Identification of Infant Rice Cereal Based by Raman Spectroscopy Combined with an Extreme Learning Machine Algorithm

Food quality and safety oversight now urgently require the identification of similar food samples. Using infant rice cereals as an example, this study collected the Raman spectra of samples from various brands and used a statistical control chart method to reveal that samples from the same brand have controllable quality fluctuations. Additionally, samples from different brands also fall within the control limit, indicating a high degree of similarity between the samples. Under conditions of raw data, the extreme learning machine algorithm and Raman spectroscopy for sample identification demonstrated a recognition rate of 77.6