Identification and Quantification of Texture Soy Protein in A Mixture with Beef Meat Using ATR-FTIR Spectroscopy in Combination with Chemometric Methods

Document Type : Research article

Authors

1 Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Science, Tehran, Iran.

2 Pharmaceutical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

3 Division of Pharmacology and Toxicology, Department of Basic Sciences, School of Veterinary Medicine, Shiraz University, Shiraz, Iran.

Abstract

Meat, as an important source of protein, is one of the main parts of many people’s diet. Due to
economic interests and thereupon adulteration, there are special concerns on its accurate labeling.
In this study Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometric
techniques (principal component analysis (PCA), artificial neural networks (ANNs), and partial
least square regression (PLS-R)) were employed for discrimination of pure beef meat from textured
soy protein plus detection and quantification of texture soy protein in a mixture with beef meat.
Spectral preprocessing was carried out on each spectra including Savitzki-Golay (SG) smoothing
filter, Standard Normal Vitiate (SNV), scatter correction (MSC), and min-max normalization.
Spectral range 1700–1071 cm-1 was selected for further analysis. Principal component analysis
showed discrete clustering of pure samples. In the next step, supervised artificial neural networks
(ANNs) were performed for classification and discrimination. The results showed classification
accuracy of 100% using this model. Furthermore, PLS-R model correlated the actual and FTIR
estimated values of texture soy protein in beef meat mixture with coefficient of determination
(R2) of 0.976. In conclusion, it was demonstrated that ATR-FTIR spectroscopy along with PCA
and ANNs analysis might potentially replace traditional laborious and time-consuming analytical
techniques to detect adulteration in beef meat as a rapid, low cost, and highly accurate method.

Keywords

Main Subjects

References

(1) Ballin NZ, Vogensen FK and Karlsson AH. Species
determination–Can we detect and quantify meat
adulteration? Meat Sci. ( 2009) 83: 165-74.
(2) Ballin NZ. Authentication of meat and meat products.
Meat Sci. (2010) 86: 577-87.
(3) Hargin K. Authenticity issues in meat and meat
products. Meat Sci. (1996) 43: 277-89.
(4) Moreira MJP, Silva A, Saraiva C and Marques
Martins de Almeida JM. Prediction of adulteration
of game meat using FTIR and chemometrics. Nutr.
Food Sci. (2018) 48: 245-58.
(5) Zhao M, Downey G and O’Donnell CP. Detection of
adulteration in fresh and frozen beefburger products
by beef offal using mid-infrared ATR spectroscopy
and multivariate data analysis. Meat Sci. (2014) 96:
1003-11.
(6) Meza-Márquez OG, Gallardo-Velázquez T and
Osorio-Revilla G. Application of mid-infrared
spectroscopy with multivariate analysis and soft
independent modeling of class analogies (SIMCA)
for the detection of adulterants in minced beef. Meat
Sci. (2010) 86: 511-9.
(7) Lamyaa M. Discrimination of pork content in
mixtures with raw minced camel and buffalo meat
using FTIR spectroscopic technique. Int. Food Res.
J. (2013) 20: 1389-94.
(8) Rohman A, Erwanto Y and Man YBC. Analysis of
pork adulteration in beef meatball using Fourier
transform infrared (FTIR) spectroscopy. Meat Sci.
(2011) 88: 91-5.
(9) Al-Kahtani HA, Ismail EA and Ahmed MA.
Pork detection in binary meat mixtures and some
commercial food products using conventional and
real-time PCR techniques. Food Chem. (2017) 219:
54-60.
(10) Ashtarinezhad A, Panahyab A, Mohamadzadehasl
B, Vatanpour H and Shirazi FH. FTIR
microspectroscopy reveals chemical changes in
mice fetus following phenobarbital administration.
Iran. J. Pharm. Res. (2015) 14 (Suppl): 121-30.
(11) Sousa N, Moreira MJ, Saraiva C and de Almeida
JM. Applying fourier transform mid infrared
spectroscopy to detect the adulteration of salmo
salar with oncorhynchus mykiss. Foods (2018) 7:
55.
(12) Abdi H. Partial least square regression (PLS
regression). In: Lewis-Beck M, Bryman A and
Futing T. (eds.) Encyclopedia of Social Sciences
Research Methods. 2nd ed. Sage Publications, CA
(2003) 792-5.
(13) Anjos O, Campos MG, Ruiz PC and Antunes P.
Application of FTIR-ATR spectroscopy to the
quantification of sugar in honey. Food Chem.
(2015) 169: 218-23.
(14) Frank LE and Friedman JH. A statistical view of some
chemometrics regression tools. Technometrics
(1993) 35: 109-35.
(15) Geladi P. Chemometrics in spectroscopy. Part 1.
Classical chemometrics. Spectrochim. Acta B: At.
Spectrosc. (2003) 58: 767-82.
(16) Ashtarinezhad A, Panahyab A, Vatanpour H and
Shirazi F. FTIR determination of miconazole
effects on mice fetus brain tissue. Iran. J. Pharm.
Res. (2014) 10: 79-84.
(17) Beebe KR, Pell RJ and Seasholtz MB. (eds.)
Chemometrics: a practical guide. 1st ed. John
Wiley and Sons, New York (1998) 156-9
(18) Ashtarinezhad A, Panahyab A, ShaterzadehOskouei S, Khoshniat H, Mohamadzadehasl B and
Shirazi FH. Teratogenic study of phenobarbital
and levamisole on mouse fetus liver tissue using
biospectroscopy. J. Pharm. Biomed. Anal. (2016)
128: 174-83.
(19) Ashtarinezhad A, Mohamadzadehasl B, Panahyab
A, Vatanpour H and Shirazi F. FTIR spectroscopy
reveals chemical changes in mice fetus following
phenobarbital administration. Iran. J. Pharm. Res.
(2012) 7: 147.
(20) Hu Y, Zou L, Huang X and Lu X. Detection and
quantification of offal content in ground beef meat
using vibrational spectroscopic-based chemometric
analysis. Sci. Rep. (2017) 7: 15162.
(21) Al-Jowder O, Kemsley E and Wilson RH. Detection
of adulteration in cooked meat products by midinfrared spectroscopy. J. Agr. Food Chem. (2002)
50: 1325-9.
(22) Arsalane A, El Barbri N, Tabyaoui A, Klilou A,
Rhofir K and Halimi A. An embedded system
based on DSP platform and PCA-SVM algorithms
for rapid beef meat freshness prediction and
identification. Comput. Electron. Agr. (2018) 152:
385-92.
(23) Marshall S, Kelman T, Qiao T, Murray P and
Zabalza J. (eds.) Hyperspectral Imaging for Food
Applications. 23rd European Signal Processing
Conference (EUSIPCO), IEEE (2015).
(24) Geladi P and Kowalski BR. Partial least-squares
regression: a tutorial. Anal. Chim. Acta (1986) 185:
1-17.
(25) Moreira MJP, Silva AC, de Almeida JM and Saraiva
C. Characterization of deterioration of fallow deer
and goat meat using microbial and mid infrared
spectroscopy in tandem with chemometrics. Food
Packag. Shelf Life (2018) 15: 169-80.
(26) Baker MJ, Trevisan J, Bassan P, Bhargava R,
Butler HJ, Dorling KM, Fielden PR, Fogarty 
197
Keshavarzi Z et al. / IJPR (2019), 18 (Special Issue): 190-197
SW, Fullwood NJ, Heys KA, Hughes C, Lasch P,
Martin-Hirsch PL, Obinaju B, Sockalingum GD,
Sulé-Suso J, Strong RJ, Walsh MJ, Wood BR,
Gardner P and Martin FL. Using fourier transform
IR spectroscopy to analyze biological materials.
Nat. Protoc. (2014) 9: 1771-91.
(27) Old OJ, Lloyd GR, Nallala J, Isabelle M, Almond
LM, Shepherd NA, Kendall CA, Shore AC,
Barr H and Stone N. Rapid infrared mapping for
highly accurate automated histology in Barrett’s
oesophagus. Analyst. (2017) 142: 1227-34.
(28) Griffiths PR and De Haseth JA. (eds.) Fourier
Transform Infrared Spectrometry. 2nd ed. John
Wiley and Sons, New Jersey (2007) 390.
(29) Ashtarinezhad A, Panahyab A, Mohamadzadehasl
B and Shirazi FH. Characterization of miconazole
effects on mice fetus liver tissue using FTIR-MSP.
Iran. J. Pharm. Res. (2017) 16: 677-84.
(30) Jackson M and Mantsch HH. The use and misuse of
FTIR spectroscopy in the determination of protein
structure. Crit. Rev. Biochem. Mol. Biol. (1995) 30:
95-120.
(31) Yang H, Yang S, Kong J, Dong A and Yu S.
Obtaining information about protein secondary
structures in aqueous solution using Fourier
transform IR spectroscopy. Nat. Protoc. (2015) 10:
382-96.
(32) Berrueta LA, Alonso-Salces RM and Héberger K.
Supervised pattern recognition in food analysis. J.
Chromatogr. A (2007) 1158: 196-214.
(33) Grunert T, Wenning M, Barbagelata MS, Fricker
M, Sordelli DO, Buzzola FR and Ehling-Schulz M.
Rapid and reliable identification of Staphylococcus
aureus capsular serotypes by means of artificial
neural network-assisted Fourier-Transform
infrared spectroscopy. J. Clin. Microbiol. (2013)
51: 2261.
(34) Guiné RP, Barroca MJ, Gonçalves FJ, Alves M,
Oliveira S and Mendes M. Artificial neural network
modelling of the antioxidant activity and phenolic
compounds of bananas submitted to different
drying treatments. Food Chem. (2015) 168: 454-9.
(35) Taylor KD, Goel R, Shirazi FH, Molepo M,
Stewart DJ and Wong PT. Pressure tuninig
infrared spectroscopic studey of cisplatin-induced
structureal changes in a phosphatidylserine model
membrane. Br. J. Cancer (1995) 72: 1400-5.
Iranian Journal of Pharmaceutical Research
Volume 18, Special Issue
Autumn 2019
Pages 190-197

Files

Share

How to cite

Statistics