Language Comes Before Hardware
Analytical instruments exist in language long before they exist in the laboratory. Datasheets, methods, manuals, specifications, and service reports shape expectations well before the first sample is analyzed. Decisions are made, comparisons are drawn, and problems are anticipated based on words.
When terminology is misunderstood, people may believe they agree while actually referring to different things. Discussions sound aligned, but expectations are not. Later, the instrument appears to “underperform”, methods seem unstable, and troubleshooting turns into argument instead of problem solving. In many cases, the root cause is not a technical failure, but a mismatch in language.
In analytical work, words are not decoration. They define how performance is judged and how results are interpreted.
The Same Words, Different Meanings
Technical terminology is not universal across analytical techniques. The same word can describe fundamentally different things depending on context.
“Resolution” is a clear example. In chromatography, resolution describes how well two peaks are separated in time and is calculated from retention behavior and peak width. In mass spectrometry, resolution describes the ability to distinguish ions with similar mass-to-charge ratios and depends on analyzer design and operating mode. In spectroscopy, resolution refers to the ability of an optical system to separate closely spaced wavelengths or frequencies. The same word is used, but it refers to different physical limits, different hardware constraints, and different practical consequences. This difference becomes especially visible in mass spectrometry, where “mass resolution” and “mass resolving power” are often used interchangeably despite describing different concepts - a distinction discussed in detail in a dedicated article on analyte.me.
The same pattern appears with other common terms. Words like “scan”, “cycle”, or “method” are widely used, but they often point to different things: a data acquisition approach, a software routine, or a validated analytical procedure. The vocabulary feels shared, while the meaning quietly shifts.
When Meanings Shift
Terminology does not only differ between techniques; it also shifts with use. Technical terms are reused in marketing materials, specifications, and documentation with changes in emphasis. Statements may remain technically correct while relying on assumptions that rarely hold in routine laboratory work. Conditions may be implied instead of stated. Limitations may be omitted. “Typical” performance may be presented in place of guaranteed behavior.
This is not always intentional deception. But it requires readers to understand what a term includes, what it excludes, and under which conditions it applies.
Why AI Struggles With Terminology
Many expect artificial intelligence to help clarify terminology. In practice, it often creates new confusion. AI systems reproduce language patterns but do not understand physical reality or technique-specific constraints. When terms have multiple meanings, AI tends to merge them or produce confident explanations that sound reasonable but are wrong.
In analytical work, such errors are especially problematic. A convincing explanation that is incorrect is harder to detect than an obvious gap in knowledge.
Making Terminology Explicit
Understanding terminology is not about memorizing definitions. It is about knowing where a term applies, where it does not, and which assumptions come with it. This understanding is contextual and grows from real work with instruments, methods, and data.
This is why analyte.me maintains a Glossary of Terms. It is not a standalone dictionary meant to be read in isolation. Terms from the glossary are linked directly within instrument descriptions and comparison tables, next to the parameters where they are used. This allows readers to see terminology in context, understand why a specific term matters for a specific instrument, and evaluate its practical implications at the moment decisions are made.
The goal is not to explain terminology in the abstract, but to make it usable - exactly where specifications are compared and expectations are formed.