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Predicting the soluble solids content of honey peach based on bioimpedance spectroscopy and machine learning
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DOI：10.1007/s11694-026-04822-9.png)
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Abstract
En 中文
Soluble solids content (SSC) is a key intrinsic indicator of honey peach flavor, ripeness, and commercial value. This study proposes a nondestructive method for predicting SSC in honey peaches using bioimpedance spectroscopy (BIS) and machine learning. Bioimpedance data from ‘Hujingmilu’ honey peaches were collected over the frequency range of 1 Hz to 100 kHz. A Single-Cole-Warburg equivalent circuit model was constructed to extract seven physiologically relevant electrical parameters. Subsequently, five regression models were trained and evaluated under four feature combinations: partial least squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and convolutional neural network regression (CNNR). Among the models evaluated, CNNR, XGBoost, and RFR showed comparable and superior performance. CNNR achieved an Rp of 0.864, an R2 p of 0.728, and an RMSEP of 0.982 on the prediction set, closely followed by XGBoost (Rp = 0.860) and RFR (Rp = 0.858). Shapley additive explanations (SHAP) analysis further indicated that equivalent circuit parameters and phase-angle features were the primary contributors to the model’s predictions. Moreover, four feature selection methods, namely genetic algorithm (GA), ant colony optimization (ACO), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were used to construct simplified models. Simplified models based on only five frequency points retained comparable predictive performance. Among them, the GA-CNNR model performed best (Rp = 0.856, R2 p = 0.706, RMSEP = 1.029). These results indicate that BIS data contain substantial information redundancy. Overall, BIS combined with machine learning provides an effective approach for nondestructive SSC assessment in honey peaches.
Keywords:
Honey peach
Soluble solids content
Bioimpedance spectroscopy
Machine learning
Nondestructive detection
Shapley additive explanations
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J
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3.3
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1.3K
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1.1W
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