Abstract
Infant formula is related to children's life and health. However, the existing identification methods for infant formula are time-consuming, costly and prone to environmental pollution. Therefore, a simple, efficient and less polluting identification method for infant formula is urgently needed. The aim of this study was to distinguish between goat and cow infant formula using HS-SPME-GC–MS and E-nose combined with triple-channel models. The results indicated that the main difference of them attributed to thirteen volatile compounds and three sensor variables. Based on this, the linear discriminant and partial least squares discriminant analyses were conducted, and a multilayer perceptron neural network model was constructed to identify the commercial samples. There was a high percentage of correct classifications (>90%) in samples. Together, our work demonstrated that the volatile compounds of infant formula combined with chemometric analysis were effective and rapid for detecting two infant formulas.
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