Real-time detection and analysis of foodborne pathogens via machine learning based fiber-optic Raman sensor

Zhang, Bohong; Rahman, Md Asad; Liu, Jinling; Huang, Jie; Yang, Qingbo

doi:10.1016/j.measurement.2023.113121

Cited by 23 publications

(2 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These features capture the key characteristics of the optical signals generated by the sensing module in response to the presence of foodborne pathogens in the produce samples. 15 …”

Section: Machine Learning Algorithms For Pathogen Detectionmentioning

confidence: 99%

“…These features capture the key characteristics of the optical signals generated by the sensing module in response to the presence of foodborne pathogens in the produce samples. 15 Training and Testing-We trained and tested our machine learning algorithms using a dataset of optical data collected from fresh produce samples spiked with different concentrations of Escherichia coli (E. coli) and Salmonella enteric. 6 We used a random forest (RF) classifier and a support vector machine (SVM) classifier to predict the presence or absence of pathogen contamination in the samples.…”

Section: Machine Learning Algorithms For Pathogen Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning

Sharma,

Tharani

2024

Science Progress

View full text Add to dashboard Cite

Contaminated fresh produce remains a prominent catalyst for food-borne illnesses, prompting the need for swift and precise pathogen detection to mitigate health risks. This paper introduces an innovative strategy for identifying food-borne pathogens in fresh produce samples from local markets and grocery stores, utilizing optical sensing and machine learning. The core of our approach is a photonics-based sensor system, which instantaneously generates optical signals to detect pathogen presence. Machine learning algorithms process the copious sensor data to predict contamination probabilities in real time. Our study reveals compelling results, affirming the efficacy of our method in identifying prevalent food-borne pathogens, including Escherichia coli ( E. coli) and Salmonella enteric, across diverse fresh produce samples. The outcomes underline our approach's precision, achieving detection accuracies of up to 95%, surpassing traditional, time-consuming, and less accurate methods. Our method's key advantages encompass real-time capabilities, heightened accuracy, and cost-effectiveness, facilitating its adoption by both food industry stakeholders and regulatory bodies for quality assurance and safety oversight. Implementation holds the potential to elevate food safety and reduce wastage. Our research signifies a substantial stride toward the development of a dependable, real-time food safety monitoring system for fresh produce. Future research endeavors will be dedicated to optimizing system performance, crafting portable field sensors, and broadening pathogen detection capabilities. This novel approach promises substantial enhancements in food safety and public health.

show abstract

“…These features capture the key characteristics of the optical signals generated by the sensing module in response to the presence of foodborne pathogens in the produce samples. 15 …”

Section: Machine Learning Algorithms For Pathogen Detectionmentioning

confidence: 99%

Section: Machine Learning Algorithms For Pathogen Detectionmentioning

confidence: 99%