## 1 Introduction
They concluded that, although one method may be preferable to another depending on the circumstances, obtaining the confidence intervals can help to improve the characterisation of the properties of a material and aid early failure detection. In a later study, also using test data, these authors noted the importance of obtaining the confidence intervals, but in this case, they applied Bayesian methods for the estimate [30]. [PERSON] et al. [31] noted the importance of estimating confidence intervals in medium-density fibreboard (MDF) production processes, reporting that bootstrapping provides an efficient solution to estimate confidence intervals when other approaches are inefficient or less realistic. They used 2000 resamplings for the bootstrapping, less than half the number applied in the present article, to calculate the lower percentiles of the internal bond from board manufacturing data. [PERSON] et al. [32] used data from various fields, including mathematics, medicine, transport, environmental studies, electronics, and production processes, and trained the neural network so that instead of providing a simulation of the result, it would provide the confidence intervals of the result, using two neurons in the output layer. [PERSON] et al. [33]also conducted a comparative study of various techniques used to obtain confidence intervals from neural network results and the delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods. They concluded that bootstrapping is a powerful tool for constructing confidence intervals that balances current computational power with the accuracy of the result. Similarly, [PERSON] et al. [34] reaffirmed the bootstrap technique as a powerful tool for generating confidence intervals. In this study, they combined artificial neural networks and bootstrap methods to obtain confidence intervals using the estimate of variance and real-world and simulated data to assess the reliability of the method. [PERSON] et al. [35] addressed the need to know the confidence intervals of a neural network prediction, and the problem of neural networks being regarded as some sort of black box. They addressed the problem using a Monte Carlo simulation, a slightly different resampling technique from bootstrapping, and concluded that Monte Carlo is also a useful method to obtain confidence intervals. [PERSON] et al. [36; 37] proposed a combination of neural networks to generate prediction confidence intervals, using a weighted average of each network in the final result and improving the quality of the confidence intervals with regard to the accuracy and the coverage index. The objective of this study was to use bootstrapping to determine the confidence intervals of the prediction of a standard particleboard internal bond obtained by artificial intelligence techniques, enabling greater reliability in factory decision making about specification compliance. Following [PERSON] et al. [38] and [PERSON] et al. [39], bootstrapping is the most appropriate method to address input data uncertainty because it enables an optimum performance while also minimising computation costs. Moreover, bootstrapping is relatively simple compared to other methods of constructing prediction intervals (PI), such as the delta [40] or Bayesian [41] techniques. As bootstrap-generated PI tend to be narrower, they generate better information about the result. Unlike alternative approaches, bootstrapping does not require a calculation of complex matrices such as the Jacobian or Hessian matrices. This improves the stability of the method and avoids problems associated with mathematical singularities [25; 34]. To this end, the internal bond was calculated by combining the outputs of 20 artificial neural networks to improve the accuracy of the overall result.