Predicting enzymatic starch hydrolysis mechanism during paddy malting by vibrational spectroscopy and multivariate calibration analysis
•Starch hydrolysis during paddy germination was predicted by vibrational spectroscopy.•Enzymes synthesis and starch degradation depicted by intensities of spectral bands.•Cleaving of glycosidic linkages results in α and β-d-glucose formation.•Principal Component Analysis correlated biochemical chang...
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Published in | Food chemistry Vol. 259; pp. 89 - 98 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
Elsevier Ltd
01.09.2018
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Subjects | |
Online Access | Get full text |
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Summary: | •Starch hydrolysis during paddy germination was predicted by vibrational spectroscopy.•Enzymes synthesis and starch degradation depicted by intensities of spectral bands.•Cleaving of glycosidic linkages results in α and β-d-glucose formation.•Principal Component Analysis correlated biochemical changes with Raman spectrum.•Partial Least Square calibration model showed high prediction ability with low RMSE.
Vibrational spectroscopic techniques were employed to predict the mechanism of starch hydrolysis based on structural changes during germination of paddy. The proposed mechanism for starch hydrolysis dealt with the synthesis of amylase at the onset of germination, depicting an increased intensity of spectral bands at amide I, II and III regions. The process commenced with the enzyme actions on skeletal mode of pyranose ring structure of glucose units followed by cleavage of the glycosidic linkage by the process of multiple and multi-chain attack resulting in decrease of the bands (400–900 cm−1). The increased intensity of the bands (1200–1500 cm−1) indicated the process of starch hydrolysis and formation of d-glucose. Multivariate calibration analysis (PCA and PLS) was employed to correlate Raman spectral data with biochemical changes during germination and to develop a calibration model. The model showed a high prediction ability with low root mean square error of prediction (RMSEP) (0.043–0.568). |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0308-8146 1873-7072 |
DOI: | 10.1016/j.foodchem.2018.03.094 |