The Intersection of Food Safety and Data Science

By the bioMérieux Connection Editors

Last month, the International Dairy Foods Association (IDFA) presented A New Era of Smarter Food Safety: The Intersection of Food Safety and Data Science, a webinar hosted by Nicholas Siciliano, Ph.D., Chief Executive Officer and Co-Founder of Invisible Sentinel, Inc. Dr. Siciliano answered key questions on how predictive data can impact the bottom line of food and beverage manufacturers. The webinar is now on-demand and we’ve recapped the most important takeaways.

Predictive Technology Protects Your Product

Dr. Siciliano discusses how utilizing data science applications can help predict uncertain and potentially adverse outcomes for your product, brand, and consumer. He argues that predictive technology is essential for optimized efficiency and maximized profit. Organizations can start with small steps, such as determining their largest problem or a handful of small problems. Once key problems are identified, they can then focus on pinpointing relevant data and associated metadata, harmonizing data collection, and standardizing the data retrieval processes.

“Today data is all around us [and] we all live in this digitized age,” says Dr. Siciliano. “There are so many data points out there that it is easy to get lost and to start wondering ‘What data should I be collecting?”

Proactive Data Analysis > Reactive Problem Solving

The food manufacturing industry has undergone major shifts with the introduction of new technologies, which have disrupted the way manufacturers tackle safety and quality. Dr. Siciliano explains how new technologies also impact an organization’s data strategy. Critical data points related to food safety and quality exist throughout the supply chain—during the raw material, quality control, and final product stages. This wealth of information and data, when taken holistically, can add tremendous value to an organization.

Dr. Siciliano urges food manufacturers to focus on being proactive instead of reactive. He explains that each data point being collected is more than a single data point—it’s a window into your facility.

“If [you] can harvest the data you already collected to do retrospective analytics, that allows [you] to identify correlates quicker and to get you going from a reactive to a predictive mode earlier,” explains Dr. Siciliano.

Objective: Quantify Risk at any Time

The overall goal of predictive technology is to be able to calculate and quantify risk at any point in time and determine how that risk could impact the bottom line. This goal is supported by digitalized, searchable data and can be supplemented with next generation tools such as whole genome sequencing, pathogen monitoring, spoilage profiles, and supply chain monitoring and evaluation. This is only the beginning of a new era of smarter food safety.


Opinions expressed in this article are not necessarily those of bioMérieux, Inc.

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