From Lab to Field: Developing a Benchtop Raman Spectroscopy for Pesticide Detection in Food-2

 
 
Project: From Lab to Field: Developing a Benchtop Raman Spectroscopy for Pesticide Detection in Food-2
Work Package 5: Processing of pesticide RS fingerprint data using artificial intelligence and deep learning methods and creating an algorithm
 
This work package focuses on training an artificial intelligence algorithm using data from WP4 and WP6 (Raman spectra of various pesticides at various concentrations and obtained using different nanofabricated surfaces) to classify pesticides and estimate quantities. Deep neural networks will form the basis for prediction, complemented by scalable machine learning algorithms to address potential issues with data interpretation and result scalability.
Data preparation will involve coding Raman wavelengths and intensities, subtracting control spectrum data, and background cleaning. Dimensionality reduction techniques like PCA and autoencoders will be employed to enhance AI algorithm accuracy. Band intensity normalization will improve pesticide quantity estimates.
Algorithm design will prioritize two criteria: Accuracy and Explainability. While neural networks are expected to provide the best accuracy, they lack explainability. Therefore, white-box models like Explainable Boosting Machines (EBM) will be used alongside neural networks to interpret which peak values influence specific pesticide predictions. Main success criteria: Pesticide classification: 90% accuracy rate and Pesticide quantity estimates: 0.9 R2 score. Various algorithms and combinations will be tested throughout the project to meet these criteria.
 
References
https://www.lignonanoplatform.net/arastirma-programlari/arastirma-programi-4