Near-infrared spectroscopy and partial least squares-class modeling (PLS-CM) for metabolomics fingerprinting discrimination of intervention breakfasts ingested by obese individuals
Near-infrared spectroscopy has been used in nutritional metabolomics fingerprinting for the assessment of the intake of intervention breakfasts prepared with four different vegetable oils that were previously subjected to a deep frying process of 20 cycles for 5min at 180 degrees C. The target oils were an extra virgin olive oil and three varieties of refined sunflower oil. Of the three latter, one of them was used as such, other was spiked with a synthetic oxidation inhibitor (dimethylsiloxane) and, finally, the last one was enriched with an extract of phenolic compounds from olive pomace, the antioxidant properties of which are well known. Urine sampled from individuals before intake and 2 and 4h after intake was directly analyzed by NIRS to obtain fingerprint characteristics of the metabolome composition. The resulting urinary patterns were combined for statistical analysis by unsupervised and supervised approaches. Partial least squares-class modeling enabled to develop class-models for each intervention breakfast, thus achieving discrimination of urinary fingerprints from individuals after breakfast intake. The models were statistically characterized by estimation of sensitivity and specificity parameters for the training and evaluation (validation) steps. The application of variable importance in projection algorithm enabled to detect the spectral regions with higher significance to explain the variability observed in the partial least squares class-models. Quantitative differences of variable importance in projection scores discriminated among the different classes under study. Copyright (c) 2013 John Wiley & Sons, Ltd.
partial least squares-class modeling (pls-cm) metabolomic fingerprinting urine edible oils near infrared spectroscopy nutrimetabolomics