Fig. 2: Unsupervised classification of meat flosses using principal component analysis (PCA) integrated with time window slicing method. | npj Science of Food

Fig. 2: Unsupervised classification of meat flosses using principal component analysis (PCA) 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. 2

PCA is implemented to analyze output sensing signals that are preprocessed with four different extracted features: a maximum, b minimum, c mean, and d median values. Time window slicing method is applied to construct different window numbers in the data (i.e., 1 window (W1) and 5 windows (W5)). The condition without window (W0) is also analyzed as reference. PCA can create separated clusters between pork and non-pork meat flosses in the data measured by e-nose, despite the existing overlap between beef and chicken classes.

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