Academic Journal
Unleashing the power of visible-near infrared spectroscopy: predicting Golden Delicious apple enzyme activity.
| Τίτλος: | Unleashing the power of visible-near infrared spectroscopy: predicting Golden Delicious apple enzyme activity. |
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| Συγγραφείς: | Mesri Gundoshmian T; Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran., Razavi MS; Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran., Akbari R; Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran., Tahmasebi M; Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran., Locatelli I; Department of Food, Environmental, and Nutritional Sciences (DeFENS), University of Milan, Milan, Italy., Grassi S; Department of Food, Environmental, and Nutritional Sciences (DeFENS), University of Milan, Milan, Italy. |
| Πηγή: | Journal of the science of food and agriculture [J Sci Food Agric] 2026 Sep; Vol. 106 (12), pp. 7503-7511. Date of Electronic Publication: 2026 Jun 09. |
| Τύπος έκδοσης: | Journal Article; Evaluation Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: John Wiley & Sons Country of Publication: England NLM ID: 0376334 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0010 (Electronic) Linking ISSN: 00225142 NLM ISO Abbreviation: J Sci Food Agric Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2005-> : Chichester, West Sussex : John Wiley & Sons Original Publication: London, Society of Chemical Industry. |
| Ιατρικοί όροι (MeSH): | Malus*/enzymology , Malus*/chemistry , Catechol Oxidase*/chemistry , Catechol Oxidase*/metabolism , Fruit*/chemistry , Fruit*/enzymology , Peroxidase*/chemistry , Peroxidase*/metabolism , Plant Proteins*/chemistry , Plant Proteins*/metabolism, Spectroscopy, Near-Infrared/methods ; Support Vector Machine ; Color ; Particle Swarm Optimization ; Prediction Algorithms |
| Περίληψη: | Background: Enzymatic browning is a significant reaction in fruits that affects their color, appearance, and quality. The quality of apples, as a perishable product, is mainly influenced by the activity of two browning-related enzymes, polyphenol oxidase (PPO) and peroxidase (POD), during storage. Assessment of these enzymes using conventional methods is often destructive and time-consuming, preventing rapid and non-invasive monitoring of fruit quality. In this study, a visible-near infrared (visible-NIR) spectroscopy approach was developed to predict the enzymatic activity of PPO and POD in intact Golden Delicious apples, aiming to enable rapid, non-destructive evaluation and to identify the most informative spectral regions for industrial applications. Results: Both support vector regression (SVR) and decision tree (DT) algorithms achieved high performance when combined with non-linear feature selection algorithms. The best performance, in terms of elapsed time and figure of merits, was achieved by combining particle swarm optimization (PSO) with SVR and DT. However, partial least squares (PLS) models outperformed both SVR-PSO and DT-PSO. Conclusions: This study is an advanced proof of concept of the use of visible-NIR spectroscopy - combined with variable selection and machine learning algorithms - for predicting browning-related enzyme activity in apples. The SVR and DT algorithms, coupled with metaheuristic strategies, reached lower performances than PLS, but the success of the variable selection strategy lays the groundwork for developing a miniaturized sensor for assessing apple quality during storage and controlling browning. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. (© 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.) |
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| Grant Information: | This work was supported financially by the University of Mohaghegh Ardabili. |
| Contributed Indexing: | Keywords: Golden Delicious apples; machine learning; metaheuristic algorithms; non‐destructive quality assessment; peroxidase; polyphenol oxidase; visible‐NIR spectroscopy |
| Substance Nomenclature: | EC 1.10.3.1 (Catechol Oxidase) EC 1.11.1.7 (Peroxidase) 0 (Plant Proteins) |
| Entry Date(s): | Date Created: 20260610 Date Completed: 20260806 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC13442799 |
| DOI: | 10.1002/jsfa.70782 |
| PMID: | 42265833 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1097-0010 |
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| DOI: | 10.1002/jsfa.70782 |