Predicting multiple taste sensations with a multiobjective machine learning method

Kershaw, J. C. & Mattes, R. D. Nutrition and taste and smell dysfunction. World J. Otorhinolaryngol.—Head. Neck Surg. 4, 3–10 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pallante, L. et al. On the human taste perception: molecular-level understanding empowered by computational methods. Trends Food Sci. Technol. 116, 445–459 (2021).Article 
CAS 

Google Scholar 
Töle, J. C., Behrens, M. & Meyerhof, W. Taste receptor function. Handb. Clin. Neurol. 164, 173–185 (2019).Article 
PubMed 

Google Scholar 
Lim, J. & Pullicin, A. J. Oral carbohydrate sensing: beyond sweet taste. Physiol. Behav. 202, 14–25 (2019).Article 
CAS 
PubMed 

Google Scholar 
Behrens, M. & Meyerhof, W. Bitter taste receptors and human bitter taste perception. Cell. Mol. Life Sci. 63, 1501–1509 (2006).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Neta, E. R. D. C., Johanningsmeier, S. D. & McFeeters, R. F. The chemistry and physiology of sour taste—a review. J. Food Sci. 72, R33–R38 (2007).
Google Scholar 
Roper, S. D. The taste of table salt. Pflug. Arch. 467, 457–463 (2015).Article 
CAS 

Google Scholar 
Wang, W., Zhou, X. & Liu, Y. Characterization and evaluation of umami taste: A review. TrAC—Trends Anal. Chem. 127, 115876 (2020).Article 
CAS 

Google Scholar 
Breslin, P. A. S. An evolutionary perspective on food and human taste. Curr. Biol. 23, R409–R418 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tan, S. Y. & Tucker, R. M. Sweet taste as a predictor of dietary intake: a systematic review. Nutrients 11, 94 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kinnamon, S. C. Taste receptor signalling—from tongues to lungs. Acta Physiol. 204, 158–168 (2012).Article 
CAS 

Google Scholar 
Servant, G., Kenakin, T., Zhang, L., Williams, M. & Servant, N. The function and allosteric control of the human sweet taste receptor. Adv. Pharmacol. 88, 59–82 (2020).Article 
CAS 
PubMed 

Google Scholar 
Malavolta, M. et al. A survey on computational taste predictors. Eur. Food Res. Technol. 248, 2215–2235 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang, W. et al. BitterX: a tool for understanding bitter taste in humans. Sci. Rep. 6, 1–8 (2016).
Google Scholar 
Dagan-Wiener, A. et al. Bitter or not? BitterPredict, a tool for predicting taste from chemical structure. Sci. Rep. 7, 12074 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Zheng, S. et al. e-Bitter: bitterant prediction by the consensus voting from the machine-learning methods. Front. Chem. 6, 82 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Charoenkwan, P. et al. iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics 112, 2813–2822 (2020).Article 
CAS 
PubMed 

Google Scholar 
Charoenkwan, P., Nantasenamat, C., Hasan, M. M., Manavalan, B. & Shoombuatong, W. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides. Bioinformatics https://doi.org/10.1093/bioinformatics/btab133 (2021).Article 
PubMed 

Google Scholar 
Charoenkwan, P. et al. iBitter-Fuse: a novel sequence-based bitter peptide predictor by fusing multi-view features. Int. J. Mol. Sci. 22, 8958 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rojas, C. et al. A QSTR-based expert system to predict sweetness of molecules. Front. Chem. 5, 1–12 (2017).Article 

Google Scholar 
Zheng, S., Chang, W., Xu, W., Xu, Y. & Lin, F. e-Sweet: a machine-learning based platform for the prediction of sweetener and its relative sweetness. Front. Chem. 7, 35 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bouysset, C., Belloir, C., Antonczak, S., Briand, L. & Fiorucci, S. Novel scaffold of natural compound eliciting sweet taste revealed by machine learning. Food Chem. 324, 126864 (2020).Article 
CAS 
PubMed 

Google Scholar 
Lee, J., Song, B. S., Chung, Y. K., Jang, J. H. & Huh, J. BoostSweet: learning molecular perceptual representations of sweeteners. Food Chem. 383, 132435 (2022).Article 
CAS 
PubMed 

Google Scholar 
Banerjee, P. & Preissner, R. BitterSweetForest: a random forest based binary classifier to predict bitterness and sweetness of chemical compounds. Front. Chem. 6, 93 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tuwani, R., Wadhwa, S. & Bagler, G. BitterSweet: building machine learning models for predicting the bitter and sweet taste of small molecules. Sci. Rep. 9, 7155 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Bo, W. et al. Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network. Food Res. Int. 153, 110974 (2022).Article 
CAS 
PubMed 

Google Scholar 
Maroni, G. et al. Informed classification of sweeteners/bitterants compounds via explainable machine learning. Curr. Res. Food Sci. 5, 2270–2280 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Charoenkwan, P., Yana, J., Nantasenamat, C., Hasan, M. M. & Shoombuatong, W. IUmami-SCM: a novel sequence-based predictor for prediction and analysis of umami peptides using a scoring card method with propensity scores of dipeptides. J. Chem. Inf. Model. 60, 6666–6678 (2020).Article 
CAS 
PubMed 

Google Scholar 
Charoenkwan, P. et al. UMPred-FRL: a new approach for accurate prediction of umami peptides using feature representation learning. Int. J. Mol. Sci. 22, 13124 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pallante, L. et al. Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Sci. Rep. 12, 21735 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Qi, L. et al. Umami-MRNN: deep learning-based prediction of umami peptide using RNN and MLP. Food Chem. 405, 134935 (2023).Article 
CAS 

Google Scholar 
Zhang, J. et al. Umami-BERT: an interpretable BERT-based model for umami peptides prediction. Food Res. Int. 172, 113142 (2023).Article 
CAS 
PubMed 

Google Scholar 
Eberly, L. E. Multiple linear regression. Methods Mol. Biol. (Clifton, N. J.) 404, 165–187 (2007).Article 

Google Scholar 
Shanmugasundar, G. et al. A comparative study of linear, random forest and adaboost regressions for modeling non-traditional machining. Processes 9, 2015 (2021).Article 
CAS 

Google Scholar 
Tiryaki, S. & Aydin, A. An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Constr. Build. Mater. 62, 102–108 (2014).Article 

Google Scholar 
Bolaños, M., Ferrà, A. & Radeva, P. Food ingredients recognition through multi-label learning. Lect. Notes Computer Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.) 10590, 394–402 (2017).
Google Scholar 
Cappellin, L. et al. Multiclass methods in the analysis of metabolomic datasets: the example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS. Food Res. Int. 54, 1313–1320 (2013).Article 
CAS 

Google Scholar 
Reda, A., Fakharany, E. & Hazman, M. Early prediction of wheat diseases using SVM multiclass. Adv. Intell. Syst. Comput. 639, 257–269 (2018).Article 

Google Scholar 
Saha, P., Ghorai, S., Tudu, B., Bandyopadhyay, R. & Bhattacharyya, N. Multi-class support vector machine for quality estimation of black tea using electronic nose. In: Proc. International Conference on Sensing Technology, ICST 571–576 (IEEE, 2012). https://doi.org/10.1109/ICSensT.2012.6461744.Damarla, S. & Kundu, M. Classification of tea samples using learning vector quantization neural network. In: Proc. 2020 IEEE Applied Signal Processing Conference, ASPCON 2020 99–103 (IEEE, 2020). https://doi.org/10.1109/ASPCON49795.2020.9276662.Monforte, A. R., Martins, S. I. F. S. & Silva Ferreira, A. C. Discrimination of white wine ageing based on untarget peak picking approach with multi-class target coupled with machine learning algorithms. Food Chem. 352, 129288 (2021).Article 
CAS 
PubMed 

Google Scholar 
Tsakanikas, P., Karnavas, A., Panagou, E. Z. & Nychas, G. J. A machine learning workflow for raw food spectroscopic classification in a future industry. Sci. Rep. 10, 11212 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).Article 

Google Scholar 
Shapiro, S. & Wilk, M. An analysis of variance test for normality (complete samples). Biometrika 52, 691–611 (2007).Ferreira, J. A. & Zwinderman, A. H. On the Benjamini–Hochberg method. Ann. Stat. 34, 1827–1849 (2006).Article 

Google Scholar 
Hasan, M. M., Manavalan, B., Shoombuatong, W., Khatun, M. S. & Kurata, H. i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation. Plant Mol. Biol. 103, 225–234 (2020).Article 
CAS 
PubMed 

Google Scholar 
Hasan, M. M. et al. Meta-i6mA: an interspecies predictor for identifying DNA N 6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework. Brief. Bioinform. 22, 1–16 (2021).Article 

Google Scholar 
Mota-Merlo, M. & Martos, V. Use of machine learning to establish limits in the classification of hyperaccumulator plants growing on serpentine, gypsum and dolomite soils. Mediterr. Bot. 42, e67609 (2021).Article 

Google Scholar 
Michelucci, U., Sperti, M., Piga, D., Venturini, F. & Deriu, M. A. A model-agnostic algorithm for bayes error determination in binary classification. Algorithms 14, 301 (2021).Article 

Google Scholar 
Venturini, F. et al. Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques. Foods 10, 1010 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ahmad, A., Ordoñez, J., Cartujo, P. & Martos, V. Remotely Piloted Aircraft (RPA) in agriculture: a pursuit of sustainability. Agronomy 11, 7 (2020).Article 

Google Scholar 
Martos, V., Ahmad, A., Cartujo, P. & Ordoñez, J. Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Appl. Sci. 11, 5911 (2021).Article 
CAS 

Google Scholar 
Fu, B. et al. Three novel umami peptides derived from the alcohol extract of the Pacific oyster (Crassostrea gigas): identification, characterizations and interactions with T1R1/T1R3 taste receptors. Food Sci. Hum. Wellness 13, 146–153 (2024).Article 
CAS 

Google Scholar 
Fritz, F., Preissner, R. & Banerjee, P. VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds. Nucleic Acids Res. 49, W679–W684 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rojas, C. et al. ChemTastesDB: a curated database of molecular tastants. Food Chem. Mol. Sci. 4, 100090 (2022).Article 
CAS 

Google Scholar 
Landrum, G. RDKit Documentation. Read. Writ. 1, 4 (2013).
Google Scholar 
Yu, Z. et al. Taste, umami-enhance effect and amino acid sequence of peptides separated from silkworm pupa hydrolysate. Food Res. Int. 108, 144–150 (2018).Article 
CAS 
PubMed 

Google Scholar 
Zhang, J., Zhao, M., Su, G. & Lin, L. Identification and taste characteristics of novel umami and umami-enhancing peptides separated from peanut protein isolate hydrolysate by consecutive chromatography and UPLC–ESI–QTOF–MS/MS. Food Chem. 278, 674–682 (2019).Article 
CAS 
PubMed 

Google Scholar 
Minkiewicz, P., Iwaniak, A. & Darewicz, M. BIOPEP-UWM database of bioactive peptides: current opportunities. Int. J. Mol. Sci. 20, 5978 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Burdock, G. A. Fenaroli’s Handbook of Flavor Ingredients. Fenaroli’s Handbook of Flavor Ingredients (CRC Press, 2004). https://doi.org/10.1201/9781420037876.Rojas, C. et al. Quantitative structure–activity relationships to predict sweet and non-sweet tastes. Theor. Chem. Acc. 135, 1–13 (2016).Article 
CAS 

Google Scholar 
ToxNet.Bento, A. P. et al. An open source chemical structure curation pipeline using RDKit. J. Cheminformatics 12, 51 (2020).Article 
CAS 

Google Scholar 
Moriwaki, H., Tian, Y. S., Kawashita, N. & Takagi, T. Mordred: a molecular descriptor calculator. J. Cheminformatics 10, 4 (2018).Article 

Google Scholar 
Zhang, S. Nearest neighbor selection for iteratively kNN imputation. J. Syst. Softw. 85, 2541–2552 (2012).Article 

Google Scholar 
InSyBio – Biomarkers.CAO, Y., MIAO, Q.-G., LIU, J.-C. & GAO, L. Advance and prospects of AdaBoost algorithm. Acta Autom. Sin. 39, 745–758 (2013).Article 

Google Scholar 
Lundberg, S. & Lee, S.-I. A unified approach to interpreting model predictions https://doi.org/10.48550/ARXIV.1705.07874 (2017).