Discovery of food identity markers by metabolomics and machine learning technology

Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to screen food markers when coupled to efficient data analysis. This study explored feasibility of random forest (RF) machine learning, specifically its inherent feature extraction for non-targeted metabolic marker discovery. The distinction of chia, linseed, and sesame that have gained attention as “superfoods” served as test case. Chemical fractions of non-processed seeds and of wheat cookies with seed ingredients were profiled. RF technology classified original seeds unambiguously but appeared overdesigned for material with unique secondary metabolites, like sesamol or rosmarinic acid in the Lamiaceae, chia. Most unique metabolites were diluted or lost during cookie production but RF technology classified the presence of the seed ingredients in cookies with 6.7% overall error and revealed food processing markers, like 4-hydroxybenzaldehyde for chia and succinic acid monomethylester for linseed additions. RF based feature extraction was adequate for difficult classifications but marker selection should not be without human supervision. Combination with alternative data analysis technologies is advised and further testing of a wide range of seeds and food processing methods.Fil: Erban, Alexander. Max-Planck-Institute of Molecular Plant Physiology. Department of Molecular Physiology; AlemaniaFil: Fehrle, Ines. Max-Planck-Institute of Molecular Plant Physiology. Department of Molecular Physiology; AlemaniaFil: Martinez-Seidel, Federico. Max-Planck-Institute of Molecular Plant Physiology. Department of Molecular Physiology; AlemaniaFil: Brigante, Federico Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencia y Tecnología de Alimentos Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Ciencia y Tecnología de Alimentos Córdoba; ArgentinaFil: Lucini Mas, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencia y Tecnología de Alimentos Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Ciencia y Tecnología de Alimentos Córdoba; ArgentinaFil: Baroni, María Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencia y Tecnología de Alimentos Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Ciencia y Tecnología de Alimentos Córdoba; ArgentinaFil: Wunderlin, Daniel Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencia y Tecnología de Alimentos Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Ciencia y Tecnología de Alimentos Córdoba; ArgentinaFil: Kopka, Joachim. Max-Planck-Institute of Molecular Plant Physiology. Department of Molecular Physiology; Alemani

Metabolic profiling of Arabidopsis thaliana epidermal cells

Metabolic phenotyping at cellular resolution may be considered one of the challenges in current plant physiology. A method is described which enables the cell type-specific metabolic analysis of epidermal cell types in Arabidopsis thaliana pavement, basal, and trichome cells. To achieve the required high spatial resolution, single cell sampling using microcapillaries was combined with routine gas chromatography-time of flight-mass spectrometry (GC-TOF-MS) based metabolite profiling. The identification and relative quantification of 117 mostly primary metabolites has been demonstrated. The majority, namely 90 compounds, were accessible without analytical background correction. Analyses were performed using cell type-specific pools of 200 microsampled individual cells. Moreover, among these identified metabolites, 38 exhibited differential pool sizes in trichomes, basal or pavement cells. The application of an independent component analysis confirmed the cell type-specific metabolic phenotypes. Significant pool size changes between individual cells were detectable within several classes of metabolites, namely amino acids, fatty acids and alcohols, alkanes, lipids, N-compounds, organic acids and polyhydroxy acids, polyols, sugars, sugar conjugates and phenylpropanoids. It is demonstrated here that the combination of microsampling and GC-MS based metabolite profiling provides a method to investigate the cellular metabolism of fully differentiated plant cell types in vivo

Downregulation of Cinnamoyl-Coenzyme A Reductase in Poplar: Multiple-Level Phenotyping Reveals Effects on Cell Wall Polymer Metabolism and Structure[W]

Cinnamoyl-CoA reductase (CCR) catalyzes the penultimate step in monolignol biosynthesis. We show that downregulation of CCR in transgenic poplar (Populus tremula × Populus alba) was associated with up to 50% reduced lignin content and an orange-brown, often patchy, coloration of the outer xylem. Thioacidolysis, nuclear magnetic resonance (NMR), immunocytochemistry of lignin epitopes, and oligolignol profiling indicated that lignin was relatively more reduced in syringyl than in guaiacyl units. The cohesion of the walls was affected, particularly at sites that are generally richer in syringyl units in wild-type poplar. Ferulic acid was incorporated into the lignin via ether bonds, as evidenced independently by thioacidolysis and by NMR. A synthetic lignin incorporating ferulic acid had a red-brown coloration, suggesting that the xylem coloration was due to the presence of ferulic acid during lignification. Elevated ferulic acid levels were also observed in the form of esters. Transcript and metabolite profiling were used as comprehensive phenotyping tools to investigate how CCR downregulation impacted metabolism and the biosynthesis of other cell wall polymers. Both methods suggested reduced biosynthesis and increased breakdown or remodeling of noncellulosic cell wall polymers, which was further supported by Fourier transform infrared spectroscopy and wet chemistry analysis. The reduced levels of lignin and hemicellulose were associated with an increased proportion of cellulose. Furthermore, the transcript and metabolite profiling data pointed toward a stress response induced by the altered cell wall structure. Finally, chemical pulping of wood derived from 5-year-old, field-grown transgenic lines revealed improved pulping characteristics, but growth was affected in all transgenic lines tested