Identification and Evaluation of Composition in Food Powder Using Point-Scan Raman Spectral Imaging

This study used Raman spectral imaging coupled with self-modeling mixture analysis (SMA) for identification of three components mixed into a complex food powder mixture. Vanillin, melamine, and sugar were mixed together at 10 different concentration level (1% to 10%, w/w) into powdered non-dairy creamer. SMA was used to decompose the complex multi-component spectra and extract the pure component spectra and corresponding contribution images. Spectral information divergence (SID) values of the extracted pure component spectra and reference component spectra were computed to identify the components corresponding to the extracted spectra. The contribution images obtained via SMA were used to create Raman chemical images of the mixtures samples, to which threshold values were applied to obtain binary detection images of the components at all concentration levels. The detected numbers of pixels of each component in the binary images was found to be strongly correlated with the actual sample concentrations (correlation coefficient of 0.99 for all components). The results show that this method can be used for simultaneous identification of different components and estimation of their concentrations for authentication or quantitative inspection purposes.

[1]  Chein-I Chang,et al.  An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis , 2000, IEEE Trans. Inf. Theory.

[2]  Ali Topcu,et al.  Monitoring multiple components in vinegar fermentation using Raman spectroscopy. , 2013, Food chemistry.

[3]  Zhenhe Ma,et al.  Real time monitoring of multiple components in wine fermentation using an on-line auto-calibration Raman spectroscopy , 2014 .

[4]  H B Ding,et al.  Near-infrared spectroscopic technique for detection of beef hamburger adulteration. , 2000, Journal of agricultural and food chemistry.

[5]  Jianwei Qin,et al.  Simultaneous detection of multiple adulterants in dry milk using macro-scale Raman chemical imaging. , 2013, Food chemistry.

[6]  W. Windig,et al.  Interactive self-modeling mixture analysis , 1991 .

[7]  Sagar Dhakal,et al.  Prediction of Egg's Freshness Using Backward Propagation Neural Network , 2011 .

[8]  Sagar Dhakal,et al.  Parameter Selection for Raman Spectroscopy-Based Detection of Chemical Contaminants in Food Powders , 2016 .

[9]  Jens Michael Carstensen,et al.  Potential of multispectral imaging technology for rapid and non-destructive determination of the microbiological quality of beef filets during aerobic storage. , 2014, International journal of food microbiology.

[10]  Sagar Dhakal,et al.  Evaluation of Turmeric Powder Adulterated with Metanil Yellow Using FT-Raman and FT-IR Spectroscopy , 2016, Foods.

[11]  Yankun Peng,et al.  A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging , 2014, Food and Bioprocess Technology.

[12]  G. Marosi,et al.  Comparison of chemometric methods in the analysis of pharmaceuticals with hyperspectral Raman imaging , 2011 .

[13]  C Borggaard,et al.  Optical measurements of pH in meat. , 1999, Meat science.

[14]  Scott A Hale,et al.  Quantitative analysis and detection of adulteration in crab meat using visible and near-infrared spectroscopy. , 2006, Journal of agricultural and food chemistry.

[15]  A. K Lockley,et al.  DNA-based methods for food authentication , 2000 .

[16]  Ding Hb,et al.  Near-infrared spectroscopic technique for detection of beef hamburger adulteration. , 2000 .

[17]  Carmen Gallo,et al.  Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. , 2012, Meat science.

[18]  George-John E. Nychas,et al.  A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage , 2013 .

[19]  Yankun Peng,et al.  Prototype instrument development for non-destructive detection of pesticide residue in apple surface using Raman technology , 2014 .

[20]  Jianwei Qin,et al.  Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method , 2008 .

[21]  Josse De Baerdemaeker,et al.  A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products , 2007 .

[22]  Yankun Peng,et al.  Extraction and identification of mixed pesticides' Raman signal and establishment of their prediction models , 2017 .

[23]  Christophe Blecker,et al.  Fluorescence Spectroscopy Measurement for Quality Assessment of Food Systems—a Review , 2011 .

[24]  S. Lanteri,et al.  Detection of minced beef adulteration with turkey meat by UV-vis, NIR and MIR spectroscopy , 2013 .

[25]  Moon S. Kim,et al.  A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on spinach leaves , 2014 .

[26]  Y. R. Chen,et al.  Hyperspectral-multispectral line-scan imaging system for automated poultry carcass inspection applications for food safety. , 2007, Poultry science.

[27]  Lena Osterhagen,et al.  Mixing In The Process Industries , 2016 .

[28]  Yankun Peng,et al.  Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique , 2012 .

[29]  J. Bosset,et al.  Application of strontium isotope abundance ratios measured by MC-ICP-MS for food authentication , 2004 .

[30]  Havva Tümay Temiz,et al.  A novel method for discrimination of beef and horsemeat using Raman spectroscopy. , 2014, Food chemistry.

[31]  Jianwei Qin,et al.  Raman Chemical Imaging System for Food Safety and Quality Inspection , 2010 .

[32]  M. F. Edwards,et al.  Mixing in the process industries , 1985 .

[33]  D. E. Chan,et al.  High Throughput Spectral Imaging System for Wholesomeness Inspection of Chicken , 2008 .

[34]  Sagar Dhakal,et al.  Raman spectral imaging for quantitative contaminant evaluation in skim milk powder , 2016, Journal of Food Measurement and Characterization.

[35]  Yankun Peng,et al.  A machine vision system for identification of micro-crack in egg shell , 2012 .

[36]  Yankun Peng,et al.  Optical Methods and Techniques for Meat Quality Inspection , 2015 .

[37]  Barry M. Wise,et al.  A new approach for interactive self-modeling mixture analysis , 2005 .

[38]  Isabel Mafra,et al.  Food authentication by PCR-based methods , 2008 .

[39]  Royston Goodacre,et al.  Rapid quantitative assessment of the adulteration of virgin olive oils with hazelnut oils using Raman spectroscopy and chemometrics. , 2003, Journal of agricultural and food chemistry.

[40]  Gamal ElMasry,et al.  Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef , 2012 .

[41]  D. Cozzolino,et al.  Feasibility study on the use of visible and near-infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins. , 2003, Journal of agricultural and food chemistry.

[42]  M. Kim,et al.  Optimal Optical Filters of Fluorescence Excitation and Emission for Poultry Fecal Detection , 2012 .