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Principal component analysis (PCA) is a widely used technique in optical emission spectroscopy (OES) sensor data analysis for the low dimension representation of high dimensional datasets. While PCA produces a linear combination of all the variables in each loading, sparse principal component analysis (SPCA) focuses on using a subset of variables in each loading. Therefore, SPCA can be used as a key variable selection technique. This paper shows that, using SPCA to analyze 2046 variable OES data sets, the number of selected variables can be traded off against variance explained to identifying a subset of key wavelengths, with an acceptable level of variance explained. SPCA-related issues such as selection of the tuning parameter and the grouping effect are discussed.