Experimental design and big data automation - Prof. Marcello Mascini - a.a. 2025/2026
Indice degli argomenti
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The course aims to increase the knowledge of pre and post processing experimental data with multivariate statistical techniques applied to the analysis of foods. This course will emphasize computer approaches to multivariate statistical analysis and automation. We will discuss how to design, conduct, and analyze experiments in food sciences
Because of the practical application nature of this course there is no mandatory textbook. Instead, you should purchase a text that suits your needs (e.g., practical application versus mathematical statistics). Recommended texts are:
Johnson, Dallas E. (1998). Applied multivariate methods for data analysis. Pacific Grove, CA: Duxbury Press. Good balance between theory and practice.
Tabachnick, B. G. & Fideii,L.S. (2000). Using Multivariate Statistics, 4th Ed. New York: Allyn & Bacon. A traditional and popular text that focuses on practical applications.
Oehlert, Gary W. (2010). A first course in design and analysis of experiments. (http://users.stat.umn.edu/~gary/book/fcdae.pdf)
Barrentine Larry B. (1999) An Introduction to Design of Experiments: A Simplified Approach Amer Society for Quality
slides of the lessons and eLearning tools
The course consists of theoretical lessons enriched by practical examples, exercises and exercises of data processing using open access software of recognized international validity.
Teaching is carried out with lectures in English.
The course is split into 4 units:
UNIT 1: univariate analysis
Data, information, models, data types, analytical representation of data
Calibration and regression, Introduction to Statistics
Media & Variance
The Normal distribution, theory of measurement errors, the central limit theorem and the theorem of Gauss
Maximum likelihood, method of least squares, Generalization of the method of least squares
Polynomial regression, non-linear regression, the χ2 method, Validation of the model
UNIT 2: multivariate analysis
Correlation
Multiple linear regression
Principal component analysis (PCA)
Principal component regression (PCR) and Partial least squares regression - (PLS)
UNIT 3: Design of Experiments
Basic design of experiments and analysis of the resulting data
Analysis of variance, blocking and nuisance variables
Factorial designs
Fractional factorial designs
Overview of other types of experimental designs (Plackett–Burman designs, D-optimal designs, Supersaturated designs, Asymmetrical designs)
Response surface methods and designs
Applications of designed experiments from various fields of food science
UNIT 4: Elements of Pattern recognition
cluster analysis
Potential Method
normalization
The space representation (PCA)
Examples of PCA
Discriminant analysis (DA)
PLS-DA
Examples of PLS-DA
The teacher manages the course through the web platform http://elearning.unite.it/ . After sign up Students can download all electronics materials of the course. Agenda of the practical use of academic-free programs and of multi-choice tests and reports will be planned at the beginning of the course and uploaded on the web platform. Students can download all learning tools (pdf files, software, excel files ect) before classes.
Workload:
Face-to-face teaching: 30 hours
Interactive teaching (groups, individuals): 10 hours
Virtual Lab: 10
Individual study: 40 hours
Throughout the duration of the course, students will be invited to actively participate in learning (via smartphone or laptop) using the wooclap interactive platform with interactive presentations (multiple choice questions, word cloud, open questions, etc.).
On the e-learning website, students will be able to download all learning tools. In particular, specific activities will be available for working students such as access to online lessons, recorded mini-lessons, commented slides.
For working students, there will be periodic in-depth meetings and additional receptions, also remotely.
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The Link to partecipate to the students reception Online. Please write me an email to book an appointment:)
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