Infrared spectroscopic and chemometric approach for identifying morphology in embryo culture medium samples

[Display omitted] •PLS-DA and KNN supervised classification methods with GA as a feature selection and MC, MSC and OSC pretreatment methods for pattern recognition of the ECM samples were examined.•GA-PLS-DA classification model with OSC pretreatment showed the best performance for classification of the ECM samples in IVF.•The performance of the supervised classification algorithms in the ECM samples was compared by the SRD method. The present study aimed to examine embryo culture medium (ECM) samples in in vitro fertilization (IVF) using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy coupled with the pattern recognition methods in order to differentiate between morphology A, morphology AB and morphology B results. In this work, 45 the ECM samples analyzed in the 1100–3000 cm−1 spectral region. The FTIR data from ECM samples were subjected to multivariate analyses by the unsupervised and supervised pattern recognition techniques. Cluster analysis (CA) after diagnosis and elimination outlier detection was performed to process FTIR data. Also, partial least squares discriminant analysis (PLS-DA) as a parametric and linear supervised classification algorithm and K-nearest neighbors (KNN) as a non-parametric and linear supervised classification algorithm were performed in spectral data analysis for the classification. The supervised classification algorithms were also tested via the variable selection by means of genetic algorithm (GA). The classification efficiency parameters, including accuracy (ACC), error rate (ER) and non-error rate (NER) were calculated and finally, sum of the ranking differences (SRD) procedure was used to compare chemometrics methods. The GA-PLS-DA approach with orthogonal signal correction (OSC) preprocessing (Case 4) for classification of the ECM samples exhibited the best performance and obtained ACC, ER, NER of 91%, 4% and 96% in the test set, respectively. Hence, the proposed method is rapid, simple, without any chemical preparation and accurate for the discrimination of the ECM samples based on their morphology results. In addition, the SRD procedure was used to order and group chemometric methods applied for the discrimination of the ECM samples based on their morphology results.