Development of rapid analytical methods for determining corn quality

The study proposed to develop a rapid method for classifying corn according to drying temperature, to develop methods for predicting corn quality characteristics relating to wet-milling, dry-milling and alkaline cooking, and to improve a high performance liquid chromatography (HPLC) technique for me...

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Main Author: Chawnua, Anuchita
Format: Text
Language:English
Published: DigitalCommons@University of Nebraska - Lincoln 2000
Subjects:
DML
Online Access:https://digitalcommons.unl.edu/dissertations/AAI9991976
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spelling ftunivnebraskali:oai:digitalcommons.unl.edu:dissertations-9381 2023-11-12T04:16:27+01:00 Development of rapid analytical methods for determining corn quality Chawnua, Anuchita 2000-01-01T08:00:00Z https://digitalcommons.unl.edu/dissertations/AAI9991976 ENG eng DigitalCommons@University of Nebraska - Lincoln https://digitalcommons.unl.edu/dissertations/AAI9991976 ETD collection for University of Nebraska - Lincoln Food science text 2000 ftunivnebraskali 2023-10-30T10:09:17Z The study proposed to develop a rapid method for classifying corn according to drying temperature, to develop methods for predicting corn quality characteristics relating to wet-milling, dry-milling and alkaline cooking, and to improve a high performance liquid chromatography (HPLC) technique for measuring fumonisin B1 (FB1) in extruded corn. To evaluate the ability of Near Infrared Spectroscopy (NIRS) to classify dried corn according to drying temperature, whole kernel corn samples were dried under different temperatures. Spectra were collected from the visible and near infrared regions. Discriminant analysis based on Mahalanobis distances was applied to classify the samples. The results indicated that NIRS was a promising technique for classifying corn by drying temperature, as correct classification rates of 84.3% were achieved. Corn characteristics, including starch yield, tangential abrasive dehulling device (TADD) index, dry matter loss (DML), and nixtamal moisture content, were measured using NIRS. In establishing calibration models, Partial Least Squares (PLS) and Multiple Linear Regression (MLR) were applied. The results revealed that NIRS has the ability to predict starch yield with a high correlation coefficient of validation (r-value = 0.898). A high drying temperature lowered the ability of NIRS to predict. For TADD index evaluation, the study suggested that NIRS has potential to predict this parameter, and drying temperature had no affect on its ability. In the assessment of the ability of NIRS to predict corn characteristics for alkaline cooking, the study showed that the ability of NIRS to predict DML was poor, but the technique could successfully predict nixtamal moisture content. The drying temperature had a slight effect on the NIRS prediction. Use of an enzyme to extract FB1 from extruded corn prior to HPLC determination and the use of 6-aminoquinolyl-n-hydroxysuccinimidyl carbamate (AQC) for derivatizing FB1 were investigated. The results indicated that using an amylase enzyme significantly ... Text DML University of Nebraska-Lincoln: DigitalCommons@UNL
institution Open Polar
collection University of Nebraska-Lincoln: DigitalCommons@UNL
op_collection_id ftunivnebraskali
language English
topic Food science
spellingShingle Food science
Chawnua, Anuchita
Development of rapid analytical methods for determining corn quality
topic_facet Food science
description The study proposed to develop a rapid method for classifying corn according to drying temperature, to develop methods for predicting corn quality characteristics relating to wet-milling, dry-milling and alkaline cooking, and to improve a high performance liquid chromatography (HPLC) technique for measuring fumonisin B1 (FB1) in extruded corn. To evaluate the ability of Near Infrared Spectroscopy (NIRS) to classify dried corn according to drying temperature, whole kernel corn samples were dried under different temperatures. Spectra were collected from the visible and near infrared regions. Discriminant analysis based on Mahalanobis distances was applied to classify the samples. The results indicated that NIRS was a promising technique for classifying corn by drying temperature, as correct classification rates of 84.3% were achieved. Corn characteristics, including starch yield, tangential abrasive dehulling device (TADD) index, dry matter loss (DML), and nixtamal moisture content, were measured using NIRS. In establishing calibration models, Partial Least Squares (PLS) and Multiple Linear Regression (MLR) were applied. The results revealed that NIRS has the ability to predict starch yield with a high correlation coefficient of validation (r-value = 0.898). A high drying temperature lowered the ability of NIRS to predict. For TADD index evaluation, the study suggested that NIRS has potential to predict this parameter, and drying temperature had no affect on its ability. In the assessment of the ability of NIRS to predict corn characteristics for alkaline cooking, the study showed that the ability of NIRS to predict DML was poor, but the technique could successfully predict nixtamal moisture content. The drying temperature had a slight effect on the NIRS prediction. Use of an enzyme to extract FB1 from extruded corn prior to HPLC determination and the use of 6-aminoquinolyl-n-hydroxysuccinimidyl carbamate (AQC) for derivatizing FB1 were investigated. The results indicated that using an amylase enzyme significantly ...
format Text
author Chawnua, Anuchita
author_facet Chawnua, Anuchita
author_sort Chawnua, Anuchita
title Development of rapid analytical methods for determining corn quality
title_short Development of rapid analytical methods for determining corn quality
title_full Development of rapid analytical methods for determining corn quality
title_fullStr Development of rapid analytical methods for determining corn quality
title_full_unstemmed Development of rapid analytical methods for determining corn quality
title_sort development of rapid analytical methods for determining corn quality
publisher DigitalCommons@University of Nebraska - Lincoln
publishDate 2000
url https://digitalcommons.unl.edu/dissertations/AAI9991976
genre DML
genre_facet DML
op_source ETD collection for University of Nebraska - Lincoln
op_relation https://digitalcommons.unl.edu/dissertations/AAI9991976
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