A method for establishing an unbiased model using the metabolic profile and phenotypic or trait profile of at least two groups of plants, said method comprising: a) characterizing the phenotypic or trait profiles of said at least two groups of plants, wherein said at least two groups of plants have different phenotypes or traits; b) extracting metabolites from said at least two groups of plants; c) separating said metabolites by chromatography to generate a first set of data; d) detecting the mass-to-charge fragments produced by said metabolites using mass spectrometry to generate a second set of data; e) pre-processing said first set of data and said second set of data to align, reduce noise, and normalizing said mass-to-charge fragment x retention times or index intensities for internal standard mass-to-charge intensity and sample dry weight; f) using the pre-processed data of step e) to build a partial least squares multivariate calibration to predict quantitative outcomes and using validation or cross-validation to select latent variables; and, g) providing an output to a user of said unbiased model that can be used to predict the development of a phenotype or trait in an independent plant where only the metabolic profile has been characterized, wherein the metabolic profile forming the output of step (e) comprises the entire set of metabolites which are detected and pre-processed, and further comprising selecting a plant that is predisposed to express a phenotype or trait of interest, said method comprising: h) using the method of steps a) to i) to predict expression of said phenotype or trait of interest in said at least one independent plant; and, i) selecting said at least one independent plant which is predicted to express said phenotype or trait of interest. a) characterizing the phenotypic or trait profiles of said at least two groups of plants, wherein said at least two groups of plants have different phenotypes or traits; b) extracting metabolites from said at least two groups of plants; c) separating said metabolites by chromatography to generate a first set of data; d) detecting the mass-to-charge fragments produced by said metabolites using mass spectrometry to generate a second set of data; e) pre-processing said first set of data and said second set of data to align, reduce noise, and normalizing said mass-to-charge fragment x retention times or index intensities for internal standard mass-to-charge intensity and sample dry weight; f) using the pre-processed data of step e) to build a partial least squares multivariate calibration to predict quantitative outcomes and using validation or cross-validation to select latent variables; and, g) providing an output to a user of said unbiased model that can be used to predict the development of a phenotype or trait in an independent plant where only the metabolic profile has been characterized, wherein the metabolic profile forming the output of step (e) comprises the entire set of metabolites which are detected and pre-processed, and h) using the method of steps a) to i) to predict expression of said phenotype or trait of interest in said at least one independent plant; and, i) selecting said at least one independent plant which is predicted to express said phenotype or trait of interest. <EOS>