A simple, quick, selective, sensitive, and effective field‐friendly method capable of being used by nonexperts has been developed for detecting mitragynine in Mitragyna speciosa (kratom) using surface‐enhanced Raman spectroscopy (SERS). Over 100 samples and blanks (known to be either positive or negative for the presence of mitragynine) were examined in duplicate using five identical handheld Raman spectrometers, which provided a data set of over 1,000 examinations. Based on the results of these analyses, the method yielded a true‐positive rate of 99.3%, a true‐negative rate of 97.9%, a false‐positive rate of 2.1%, and a false‐negative rate of 0.7%. The average minimum detectable concentration (Cm) of mitragynine that reproducibly yielded a match for one of the library spectra on all five instruments was determined to be 342 ng/mL (ppb). This Cm value is a conservative estimate considering that the extraction process was not fully optimized by this study, which was not necessary since the Cm value achieved was well below typical mitragynine concentrations in kratom (1.3–2.3%). The method is ideal (i) for prioritizing samples for additional testing using other more time‐consuming laboratory‐based techniques needed to detect and quantify mitragynine and (ii) for field use at international mail facility (IMF) satellite laboratories to help interdict kratom and prevent this dangerous product from reaching the U.S. supply chain.

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This paper is concerned with contrasting the impact of globalization pressures on industrial development in particular localities, with specific reference to the relative performance of regional clusters. A multiple case study approach is adopted in order to examine the decline of volume yacht manufacturing in a long-established English cluster and to compare its responses to globalization with those of major competitors located in other parts of Europe. The case study opens with an analysis of three sector-specific drivers of globalization that have exercised a decisive impact on the sector over the last three decades. In the main analytical section, two alternative approaches to the analysis of clusters (Porter 1990, Best 2001) are applied to the empirical material. The application of Porter's 'diamond' framework suggests some distinctive performance-related characteristics, while Best's 'cluster dynamics' model provides a more sophisticated explanation of the differential responses and outcomes identified in the English case. The implications for policy are that cluster-level outcomes may be predicated on the internal dynamics of their respective 'entrepreneurial fïrms', and that regional development initiatives would benefit from conceptual and empirical studies that can better address the historical and spatial complexity of the underlying processes.

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