Authenticity Verification of Alcoholic Beverages in IoT Environment through Fluorescence Hyperspectral Technology and Machine Learning

Abstract

In the Internet of Things (IoT) environment, verifying the authenticity of beverages is an important issue. Baijiu, a unique Chinese distilled liquor, has gained popularity among people due to its distinctive flavor. In recent years, there has been increasing concern about the adulteration of baijiu. Many studies in this field have attempted to combine chemometrics, electronic nose, Raman spectroscopy, and other detection technologies with machine learning for baijiu detection. However, no research has yet applied the mature detection technology of fluorescence hyperspectral technology to this field. Additionally, existing studies have mainly focused on branded baijiu, without addressing the problem of adulteration in bulk baijiu, which remains a long-standing issue. In this study, a combination of fluorescence hyperspectral and machine learning was used for rapid and nondestructive adulteration detection of bulk liquor in Chengdu. The fluorescence hyperspectral data of three types of samples, namely, original liquor, adulterated liquor with water, and adulterated liquor with industrial alcohol, were obtained, and the raw data were preprocessed using Savitzky–Golay (SG), baseline correction (BL), multivariate scatter correction (MSC), and the combination of Savitzky–Golay and baseline correction (SG-BL). Finally, PCA and MDS are also used to extract spectral data characteristics, respectively. For model selection, three models were chosen: back-propagation artificial neural network (BP-ANN), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN). For model evaluation, we choose accuracy, recall, precision, F1-score, memory occupied, and time consumed by the model to evaluate. The experiments show that the combination of fluorescence hyperspectral technology and machine learning can provide a new method, MSC-PCA-SVM, for bulk liquor detection, with a final classification accuracy, recall, precision, and F1-score all reaching 100%, The effectiveness and application potential of this method in the IoT environment have been demonstrated. The results of this study can not only be applied to liquor detection but also have the value of reference for the adulthood and detection of the entire food industry. It is of great significance to standardize the market and protect consumer rights.