Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties

This study investigated fluorescence spectroscopy for evaluating honey quality changes during storage. Twenty-two honey varieties were stored at high (35 °C) and low (4 °C) temperatures for six months, with excitation-emission matrix (EEM) and total polyphenol content (TPC) measured every two months...

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Main Authors: Takumi Murai, Teruki Tobari, Sota Kudo, Yoshito Saito
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Applied Food Research
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Online Access:http://www.sciencedirect.com/science/article/pii/S277250222500455X
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author Takumi Murai
Teruki Tobari
Sota Kudo
Yoshito Saito
author_facet Takumi Murai
Teruki Tobari
Sota Kudo
Yoshito Saito
author_sort Takumi Murai
collection DOAJ
description This study investigated fluorescence spectroscopy for evaluating honey quality changes during storage. Twenty-two honey varieties were stored at high (35 °C) and low (4 °C) temperatures for six months, with excitation-emission matrix (EEM) and total polyphenol content (TPC) measured every two months. Under high-temperature storage, TPC increased significantly while remaining stable at low-temperature. EEM measurements revealed five characteristic fluorescence peaks attributed to various compounds including amino acids, flavonoids, phenolic acids and Maillard reaction products. Using principal component scores obtained from principal component analysis (PCA) dimensionality reduction, support vector machine (SVM) classification achieved 81.82 % accuracy in distinguishing between early storage periods and late storage periods for high-temperature samples, while maintaining 59.09 % accuracy for low-temperature samples. Partial least squares regression (PLSR) models constructed using EEM data demonstrated robust TPC prediction capability with R²cv of 0.92, root mean square error cross validation (RMSECV) of 40.66 μg gallic acid equivalent/g and residual prediction deviation (RPD) of 3.61. Variable importance in projection (VIP) analysis indicated that fluorescence regions associated with flavonoids, phenolic acids and Maillard reaction products significantly contributed to TPC prediction. These findings demonstrate the potential of fluorescence spectroscopy as a non-destructive method for evaluating honey quality changes during storage, particularly under high-temperature conditions.
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spelling doaj-art-eff28785de2c4113be215077542bd3bb2025-08-20T03:13:22ZengElsevierApplied Food Research2772-50222025-12-015210115010.1016/j.afres.2025.101150Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence propertiesTakumi Murai0Teruki Tobari1Sota Kudo2Yoshito Saito3Graduate School of Science and Technology, Niigata University, 8050 2-no-cho, Ikarashi, Nishi-ku, Niigata 950-2181, JapanFaculty of agriculture, Niigata University, 8050 2-no-cho, Ikarashi, Nishi-ku, Niigata 950-2181, JapanFaculty of agriculture, Niigata University, 8050 2-no-cho, Ikarashi, Nishi-ku, Niigata 950-2181, JapanGraduate School of Science and Technology, Niigata University, 8050 2-no-cho, Ikarashi, Nishi-ku, Niigata 950-2181, Japan; Corresponding author.This study investigated fluorescence spectroscopy for evaluating honey quality changes during storage. Twenty-two honey varieties were stored at high (35 °C) and low (4 °C) temperatures for six months, with excitation-emission matrix (EEM) and total polyphenol content (TPC) measured every two months. Under high-temperature storage, TPC increased significantly while remaining stable at low-temperature. EEM measurements revealed five characteristic fluorescence peaks attributed to various compounds including amino acids, flavonoids, phenolic acids and Maillard reaction products. Using principal component scores obtained from principal component analysis (PCA) dimensionality reduction, support vector machine (SVM) classification achieved 81.82 % accuracy in distinguishing between early storage periods and late storage periods for high-temperature samples, while maintaining 59.09 % accuracy for low-temperature samples. Partial least squares regression (PLSR) models constructed using EEM data demonstrated robust TPC prediction capability with R²cv of 0.92, root mean square error cross validation (RMSECV) of 40.66 μg gallic acid equivalent/g and residual prediction deviation (RPD) of 3.61. Variable importance in projection (VIP) analysis indicated that fluorescence regions associated with flavonoids, phenolic acids and Maillard reaction products significantly contributed to TPC prediction. These findings demonstrate the potential of fluorescence spectroscopy as a non-destructive method for evaluating honey quality changes during storage, particularly under high-temperature conditions.http://www.sciencedirect.com/science/article/pii/S277250222500455XHoneyTotal phenolic compoundsExcitation-emission matrixSupport vector machinePartial least squares regression
spellingShingle Takumi Murai
Teruki Tobari
Sota Kudo
Yoshito Saito
Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties
Applied Food Research
Honey
Total phenolic compounds
Excitation-emission matrix
Support vector machine
Partial least squares regression
title Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties
title_full Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties
title_fullStr Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties
title_full_unstemmed Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties
title_short Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties
title_sort time series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties
topic Honey
Total phenolic compounds
Excitation-emission matrix
Support vector machine
Partial least squares regression
url http://www.sciencedirect.com/science/article/pii/S277250222500455X
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AT sotakudo timeseriescharacterizationofvarioushoneytypesunderdifferentstorageconditionsbasedontotalpolyphenolcontentandfluorescenceproperties
AT yoshitosaito timeseriescharacterizationofvarioushoneytypesunderdifferentstorageconditionsbasedontotalpolyphenolcontentandfluorescenceproperties