Condensation: The Hidden Enemy of Food Safety?
Microbes are ubiquitous in nature. Their prevalence and spread – even with sanitisation procedures – is evident in food production environments. But what about condensation? Could this be just as big an enemy?
Dietary Intake Assessments – When Experience Counts
The US based NCI created a method to estimate the usual or habitual amount of food, or nutrients eaten by populations, known as the NCI method.
AI – The Time is Now
The benefit and power of Artificial Intelligence (AI) has been discussed for years. However, recent challenges in the food industry have accelerated it’s implementation. AI is now part of the food industry revolution, and it’s here to stay.
Using AI for a Safer Factory
Food safety and consumer health are of utmost importance for the food industry. But even with so many controls in place, foodborne illness can still occur. New approaches are required to enable companies to take a more preventative approach to food safety. So why are companies turning to AI to solve this challenge?
A Revolution In Preventive Food Safety
“Each year – Unsafe food causes 600 million cases of foodborne diseases and 420 000 deaths.” World Health Organisation
Future of Food Safety – Talks from Creme Global
Cronan McNamara, CEO, Creme GlobalAI and Predictive Modelling in the Food Industry.What Can It Do for You?IAFP 2021, Wednesday, 28 April 2021 Brendan Ring, Commercial Director, Creme GlobalEnabling a Preventative […]
Balancing Sweetness: A How-to Guide
When it comes to beverages, the reformulation challenge is notably simpler, as the sweetness of the sugars can be replaced with far smaller quantities of sweeteners which delivers the desired calorie reduction.
Creme Global team making an impact globally
As part of the PROTECT Education and Outreach program, two of the Creme team visited two schools in Laxmipuram village, in the Andhra Pradesh State in India. Part of the […]
All Models Are Not Born Equal – Empirical vs. Mechanistic Models
One of the most valuable applications for mathematical or scientific models is to be able to accurately predict the future. In the absence of a crystal ball, how you go about that depends on what you want to predict, and on what you already know.
