Multivariate Standard Addition Cobalt Electrochemistry Data Fusion for Determining Phosphate Concentration in Hydroponic Solution

Abstract

Phosphate is one of the major elements affecting agricultural production. The accurate determination of phosphate concentration essential for plant growth, especially in a hydroponics system, allows regulating the balanced and suitable range set of nutrients to plants efficiently. This study proposed a data fusion model based on 70 samples for calibration and 30 samples for predicting concentrations of phosphate in an eggplant nutrient solution. Three multivariate analysis methods i.e. partial least squares model (PLS), Gaussian process regression (GPR), and artificial neural network (ANN) were studied and compared for their performance efficiencies. The results showed that combining the multivariate standard addition method (MSAM) in acquiring data from cobalt electrodes and ANN data fusion model came up with satisfactory outcomes. Both the method provided good performance with R 2 values of 0.98 and 0.96, and the root mean square error (RMSE) of 50 and 66 mg. L −1 respectively in calibration and evaluation tests. These values were much higher than those of conventional processing techniques. Moreover, the normal direct calibration method in acquisition signal from cobalt electrodes was also applied, which provided R 2 values of 0.7 to 0.8. These high values are sufficient for development to measure phosphate concentration in hydroponic solutions. INDEX TERMS Phosphate sensing, multi-sensor data fusion, multivariate standard addition method (MSAM), partial least squares model (PLS), Gaussian process regression (GPR), neural network-ANN.

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

“…The MSAM sampling method [ 29 ] was utilized to prepare the dataset for developing the models. Deionized water was mixed with a sufficient amount of stock solution of targeted ions to dilute the rinsed water (RW) in which the concentration of each ion species approximated to the lower limit value ( ) of linearity range of the ISEs (roughly 1 ).…”

“…Recently, deep kernel learning (DKL), a combination of the deep learning structure and kernel methods, was proposed as an elegant and flexible algorithm [ 35 ]. In this study, the multi-ion concentration of the hydroponic nutrient solution was determined by combining the MSAM technique [ 29 ] with DKL signal processing to enhance the accuracies and feasibilities of the ISEs, as shown in Figure 1 . The collected signals from sensor array extracted more significant information by using the feature enrichment technique.…”

“… , “enriched data value,” represents the values of differences between and , which were used to improve the performance of the model. The details of this technique were reported by Tuan et al [ 29 ]. The 17-dimensional input vector (16 enriched features plus one temperature sensor) was then introduced to the DKL, as shown in Figure 1 .…”

“…However, none of technical strategies has radically resolved the drawbacks of ISEs. Other investigations focused on predicting the ions that are not available through an ion-selective electrode (as such , , and Mg) were examined by fusing the datasets acquired from an array of available ISEs [ 20 , 27 , 28 , 29 ].…”

“…Therefore, the ANN does not work satisfactorily with a small dataset of 27 samples [ 19 ]. In case of a limited number of samples, GP is an appropriate choice [ 29 ]. However, the basic GP model does not fit well in high-dimensional input-output spaces [ 33 ].…”