|HOOPER, SHARON - Michigan State University|
|Harnly, James - Jim|
Submitted to: ACS Food Science and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/19/2022
Publication Date: 5/5/2022
Citation: Geng, P., Hooper, S., Sun, J., Chen, P., Cichy, K.A., Harnly, J.M. 2022. Contrast study on secondary metabolite profile between pastas made from three single varietal common bean (Phaseolus vulgaris L.) and durum wheat (Triticum durum). ACS Food Science and Technology. 2(5):895–904. https://doi.org/10.1021/acsfoodscitech.2c00050.
Interpretive Summary: Common bean (Phaseolus vulgaris) is an important food source worldwide. Beans are packed with protein, lipids, dietary fiber, and micronutrients. As a gluten-free nutrient-rich protein source, the utilization of bean flour has drawn more and more attention. Consumption has increased in both developing and developed countries. The chemical composition study of bean flours focused on the nutrition content. Only limited attention has been given to secondary metabolites. This study revealed the metabolomic difference between gluten free bean pasta and traditional durum wheat pasta.
Technical Abstract: Gluten free bean pastas utilizing bean composite flour (bean flour 90%, tapioca starch 9% and xanthan gum 1%) from three single varietal bean and the controlled durum wheat pasta were analyzed by ultra-high performance liquid chromatography high-resolution accurate-mass multi-stage mass spectrometry (UHPLC-HRAM-MSn) with electrospray ionization (ESI). The secondary metabolites profiles of pastas were compared between each variety of bean as well as bean and wheat by visual inspection and chemometric approach. The impact of genotype of bean, cooking status of pasta (raw and cooked) and manufacturing conditions (drying temperature and extrusion temperature) on metabolites profiles were investigated using factorial multivariate analysis of variance (MANOVA). The results suggested that only the genotype has a significant effect on the metabolites profiles, whereas other experiment factors do not contribute to the disparities in chemical profiles significantly. Principal component analysis (PCA) was performed on the genotype data matrix obtained from MANOVA. Characteristic secondary metabolites were recognized for each pasta from the PCA loading plot.