Fig. 3: Supervised classification of meat flosses using linear discriminant analysis (LDA) integrated with time window slicing method. | npj Science of Food

Fig. 3: Supervised classification of meat flosses using linear discriminant analysis (LDA) integrated with time window slicing method.

From: Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Fig. 3

LDA was employed to analyze output sensing signals that were preprocessed with four different extracted features: a maximum, b minimum, c mean, and d median values. Time window slicing method was applied to construct different window numbers in the data (i.e., 1 window (W1) and 5 windows (W5)). The condition without window (W0) was also analyzed as reference. A clear cluster separation among three different meat flosses (beef, chicken, and pork) was yielded by the LDA at W5.

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