A new method of predicting of gas chromatographic retention indices for polychlorinated dibenzofurans (PCDFs)

Quantitative structure-retention relationship for polychlorinated dibenzofurans based on molecular interaction field analysis

“…Since the experimental error of the DB-5 RI column is known to be approximately 0.3%, thus the mean error 0.310% of this MBPLS model is considered excellent. It agrees with previous studies on the same dataset [7,8,11] . The scatter plot of RI (Figure 3) shows excellent agreement between the experimental data and the calculated values.…”

“…The scatter plot of RI (Figure 3) shows excellent agreement between the experimental data and the calculated values. When compared with traditional QSRR model [7,8,11] , the QSRR model developed in this study can give more information than prediction, e.g., the retention mechanisms. The model is built on not only the chemical structure descriptors, but also the interaction between chemicals and the matrix that those chemicals presented, e.g., the stationary in this study.…”

“…Many QSRR models for PCDFs have been reported previously using various descriptors [7,8,10,11] . However, there are no studies attempting to develop QSRR models using the molecular interaction field (MIF) as descriptors.…”

Development of a Multiple-Class High-Resolution Gas Chromatographic Relative Retention Time Model for Halogenated Environmental Contaminants

“…However, given the large number of potential contaminants and the lack of analytical standards, predictive tools are needed to assist researchers in screening environmental samples for novel compounds. Several models have been developed to predict relative GC retention times (GC-RRTs) of individual halogenated organic contaminant classes, such as those for PCBs, − PCDEs, PCDDs, , PCDFs, ,, and PBDDs, yet no further attempts have been made to develop a predictive GC-RRT model for more than one contaminant class after such an approach was successfully demonstrated by Ong and Hites …”

“…However, given the large number of potential contaminants and the lack of analytical standards, predictive tools are needed to assist researchers in screening environmental samples for novel compounds. Several models have been developed to predict relative GC retention times (GC-RRTs) of individual halogenated organic contaminant classes, such as those for PCBs, [4][5][6] PCDEs, 7 PCDDs, 4,8 PCDFs, 4,9,10 and PBDDs, 11 yet no further attempts have been made to develop a predictive GC-RRT model for more than one contaminant class after such an approach was successfully demonstrated by Ong and Hites. 4 The objective of this work was to identify a set of easily calculated variables that could be successfully used as descriptors in a predictive GC-RRT model for each of the following individual halogenated contaminant classes: PBDEs, PCDEs, PCBs, PCNs, PCDDs, PCDFs, PBDDs, PBDFs, and organochlorine pesticides.…”