AOAC India
The organization is committed to being a proactive, worldwide provider and facilitator in the development
The organization is committed to being a proactive, worldwide provider and facilitator in the development
About Speaker
Abhishek Mandal – Spl: Agricultural Chemicals; Detection of Pesticide Residues; Synthesis of Nano-particles; Big Data Analysis; Dsg: Scientist, ICAR-Indian Agricultural Research Institute, New Delhi.
Food authenticity is about misrepresentation by either mislabeling or by adulterating usually with lower cost material.
Typical examples are:
Addressing Food Authentication Challenges
Analytical approaches: fundamental classifications
Irrespective of the analytical technique, there are some fundamental ways of classifying the analytical approach. Many of the techniques can be used for multiple approaches, depending on how they are configured. Typical ways of classifying the approach are:
Data Analysis: Chemometric Approach
Unsupervised: classification of sample without any knowledge about the origin
Supervised: similarity of unknown to authentic material
Statistical and Machine Learning Data Analysis used in food authenticity data analyses. They include:
Fold Change Analysis
T-tests
Volcano Plot
One-way ANOVA and post-hoc analysis
Correlation analysis
Principal Component Analysis (PCA)
Partial Least Squares – Discriminant Analysis (PLS-DA)
Orthogonal-Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA)
Significance Analysis of Microarray (SAM)
Empirical Bayesian Analysis of Microarray (EBAM)
Hierarchical Clustering
Partitional Clustering
Random Forest
Support Vector Machine (SVM)
Applications/Case studies of the Statistical and Machine Learning Data Analysis used in food authenticity data analyses
Individual case studies outlining the importance and application of the various multivariate analytical tools/ chemometrics in food authenticity/food fraud detection data analysis.