Table 4.

The strengths points and limitations of the elaborated Tools

Tool	Benefits	Limitations
ANN	• Eliminates the error reverting to employing a single wavelength regression such as in univariate UV approaches • Does not demand a monotonous validation process • Can be trained on diverse ratios of mixes	• Needs many tests to obtain accurate prediction outcomes, which needs to adjust many parameters like transfer functions, layers, neuron number, goal, and the learning rate • The improper adjustment of one of the previous parameters may lead to learning errors or the occurrence of overfitting issues
FSD	• Enhances the overlaid spectra resolution • Does not impact the signal/noise ratio • Doesn’t require a special program or (cos, sin) transformation for performing like in Discrete Fourier Transform	• Crucial measurements of the signals at the selected wavelength • Influenced by the increment of wavelength
MC	• The mean-centered signals are measured at the maximum points, for higher sensitivity • Does not impact the signal/noise ratio	• The computed arithmetic mean is greatly affected by skewed data • Needs to test the best divisor concentration and the best wavelength range for the mean centering process