Non-Destructive Assessment of Citrus Mastication Quality Using Magnetic Induction Tomography and Machine Learning

Mastication quality is a key determinant of citrus eating quality, but conventional sensory evaluation and instrumental texture analysis are subjective, destructive, or time-consuming. This study combined magnetic induction tomography (MIT) with machine learning to assess citrus mastication quality non-destructively. MIT signals were acquired from 300 ‘Miyagawa’ satsuma mandarin fruits at 21 frequencies (15–35 MHz). Puncture force, shear force, and chewiness were measured instrumentally. These measurements and sensory scores were integrated into an Analytic Hierarchy Process (AHP)-weighted comprehensive mastication score (0–10), which was also divided into four quality grades (A–D). Feature selection ranked raw features by univariate linear-regression F-test p-values for the regression targets or absolute Kendall’s τ values for the classification target. The top 5% of ranked features were retained, and PCA was then fitted to the retained features. Feature distributions were summarized using pooled out-of-fold predictions from nested cross-validation, with five-fold cross-validation used only for inner Bayesian hyperparameter tuning. SVM achieved the highest R2 for puncture force (0.825) and shear force (0.762), Random Forest achieved the highest R2 for chewiness (0.849) and the highest grade-classification accuracy (64.3%) and macro-averaged AUC (0.871), and XGBoost achieved the highest R2 for the comprehensive mastication score (0.835). These results demonstrate the feasibility of combining MIT with machine learning for non-destructive assessment of ‘Miyagawa’ satsuma mandarin citrus mastication quality and identify frequency–spatial patterns that may guide sensor optimization and future grading applications in other citrus fruits.