Abstract
Electronic nose, as a non-destructive instrument, is widely used in the field of gas analysis. In this work, E-nose was employed to distinguish wines and Chinese liquors by means of a machine learning technique. First, a multi-hidden layers Back-Propagation Neural Network (BPNN) was designed to build an identification model for the classification of different wines. Then, a BPNN-based transfer-learning framework was developed with minimal changes to the architecture of the BPNN-based model which was trained on the wine sample dataset. Experimental results revealed that the BPNN-based model performed with a 98.27% accuracy in identifying different wines, and the BPNN-based transfer-learning framework performed with a 93.4% accuracy in identifying Chinese liquors by only re-training the output layer. This reduced the model training costs compared with the complete retraining of a new classification model. Results demonstrated the effectiveness of the proposed BPNN-based transfer-learning model, which was capable of identifying different kinds of wines based on their own properties and could be easily applied to the classification of Chinese liquors. The model-based transfer learning framework offered promising potential for different classification tasks of various beverage.
Highlights
Wine is a popular beverage around the world that has always played an important role in social gatherings [1]
In the linear subspace transformed by PRINCIPAL COMPONENT ANALYSIS (PCA), the data points that are very similar to each other tend to cluster in the score plot, the dissimilar data points scatter in the score plot [28]
The obtained conclusions are presented below: (1) In this study, PCA performed poorly in distinguishing different wines and Chinese liquors, indicating that the details of the beverage aromas were missed after the PCA processing
Summary
Wine (generally 10–15% ethanol by volume) is a popular beverage around the world that has always played an important role in social gatherings [1]. In 2018, world wine production was approximately 29.23 billion liters and the exports reached 31.3 billion Euros (International Organization of Vine and Wine). China was the world’s fifth largest wine consumer, with consumption reaching 1.8 billion liters [2]. All wines consist mainly of water, ethanol, and acids, but the elements that distinguish one wine from another, such as their quality, area of origin, vintage, etc. Aroma is one of the most important aspects of wine quality [4], but the compounds being responsible for wine aroma are volatile [5].
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