Palermo","year":"2012","journal-title":"J. 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