Training Courses

This two-day workshop is designed to provide participants with the knowledge and practical skills needed to leverage Excel for analyzing and interpreting data in analytical chemistry. The workshop focuses on essential and advanced Excel functions and their applications in real- world chemical data analysis, such as method validation, measurement uncertainty, error propagation, and statistical methods tailored for laboratory work. Through hands-on exercises and real-world examples, attendees will explore methods for visualizing data, calculating descriptive statistics, conducting hypothesis testing, and handling complex data sets using Excel’s built-in tools and custom formulas. This workshop is particularly beneficial for professionals and students in chemistry, environmental science, biology, or related fields who need to enhance their data analysis skills for laboratory and research applications. The course will also cover techniques to automate calculations and error analysis, allowing participants to streamline their data workflows. By the end of the workshop, participants will have the confidence and expertise to apply Excel effectively in their daily analytical tasks, improving accuracy, productivity, and data interpretation in scientific research.

Course Content

• Overview of Excel Interface
• Data Entry and Formatting
• Importing and Exporting Data
• Calculating Key Statistical Parameters
• Using Formulas and Functions
• Creating Graphs for Data Representation
• Trendlines and Calibration Curves
• Introduction to Excel Data Analysis AddIns
• Data Cleaning Techniques
• Outlier Detection in Experimental Data
• Correlation and Covariance Analysis
• Regression Analysis in Analytical Chemistry
• Advanced Curve Fitting
• T-test (Paired and Unpaired)
• Z-test for Large Sample Comparisons
• ANOVA Single Factor & Two Factor (With/Without Replication)
• F-Test Two Samples for Variances

Course Content

1) Basic Tools and Operations of Laboratory Experiments

• handling and care of basic laboratory instruments
• analytical balances, glassware, pH meter, preparation of standard solutions, instrument uncertainty

2) Workplace Safety

• prevention, PPE, waste management, biohazards and chemical waste

3) Data collection and simple statistics

• units of measurements
• significant figures

4) Validation for Instrumental Analysis

• performance characteristics (accuracy, precision, LOD, LOQ, selectivity, sensitivity, bias and variance, linear and dynamic ranges, repeatability, reproducibility, system stability and robustness).

Course Content

• Basic statistics for risk in conformity assessment
• ISO 17025: 2017 requirements on reporting statement of conformity
• Decision rules
• Process and approaches in conformity assessment
• Hypothesis (significance) testing
• Reporting statement of conformity

Course Content

• General QA principles in accreditation
• Introduction to QA and QC for laboratories
• Personnel qualifications and training
• Internal quality audits and quality review
• Reference materials and traceability
• Method validation and verification
• Proficiency testing
• Measurement uncertainty General QC procedures in testing
• Replicate analysis
• Spike recovery analysis with certified references materials
• Instrument performance and calibration
• Use of control charts

Course Content

Module 1: Introduction to Laboratory Safety and HIRARC

• Introduction to Occupational Safety and Health (OSH)
• Importance of laboratory safety.
• Common laboratory accidents.
• Introduction to HIRARC implementation in laboratory
• Roles and responsibilities of laboratory personnel

Module 2: Legal Requirements & Guidelines

• Occupational Safety and Health Act 1994 (OSHA 1994)
• OSHA Amendment 2022.
• USECHH Regulations 2000
• CLASS Regulations.
• Chemical Health Risk Assessment (CHRA)
• DOSH Guidelines related to HIRARC.
• Laboratory safety requirements and compliance.

Module 3: Laboratory Hazard Identification

• Definition of hazard and risk
• Types of laboratory hazards
• Hazard identification techniques
• Safety Data Sheet understanding
• Laboratory inspection techniques
• Unsafe acts and unsafe conditions

Module 4: Risk Assessment Process

• Risk assessment principles.
• Likelihood and severity concepts
• Risk matrix methodology
• Risk rating determination
• Prioritizing laboratory risk

Module 5: Risk Control Measures in Laboratory

• Hierarchy of Control
• Laboratory engineering controls
• Safe Chemical handling
• Chemical Storage and Segregation
• Emergency preparedness

Module 6: Laboratory HIRARC Documentation

• HIRARC documentation format
• Developing HIRARC forms
• Recording findings and recommendations
• Monitoring and review process
• Continuous improvement

Module 7: Incident Investigation & Case Study

• Laboratory incident reporting
• Root cause analysis
• Corrective and preventive actions
• Lesson learned from laboratory incidents

*eligible for 10 CEP Points by DOSH

*eligible for 12 CPD Hours by EiMAS

Course Content

Topic 1: Overview of Management of Chemicals

1. Chemicals discovered or made
2. Chemicals used in Industries and laboratories
3. Hazards in the workplace
4. Causes of accident in workplace
5. Chemical health risk assessment
6. Safe management of chemicals

Topic 2: Local Laws & Regulations on Chemical Substances

1. General outlines of Local laws : • Factories & Machinery Act 1967 • Pesticides Act 1994 • Environmental Quality Act 1974 and Regulations 2005 • Poisons Act 1952 and Regulations
2. OSHA 1994 (Occupational Safety and Health Act 1994) a. USECHH Reg.2000 b. Class Regulation

Topic 3: Management of Chemicals – Cradle to Grave concept.

1. Introduction to management of chemicals
2. State the concept of Cradle to Grave:
   i. Manufacturing & Production
   ii. Chemical Purchasing
   iii. Transportation; and vital information on • HAZCHEM code
   iv. Receiving & Internal Transportation
   v. Storage
   vi. Usage and Handling
   vii. Disposal
3. Explain elements of “From the Cradle to the Grave” concept.
4. Explain other control strategies

Topic 4: Overview of Chemical Wastes / Laboratory wastes

1. Introduction
2. Explain the types of waste
3. Environmental issues
4. Definition of hazardous wastes
5. Explain the characteristics of wastes
6. Environmental & health effects of hazardous waste

Topic 5: Local laws & Regulations on Chemical Wastes / Laboratory wastes

1. Waste (Under EQA 1974)
2. Environmental Quality (Industrial Effluent) Regulations 2009
3. Hazardous Waste Management [Under EQ (Scheduled Wastes) Regulations 2005]

Topic 6: Wastes Management

1. Introduction to waste management plan
2. Wastes minimization, source reduction reuse and recycle
3. Types of wastes a. Industrial wastes
4. Storage of Schedule Wastes
5. Industrial Effluent Treatment Systems
   i. Chemical treatment
   ii. Biological treatment
   iii. Anaerobic treatment
6. Waste disposal by:
   i. Incineration of scheduled wastes
   ii. Solidification
   iii. Secure landfill

Course Content

• Sampling Statistics
• General Procedures of Estimation Uncertainties in Chemical Measurements by the GUM & the EURACHEM principles
• Basic Procedures for MU with worked examples and exercises
• Worked examples of MU by component-by-component approach
• Strategy in implementing MU in Chemical Laboratory
• Recent International Development of MU for Chemical Analysis

*eligible for 10 CEP Points by DOSH

*eligible for 12 CPD Hours by EiMAS

Course Content

This two-day intensive training program on “MS 1042:2023 Safety in Laboratory” is designed to provide participants with a comprehensive understanding of the safety standards and best practices in laboratory environments. The course will cover essential aspects of laboratory safety, including hazard identification, risk assessment, safe handling of chemicals, biological safety, fire safety, and emergency response. Participants will gain the knowledge and skills necessary to ensure a safe and compliant laboratory environment, in accordance with the MS 1042:2023 standard.

Course Content

• SAMM Requirements for Internal Audit
• Overview of the Internal Audit Process
• Audit Schedule
• Audit planning
• Development of auditors skills
• Conducting an Audit
• The Audit Report and Follow-up

Course Content

This training brings practical guidance for laboratories to organize interlaboratory comparisons (ILCs)
with a small number of participants (n = 2–7) which is not normally arranged by a proficiency testing
provider. The methodological guideline aims to provide practical approaches following the technical
requirements of organizing a small ILC.
Topics covered:

• ISO 17025 and laboratory accreditation requirements on ILC
• Design of small ILC
• Preparation of test items
• Distribution of ILC test items
• Statistical evaluation of the results
• Participation report

Course Content

• Approaches to method validation
• Performance characteristic of a method
• Documentation and use of method validation data
• Use of method validation data for quantification of measurement uncertainty and conformity assessment

Course Content

This intensive two-day programme provides participants with a strong and practical foundation in Six Sigma methodology for achieving operational excellence, process consistency, and quality improvement in chemical and process-based industries. Designed for engineers, scientists, researchers, and industry practitioners, the course delivers a structured and industry-relevant learning experience that simplifies the application of Six Sigma concepts within real industrial environments. Participants will develop a clear understanding of how organizations systematically reduce process variation, minimise defects, improve product quality, and enhance operational efficiency through data-driven decision making.

The programme introduces the core philosophy of Six Sigma and the structured DMAIC (Define–Measure–Analyse–Improve–Control) improvement framework, with strong emphasis on its application in chemical manufacturing, process optimisation, and continuous improvement initiatives. Participants will also be exposed to the Seven Quality Control (7 QC) Tools, variability analysis techniques, and essential problem-solving methodologies widely used in industrial quality improvement programs.

Key analytical and quality improvement tools covered include:

• Root Cause Analysis techniques such as Fishbone (Ishikawa) Diagram and 5 Whys
• Pareto Analysis (80/20 Rule) for prioritisation of critical issues
• Histograms and Scatter Diagrams for process variation and relationship analysis
• Cause-and-Effect Matrix for identifying critical process variables
• Fundamentals of Regression Analysis and process relationship interpretation
• Introduction to Control Charts for process monitoring and stability assessment
• Basic concepts of Hypothesis Testing (introductory level)
• Parameter in Data Sets for understanding process relationships using Y=f(x), where process outputs are influenced by controllable process variables and operating conditions

Through guided industrial case studies and practical examples related to chemical and process industries, participants will learn how to interpret process variation, analyse operational performance, identify root causes of process inefficiencies, and implement systematic quality improvement strategies for process optimisation and operational excellence.

By the end of the course, participants will gain valuable insight into reducing variability, improving yield and consistency, strengthening product quality, enhancing process reliability, and supporting continuous improvement initiatives—critical elements for competitiveness and sustainability in today’s chemical and advanced manufacturing industries.

Six Sigma Concept for Quality & Process Improvement

Session 1: Introduction to Six Sigma

• Opening
• History and development of Six Sigma
• Benefits of applying Six Sigma in Chemical industry: reduced variation, improved quality, and higher reliability.

Session 2: Introduction to Six Sigma

• What is Six Sigma
• DMAIC philosophy

Session 3: Quality Philosophy

• Six Sigma as Quality Philosophy
• Six Sigma as a Method: Y = f (x)

Session 4: Process Variability

• Variability, Variation and Standard Deviation
• Parameter Study
• Voice of the Customer (VOC)
• Quality Function Deployment (QFD)

Session 5: Six Sigma in Management

• Six Sigma as a Quality Tool
• Strategy by Phases

Session 6: Benefits of Six Sigma

• Benefits of Six Sigma and its Application
• Yellow Belt + Green Belt + Black Belt

Session 7: Introduction of 7 QC Tools

• Tools and Techniques used: Management and Technical field

Session 8: 8D TOPS

• 8D TOPS Problem-Solving Method

Session 9: Parameters in Data Sets

• Six Sigma principles for Parameters Y = f (x)
• The DMAIC (Define, Measure, Analyze, Improve, Control) methodology.

Session 10: DMAIC methodology – DEFINE.

• Purpose of the Define phase.
• Project charter components (problem statement, goal statement, scope, timeline).

Session 11: DMAIC methodology – MEASURE

• Data collection planning (what, how, and from where to collect).
• Types of data (discrete vs. continuous).
• Basic statistics (mean, median, mode, standard deviation)

Session 12: DMAIC methodology – ANALYZE using 7 QC Tools

• Root Cause Analysis techniques (Fishbone/Ishikawa diagram, 5 Whys).
• Pareto analysis (80/20 rule)
• Checksheet
• Histograms
• Regression analysis
• Scatter Diagram

Session 13: DMAIC methodology – IMPROVE

• Brainstorming and solution selection tools
• Failure Modes and Effects Analysis (FMEA).
• Plan-Do-Check-Act (PDCA) cycle

Session 14: DMAIC methodology – CONTROL

• Control plans and documentation.
• Statistical Process Control (SPC) basics
• Control charts (X-bar, R-chart)

Course Content

This intensive two-day programme provides participants with a strong and practical foundation in Six Sigma methodology for achieving operational excellence, process consistency, and quality improvement in chemical and process-based industries. Designed for engineers, scientists, researchers, and industry practitioners, the course delivers a structured and industry-relevant learning experience that simplifies the application of Six Sigma concepts within real industrial environments. Participants will develop a clear understanding of how organizations systematically reduce process variation, minimise defects, improve product quality, and enhance operational efficiency through data-driven decision making.

The programme introduces the core philosophy of Six Sigma and the structured DMAIC (Define–Measure–Analyse–Improve–Control) improvement framework, with strong emphasis on its application in chemical manufacturing, process optimisation, and continuous improvement initiatives. Participants will also be exposed to the Seven Quality Control (7 QC) Tools, variability analysis techniques, and essential problem-solving methodologies widely used in industrial quality improvement programs.

Key analytical and quality improvement tools covered include:

• Root Cause Analysis techniques such as Fishbone (Ishikawa) Diagram and 5 Whys
• Pareto Analysis (80/20 Rule) for prioritisation of critical issues
• Histograms and Scatter Diagrams for process variation and relationship analysis
• Cause-and-Effect Matrix for identifying critical process variables
• Fundamentals of Regression Analysis and process relationship interpretation
• Introduction to Control Charts for process monitoring and stability assessment
• Basic concepts of Hypothesis Testing (introductory level)
• Parameter in Data Sets for understanding process relationships using Y=f(x), where process outputs are influenced by controllable process variables and operating conditions

Through guided industrial case studies and practical examples related to chemical and process industries, participants will learn how to interpret process variation, analyse operational performance, identify root causes of process inefficiencies, and implement systematic quality improvement strategies for process optimisation and operational excellence.

By the end of the course, participants will gain valuable insight into reducing variability, improving yield and consistency, strengthening product quality, enhancing process reliability, and supporting continuous improvement initiatives—critical elements for competitiveness and sustainability in today’s chemical and advanced manufacturing industries.

Six Sigma Concept for Quality & Process Improvement

Session 1: Introduction to Six Sigma

• Opening
• History and development of Six Sigma
• Benefits of applying Six Sigma in Chemical industry: reduced variation, improved quality, and higher reliability.

Session 2: Introduction to Six Sigma

• What is Six Sigma
• DMAIC philosophy

Session 3: Quality Philosophy

• Six Sigma as Quality Philosophy
• Six Sigma as a Method: Y = f (x)

Session 4: Process Variability

• Variability, Variation and Standard Deviation
• Parameter Study
• Voice of the Customer (VOC)
• Quality Function Deployment (QFD)

Session 5: Six Sigma in Management

• Six Sigma as a Quality Tool
• Strategy by Phases

Session 6: Benefits of Six Sigma

• Benefits of Six Sigma and its Application
• Yellow Belt + Green Belt + Black Belt

Session 7: Introduction of 7 QC Tools

• Tools and Techniques used: Management and Technical field

Session 8: 8D TOPS

• 8D TOPS Problem-Solving Method

Session 9: Parameters in Data Sets

• Six Sigma principles for Parameters Y = f (x)
• The DMAIC (Define, Measure, Analyze, Improve, Control) methodology.

Session 10: DMAIC methodology – DEFINE.

• Purpose of the Define phase.
• Project charter components (problem statement, goal statement, scope, timeline).

Session 11: DMAIC methodology – MEASURE

• Data collection planning (what, how, and from where to collect).
• Types of data (discrete vs. continuous).
• Basic statistics (mean, median, mode, standard deviation)

Session 12: DMAIC methodology – ANALYZE using 7 QC Tools

• Root Cause Analysis techniques (Fishbone/Ishikawa diagram, 5 Whys).
• Pareto analysis (80/20 rule)
• Checksheet
• Histograms
• Regression analysis
• Scatter Diagram

Session 13: DMAIC methodology – IMPROVE

• Brainstorming and solution selection tools
• Failure Modes and Effects Analysis (FMEA).
• Plan-Do-Check-Act (PDCA) cycle

Session 14: DMAIC methodology – CONTROL

• Control plans and documentation.
• Statistical Process Control (SPC) basics
• Control charts (X-bar, R-chart)

Course Overview

Designed for analytical chemists, this practice-oriented course provides a step-by-step framework for evaluating and reporting measurement uncertainty under ISO/IEC 17025. It focuses on top-down approaches leveraging existing method validation, QC records, and statistical techniques across routine environmental testing scenarios.

Key Learning Outcomes

• Utilize Existing Validation Data: Extract uncertainty components directly from routine validation and QC processes.
• Design Precision & Bias Studies: Structure experiments using CRMs, QC data, PT data, and recovery studies.
• Apply Practical Statistics: Confidently evaluate analytical variability, bias, and expanded uncertainty.
• Environmental Method Applications: Work through step-by-step workflows for up to 8 environmental test methods.
• Audit-Ready Reporting: Prepare standardized, compliant uncertainty budgets for accreditation audits.

Course Module & Key Topics Covered

Module 1: Introduction & Framework

• Transitioning to top-down approaches; alignment with ISO/IEC 17025 and GUM principles using validation/QC data.

Module 2: Statistical Techniques

• Statistical tools, errors in measurements, combining standard uncertainties (precision, bias), and expanded uncertainty (k-factor).

Module 3: Precision & Bias Design

• Experimental design for CRMs, analytical quality control, proficiency testing (PT) evaluation, and recovery across matrices/ranges.

Module 4: Practical Worked Examples

• Step-by-step calculations for up to 8 environmental parameters (water, soil, air, wastewater) across varied ranges.

Module 5: Documentation & Audits

• Structuring compliant measurement uncertainty budgets and reports for accreditation audit readiness.

Course Content

• ISO 17025 requirements and SAMM Policy on proficiency testing (PT)
• Organisation of PT schemes
• Statistics in PT performance evaluation
• Root cause analysis for poor performance
• Corrective actions

Course Content

• Introduction to Method Validation
• SAMM Scheme
• Descriptive Statistics
• Outlier Tests – Dixon, Grubb and Cochran
• Standard error of the mean
• Confidence Intervals
• Significance Testing, the Student t-test, F-test, one-way ANOVA, Analysis of Variance, Linear Regression, Standard Curve & its Confidence Intervals, Determination of method’s measurement uncertainty

Course Content

• Introduction
• General requirements
• Structural requirements
• Resource requirements
• Process requirements
• Management systems requirements