[Response] Thanks for your comments.
34. How does the author know that ANN was not overfitting? According to Beer’s law, the relationship between absorbance and the analyte of interest is linear. How does the author justify the use of ANN that models non-linear relationships for an application were the linear relationship is proven? Is it possible that he/she were modeling interferences in the frying process?
[Response] Thanks for your comments. Multivariate approaches suffer from overfitting, thus validation is an obligatory component of any analysis (Cavanna, Righetti, Elliott, & Suman, 2018)). Typically, cross-validation approaches are used, in which a proportion of the data (e.g., 10–40%, the “validation set”) are randomly removed, and the model is built with the remaining “training set”. This procedure is repeated many times until each sample has been in the test set exactly once (leave-n-out procedure). The accuracy of the model on these left-out samples gives an estimate of the predictive power for unseen samples and also the robustness of the model to perturbations of the data. Therefore, we applied the validation model so as to evaluate the model is not overfitting. There is no one-to-one correspondent relationship between thesechemical indexes (polar compounds, free fatty acids, hydroperoxide) and the selected peak areas, therefore they may be not absolutely the linear relationships between the chemical indexes and the selected peaks. Many researches using the ANN to calibrate the model with the spectra and other chemical index. indicating it is available for choosing ANN for this spectroscopic application (Özbalci, et al., 2013; Wenning et al., 2010; Barmpalexis, 2018; Li, et al., 1999; Agatonovic-Kustrin, 2013).