Authentication of Whey Protein Powders by Portable Mid-Infrared Spectrometers Combined with Pattern Recognition Analysis.
The objective of this study was to develop a simple and rapid method to differentiate whey protein types (WPC, WPI, and WPH) used for beverage manufacturing by combining the spectral signature collected from portable mid-infrared spectrometers and pattern recognition analysis. Whey protein powders from different suppliers are produced using a large number of processing and compositional variables, resulting in variation in composition, concentration, protein structure, and thus functionality. Whey protein powders including whey protein isolates, whey protein concentrates and whey protein hydrolysates were obtained from different suppliers and their spectra collected using portable mid-infrared spectrometers (single and triple reflection) by pressing the powder onto an Attenuated Total Reflectance (ATR) diamond crystal with a pressure clamp. Spectra were analyzed by soft independent modeling of class analogy (SIMCA) generating a classification model showing the ability to differentiate whey protein types by forming tight clusters with interclass distance values of >3, considered to be significantly different from each other. The major bands centered at 1640 and 1580 cm(-1) were responsible for separation and were associated with differences in amide I and amide II vibrations of proteins, respectively. Another important band in whey protein clustering was associated with carboxylate vibrations of acidic amino acids (∼1570 cm(-1)). The use of a portable mid-IR spectrometer combined with pattern recognition analysis showed potential for discriminating whey protein ingredients that can help to streamline the analytical procedure so that it is more applicable for field-based screening of ingredients.


CONCLUSIONS
A rapid, simple and accurate method was developed to authenticate commercial whey protein products by using portable mid-infrared spectrometers combined with chemometrics, which could help ensure the functionality of whey protein ingredients in food applications.
Dept. of Food Science and Technology, The Ohio State Univ., 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, Ohio, 43210, U.S.A.
Ting
T
Wang
Pepsi-Cola Company, 100 Stevens Ave, Valhalla, N.Y., 10595, U.S.A.
Siow Ying
SY
Tan
Pepsi-Cola Company, 100 Stevens Ave, Valhalla, N.Y., 10595, U.S.A.
William
W
Mutilangi
Dept. of Food Science and Technology, The Ohio State Univ., 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, Ohio, 43210, U.S.A.|Dept. of Food Engineering, Faculty of Engineering, Adnan Menderes Univ, Aydin, 09100, Turkey.
Didem P
DP
Aykas
Dept. of Food Science and Technology, The Ohio State Univ., 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, Ohio, 43210, U.S.A.
Luis E
LE
Rodriguez-Saona
D001628:Beverages; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D011208:Powders; D011492:Protein Hydrolysates / Q000032:analysis / Q000737:chemistry; D013055:Spectrophotometry, Infrared / Q000379:methods*; D000067796:Whey; D000067816:Whey Proteins / Q000032:analysis* / Q000737:chemistry / Q000145:classification
D011208:Powders; D011492:Protein Hydrolysates; D000067816:Whey Proteins
ingredient authentication; pattern recognition; portable mid-infrared spectrometer; whey protein powder