Jerome Workman, Jr.

Artificial intelligence (AI), and its subfield machine learning (ML), are major buzzwords in today’s technology world. In a two-part series, let’s begin to take a look under the hood and behind the scenes to see what AI is and its applicability to analytical chemistry and spectroscopy for future discussion and elaboration. What are the benefits and limitations, as well as the praises and detractions, of AI? How does AI relate to chemometrics?

As with any modeling technique, it might serve us well to remember Robert A. Heinlein’s reference: “There ain’t no such thing as a free lunch (TANSTAAFL)” (1), and supercalibratestatisticexpedientalgorithmadnauseous, a play-on word inspired by the wisdom of Mary Poppins (2), reminding us about the hype of new algorithms. The term was coined to mock the then-current practice of trying all-possible combinations of available algorithms and test statistics to automate the process of developing calibration models for near-infrared (NIR) analysis. It took a long time, but eventually, the NIR community recognized the futility of that methodology. Remember, if you are going to make mistakes, automation allows you to make them more quickly and repetitively. One must be cautioned when using powerful and automated regression-based methods to remember that modeling algorithms are only able to build an accurate predictive model based on the information they are presented. As Mark Twain once said, “There are three kinds of lies: lies, damn lies, and statistics” (3–5). And as Rasmus Bro, a leader in the field of chemometrics, has said:

“As most people are aware, there is currently a hype on machine learning and artificial intelligence (AI), and that is all fine and good. Part of the hype is that these methods are sometimes oversold, and so then people are relieved that now they don’t have to think about things, saying, ‘I don’t have to think about experimental design, sampling error, analytical quality, etc.’ And when that happens, those projects typically fail, because these new and excellent methods don’t actually replace the responsibility for you to actually know what you are doing” (6).

With all these precautionary tales in mind, we now move ahead with our initial overview discussion of AI and chemometrics.

Basically, AI and machine learning (ML), as they are used today, consist of applying signal processing and multiple layer neural networks to model a function or set of functions that has been loosely associated with the intelligent activity of living organisms. The same challenges for building successful models may be present in all modeling activities, including chemometrics; these may include sparse or limited data, noisy data, unknown limits to the potential data and predicted values, a lack of basic understanding of the nature of the data, and so forth. In chemometrics, it is generally accepted that a chemist or chemical engineer is deriving (or at least interpreting) a quantitative, qualitative, or classification model for a chemical system, and therefore has domain knowledge of the nature of the data and the expected model prediction results. 

, or fundamental knowledge of the system one is working with, is essential to avoid major errors, using computer modeling. That is why, if one is using AI to build a self-driving automobile, it would be very handy to have expert drivers involved during the modeling process. Without domain knowledge, one will undoubtedly see major errors in relying on models generated without such knowledge used as input information. These caveats also apply to modeling weather (where one asteroid or erupting volcano changes everything), the future economy (with the potential for wars and famines), and other unbounded or somewhat unknown domains.

For an “official” AI response, we asked the question, “How are machine learning and chemometrics related?” to the program Chat.OpenAI.com (7), an AI program in development. The AI answer is transcribed below. Note that as the AI program “learns,” its responses will change over time.

“Machine learning and chemometrics are both fields that involve the use of statistical and computational methods to analyze and interpret data. However, they are applied in different contexts and for different purposes. Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that can learn from and make predictions or decisions based on data. These algorithms and models are trained on a set of labeled data, which consists of input data and corresponding output data, and are then used to make predictions or decisions about new, unseen data. Machine learning methods are widely used in a variety of applications, including image and speech recognition, natural language processing, and predictive modeling.

Chemometrics, on the other hand, is the application of statistical and mathematical methods to the analysis of chemical data. It is used to extract meaningful information and knowledge from chemical data sets, such as those obtained from spectroscopic or chromatographic measurements. Chemometrics methods are used to analyze and interpret chemical data in order to gain insights into the underlying chemical processes and to make predictions 

about the properties and behavior of chemical substances.

Both machine learning and chemometrics involve the use of statistical and computational techniques to analyze and interpret data. However, while machine learning is generally applied to a wide range of data types and applications, chemometrics is specifically focused on the analysis of chemical data.”

As with many technical matters, it is useful to begin by discussing the various concepts involved with the subject of interest, and compose a list of (and a set of definitions for) those terms. If one is to develop AI technology and ML algorithms, it is helpful to know what 

 are. These concepts seem self-evident or intuitive to sentient beings; however, clear definitions of terms are helpful for classification and understanding. There have been many attempts to precisely define 

 from Merriam-Webster includes the following:

1) “...(T)he ability to learn or understand or to deal with new or trying situations: Reason; also: the skilled use of reason; and 2): the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by objective criteria (such as tests)” (8).

The definition of intelligence from Oxford Learner’s Dictionaries is

“...(T)he ability to learn, understand, and think in a logical way about things; the ability to do this well....” (9).

A definition of AI from the Oxford English Dictionary is as follows:

“(T)he capacity of computers or other machines to exhibit or simulate intelligent behavior; the field of study con- cerned with this. Abbreviated AI” (10).

The often-referred to Wikipedia source has a more detailed definition:

“AI is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans....”

Wikipedia notes that older definitions of AI referred to machines that mimic human cognitive skills, such as learning and problem solving. A more modern definition if AI uses phrases such as “machines that act rationally,” which somewhat muddies the waters in terms of a precise definition (11). We might summarize the various ideas about a definition of AI, for our purposes, as:

“(A)n automated technology using computers and algorithms that is capable of simulating learning, thought, creativity, rationality, and analysis; and that is able to perform routine or complex tasks that have been historically accomplished by humans.”

ML may then be defined as a subfield of AI where computers (machines) are programmed in such a way that they are able to self-iterate and optimize problem solving for modeling data in an automated way, using computational algorithms specifically designed for such purposes. Let us be frank here: the perceived intelligence or learning is a computer simulation using automation and optimization routines of computer algorithms—no more and no less. Once programmed with appropriate algorithms, the computer (machine) is capable of simulating problem solving and learning functions that are normally attributable to humans.

The foundation of learning AI is to understand the variety of ML (deep learning) algorithms, and then learn how to apply these ML algorithms for various applications. Next, one may design a user interface to interact with the AI programming. The main subfields involved include using the various models for defining an AI problem, learning how to design AI programs, deciding how humans will interact with the AI programs, and learning how to modify and teach an AI program. One may add here that it is also important that experts evaluate or check the output of an AI program to test its legitimacy.

It may surprise those analytical chemists completely unfamiliar with AI that many of the chemometrics (multivariate) algorithms they are familiar with are also classified as AI algorithms—these are algorithms that are routinely taught and used by AI programmers. We refer our readers to the following articles for a head start on this discussion (12–14). This previous series of articles provides definitions and a set of references for signal preprocessing, component analysis, quantitative (calibration) methods, qualitative (classification) methods, and programming platforms.

Table I shows a comparison of the chemometrics algorithms and the AI and ML algorithms as derived by the authors. Table II is a comparison table generated by an AI program (Chat.OpenAI.com) that compares AI and ML algorithms with chemometrics algorithms using its own AI terminology.

Figure 1 shows a basic flow chart of commonality between AI approaches and chemometric modeling. Note that any modeling system is only capable of accurately modeling the data it has been shown or data that follow a logical framework, such as game theory. Within this limited framework, modeling techniques are able to interpolate within its known boundaries and generally produce an accurate result, or at least, a result with a bounded estimate of error. If the modeling system has not seen the data, or if there is not a rational or known framework for potential results, then the results are merely an automated or computerized guess.

FIGURE 1: Flow chart of commonality between model optimization in AI approaches and chemometric modeling. A finished model must include all possible data variables to be a closed system (interpolation) or be forced to predict outside the modeling data space with potential for greater error (failure to predict accurately) if in an open system (extrapolation). The red rectangle illustrates the “never-ending” process of collecting more data when modeling applications try to predict “open systems.” The iterative process of modeling, testing, and adding additional data is illustrated. Note that the algorithms and modeling steps may also be varied with more modeling iterations.

The concept of an open or closed model is helpful at this point, because we may imagine what could happen when we model a constrained data set and when the nature of the entire potential data set is not well understood. As Figure 2 illustrates, the limitation of models using powerful fitting algorithms is the “completeness” of the data used and the assumptions made about the nature of the data. Modeling “uncharted” data in science can be, and often is, problematic and unpredictable. Model prediction outside of a closed system is based on hypothesized assumptions about the nature of the data. If the nature of the data and the assumptions about the data are unknown or not clearly understood, then the ability of any modeling system to predict a result accurately is also unknown. Therefore, caution when using extrapolation in a modeling system is wise.

FIGURE 2: Illustration of closed and open model data showing commonality between AI approaches and chemometric modeling. Data shown only in three dimensions (D1, D2, D3). A finished model must include all possible data variables to be closed (interpolation) or forced to predict outside the modeling data space with potential for greater error if open (extrapolation). Note that a model based on fitting to the black dots would not necessarily accurately predict either the green dots or red dots. The limitation of models using powerful fitting algorithms is the “completeness” of the data used and the assumptions made about the nature of the data. Modeling “uncharted” data in science can, and often is, problematic and unpredictable. Model prediction outside of a closed system is based on hypothesized assumptions about the nature of the data.

One can imagine that game playing is a good example of what a modeling system or AI could easily master, because the universe of possible game moves is well-known and constrained to a finite set of options. With the high speed of computation possible in today’s computers, a modeling system is able to compute all possible combinations of moves and describe the best option (or options) available based on probability, separate decision algorithms, or a competitor’s responses to its moves. It would seem that a well-written AI or computer system would be the master of game playing and associated tasks. A next level of complexity may arise in self-driving automobiles, but this activity is still fairly well-defined under normal circumstances. That being said, there are occasionally extreme circumstances, such as weather, unusual sky or light reflections, sudden objects appearing on the roadway, multiple car collisions, and so forth, that would be extremely difficult to model and negotiate.

AI computers would also seem to be able to master inventory, logistics, traffic control, basic war games, and prediction of basic human behaviors—all with relatively constrained circumstances. Even a form of pseudo-creativity would seem to be relatively straightforward with access to the Internet and computerized data processing. For new inventions, a computer system is capable of reviewing the prior patent art and literature, evaluating the known physical laws and basic science regarding the subject, and then “suggesting” potentially novel ideas not yet tried. However, issues of compassion, love, sacrifice, selflessness, and generosity would be extremely difficult to model; these are also factors that weigh in heavily for positive human decisions.

So let us get back to the task at hand, which is to discuss AI for applications in analytical spectroscopy. Potential uses would be for instrument alignment (instrument calibration) and diagnostics, instrument measurement conditions and parameter optimization, experimental design, signal preprocessing, quantitative and qualitative calibration, library searching, and automated imaging and image enhancement functions.

There are several steps in developing both AI and chemometric models that are basic and somewhat unchangeable. These would include the following steps, which can be accomplished either manually or in an automated fashion:

1) Define the problem that needs to be solved.

2) Define the type and amount of data required to train the model.

8) Repeat the process to optimize the model .

There are three main skill sets that are essential in learning how to work with AI. They are as follows:

1) Learn how to operate basic software tools (such as Python), language, and syntax, and the types of algorithms needed.

2) Learn the basics of machine learning algorithms: regression, k-nearest neighbors (KNN), support vector machine (SVM), artificial neural networks (ANNs), reinforcement learning methods, computer vision tools, and so forth.

3) Learn how to specifically solve the problems you are interested in by researching other examples and those researchers already problem solving in your area of interest.

In Part I of this two-part set of introductory articles on AI, we have attempted to give a very concise and top-level overview of AI, a technology topic that is now taking the world by storm. AI discussions are now ubiquitous across media, politics, business, academia, and especially in science and engineering. There is so much more to be said, but we have here brought an introduction to a topic we may continue to explore through additional articles and social media, such as in the “Analytically Speaking” podcast series, Episode 9, featuring Rasmus Bro as the guest speaker (6). In addition, in Part II of this written series, we plan on exploring actual applications of AI and ML to different vibrational spectroscopy methods, such as Raman, Fourier transform infrared (FT-IR), NIR, and UV–visible (UV-vis) spectroscopic techniques.

 (John Wiley & Sons, New York, NY, 1991), pp. ix.

 (University of California Press, Berkeley, CA, 2010), p. 228. (Attributed to Twain dictating this to his secretary in Florence, April, 1904, in “Notes on “Innocents Abroad”). 

http://gutenberg.net.au/ebooks02/0200551h.html

https://www.york.ac.uk/depts/maths/histstat/lies.htm

(5) Velleman, P.F. Truth, Damn Truth, and Statistics. 

https://doi.org/10.1080/10691898.2008.11889565

(6) Spectroscopy, Analytically Speaking Podcast, Episode 9, Feb. 1, 2023. 

https://www.spectroscopyonline.com/analytically-speaking-podcast 

(7) OpenAI, Build Next-Gen Apps With OpenAI’s Powerful Models. 

(8) Merriam-Webster Dictionary, Intelligence. 

https://www.merriam-webster.com/dictionary/ intelligence#:~:text=1%20%3A%20 the%20ability%20to%20learn,noun 

(9) Oxford Learners Dictionaries, Intelligence. 

https://www.oxfordlearnersdictionaries.com/us/definition/english/intelligence

(10) Oxford English Dictionary, Artificial intelligence, n. 

https://www.oed.com/viewdictionaryentry/Entry/271625

(11) Wikipedia, Artificial Intelligence. 

https://en.wikipedia.org/wiki/Artificial_intelligence

(12) Workman, Jr, J.; Mark, H. A Survey of Chemometric Methods Used in Spectroscopy, 

(13) Workman, Jr, J.; Mark, H. Survey of Key Descriptive References for Chemometric Methods Used for Spectroscopy: Part I, 

(14) Workman, Jr, J.; Mark, H. Survey of Key Descriptive References for Chemometric Methods Used for Spectroscopy: Part II, 

serves on the Editorial Advisory Board of Spectroscopy and is the Senior Technical Editor for 

. He is also a Certified Core Adjunct Professor at U.S. National University in La Jolla, California. He was formerly the Executive Vice President of Research and Engineering for Unity Scientific and Process Sensors Corporation.

 serves on the Editorial Advisory Board of Spectroscopy, and runs a consulting service, Mark Electronics, in Suffern, New York. Direct correspondence to: 

There were some errors in the October 2022 Chemometrics in Spectroscopy column that evaded detection until after the column was published (1). Therefore, we hereby publish these corrections:

 was given as 10000; this number contains one more zero than the correct amount. The correct expression for 8

In the first text column on page 20, under “Errors in Representation,” there were several instances of inserting the incorrect subscript to define the number system the specified number belongs to. For example, in the passage“. . . 2

 = 0.25 is too small...” it is clear that the digit 2 cannot belong to a binary number, since a binary number can only contain the digits 0 and 1, as we stated several times. The correct statement is “. . . 2

The same error occurred several times over the subsequent six lines. Instead of belaboring each occurrence of the error, here we simply present the corrected passage, starting with “for example”:

, which is again too large. We note that 0.0101

, which is still too large, while 0.01001

, is again too small. Finally, we are closing in on 0.3

In Table III, the last two entries under the column heading “Truncated Decimal Number” simply repeat the second-to-last entry instead of reflecting the continuing truncation of pi. The correct table entries are 3.141000000000000

(1) Mark, H.; Workman, Jr, J. Decimal Versus Binary Representation of Numbers in Computers. 

https://doi.org/10.56530/spectroscopy.mm1179p4

Articles

Are you intrigued by artificial intelligence, but unsure what it really means for analytical chemistry? Read on.

Artificial Intelligence in Analytical Spectroscopy, Part I: Basic Concepts and Discussion

This year’s Atomic Spectroscopy Award recipient is Andreas Riedo. For the past decade, Riedo’s research has focused on the development of fundamental measurement methodologies using miniaturized laser ablation–desorption ionization mass spectrometry (LIMS or LMS) for the chemical (elements, isotopes, and molecules) analysis of complex mineral surfaces, including semiconductor crystalline solids and materials found in space exploration.

Andreas Riedo of the Physics Institute at the University of Bern has won the 2023 Emerging Leader in Atomic Spectroscopy Award, which is presented by 

 magazine. This annual award, begun in 2017, recognizes the achievements and aspirations of a talented young atomic spectroscopist, selected by an independent scientific committee. The award will be presented to Riedo at the 2023 European Winter Conference on Plasma Spectrochemistry (Ljubljana, Slovenia, January 29–February 3, 2023), where he will give an award lecture.

Andreas Riedo of the Physics Institute at the University of Bern, Switzerland.

Riedo’s research has focused on the development of fundamental measurement methodologies using laser ablation (LA) and laser ablation–desorption ionization mass spectrometry (LIMS) for the chemical analysis of complex solid materials and organic molecules that are related to space research and astrobiology.

Riedo received his PhD in physics in 2014 and has been actively involved in the field of space research and planetary exploration, focusing on the design, development, and qualification of space-capable analytical instrumentation. His interest and work in the field of laser-based MS began during his PhD studies with Prof. Peter Wurz at the Physics Institute in the division of Space Research and Planetary Sciences at the University of Bern. As a graduate student, Riedo optimized an existing LIMS system intended for in situ space research. The instrument was enhanced as a result of his work to perform analysis for quantitative element composition, isotope abundances, depth profiling, and two- and three-dimensional (3D) chemical imaging and analysis.

As a postdoctoral researcher, Riedo specialized in applying LIMS for potential astrobiology applications. Specifically, this research seeks to identify chemical biosignatures that may indicate past or present occurrence of life forms in extraterrestrial environments. To further advance the techniques required for detection of current extraterrestrial life, Riedo has measured carbon, hydrogen, nitrogen, oxygen, phosphorus, and sulfur (CHNOPS) biomarkers in selected terrestrial desert soil samples. He has demonstrated single-microbe detection in such soil samples for sparse life identification. In addition, he has shown that sensitive LIMS is capable of accurate isotope ratio analysis of sulfur isotopes, which are an important class of potential life biomarkers.

After his postdoctoral work, Riedo built his own laboratory as an indepen- dent scientist at the University of Bern, where he has applied laser desorption mass spectrometry to detect complex molecules potentially relevant for Earth-like lifeforms. These molecule types include polycyclic aromatic hydrocarbons (PAHs), lipids, and amino acids. Currently, Riedo is a scientist and project manager for the Neutral gas and Ion Mass spectrometer (NIM) instrument, which is part of the scientific payload of the Jupiter’s Icy Moons explorer (JUICE) space mission of the European Space Agency (ESA).

The Laser Mass Spectrometry Laboratory, an ISO 5 cleanroom, in spring 2022, showing Andreas Riedo in the foreground and his PhD student Peter Keresztes Schmidt in the background. They are working on the refurbishment of the ion-optical part of the LIMS (LMS) instrument. The detector unit with the bottom part of the ion-optical elements is directly in front of them, and the top part of the ion-optical instrument is to the front-right (the metallic tubular structure with the white ring on top). (Photo courtesy of Peter Wurz).

Riedo at work in The Laser Mass Spectrometry Laboratory in 2022. (Photo courtesy of Andreas Riedo).

Developing and Refining Instrumentation to Advance Astrobiology: a Miniature Laser Ablation Time-of-Flight Mass Spectrometer

Riedo has gained significant recognition in the field of experimental astrobiology. He was invited to join the studies group for instrumentation that will be part of the National Aeronautics and Space Administration (NASA) Europa Lander. His instrument project for a potential future mission would look for signs of life on the surface of Europa, one of Jupiter’s moons. Europa is slightly smaller than Earth’s moon and is primarily made of silicate rock with a water-ice crust. He is also a project manager for two LIMS instruments planned for NASA’s Artemis program on the moon. The Artemis program will land the first woman and next man on the moon by 2024 and test new technologies and approaches to gather information for sending astronauts to Mars. In addition, his Organics Information Gathering Instrument (ORIGIN) has been selected for a mission to Venus (8). “The ORIGIN space instrument is a miniature laser desorption–laser ablation ionization mass spectrometry (LIMS) instrument designed to detect large, non-volatile molecules, specifically biomolecules such as amino acids and lipids; as well as to be used in ablation mode for elemental composition analysis of extraterrestrial materials. Being able to successfully achieve an innovative system to be included on a Venus Life Finder Mission is a significant contribution,” said Jorge Pisonero of the University of Oviedo in Spain.

“Dr. Andreas Riedo designed and constructed independently a novel mass spectrometric setup based on laser desorption mass spectrometry,” Pascale Ehrenfreund from George Washington University said. “Riedo’s instrument allows the direct desorption of biomolecules that are highly relevant for life detection on planetary objects other than Earth.” When addressing Riedo’s most important contributions, Frances Westall from the CNRS-Centre de Biophysique Moléculaire in Orléans, France said. “His space-developed instrumentation is capable of providing the essential context information from a rock sample potentially hosting carbonaceous traces of life, then analyzing these specific traces.”

The AbSciCon Conference in May 2022 showing Riedo (right) with PhD student Nikita Boeren (left). (Photo courtesy of Andreas Riedo).

Developing the Miniature Laser Ablation Time‐of‐Flight Mass Spectrometer

Indeed, colleagues and the community at large are impressed by Riedo’s work on instrumentation for space exploration. “Riedo has proven his abilities to develop innovative new methods to study samples from other planetary environments,” notes Charles Cockell, a collaborator from the University of Edinburgh. Riedo’s paper reporting on the analytical performance of a miniature LMS designed for in situ investigations of planetary surfaces is his most cited paper to date (1) (Table I). The high sensitivity and high spatial resolution mass spectrometer for this instrument was designed for elemental and isotopic analysis of naturally occurring materials. An UV laser, rather than infrared (IR), was used for sample ablation with an irradiance of ~1 GW/cm

. Implementing this laser achieved stable ion formation with low sample weight loss. This instrument exhibited good reproducibility because of the use of computer control of the experimental parameters, such as laser ablation characteristics, ion-optical parameters, and sample position. The dynamic range of the instrument was reported to be more than eight decades, and the detection sensitivity for both metallic and non-metallic elements was excellent. Abundance down to tens of parts per billion (ppb) was demonstrated together with detailed isotopic patterns. This instrument has demonstrated improved performance over established instruments in planetary in situ research. “This very compact instrument shows much better performance than instrumentation used so far in planetary research,” said Peter Wurz, Riedo’s doctoral supervisor and mentor at the Physics Institute at the University of Bern in Switzerland said. “None of the established instruments in planetary in situ research has such performance.”

As part of this work, Riedo and his colleagues reported the first mass spectrometric analysis of elemental and isotopic composition of several National Institute of Standards and Technology (NIST) standards using their miniature LMS (2). This research demonstrates improvements to previous LMS systems by varying laser optics, type and wavelength, as well as data acquisition and analysis procedures. The selected femtosecond (fs)-laser ablation ion source used was a 775 nm, 190 fs, 1 kHz laser, which has exhibited reduced thermal effects and negligible sample damage, as well as substantial improvements in instrument performance, as discussed above. This work, which shows the successful introduction of femtosecond lasers in his mass spectrometry instrument, stands out to Wurz. “This paper is exemplary for the continuous development by Andreas Riedo of the analytical improvements he has pursued,” Wurz said.

In 2013, Riedo and coworkers expanded the use of the miniature LMS for precise and accurate measurements of isotope abundances (3). A pulsed UV laser (266 nm, 3 ns, and 20 Hz) was applied for ablation, atomization, and ionization for measurements of several untreated NIST standards and to measure galena, a naturally occurring mineral form of lead(II) sulfide. Relative accuracy and precision for this instrument for these samples was shown to be in the parts-per-thousand level. The results for galena were surprisingly similar to those obtained using a thermal ionization MS (TIMS) system. It was determined that a lower isotope composition in the sample (for example below 100 ppm), resulted in a measured accuracy and precision in the percentage levels. The instrument is able to estimate the age of minerals using measurements of the lead isotope 

Work published from 2017 demonstrated the importance of high-speed and high dynamic range detector systems for optimum resolution and sensitivity time-of-flight MS (12). To achieve the appropriate analytical performance, a multi-anode multichannel plate detector with high-speed data acquisition was tested. This design resulted in a lightweight, low power, compact detector system that was suitable for portable or space payload instrumentation.

Analysis of rock and soil samples on extraterrestrial planets is an important part of upcoming space missions. To that end, Riedo and collaborators reported on the development and testing of a miniaturized LMS for in situ spatial and elemental profiles of a heterogeneous rock sample to a depth of several nanometers (17). This work demonstrated that highly detailed 3D composition information, including oxidation properties could be obtained for natural, heterogeneous rock samples by applying the LMS technology. When combined with Raman spectroscopy, LMS provides even more comprehensive mineralogical analysis details. These details are important for characterization and determination of probe drilling locations that will yield the greatest information on remote planet landscapes.

The Atacama desert, representing a terrestrial Mars analogue site, in September 2019. (Photo courtesy of Andreas Riedo).

Riedo performing field work in the Atacama desert (as a Mars analogue site) in September 2019. (Photo courtesy of Andreas Riedo).

The most recent developments of instrumentation and applications carried out by Riedo and coworkers has been for the detection of lipids, sulfur isotopes, elemental signatures of microbes, and amino acids on extraterrestrial and planetary analogue surfaces (7,9,10–11). These most recent publications are listed in Table II. This research further addresses advanced applications of LIMS, LMS, and Raman spectroscopy for materials characterization, such as 3D chemical imaging, structural analysis of contaminants in electrodeposited metal films, chemical depth profiling of solid materials, and in situ analysis of elemental and isotopic composition of heterogeneous materials, such as meteorites, and terrestrial rocks and minerals.

Riedo and colleagues have demonstrated that chemical imaging of metallic samples is possible using fs LIMS, with results in agreement with inductively coupled plasma–collision/reaction interface–mass spectrometry (ICP–CRI-MS) and laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS). Riedo’s work has been applied to probe the spatial element composition of three ternary Cu–Sn–Pb model bronze alloys (lead bronzes: CuSn10Pb10, CuSn7Pb15, and CuSn5Pb20) (13). These alloys were selected as representing excellent cathode materials used for electro-organic synthesis involving dehalogenation or deoxygenation of pharmaceutical products. The chemical imaging was performed using spotwise measurements, by ablating material from various surface locations using a 4 × 4 raster array (50 μm pitch distance, with each ablation crater diameter of ~20 μm). Major elements (Cu, Sn, and Pb), minor elements (Ni, Zn, Ag, and Sb) and trace elements (C, P, Fe, and As) were quantified and available for constructing elemental image generation of the solid samples.

Chemical Analysis Using a Miniature Laser-Ablation Mass Analyzer

Part of the work being done by Riedo and coworkers to advance instrumentation for space exploration is to ensure its capability to detect potential extraterrestrial life. The detection of the signs of life on other planets involves the analysis of rock and soil samples for chemical and isotopic composition. Riedo and colleagues have described the method and apparatus for analysis of the chemical composition of naturally occurring microfossils using their miniature LMS system approach (5). The reported chemical analysis studies were conducted with high spatial resolution of approximately 15 μm W x 20–200nm H. The purpose of this investigation was to evaluate micrometer-sized samples for isotope abundance, isotope ratios, and element composition—and to summarize the analysis data into accurate chemical compositional maps of these samples. Results for the chemical mapping were successful, noting that an improved signal to noise ratio is required for accurate isotope ratio determination.

An LMS, designed and built at the University of Bern in the Department of Space Research and Planetary Sciences, was used to measure and characterize the elemental composition, mineralogy, and petrology of an Allende meteorite sample (6). This experiment demonstrated the feasibility for applying LMS to analyze rock samples for remote extraterrestrial applications. The mineralogical and volatile element content of the Allende meteorite—the largest carbonaceous chondrite ever found on Earth, composed mainly of silicates, oxides and sulfides, graphite, carbonates and organic compounds, including amino acids—was able to be accurately determined without sample preparation.

Field studies have been conducted by NASA teams in the Atacama desert (as a Mars analogue site), as part of the Subglacial Lake Exploration: Diversity, Geochemistry, Ecology (SLEDGE) project of the European Union to determine if Earth-based microbes of sparse life in soil samples could be detected based on element analysis (10). The purpose of this study was to test and improve the measurement capabilities of the miniature LMS instrument designed by Riedo and coworkers. For the study, 14 Martian mudstone analogue samples were used—half artificially inoculated with microbes, and half used as abiotic controls. The samples were treated in four different ways—biotic samples were cultured anaerobically and aerobically; abiotic samples were incubated with water, and some remained dry. A few of the samples were irradiated with a high dose of gamma radiation, while others were not irradiated. The study demonstrated that their instrumentation is capable of detecting microbes of sparse life in terrestrial soil samples. “This demonstrated capability allowed him to become a member of NASA teams for field studies in Mars analogue sites,” Wurz said. In summary, this research showed the first results on the detection of biogenic element pat- terns of microbial life using a miniature, space-mission-capable LIMS system.

In 2016, Riedo and his colleagues determined that direct laser desorption MS (LD-MS) was capable of measuring low molecular weight biomarkers in geological samples (14). It was following this 2016 work that the miniature LMS was first designed, built, and tested for in situ elemental and isotope analysis of solids in space research.

Chemical Depth Profiling of Solid Materials

The miniature LMS system has also been developed for applications on earth, such as chemical depth profiling of solid materials. In 2015, LMS was used for high-resolution chemical depth profiling measurements of copper films as a feasibility study to determine its spatial analysis capabilities for semiconduc- tor wafer plating and surfaces of space weathered samples (4). For this work, the custom-developed LMS used an ultrashort pulsed laser system (τ ~ 190 fs, 

 = 775 nm) for ablation and ionization of the sample surface with a subnanometer ablation rate per single laser shot. The instrument demonstrated capabilities for high vertical depth resolution, excellent sensitivity (near 10 ppb), and a dynamic range greater than 108.

In early 2016, Riedo published work describing the structural analysis of plating additives in copper films using LIMS combined with secondary ion mass spectrometry (SIMS) and focused ion beam (FIB) analysis for sample depth profiling of electrodeposited copper films (15). The LIMS technique demonstrated nanometer depth-resolution. Using the combined analytical techniques allowed for a detailed assessment of the additive’s interaction and location within the copper films.

Another published paper by Riedo in 2016 was able to demonstrate high spatial resolution using LMS for direct chemical quantitation of solids (16). In recent years, the analytical methods of choice for chemical analysis of solid materials have been SIMS, LA-ICP-MS, and glow discharge MS (GD-MS). For remote space measurements and for characterization of materials used for manufacturing semiconductors, a miniaturized LMS instrument also provides high spatial resolution measurements of such solids as metal alloys, meteorite specimens, and terrestrial fossils.

In 2015, Riedo and associates published the first paper where pulsed laser ablation was used for high spatial resolution depth profiling for solid materials (18). This work was expanded to two-dimensional surface imaging, and then to 3D composition profiling of solids. These profiles have been shown to demonstrate a nanometer scale vertical spatial resolution, while providing chemical composition information. Such a technique provides detailed information for metal layering and deposition processes as well as solid materials and geological samples.

People always seem to be interested in what makes some individuals so successful. Is it personality, enthusiasm, brilliance, work ethic, organizational skills, communication skills, or the ability to network and successfully establish important collaborative relationships in one’s field? In Riedo’s case, he clearly possesses many of these qualities.

“Andreas is a super enthusiastic person with a great knowledge about his research topics,” Pisonero said. “Andreas is an incredibly friendly person, very talkative and with great communication skills.”

“Andreas is really a hands-on person, gets fully involved in all experimental aspects and data analysis,” Wurz said. “He is also good in including students in his research, from the bachelors level all the way to PhD students.” Wurz further elaborates, “Andreas has strong networking capabilities and successfully established important collaborations in Europe and the US in the research field of astrobiology.”

“I am generally impressed by Andreas’s energy, intelligence and pursuit of his objectives,” Westall said. “In this, he is a hard worker but is also companionable and enjoys social interaction.”

“Dr. Andreas Riedo has a very positive attitude and is always constructive. He is hard working, decisive, and perseverant,” Ehrenfreund said. “He is a very positive and pragmatic person and extremely good at multitasking. He is very dedicated to outreach, which is a rare and important quality for researchers today.”

Westall summed up the feelings of several collaborators: “I am happy to have to opportunity to know and work with Andreas,” he said. “I appreciate his youthful enthusiasm, dynamism and drive. I hope, one day, to see his instruments flying!”

(1) Riedo, A.; Bieler, A.; Neuland, M.; Tulej, M.; Wurz, P. Performance Evaluation of a Miniature Laser Ablation Time‐of‐Flight Mass Spectrometer Designed for In Situ Investigations in Planetary Space Research. 

(2) Riedo, A.; Neuland, M.; Meyer, S.; Tulej, M.; Wurz, P. Coupling of LMS With a FSLaser Ablation Ion Source: Elemental and Isotope Composition Measurements. 

(3) Riedo, A.; Meyer, S.; Heredia, B.; Neuland, M.B.; Bieler, A.; Tulej, M.; Leya, I.; Iakovleva, M.; Mezger, K.; Wurz, P. Highly Accurate Isotope Composition Measurements by a Miniature Laser Ablation Mass Spectrometer Designed for In Situ Investigations on Planetary Surfaces. 

(4) Grimaudo, V.; Moreno-Garc.a, P.; Riedo, A.; Neuland, M.B.; Tulej, M.; Broekmann, P.; Wurz, P. High-Resolution Chemical Depth Profiling of Solid Material Using a Miniature Laser Ablation/Ionization Mass Spectrometer. 

(5) Tulej, M.; Neubeck, A.; Ivarsson, M.; Riedo, A.; Neuland, M.B.; Meyer, S.; Wurz, P. Chemical Composition of Micrometer-Sized Filaments in an Aragonite Host by a Miniature Laser Ablation/Ionization Mass Spectrometer. 

(6) Neuland, M.B.; Meyer, S.; Mezger, K.; Riedo, A.; Tulej, M.; Wurz, P. Probing the Allende Meteorite with a Miniature Laser-Ablation Mass Analyzer for Space Application. 

(7) Boeren, N.J.; Gruchola, S.; de Koning, C.P.; Keresztes Schmidt, P.; Kipfer, K.A.; Ligterink, N.F.W.; Tulej, M.; Wurz, P.; Riedo, A. Detecting lipids on planetary surfaces with laser desorption ionization mass spectrometry. 

(8) Ligterink, N.F.; Kipfer, K.A.; Gruchola, S.; Boeren, N.J.; Keresztes Schmidt, P.; de Koning, C.P.; Tulej, M.; Wurz, P.; Riedo, A. The ORIGIN Space Instrument for Detecting Biosignatures and Habitability Indicators on a Venus Life Finder Mission. 

(9) Riedo, A.; Grimaudo, V.; Aerts, J.W.; Lukmanov, R.; Tulej, M.; Broekmann, P.; Lindner, R.; Wurz, P.; Ehrenfreund, P. Laser Ablation Ionization Mass Spectrometry: A Space Prototype System for In Situ Sulphur Isotope Fractionation Analysis on Planetary Surfaces. 

(10) Riedo, A.; de Koning, C.; Stevens, A.H.; Cockell, C.S.; McDonald, A.; Cede Lopez, A.; Grimaudo, V.; Tulej, M.; Wurz, P.; Ehrenfreund, P. The Detection of Elemental Signatures of Microbes in Martian Mudstone Analogues Using High-Spatial Resolution Laser Ablation Ionization Mass Spectrometry. 

(11) Ligterink, N.F.W.; Grimaudo, V.; Moreno-Garcia, P.; Lukmanov, R.; Tulej, M.; Leya, I.; Lindner, R.; Wurz, P.; Cockell, C.S.; Ehrenfreund, P.; Riedo, A. ORIGIN: A Novel and Compact Laser Desorption–Mass Spectrometry System for Sensitive In Situ Detection of Amino Acids on Extraterrestrial Surfaces. 

(12) Riedo, A.; Tulej, M.; Rohner, U.; Wurz, P. High-Speed Strip-Line Multi-Anode Multichannel Plate Detector System. 

(13) Grimaudo, V.; Moreno-Garc.a, P.; Riedo, A.; Meyer, S.; Tulej, M.; Neuland, M.B.; Mohos, M.; Gütz, C.; Waldvogel, S.R.; Wurz, P.; Broekmann, P. Toward Three-Dimensional Chemical Imaging of Ternary Cu–Sn–Pb Alloys Using Femtosecond Laser Ablation/Ionization Mass Spectrometry. 

(14) Moreno-Garc.a, P.; Grimaudo, V.; Riedo, A.; Tulej, M.; Wurz, P.; and Broekmann, P. Towards Matrix-Free Femtosecond-Laser Desorption Mass Spectrometry for In Situ Space Research. 

(15) Moreno-Garcia, P.; Grimaudo, V.; Riedo, A.; Tulej, M.; Neuland, M.B.; Wurz, Broekmann, P. Towards Structural Analysis of Polymeric Contaminants in Electrodeposited Cu Films. 

(16) Riedo, A.; Grimaudo, V.; Moreno-Garcia, P.; Neuland, M.B.; Tulej, M.; Broekmann, P.; and Wurz, P. Laser Ablation/Ionisation Mass Spectrometry: Sensitive and Quantitative Chemical Depth Profiling of Solid Materials. 

(17) Neubeck, A.; Tulej, M.; Ivarsson, M.; Broman, C.; Riedo, A.; McMahon, S.; Wurz, P.; Bengtson, S. Mineralogical Determination In Situ of a Highly Heterogeneous Material Using a Miniaturized Laser Ablation Mass Spectrometer with High Spatial Resolution. 

(18) Riedo, A.; Grimaudo, V.; Moreno-Garcia, P.; Neuland, M.B.; Tulej, M.; Wurz, P.; Broekmann, P. High Depth-Resolution Laser Ablation Chemical Analysis of Additive-Assisted Cu Electroplating of Microchip Architectures. 

. Direct correspondence about this article to 

Andreas Riedo of the Physics Institute at the University of Bern, the 2023 winner of the Emerging Leader in Atomic Spectroscopy Award, is using laser ablation–desorption ionization mass spectrometry (LIMS) to chemically analyze complex mineral surfaces found in space exploration.

The 2023 Emerging Leader in Atomic Spectroscopy Award

salary and employment survey extracted from calendar year 2018 trends shows a marked decrease in salaries from previous survey years. At the same time, nearly two-thirds of respondents said they were either highly satisfied or satisfied with their current positions.

 salary survey results showed a 14% decrease in average base salaries reported, as compared to 2018 survey numbers. A comparison of the survey sampling population from 2018 to 2019 indicated a slight decrease in the average age of respondents this year, with more respondents from the Midwest (USA), and fewer from industry (54.6% versus 67.9%); these factors would be expected to reduce the average salary values somewhat. Last year, 38.3% reported their job titles as laboratory director or manager, whereas this year only 17.2% reported similar job titles; this significantly reduced the number of $125,000-plus salaries within the survey data (see the box insert within this article to learn how the survey was conducted). The salary trends over the history of this survey from 2001 to present are shown in Table I and Figure 1. Given that these results and our corresponding analysis were so surprising, we calculated the results multiple times; each calculation yielded the same results. The high number of survey responses this year (n = 405) provided a good sample population for salary calculations; possibly the salaries will once again trend upward in future years. For the statisticians out there, the median and mean values were not widely different, suggesting a somewhat normal distribution of salary values. One might also observe from the past five years (1–5) that last year's reported salary increases may be somewhat of an anomaly as well (Table I and Figure 1). Subsequent years may reveal additional cause(s) for such dramatic changes.


	Table I: Average reported base salaries from 2001 to 2019

Average Salaries by Gender, Sector, Education, and Age

	As shown in Table II, the average male base salary of $84,602 is comparable to the 2015 survey average, and the lowest recorded over the past five years. Female salaries followed a similar trend, with an average value of $67,235, also representing the lowest survey results over the past six years. As for the gender gap in salaries, there is a clear and significant difference between male and female salaries. However unless the age effect and other effects are accounted for, such as education and years of experience, the raw differences are difficult to interpret precisely. That being said, the salaries for full-time male workers this past year averaged $84,602, compared to $67,235 for full-time female workers. This is a negative gap of 25.9%, which is statistically meaningful.


	Figure 1: Trend line of average reported base salaries from 2001 to 2019.

	When comparing sector data, average survey base salaries decreased in academia to an average of $58,250, a large downward trend of $15,231. For the industrial sector, the average salary was $86,800, a decrease of $11,662 over last year. Reported government salaries were also lower, with an average of $77,111, a decrease of $3,252 over the 2018 survey.


	Table II: Average salaries by gender, sector, education, and age

	Breaking the average salaries down by education level reveals a startling reduction of 23% from last year for those who have completed a PhD. The current average of $94,939 is roughly equivalent to that of 2015. This year's data also indicated that our sampling population with bachelor's degrees had higher average salaries than those at the master's degree level. This ranking of salaries was also observed in the 2014 and 2016 surveys. Master's level salary reductions from 2018 to 2019 were 23%. The bachelor's level salary decrease from last year to this year was much less exhibiting a 0.5% decrease, indicating some solidity in bachelor's level career positions.

	Survey data indicated that salaries have declined for all age groups compared to last year's survey. The 60+ year age group had an average salary of $83,212, down from $97,816 last year; this was a –17.6% change. The 50–60 age group had the most dramatic change, dropping 27.7%, from $106,573 last year to $83,488 this year. The 40–50 age group averaged $82,725 this year, down 2.8% from last year's value of $85,004. The under 40 age group averaged $62,365 this year versus $70,589 in the 2018 survey, a change of –13.2%.


	Table III: United States regional breakdown of employment sector and average salary

Average Regional Salary Differences in the United States 

	Table III and Figure 2 show average base salaries for full-time workers in different geographical regions in the continental United States. The Southwest and Southeast had a marked increase in average salaries compared to last year. The Southwest had the largest uptick, from $84,132 to $94,049,, an increase of 11.8%. Salaries in the Southeast grew 4.3%, from $84,000 to $87,622. Other sectors had decreases in average salaries, with the most dramatic drop in the Northeast, at –20%, a drop from $92,381 to $73,822. This is followed by a decrease in the Northeast of –14.0%, with average salaries dipping from $107,313 to $92,295. Midwest salaries decreased 6.5%, from $88,940 to $83,172. Overall, the Southwest exhibited the highest salaries followed by the Northeast, then the Southeast, Midwest, and Northwest.


	Figure 2: The average base salaries by U.S. region for the 2019 survey from low to high.

	When we look at the ongoing gender pay gap (Table IV), we first note that all groups, irrespective of gender, reported a downtrend in salaries for this year versus last year. For males working in industry, the reported average salaries dropped from $108,068 last year to $94,765 this year, a –14.0% drop. For females working within the industrial sector, salaries dropped from last year's $85,161 to this year's $71,621, a –18.9% dip. In academia, average salary changes from last year to this year were $78,946 to $61,263 for males, and $69,231 to $54,778 for females. In government work, average salary changes from last year to this year were $81,247 to $80,042 for males, and $76,800 to $69,200 for females.


	Table IV: Male versus female average salary, 2016–2019

	If we are to compare salary gaps over the past five years in dollars (Table II) and percentage (Figure 3) for all categories, we see that salary gaps between male and female worker's salaries for all groups are significant, and remain that way. Even though there have been variations from year to year, the problem seems to be changing very little, according to our survey data.


	Figure 3: Salary gap between male and female workers over past six years (as percentage average salary difference).

	The average annual base salary for 2018 (2019 survey) for full-time workers was $79,711. Fewer than half of our respondents (46.42%) received a bonus, whereas 53.6% did not. For those who did receive bonuses, the average bonus was $12,026 for U.S. full time workers and $11,962 for all full-time workers (U.S. plus international).


	Table V: Respondents showing actual versus expected salary increases for 2018

	Salary increases by percentage for this past year are shown in Table V. From the table we can see that 17.22% did not expect any increase, but 27.41% faced this reality; fully more than 10% of respondents that expected a raise did not receive one. From the table, one can see that 16.01% (8.46% + 2.11% + 5.44%) expected an increase of 5% or more, and 15.66% (6.02% + 4.22% + 5.42%) actually received this level of raise. Nearly 84% (17.22% + 37.46% + 29.31%) of respondents expected to receive less than a 4% increase, and, lo and behold, 84% (27.41% + 34.64% + 22.29%) actually did receive a salary increase of 4% or less during the survey year–remarkable! We can see from the table that, even though expectations for salary increases among respondents is not overly optimistic, even these low expectations were not met. From column 4, we can observe that over 20% (the sum of column 4, Table V) did not have their salary increase expectations met.


	Figure 4: Breakdown of perceived basis (personal, team, or organizational success) for salary increases.

	For most respondents (63%), salary increases were perceived to be based on organizational success, whereas 31% believe their salary increases were based on personal achievements (Figure 4). When asked if salary increases fairly reflected personal performance, 64% did not agree, and when asked if their annual salary increase keeps up with inflation, 70% said it did not. When asked if compensation received was competitive with salaries for similar positions in other organizations, 54% disagreed. When asked the ultimate question if one would consider leaving a science career to make more money, only 39.3% said they would.


	Figure 5: Percent of respondents indicating various perceived causes for their increased workload.

	During this past year, 56.0% of respondents reported an increased workload, 38.8% said their workload stayed the same, and 4.6% reported a decrease in workload. Last year, 63.5% stated their workload had increased over previous years. The perceived reasons for increased workload during this past year are shown as Figure 5. Increased business and added responsibility for an existing position were the main causes for workload increases. New equipment, staffing cuts, and new regulations comprised the next set of factors. From Table VI, we see that, although only 26.78% (22.68% + 4.10%) of respondents are contracted to work more than 40 or hours per week, 58.47% actually worked more than 40 hours per week, and almost 15% admitted to working more than 50 hours per week on average. These work numbers are as expected for professionals.


	Table VI: Percentage of respondents showing the number of contracted hours expected per week versus actual hours worked

	Allotted paid vacation for respondents during 2018 is shown in Table VII. We see from the table that 28.15% or respondents are given five weeks or more of paid vacation, while 26.23% receive two weeks or fewer of paid time off. We should consider that 39.25% of respondents have been with the same organization for 10 or more years; 18.0% of the entire surveyed group has remained with the same organization for more than 20 years. Happily, more than half (51.1%) of respondents have four or more weeks of vacation.


	Table VII: Entitlement for paid vacation in 2018

	For this 2019 survey, 65% of respondents said they were either highly satisfied or satisfied with their current position, whereas 35% were either highly dissatisfied or somewhat dissatisfied. In another form of this question, 60% of respondents said their job met their career expectations, while 40% said it did not. The survey asked a number of detailed questions related to career fulfillment, and these responses are given in Table VIII. A full 76% felt that their work was valued by their employer, and 83% felt their job was secure. A remarkable 75.7% responded that they use their skills and training to its fullest extent in their current position. Overall, the career fulfillment responses were extremely positive!


	Table VIII: Career fulfillment factors showing detailed responses

	Last year's survey dealt heavily with sexual harassment in the workplace. This year, the editors decided to look more closely at another issue that receives far less attention, but that can also cause serious work stress without much opportunity for workers to get relief: bullying. We asked a number of related questions with some surprising results (Table IX). Just under half (47.7%) of respondents feel unable to express their concerns at work. The same percentage of respondents feel that they do not have sufficient authority to accomplish their jobs. A total of 23% feel bullied or intimidated at work, with 10.8% admitting they have been publicly humiliated by their boss. Note that work stress levels in 2018 were perceived to have increased for 48.0% of respondents; 45.2 % said that work stress remained unchanged, with only 6.7% stating that they had a decrease in work related stress during 2018.


	Table IX: Responses related to workplace bullying (in rank order)

	We also asked our respondents about the factors they consider most important to them when evaluating a job (Table X). The top-ranked factors were salary and bonus structure, work–life balance, and "the team I work with." Other factors, including job security, intellectual challenge, and a tolerant work place, also rated above 85%. The three least important factors were prestige of the organization, the ability to work from home, and maternity or paternity leave (although we noted that 67.9% of our respondents are age 40 or over).


	Table X: Factors important for choosing a job (more important or less important and ranked by importance)

Other Benefits and Job Fulfillment Issues

	The survey also asked about other work benefits, such as sponsored technical conference attendance, payment of professional dues, provision for participation in inventions and patent work, and recognition of achievement by management. From Table XI, one can see that only 5.16% are permitted to attend five or more conferences, while 66.06% may attend none or just one sponsored conference per year. When asked if their organization pays their professional dues, only 35.5% answered yes. When asked if the organization allows workers to participate in inventions or patents, just under half (49.4%) said they did. When asked if individuals or teams received special recognition from management during 2018, only 28.7% said they had, with a large majority of 71.34% telling us they had not.


	Table XI: Number of technical conferences respondents were allowed to attend in 2018

	The job market for spectroscopy related positions was also considered in this survey. From a series of questions, 23% of respondents said competition was strong for such positions in their respective locations; 50% said it was moderate, and 27% said that the competition was weak (which suggests that employers would have to compete for good candidates to fill these skilled roles). A series of questions was asked about the difficulty in finding a satisfactory position if one had to change jobs. 12.3% said it would be straightforward to find an equivalent position; 40.2% said it would take some time, but it would be possible to find a comparable position; 21.7% said they could find another job, but it probably wouldn't be as good as their current position; and 25.8% said a search would be difficult, and they would take whatever job they could find. Thus, just under half (47.5%) of respondents present a poor outlook for job opportunities.

	Just over half (51.3%) of our respondents have a recognized management role at work, with 63.9% of those having 1–5 direct reports, and 5.4% of managers having greater than 30 direct reports (Figure 6). For the total respondent population, just 20% said they had formal academic credentials in business or management. Of those that have had formal business or management training, a series of questions was asked to determine the perceived value in taking additional time and expense to complete formal academic training in addition to their scientific or engineering training. The responses are given in Table XII. Those formally trained in business management are in strong agreement that this training has given them a marked advantage in the workplace, that the time and money invested was worth it, and that this training has led to greater job responsibility. One might note here that, historically, the base salaries for managers have averaged much higher than for nonmanagers.


	Figure 6: The percentage of managers with a specified number of direct reports.

	In 2019, there was little base salary difference reported between management and nonmanagement positions for women ($67,098 versus $67,354, respectively).However, there was a significant difference for men ($93,935 versus $74,818) (see Table IV). We note that, over the past four years, the average salaries for management versus nonmanagement, irrespective of gender or location, are $91,821 and $73,318, respectively. This trend represents a 27.5% base salary advantage for those in management positions, though in this year's data, women did not benefit in this regard.


	Table XII: Perceived benefits of formal academic business or management training (in rank order)

 salary survey results showed a 14% decrease in average base salaries compared to 2018. Of course, the demographics of the survey have a major effect on results and we noted specific differences in this year's respondent pool. Even though there was a decrease in salaries this year, there is still financial advantage for acquiring an advanced technical degree, and even formal management or business training. The distinction between bachelor's and master's degrees is not so clear, but obtaining a PhD has yielded significant salary benefits, according to the data we have seen over the 19 years of this survey. The Northeast and Southeast regions of the United States traditionally yield the highest salaries, and, as in other professions, years of experience generally provide proportional increases in wages. Fewer than half of respondents received bonuses, and these averaged nearly $12,000. Salary gaps by gender are significant, and remain that way. Even though there have been variations from year to year, the gender gap seems to be changing very little, according to our survey data over the years.

	The survey indicated that over one- fourth of respondents received no increase in pay last year, and only one in seven received a raise of 5% or more. More than 80% received a salary increase of 4% or less. Most respondents believe that salary increases were based on organizational success, that salary increases did not fairly reflect personal performance, and that increases did not keep up with inflation. Well over half of those surveyed said their workload had increased, with respondents reporting that increased business within their organizations or added responsibility for an existing position were the main causes for this increase. Well over half of the survey respondents work more than 40 hours per week, with one in seven working more than 50 hours per week on average. Over half of respondents receive four or more weeks of vacation, while one-fourth receive two weeks or less.

	In spite of all this, two-thirds of respondents said they were either highly satisfied or satisfied with their current positions, 60% of respondents said their job met their career expectations, and a full three-fourths felt that their work was valued by their employer. Over 80% of those that completed the survey felt their job was secure. Yet one in four expressed feelings of being bullied or intimidated at work, with one in ten admitting to having been publicly humiliated by a boss. Nearly half of respondents stated there was an increase in work stress during this past year. Salary and bonus structure, work and life balance, and "the team I work with," were clearly the most important factors to job satisfaction.

	Two-thirds of responders are allowed to attend one (or none) conferences per year; just one third have their professional dues paid, and just under half participate in invention or patent work. Only one in four was formally recognized for their work performance by their organizations last year. Just one in eight feel it would be straightforward to find another equivalent position; one in four said a search for a new position would be difficult, and they would take whatever job they could find.

	Over half of our respondents have a formal management role in their current job function, with nearly two- thirds of those having 1–5 direct reports and 5% of managers having more than 30 direct reports. We note that one in five of all respondents have formal academic credentials in business or management. Over the past four years, there has been an average base salary advantage of 27.5% for management versus nonmanagement positions, irrespective of gender or location.

Our annual salary and employment survey looks at salaries, workload, job satisfaction, and the overall job market for spectroscopists. This year, we had some surprising results.

Jerome Workman, Jr.

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