Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis

Bibliographic Details
Main Author:	Towers, Brittany N.
Language:	English
Published:	The Ohio State University / OhioLINK 2013
Subjects:	Food Science
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=osu1366368685

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-osu13663686852021-08-03T05:22:27Z Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis Towers, Brittany N. Food Science In the food industry, there has been a demand for a rapid quality control technique and need for real time quality analysis of food products. Off-flavors arising immediately after processing were detected in several corn-based snack products detrimentally affecting their final quality. Our objective was to evaluate infrared spectroscopy and selected ion flow tube mass spectroscopy (SIFT-MS) for assessing the development of off-flavors in corn-based snacks and identifying the components contributing to the undesirable aroma. A local manufacturer of grain-based snacks provided test samples that developed off-flavors and their corresponding controls. The volatile profiles of the samples were analyzed by SIFT-MS. In addition, ground samples were directly measured on near-infrared (NIR) and mid-infrared (MIR) systems. The SIFT-MS and IR spectra were evaluated by pattern recognition analysis (SIMCA) to classify anomalous from the control samples. SIMCA analysis of SIFT-MS showed that the off-flavor discrimination was associated to compounds with m/z 93 (NO+) and 109 (H3O+) related to pyridine and pyrazine levels. SIMCA analysis of infrared spectra clustered the different samples based on off-flavor character, confirming that samples contained differences in their chemical composition. The infrared analysis indicated the role of nitrogen heterocyclic compounds with major absorption in 4200-4400 cm-1 and 700-900 cm-1 regions, associated to the N–H stretching and out-of-plane C–H bending bands. Our data strongly support unique SIFT-MS and infrared marker profiles that can effectively differentiate samples exhibiting quality defects, which can provide rapid quality control tool to monitor off-flavor development and to be able to take effective corrective actions.The demand for organic food products has increased, a trend expected to continue as organic products become mainstreamed into major grocery chains. Besides the higher cost of production and premium price, quality variability of organic cornmeal can affect the final extruded product. Our objective was to evaluate the use of a portable infrared spectroscopy system for differentiating between organic and conventional cornmeal and to assess the quality differences (moisture content and final viscosity) between organic and conventional cornmeal used for snack food production. A local manufacturer of grain-based products provided test samples of organic and conventional cornmeal used to manufacture extruded snack products. Reference methods included moisture content (oven method) and final viscosity (Rapid Visco-Analyzer, RVA). Cornmeal samples were measured directly using a portable mid-infrared system to collect the spectral data, which was evaluated using multivariate analysis techniques (SIMCA and PLSR). The SIMCA analysis accurately classified between organic and conventional cornmeal samples and the discrimination was associated with the major band at 1710 cm-1 from protonated carboxylic groups of acidic amino acids. The PLSR data show strong regression for the moisture content (SECV=0.20, Rval=0.96) and final viscosity (SECV=579, Rval=0.96) parameters showing promise for predicting these parameters in unknown corn meal samples. Our data strongly support the capability of a portable infrared system to classify between organic and conventional cornmeal and predict important quality attributes for the snack industry. 2013-08-06 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1366368685 http://rave.ohiolink.edu/etdc/view?acc_num=osu1366368685 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Food Science
spellingShingle	Food Science Towers, Brittany N. Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis
author	Towers, Brittany N.
author_facet	Towers, Brittany N.
author_sort	Towers, Brittany N.
title	Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis
title_short	Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis
title_full	Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis
title_fullStr	Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis
title_full_unstemmed	Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis
title_sort	rapid quality assessment of corn-based products by infrared spectroscopy and selected ion flow tube mass spectroscopy with multivariate analysis
publisher	The Ohio State University / OhioLINK
publishDate	2013
url	http://rave.ohiolink.edu/etdc/view?acc_num=osu1366368685
work_keys_str_mv	AT towersbrittanyn rapidqualityassessmentofcornbasedproductsbyinfraredspectroscopyandselectedionflowtubemassspectroscopywithmultivariateanalysis
_version_	1719419248364748800

Rapid Quality Assessment of Corn-Based products by Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectroscopy with Multivariate Analysis

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