Automated Classification of 6-n-Propylthiouracil Taster Status with Machine Learning

Abstract

Several studies have used taste sensitivity to 6-n-propylthiouracil (PROP) to evaluate interindividual taste variability and its impact on food preferences, nutrition, and health. We used a supervised learning (SL) approach for the automatic identification of the PROP taster categories (super taster (ST); medium taster (MT); and non-taster (NT)) of 84 subjects (aged 18–40 years). Biological features determined from subjects were included for the training system. Results showed that SL enables the automatic identification of objective PROP taster status, with high precision (97%). The biological features were classified in order of importance in facilitating learning and as prediction factors. The ratings of perceived taste intensity for PROP paper disks (50 mM) and PROP solution (3.2 mM), along with fungiform papilla density, were the most important features, and high estimated values pushed toward ST prediction, while low values leaned toward NT prediction. Furthermore, TAS2R38 genotypes were significant features (AVI/AVI, PAV/PAV, and PAV/AVI to classify NTs, STs, and MTs, respectively). These results, in showing that the SL approach enables an automatic, immediate, scalable, and high-precision classification of PROP taster status, suggest that it may represent an objective and reliable tool in taste physiology studies, with applications ranging from basic science and medicine to food sciences.

Application of Artificial Neural Networks (ANN) to Elucidate the Connections among Smell, Obesity with Related Metabolic Alterations, and Eating Habit in Patients with Weight Excess

“…In the last decade, AutoCM has been successfully tested across the medical field. Recently, a machine learning model has been created and used to predict with high precision the taste function of subjects [ 73 ]. The description of the 3-layer architecture and the mathematical models of Auto-CM is reported in Buscema and Grossi [ 74 ].…”

Automated identification of the genetic variants of TAS2R38 bitter taste receptor with supervised learning

“…The automatic recognition of the TAS2R38 genotype of participants was carried out with SL algorithms capable of distinguishing the three genotypes (PAV/PAV, PAV/AVI, and AVI/AVI), by using the participants’ features presented in the data model as predictive variables, as already performed by Naciri et al with the aim of identifying PROP taster categories [89] .…”

“…The following algorithms were used: Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and CatBoost classifier according to Naciri et al [89] . During training, the algorithms search for the correlation of the features with the TAS2R38 genotype groups, then they take new unknown inputs and assign them to the appropriate category.…”

“…Ry recognize, with high precision, the PROP phenotype, by using Machine Learning (ML) [89] . Given the implications that the genetic variants of TAS2R38 bitter receptor have in tasting and non-tasting functions and disorders [2] , [9] , [36] , [38] , [52] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [83] , [84] , [85] , we addressed the issue of assessing the efficacy of ML classifiers in the automatic and highly accurate distinction between TAS2R38 genotypes, by exploiting biological features of participants which were used as predictive variables in the data set.…”

A Supervised Learning Regression method for the analysis of oral sensitivity of healthy subjects and patients with chemosensory loss

“…Data processing operations and the resolution of the over tting or under tting problems, which are crucial phases in the running of an SL experiment, were performed according to Naciri et al 2022, 2023 22,23 . They included handling of missing values; elimination of the duplicate values; the converting of the dataset's content into a structure that machine learning algorithms can employ; the models' hyperparameter optimization that allows nding the optimal value of the number of model iterations, learning rate, and depth of the SL regressors.…”