Analisis kimia pendahuluan 2026

Summary

The lecture introduces analytical chemistry as the science of determining what substances are present in a sample, how much is present, and what properties or structure those substances have. In pharmacy, analytical chemistry helps confirm a medicine’s identity and dose, detect impurities or toxic substances, and assess product quality, safety, and therapeutic usefulness.

Main concepts

Three aims of analysis

• Identification: Determine which substance or substances are present.
• Quantification: Determine their amount or concentration.
• Characterization: Examine properties such as structure, pH, melting or boiling point, and spectral features.

Good analysis depends on the entire process, not just the instrument or measurement. It includes selecting representative samples, preparing them appropriately, taking measurements, interpreting the data, and communicating results with suitable precision and uncertainty.

Choosing an analytical method

Analytical methods should be chosen to fit the problem and sample. Important considerations include the target substance, sample matrix, concentration, possible interferences, cost, sensitivity, and selectivity.

• Sensitivity is the ability to detect an analyte, including at low concentrations.
• Selectivity is the ability to measure the target in the presence of other substances.
• Efficiency includes practical considerations such as time, reagent use, and cost.
• Accuracy describes closeness to a reference value; precision describes how closely repeated results agree. A method can be precise without being accurate, or accurate on average without being precise.

Ways of classifying methods

Analytical methods can be classified in several ways:

• By purpose: qualitative (what is present) or quantitative (how much is present).
• By approach: chemical methods, which rely on reactions, or physical methods, which measure physical properties or changes.
• By technique: classical methods, such as titration, gravimetry, and qualitative chemical reactions, or instrumental methods.

Instrumental approaches include spectroscopy (UV–visible, infrared, Raman, NMR, atomic absorption, and mass spectrometry), chromatography (such as HPLC and gas chromatography), and electrochemical methods. The lecture also mentions biological and molecular techniques such as ELISA, PCR, electrophoresis, and immunochemical methods.

The lecturer emphasizes that sophisticated instruments are not automatically better for every task. A classical method may be suitable if it meets the requirements for the sample and produces reliable results at an acceptable cost.

General workflow for an analysis

1. Define the problem and objective. Specify what substance is being measured, in what sample, and what result is needed.
2. Understand the sample matrix. Consider its composition and which substances might interfere with the measurement.
3. Choose an appropriate method. Match the method to the analyte’s properties, expected concentration, required sensitivity and selectivity, available equipment, and cost.
4. Take representative samples. Sampling should reflect the population or material being studied. Depending on the sample, collection from different locations or depths may be necessary.
5. Prepare the sample. Make it suitable for measurement—for example, by homogenizing or dissolving it, concentrating it, or removing interfering substances.
6. Measure the analyte. Use the selected method, along with suitable standards or controls where appropriate.
7. Process and assess the data. Convert instrument readings into meaningful quantities, evaluate repeated measurements, and check whether results are reliable.
8. Report the results clearly. Include appropriate units, significant figures, and uncertainty, and explain what the results mean.

A method reported in the literature may not automatically suit a different sample type. For example, a procedure developed for a tablet may not work directly on blood, whose components can interfere with the analysis.

Example: Investigating possible arsenic poisoning

The lecturer uses a hypothetical investigation of deer deaths to illustrate the analytical process:

• Kidney tissue and nearby grass or water are selected because they may help investigate exposure.
• Tissue is homogenized so that portions taken for analysis are more representative. Replicate measurements help assess precision.
• The sample is prepared to remove or separate interfering materials. The example describes converting arsenic into a form that can be separated and measured using a color-based method, then comparing the response with standards.
• The lecture cites estimated arsenic results of 16 and 22 ppm in kidney samples and 600 ppm in grass. It states that levels above 10 ppm in kidney may indicate serious toxicity.
• Finding arsenic does not, by itself, prove the cause of death. Exposure history, tissue findings, and other possible causes must also be considered.
• The example involves arsine gas, which the lecturer identifies as highly toxic and requiring stringent laboratory safety controls.

Measurement errors and reliability

The lecture distinguishes three kinds of error:

• Random errors: Unpredictable variation between measurements, potentially associated with environmental changes or differences in handling. Repeated measurements and statistical measures such as standard deviation or coefficient of variation can help assess their impact.
• Systematic errors: Consistent bias caused by factors such as an uncalibrated instrument, an unsuitable method, contaminated reagents, or uncontrolled conditions. These should be investigated and corrected through calibration, suitable standards and blanks, equipment checks, and control of environmental conditions.
• Blunders: Avoidable mistakes caused by carelessness or misunderstanding instructions. Training, following protocols, checking measurements, and using checklists can help prevent them.

Measurement errors cannot be eliminated entirely. Results should therefore be checked against appropriate standards and supported by replicate measurements. Absolute error is the difference between the measured and reference values; relative and percentage errors express that difference in relation to the reference value.

Uncertainty and significant figures

Measurements have limits: the instrument, sample handling, standard purity, and reading technique can all affect a result. Results should be reported with appropriate uncertainty, rather than implying that a measurement is exact.

Significant figures communicate the precision of a reported value. The lecture’s main rules are:

• Non-zero digits are significant.
• Zeros between significant digits are significant.
• Leading zeros are not significant.
• In multiplication and division, the result is generally limited by the factor with the fewest significant figures.
• In addition and subtraction, the result is generally limited by the least precise decimal place.

The lecturer uses values such as 1 and 1.0 to show that equal numerical values can communicate different levels of reported precision. Some detailed numerical examples in the subtitles are garbled, so their specific calculations should be treated cautiously.

Course outline

The lecture previews topics including calculations and sample preparation; qualitative analysis of ions and functional groups; titrations; spectroscopy and chromatography; electrochemical methods; mass spectrometry; immunochemical and molecular techniques; and practical sessions involving laboratory equipment, ion analysis, titration, UV–visible and infrared spectroscopy, colorimetric methods, and atomic absorption.

Speakers and sources featured

• Lecturer: One instructor delivers the lecture; the subtitles do not state the speaker’s name.
• Channel attribution: Nursalam Hamzah, as listed in the video metadata.
• Other speakers or sources: None are identifiable in the provided subtitles.