Evidence map›Paper›PMID 39581930›Full record

ArticleProbiotics and antimicrobial proteins2025

Uncovering the Probiotic Supplement Landscape: Market Offerings, Sales Patterns, and Future Forecasts Using Machine Learning Approach - A Case Study of Montenegro.

Martinović Anđela, Martinović Ivan, Diego Mora, Stefania Arioli

Abstract read
In one paragraph

Article in Probiotics and antimicrobial proteins, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Martinović AnđelaDepartment of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, Via Celoria 2, 20133, Milan, Italy. andjela.martinovic@gmail.com.ORCID http://orcid.org/0000-0001-5972-6701
Martinović IvanFaculty of Electrical Engineering, University of Montenegro, Džordža Vašingtona Bb, 81000, Podgorica, Montenegro.
Diego MoraDepartment of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, Via Celoria 2, 20133, Milan, Italy.
Stefania ArioliDepartment of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, Via Celoria 2, 20133, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global increasing awareness about the health benefits of probiotics resulted to explorational growth in probiotic food supplement market. However, in some countries such as Montenegro, specific probiotic supplement regulation and comprehensive market analysis are absent, hampering the understanding of consumer preferences, market trends, and potential economic impacts of this industry. This article aims to delve into the Montenegrin market of probiotic food supplements, thoroughly examining various product types and their key characteristics. Using the case study of a pharmacy chain, as an example of organizational level, the sales, sale patterns, and trends are examined. Furthermore, we developed and employed a machine learning model for forecasting future sales. The market analysis highlighted the importance of setting national probiotic supplement regulations to enhance Montenegrin consumer understanding and trust, ensuring product efficacy and safety. Our study clearly showed increased interest in probiotic supplements as well as a constant positive trend in probiotic supplement sales. Furthermore, we found the correlation between foreign tourist visits in Montenegro and the yearly seasonality of probiotic supplement sales. Developed support vector regression machine learning model on time series data showed a good forecasting accuracy, clearly indicating that the same could be used for national sales forecasting. The insights from this study could promote the establishment of national probiotic supplement regulations, enhancing consumer protection and market credibility. Additionally, developed machine learning model provides the industry with valuable predictive tool, enabling companies to optimize their supply chains, effectively meet demand, and make data-driven decisions that could support sustainable market growth.

Indexed as

CommerceDietary SupplementsMachine LearningProbioticsForecastingHumansMontenegroArtificial intelligenceCOVID-19HealthPredictionProbioticsRegulations

Identifiers

PMID39581930
PMCPMC12634760

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.