Simultaneous Detection of Nitrate and Nitrite Based on UV Absorption Spectroscopy & Machine Learning

Impact of Measurement Protocol on ICP-MS Data Quality Objectives

A Look at Spectral Information and Remote Sensing Data Using Vis-NIR

Inversion of Low-Grade Copper Mining Areas

A Molecular Spectroscopy Workbench Column

Raman Spectroscopy and Chemical Bonding of BaFCl, BaFBr, and BaFl

Donald J. Douglas has won the 2022 Winter Conference Lifetime Achievement Award in Plasma Spectroscopy. Douglas will receive the award, presented by Thermo Fisher Scientific, on Monday, January 17, at the Winter Conference on Plasma Spectrochemistry in Tuscon, Arizona.

Douglas, one of the pioneers of inductively coupled plasma–mass spectrometry (ICP-MS), is being recognized for understanding critical fundamental challenges of the new technology and contributing fundamentally to its development as a widespread analytical tool. His publications describe the problems and solutions that include molecular-beam-style sampling of the ICP (the now-ubiquitous sampler-skimmer); and capacitive coupling leading to a “pinch” discharge, and the balanced load coil that suppresses this. He was one of the first to study the use of collision and reaction cells with ICP-MS and is likely the first to consider specific ion-molecule reactions to resolve isobaric interferences.

Douglas is a Professor Emeritus and Honorary Professor at the University of British Columbia (Kelowna, British Columbia, Canada). He received his BSc from McMaster University (Hamilton, Canada), and his PhD from the University of Toronto (Canada).

This award has been presented biennially since 2010. Past winners are:

The award is sponsored by Thermo Fisher Scientific, but winners are chosen by an independent scientific committee. The winner is awarded a sum of $5,000 USD, travel support to the conference, and meeting registration. More information about the awards is available on the Thermo Fisher Scientific 

Articles

Donald J. Douglas has won the 2022 Winter Conference Lifetime Achievement Award in Plasma Spectroscopy. Douglas will receive the award, presented by Thermo Fisher Scientific, on Monday, January 17, at the Winter Conference on Plasma Spectrochemistry in Tuscon, Arizona.



Donald J. Douglas Wins 2022 Winter Conference Lifetime Achievement Award in Plasma Spectroscopy

Denise M. Mitrano is an Assistant Professor of Environmental Chemistry of Anthropogenic Materials at ETH Zurich in the Department of Environmental Systems Science. Her research is directed to understanding the impact and interaction of nanoparticles in the environment using atomic spectroscopy techniques, such as inductively coupled plasma–mass spectrometry (ICP-MS) and single-particle ICP-MS (sp-ICP-MS). We recently interviewed Mitrano about her work.

Jerome Workman Jr. serves on the Editorial Advisory Board of Spectroscopy and is currently with Unity Scientific LLC. He is also an adjunct professor at Liberty University and U.S. National University. He can be reached at jworkman04@gsb.columbia.edu.

Denise M. Mitrano is an Assistant Professor of Environmental Chemistry of Anthropogenic Materials at ETH Zurich in the Department of Environmental Systems Science. Her research is directed to understanding the impact and interaction of nanoparticles in the environment using atomic spectroscopy techniques, such as inductively coupled plasma–mass spectrometry (ICP-MS) and single-particle ICP-MS (sp-ICP-MS). She is the winner of the 2022 Emerging Leader in Atomic Spectroscopy Award. Chosen by an independent committee, the Emerging Leader in Atomic Spectroscopy Award recognizes the achievements and aspirations of a talented young atomic spectroscopist who has made strides early in his or her career toward the advancement of atomic spectroscopy techniques and applications. We recently interviewed Mitrano about her work.

You and your colleagues have published a paper describing the release of microplastic fibers from textiles from abrasion, which can contribute towards the concentration of microplastics in the environment (1). What was most surprising to you about this finding?

Microplastics can enter the environment from a variety of different sources: either as the breakdown of macroplastic litter, such as plastic bottles and mismanaged waste, or from daily use of everyday items, such as textiles. Polyester textiles make up a large proportion of the clothes we wear, and thus can represent an important source of microplastic fibers when they are washed or worn. How the textile is processed, finished, and washed throughout its lifecycle can dictate the amount of microplastic fibers that are shed over time—and so the material choice, manufacturing, and use can all impact microplastic fiber release. Because of this, I am often asked if consumers should choose to purchase garments made of certain materials to be more environmentally friendly, but there is no easy answer. Synthetic textiles can shed microplastic fibers that may negatively impact the environment, but growing cotton consumes a lot of water, and with other natural fibers, such as wool, one must consider animal welfare. When it comes to clothing in particular, my advice is always simply to consume less and use the garments you do own for longer—this is certainly the best way to reduce environmental impacts on many levels when it comes to the fashion industry.

Biogeochemistry of Anthropogenic Particles, 

which you, Praetorius, Lespes, and Slaveykova edited (2), is a collection of articles on the biogeochemical cycle of human-produced particles in the environment, emphasizing the distribution of nano- and micro-scale sizes. What motivated you to publish this book and how much coverage do you give to analytical methods in it?

As scientists long working in the field of nanoparticle analysis, we were interested in how different methods and concepts of the fate, transport, and biological interactions of nanomaterials could be translated across different areas of research, especially considering that anthropogenic particles (engineered nanomaterials, microplastics, incidental nanomaterials) have been receiving increased attention compared to natural colloids. It was interesting to note that researchers across the collection emphasized the importance of good measurement and analytical practices, and continue to develop and use methods that were initially developed in the context of environmental health and safety of engineered nanomaterials in new ways. What was once considered a specialist technique, such as sp-ICP-MS, which I helped to develop during my PhD, is becoming increasingly commonplace and standardized. Collectively, the boundaries continue to be pushed to measure smaller, more diverse particles in complex matrices with greater ease—so it is exciting to imagine what will be possible in the near future.

One of your most highly cited papers involves analysis of silver nanoparticles (AgNPs) using sp-ICP-MS (3). What is unique or special about using ICP-MS, operated in a single-particle counting mode (sp-ICP-MS), for nanoparticle characterization? How did you arrive at this approach?

While I always had an interest in analytical chemistry, as new student in the area of nanometrology I certainly owe a significant amount of gratitude to my PhD supervisor, Jim Ranville, for seeing the promise of this technique, how it could be utilized in environmental contexts, and for suggesting that I further develop this methodology early on in my career! Single-particle ICP-MS is a technique that is capable of counting and sizing individual inorganic nanoparticles at trace concentrations. This method paved the way for being able to understand the behavior of nanoparticles at more environmentally and biologically relevant concentrations. Even when I first began to present this method at scientific conferences, it garnered a considerable amount of attention as a promising technique in the field of environmental chemistry, and other researchers were keen to implement it in their laboratory. This was eventually facilitated both by the standardization of the technique (as an ISO method) as well as by instrumentation and software developments specifically dedicated to process data in single-particle mode. In recent years, the method has continued to mature with additional development in how to capture and process data as well as in the diversity of materials that can be studied (including multi-element particle analysis when using an ICP-TOF-MS instrument instead of a quadrupole ICP-MS system).

In another of your papers, you described the application of sp-ICP-MS and asymmetrical flow field flow fractionation ICP-MS (AF4-ICP-MS) for characterizing silver engineered nanoparticles in the environment (4). Which of these techniques is preferred for characterizing silver engineered nanoparticles ?

Both sp-ICP-MS and AF4-ICP-MS can be used for many inorganic nanoparticles, not only Ag, and thus are suitable to be used for a wide range of applications and study systems. The two techniques also provide different but potentially complementary data. However, as with any analytical method, there are pros and cons of each technique, and each has its limitations, depending on the research question. SP-ICP-MS can count and size individual nanoparticles at very low concentrations, but one is not able to assess the aggregation state, for example, if a nanoparticle is aggregated to an organic particle (which wouldn’t be detected in single-particle mode with a quadrupole ICP-MS used in this approach); or, if many small nanoparticles were homoaggregated, the signal would register as one, larger particle. Conversely, AF4-ICP-MS is able to provide more information as to the size of a more complex, particle agglomerate (in addition to measuring the size of individual particles), but finding the “best” operational conditions in this chromatography-like technique is often much less straightforward compared to sp-ICP-MS, and the detection limits are higher. Knowing the strengths and limitations of each of the techniques will allow the researcher to select the best method for the objective at hand, or potentially design a series of tests utilizing both methods to have a more complete understanding of the particles in their system.

While your initial field of research was focused on the characterization of engineered nanomaterials, more recently you have been focused on nanoplastics and microplastics research. How were you able to bridge this gap and how did the analytical tools you use to approach these two different fields change?

I was very interested in applying the skills and process understanding I gained in studying engineered nanomaterials to different particulate materials of emerging concern, including plastics. However, analytically, measuring plastics can be very different and more challenging than measuring inorganic (nano)particles. Therefore, I developed a new approach to synthesize nano- and microplastics doped with a trace metal to more easily, and quickly, quantify them in complex matrices using techniques that are more standardized in metals analysis, including ICP-MS and sp-ICP-MS. By using the metal as a proxy for the plastics, we could spike them into a variety of laboratory- and pilot-scale facilities, which allowed us to investigate the fate and transport of plastics in environmental systems (waterways, porous media[what does “porous media” mean as an environmental system?]) and in wastewater and drinking water treatment plants, and to study biological uptake and interactions of plastics. This approach has opened up a completely new avenue for those studying plastic pollution and has provided many new opportunities to utilize atomic spectroscopy in a field where that would not have been possible otherwise.

What do you find most interesting about being an analytical chemist working in the field of environmental chemistry?

The thing I like most about being an analytical chemist is that other researchers are able to use our methods and take them even further. In a way, this amplifies my contributions across the field of environmental science to provide others with the tools to answer their most interesting questions. Being an expert in nanometrology has allowed me to collaborate across many different research fields with colleagues whose expertise is very different from mine, and so I’m constantly able to learn about new areas of science.

What is it about science that motivates you? Would you share some of your work and organizational habits that have helped you be productive and successful professionally?

On a day-to-day basis, to me, science, and especially analytical method development, is about problem solving and the excitement when you have finally accomplished a difficult puzzle (sometimes after much trial and error!). But the implications of our work go beyond the laboratory. The natural environment is experiencing ever-increasing pressures from anthropogenic stressors. Understanding how human activities influence physical, chemical, and biological cycles is a central component of modern geosciences and I find it very rewarding to contribute knowledge that can lead toward the protection of our waterways and soils. Additionally, mentoring students to become independent scientists, and more generally independent thinkers, is a key motivator—we get to have a lot of fun with science.

Perhaps the two things that have helped my professional success the most are to keep an open mind and to ask many questions. This has opened the door for a lot of new ideas and collaborations that wouldn’t have come about if I were solely focused on my day-to-day work. Naturally, learning to balance the demands of organizing multiple projects on different topical subjects simultaneously took time, but in the end, I feel that I am gaining an increasingly holistic view of my field, which helps me to better identify key research gaps and develop better research objectives.

What is your next area of interest for your research?

In the field of environmental health and safety of engineered nanomaterials, I saw first-hand how important it was to develop standardized methods for particle analysis. Therefore, beyond developing novel techniques to measure nano- and microplastics, I am also keen to work toward standardization of promising methodologies together with others so that as a community we can have more trustworthy and comparable results when assessing plastics concentration in the environment.

In recent years, we have laid the foundation for using the metal-doped plastics we synthesized to assess plastic fate and transport in a variety of systems. However, there are still many other aspects to explore to understand the risks of plastics in the environment and therefore the time is ripe to expand upon this foundation to investigate particle fluxes in other key environmental and biological systems where plastics are known to be in high concentrations.

Do you have any words of advice for young people desiring a career in science?

Be brave and think outside the box! As a young scientist, you have the opportunity to focus on a new field and develop innovative methods that are not yet established. This may entail risks, but in the best-case scenario, you can become a pioneer in your own field.

(1) Y. Cai, D.M. Mitrano, R. Hufenus, and B. Nowack, Formation of Fiber Fragments during Abrasion of Polyester Textiles, 

(2) D.M. Mitrano, A. Praetorius, G. Lespes, and V.I. Slaveykova, eds., 

Biogeochemistry of Anthropogenic Particles 

https://books.google.com/books?hl=en&lr=&id=chAvEAAAQBAJ&oi=fnd&pg=PP1&ots=KoKGXq9Plf&sig=kILs9mkanNGjhPjpWybbvIT2BSs#v=onepage&q&f=false

(3) D.M. Mitrano, E.K. Lesher, A. Bednar, J. Monserud, C.P. Higgins, and J.F. Ranville, Detecting nanoparticulate silver using single‐particle inductively coupled plasma–mass spectrometry, 

(4) D.M. Mitrano, A. Barber, A. Bednar, P. Westerhoff, C.P. Higgins, and J.F. Ranville, Silver nanoparticle characterization using single particle ICP-MS (SP-ICP-MS) and asymmetrical flow field flow fractionation ICP-MS (AF4-ICP-MS), 

Denise M. Mitrano, the 2022 winner of the Emerging Leader in Atomic Spectroscopy Award, is an Assistant Professor of Environmental Chemistry of Anthropogenic Materials at ETH Zurich in the Department of Environmental Systems Science. Mitrano earned her BS degree (cum laude) in chemistry from Salve Regina University in Newport, Rhode Island, in 2008, and received her PhD in 2012 in geochemistry from the Colorado School of Mines, with advisement from Professor James F. Ranville. Her thesis title was, “Development of ICP-MS Based Nanometrology Techniques for Characterization of Nanoparticles in Environmental Systems.” She has become a leader in the area of nanometrology, including the detection and transformation of particles in consumer products and in the environment. She is particularly interested in developing new analytical tools to systematically understand the mechanisms and processes driving the fate, transport, and biological interactions of particles, including engineered nanomaterials and nano- and microplastics. Her interest in a “safer by design” approach for both nanomaterials and plastics is exemplified by her work on the boundaries of environmental science, materials science, and policy to promote sustainability and environmental health and safety of new materials. She has recently been awarded the SNSF Marie Heim Vögtlin Prize for Outstanding Young Woman Researcher of the Year in Switzerland (2022) and the 2022 James J. Morgan Early Career Award from ES&T and the ACS Division of Environmental Chemistry.

Gaining an Understanding of Nanoparticles in the Environment Using Atomic Spectroscopy: Denise M. Mitrano, the 2022 winner of the Emerging Leader in Atomic Spectroscopy Award 

Since we have been beset by the global Covid-19 pandemic in early 2020, the scientific community has rallied mightily to produce both testing methods and vaccines. Over 1.5 billion Covid tests have been administered in just the United States, United Kingdom, and India alone. Both the common polymerase chain reaction (PCR) and the antigen tests rely on long-established technologies. Here, we explore how spectroscopy might play a part in next-generation Covid and pathogen testing.

It is hard to underestimate the global dislocations caused over the previous two years by the Covid-19 pandemic. Since its discovery during the initial outbreak in Wuhan, China, it has spread throughout the world, infecting more than 250 million people and causing more than 5 million global deaths. Thanks to the rapid response of the global scientific community, over 8 billion vaccine doses have been administered as of early December 2021.

Given its transmissibility, animal reservoirs, and the impossibility of vaccinating the entire human population, it seems highly likely that Covid-19 will become endemic. At the same time, current testing methods have substantial drawbacks. Although reverse transcription polymerase chain reaction (RT-PCR) tests are currently the gold standard for testing, there is a long period of incubation for Covid, and the RT-PCR test has a substantial false negative rate depending on the timing of the testing with respect to the peak infectiousness of an individual. As an example, a recent study published in 

 showed that in one super-spreader event, 36% of 148 infected close contacts tested with an RT-PCR test were initially missed (1), which coheres with the RT-PCR true positivity rates for nasal swabs of 63% observed in a study by Wang and others, where they also found higher true positivity rates (72%) for sputum samples (2).

This study suggests that other more rapid and accurate methods of testing may find traction in the coming years. Such methods should be easier to administer, such as the rapid antigen tests or lateral flow tests, and should also improve on the accuracy of the RT-PCR test. Substantial work is underway to apply optical methods that could meet these criteria. Efforts are concentrated in surface-enhanced Raman spectroscopy (SERS) and attenuated total reflection Fourier transform infrared spectroscopy (ATR-FT-IR). We briefly review progress of both methods in this article.

Surface-Enhanced Raman Spectroscopy (SERS)

In traditional Raman spectroscopy, the efficiency of the Raman effect is quite small; it is often cited that only one in a million of the incident photons scatters with a Raman shift, which creates quite a challenge for sensitive detection. In SERS, the presence of a localized surface plasmon on particular conducting surfaces, such as gold or silver nanoparticles, enhances the Raman effect. With SERS, Raman scattering intensity can be increased by a factor of 10

 or more, resulting in single-molecule detection.

Recently published results indicate that reagent-free SERS on saliva samples has promise. For example, a team of Italian researchers tested approximately 90 subjects that were divided as equally as possible between Covid-positive, Covid-negative, and control subjects. Saliva samples were collected and centrifuged (to remove food debris), then 3 μL of the saliva samples were deposited on a glass slide covered in commercially available aluminum foil to promote the SERS effect. A Horiba Raman microscope was used with a 785 nm laser to collect the SERS spectra. The averaged spectra from the control, Covid-positive, and Covid-negative subjects are shown in Figure 1. When spectra were analyzed using a multivariate chemometric model and a convolutional neural network, the method could distinguish between the three types of subjects with 84% sensitivity and could detect Covid-positive subjects 92% of the time (3). One disadvantage of this study was that it was conducted on an older population with a large number of patients who were critically ill and who, upon imaging, exhibited symptoms of pneumonia.

FIGURE 1: Differences in control (black) Covid-positive (red), and Covid-negative (green) SERS spectra of saliva, from reference (3). Shared under Creative Commons Attribution 4.0 International License.

A follow-up study by a team of Canadian researchers took a wider cohort of the population. Their population sample size consisted of males and females between 10 and 61 years old who volunteered to donate samples at a Covid-19 testing clinic. This cohort included asymptomatic volunteers as well as volunteers with and without respiratory symptoms. Similar to the Italian study, the researchers used a Renishaw Raman microscope with the excitation set at 785 nm, and with similar sample preparation. The spectra were then fed through a machine learning pipeline for discrimination. This study could detect Covid-positive males with 79% sensitivity and 75% specificity, and Covid-positive females with 84% sensitivity and 64% specificity (4).

Both of these studies seem to indicate comparable or better performance than the gold-standard RT-PCR tests, possibly without the uncomfortable nasal swab as well. Numerous other research groups are working in this area—for example, Miguel José Yacamán’s group at Northern Arizona University and Andrea Tao’s group at University of California San Diego. Several companies are working on clinical trials and preclinical trials, including Botanisol Analytics from Phoenix, Arizona, in cooperation with Ocean Insight, and GreenTropism from Paris, France, in cooperation with Horiba France SAS. The combination of a lateral flow immunoassay (which has limited sensitivity) with SERS has been suggested as a modality to optimize rapid point-of-care Covid detection (5).

Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FT-IR)

Given the success of Raman spectroscopy methods for Covid- 19 detection, it is natural to think that FT-IR spectroscopy may also find success in determining an individual’s Covid status. Recently, Kitane and others (6) investigated this possibility, using RNA extracted from nasopharyngeal samples. In their study, Kitane and others took 3 μL of extract that was directly scanned with a Jasco 4600 ATR-FT-IR spectrometer using a single-reflection ATR. Spectra collected at 2 cm

 resolution in the range of 600–4500 cm

 are then processed using a variety of machine learning techniques. The authors note that none of the features in the spectra could be directly attributed to Covid status. A subset of carbon- and phosphorous-related features were used to separate positives and negatives.

The results of the study, conducted on 280 samples (100 Covid-positive, 180 Covid-negative) of mostly asymptomatic males and females between the ages of 11 and 67 years old indicate that ATR-FT-IR has an estimated 98% accuracy, 97% sensitivity, and 98% specificity. These statistics are similar to clustered regularly interspaced short palindromic repeats (CRISPR)-based statistics. The authors suggest that the total analysis time could be as little as 1 to 1.5 min. The limit of detection of 10 RNA copies per milliliter is also competitive with CRISPR and the low-end of RT-PCR detection limits.

There are a number of other optical methods for Covid detection that are not potentially as fast as SERS and ATR-FT-IR, but may be useful because of their specificity. For example, a tagged aptamer (short single-strand RNA or DNA sequence) can bind to specific proteins, such as the spike protein on Covid-19. The aptamer can then be detected using a number of methods, such as bilayer interferometry, plasmon resonance, or SERS (7). Similarly, particular nanoparticles can be conjugated to Covid-19 antibodies. Optical detection of the particles once they have attached to the Covid virus is then possible. Likewise, fluorescence of probe molecules or tip-enhanced Raman spectroscopy (TERS) have shown high specificity and low detection limits. Unfortunately, none of these methods have been made rapid enough to potentially qualify as a point-of-care diagnostics, as sample preparation and measurement times are typically measured in hours.

Both SERS and ATR-FT-IR have substantial potential as next-generation Covid-19 detection methods. Key attributes include high accuracy, sensitivity, and specificity, combined with speed and ease of use. As the Covid-19 virus becomes increasingly endemic, we should hope that our Covid-19 testing methods become both less intrusive and more accurate. Collecting saliva samples is much easier and more comfortable than nasal swabbing, for example. Even the RT-PCR gold standard has a non-negligible error rate at present.

If these optical methods provide the speed and accuracy in the field that has been shown in the laboratory, expect to see rapid adoption of SERS and ATR-FT-IR Covid test protocols in point-of-care situations as favorable clinical trial data are released. Furthermore, the machine learning algorithms employed will continue to evolve and improve with additional testing data, and the possibility exists to detect new Covid variants as well. Efforts within the photonics community toward Covid detection are well-placed, and the evolution of these methods over a short amount of time represents a great achievement!

, 4943 (2021). https://doi.org/10.1038/s41598-021-84565-3

, preprint. https://doi.org/10.1101/2021.09.21.21262619

(4), 2974–2995 (2021). doi:https://doi.org/10.1021/acsabm.1c00102

, 16740 (2021). https://doi.org/10.1038/s41598-021-95568-5.

(9), 6404–6413 (2021). https://doi.org/10.1021/acsomega.1c00008.

, is the General Manager of the Applied Systems business at Ocean Insight, an affiliate associate professor at the University of Washington, and has started and advised numerous companies in spectroscopy and in applications of machine learning. He has approximately 40 peer-reviewed publications and 6 patents. His work in practical optical spectroscopy, such as LIBS, Raman, and TDL spectroscopy, dovetails with the coverage in this column, which reviews methods (new and old) in laser-based spectroscopy and optical sensing. Direct correspondence to: 

The Covid-19 pandemic has led to extraordinary developments of both testing methods and vaccines. Can spectroscopy play a role in next-generation testing for Covid and other pathogens?

Catching Covid: Rapid Spectroscopic Methods Show Promise

In this study, we proposed a multiscale convolutional neural network (MsCNN) that can screen the Raman spectra of the hepatitis B (HB) serum rapidly without baseline correction. First, the Raman spectra were measured in the serums of 435 patients diagnosed with a HB virus (HBV) infection and 499 patients with non-HBV infections. The analysis showed that the Raman spectra of the serums were significantly different in the range of 400– 3000 cm

 between HB patients and non-HB patients. Then, the MsCNN model was used to extract the non-linear features from coarse to fine in the Raman spectrum. Finally, extracted fine-grained features were placed into the fully connected layer for classification. The results demonstrated that the accuracy, sensitivity, and specificity of the MsCNN model are 97.86%, 98.94%, and 96.79%, respectively, without baseline correction. Compared to the traditional machine learning method, the model achieved the highest classification accuracy on the HB data set. Therefore, multiscale convolutional neural network provides an effective technical means for Raman spectroscopy of the HBV serum.

In this study, we proposed a multiscale convolutional neural network (MsCNN) that can screen the Raman spectra of the hepatitis B (HB) serum rapidly without baseline correction. First, the Raman spectra were measured in the serums of 435 patients diagnosed with a HB virus (HBV) infection and 499 patients with non-HBV infections. The analysis showed that the Raman spectra of the serums were significantly different in the range of 400–3000 cm

Hepatitis B (HB) is caused by infection with the HB virus (HBV), a kind of infectious disease dominated by liver inflammatory lesions. There are approximately two billion people infected with HBV in the world. Every year, more than one million people die of hepatic failure, cirrhosis, and hepatocellular carcinoma because of HBV infection. The incidence of HB is higher in China, and the infection rate is more than 7% (1). Therefore, we adopted the measures of “early detection, diagnosis, and treatment,” because it is the only way to improve the survival rate for HB patients.

Raman spectroscopy is a noninvasive sensitive optical analysis technique based on inelastic scattering. It also provides more molecular structure and specific fingerprint type information. It has been widely used in diagnosing the dengue fever (2), nasopharyngeal carcinoma (3), prostate cancer (4), and gastric cancer (5). Almost all types of diseases, such as cancer and infectious diseases, occur at the molecular level (6). The characteristic peak position and intensity change of Raman spectroscopy provide a basis for diagnosing diseases. Although traditional machine learning methods have been proven to be the main method for extracting feature information from Raman spectroscopy data sets in real-time, these methods require pre-processing of the data. The machine learning classification system based on Raman spectroscopy includes the following sequential preprocessing: removal of cosmic ray interference (7), smoothing, baseline correction, and using principal component analysis (PCA) to reduce the dimensionality of data (8).

The main component of the Raman spectrum background signal is caused by fluorescence, which is several orders of magnitude stronger than the actual Raman scattering (9) and resulting in inaccurate Raman data, which has a severe impact on the classification performance of machine learning. Though many scholars have achieved excellent results in this area, baseline correction remains challenging. Until now, scholars from all over the world have proposed many methods to resolve baseline correction issues, such as the polynomial baseline model (10) and simulation-based methods (11). Leon-Bejarano and others (12) proposed applying the empirical mode decomposition (EMD) method in baseline correction. Lieber and others (10) presented a modified least-squares polynomial curve fitting for fluorescence subtraction, which was shown to be effective. Zhang and others (13) proposed a variant of penalized least squares, called the 

adaptive iteratively re-weighted penalized least squares (AirPLS)

. It iteratively adapts weights controlling the residual between the estimated baseline and the original signal.

Before we lucubrate, we should compare the application of the machine learning algorithm in Raman spectroscopy. Support vector machine (SVM) learning has been widely used in the two-class problem of Raman spectroscopy and a large part of it is related to medical applications. In this context, SVM outperforms linear discriminant analysis (LDA) and 

-nearest neighbor classification (KNN) in the diagnosis of high renin hypertension (14). Although SVM excels in classification accuracy, training a nonlinear SVM is not feasible for large-scale problems involving thousands of classes. Random forest (RF) (15) is a viable alternative to high-dimensional data SVM. RF is an integrated learning method based on multidecision trees, which avoids over-delivering models to training sets. In the decade before the deep learning technique become prevalent, it was essential in the field of machine learning. However, compared with PCA-LDA and PCA-SVM (16), RF does not perform well in the classification of white blood cell subtype Raman spectra. The closest approach to convolutional neural networks is a fully connected artificial neural network (ANN). The sensitivity and specificity of ANN in Alzheimer’s disease identification is more than 95% (17). Unlike convolutional neural networks (CNN), ANN is a shallow architecture that does not have enough capacity to solve large-scale problems. Maquel and others (18) determined the primary grouping of data before conducting multi-layer artificial neural network analysis. However, Yan and others (19) used the convolutional neural network to share convolution kernels and easily process high-dimensional data and identified the sensitivity and specificity of tongue squamous cell carcinoma reaching 99.07 and 95.37%. Wang and others (20) combined serum Raman spectroscopy technology and long-short-term memory neural network (LSTM) to quickly screen HB patients with an accuracy rate of 97.32%, but the author did not consider the impact of baseline correction on the classification of machine learning model and deep learning model.

One disadvantage associated with traditional machine learning methods such as SVM, LDA, and RF is that they all require baseline corrections for Raman spectroscopy and are not easily extended to problems involving a large number of classes. Because deep learning techniques have had enormous success in computer vision, natural language processing, and bioinformatics in recent years, we proposed a network architecture for one-dimensional spectral data classification. Different from current machine learning methods, MsCNN combines pre-processing, feature extraction, and classification into a single architecture. Another important note regarding MsCNN is that it also removes the steps of baseline correction and smoothing.

Preparation of Serum Samples and Determination of Raman Spectroscopy 

In this study, 435 HB patients and 499 non-HB patients from the First Affiliated Hospital of Xinjiang Medical University were included in the study group. Then, 5 mL of peripheral blood samples were collected from each subject to the blood collection tube without any anticoagulant. After the blood was fully clotting, the blood samples were centrifuged at 3000 r/min (1730 g) for 10 min to remove blood corpuscle, fibrinogen, and other components to obtain serum.

We used a laser Raman spectrometer to generate laser light at 532 nm. The spectrum was recorded in the Raman shift range of 400–3000 cm

, and the recording time was 1.0 s. The LabSpec6 software package of Horiba Scientific was used to analyze the Raman spectrum data sets.

All of the above samples were from the First Affiliated Hospital of Xinjiang Medical University. With the approval of the Ethics Committee, the human serum samples were studied.

In this paper, we used the modified residual network structure (21) as the building block of the multiscale convolutional neural network model. The original and our modified building blocks are shown in Figure 1. The modified building blocks are marked as 

. We found that after the quick connection of the original remaining components, removing the modified linear unit and adding the Max-pool unit can improve the convergence speed during training.

FIGURE 1: (a) and (b) respectively show the structure of the original residual block and the modified residual block. For blocks with strides, the original residual block uses a convolution operation with a stride of two to align the output shape. The 

To preserve the fine-level information and simultaneously mine the coarse and intermediate information, the input and output of the network are in the form of a Gaussian pyramid. Compared with the common architecture, the residual network structure can be used to implement a deeper CNN. Because of the addition of the Max-pool unit, the size of the feature map will become half the size of the previous feature map after each Mresblock block is stacked. Therefore, the number of Mresblocks for each scale stack is different, and the number of layers stacked from top to bottom is {5, 4, 3}, and the total number of convolutions of the model is 42. At the front end of the network are three convolutional layers of different sizes (51 × 1, 25 × 1, and 12 × 1), respectively, generating 64 feature maps, and extracting spectral features of different input scales by stacking Mresblock. In addition, the information from the coarsest level output will be passed to the next stage, learning more detailed features, using up-convolution to learn the appropriate features helps to remove redundant information (22). Then, the extracted different scale features are feature-fused, and the best features learned by each scale are retained. Finally, the classification results are obtained through the fully connected layer and the classification layer. Figure 2 shows the graphical description of the network.

FIGURE 2: Multiscale convolutional neural network architecture. The input to the original spectrum is in the form of a pyramid and each scale is trained. At the time of testing, the raw spectrum size was entered.

Multiscale CNN for Raman Spectral Data Classification

The input of MsCNN in Raman spectral classification is one-dimensional, and the convolution layer is represented as follows:

 is the convolution kernel, the size of the convolution kernel in Mresblock is 3 × 1; “*” is the convolution operation; and 

We use the activation function ReLU (23), which is defined as:

Max-pooling is a further operation of spectral data, which can reduce program parameters and retain important features.

 represent the input and output of the pooling layer, and 

The fully connected layer follows the convolutional layer, and finally, the number of outputs from the Softmax layer is equal to the number of classes considered. In the fully connected layer, we use ReLU as the activation function. Softmax is a compression function. It can compress a 

-dimensional vector z containing any real number into another 

-dimensional real vector so that the range of each element is between [0, 1], and the sum of all elements is 1.

The coarse-to-fine approach desires that every intermediate output becomes the feature map of the corresponding scale. Therefore, the intermediate output of our training network should form the form of a Gaussian pyramid. The MSE standard applies to every level of the pyramid. The loss function is defined as follows:

The training of the MsCNN was performed using the Adam algorithm, which is a variant of stochastic gradient descent, for 100 epochs with learning rate equal to 1e-3, 

 = 1e-8. The layers were initialized from a Gaussian distribution with a zero mean and variance equal to 0.05. The training was performed on a single Nvidia RTX-2070 GPU.

We tested the MsCNN model on the Raman spectroscopy data set of HB serum. First, we conduct a series of ablation experiments on the original spectral data. Then compared with many machine learning algorithms in the past for spectral recognition. The contrast experiment was divided into two groups. The first group was the pre-processed spectral data, and the second group was the original spectral data. During the preprocessing of data, Savitzky-Golay (with nine points and 5th order polynomial fitting) (24) method was used to smoothen the spectral data. Then adaptive iterative reweighted penalty least squares (AirPLS) algorithm was used to correct the baseline (13). We use principal component analysis to extract key features of the data, retaining 99.99% of the total variance (51 dimensions). During model training, five-fold cross-validation is used to evaluate the model.

The machine learning algorithms involved in the comparison included KNN, RF, LDA, ANN, SVM, CNN, and ResNet. The evaluation indicators of the above algorithms included accuracy, sensitivity, and specificity. Accuracy takes into account both positive and negative probabilities of the model, which is of high global significance. Sensitivity is also called a valid positive rate—the higher sensitivity, the lower likelihood of missed diagnosis. Specificity is also called exact negative rate—the higher specificity, the higher probability of confirmed diagnosis. The proposed MsCNN was applied using Keras (25) and Tensorflow (26). All other machine learning methods are implemented using Scikit-learn (27).

Figure 3 shows the mean spectra and difference spectrum after baseline correction of serum from HBV patients’ samples and non-HBV patients’ samples in the range of 400 cm

. It demonstrates that the mean spectra of HBV and non-HBV samples are almost identical in structure, with only minor changes in the intensity of some Raman bands that can’t be detected by naked eyes. Direct visual interpretation can very likely result in misdiagnosis. There is an urgent need for a method to distinguish the lesion sample from the standard sample accurately.

FIGURE 3: Normalized average spectra and difference spectrum (black) of samples from hepatitis B patients (red) and non-hepatitis B patients (green).

Raman peaks at 426, 509, 875, 957, 1003, 1155, 1280, 1446, 1510, 1652, 2307, 2660, and 2932 cm

 can be observed in both groups, with the most energetic intensities at 1003, 1155, 1510, and 2932 cm

. These intensity differences in the Raman spectra can be viewed more clearly at the bottom of Figure 3 that means, there is excellent potential for identifying HBV patients and healthy person using Raman spectra of serum.

We conducted a series of ablation experiments on the original data set. These data retained 99.99% of the total variance (51 dimensions) through principal component analysis. The first scale is 51 dimensions, and the remaining scale is 1/2 of the previous scale. Specific experimental results are shown in Table I.

As can be seen from Table I, when using two different scales (line 2 of Table I) to classify the primitive data, the performance is better than the model using the same scale, and its accuracy, sensitivity, and specificity are 96.11%, 95.37%, and 95.79%, respectively. In addition, in the experimental comparison of different scales, the overall results of the MsCNN model using three scales are superior to other scales. Therefore, we ended up using three different scales of MsCNN as the model in this paper.

Baseline Correction Spectral Classification

We evaluated our MsCNN method on a HB data set. These spectra had been baseline corrected, and cosmic rays had been removed too. Then, we followed the pre-processing techniques and evaluation criteria described in Section 2.5. The experimental results are shown in Table II.

It can be seen from Table II that the KNN model has the worst classification effect, with an accuracy rate of only 78.07%. The ANN model has the highest sensitivity (98.00%), but as the number of neurons increased during the training process, the slower the training speed and the lower the efficiency. ResNet uses its deeper network structure to mine more information, and the accuracy is up to 96.31%, which is 1.52% higher than the ordinary CNN model. As one of the most commonly used machine learning classification models, SVM has an accuracy rate of 94.25%, which is 2.27% and 6.55% higher than the accuracy of the RF and LDA models, respectively. In addition, the use of deep learning methods to identify baseline-corrected spectra is better than previous machine learning methods. The accuracy of the MsCNN model is the highest among all models (96.79%), which demonstrates the effectiveness of the improved Mresblock module in this article. And it proves that the deep learning method has great potential in extracting Raman spectral feature information at the same time.

To understand the MsCNN model better, we also studied the model prediction, especially those were not consistent with the correct labels. Figure 4 shows the spectra of the HB patient (shown in red) and the non-HB patient (shown in green) that have the most accurate model predictions. It also shows the spectra with wrong predictions. We found that the processed data has a highly similar spectral structure. By observing the two correctly classified spectra, we saw that the Raman peak of HB patient is much higher in value than non-HB patient. Performing similarity analysis from the peak, MsCNN model may classify HB patient in group B as non-HB patient. Because of the predicted spectrum, B has a high peak similarity to the correctly classified spectrum in A. These plots demonstrate that the MsCNN can match the peaks characteristic of a particular species even when the prediction did not agree with the correct label.

FIGURE 4: Examples of successful and unsuccessful classification. In the figure, the green (a) and red (b) spectra in the first row represent the predicted spectra of non-HB patients and HB patients, respectively. The second and third rows are the corresponding category spectra that were incorrectly predicted. The prediction score is also shown in each figure, reflecting the confidence of the prediction.

The results show that MsCNN is able to achieve significantly better accuracy compared to other conventional machine learning methods on the baseline corrected spectra. In this section, we explored the performance of MsCNN on data without baseline processing. Before the experiment, except for RF, only 99.99% of the total variance of PCA had been used to reduce the data dimension, because we found that PCA reduces the performance of RF. The experimental results are shown in Table III.

As can be seen from Table III, the deep learning method has a great advantage in processing the original high-dimensional data. Analyzing and contrasting the results in the “Multiscale Ablation Experiments” section and the experimental results in this section, we can observe that baseline correction significantly improves the performance of all the conventional methods by 4–5%. That is because the baseline correction removes the effects of signal compression and other noise from natural bioluminescent vectors. On the other hand, the performance of MsCNN drops by approximately 0.5–2% when combined with baseline correction methods. This result may indicate that MsCNN can learn the technique of dealing with baseline interference more effectively than the baseline correction method and retain more identification information. The use of coarse-to-fine methods for training in multi-scale spaces is better suited for Raman spectroscopy.

Here, we reported the application of the Raman spectrum of serum combined with multiscale CNN methods for HB patients screening. Through the analysis of the mean spectrum of HBV infection and non-HBV infection groups, we found that peak changes in Raman spectra of serum may be related to many vibrational modes of various biomolecules such as lipids, proteins, and nucleic acid molecules. Table IV lists tentative assignments for the dominant spectral bands according to the previous literature (28).

For some peaks, such as 509, 957, 1003, 1155, 1510, and 2660 cm

, the intensities of the pathological samples are higher than the standard samples. An increase in the intensity of Raman peak at 509 cm

 is because of cysteine. Cysteine is a sulfur-containing amino acid. If its value is too high, it may cause abnormal liver function, resulting in severe functional disorders such as hepatocyte synthesis and metabolism. When liver cell damage is mild, the healthy metabolic function of the liver cell will not cause the change of cysteine level (29). Raman peak at 2660 cm

 is assigned for amino acid methionine, in the literature (30), the researchers found that plasma fragrant amino acids and methionine levels were significantly elevated in patients with liver disease. Raman peak at 957 cm

 is because of cholesterol. High cholesterol content will further damage the liver of HB patients, increases the burden on the liver, causes fat metabolism disorder, and affects the digestive function of HB patients, this is consistent with the conclusion that the serum cholesterol levels of patients with coronary heart disease and liver disease are relatively elevated in the literature (31). Raman peak at 1510 cm

 is mainly because of the ring breathing mode of DNA bases (28). The Raman peak of Phenylalanine in serum albumin is 1003 cm

. An elevated level of Phenylalanine is most likely because of dysfunction of the liver in HBV infected patients (32,33). Increases in these intensities of Raman peaks indicate the augment of albumin concentration in the blood of HBV infected person. The peak at 1155 cm

 belongs to the vibration of carotenoids (34). It can be seen from Figure 3 that the carotenoid content in the blood of patients with HB is much higher than that in non-HB patients. Carotenoids are an antioxidant that plays an important role in protecting cells and organisms from free radicals (ROS) and reactive nitrogen (RNS) free radicals. Li and others (35) showed that the application of antioxidants is a reasonable treatment strategy for the prevention and treatment of liver diseases. Therefore, the content of carotenoids in patients with HB is much higher than that in patients with non-HB. It may be to relieve liver cirrhosis and even liver cancer in patients with HB.

In contrast to the above, Raman bands 875, 1280, 1446, 1652, and 2932 cm

, decreased intensities, have been observed for the diseased sample. A peak at 875 cm

 is because of antisymmetric stretch vibration of choline group N

. Phosphatidylcholine has the effect of repairing an alcoholic liver injury, fatty liver, and preventing cirrhosis (36). Non-HBV patients have a slightly higher peak at 875 cm

 than HBV patients, probably because phosphatidylcholine metabolism is relatively active in non-HBV (37). Likewise, the intensity of Raman peak assigned to lipids owning to the C=C stretch (28) also decreased at 1652 cm

 are because of C-H stretching and assigned for amide III. A decrease in the peak intensity at 1280 and 2933 cm

 shows decrease concentration of β-sheet in HBV infected patients, which is in agreement with the existing literature that reported that a decrease in beta structure compared to the helical structure in the amide III region is an indication of interferon secretion by lymphocytes as an immune response against HBV infections (38).

In this study, we proposed a Raman spectral classification method, which not only has an excellent performance but also avoids baseline preprocessing, for MsCNN. Our approach has been validated on the HB data set and has proven to be largely superior to other traditional machine learning methods. Although we focused our study on Raman data of HB serum, we believe the technique is also applicable to Raman spectroscopy of serum for other diseases. We speculate that Raman spectroscopy of serum has similarities, and fine-tuning our network can make it very practical to classify new problems. This process is called 

, which has been widely used in image recognition and natural language processing.

This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61765014); Reserve Talents Project of National High-Level Personnel of Special Support Program (QN2016YX0324); Urumqi Science and Technology Project (No. P161310002 and Y161010025); and Reserve Talents Project of National High-level Personnel of Special Support Program (Xinjiang [2014]22).

The authors declare no conflicts of interest.

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A multiscale convolutional neural network (MsCNN) was used to screen Raman spectra of the hepatitis B serum, achieving higher classification accuracy compared to traditional machine learning methods.

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