Potassium represents one of the most crucial minerals in infant formula that supports healthy growth and development of infants. Here, a novel strategy for the real-time quantification of potassium in infant formula samples is introduced. Using laser-induced breakdown spectroscopy (LIBS) in a data-driven approach, a modified random frog algorithm (MRFA) is adopted in a higher-density discrete wavelet transform (HDWT) domain for the selection of the most important features related to potassium, which is named as DD-LIBS. In DD-LIBS, the HDWT oversamples the LIBS signals in both time and frequency domains by a factor of two, enhancing the spectral expandability in an approximately shift-invariant way. The MRFA is thus capable of isolating the features of potassium with experience accumulated from the collected LIBS data. Such pretreatment combined with a partial least squared (PLS) model can significantly suppress the uncontrolled shift and broadening effects on multivariate calibration, improving the capability of LIBS for accurate quantification of potassium. The present work demonstrates the feasibility of DD-LIBS for the quantification of potassium content of 90 commercial infant formula samples. A satisfactory result illustrates DD-LIBS as a feasible tool for real-time analysis of potassium content with little sample preparation. This strategy may be well extended to other element detection in the presence of uncontrolled interference.
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