Prediction of the soluble solid contents for apple fruit using electrical parameters

Abstract: Soluble Solids Content (SSC) is one of the most important physiochemical parameters to evaluate the internal quality of apple fruits. However, the traditional SSC detection can cause some mechanical damages to the fruits under the static and dynamic forces. It is necessary to explore the non-destructive SSC prediction in modern agriculture. Fortunately, the dielectric spectrum detection has been widely used in the quality evaluation of fruits and vegetables in recent years, due to the simple procedure and measurement requirements for the samples. Nevertheless, the nondestructive quality detection can result in the high complexity of the prediction model, due mainly to the data redundancy in a large number of measured electrical parameters at different frequency points. In this study, the linear and nonlinear feature extraction of electrical parameters was proposed to simplify the input of regression model, in order to accurately and rapidly predict the SSC of apples. The electrical parameters of three hundred Fuji apples were measured at the nine frequencies in the range of 0.158-3 980 kHz by high-frequency LCR (Inductance (L), Capacitance (C), and Resistance (R)) meter. Among them, 15 electrical parameters were collected at each frequency, where each apple sample was contained 135 electrical parameters. All the electrical parameters of each apple were measured non-destructively. The SSC was also obtained by the special physical and chemical analysis machine after wholly destroying the samples. Finally, the regression prediction model was constructed using the key genomic parameters of apple. Two nonlinear regression models (namely multi-layer perceptron (MLP) and XGBoost model) were employed, according to the electrical characteristic parameters and SSC. Furthermore, 300 samples of apple were randomly divided into the calibration set and prediction set. The calibration set was used to construct the prediction model, whereas, the stability of the model was then tested by the prediction set. Firstly, the MLP and XGBoost were set as the input of nonlinear regression models. 40 dielectric characteristics were obtained by the linear feature extraction of principal component analysis (PCA), with a cumulative contribution rate of 98.9%. More importantly, the evaluation index of PCA-XGBoost model was higher than that of PCA-MLP model in the calibration set and prediction set. The residual prediction deviation of PCA-MLP model was lower than PCA-XGBoost model. Furthermore, the nonlinear characteristics of the electrical parameters were ignored in the linear feature extraction, due to the nonlinear relationship between the electrical parameters of apple samples at different frequencies. Long short-term memory encoder-decoder model (LSTMED) was also utilized to extract the non-linear characteristics for the input of the nonlinear regression model of MLP and XGBoost. A comparison was made for the prediction accuracy of LSTMED-MLP and LSTMED-XGBoost. The experiments showed that the residual prediction deviations of LSTMED were higher than those of PCA, respectively, indicating the better feasibility. The LSTMED-MLP model performed the best on the calibration sets and prediction sets, followed by the LSTMED-XGBoost model. The predicted correlation coefficients were 0.91 and 0.85, respectively. The predicted root mean square errors were 0.84 and 0.95, respectively. Therefore, the LSTMED demonstrated the effective performance of feature extraction and data reduction for the non-linear parameters. The wide prediction suitability of the improved model can be expected for the inner quality parameters of fruit and vegetables.