Multiple pattern matching in strings is a fundamental problem in text processing applications such as regular expressions or tokenization. This paper studies efficient implementations of \emph{double-array Aho--Corasick automata (DAACs)}, data structures for quickly performing the multiple pattern matching. The practical performance of DAACs is improved by carefully designing the data structure, and many implementation techniques have been proposed thus far. A problem in DAACs is that their ideas are not aggregated. Since comprehensive descriptions and experimental analyses are unavailable, engineers face difficulties in implementing an efficient DAAC. In this paper, we review implementation techniques for DAACs and provide a comprehensive description of them. We also propose several new techniques for further improvement. We conduct exhaustive experiments through real-world datasets and reveal the best combination of techniques to achieve a higher performance in DAACs. The best combination is different from those used in the most popular libraries of DAACs, which demonstrates that their performance can be further enhanced. On the basis of our experimental analysis, we developed a new Rust library for fast multiple pattern matching using DAACs, named \emph{Daachorse}, as open-source software at \url{https://github.com/daac-tools/daachorse}. Experiments demonstrate that Daachorse outperforms other AC-automaton implementations, indicating its suitability as a fast alternative for multiple pattern matching in many applications.