The use of infrared spectrometers to predict quality parameters of cornmeal (corn grits) and differentiate between organic and conventional practices. (March 2015)

• Record Type: Journal Article
• Authors: Ayvaz, Huseyin; Plans, Marçal; Towers, Brittany N.; Auer, Angela; Rodriguez-Saona, Luis E.
• Is Part Of: Journal of cereal science. Volume 62(2015)
• Page Start: 22
• Page End: 30
• Publication Date: 2015-03
• Subjects: Cornmeal -- Organic -- Handheld and portable spectrometers -- Multivariate analysis; ATR attenuated total reflectance -- BKD breakdown -- CV cross validation -- FV final viscosity -- ICD interclass distance -- MIR mid-infrared -- NIR near infrared -- PCA principal component analysis -- PV peak viscosity -- PLSR partial least squares regression -- RPD residual predictive deviation -- RVA rapid visco analyzer -- RVU rapid visco units -- SEP standard error of prediction -- SET setback -- SIMCA soft independent modeling of class analogies -- THR trough; Grain -- Periodicals; Cereal products -- Periodicals; Céréales -- Périodiques; Produits céréaliers -- Périodiques; Cereal products; Grain; Periodicals; 664.705
• DOI: 10.1016/j.jcs.2014.12.004
• Languages: English
• ISSNs: 0733-5210

Abstract

Abstract: Benchtop and handheld NIR and portable mid-infrared (MIR) spectrometers were evaluated as rapid methods for differentiating between organic and conventional cornmeal and to measure quality parameters of cornmeal used for production of snack foods. Twenty-seven conventional and eleven organic cornmeal samples were obtained from a local manufacturer of grain-based products. Reference quality parameters measured included moisture content, ash content, pasting properties and particle size. Soft independent modeling of class analogy (SIMCA) analysis accurately classified between organic and conventional cornmeal samples (interclass distance > 3.7) based on differences in the CO signal associated with side chain vibrations of acidic amino acids. Residual predictive deviation (RPD) values for partial least squares regression (PLSR) models developed, ranged between 2.3 and 9.6. Overall, our data supports the capability of infrared systems to classify between organic and conventional cornmeal, and to predict important quality attributes of cornmeal for the snack food industry. Graphical abstract: Highlights: Organic/conventional cornmeal samples were analyzed for their quality parameters. Handheld/portable spectrophotometers were employed for infrared analysis. Rapid IR-based authentication of organic cornmeal was possible. Good quantitative predictions in independent validation sets were achieved.