Foodinformatics: Quantitative Structure‐Property Relationship Modeling of Volatile Organic Compounds in Peppers

Abstract

The aim of this work was the foodinformatic (chemoinformatic) modeling of volatile organic compounds (VOCs) of different samples of peppers based on a quantitative structure‐property relationship (QSPR) for the retention indices of 273 identified compounds. The experimental retention indices were measured by means of comprehensive two‐dimensional gas chromatography combined with quadrupole‐mass spectrometry (GC × GC/qMS) using the BPX5 and BP20 column coupled system. All the VOCs were represented by means of both conformation‐independent molecular descriptors and molecular fingerprints calculated in the Dragon and PaDEL‐Descriptor software. The dataset was divided into training, validation and test sets of molecules according to the Balanced Subsets Method (BSM). Subsequently, the V‐WSP unsupervised variable reduction method was used to reduce the presence of multicollinearity, redundancy, and noise in the initial pool of 4,336 molecular descriptors and fingerprints. Using this method, a reduced pool of 1,664 was submitted to the supervised selection by means of the replacement method (RM) variable subset selection in order to define a four‐descriptor model. The quality of the model was measured by means of the coefficient of determination and the root‐mean‐square deviation in fitting (Rtrain2=0.879 and italicRMSDitalictrain=72.1), validation (Rval2=0.832 and italicRMSDitalicval=91.7), and prediction (Rtest2=0.915 and italicRMSDitalictest=55.4). The negligible differences among the parameters in the three sets indicate a stable and predictive QSPR model. This quantitative structure‐activity relationship was developed keeping in mind the five principles defined by the Organization for Economic Co‐operation and Development (OECD) to make it applicable. Practical Application This predictive mathematical model developed from the retention indices of 273 volatile organic compounds (VOCs) detected in pepper samples could be useful for chromatographers working on the identification of other common VOCs in peppers or other foods by means of comprehensive two‐dimensional gas chromatography combined with quadrupole‐mass spectrometry (GC × GC/qMS) using a bi‐dimensional stationary phase coupled system (BPX5 and BP20).

Comprehensive Two-Dimensional Gas Chromatography Advances in Technology and Applications: Biennial Update

“…This is based on use of computational modeling of descriptors for a given molecule, and the concept of quantitative structure–property relationships (QSPR). With suitable feature selection algorithms and correlation between the training set of experiments and simulated molecular descriptors, retention indices and retention can be reliably predicted for a given molecular structure . This was demonstrated for a set of 273 volatiles in pepper samples analyzed by using GC×GC−qMS with a BPX5/BP20 column set.…”

“…With suitable feature selection algorithms and correlation between the training set of experiments and simulated molecular descriptors, retention indices and retention can be reliably predicted for a given molecular structure. 82 This was demonstrated for a set of 273 volatiles in pepper samples analyzed by using GC×GC−qMS with a BPX5/BP20 column set. Furthermore, combined with the new PEG-based retention index, the retention time of unknown compounds can also be predicted 83 and applied to positively identify 12 wrongly assigned (unknown) contaminants with GC×GC.…”

Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap

“…One of the strategies proved to achieve this goal is the Balanced Subsets Method (BSM) (Rojas et al, 2015), which was applied elsewhere in foodinformatic studies when dealing with retention indices of volatile organic compounds (VOCs) detected by SPME-GC-MS (Rojas et al, 2019;Rojas et al, 2018). In brief, the BSM approach creates clusters of molecules based on the k-means cluster analysis (k-MCA) by using conformation-independent molecular descriptors and the experimental retention time.…”

“…Thus, several QSPR studies were reported in the literature to predict the t R of pesticide residues (Dashtbozorgi et al, 2013;Torrens & Castellano, 2014;Zdravković et al, 2018). Our research group has also been interested in QSPR studies for the prediction of chromatographic retention indices in the field of food science (foodinformatics) (Rojas et al, 2019;Rojas et al, 2018), as well as the in silico modeling of the water solubility of pesticides (Fioressi et al, 2019).…”

Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method

“…The coefficient of determination and root mean square deviation for predictions were 0.915 and 55.4, respectively. 11 Veenaas et al implemented partial least squares (PLS) to predict RI values and the average deviation between the predicted and the experimental value was 5%. 12 Therefore, the QSRR methodology was employed to obtain more RI of flavor compounds in beer and more accurate identification.…”