2026-09-23T00:23:06Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/127418 2026-02-03 col_2142_5131 col_2142_14847 com_2142_5130 com_2142_683 com_2142_397

application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01 The student, Nathaniel Schumer, accepted the attached license on 2024-12-13 at 09:25. The student, Nathaniel Schumer, submitted this Thesis for approval on 2024-12-13 at 09:35. This Thesis was approved for publication on 2024-12-13 at 15:39. DSpace SAF Submission Ingestion Package generated from Vireo submission #21595 on 2025-03-28 at 14:45:06 Chemometric-based compliance approach for detection of economically motivated fraud in multiple organic spices using NIR spectroscopy Schumer, Nathaniel Glen 2024-12-13 Kamruzzaman, Mohammed Singh, Vijay Rausch, Kent Chemometrics Spices Multivariate Analysis Spectroscopy Fraud eng Spices hold significant cultural, culinary, and economic value worldwide, serving as essential ingredients in diverse cuisines and playing a vital role in the global food industry. However, spices are often adulterated to achieve economic gain, as fraudulent practices involving cheaper filler substances can increase profit margins. Even in small amounts, such adulteration with filler material does not significantly alter the aroma, flavor, or color characteristics of the adulterated product. Current regulations offer no definitions or standards for identifying or preventing the sale of adulterated spices. Prediction and identification of these filler materials are determined with conventional techniques and expensive methods that require chemicals and are destructive in nature. This work investigates a chemometric-based fast and non-destructive approach for adulteration detection in multiple species to stop economic fraud. The spices examined were cardamon, cinnamon, coriander, cloves, mustard, and nutmeg. 144 spice samples adulterated with 0-10% corn starch (w/w) were scanned using a benchtop near-infrared (NIR) spectrometer in the 867-2535 nm spectral range. Multivariate analysis was utilized to develop calibration models using partial least-squares regression (PLSR) with raw spectra and various preprocessing approaches applied to the spectra. The calibration and validation model obtained for all spices (R2>0.9, RMSE<1%, and RPD>3) is promising in displaying great predictive ability and performance. 2024-12 Thesis https://hdl.handle.net/2142/127418 Copyright 2024 Nathaniel Schumer Engineering Administration Agricultural & Biological Engr University of Illinois at Urbana-Champaign M.S. Thesis