Abstract
The soluble solids content (SSC) affects the flavor of green plums and is an important parameter during processing. In recent years, the hyperspectral technology has been widely used in the nondestructive testing of fruit ingredients. However, the prediction accuracy of most models can hardly be improved further. The rapid development of deep learning technology has established the foundation for the improvement of building models. A new hyperspectral imaging system aimed at measuring the green plum SSC is developed, and a sparse autoencoder (SAE)–partial least squares regression (PLSR) model is combined to further improve the accuracy of component prediction. The results of the experiment show that the SAE–PLSR model, which has a correlation coefficient of 0.938 and root mean square error of 0.654 for the prediction set, can achieve better performance for the SSC prediction of green plums than the three traditional methods. In this paper, integration approaches have combined three different pretreatment methods with PLSR to predict the SSC in green plums. The SAE–PLSR model has shown good prediction performance, indicating that the proposed SAE–PLSR model can effectively detect the SSC in green plums.
Highlights
Green plum, known as sour plum, is one of the traditional fruits that has been cultivated for thousands of years in China
The RP of the sparse autoencoder (SAE)–back propagation (BP) and the SAE–support vector regression (SVR) models decreased by 0.9% and 1.2%, respectively, and the RMSEP of the SAE–BP and the SAE–SVR models increased by 2.1% and 3.8%
These results indicated that the SAE–partial least squares regression (PLSR) model had good performance in the the PLSR model in the SAE–PLSR model was replaced with BP and SVR for prediction and analysis
Summary
Known as sour plum, is one of the traditional fruits that has been cultivated for thousands of years in China. The nondestructive detection method of green plum SSC based on hyperspectral imaging technology is studied in this paper to solve these problems. 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]. This study used green plums as the research object and aims to predict the SSC in green plums to eliminate the limitations of traditional methods and further improve the prediction performance of the model. On the basis of the deep learning technology, a model combining SAE and PLSR is constructed in order to improve the prediction accuracy of the SSC in green plums. The prediction results of different models are analyzed and visualized to reflect the SSC prediction result of each green plum to facilitate the subsequent sorting of green plums
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