Abstract
This paper described the development of a multivariate classification methodology to detect frauds in bovine meat based on mid-infrared spectroscopy and partial least squares discriminant analysis (PLS-DA). These frauds consisted of adding carrageenan, sodium chloride, and tripolyphosphate, ingredients that increase meat water holding capacity aiming to obtain economic gains. Meat pieces (fresh beef muscle) of the same bovine cut, M. semitendinosus, from different origins were injected with single to ternary mixtures of adulterants, and their purges were analyzed totaling 176 spectra. Multiclass PLS-DA models for specifically detecting each adulterant provided good results (correctly classification rates > 90%) only for tripolyphosphate. Nevertheless, a two-class PLS-DA model discriminating adulterated and non-adulterated meat provided high success rates (≥ 95%). Aiming to verify the model’s ability to detect other (non-trained) adulterant, this last model was combined with outlier detection in a soft version of a discriminant model that was able to correctly detect 100% of a new validation set consisting of 20 meat samples containing maltodextrin.
Similar content being viewed by others
References
Abbas O, Zadravec M, Baeten V, Mikus T, Lesic T, Vulic A, Prpic J, Jemersic L, Pleadin J (2018) Analytical methods used for the authentication of food of animal origin. Food Chem 246:6–17
Alamprese C, Casale M, Sinelli N, Lanteri S, Casiraghi E (2013) Detection of minced beef adulteration with turkey meat by UV–vis, NIR and MIR spectroscopy. LWT – Food Sci Technol 53:225–232
Alamprese C, Amigo JM, Casiraghi E, Engelsen SB (2016) Identification and quantification of turkey meat adulteration in fresh, frozen-thawed and cooked minced beef by FT-NIR spectroscopy and chemometrics. Meat Sci 121:175–181
Ayadi MA, Kechaou A, Makni I, Attia H (2009) Influence of carrageenan addition on turkey meat sausages properties. J Food Eng 93:278–283
Ballin NZ (2010) Authentication of meat and meat products. Meat Sci 86:577–587
Barnett J, Begen F, Howes S, Regan A, McConnon A, Marcu A, Rowntree S, Verbeke W (2016) Consumers’ confidence, reflections and response strategies following the horsemeat incident. Food Control 59:721–730
Biancolillo A, Firmani P, Bucci R, Magri A, Marini F (2019) Determination of insect infestation on stores rice by near infrared (NIR) spectroscopy. Microchem J 145:252–258
Botelho BG, Reis N, Oliveira LS, Sena MM (2015) Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA. Food Chem 181:31–37
Boyaci IH, Temiz HT, Uysal RS, Velioglu HM, Yadegari RJ, Rishkan MM (2014) A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chem 148:37–41
Burikov SA, Dolenko TA, Fadeev VV, Sugonyaev AV (2007) Identification of inorganic salts and determination of their concentrations in aqueous solutions based on the valence Raman band of water using artificial neural networks. Pattern Recogn Image Anal 17:554–559
Cao H, Xu S (2008) Purification and characterization of type II collagen from chick sternal cartilage. Food Chem 108:439–445
Cheng Q, Sun DW (2008) Factors affecting the water holding capacity of red meat products: a review of recent research advances. Crit. Rev. Food Sci Nutr 48:137–159
da Silva VAG, Talhavini M, Zacca JJ, Trindade BR, Braga JWB (2014) Discrimination of black pen inks on writing documents using visible reflectance spectroscopy and PLS-DA. J Braz Chem Soc 25:1552–1564
EC (2005) Commission Recommendation concerning a coordinated programme for the official control offoodstuffs. Official Journal of the European Union L59 27-39 Brussels, European Community
Engel J, Gerretzen J, Szymanska E, Jansen JJ, Downey G, Blanchet L, Buydens LMC (2013) Breaking with trends in pre-processing? TrAC – Trends Anal Chem 50:96–106
Esteki M, Regueiro J, Simal-Gándara J (2019) Tackling fraudsters with global strategies to expose fraud in the food chain. Compr Rev Food Sci Food Saf 18:425–440
Honikel KO (1998) Reference methods for the assessment of physical characteristics of meat. Meat Sci 49:447–457
Kamruzzaman M, Sun DW, ElMarsy G, Allen P (2013) Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis. Talanta 103:130–136
Kamruzzaman M, Makino Y, Oshita S (2016) Hyperspectral imaging for real-time monitoring of water holding capacity in red meat. LWT - Food Sci Technol 66:685–691
Khanmohammadi M, Ashori A, Kargosha K, Garmarudi AB (2007) Simultaneous determination of sodium tripolyphosphate, sodium sulfate and linear alkylbenzensulfonate in washing powder by attenuated total reflectance: fourier transform infrared spectrometry. J Surfactants Deterg 10:81–86
Küpper L, Heise HM, Butvina LN (2001) Novel developments in mid-IR fiber-optic spectroscopy for analytical applications. J Mol Struct 563:173–181
Kutsanedzie FYH, Guo Z, Chen Q (2019) Advances in nondestructive methods for meat quality and safety monitoring. Food Rev Int 35:536–562
Lopez-Maestresalas A, Insausti K, Jarén C, Pérez-Roncal C, Urrutia O, Beriain ML, Arazuri S (2019) Detection of minced lamb and beef fraud using NIR spectroscopy. Food Control 98:465–473
Martins AR, Talhavini M, Vieira ML, Zacca JJ, Braga JWB (2017) Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis. Food Chem 229:142–151
Meza-Marquez OG, Gallardo-Velazquez T, Osorio-Revilla G (2010) Application of midinfrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef. Meat Sci 86:511–519
Miaw CSW, Sena MM, Souza SVC, Callao MP, Ruisánchez I (2018) Detection of adulterants in grape nectars by attenuated total reflectance Fourier-transform mid-infrared spectroscopy and multivariate classification strategies. Food Chem 266:254–261
Nunes KM, Andrade MVO, Santos Filho AMP, Lasmar MC, Sena MM (2016) Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parametersand ATR-FTIR spectroscopy. Food Chem 205:14–22
Nunes KM, Andrade MVO, Almeida MR, Fantini C, Sena MM (2019) Raman spectroscopy and discriminant analysis applied to the detection of frauds in bovine meat by the addition of salts and carrageenan. Microchem J 147:582–589
Oliveri P, Downey G (2012) Multivariate class modeling for the verification of food-authenticity claims. TrAC - Trends Anal Chem 35:74–86
Pavlidis DE, Mallouchos A, Ercolini D, Panagou EZ, Nychas GJE (2019) A volatilomics approach for off-line discrimination of minced beef and pork meat and their admixture using HS-SPME GC/MS in tandem with multivariate data analysis. Meat Sci 151:43–53
Perisic N, Afseth NK, Ofstad R, Kohler A (2011) Monitoring protein structural changes and hydration in bovine meat tissue due to salt substitutes by Fourier transform infrared (FTIR) microspectroscopy. J Agric Food Chem 591:0052–10061
Perisic N, Afseth NK, Ofstad R, Scheel J, Kohler A (2013) Characterizing salt substitution in beef meat processing by vibrational spectroscopy and sensory analysis. Meat Sci 95:576–585
Pour SO, Fowler SM, Hopkins DL, Torley PJ, Gill H, Blanch EW (2019) Investigation of chemical composition of meat using spatially off-set Raman spectroscopy. Analyst 144:2618–2627
Prevolnik M, Candek-Potokar M, Skorjanc D (2010) Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods. J Food Eng 98:347–352
Rodionova OY, Titova AV, Pomerantsev AL (2016) Discriminant analysis is an inappropriate method of authentication. TrAC - Trends Anal Chem 78:17–22
Santos PM, Simeone MLF, Pimentel MAG, Sena MM (2019) Non-destructive screening method for detecting the presence of insects in sorghum grains using near infrared spectroscopy and discriminant analysis. Microchem J 149:104057
Smrcková P, Horsky J, Sárka E, Kolácek J, Netopilik M, Wallterová Z, Krulis Z, Synytsyaa A, Hrusková K (2013) Hydrolysis of wheat B-starch and characterization of acetylated maltodextrin. Carbohydr Polym 98:43–49
Soares LF, da Silva DC, Bergo MCJ, Coradin VTR, Braga JWB, Pastore TCM (2017) Evaluation of a NIR handheld device and PLS-DA for discrimination of six similar Amazonian wood species. Quim Nova 40:418–426
Trout GR (1988) Techniques for measuring water-binding capacity in muscle foods - a review of methodology. Meat Sci 23:235–252
Volery P, Besson R, Schaffer-Lequart C (2004) Characterization of commercial carrageenans by Fourier transform infrared spectroscopy using single-reflection attenuated total reflection. J Agric Food Chem 52:7457–7463
Wang W, Peng Y, Sun H, Zheng X, Wei W (2018) Spectral detection techniques for non-destructively monitoring the quality, safety, and classification of fresh red meat. Food Anal Methods 11:2707–2730
Wu T, Zhong N, Yang L (2018) Identification of adulterated and non-adulterated Norwegian salmon using FTIR and an improved PLS-DA method. Food Anal Methods 11:1501–1509
Acknowledgments
Particularly, K.M.N. thanks the Projeto Ciências Forenses (CAPES Call PROFORENSE 2014, AUXPE 3353/2014, Process 23038.006843/2014-00, Finance Code 001) for a fellowship.
Funding
The authors acknowledge the financial support of Brazilian agencies: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Karen Monique Nunes declares that she has no conflict of interest. Marcus Vinícius Oliveira Andrade declares that he has no conflict of interest. Mariana Ramos Almeida declares that he has no conflict of interest. Marcelo Martins Sena declares that he has no conflict of interest.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed Consent
Not applicable.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 828 kb)
Rights and permissions
About this article
Cite this article
Nunes, K.M., Andrade, M.V.O., Almeida, M.R. et al. A soft discriminant model based on mid-infrared spectra of bovine meat purges to detect economic motivated adulteration by the addition of non-meat ingredients. Food Anal. Methods 13, 1699–1709 (2020). https://doi.org/10.1007/s12161-020-01795-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12161-020-01795-3