Contents lists available at ScienceDirect Food Chemistry journal homepage: www.elsevier.com/locate/foodchem Raman and IR spectroscopic modality for authentication of turmeric powder Kuanglin Chao a, ⁎ , Sagar Dhakal a , Walter F. Schmidt a , Jianwei Qin a , Moon Kim a , Yankun Peng b , Qing Huang c a Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, 10300 Baltimore Avenue, Bldg. 303 BARC- East, Beltsville, MD 20705, USA b National R&D Centre for Agro-Processing, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China c Hefei Institute of Physical Sciences, CAS 350 Shushanhu Road, Hefei 230031, China ARTICLE INFO Keywords: Raman Infrared Food safety Adulteration Turmeric ABSTRACT Deliberate chemical contamination of food powders has become a major food safety concern worldwide. This study used Raman imaging and FT-IR spectroscopy to detect Sudan Red and white turmeric adulteration in turmeric powder. While Sudan Red Raman spectral peaks were identifiable in turmeric-Sudan Red samples, Sudan Red false positive detection was observed in binary Raman images, limiting effective quantitative de- tection. In addition, white turmeric Raman spectral peaks were unidentifiable in turmeric-white turmeric mixtures. However, IR spectra of turmeric-Sudan Red and turmeric-white turmeric samples provided discrete identifier peaks for both the adulterants. Partial least squares regression models were developed using IR spectra for each mixture type. The models estimated Sudan Red and white turmeric concentrations with correlation coefficients of 0.97 and 0.95, respectively. Priority should be given to developing an IR imaging system and incorporating it with Raman system to simultaneously measure of food samples for detection of adulterants. 1. Introduction Food powders are ubiquitous in the consumer market, but pose a continual food safety problem because deliberate adulteration with chemical contaminants or additives is relatively facile. Adulteration is almost always economic in nature, but may also have malicious intent or pose a threat to public safety. For example, over 135 cases of adulterated skim milk powder across 18 countries were documented between 1984 and 2012. Food powder adulteration has also been documented for many other ingredients such as wheat gluten, starch, turmeric, and curry powder (Moyer, DeVries, & Spink, 2017; Peng et al., 2017; Sasikumar, Syamkumar, Remya, & Zachariah, 2004; Dhakal et al., 2018). Analytical techniques such as high-performance liquid chromato- graphy (HPLC) and gas chromatography are commonly used to detect additives and contaminants in food powders (Mazzetti et al., 2004; He et al., 2007). However, these conventional technologies are destructive, expensive, time-consuming and require skilled operators and large solvent volumes. Spectroscopic techniques including UV–visible, in- frared (IR) and Raman have been gaining importance for rapid, in- expensive detection (Di Anibal, Odena, Ruisanchez, & Callao, 2009; Ding & Kokot, 2015; Cheng et al., 2010). Our research group at the United States Department of Agriculture (USDA) has developed a 785- nm point-scan Raman hyperspectral imaging system and analysis method for non-destructive detection of chemical contaminants and adulterants in food powders (Qin, Chao, & Kim, 2010). The system acquires Raman spectra from the entire surface area of a sample, and accumulates them to form a hyperspectral image. The spectrum of each pixel in the image is analyzed to visualize the spatial distribution of contaminant particles. Multiple chemical contaminants and/or ad- ditives in food powders can be identified simultaneously (Qin et al., 2010; Qin, Chao, & Kim, 2013; Dhakal et al., 2016a). Laser intensity, penetration depth, exposure time, and spatial re- solution are important parameters for Raman spectral imaging of powdered samples. Contaminant particles located at depths where the laser cannot penetrate are not detected. Low spatial resolution fails to detect particles, but very high spatial resolution detects the same par- ticle multiple times. Thus, our system was optimized for milk powder, starch and flour, and the resultant parameters were utilized to detect melamine in milk powder, maleic anhydride in starch, and benzoyl peroxide in flour with a 50 ppm detection limit for all three con- taminants (Dhakal et al., 2016b; Dhakal et al., 2016c). For high https://doi.org/10.1016/j.foodchem.2020.126567 Received 18 September 2019; Received in revised form 3 March 2020; Accepted 4 March 2020 ⁎ Corresponding author. E-mail addresses: kevin.chao@usda.gov (K. Chao), sagar.dhakal@usda.gov (S. Dhakal), walter.schmidt@usda.gov (W.F. Schmidt), moon.kim@usda.gov (M. Kim), ypeng@cau.edu.cn (Y. Peng), huangq@ipp.ac.cn (Q. Huang). Food Chemistry 320 (2020) 126567 Available online 05 March 2020 0308-8146/ © 2020 Elsevier Ltd. All rights reserved. T