Chemometrics in Spectroscopy ? Linearity in Calibration: Quantifying Nonlinearity, Part II (PDF)

In this tutorial, Thomas G. Mayerhöfer and Jürgen Popp introduce complex-valued chemometrics as a more physically grounded alternative to traditional intensity-based spectroscopy measurement methods. By incorporating both the real and imaginary parts of the complex refractive index of a sample, this approach preserves phase information and improves linearity with sample analyte concentration. The result is more robust and interpretable multivariate models, especially in systems affected by nonlinear effects or strong solvent and analyte interactions.

Researchers from Hebei University and Hebei University of Engineering have developed a hyperspectral imaging method combined with data fusion and machine learning to accurately and non-destructively assess walnut quality and classify storage periods.

Chinese researchers have developed a powerful new method using near-infrared (NIR) hyperspectral imaging combined with a convolutional neural network (CNN) to identify hazardous explosive materials, like trinitrotoluene (TNT) and ammonium nitrate, from a distance, even when concealed by clothing or packaging.

Researchers have developed an analytical method combining remote near-infrared and Raman spectroscopy with machine learning to noninvasively map moisture and salt damage in historic buildings, offering critical insight into ongoing structural deterioration.

A new study highlights how chemometrics-powered spectroscopic techniques offer a fast, non-destructive, and cost-effective method for detecting phenolics and vitamins in foods.

Researchers from Jinan University and Guangzhou Customs Technology Center have developed a cost-effective UV-vis spectroscopy and machine learning method to accurately identify recycled PET content as low as 10%, advancing sustainable packaging and circular economy efforts.