Raman Imaging for the Detection of Adulterants in Paprika Powder: A Comparison of Data Analysis Methods

Abstract

Raman imaging requires the effective extraction of chemical information from the corresponding datasets, which can be achieved by a range of analytical methods. However, since each of these methods exhibits both strengths and weaknesses, we herein directly compare univariate, bivariate, and multivariate analyses of Raman imaging data by evaluating their performance in the quantitation of two adulterants in paprika powder. Univariate and bivariate models were developed based on the spectral features of the target adulterants, whereas spectral angle mapper (SAM), adopted as a multivariate analysis method, utilized the complete dataset. The obtained results demonstrate that despite being simple and easily implementable, the univariate method affords false positive pixels in the presence of background noise. Luckily, the above problem can be easily resolved using the bivariate method, which utilizes the multiplication of two band images wherein the same adulterant shows high-intensity peaks exhibiting the least overlap with those of other sample constituents. Finally, images produced by SAM contain abundant false negative pixels of adulterants, particularly for low-concentration samples. Notably, the bivariate method affords results closely matching the theoretical adulterant content, exhibiting the advantages of using non-complex data (only two bands are utilized) and being well suited to online applications of Raman imaging in the agro-food sector.

Improving Sensitivity in Raman Imaging for Thin Layered and Powdered Food Analysis Utilizing a Reflection Mirror

“…Most recently, Raman imaging on both the micro- and macroscale levels have been successfully tested for powdered food authenticity analysis [13,14,15,16,17,18,19]. Since a laser beam can penetrate a certain depth into a sample, the penetration depth was first determined to ensure that the adulterant particles at the very bottom of the sample could be detected [18,20].…”

Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology

“…It was found that there were significant metabolic differences between high-grade medulloblastoma and normal brain tissue. Lohumi et al [16] used Raman imaging to detect the content of dopants in paprika, by analyzing the univariate, bivariate, and multivariate of the Raman spectra of the substances, the method has effectively reduced the errors caused by the background noise interference. Hauke et al [17] presented a novel approach to in situ studies for the sintering process of silicate ceramics by hyperspectral Raman imaging, which demonstrated the power of hyperspectral Raman imaging for in situ studies of the mechanism(s) of solid-solid or solid-melt reactions at high-temperature with a micrometer-scale resolution.…”