Chemometric-based compliance approach for detection of economically motivated fraud in multiple organic spices using NIR spectroscopy
Schumer, Nathaniel Glen
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https://hdl.handle.net/2142/127418
Description
Title
Chemometric-based compliance approach for detection of economically motivated fraud in multiple organic spices using NIR spectroscopy
Author(s)
Schumer, Nathaniel Glen
Issue Date
2024-12-13
Director of Research (if dissertation) or Advisor (if thesis)
Kamruzzaman, Mohammed
Committee Member(s)
Singh, Vijay
Rausch, Kent
Department of Study
Engineering Administration
Discipline
Agricultural & Biological Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Chemometrics
Spices
Multivariate Analysis
Spectroscopy
Fraud
Abstract
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.
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