**7.1. Why miniaturization?**

We have been developing and characterizing miniaturized plasmas in the form of microplasmas [41–48] that we fabricated using a variety of fabrications technologies [49–52] for *taking part of lab to the sample types of applications* [53–60]. Microplasmas are arbitrarily defined as those with one critical dimension in the micrometer regime. To enable use of a non-thermal, non-equilibrium microplasma with an optical emission spectrometer outside of a lab, use of a portable spectrometer is also required.

It is expected that a miniaturized system of the type shown in **Figure 12** will find wide applicability in many analytical situations. For example, in environmental monitoring for testing of water quality *on-site* (e.g., drinking water, lake water, river water, and ground water). Water monitoring is significant to this geographical area because Ontario alone has 250,000 lakes. Outside of Ontario (and Canada), and as already mentioned, Arsenic (As) in water wells affects more than 100 million inhabitants worldwide. Other examples of potential environmental uses include air quality monitoring (e.g., sick building syndrome), safety and security. In clinical analysis, such a portable, *"shoe-box size"* instrument could be used to test

The predictive ability of ANNs (on the average) was 5.0%, and of PLS was (on the average) 5.1%. As expected, prediction errors increased as noise levels increased. A key difference between ANNs and PLS is that PLS did poorly when Δ*λ* was 0 pm (even at very low noise levels). Interestingly, from the simulated spectral scans when 0% noise was added (**Figure 11**) both ANNs and PLS had a prediction errors (on the average) of less than 1%, essentially the predictions were error free. Thus, it can be concluded that (likely) predictive ability was noise-

Shallow depth ANNs for spectral interference correction were experimentally evaluated using a large-size ICP spectrometer with a scanning monochromator (**Figure 8**) that had resolution typical of commercial systems. The ability of ANNs to predict the concentration of an Analyte (**A**) in a mixture of **A** with an Interferent (**I**) was used a key figure-of-merit and it was studied extensively (**Figures 10** and **11**). To validate predictive ability, predicted **A** concentrations by ANNs were compared with those obtained by PLS. Using experimental spectral scans, the average prediction error for ANNs was 4.1% and for PLS was 4.4%. Simulations were used to understand the origin of prediction errors for both of these methods. The average errors in predictive ability for simulated spectral scans and for Analyte (**A)** by ANNs was 5.0% and for PLS was 5.1%. The higher errors obtained when using simulations over those obtained when using experimentally obtained spectral scans is likely due to use of high (by experimental standards) levels of noise. When low levels of noise were used, the prediction errors were less than 1% (**Figure 11**). In other words the predicted concentrations by both methods were essentially error free. Clearly, methods capable of better discriminating between signals and noise are desirable. ANNs may also find applicability in portable, miniaturized systems that can be used for *"taking part of the lab to the sample"* types of applications. Due to the short focal length of portable spectrometers employed in miniaturized systems, such spectrometers suffer from significant spectral overlaps (but not from wavelength shift). Interference using miniaturized systems

**7. Spectral interference correction in miniaturization using ANN-based,** 

We have been developing and characterizing miniaturized plasmas in the form of microplasmas [41–48] that we fabricated using a variety of fabrications technologies [49–52] for *taking part of lab to the sample types of applications* [53–60]. Microplasmas are arbitrarily defined as those with one critical dimension in the micrometer regime. To enable use of a non-thermal, non-equilibrium microplasma with an optical emission spectrometer outside of a lab, use of a

**6. Conclusions (when using a large-size ICP spectrometer)**

depended (or noise-limited).

240 Advanced Applications for Artificial Neural Networks

will be discussed next.

**deep learning approaches**

portable spectrometer is also required.

**7.1. Why miniaturization?**

**Figure 12.** Illustration of a miniaturized microplasma-based optical emission system. Such a system may find use for chemical analysis *on-site*. The ability to obtain analytical results *on-site* and in (near) *real-time* is in stark contrast to the traditional sample collection-in-a-field and chemical analysis-in-a-lab approaches. *On-site* analysis has the potential to alter the traditional chemical analysis paradigm in which samples are collected in a field and are brought to a lab for analysis.

Pb (Lead) concentrations in blood (in the US alone, every child under the age of 7 has to be tested for potential elevated Pb concentrations in their blood); Na and K have to be measured in blood (Na and K concentration-determinations are mandatory in any medical checkup); Li (Lithium) in blood must be measured in certain cases because Li-containing medications are often prescribed as mood stabilizers. In space exploration, a system of the type shown in **Figure 12** may be used in studies involving Ca loss in bones during space flight. This limited set of examples has been included here to highlight potential applicability of miniaturized, microplasma-based optical emission spectrometry systems.

An example of a battery-operated, 3D-printed microplasma coupled to a portable, fiber-optic emission spectrometer is shown in **Figure 12**. All components shown in **Figure 12 can fit inside a shoe box**.

For a system of the type shown in **Figure 12**, in addition to requiring a battery-operated, lightweight, miniaturized plasma source (for use *on-site* rather than in a lab), the spectrometer must also be portable (generally meaning that it must have a short focal length). However, as the focal length of the optical spectrometer is reduced (e.g., from 75 cm as shown in **Figure 8** to about 12.5 cm as shown in **Figure 12**), resolution decreases and spectral overlaps become more prevalent. An example is shown in **Figure 13**.

**Some specifics:** in optical emission spectrometry, resolution (R = *λ*/Δ*λ*) is defined as the ability to baseline resolve two closely spaced wavelengths (**Figure 13**, left frame). As already mentioned, for use *on-site*, a portable optical spectrometer (with a short focal length) is required. But as focal length decreases, resolution degrades. And as resolution degrades, spectral overlaps become more severe, thus making spectral interference correction essential. To demonstrate the severity of spectral overlaps and the need for spectral interference correction, the superimposed spectra of Europium (Eu), Strontium (Sr), and Lead (Pb) shown in **Figure 14** and will be used as an example.

Shown in **Figure 14** are three spectra obtained by introducing individually (or separately) into a microplasma (**Figure 12**) equal concentrations of Eu, Pb, and Sr. and to facilitate discussion, the spectra have been superimposed and graphed together. In a hypothetical sample

**Figure 13.** Left frame: Spectral window showing two Cu lines (acquired using a long focal length spectrometer, for example, one shown in **Figure 8**) demonstrating baseline resolution. Right frame, spectra from 200 to 850 nm acquired using a spectrometer with a short focal length (**Figure 12**, StellarNet co, http://www.stellarnet.us/). Right frame (insert): In this case, the same Cu lines cannot be resolved. Each 200–850 nm spectrum (or full spectral scan) is 2048 data points.

**Figure 14.** Spectral overlaps observed when using Eu (solid line), Sr (dashed line), and Pb (dotted line).

containing these three elements, Sr. is the analyte and Eu and Pb are the interferents. The most intense Sr. line (at ~460 nm) is directly overlapped by two Eu lines (at ~459 nm). The second most intense Sr. line (at around 407 nm) is overlapped by the most intense Pb line (at 405 nm), and the third most intense Sr. line (~ 421 nm) is also directly overlapped by a Eu spectral line. Clearly in this example interference free determinations of Sr. are not possible, thus making spectral interference correction essential. In this laboratory, an ambitious goal of using ANNbased deep learning approaches is being pursuit. Deep learning approaches can handle larger amounts of data (as opposed to shallow depth ANNs), and they are claimed to have improved abilities to distinguish signals from noise, thus likely having improved predictive abilities.

#### **7.2. ANN-based deep learning**

Pb (Lead) concentrations in blood (in the US alone, every child under the age of 7 has to be tested for potential elevated Pb concentrations in their blood); Na and K have to be measured in blood (Na and K concentration-determinations are mandatory in any medical checkup); Li (Lithium) in blood must be measured in certain cases because Li-containing medications are often prescribed as mood stabilizers. In space exploration, a system of the type shown in **Figure 12** may be used in studies involving Ca loss in bones during space flight. This limited set of examples has been included here to highlight potential applicability of miniaturized,

An example of a battery-operated, 3D-printed microplasma coupled to a portable, fiber-optic emission spectrometer is shown in **Figure 12**. All components shown in **Figure 12 can fit** 

For a system of the type shown in **Figure 12**, in addition to requiring a battery-operated, lightweight, miniaturized plasma source (for use *on-site* rather than in a lab), the spectrometer must also be portable (generally meaning that it must have a short focal length). However, as the focal length of the optical spectrometer is reduced (e.g., from 75 cm as shown in **Figure 8** to about 12.5 cm as shown in **Figure 12**), resolution decreases and spectral overlaps become

**Some specifics:** in optical emission spectrometry, resolution (R = *λ*/Δ*λ*) is defined as the ability to baseline resolve two closely spaced wavelengths (**Figure 13**, left frame). As already mentioned, for use *on-site*, a portable optical spectrometer (with a short focal length) is required. But as focal length decreases, resolution degrades. And as resolution degrades, spectral overlaps become more severe, thus making spectral interference correction essential. To demonstrate the severity of spectral overlaps and the need for spectral interference correction, the superimposed spectra of Europium (Eu), Strontium (Sr), and Lead (Pb) shown in **Figure 14**

Shown in **Figure 14** are three spectra obtained by introducing individually (or separately) into a microplasma (**Figure 12**) equal concentrations of Eu, Pb, and Sr. and to facilitate discussion, the spectra have been superimposed and graphed together. In a hypothetical sample

**Figure 13.** Left frame: Spectral window showing two Cu lines (acquired using a long focal length spectrometer, for example, one shown in **Figure 8**) demonstrating baseline resolution. Right frame, spectra from 200 to 850 nm acquired using a spectrometer with a short focal length (**Figure 12**, StellarNet co, http://www.stellarnet.us/). Right frame (insert): In this case, the same Cu lines cannot be resolved. Each 200–850 nm spectrum (or full spectral scan) is 2048 data points.

microplasma-based optical emission spectrometry systems.

more prevalent. An example is shown in **Figure 13**.

and will be used as an example.

**inside a shoe box**.

242 Advanced Applications for Artificial Neural Networks

Similar to conventional or shallow depth ANNs (i.e., those with a few network layers, **Figure 4**), deep learning neural nets (or deep neural networks, or DNNs for short), have numerous hidden layers. An example is shown in **Figure 15a**. A key advantage of deep learning [61–65] ANNs versus shallow depth ANNs is that in deep learning the network continues to learn as the amount of data increases, thus increasing its learning and (likely) predictive abilities (thus reducing %|Error| of prediction). In sharp contrast, shallow depth ANNs (e.g., with a few layers) although they may outperform deep learning when using relatively small data sets, their learning and predictive abilities plateau (**Figure 15b**).

At present, deep learning is receiving significant attention. A limited number of examples include **IBM**'s Watson [66], a reportedly \$24 billion investment so far); **Google** is offering a time-limited free access to cloud machine learning [67]; **Mobile Eye** is marketing their advanced driver assist system (ADAS) claiming that it has been installed in many self-driving cars [68]; **NVIDIA** is marketing graphics processing units (GPUs) with deep learning abilities [69]; Intel, a semiconductor fabrication house has a sizable investment on deep learning [70]; **OpenText** [71] is using deep learning in the name of Magellan to rival IBM's Watson; **Qualcomm** is a fabrication house of CPUs for smartphones offers a software kit for neural

**Figure 15.** (a) Deep learning neural nets with every neuron connected to all neurons in neighboring layers (where n maybe in the millions of layers). (b) Sketch of predictive (and learning) abilities of shallow depth and deep learning ANNs versus the amount data used in a training set.

networks [72]; **Samsung** is developing deep learning approaches for heath applications [73]; Noah'sArkLab [74] funded by **Huawei** (a telecom company), is heavily investing in deep learning; **Microsoft** is offering a (currently free) "cognitive toolkit" for deployment of deep learning approaches [75]; and **Apple** is offering developers machine learning tools [76] for inclusion into any iOS app. Apple is also publishing their Machine Learning Journal. Overall and one way or another, all of these companies are either using or promoting use of ANNbased deep learning.

We are experimenting with deep learning for spectral interference correction using a miniaturized, portable spectrometer (of the type shown in **Figure 12**) using data of the type shown in **Figures 13** and **14**.
