Smart Sampling and Probing: Are You Getting All the Relevant Information?

Area of Science:

• Analytical Chemistry
• Data Science

Background:

• Sampling and probing are crucial for characterizing complex systems and samples in analytical science.
• Inefficient analytical situations often arise from oversampling and overprobing, leading to suboptimal data collection.

Purpose of the Study:

• To provide a method for evaluating the number of factors and objects in a dataset to avoid oversampling and overprobing.
• To enhance the efficiency of analytical processes through data-driven insights.

Main Methods:

• Utilizes principal component analysis and principal object analysis for data analysis.
• Employs eigenvalue decomposition to determine the number of factors and object contributions.
• Validates the methodology through simulations and controlled experiments.

Main Results:

• Eigenvalue decomposition effectively identifies the number of relevant features within datasets.
• The approach allows for the supervision of sampling and probing processes.
• Demonstrated ability to retrieve the exact number of factors and objects in datasets.

Conclusions:

• The proposed numerical approach, using eigenvalue decomposition, optimizes sampling and probing for efficient analysis.
• Applicable to complex systems, including food and environmental samples.
• Aids researchers in achieving more accurate and efficient characterization of samples.