Revolutionizing Food Safety with AI: Visualizing Hyperparameter Optimization in NIR Spectroscopy!
Revolutionizing Food Safety with AI: Visualizing Hyperparameter Optimization in NIR Spectroscopy!
This video showcases the optimization process for Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) using Optuna, applied to NIR spectral data with various preprocessing treatments (e.g., RAW, SNV, Der1+SNV, Der2+SNV).🔹 What’s in the video?
X-Axis: Learning Rate (LR) – Key to model convergence.
Y-Axis: Units/Filters in the first layer – Representing network complexity.
Z-Axis: Accuracy – Up to 1.0 in top models!
Colors: Blue for ANN, Red for CNN.
Animation: Rotates the 3D scatter plot to explore how hyperparameters evolve across treatments, revealing insights into which combos yield the best performance for detecting food adulteration.
This work highlights how AI can enhance chemometric analysis in food science, improving accuracy in identifying adulterants like those in oils, spices, or dairy products. CNN models often outperform ANN in spectral data, achieving near-perfect scores with optimal preprocessing!
If you’re in #NIRSpectroscopy, #AIFoodSafety, or battling #FoodAdulteration, let’s connect!
