Individual biochemical indicators are insufficient for comprehensive tomato food flavor quality assessment, necessitating multi-parameter models of the core soluble taste matrix. We hypothesized that age stratification of trained sensory assessors would expose differential biochemical variable importance profiles in flavor quality prediction. Accordingly, this study aimed to: (1) construct and compare multiple regression models linking eight biochemical indicators to sensory scores, (2) identify key quality drivers via feature selection, and (3) examine whether age stratification alters the identified sensory drivers. Eight baseline taste indicators across 62 tomato cultivars were evaluated by 30 age-stratified trained sensory panelists (<40 and ≥40 years), using cross-validation to ensure model robustness against small-sample constraints. Partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Boruta were applied. Random forest achieved the best performance (R² = 0.82). In the full panel model, key variables were fructose, total free amino acids, and vitamin C. After age stratification, the under-40 group retained these variables, whereas the ≥40 group replaced vitamin C with soluble solids. Fructose and total free amino acids were consistently robust drivers, while total acidity remained least important. Deploying the RF–Boruta framework within an age-stratified context provides a structured analytical framework for investigating flavor perception from biochemical data. These findings suggest that fructose and total free amino acids represent highly robust candidate indicators for flavor quality prediction, while age-stratified variances suggest the utility of integrating demographic-specific metrics into precision breeding frameworks.

Seed germination is the first critical transition in the plant life cycle, strongly influencing seedling establishment, crop uniformity, and subsequent productivity. However, the genetic basis of seed germination in tomato remains incompletely understood. Here, we performed a genome-wide association study (GWAS) using 319 diverse tomato accessions to identify new loci and candidate genes associated with seed germination. Five significant variants were detected across the genome. Among them, SNP_59508294 on chromosome 3 was consistently associated with the seed germination index (SGI), and accessions carrying the GG haplotype exhibited a significantly higher SGI than those carrying the TT haplotype. Seven α-expansin (EXPA) genes were located within or near the associated interval. Integration of transcriptomic data from contrasting accessions and expression profiles across tomato tissues showed SlEXPA10 to be the most plausible candidate gene. The InDels in the SlEXPA10 promoter were highly associated with its low expression level in low SGI varieties. CRISPR/Cas9-mediated disruption of SlEXPA10 significantly reduced the rate and speed of seed germination relative to the wild type. Collectively, these results identify SlEXPA10 as a positive regulator of tomato seed germination and provide a candidate locus for improving seed vigor and emergence uniformity.

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.