# Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder  
[PERSON] [PERSON]  
1 College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China;  
[EMAIL_ADDRESS] (L.S. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (Y.F.) 1  
[PERSON]  
quality cannot be guaranteed. The nondestructive detection method of green plum SSC based on hyperspectral imaging technology is studied in this paper to solve these problems. In recent years, several studies have reported on the nondestructive testing of agricultural products based on spectral imaging techniques [3; 4; 5; 6; 7]. The commonly used modeling methods are partial least squares regression (PLSR), support vector regression (SVR), and back propagation (BP) neutral network [8; 9]. [PERSON] [10] has studied the nondestructive detection of protein content in wheat by using the PLSR model for prediction and observed correlation coefficient (\(R\)) and root mean square error (RMSE) values of 0.79 and 0.94%, respectively, in the prediction set. [PERSON] [11] has established the SVR model to measure the starch content in rice and observed \(R\) and RMSE values of 0.991 and 0.669%, respectively, in the prediction set, thereby indicating the effective determination of the starch content in rice. However, part of the research is limited to single traditional modeling methods and often has limitations in target prediction. Some studies have found that the extraction of feature bands from the original spectrum can effectively improve the prediction performance of the models. The spectral data in some bands of the original spectral data have a low signal-noise ratio (SNR) and a low contribution rate of predicting the target component, thereby reducing the prediction accuracy in model calibration. In addition, a linear correlation between spectral data in different bands reduces the prediction accuracy of the model. Therefore, on the basis of the traditional prediction models, the pretreatment of the raw data by using the selected feature wavelengths method can effectively solve the abovementioned problems and improve the prediction performance of the model [12; 13; 14]. [PERSON] [15] has studied the nondestructive detection of counterfeit beef based on hyperspectral technology. The principal component analysis (PCA), locally linear embedding (LLE), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and other methods are compared during the model establishment. The results of the experiment have shown that the model using CARS combined with PLSR have worked best and revealed \(R\) and RMSE values of 0.960 and 2.758, respectively, in the prediction set. With the development of deep learning technology, more deep learning models are applied to spectral data processing [16; 17; 18]. The sparse autoencoder (SAE) is an unsupervised deep learning algorithm used in the reduction of data dimension and extraction of features [19; 20]. The goal of the autoencoder is to reconstruct the original input as accurately as possible in the output layer by the random initialization and the update of the weights and offsets through continuous learning. Moreover, adding sparseness restrictions to neurons can effectively reduce the raw data dimension, remove the redundant parts of the information, and achieve better robustness. [PERSON] [21] has built a model based on SAEs to extract spatial spectral features and classify hyperspectral remote sensing images. The performance of the model is verified by data, and the accuracy has reached 90.03% on the hyperspectral image dataset (Pavia University), which is superior to other traditional methods. [PERSON] [22] has built a model based on SAEs to detect the presence of tapeworms in rice and achieved good results, with an overall accuracy of 93.44%, which is more superior and more stable compared with that of other traditional methods. SAEs have more applications in image classification than component prediction.