Abstract
Amino acid nitrogen and total acid are two most important quality indices to assess the quality of soy sauce in China. This work employed near infrared spectroscopy combined with synergy interval partial least square and genetic algorithm to detect amino acid nitrogen and total acid content in soy sauce. First, synergy interval partial least square was used to select efficient spectral regions from the full spectrum region; and then, genetic algorithm was used to selected variables from the efficient spectral regions, to build partial least square model. The optimal genetic algorithm synergy interval partial least square models were obtained as follows: Rc = 0.9988 and Rp = 0.9988 for amino acid nitrogen content model using 64 variables; Rc = 0.9917 and Rp = 0.9902 for total acid content model using 81 variables. Genetic algorithm synergy interval partial least square models showed superiority over the partial least square and synergy interval partial least square models. The results indicated that amino acid nitrogen and total acid content in soy sauce could be rapidly determined by near infrared spectroscopy technique. Also, the results indicated that genetic algorithm synergy interval partial least square can improve the performance in measurement of amino acid nitrogen and total acid content by near infrared spectroscopy.
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