Abstract
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%, suggesting that the machine learning algorithm has some identification effect. After optimizing the conditions, it was found that the recognition accuracy was significantly improved, reaching 99.9%, based on coif3 wavelet denoising and 0~0.1 normalization processing. During the experiment, it took 100 seconds to collect the Raman spectrum signal of a single sample and running the intelligent algorithm only took less than one second to obtain the calculation results. The optimization and identification methods proposed in this work have the advantages of efficiency and accuracy, which can provide a reference for the identification of similar samples.
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Funding
This research was financially supported by the Excellent Young Backbone Teachers of “Blue Project” in Jiangsu Universities in 2021, the Industry University Research Collaboration Foundation of Jiangsu Province (BY2022611), Zhejiang Provincial Natural Science Foundation of China (LQ20C200004), Fundamental Research Funds for the Provincial Universities of Zhejiang from the Zhejiang Gongshang University (QRK23020), and the National Natural Science Foundation of China (61602217, 22304158, 62376249, 91746202).
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Zhao, YJ., Zhang, ZY., Zhang, YS. et al. Identification of Infant Rice Cereal Based by Raman Spectroscopy Combined with an Extreme Learning Machine Algorithm. J Anal Chem 79, 447–455 (2024). https://doi.org/10.1134/S1061934824040154
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DOI: https://doi.org/10.1134/S1061934824040154