Predicting the Heating Temperature of Soymilk Products Using Fluorescence Fingerprints

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• 作者：Mito Kokawa ; Kotaro Nishi ; Hiroko Ashida…
• 关键词：Excitation–emission matrix (EEM) ; Partial least squares (PLS) ; Colorimeter ; L*a*b* ; Maillard products
• 刊名：Food and Bioprocess Technology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：10
• 期：3
• 页码：462-468
• 全文大小：
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Food Science; Chemistry/Food Science, general; Agriculture; Biotechnology;
• 出版者：Springer US
• ISSN：1935-5149
• 卷排序：10

文摘

A novel measurement technique using fluorescence fingerprints (FFs) was developed to measure the degree of heat treatment applied to soymilk. FFs are a set of fluorescence spectra acquired at consecutive excitation wavelengths. Soymilk was heated at 50, 60, 70, 80, or 90 °C, for 10 min, and the samples were measured both in the liquid and freeze-dried forms. Partial least squares (PLS) regression models were constructed to predict heating temperature from the FFs of liquid soymilk and freeze-dried soymilk. Heating temperatures were predicted from soymilk FFs with root-mean-square errors of prediction (RMSEP) and R2P of 7.20 °C and 0.92 and from freeze-dried soymilk FFs with RMSEP and R2P of 9.00 °C and 0.89, respectively. The fluorescence of aromatic amino acids and Maillard products mainly contributed to the prediction models. FF measurement proved to be effective for the objective control of the soymilk heating process.