Seolah Kim, Sun Woo Chung, Hyun Joo An, Chan Kyu Lim, Mi Kyoung Jeon, and Yeon Jin Jang. Journal of the Korean Society of Food Science and Nutrition 2022;51:344-51. https://doi.org/10.3746/jkfn.2022.51.4.344

Papaya fruit (Carica papaya) has different degrees of ripening within each fruit, affecting its commercial market value. The fruit characteristics of “Tainung No. 2” Red papaya were investigated at the stem-end, middle, and calyx-end across 3 ripening stages and categorized based on fruit skin coloration: unripe at 16 weeks after anthesis (WAA), half-ripe at 18 WAA, and full-ripe at 20 WAA. The fruits maintained an elliptical shape during ripening with a ratio of 2.36 of the length to the width. The peel and pulp color changed from green to white to yellow during ripening, regardless of the three parts. In the pulp, soluble solid contents increased, and firmness decreased during ripening but did not differ among the three parts. Individual nutrient contents, including metabolites and minerals, changed dynamically between the ripening stages and fruit parts. Total carbohydrates and proteins, N, and K, were accumulated more at the stem-end during ripening; meanwhile, fructose, glucose, Mg, and Mn were accumulated more at the calyx-end. In the principal component analysis, ripening stages and fruit parts were distinctly determined by the first and second principal components, respectively. Understanding the nutrient and metabolite dynamics during ripening and their distribution within the fruit can help optimize cultivation practices, enhance fruit quality, and ultimately benefit both growers and consumers.

Strawberry aroma, crucial for determining quality, involves complex volatile compounds which are challenging to identify. This study explores strawberry aroma analysis using Gas Chromatography-Mass Spectrometry (GC-MS) and the Automated Mass Spectral Deconvolution and Identification System (AMDIS). Central to our research is the creation of a bespoke strawberry Volatile Organic Compounds (VOCs) user library using AMDIS, specifically for analyzing strawberry aromas. The library contains VOCs from 61 strawberry cultivars, integrating information on 104 VOCs, including mass spectra, retention index, chemical class, CAS number, formula, odor threshold, and odor description. This custom library significantly outperformed a commercial library by reducing potential false hits by 200, decreasing the size of report files by over 96%, and, most importantly, shortening AMDIS analysis processing time from 31 s to 9 s, representing an approximate 71% reduction. Further, the study demonstrates the library’s practical application by contrasting the aroma profiles of strawberries harvested in winter and spring. This comparison revealed significant VOC variations depending on seasonal temperature changes, offering insights into environmental influences on strawberry aroma. In conclusion, this research marks a significant advance in strawberry aroma quality analysis. The strawberry VOC library developed in this study is expected to greatly aid targeted metabolomics and flavor research in strawberry breeding.

In Korea, the majority of strawberry cultivation follows a forcing culture, where planting occurs in mid-September after the flower differentiation, and harvesting begins at the end of November. October and November constitute off-season, resulting in higher prices. The accelerated forcing culture involves artificially promoting flower differentiation to expedite strawberry harvest. This study aimed to identify the most suitable schedule for strawberry cultivation using the short-day and low-temperature treatments through greenhouse environmental control during the nursery stage. The selection of the most suitable cultivars for accelerated forcing culture among Korean breeding cultivars (‘Sulhyang’, ‘Kuemsil’, ‘Kingsberry’, ‘Vitaberry’, ‘Jukhyang’, and ‘Altaking’) was also part of the objectives. The nursery treatments were initiated on 4 July, 14 July, and 25 July. After approximately 5 weeks of treatment, transplanting was carried out. The control group was transplanted on 15 September. When night-chilling nursery treatment was applied on 4 July, followed by transplanting on 11 August, all six cultivars exhibited flowering earlier compared to the control group, leading to increased early yield. Particularly, the ‘Sulhyang’ cultivar showed the highest marketable yield at 68.6 g per plant in October. This research contributes to identifying the nursery cultivation schedule for off-season strawberry production and selecting suitable cultivars, and is expected to contribute to increased farm income.

Anthracnose, predominantly caused by Colletotrichum fructicola, severely reduces yield in Fragaria × ananassa production. We assessed ensemble machine learning (ML) frameworks to improve genomic prediction (GP) of resistance using a training population of 300 individuals from six full-sib families. Genotyping with the Axiom^® 50K FanaSNP array and phenotyping by AUDPC after artificial inoculation enabled evaluation of five algorithms—G-BLUP, LASSO, LightGBM, Random Forest, and XGBoost—combined with informed feature selection and resampling-based data augmentation (3×, 5×). Ensemble ML models consistently outperformed linear approaches, with Random Forest, LightGBM, and XGBoost achieving the highest accuracies. Marker prioritization revealed that a reduced SNP panel of ~200 markers provided near-maximal predictive performance (R² up to 0.991), demonstrating that compact subsets can support cost-efficient GP. Data augmentation, implemented through the resampling of existing observations rather than the creation of new alleles, improved statistical power and model stability under limited sample sizes. Collectively, this study demonstrates that (i) ensemble ML models deliver superior accuracy for predicting polygenic resistance, (ii) small SNP panels can achieve high efficiency, and (iii) augmentation enhances robustness in resource-constrained breeding populations. These findings establish a scalable and breeder-oriented GP pipeline to accelerate the development of anthracnose-resistant strawberry cultivars.

The production of Kimchi cabbage (Brassica rapa L. ssp. pekinensis) is increasingly threatened by concurrent heat and drought stress, which induce physiological disorders and severe heading failure. In this study, we implemented a dual-environment screening strategy to identify robust indicators of heat resilience in an F₂ population. First, seedling-stage heat tolerance was evaluated in a controlled growth chamber using qualitative visual scoring (Scale 1–4) for shoot and root vigor. Second, the population was validated under summer field conditions in South Korea. Machine learning (ML) analysis revealed that the seedling-stage qualitative shoot-to-root (S/R) ratio was the most robust predictor of mature heat resilience, achieving a classification accuracy of 0.770 via SVM algorithms. The study also revealed a strong positive correlation (r = 0.71) between shoot apical meristem (SAM) wilting and localized calcium deficiency (tipburn). Individuals with severe SAM wilting were predominantly associated with severe tipburn and inner-leaf decay. Genome-wide association studies (GWAS) pinpointed the MIZU-KUSSEI 1-like (MIZ1-like) gene on Chromosome 2 as a primary candidate for S/R ratio regulation, potentially acting through the optimization of root hydrotropism. Our findings highlight that qualitative seedling-stage S/R ratio screening, integrated with ML models, serves as an efficient, breeder-friendly tool for early selection of climate-resilient B. rapa cultivars before field transplantation.