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Researchers Identify Seasonal Peaks for Foodborne Infections

Rockville, Md. — Each year, thousands of pounds of food are wasted and billions of dollars in food sales lost because of recalls tied to foodborne infections. Using a newly developed approach, researchers identified seasonal peaks for foodborne infections that could be used to optimize the timing and location of food inspections. 

“We rely upon food producers, distributors, and retailers to keep food safe in fields, grocery stores, and restaurants,” said Ryan B. Simpson, doctoral candidate at Tufts University Friedman School of Nutrition Science and Policy. “A lapse in food safety practices during any step in the food delivery and supply chain can jeopardize human health, waste food resources, and threaten the national food economy.” 

Foodborne infections can be caused by a variety of pathogens such as  Listeria,  Salmonella,  and  E. coli. A single pathogen can lead to outbreaks that peak in different states at different times. Knowing the patterns for each pathogen and state could be used to design an optimized schedule for food safety inspections. 

To characterize the timing and intensity of infection peaks, Simpson and colleagues developed an analysis method that robustly determines which specific pathogens are likely to cause an outbreak at a given time. 

Simpson presented the new analysis method as part of Nutrition 2020 Live Online, a virtual conference hosted by the American Society for Nutrition (ASN). The research was performed under the supervision of Elena N. Naumova, Chair of the Department of Nutritional Epidemiology and Data Sciences at Tufts University Friedman School of Nutrition Science and Policy. 

Using their new analysis method, the researchers found that although foodborne outbreaks typically peak in July, food recalls are delayed by one to two months, peaking from mid-August through mid-September. These findings were consistent across examined states and pathogens. 

Next, the researchers aim to refine their analysis method by exploring specific foods and food groups linked to foodborne outbreaks. They also plan to examine relationships between outbreaks for particular pathogens with food preparation practices and other factors. 

“Our future research will provide valuable information that could help refine existing food safety policies while also aiding food producers, distributors, and retailers in preventing or mitigating foodborne outbreaks,” said Simpson. 

This research is supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2017-17072100002. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. This research is also supported by the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Cooperative State Research, Education, and Extension Service Fellowship. 



An international team led by NTU Singapore's Professor Chen Xiaodong (left) has invented an artificial olfactory system that mimics the mammalian nose to assess the freshness of meat accurately.
© Photo: NTU Singapore

Scientists Develop AI-Powered 'Electronic Nose' To Sniff Out Meat Freshness

Singapore — A team of scientists led by Nanyang Technological University, Singapore (NTU Singapore) has invented an artificial olfactory system that mimics the mammalian nose to assess the freshness of meat accurately.

The “electronic nose” (e-nose) features a “barcode” that changes color over time in reaction to the gases produced by meat as it decays, and a barcode “reader” in the form of a smartphone app powered by artificial intelligence (AI). The e-nose has been trained to recognize and predict meat freshness from a large library of barcode colors.

When tested on commercially packaged chicken, fish, and beef meat samples that were left to age, the team found that their deep convolutional neural network AI algorithm that powers the e-nose predicted the freshness of the meats with a 98.5% accuracy. As a comparison, the research team assessed the prediction accuracy of a commonly used algorithm to measure the response of sensors like the barcode used in this e-nose. This type of analysis showed an overall accuracy of 61.7%.

The e-nose, described in a paper published in the scientific journal  Advanced Materials  in October, could help to reduce food waste by confirming to consumers whether meat is fit for consumption, more accurately than a “Best Before” label could, said the research team from NTU Singapore, who collaborated with scientists from Jiangnan University, China, and Monash University, Australia.

A patent has been filed for this method of real-time monitoring of food freshness, and the team is now working with a Singapore agribusiness company to extend this concept to other types of perishables.



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Researchers Address the Issue of Wine Fraud

Adelaide, South Australia — University of Adelaide wine researchers are developing a fast and simple method of authenticating wine — a potential solution against the estimated billions of dollars’ worth of wine fraud globally — but also offering a possible means of building regional branding.

The team of scientists were able to identify the geographical origins of wines originating from three wine regions of Australia and from Bordeaux in France with 100% accuracy with a novel technique of molecular fingerprinting using “fluorescence spectroscopy,” a technology that analyzes  fluorescence of molecules.

“Wine fraud is a significant problem for the global wine industry, given a yearly economic impact within Australia alone estimated at several hundred million dollars, and globally thought to be in the billions of dollars,” says Ruchira Ranaweera, a Ph.D. student in the University’s Waite Research Institute, who conducted the research.

“Wine authentication can help to avoid any uncertainty around wine labeling according to origin, variety, or vintage. The application of a relatively simple technique like this could be adapted for use in the supply chain as a robust method for authentication or detection of adulterated wines.”

The researchers looked at Cabernet Sauvignon — a globally important grape variety and the second most planted in Australia — from three different wine regions of Australia and Bordeaux in France, the birthplace of Cabernet Sauvignon.

The research has been published in the journal Food Chemistry and was supported by Wine Australia and the Australian Government, the Waite Research Institute, and industry partners through the ARC Training Centre for Innovative Wine Production.

The researchers compared an existing approach for authentication, which involves measuring elements in wine samples using “inductively coupled plasma-mass spectrometry” (ICP-MS), with the more simple, rapid, and cost-effective fluorescence spectroscopy technique.

“This method provides a ‘fingerprint’ of the samples according to the presence of fluorophoric or light-emitting compounds,” says Ranaweera. “When used in combination with a robust data analysis using a particular machine learning algorithm, it is proving to be a powerful technique for authentication.”

In every wine the researchers tested using the novel combination of fluorescence spectroscopy with machine learning-driven data analysis, they were able to correctly allocate the wine to region with the fluorescence data but not with elements determined by ICP-MS. 

There are other useful applications of this technology for the wine industry that are available now or in the pipeline, such as phenolic and wine color analysis, and smoke taint detection.

Project leader Associate Professor David Jeffery, from the Waite Research Institute and the ARC Training Centre for Innovative Wine Production, says researchers hope ultimately to identify specific chemical markers that help discriminate between wine regions.

“Other than coming up with a robust method for authenticity testing, we are hoping to use the chemical information obtained from fluorescence data to identify the molecules that are differentiating the wines from the different regions,” Jeffery says.

“This may help with regional branding, by understanding how their wines’ characteristics are influenced by the region and how they differ from other regions.”