Evidence map›Paper›PMID 42650552›Full record

ArticleFoods (Basel, Switzerland)2026

Storage-Time-Aware Chemometric Deep Learning Model for Predicting Phenolic Retention and Antioxidant Capacity in Kefir Enriched with Alginate-Encapsulated Grape Seed Extract.

Özge Duygu Okur, Aytaç Altan

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Özge Duygu OkurDepartment of Food Engineering, Faculty of Engineering, Zonguldak Bülent Ecevit University, 67100 Zonguldak, Turkey.ORCID 0000-0002-5483-2983
Aytaç AltanDepartment of Electrical Electronics Engineering, Faculty of Engineering, Zonguldak Bülent Ecevit University, 67100 Zonguldak, Turkey.ORCID 0000-0001-7923-4528

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kefir offers a suitable fermented dairy matrix for delivering plant-derived bioactives, yet maintaining and predicting phenolic compounds during refrigerated storage remains a technological challenge. This study developed a storage-time-aware chemometric deep learning model to estimate phenolic retention and antioxidant capacity in kefir enriched with free and alginate-encapsulated black grape seed extract. Five formulations were prepared, including control kefir, kefir containing 1% and 3% free extract, and kefir containing 1% and 3% alginate-encapsulated extract. Samples were stored at 4 °C and analyzed on Days 1, 7, and 14 for physicochemical, textural, color and bioactive properties. Total phenolic content and Trolox-equivalent antioxidant capacity were used as target responses, while ten quality descriptors were transformed into latent chemometric features and arranged as storage-time sequences. A convolutional recurrent deep learning architecture was then optimized using the crested porcupine optimizer (CPO) and compared with baseline and ablation models. Alginate encapsulation produced structurally intact beads with high encapsulation efficiency, supporting improved phenolic retention during storage. The proposed model provided the most accurate dual prediction of total phenolic content and antioxidant capacity. It outperformed non-optimized and partially ablated alternatives in terms of reducing error and achieving agreement between the observed and predicted values. Residual, normality and Bland-Altman analyses further supported the reliability of the prediction behavior. Overall, the results demonstrate that integrating alginate encapsulation with storage-time-aware chemometric deep learning provides a practical strategy for monitoring bioactive stability in functional kefir and offers a reproducible modelling framework for fermented dairy products enriched with phenolic-rich by-products.

Indexed as

alginate encapsulationantioxidant capacityblack grape seed extractchemometric deep learningcrested porcupine optimizerkefirphenolic retention

Identifiers

PMID42650552
PMCPMC13512419

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.