Prediction of Volatile Components Retention Time in Blackstrap Molasses by Least‐Squares Support Vector Machine

Abstract

AbstractHouse flies are pestiferous insects that have the potential to spread many diseases to humans and livestock, so it is very significant for us to manage house fly populations. Many commercial types of bait are available to attract house flies, but most are designed for outdoor or limited indoor use, due to their malodorous components. This study sought to identify compounds present in blackstrap molasses that might be attractive to house flies. An effective Quantitative Structure‐Property Relationship (QSPR) model between the Retention Time (RT) and five molecular descriptors of the volatile compounds in blackstrap molasses, was built using a modified algorithm of Least‐Squares Support Vector Machine (LS‐SVM). Descriptors calculated from the molecular structures alone were used to represent the characteristics of compounds. The five molecular descriptors selected by the Heuristic Method (HM) in CODESSA were used as inputs for LS‐SVM. The results obtained by LS‐SVM were compared with those obtained by the HM. The LS‐SVM model gives better results with the predicted correlation coefficient ($\rm{ R^2 }$) 0.919 and Root Mean‐Square Errors (RMSE) 2.193 for the test set, as well as that 0.824 and 2.728 in the MLR model. The prediction results of log RT are in very good agreement with the experimental values. This paper provided a new and effective method for predicting the chromatography retention index.