## 2 Materials and Methods
### Artificial Neural Networks  
Machine learnings methods are known as data driven approaches; several different techniques were applied to the dataset to determine which algorithm could best solve the problem at hand. As a result, Artificial Neural Networks (ANNs) was selected as the optimal choice for multivariate data analysis [51]. ANNs are simplified mathematical models which resemble the human brain and can be described as several layers of interconnected \"neurons.\" Each neuron can perform mathematical operations on the input values and transfer the result to the next layer of neurons. The parameters of the mathematical operations are tuned by a learning algorithm. The power of ANNs is based on the collective behavior of many interconnected neurons and their ability to exchange and retain meaningful information. Advantageously, ANNs do not rely on data distribution and thus can derive patterns from large, complex, and noisy datasets which are typically difficult to analyze by conventional linear data analysis methods. ANNs are fault-tolerant and capable of generalization, which enables them to restore missing data and represent \"real-world\" solutions for the generalization of a problem. However, depending on the complexity of the model, ANNs can be time consuming to perform and the number of hidden layers has to be determined to completely represent all features of the dataset. Furthermore, it is not always clear how ANNs approach the solution, and as such, they are sometimes called \"black boxes\" [52; 53; 54]. ANNs tend to converge on local minima, yielding reduced generalization abilities [55; 56]. However, ANNs consistently outperform other classification methods, indicating the ability of non-linear data generalization to achieve high predictive capabilities [57; 58; 59; 27; 54]. The process of searching for a global minima can be complicated; to alleviate this problem herein, each training event during the testing phase was repeated twenty times, each time with new random starting values for the network weights; the network with the lowest error was then determined. Overtraining was assessed by performance of the model on the validation data. One established statistical technique to evaluate if a model is over-trained is to use a separate dataset for validation of the trained model. The cross-validation technique is an extension of this principle. The measurements of model accuracy on the validation dataset are accepted as an estimate of model accuracy on novel, unseen data. A well-generalized network should give good prediction results on the validation data, and these should be comparable to the prediction results on the test set [60]. To design, test, and validate ANN models, the \"neuralnet\" package in the R environment was employed [61]. The specific form of ANN used for AD diagnostics was a multilayer perceptron (MLP). The MLP architecture was designed and optimized by varying the number of hidden layers and the number of neurons in each layer; each architecture was tested using transfer and resilient back-propagation training methods. For each classification event, a random split was used on the original dataset to divide it into training (90%) and test (10%) sets during the testing phase and then during cross-validation, 10% of data was separated from the training set and used. The training dataset was used to determine the relationship between dependent and independent variables. The test set, in turn, assesses the performance of the model. The assignment of data to either the training set or the test set is done by random sampling, where multiple spectra from donors were used as individual inputs. The random split was applied to the original dataset of 480 spectra to divide it into training (90%) and test (10%) datasets; this corresponds to 48 randomly selected individual spectra used for testing and the remaining spectra (432) to be used for training.