Nutritional and Phytochemical Diversity Among Cowpea (<i>Vigna unguiculata</i>L.) Genotypes: Insights From Multivariate and Machine Learning Approaches
NEW ZEALAND JOURNAL OF CROP AND HORTICULTURAL SCIENCE, cilt.54, sa.4, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 54 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.1002/nzc2.70259
- Dergi Adı: NEW ZEALAND JOURNAL OF CROP AND HORTICULTURAL SCIENCE
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Periodicals Index Online, BIOSIS, Geobase, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Engineering Source (EBSCO)
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
Cowpea (Vigna unguiculata [L.] Walp.) is an important grain legume with considerable potential for crop diversification and the improvement of nutritional quality. Understanding genotype-dependent variation in seed nutritional composition, phytochemical attributes, and functional properties is essential for identifying promising genetic resources for breeding and food-related applications. This study evaluated the nutritional, phytochemical, and functional diversity of cowpea genotypes using comprehensive biochemical profiling, mineral and protein characterization, multivariate statistical analyses, and exploratory machine-learning modeling. Significant genotypic variation was observed in total phenolic and flavonoid contents, antioxidant capacity, enzyme inhibitory activities, protein content, and mineral composition, indicating substantial differentiation among the evaluated genotypes. Genotypes 23Vupmbs and 23Vuapk exhibited particularly strong antioxidant profiles, combining high DPPH, ABTS, CUPRAC, and FRAP activities with elevated phenolic contents. Genotype 23Vu & uuml;sbo showed the highest AChE, BChE, and tyrosinase inhibitory activities, while 23Vus & uuml;kh was characterized by the highest alpha-amylase inhibitory activity and flavonoid content. Correlation and multivariate analyses revealed distinct biochemical profiles and strong associations among phenolic content, antioxidant responses, and selected mineral traits. Exploratory machine-learning analyses identified trait-specific associations between selected predictor variables and biochemical responses; however, model performance varied among traits and was interpreted cautiously because of the limited number of independent genotypes. Collectively, the integrated analyses identified complementary nutritional and functional profiles among the evaluated cowpea genotypes and highlighted promising materials for subsequent nutritional-quality characterization and breeding-related evaluation. The observed variation provides a useful basis for genotype selection, while demonstrating the value of integrating biochemical phenotyping with multivariate and exploratory data-driven approaches.