**8. Overall conclusions**

ANNs proved effective at addressing the key problem of spectral interference encountered in optical emission spectrometry. Due to the relatively small number of data points used (e.g., 60–160), training and validation times did not become bottlenecks. To obtain *method validation* (as is typical in chemical analysis), ANNs were compared to PLS. It was concluded that the predictive ability of both methods was at ~5% and that both methods were noise-limited. Thus, our attention has now been turned to ANN-based deep learning approaches that are reported to have improved abilities to distinguish signals from noise. Deep learning is being evaluated for use in miniaturized systems (with short focal length, portable spectrometers) in which spectral interference is typically more severe than those of long focal length, large-size spectrometers. It is expected that application of deep learning approaches has the potential to lead to portable chemical analysis instruments that are *"smaller, cheaper,* **smarter** *and faster"* at producing precise and accurate analytical results *on-site* [53]. *On-site* analysis capabilities have the potential to cause a paradigm shift in classical chemical analysis (caption of **Figure 12**) by allowing practioners *"to bring part of the lab to the sample"* so that analytical results can be obtained *in-situ* and in (near) *real-time*. Although large-size and miniaturized plasma-based instruments were used as an ANN application example, it is expected that the ideas presented here will have wider applicability to include non-plasma-based chemical analysis instruments regardless of their size.
