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| EAKL6017 | @Digital Image Colorimetry and Smartphone-Based Sensing in Analytical Chemistry | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | ANALYTICAL CHEMISTRY IN PHARMACY | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Doç. Dr. Sercan YILDIRIM | | Co-Lecturer | | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | The primary objective of this course is to provide PhD students with a comprehensive theoretical and practical foundation for utilizing smartphones and digital image sensors (CMOS/CCD) within the field of analytical chemistry. The curriculum focuses on the design of low-cost, portable, and customized measurement systems as alternatives to classical spectrophotometric instruments, the optimization of camera hardware parameters for quantitative analysis, and the systematic extraction of analytical data from various color spaces. The course aims to enable students to develop controlled illumination setups designed to eliminate ambient light interference for applications in environmental, food, and material analysis, while empowering them to generate original analytical methodologies by processing digital data through advanced chemometric techniques. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Analyzes the operating principles of digital image sensors (CMOS/CCD) and the effects of camera hardware parameters (ISO, exposure, white balance) on analytical measurement precision. | 1 - 2 - 3 - 4 - 5 | 1,3, | | PO - 2 : | Grasps the theoretical background of different color spaces (RGB, HSV, CMYK, etc.) and interprets data by determining the color channel that provides the highest sensitivity for a specific chemical analysis. | | 1,3, | | PO - 3 : | Analitik ölçümler sırasında dış ışık girişimini minimize etmek ve standart aydınlatma koşullarını sağlamak için gerekli olan karanlık kutu (dark box) düzeneklerini ve optik aksamları tasarlar. | | 1,3, | | PO - 4 : | Applies data extraction techniques from digital images via image processing software and manages the processes of converting digital signals into concentration data. | | 1,3, | | PO - 5 : | Evaluates the applicability of next-generation smartphone-based analytical methods in environmental, food, and material analysis and prepares validation protocols for these methods against standard laboratory instruments. | | 1,3, | | PO - 6 : | Discusses results within a scientific framework using basic chemometric methods and statistical error analysis to enhance the reliability of data obtained from image-based analysis. | | 1,3, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| The course content begins with the fundamental principles of digital image colorimetry, providing a comparative perspective with traditional spectrophotometric methods. Within the scope of the program, CMOS/CCD sensor technologies, the specific roles of color spaces such as RGB, HSV, and CMYK in chemical data analysis, and the critical impact of smartphone camera hardware parameters-including ISO, white balance, and exposure time-on analytical precision are detailed. The application-oriented sections of the curriculum encompass the design of controlled illumination systems, dark box prototyping techniques to eliminate ambient light interference, and methodologies for quantitative data extraction via specialized image processing software. In the final stage, the course is completed by an in-depth examination of acquired data processing using chemometric tools, method validation procedures, and original analytical applications in the fields of food, environmental, and material science. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Digital Image Colorimetry: History and Comparison with Classical Spectrophotometry | | | Week 2 | Fundamentals of Digital Image Sensors: CMOS and CCD Technologies and Optical Data Acquisition | | | Week 3 | Color Spaces and Mathematical Models I: Analytical Equivalence of RGB and CMYK Systems | | | Week 4 | Color Spaces and Mathematical Models II: Analytical Equivalence of HSV, HSL, and Lab | | | Week 5 | Camera Hardware Optimization I: Impact of ISO, Shutter Speed, and Aperture on Signal-to-Noise Ratio | | | Week 6 | Camera Hardware Optimization II: White Balance Locking, JPEG Compression, and the Importance of RAW Format | | | Week 7 | Controlled Illumination Techniques: Light Sources (LED vs. Fluorescent) and Light Diffusion Standards | | | Week 8 | Hardware Design and Prototyping: Dark Box Design and 3D Printing Integration | | | Week 9 | Mid-term Exam | | | Week 10 | Digital Image Processing Software: Automated Data Extraction with ImageJ and Open Source Tools | | | Week 11 | Quantitative Analysis and Calibration Strategies: Linearity, Sensitivity, and Standard Addition Methods | | | Week 12 | Advanced Data Treatment in Image Analysis: Chemometrics and Multivariate Analysis (PCA/PLS) Applications | | | Week 13 | Environmental Analysis Applications: Water Quality Monitoring and Digital Colorimetry in Gas Sensors | | | Week 14 | Food Analysis and Safety Applications: Image-Based Methods in Food Adulteration and Quality Control | | | Week 15 | Integration of Microextraction Techniques with Image-based Sensors | | | Week 16 | Final Exam | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9. hafta | | 1 | 30 | | In-term studies (second mid-term exam) | 12. hafta | | 1 | 20 | | End-of-term exam | 16. hafta | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | | | | |
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