Multimodal Dataset for Cinnamon Powder Adulteration Detection Using Colorimetry, FTIR Spectroscopy, and RGB Images

Description

This dataset provides a comprehensive multimodal benchmark for the detection and quantification of cinnamon powder adulteration using three complementary sensing modalities: colorimetric measurements, Fourier Transform Infrared (FTIR) spectroscopy, and RGB images. The dataset was developed to support research in food authentication, adulteration detection, spectroscopy, computer vision, multimodal machine learning, and explainable artificial intelligence. The dataset comprises 5,850 verified data records, including 1,950 colorimetric measurements, 1,950 FTIR spectra, and 1,950 RGB images, collected from cinnamon powder adulterated with three economically important adulterants: peanut shell powder, walnut shell powder, and wood dust powder. Samples were prepared at thirteen adulteration levels (0%, 1%, 2%, 3%, 4%, 6%, 8%, 10%, 12%, 15%, 18%, 21%, and 24% w/w), with 50 independently prepared samples per adulteration level for each adulterant. Consistent sample identifiers are maintained across all three modalities, enabling straightforward multimodal data fusion and cross-modal analysis. Prior to publication, the complete dataset underwent systematic quality verification, including validation of sample identifiers, adulteration labels, duplicate detection, folder organization, and sample completeness. All verification checks were successfully completed.

Institutions

• Dayananda Sagar University

  Karnataka, Bengaluru
• Graphic Era University

  Uttarakhand, Dehradun

Categories

Licence