Impact of Genetics and Environment on Cranberry Fruit Metabolites

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BACKGROUND: Cranberry (Vaccinium macrocarpon Ait.) is a highly consumed fruit found in foods and supplements and grown throughout northern North America. Its tart flavor makes it a common food ingredient, rather than being directly consumed as fresh fruit. OBJECTIVE: Cranberry fruit samples of 15 genotypes (cultivars and accessions) grown in 16 locations in 4 states (MA, NJ, OR, and WI) and a Canadian province (British Columbia) were analyzed by mass spectrometry. Data were analyzed using chemometric methods to determine the correlation of composition with geographic location. METHOD: 214 cranberry samples were analyzed by nontargeted fuzzy chromatography-direct injection mass spectrometry. Data were collected for 206 ions and analyzed by multifactorial multivariate-analysis of variance-principal component analysis (MFMV-ANOVA-PCA). RESULTS: Sample composition varied statistically (P < 0.001) with respect to the major experimental factors (state/province, growing location, genotype, and analytical batch) and cross factors (genotype-state/province and genotype-growing location). PCA score plots verified a systematic variation with respect to 42 genotype-state/province pairs and 82 genotype-growing location pairs. PCA variable loadings identified major ions that varied with each of the major factors and cross factors and 56 ions were annotated. The location-ion count matrix was transposed and analyzed by hierarchical cluster analysis (HCA) producing dendrograms that grouped ions with respect to metabolic pathways for either the genotype-state/province or genotype-growing location pairs. Annotation of the ions in the hierarchical clusters allowed evaluation of the impact of genetics and location on compounds of interest. Ions expected to correlate with fruit quality measurements (brix, titratable acid, total anthocyanins, and total pro-anthocyanidins) were identified. CONCLUSIONS: This study demonstrates that mass spectral data coupled with chemometric analysis is a valuable tool for predicting the composition of specific genotypes for specific growing locations. HIGHLIGHTS: The general design of this study can be used as a model for other food plants.

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