Table 4 Physical And Chemical Property Test Results

You're staring at a PDF. Plus, page 12. Table 4. Sixteen rows, eight columns, and a headache forming behind your eyes.

The footnote says "n=3" but the standard deviation column is blank for half the entries. In practice, 2 — and the units column just says "various. And the pH values look reasonable until you notice one outlier at 11. " Someone highlighted the viscosity row in yellow. No legend explains why.

Sound familiar?

If you work in QA, R&D, regulatory affairs, or any lab-adjacent role, you've met Table 4. Or Table 7. Or "Appendix B, Summary of Physicochemical Characterization." They're everywhere: drug master files, polymer spec sheets, environmental monitoring reports, cosmetic safety assessments. And they're almost never as self-explanatory as the author thinks.

This article isn't about one specific Table 4. It's about how to read any physical and chemical property results table without losing your mind — or missing something that matters.

What Is a Physical and Chemical Property Results Table

At its core, it's a structured summary of measured attributes that define a material's identity, behavior, and compliance. Physical properties don't change the chemical composition: density, viscosity, particle size, melting point, color, refractive index. Chemical properties describe composition and reactivity: assay/purity, impurity profile, pH, water content, residual solvents, heavy metals.

Table 4 (or whatever number it gets) is where the raw data gets condensed for review. A typical layout includes:

Test parameter — what was measured
Method reference — USP <791>, ASTM D445, EP 2.2.46, internal SOP-QC-042
Acceptance criteria — the spec limits (often "NLT 98.0%", "≤ 0.1%", "5.0–7.0")
Results — actual values, sometimes per batch/lot
Statistical summary — mean, SD, RSD, min/max across replicates or lots
Pass/Fail — or "Complies/Does Not Comply"

But the devil lives in the gaps between columns.

Why These Tables Matter More Than You Think

You might treat them as administrative checkboxes. Regulators don't.

A physical/chemical property table is often the primary evidence* that:

Your starting material is what the supplier claims
Your process hasn't drifted
Your product is stable through shelf life
Your batch-to-batch consistency is real, not aspirational

I've seen a single viscosity value in Table 4 trigger a six-month stability protocol redesign. A residual solvent result 0.So 02% over limit delayed a filing by two quarters. An unexplained particle size bimodal distribution in a parenteral emulsion? That became a 483 observation.

These tables travel. They appear in:

CTD Module 3.2.Plus, s. 4.4 (drug substance) and 3.2.P.5.

If you can't read them critically, you're not just bad at your job — you're a liability.

How to Read One Without Getting Burned

Start with the method column — every time

Don't skip to results. The method tells you what the number actually means*.

"Viscosity, cP" is useless without knowing: Brookfield RV? Spindle #? Cone-and-plate? Plus, a polymer solution measured at 25°C on a Brookfield LV #2 at 60 RPM gives a different number than the same sample at 30°C on a cone-and-plate at 100 1/s. " Both could be in Table 4. Temperature? Sample prep? Both are "viscosity in cP.RPM? Only one matches your spec.

Check the method reference against your spec. If the spec says "USP <911> Viscosity" but the table cites "Internal Method TM-045 (capillary viscometer)," that's a gap. Also, maybe it's validated and equivalent. Maybe it's not. Ask.

Watch the units like a hawk

ppm vs. mg/kg — same thing, right? Usually. But "ppm w/w" vs "ppm w/v" diverges for non-aqueous densities. µg/g vs µg/mL? Different denominators.

I once caught a heavy metals result reported as "ppm" in Table 4 where the spec was in "µg/g.23. Practically speaking, the conversion factor was 1. Worth adding: the result looked* passing. Plus, " Same magnitude — but the method was ICP-MS on a digested solid, and the spec assumed solution basis. It wasn't.

Temperature units matter too. But dSC onset in K. Melting point in °C vs °F. Don't assume.

Replicates, n, and statistics — know what you're looking at

"n=3" in the footnote. Is it the mean? A single representative run? Which means results column shows one value. The median? The first* run?

If the table shows "Mean ± SD" but the SD column is blank for three rows, don't assume zero variability. Ask for the raw data. Blank SD often means "only one replicate was run" or "data not transferred from LIMS correctly." Both happen more than anyone admits.

RSD (relative standard deviation) > 5% for assay? That's a red flag for most compendial methods. This leads to rSD > 2% for pH? Something's wrong with the measurement or the sample.

And if the table pools results across different lots* into one mean/SD — stop. So that's not batch release data. Because of that, that's a summary for a different purpose. Know which purpose you're reading for.

Acceptance criteria: spec vs. typical vs. literature

Three flavors of "limits" appear in these tables:

Release/shelf-life specification — the legal commitment. Fail = OOS investigation.
Typical/expected range — internal guidance, often wider than spec. Useful for trend detection.
Literature/reference values — "for information only." Not a spec.

Table 4 doesn't always label which is which. And i've seen tables where Column D says "Limit" and it's a mix of all three. Color coding helps. Footnotes help. But you must* verify against the governing specification document (drug substance spec, drug product spec, monograph, CMC section).

For more on this topic, read our article on in this unit you learned to or check out lack of access to improved sanitation facilities in slums.

Common Mistakes That Cost Time and Money

Treating "Complies" as a binary truth

The Pass/Fail column is someone's judgment. Sometimes the person entering it didn't notice the spec says "NLT 99.Sometimes it's automated from LIMS. Also, 96% — which rounds to 99. Sometimes it's manual entry. But 0%" and the result is 98. 0% but fails* per rounding rules in USP <1111>.

Always spot-check at least 10% of the pass/fail calls

The phantom "not applicable"

"N/A" appears everywhere — but it's not always what it seems. Consider this: " Sometimes it means "test failed and we're hiding it. Sometimes it means "test not required for this lot type." Sometimes it means "we forgot to run it.

I once spent two weeks chasing a stability issue before realizing the moisture content for a critical intermediate had been marked "N/A" because the analyst's template defaulted to that when the sample weighed less than 100 mg. The test was possible — just harder.

Don't accept "N/A" without understanding why it's there.

Rounding that rounds away the truth

Specifications often say "98.Also, 03%. " A result comes back as 102.0%.03% should round to 102.Someone rounds it to 102.That's why 0–102. Day to day, 0% and marks it "Complies. " But USP <1111> says intermediate values should be rounded to the same number of decimal places as the specification limit — meaning 102.0% and pass*.

You might be surprised how often this gets overlooked.

But what if the spec limit was actually 101.0% and fails*. Then 102.8%? And 03% rounds to 102. The rounding rule saved you — or bit you.

Know your rounding rules. They're not universal.

The "average of averages" trap

Table after table shows "% RSD" calculated from means rather than individual replicates. If you have three batches each tested in triplicate, the correct RSD uses all nine data points — not the three batch means.

This mistake inflates precision and hides real variability. It's especially common in dissolution testing and content uniformity studies.

Red Flags That Demand Immediate Investigation

When you see these patterns, stop and dig:

Identical decimal places across multiple rows — suggests copy-paste or default formatting rather than actual measurements
Standard deviations that are suspiciously round numbers — like exactly 0.5 or 1.0 when the data doesn't support it
Results that cluster too perfectly around specification limits — especially just inside the pass line
Missing units in column headers — the cardinal sin that enables every other error
Different significant figures within the same column — indicates inconsistent data handling
Blank cells where data should exist — not just empty space, but actual missing test results

Practical Checklist for Table Review

Before signing off, ask yourself:

Are units explicitly stated for every column? If not, can you derive them from context?
Do the units match between specification and results? Convert if necessary.
Is n clearly defined and consistent? Are statistics calculated correctly?
Are acceptance criteria labeled by type? Spec, typical, or reference?
Does "Complies" align with the actual numerical result and rounding rules?
Are there any unexplained N/A values?
Do the statistics make sense for the method and sample type?
Has anyone verified the data against raw instrument files or LIMS entries?

The Bottom Line

Tables don't lie — but they don't tell the whole truth either. They're summaries created by humans using tools built by other humans, all operating under time pressure and competing priorities.

The most dangerous tables are the ones that look perfect. Clean formatting, logical structure, results that make sense — these can mask fundamental errors in units, calculations, or interpretation.

Your job isn't to trust the table. It's to interrogate it gently but thoroughly. Ask questions. On top of that, trace the data back to its source. Challenge assumptions about units, statistics, and acceptance criteria.

Because somewhere between the instrument and the final report, data becomes information, and information becomes decisions. If you don't verify that chain of custody — if you don't watch the units, question the statistics, and demand clarity on those three flavors of limits — then you're not reviewing data.

You're just rubber-stamping someone else's assumptions.

And in pharmaceutical analysis, assumptions kill products, delay approvals, and endanger patients.

So watch the units. Worth adding: question the statistics. Verify the criteria. And always, always ask when something doesn't add up.

The table will thank you. More importantly, so will your patients.