Infrared Spectroscopic and Chemometric Approach for Identifying Morphology in Embryo Culture Medium Samples Publisher



Zandbaaf S¹ ; Khanmohammadi Khorrami MR¹ ; Garmarudi AB¹ ; Rashidi BH² Show Affiliations
Source: Infrared Physics and Technology Published:2020

Abstract

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. © 2020 Elsevier B.V.