Instrumental Analysis Laboratory Report Discussion Examples | How to Write About Spectra, Peaks, and Quantitative Results

What Is an Instrumental Analysis Experiment?

An instrumental analysis experiment is an experiment in which the properties of chemical substances are investigated using measuring instruments.
Information that is difficult to obtain through visual observation or manual titration alone can be quantified as spectra, peaks, electrical signals, light intensity, mass-to-charge ratios, retention times, and similar data.
Therefore, instrumental analysis is used for the quantitation of trace components, identification of unknown samples, confirmation of functional groups, separation and analysis of mixtures, and other purposes.

It is important not merely to write down the data output by the instrument, but to convert those data into chemical meaning and discuss them.
For example, an IR absorption peak represents a functional group, UV-Vis absorption represents an electronic transition or concentration, HPLC and GC peaks represent component retention times and amounts, and NMR signals represent the environments of hydrogen or carbon atoms.

Main Items to Include in the Results

In the results of an instrumental analysis experiment, organize the analyte, sample-preparation method, instrument used, measurement conditions, spectra or chromatograms, peak positions, peak intensities, peak areas, calibration curves, quantitative results, and other information.
Because the results change when the measurement conditions change, it is important to describe the instrument conditions as clearly as possible.

Main Items to Include in the Results

• Analyte
• Sample name
• Sample-preparation method
• Dilution factor
• Standard solutions used
• Instrument used
• Measurement wavelength
• Measurement range
• Mobile phase and stationary phase
• Flow rate
• Column conditions
• Measurement temperature
• Spectrum or chromatogram
• Peak position
• Peak height
• Peak area
• Retention time
• Calibration curve
• Correlation coefficient
• Unknown-sample concentration
• Comparison with literature values and labeled values
• Sources of error and points for improvement

Reference Experimental Values and How to Organize an Instrumental Analysis Report

Here, reference experimental values are used to show how spectra, peaks, calibration curves, and quantitative results obtained by instrumental analysis methods such as UV-Vis, IR, NMR, GC, HPLC, and MS can be organized in a report and connected to the discussion.

In instrumental analysis, it is important not merely to list measurement values, but to explain “what was learned” and “why that judgment can be made” based on peak positions, peak intensities, areas, retention times, calibration curves, and comparisons with standard substances.

Reference Experimental Conditions

Basic Table for Organizing Analytical Results

First, listing what was confirmed by each analytical method makes the overall report easier to understand.

Example of Organizing UV-Vis Results

In UV-Vis measurements, the absorption-maximum wavelength and absorbance are organized, and the concentration is determined from the calibration curve.

Example Calculation of UV-Vis Concentration

If the calibration curve is A = 31000c and the absorbance of unknown sample A is 0.496,

c = A ÷ 31000 = 0.496 ÷ 31000 = 1.60×10⁻⁵ mol/L

For a diluted sample, the concentration in the measured solution is multiplied by the dilution factor to obtain the concentration in the original sample.

Example of Organizing IR Spectrum Results

In IR analysis, characteristic absorption peaks are organized according to wavenumber, intensity, assignment, and structural significance.

From the IR results, the unknown sample is considered highly likely to contain an ester group.
However, because IR alone makes it difficult to distinguish the carbon skeleton or isomers, the result is judged together with NMR and MS data.

Example of Organizing 1H NMR Results

In NMR, chemical shifts, integration ratios, and splitting patterns are organized together in a table.

The triplet at 1.26 ppm and the quartet at 4.12 ppm correspond to an ethyl group with an integration ratio of 3:2.
The 3H singlet at 2.05 ppm is considered to correspond to CH3CO-, which has no neighboring hydrogens.

Example of Organizing GC Results

In GC, retention time and peak area are used for component identification and comparison of relative amounts.

The area percentage is determined by dividing each peak area by the total peak area.

Area percentage (%) = Peak area of each component ÷ Total peak area × 100

However, because detector sensitivity differs among components, the area percentage cannot be regarded directly as a mass percentage.

Example of Organizing an HPLC Calibration Curve and Quantitative Results

In HPLC, a calibration curve is prepared from standard solutions, and the concentration is determined from the peak area of the unknown sample.

In this example, the calibration curve is treated as follows.

Peak area = 18250 × Concentration (mg/L)

If the peak area of the unknown sample is 456000,

Concentration = 456000 ÷ 18250 = 25.0 mg/L

Example of Organizing MS Results

In MS, molecular-ion peaks and fragment peaks are organized to estimate molecular weight and partial structures.

From the molecular ion at m/z 136, the base peak at m/z 105, and the aromatic-derived peak at m/z 77, candidates such as aromatic esters can be considered.

Example of Structural Estimation by Integrating Multiple Analytical Results

When estimating the structure of an unknown sample, it is important not to make a judgment based on only one analytical method, but to confirm whether multiple analytical results support the same structure.

How to Summarize Quantitative Results

Quantitative results become easier to understand when the measured value, calibration curve, dilution factor, and final concentration are summarized in a single table.

In a report, not only the final concentration but also which calibration curve was used and how the dilution factor was handled should be clearly stated.

Example of Checking Calibration-Curve Linearity

In quantitative analysis, confirm whether the calibration curve is linear.
If high-concentration points deviate from the straight line, the sample must be diluted before measurement.

If a value outside the calibration-curve range is extrapolated without adjustment, the concentration may be determined incorrectly.

Table for Confirming the Reliability of Peak Identification

In component identification, showing multiple lines of evidence such as retention time, spectrum, standard substance, and library-match score increases persuasiveness.

Example of Organizing Sources of Error

In an instrumental analysis report, sources of error are easier to write about when divided into “instrument,” “sample,” “operation,” and “analysis.”

Example of Connecting Results and Discussion

In UV-Vis measurement, the absorption maximum of unknown sample A was observed near 525 nm.
This wavelength agreed with the absorption maximum of the standard substance, suggesting that unknown sample A may contain the same absorbing component.
The absorbance at 525 nm was 0.496, and substitution into the calibration curve A = 31000c gave a concentration of 1.60×10⁻⁵ mol/L.

In the IR spectrum, strong C=O absorption was observed at 1740 cm⁻¹ and strong C-O absorption at 1240 cm⁻¹.
These absorptions are characteristic of an ester group.
In addition, because no broad O-H absorption was observed near 3300 cm⁻¹, the sample was considered not to be an alcohol or carboxylic acid.

In the ¹H NMR spectrum, a 3H triplet was observed at 1.26 ppm, a 2H quartet at 4.12 ppm, and a 3H singlet at 2.05 ppm.
The combination of the peaks at 1.26 ppm and 4.12 ppm corresponds to an ethyl group, and the singlet at 2.05 ppm is considered to correspond to CH3CO-.
Together with the ester group indicated by IR, the unknown sample is highly likely to be ethyl acetate.

In GC, the retention time of the main peak of the unknown sample was 2.65 min, which agreed with the retention time of standard ethyl acetate.
Because the IR, NMR, and GC results all support ethyl acetate, the main component of the unknown sample was judged to be ethyl acetate.

Examples of Writing to Avoid in the Discussion and Improved Versions

Example Calculation of Relative Error

When comparing a measured value with a standard or labeled value, using relative error makes the magnitude of the difference easier to explain.

Relative error (%) = |Measured value − Standard value| ÷ Standard value × 100

If the standard value is 25.0 mg/L and the measured value is 23.8 mg/L,

Relative error = |23.8 − 25.0| ÷ 25.0 × 100 = 4.8%

In this case, the measured value can be described as 4.8% lower than the standard value.

Example of a Results Table That Is Easy to Include in a Report

Points for Connecting the Results to the Discussion

In an instrumental analysis report, it is important not only to explain each measurement result separately, but also to combine multiple results to reach a conclusion.

• Are the peak positions in the spectrum or chromatogram shown as specific numerical values?
• Can the peak assignments be explained based on functional groups, partial structures, and comparison with standard substances?
• Is the calibration-curve equation shown, and is the calculation process for the unknown-sample concentration described?
• Are treatments such as dilution factor, blank correction, and area percentage clearly stated?
• Has it been confirmed that the quantitative result lies within the linear range of the calibration curve?
• Is the conclusion based on consistency among multiple analytical methods rather than on a single analytical result?
• Have sources of error such as peak overlap, impurities, instrument conditions, and sample preparation been discussed?
• Are the results, calculations, discussion, and conclusion connected logically?

Example Discussion

In this experiment, multiple instrumental analysis methods were used to identify and quantify the components of an unknown sample.
In UV-Vis measurement, an absorption maximum was observed at 525 nm, which agreed with the absorption maximum of the standard substance.
Substituting the absorbance of 0.496 at this wavelength into the calibration curve A = 31000c gave a concentration of 1.60×10⁻⁵ mol/L in the unknown sample.

In the IR spectrum, strong C=O absorption was observed at 1740 cm⁻¹ and strong C-O absorption at 1240 cm⁻¹.
These are absorptions characteristic of an ester group and indicate that the unknown sample has an ester structure.
On the other hand, because no broad O-H absorption was observed near 3300 cm⁻¹, the possibilities of an alcohol or carboxylic acid are low.

In the ¹H NMR spectrum, a triplet at 1.26 ppm and a quartet at 4.12 ppm were observed with an integration ratio of 3:2.
This combination corresponds to a CH3CH2- ethyl group.
In addition, because a 3H singlet was observed at 2.05 ppm, an acetyl group, CH3CO-, is considered to be present.
Together with the ester group indicated by IR, the unknown sample is highly likely to be ethyl acetate.

In GC, the retention time of the main peak of the unknown sample was 2.65 min and agreed with the retention time of standard ethyl acetate.
In MS, the molecular ion and major fragments were also consistent with the candidate compound.
From these results, the IR, NMR, GC, and MS analyses all support the same conclusion, and the main component of the unknown sample can be judged to be ethyl acetate.

Possible sources of error include dilution errors during sample preparation, variation in injection volume, settings for the peak integration range, and insufficient blank correction.
In quantitative analysis in particular, measurement outside the linear range of the calibration curve may prevent correct determination of concentration.
In addition, when peaks overlap on a chromatogram, their areas may be overestimated or underestimated.
Therefore, comparison with standard substances, repeated measurements, appropriate dilution, and confirmation of peak separation are necessary.

Summary

In an instrumental analysis report, the peaks in spectra and chromatograms are shown numerically, and what those peaks mean is explained together with the supporting evidence.
In quantitation, it is important to clearly organize the calibration curve, measured value, dilution factor, and final concentration.

This reference example used results from UV-Vis, IR, NMR, GC, HPLC, and MS to address peak assignments, component identification, concentration calculations using calibration curves, sources of error, and consistency among multiple analytical methods.
In a report, rather than merely listing measurement results, the discussion should connect the results to what can be concluded from them.

What Is a Spectrum?

A spectrum represents the distribution of signal intensity with respect to wavelength, wavenumber, frequency, mass-to-charge ratio, or a similar variable.
In UV-Vis, it is expressed as the relationship between absorbance and wavelength; in IR, as the relationship between transmittance or absorbance and wavenumber; and in NMR, as the relationship between chemical shift and signal intensity.
In mass spectrometry, the spectrum is obtained as the relationship between m/z and ion intensity.

Spectra contain information about the structure, functional groups, electronic state, molecular weight, chemical environment, and other properties of a substance.
In a report, attention is paid not only to the overall spectral shape but also to characteristic peaks and signals.

What Does a Peak Indicate?

A peak is a region of increased signal intensity in a spectrum or chromatogram.
Peak position provides clues to components, functional groups, electronic states, retention times, and other information.
Peak intensity and peak area may be related to the amount or concentration of a component.

However, the presence of a peak does not immediately prove that the target component is present.
Overlap with other components, noise, baseline disturbances, and peaks from solvents or impurities must also be considered.
When discussing a peak, its position, intensity, shape, width, and reproducibility are checked.

Discussion of Peak Position

Peak position is one of the most important pieces of information in instrumental analysis.
In IR, it indicates the type of functional group; in UV-Vis, the wavelength of absorbed light; in NMR, the chemical environment of atoms; in HPLC and GC, the retention time of components; and in mass spectrometry, m/z.
Components and structures can be estimated by comparing peak positions with literature values or standard substances.

If a peak position differs from the literature value, possible causes include solvent, pH, temperature, concentration, instrument calibration, measurement conditions, and interactions within the sample.
In a report, not only agreement of peak positions but also the reasons for any shifts should be discussed.

Discussion of Peak Intensity

Peak intensity represents the magnitude of the detected signal.
In UV-Vis it is treated as absorbance, in fluorescence analysis as fluorescence intensity, in NMR as integration value, and in chromatography as peak height or peak area.
In many cases, peak intensity is related to the amount or concentration of a component.

However, peak intensity is affected not only by concentration but also by measurement conditions, sample preparation, instrument sensitivity, cell contamination, injection volume, and peak overlap.
When it is used for quantitation, it is important to prepare a calibration curve using standard solutions and evaluate the unknown sample within that range.

Discussion of Peak Area

In chromatography and some spectral analyses, peak area is often used for quantitation rather than peak height.
Because peak area reflects the total amount of signal across the entire peak, it may allow the amount of a component to be evaluated even if the peak becomes slightly broader.
In HPLC and GC, quantitation is performed using a range in which peak area is proportional to the amount of component.

However, correct determination of peak area requires appropriate baseline setting.
If peaks overlap or the baseline is sloped, errors occur in the area calculation.
In a report, the effect of peak area on the quantitative value should be discussed.

Discussion of Peak Width and Peak Shape

Peak width and peak shape are also important pieces of information.
In chromatography, a sharper peak generally indicates better separation and less diffusion or spreading within the column.
If a peak is broad, tails, or is asymmetric, possible causes include column condition, sample amount, interactions, mobile-phase conditions, and instrument problems.

In spectra as well, peak width is affected by intermolecular interactions, concentration, temperature, instrument resolution, and other factors.
For example, a broad peak in IR may indicate hydrogen bonding, while a broad signal in NMR may indicate exchange reactions or molecular motion.

How to Write Peak Assignments

Peak assignment means explaining which component, functional group, bond, atom, or transition an observed peak originates from.
For IR, it can be written as “absorption originating from C=O stretching vibration”; for UV-Vis, “absorption originating from a π-π* transition”; for NMR, “a signal originating from aromatic protons”; and for HPLC, “a component whose retention time agrees with that of the standard substance.”

When writing an assignment, the peak position and the basis for the assignment are stated together.
Rather than merely writing that “a peak was present,” explaining “where it appeared, what it corresponds to, and what can therefore be concluded” makes the text more suitable as a discussion.

What Is a Calibration Curve?

A calibration curve is a graph prepared by measuring standard solutions of known concentration and plotting the relationship between concentration and signal intensity.
The concentration of an unknown sample is determined by applying its signal to the calibration curve.
Calibration curves are commonly used in quantitative analyses such as UV-Vis, fluorescence analysis, atomic absorption analysis, HPLC, and GC.

To use a calibration curve, it is important that the signal of the unknown sample lie within the calibration-curve range.
If a concentration is determined by extrapolating a value outside the range, the error may become large.
The correlation coefficient and linearity of the calibration curve must also be confirmed.

Example of a calibration curve: Signal intensity = Slope × Concentration + Intercept

Discussion of Calibration-Curve Linearity

The linearity of a calibration curve is closely related to the reliability of quantitative results.
When the concentrations and signals of the standard solutions have a linear relationship, the concentration of an unknown sample can be determined relatively accurately.
A correlation coefficient closer to 1 generally makes it easier to judge the linearity as high.

However, even a high correlation coefficient does not necessarily mean that the calibration curve is completely accurate.
Errors in standard-solution preparation, bias in the concentration range, blank correction, the size of the intercept, and the presence of outliers must be checked.
If high-concentration points deviate from the straight line, detector saturation or limits of the proportional relationship between absorbance and concentration may be responsible.

How to Write Quantitative Results

When writing quantitative results, include not only the determined concentration but also the calibration curve used in the calculation, dilution factor, units, number of measurements, and average value.
For example, if an unknown sample is diluted 10-fold before measurement, the concentration determined from the calibration curve is multiplied by the dilution factor to obtain the concentration in the original sample.
Forgetting to restore the dilution factor is a common mistake.

If a quantitative value differs from a literature value, labeled value, or theoretical value, the effects of sample preparation, standard-solution preparation, measurement conditions, interfering components, peak overlap, and the calibration-curve range are discussed.
It is important not to end with “the value was calculated,” but to evaluate the validity of the quantitative result.

Discussion of Spectrophotometry

In spectrophotometry, the amount of light at a specific wavelength absorbed by a sample is measured.
Absorbance is measured at a wavelength absorbed by the target component, and the concentration is determined using a calibration curve.
Because there is a range in which absorbance is proportional to concentration, it is important to perform measurements within that range.

If absorbance is too high, the result may deviate from linearity.
Cell contamination, bubbles, insufficient blank correction, sample turbidity, and absorption by coexisting substances are also sources of error.

A = εcl

Discussion of IR Spectra

In IR spectra, absorption peaks corresponding to vibrations of bonds and functional groups in molecules are observed.
Functional groups such as O-H, N-H, C=O, C-H, C=C, and C-O show absorption in characteristic wavenumber ranges.
Therefore, IR is used to confirm functional groups and structural changes before and after reactions.

In an IR discussion, state which functional group each characteristic absorption peak corresponds to.
In addition, if a peak originating from the starting material disappears after the reaction or a peak characteristic of the product appears, this change can be used to discuss reaction progress.

Discussion of NMR Spectra

In NMR spectra, differences in the chemical environments of atomic nuclei appear as chemical shifts.
In ¹H NMR, information about molecular structure can be obtained from the types of hydrogen atoms, integration values, and splitting patterns.
In ¹³C NMR, the carbon skeleton and carbon environments around functional groups can be confirmed.

In an NMR discussion, the structure is explained based on chemical shifts, integration ratios, splitting, and agreement with known spectra.
Because solvent peaks, water peaks, and impurity peaks may also appear, care must be taken not to assign every peak to the target substance.

Discussion of Chromatography

In HPLC and GC, components in mixtures are separated, and retention time and peak area are used for component identification and quantitation.
If a retention time agrees with that of a standard substance, the corresponding component may be present.
Because peak area corresponds to the amount of component, it can be quantified using a calibration curve.

In a chromatographic discussion, retention time, peak area, resolution, and peak shape are checked.
If peaks overlap, accurate quantitation becomes difficult.
Injection volume, column condition, mobile-phase composition, flow rate, and temperature also affect the results.

Discussion of Mass Spectrometry

In mass spectrometry, the m/z values of ionized molecules and fragment ions are measured.
Molecular-ion peaks and fragment peaks provide information about molecular weight and structure.
In high-resolution mass spectrometry, elemental composition may also be estimated from exact mass.

In a mass-spectrometry discussion, molecular-ion peaks, major fragments, isotope peaks, and comparisons with standard substances or theoretical values are considered.
However, depending on the ionization method, the molecular ion may be weak, adduct ions may appear, or fragmentation may occur readily.

Discussion of Atomic Absorption Analysis and ICP Analysis

Atomic absorption analysis and ICP analysis are used to quantify the concentrations of metallic elements and inorganic elements.
A calibration curve is prepared using standard solutions, and the element concentration is determined from the signal intensity of the unknown sample.
In trace-element analysis, sample preparation, dilution, contamination, and matrix effects have a large influence on the results.

In the discussion, the linearity of the calibration curve, blank value, concentration range of the standard solutions, dilution factor, and effects of coexisting components are checked.
Trace contamination from glassware, water, and reagents may also be a source of error.

Discussion of Blank Measurements

A blank measurement is an operation in which a sample containing no target component is measured to confirm signals originating from the solvent, reagents, cell, or instrument.
If the blank value is large, factors other than the target component may be affecting the signal.
In quantitative analysis, blank correction is used to obtain a more accurate signal.

If the blank is not measured appropriately, the concentration of the target component may be overestimated.
Particularly in trace analysis, the effect of the blank value becomes relatively large.

Discussion of the Baseline

The baseline is the reference line in portions where no peaks are present.
The more stable the baseline is, the easier it is to accurately determine peak height and peak area.
If the baseline is sloped, wavy, or drifting, errors occur in peak reading.

Baseline disturbances may be caused by instrument stability, solvent, temperature changes, changes in the mobile phase, cell contamination, column condition, noise, and other factors.
In a report, it is useful to explain how the baseline affected the quantitative results.

Discussion of Noise

Noise is unwanted fluctuation contained in the measurement signal.
If noise is large, detection of small peaks becomes difficult, and errors in reading peak height and peak area become larger.
The effect of noise is particularly important in the analysis of trace components.

Causes of noise include instrument sensitivity, electrical fluctuations, instability of the light source, temperature changes, sample turbidity, bubbles, and contamination of the cell or column.
If noise is large, stabilization of the measurement conditions and improvement of sample pretreatment are necessary.

Discussion When Peaks Overlap

When peaks from multiple components appear at similar positions, they overlap and make accurate identification and quantitation difficult.
In chromatography, insufficient separation prevents accurate determination of peak area.
In spectral analysis, overlapping functional-group peaks or absorption bands may make assignments ambiguous.

Methods for improving peak overlap include changing measurement conditions, reviewing separation conditions, using a different wavelength or another analytical method, and comparing with standard substances.
In a report, the effect of overlap on quantitative values and assignments is discussed.

Errors Caused by Sample Preparation

In instrumental analysis, errors in sample preparation greatly affect the results.
If errors occur during weighing, dissolution, dilution, filtration, extraction, decomposition, pretreatment, or standard-solution preparation, the measurement signals and quantitative values shift.
Mistakes in dilution factors and standard-solution preparation in particular affect both the calibration curve and the calculated concentration of the unknown sample.

In addition, if the sample is not completely dissolved, is turbid, contains precipitate, or contains bubbles, errors occur in absorbance and peak area.
It is important to confirm sample uniformity.

Errors Caused by Instrument Conditions

Instrument conditions also affect instrumental analysis results.
Changes in measurement wavelength, slit width, number of integrations, injection volume, flow rate, column temperature, detector sensitivity, ionization conditions, and light-source stability may change peak position, intensity, and sensitivity.

When comparing results with literature values or those of another group, it is necessary to confirm whether the instrument conditions are the same.
Even for the same sample, the results may not be completely identical if the conditions differ.

Causes of Quantitative Values Being Too High

Causes of quantitative values being higher than the actual value include incorrect standard-solution concentrations, insufficient blank correction, absorption by interfering components or peak overlap, concentration of the sample, calculation errors in the dilution factor, and contamination of cells or equipment.
In particular, if a coexisting component absorbs at the same wavelength as the target component or produces a peak at a similar retention time, the amount of the target component may be overestimated.

Causes of Quantitative Values Being Too Low

Causes of quantitative values being lower than the actual value include sample loss, incomplete dissolution, insufficient extraction, decomposition, adsorption, excessive dilution, insufficient measurement sensitivity, and underestimation of peak area.
If the target component adsorbs to equipment during pretreatment or is partly lost during filtration, the measured value becomes lower.

In addition, if the matrix differs between the standard solutions and unknown sample, the response of the unknown sample may be lower.
In low-concentration samples, the effect of noise also becomes large.

How to Judge the Validity of Results

The validity of instrumental-analysis results is judged by comparison with standard substances, linearity of the calibration curve, blank values, measurement reproducibility, and comparison with literature or theoretical values.
Combining multiple lines of evidence rather than relying on a single basis increases persuasiveness.

For example, if the retention time of a peak in an unknown sample agrees with that of a standard substance, the peak area lies within the calibration-curve range, and similar values are obtained upon remeasurement, the quantitative result can be considered highly reliable.
On the other hand, caution is required if peaks overlap, the value lies outside the calibration-curve range, noise is large, or reproducibility is poor.

Discussion When the Result Differs From a Literature or Labeled Value

If a value obtained by instrumental analysis differs from a literature or labeled value, possible causes include sample preparation, standard-solution preparation, measurement conditions, instrument calibration, interfering components, and sample deterioration.
In addition, if the literature value is for a pure substance while the experimental sample is a mixture or commercial product, complete agreement may not occur.

When the result differs from a literature value, rather than simply writing that “there was an error,” specify which factor may have caused the measured value to become higher or lower.

When the Results Can Be Considered Good

Instrumental-analysis results can be considered good when peaks corresponding to the target component are confirmed, they are consistent with standard substances or literature values, the calibration curve has good linearity, the signal of the unknown sample lies within the calibration-curve range, and the measurement is reproducible.
It is also important that noise and baseline disturbance be small and that the peaks be sufficiently separated.

In qualitative analysis, agreement of peak positions and spectral patterns is important, while in quantitative analysis, the reliability of peak area or absorbance and the validity of the calibration curve are important.
The points to emphasize should be made clear according to the purpose of the experiment.

Example Discussion When the Experiment Did Not Go Well

When instrumental analysis does not go well, possible causes are considered from results such as no visible peak, a small peak, overlapping peaks, large noise, a nonlinear calibration curve, a quantitative value that deviates greatly, or poor reproducibility.
Organizing the causes separately into sample preparation, instrument conditions, standard solutions, blank, baseline, and peak processing makes the discussion easier.

How to Write Points for Improvement

In the discussion of an instrumental analysis experiment, including not only sources of error but also points for improvement makes the report easier to organize.
Points for improvement can be divided into sample preparation, standard-solution preparation, measurement operations, instrument conditions, and analysis methods.

Improvements to Sample Preparation

• Dissolve the sample completely
• Manage the dilution factor accurately
• Perform filtration or degassing when necessary
• Remove turbidity and precipitates
• Prevent deterioration of the sample
• Reduce losses during transfer

Improvements to Standard Solutions and Calibration Curves

• Prepare standard solutions accurately
• Set an appropriate concentration range
• Dilute the unknown sample so that it falls within the calibration-curve range
• Perform a blank measurement
• Investigate the causes of outliers
• Perform multiple measurements to confirm reproducibility

Improvements to Measurement and Analysis

• Clean cells and equipment thoroughly
• Remove bubbles
• Allow the instrument to stabilize sufficiently
• Standardize the measurement conditions
• If peaks overlap, review the separation conditions
• Set the baseline appropriately
• When comparing with literature values, also check the measurement conditions

Difference Between a Superficial Discussion and a Good Discussion

In an instrumental analysis experiment, writing only that “a peak appeared” or “the concentration was determined” results in a superficial discussion.
In a good discussion, the meaning of the peaks, basis for assignments, validity of the calibration curve, reliability of the quantitative values, and sources of error are connected.

Examples of Expressions That Can Be Used in Reports

The following expressions can be used when writing the results and discussion of an instrumental analysis experiment.
Adjust the necessary parts according to your own experimental results.

• Because the observed peak agreed with the standard substance or literature value, it is considered to originate from the target component.
• Peak position can be used for component identification, while peak area can be used for quantitation.
• Because the calibration curve showed linearity, signal intensity is considered proportional to concentration within this concentration range.
• Because the signal of the unknown sample was within the calibration-curve range, the quantitative result was considered generally valid.
• If peaks overlap, it becomes difficult to determine the peak area accurately and errors occur in the quantitative value.
• Baseline disturbance affects the calculation of peak height and peak area.
• If blank correction is insufficient, the concentration may be calculated including signals from sources other than the target component.
• Errors in preparation of the standard solutions affect the entire calibration curve and also affect the quantitative value of the unknown sample.
• The difference from the literature value may have been caused by differences in measurement conditions, sample preparation, coexisting components, or instrument calibration.
• To improve quantitative accuracy, it is important to standardize the measurement conditions and process the standard solutions and unknown samples using the same procedure.

Points to Check When Discussing Instrumental Analysis Experiments

Checking the following points before writing the report makes the discussion easier to write.

• Are the analytical method and analyte clearly stated?
• Is the spectrum or chromatogram shown?
• Are the meanings of peak position, intensity, and area explained?
• Is there a basis for the peak assignments?
• Has the result been compared with standard substances or literature values?
• Are the calibration-curve equation and correlation coefficient shown?
• Has it been confirmed that the signal of the unknown sample lies within the calibration-curve range?
• Has the dilution factor been correctly applied?
• Has blank correction been taken into account?
• Have the effects of noise and baseline disturbance been considered?
• Have the possibilities of peak overlap and interfering components been considered?
• Do the points for improvement correspond to the sources of error?

Summary

In instrumental analysis experiments, spectra, peaks, chromatograms, calibration curves, and quantitative values are used to evaluate the components, structures, concentrations, and purity of samples.
Spectra and peaks should not simply be regarded as having “appeared” or “not appeared”; it is important to discuss which components or functional groups they originate from based on their positions, intensities, areas, and shapes.

In quantitative analysis, a calibration curve is prepared from standard solutions and the signal of the unknown sample is evaluated within that range.
The reliability of quantitative results can be judged by checking the linearity and correlation coefficient of the calibration curve, blank correction, dilution factor, and method used to calculate peak area.
If the signal of the unknown sample lies outside the calibration-curve range, dilution or remeasurement may be necessary.

In a report, rather than merely writing that “a peak was observed” or “the concentration was determined,” organize and discuss the basis for peak assignments, validity of the calibration curve, differences from literature values, noise and baseline effects, sample-preparation errors, and the influence of instrument conditions.
In instrumental analysis, it is important to convert measurement data into chemical meaning and explain what the results indicate.