Development of Predictive Models and Quantitative Risk Assessment of Pathogens in Green Vegetables Produced by Local Farmers in Indonesia Under Climate Change Scenarios: A Case Study in Central Java

Wed, 01 Jan 2025 00:00:00 +0000

Project Overview

This research project aims to evaluate and mitigate the microbial safety risks of green vegetables in Indonesia by integrating rapid pathogen detection, predictive modeling, cross-contamination analysis, and Quantitative Microbial Risk Assessment (QMRA) in response to climate change challenges.

For detailed information, please refer to the original proposal document: “Proposal - Microbiological safety of vegetables (Ind)_rev1.docx”.

Principal Investigator: Nodali Ndraha, Ph.D.
Duration: 3 Years (2025 – 2027)
Funding Agency: Research and Innovation for Advanced Indonesia (RIIM) Competition - BRIN
Total Funding Amount: IDR 819,500,000

Research Team

Principal Investigator: Nodali Ndraha, Ph.D. (Research Center for Food Technology and Processing, BRIN)
Members (BRIN):
- Siska Aditya, Ph.D.
- Dr. Ema Damayanti
- Lino Garda Denaro, Ph.D.
- Nikmatul Hidayah, M.Si.
- Dr. drh. Widodo Suwito
- Radestya Triwibowo, Ph.D.
External Collaborators:
- Dian Anggraini Suroto, Ph.D. (Universitas Gadjah Mada - UGM, Indonesia)
- Fenny Crista Anastasia Panjaitan, M.Sc. (Politeknik Kelautan dan Perikanan Jembrana - PKPJ, Indonesia)
- Prof. Hsin-I Hsiao, Ph.D. (National Taiwan Ocean University - NTOU, Taiwan)

Project Summary

The global and national increase in the consumption of fresh vegetables has been accompanied by a high frequency of foodborne illness outbreaks. Pathogens such as Salmonella enterica, Listeria monocytogenes, and Staphylococcus aureus pose serious health risks. Furthermore, climate change—characterized by shifting temperatures, precipitation patterns, and extreme weather events—adds complexity to food safety by potentially accelerating the spread and growth of these pathogens.

Focused on green vegetables (spinach, water spinach, cabbage, and mustard greens) cultivated in Central Java, Indonesia, this comprehensive three-year study is divided into several key phases:

Advanced Pathogen Detection (Year 1): Developing sensitive, specific, and rapid detection methods using a multiplex PCR approach combined with the Most Probable Number (MPN) method to simultaneously detect and quantify target pathogens directly from farm samples.
Predictive Modeling (Year 2): Utilizing machine learning algorithms (such as Random Forest and Elastic Net) to build predictive models. These models will combine local climatological data (temperature, humidity, precipitation) with pathogen detection data to forecast the occurrence and concentration of pathogens in vegetables and their cultivation environments.
Cross-Contamination & Risk Assessment (Year 3): Evaluating the pathogen transfer ratio from food-handling surfaces to vegetables based on local processing behaviors. Ultimately, all findings will be integrated into a robust Quantitative Microbial Risk Assessment (QMRA) model to estimate the health risks associated with vegetable consumption under various future climate change scenarios (IPCC SSPs) and to evaluate potential mitigation strategies.

This research will provide a better understanding of how climate change impacts microbial food safety and will deliver science-based tools (like early warning systems) to help the industry and food safety authorities protect public health.

Development of Pathogen and Antibiotic Resistance Detection for Bacterial Isolates from the Artisan Cheese Production Environment

Mon, 07 Oct 2024 00:00:00 +0000

Project Overview

This project focuses on enhancing the food safety of locally produced artisan cheese in Indonesia by developing advanced, rapid detection methods for foodborne pathogens and their antimicrobial resistance.

The detailed proposal for this project can be found in the document: “Baru_OR Pertanian dan Pangan_PR Teknologi dan Proses Pangan_Nodali Ndraha.pdf”.

Principal Investigator: Nodali Ndraha, Ph.D.
Duration: 2 Years (2025 – 2026)
Funding Agency: National Research and Innovation Agency (BRIN) - Research Organization for Agriculture and Food (ORPP)
Total Funding Amount: IDR 292,524,690

Research Team

Principal Investigator: Nodali Ndraha, Ph.D. (Research Center for Food Technology and Processing, BRIN)
Members:
- Moh. Faiz Karimy, M.Biotech (Research Center for Food Technology and Processing, BRIN)
- Siska Aditya, Ph.D. (Research Center for Food Technology and Processing, BRIN)
- Dr. Widodo Suwito (Research Center for Food Technology and Processing, BRIN)
- Dr. Koko Tampubolon (Research Center for Food Crops, BRIN)
- Sunarti Sinaga, Ph.D. (Research Center for Marine and Land Bioindustry, BRIN)
- Khairul Anam, Ph.D. (Research Center for Applied Microbiology, BRIN)
Industry Partner: Cahyaningsih (Rumah Keju Jogja / Jogja Cheese House)

Project Summary

The production and consumption of artisan cheese in Indonesia are steadily increasing, yet this trend brings concerns regarding microbial safety, particularly the risk of contamination by pathogens such as Staphylococcus aureus and Shiga toxin-producing Escherichia coli (STEC). These pathogens can cause severe food poisoning, which is further complicated by the rising threat of antimicrobial resistance (AMR).

Currently, there is a lack of specific data on the prevalence of these pathogens in Indonesian artisan cheese, hindering effective risk management. This project aims to bridge this gap by:

Developing Advanced Detection Methods: Formulating and optimizing nucleic acid-based molecular approaches (Multiplex PCR) to rapidly and accurately detect and quantify S. aureus and STEC, including their antimicrobial-resistant strains, in cheese raw materials and products.
Prevalence and Concentration Assessment: Utilizing the developed methods to establish the prevalence and contamination levels of these pathogens across different stages of the local cheese supply chain in Yogyakarta.
Predictive Modeling: Utilizing machine learning algorithms (such as Elastic Net) to build predictive models capable of estimating the emergence and concentration of these pathogens based on local environmental and climatological factors.

The ultimate goal of this research is to contribute significantly to the food safety of artisan cheese in Indonesia, providing vital data for early warning systems and helping both producers and food authorities establish more effective risk mitigation strategies.

Predictive Modeling | Dr. Nodali Ndraha

Development of Predictive Models and Quantitative Risk Assessment of Pathogens in Green Vegetables Produced by Local Farmers in Indonesia Under Climate Change Scenarios: A Case Study in Central Java

Project Overview

Research Team

Project Summary

Development of Pathogen and Antibiotic Resistance Detection for Bacterial Isolates from the Artisan Cheese Production Environment

Project Overview

Research Team

Project Summary