Morphometric analysis of sand fly(Diptera:Psychodidae:Phlebotominae), Sergentomyia anodontis Quate and Fairchild, 1961, populations in caves of southern Thailand

Objective:To determine the morphological characteristics of variations in populations of female adult sand fly,Sergentomyia anodontis Quate and Fairchild,1961 in caves in southern Thailand using morphometric analysis.Methods:A total of 107 female Sergentomyia anodontis were isolated from 651 sand flies captured by CDC light traps overnight in caves in Surat Thani,Nakhon Si Thammarat,Satun and Songkhla provinces from February to December 2017.Measurement of 23 external and internal morphological characteristics was conducted.Data were tested with preliminary statistics(Kolmogorov-Smirnov test,Levene’s test and Box’s test of equality of covariance matrices)and by one-way ANOVA or Kruskal-Wallis test.Measurements were analyzed using canonical discriminant analysis.Results:There were 11 morphological characteristics with high variability while two characteristics exhibited low variation.The sand fly populations from Nakhon Si Thammarat,Satun and Songkhla provinces were very similar but were separate from that in Surat Thani province based on canonical discriminant analysis data.This indicates that the morphological variation founding is a result of the diversity of habitats in each population and the geographic features of caves in each area,such as their altitude above sea level.Conclusions:There is a certain variation in the morphology of Sergentomyia anodontis sand flies at the population level which may be used for future classification of sand flies.

Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics

Objectives:Near-infra red(NIR)spectroscopy is a rapid technique able to assess meat quality even if its capability to determine the shelf life of chicken fresh cuts is still debated,especially for portable devices.The aim of the study was to compare bench-top and portable NIR instruments in discriminating between four chicken breast refrigeration times(RT),coupled with multivariate classifier models.Materials and Methods:Ninety-six samples were analysed by both NIR tools at 2,6,10 and 14 days post mortem.NIR data were subsequently submitted to partial least squares discriminant analysis(PLS-DA)and canonical discriminant analysis(CDA).The latter was preceded by double feature selection based on Boruta and Stepwise procedures.Results:PLS-DA sorted moderate separation of RT theses,while shelf life assessment was more accurate on application of Stepwise-CDA.Bench-top tool had better performance than portable one,probably because it captured more informative spectral data as shown by the variable importance in projection(VIP)and restricted pool of Stepwise-CDA predictive scores(SPS).Conclusions:NIR tools coupled with a multivariate model provide deep insight into the physicochemical processes occurring during storage.Spectroscopy showed reliable effectiveness to recognise a 7-day shelf life threshold of breasts,suitable for routine at-line application for screening of meat quality.

KDLPCCA-Based Projection for Feature Extraction in SSVEP-Based Brain-Computer Interfaces

An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.