Abstract

A technique for the analysis of saccharification reactions by a specific enzyme was developed on the basis of Raman spectroscopy using multivariate analysis. It is a microvolume, quantitative, and in situ technique, which can be used for studying saccharification processes in plant tissues. Prediction models for quantitative analysis of maltose, glucose, and starch were built with partial least squares regression (PLSR) analysis to monitor the saccharification process caused by α-amylase. We examined the reliability of the prediction models built using seven test samples. The spectral regions used to build the models were optimized for each sugar and were selected in such a manner that they did not overlap with strong protein and lipid bands that generally exist in plant tissues. The models were validated by monitoring the composition of reduced sugars and starch in a reactor and by comparing the results with those obtained by a conventional method. The results of Raman analysis and the conventional method showed good agreement for the reaction with α-amylase; however, it is not perfect for reactions with a different enzyme, especially β-amylase. The results suggest that the present Raman technique is reliable and useful for sugar analysis. However, the prediction model built for a specific enzyme is valid only for that enzyme.

© 2018 The Author(s)

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