Soup's On! How Chemical Imaging and Chemometrics are Revolutionizing Food Analysis
"Uncover hidden ingredients and ensure food safety with the power of hyperspectral chemical imaging."
Applications of hyperspectral chemical imaging (HCI) are rapidly expanding. Its integration into analytical chemistry relies heavily on chemometrics. A single hyperspectral image holds vast amounts of data, requiring chemometric techniques to extract meaningful chemical information. Chemometrics unlocks the understanding of HCI, and conversely, HCI enhances our understanding of chemometrics.
When exploring chemometric principles, visual perception plays a crucial role, particularly in interpreting scatter plots and histograms. HCI takes this further by introducing spatial images, speeding up analysis through visual processing. This allows for pattern recognition, spatial distribution analysis, and outlier detection. HCI's "image advantage" allows us to observe the effects of processing on familiar scenes.
This paper explores the fundamental properties of basic chemometric techniques by applying them to an example image: a near-infrared (NIR) hyperspectral image of a powdered soup mix contaminated with foreign objects like string, wood, and a paperclip. The HCI hypercube has spatial dimensions of 418 pixels × 318 pixels and 207 wavelength channels, acquired with a BurgerMetrics HyperPro NIR imaging system.
Unveiling Food Secrets: Principal Components Analysis (PCA) with HCI
PCA is a cornerstone of chemometrics, ideal for exploring and predicting data. In NIR spectroscopy, some wavelength regions hold key information, while others primarily add noise. PCA compresses data, projecting high-dimensional spectral data into a lower-dimensional space, separating signal from noise. This process yields principal components (PCs) consisting of loading and score vectors, ordered to explain decreasing amounts of signal variance.
- PC-1: Captures maximum variance, often highlighting physical variations (e.g., soup mix surface).
- PC-2: Shows reduced contrast, making contaminants (e.g., paperclip) more distinguishable.
- Later PCs: Gradually decrease in contrast, dominated by specular reflection or noise, indicating diminishing signal content.
HCI: A Powerful Tool for Chemometric Insights
HCI serves as an intuitive tool for investigating chemometrics. Our visual processing skills are crucial when understanding spatial scene details, while the detection of subtle differences in score images and prediction maps optimizes models and parameters, like latent variables or classification thresholds.
Techniques applied to PCA and PLS-DA are easily extended to other chemometric methods. As a powerful technique for quickly amassing large sets of spectral data, HCI is ideal for training and validating chemometric models.
The incorporation of HCI as a teaching aid in introductory chemometrics courses should be further developed, enhancing the understanding of chemical processes and food analysis techniques among emerging scientists and engineers.