The purpose of this study is to develop the quantitative PCR(qPCR) assay that would enable the rapid identification and simultaneous detection of six different endodontic pathogenic bacteria in a single reaction. In this study, six pairs of primers for Treponema denticola, Porphyromonas gingivalis, Fusobacterium nucleatum, Prevotella intermedia, Streptococcus mutans, and Staphylococcus aureus and two pairs of housekeeping genes were designed for a multiplex qPCR based on the SYBR Green method. The genomic DNA was extracted from reference strains and submitted to the qPCR reaction. The specificity of the amplified products was analyzed by melting curves. As a result, six distinct melting peaks were identified by the melting curve analysis and all of the target species were simultaneously discriminated. Therefore, the multiplex qPCR assay developed in this study can be used for rapid identification and detection of T. denticola, P. gingivalis, F. nucleatum, P. intermedia, S. mutans, and S. aureus at the same time. In combination with the melting curve analysis, the level of the target species and total bacterial load can be obtained.

Multivariate control charts are widely needed to monitor the production processes in various industry. Among the several multivariate control charts, control chart have been used of the typical technique. The control chart shows a statistic that represents observed variables and monitors the process through the statistic. In this case, the statistic generally have the limit that any variables affect to that statistic. To solve this problem, some studies have been progressed in the meantime. The representative method is to disassemble total statistic into each of the variable value and make a decision the parameters with large values than threshold value as a main cause. However, the means is requested to follow the normal distribution. To settle this problem, the bootstrap technique that don't be needed the probability distribution was introduced in 2011. In this paper, I introduced the detection technique of the fault variables using multiple regression analysis. There are two advantages; First, it is possible to use less samples than the ascertainment technique applying to bootstrap. Second, the technique using the regression analysis is easy to apply to the actual environment because the global threshold value is used.