In-situ Detection of Rice Using Laser Induced Breakdown Spectroscopy and Machine Learning

Heavy metal poisoning via food is a leading cause of kidney disease and cancer in many parts of the world. Pollution of staple food products has led to serious health effects across the globe. This research investigates the application of laser-induced breakdown spectroscopy (LIBS) and machine learning (ML) for detecting elemental composition of food, using rice as an example. Six varieties of rice samples were measured using LIBS. The resulting spectral data were analyzed, and elemental assignments were made based on characteristic wavelengths. The study leveraged the inherent capabilities of LIBS to provide rapid and non-destructive elemental analysis of the samples. Principal component analysis (PCA) was performed to extract meaningful information from the complex LIBS spectral dataset to obtain a holistic understanding of the variations and similarities among the rice samples. A back propagation artificial neural network (BP-ANN) was trained using the PCA scores, reaching an overall classification accuracy of 88.68% on test data. The correlation between specific wavelengths and elemental content was established, providing an understanding on the elemental variability in different rice varieties.