ProfilePrint leverages the power of Machine Learning (ML) to transform digital fingerprints into valuable inputs for informed decision-making, offering a revolutionary approach to food quality assessment.
From Light to Insight: Pre-processing Spectral Data for Prediction
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ProfilePrint’s analyser captures the chemical signature of ingredients (left). The raw spectra data is then processed and shown on the platform (right).
The process begins with ProfilePrint’s analyser capturing the chemical signature of an ingredient using electromagnetic waves, effectively smelling and tasting by analysing its spectrum. This raw spectral data is then processed through specially customised methods to remove noise and align spectra collected under varying environmental conditions. These preprocessing steps enhance the performance of the subsequent models, improving their ability to accurately predict on new, unseen samples.
Beyond the Single Molecule:
ProfilePrint's Toolbox for Sensory Prediction (and more!)
Coffee sensory prediction by ProfilePrint as featured on its platform
ProfilePrint’s proprietary Toolbox is pivotal in translating complex chemical data into meaningful insights. Sensory attributes like bitterness, mouthfeel, and umami often result from interactions between multiple chemical components at varying concentrations, making them difficult to predict by examining only a few compounds. By training AI on data provided by expert tasters, ProfilePrint’s Toolbox experiments with a vast library of algorithms to identify the most effective ones for predicting these sensory qualities accurately. The technology recognises patterns and correlations between molecular signatures and various quality attributes, enabling consistent and objective food quality assessments that overcome the limitations of human variability.
Coffee blend recommendations generated by ProfilePrint as featured on its platform
Furthermore, ProfilePrint utilises ML for additional applications, such as combinatorial optimisation to help users find the best blends that mimic a target sample, and anomaly detection to identify samples that may be anomalous or out of control during the manufacturing process.
Unlocking the Potential of Food Quality Assessment with ProfilePrint's ML
In summary, ProfilePrint’s ML-driven approach to data processing and analysis enhances the accuracy, consistency, and efficiency of food quality assessments. By transforming complex molecular data into actionable insights, ProfilePrint significantly advances the agri-food industry, offering robust solutions for quality control and product development.