Abstract
In this paper, we proposed artificial fish swarm algorithm (AF) for synchronous selection of wavelengths and pretreatment methods during quantification of beef adulteration with spoiled beef based on the analysis of ultraviolet (UV), visible and near-infrared (Vis-NIR), and UV-Vis-NIR spectra. The best partial least squares regression model was obtained when the wavelength-then-pretreatment scheme was adopted in the Vis-NIR range where 11 wavelengths were utilized to produce an RPD value of 2.68. The coefficients of determination R2 and root mean squared errors of the model were 0.91 and 0.08 as well as 0.87 and 0.11 for calibration and prediction, respectively. It was demonstrated that AF was a useful tool for model optimization as compared with genetic algorithm. Moreover, the sequence for selecting wavelengths and spectral pretreatment methods had great influence in model performance and could be decided by try-out approach since the performance due to selection sequence was sensitive to spectral range and optimization algorithm employed. Nevertheless, wavelength-then-pretreatment scheme was preferred due to simpler model structure and reduced computation loads. It was shown that Vis-NIR spectroscopy was feasible in quantifying beef adulteration, and AF was an advance method for optimizing model performance. Moreover, AF could be expended in more studies for optimizing food quality and safety analysis.
Similar content being viewed by others
References
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
Al-Jowder O, Kemsley EK, Wilson RH (2002) Detection of adulteration in cooked meat products by mid-infrared spectroscopy. J. Agric. Food Chem. 50:1325–1329
Arakawa M, Yamashita Y, Funatsu K (2011) Genetic algorithm-based wavelength selection method for spectral calibration. J Chemom 25:10–19
Ballin NZ, Vogensen FK, Karlsson AH (2009) Species determination—can we detect and quantify meat adulteration? Meat Sci 83:165–174
Cai W, Li Y, Shao X (2008) A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemom. Intell. Lab. Syst. 90:188–194
Chen Y, Chen J, Pan T, Han Y, Yao L (2015) Correlation coefficient optimization in partial least-squares regression with application to ATR-FTIR spectroscopic analysis. Anal Methods 7:5780–5786
Ding HB, Xu RJ (2000) Near-infrared spectroscopic technique for detection of beef hamburger adulteration. J. Agric. Food Chem. 48:2193–2198
Ding Q, Small GW, Arnold MA (1998) Genetic algorithm-based wavelength selection for the near-infrared determination of glucose in biological matrixes: initialization strategies and effects of spectral resolution. Anal Chem 70:4472–4479
Ellis D, Brewster V, Dunn W, Allwood W, Golovanov A, Goodacre R (2012) Fingerprinting food: current technologies for the detection of food adulteration and contamination. Chem Soc Rev 41:5706–5727
Feng Y-Z, Sun D-W (2013) Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of pseudomonas loads in chicken fillets. Talanta 109:74–83
Gao L, Zhao S, Gao J, University LT (2013) Application of artificial fish-swarm algorithm in SVM parameter optimization selection. Comput. Eng. Appl. 49:86–90
Gemperline P (2006) Practical guide to chemometrics, 2nd edn. Crc Press, Boca Raton
Ghasemi J, Niazi A, Leardi R (2003) Genetic-algorithm-based wavelength selection in multicomponent spectrophotometric determination by PLS: application on copper and zinc mixture. Talanta 59:311–317
Gozde G, Banu O (2009) Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data. Food Chem 116:519–525
Huang Z (2013) A classification rules extraction algorithm base on fish swarm optimization. Int J Eng Sci Invent 2:31–33
Huang Z, Chen Y (2015) Log-linear model based behavior selection method for artificial fish swarm algorithm. Comput. Intell. Neurosci. 2015:685404
Jackson RJ, Elvers KT, Lee LJ, Gidley MD, Wainwright LM, Lightfoot J, Park SF, Poole RK (2007) Oxygen reactivity of both respiratory oxidases in Campylobacter jejuni: the cydAB genes encode a cyanide-resistant, low-affinity oxidase that is not of the cytochrome bd type. J Bacteriol 189:1604–1615
Jin H, Haick H (2016) UV regulation of non-equilibrated electrochemical reaction for detecting aromatic volatile organic compounds. Sensors Actuators B Chem 237:30–40
Kamruzzaman M, Makino Y, Oshita S, Liu S (2015) Assessment of visible near-infrared hyperspectral imaging as a tool for detection of horsemeat adulteration in minced beef. Food Bioprocess Technol 8:1054–1062
Kamruzzaman M, Makino Y, Oshita S (2016) Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. J Food Eng 170:8–15
Kelly JF, Downey G, Fouratier V (2004) Initial study of honey adulteration by sugar solutions using midinfrared (MIR) spectroscopy and chemometrics. J. Agric. Food Chem. 52:33–39
Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148
Li X, Shao Z, Qian J (2002) An optimizing method base on autonomous animates: fish swarm algorithm. Syst. Eng. Theory Pract. 22:32–38
Liu Y, Chen YR, Ozaki Y (2000) Two-dimensional visible/near-infrared correlation spectroscopy study of thermal treatment of chicken meats. J. Agric. Food Chem. 48:901–908
Malmheden YI, Sandberg K (1987) Differentiation of meat from horse, donkey and their hybrids (mule/hinny) by electrophoretic separation of albumin. Meat Sci 21:15–23
Man YBC, Syahariza ZA, Mirghani MES, Jinap S, Bakar J (2005) Analysis of potential lard adulteration in chocolate and chocolate products using Fourier transform infrared spectroscopy. Food Chem 90:815–819
Manap H, Dooly G, O’Keeffe S, Lewis E (2011) Cross-sensitivity evaluation for ammonia sensing using absorption spectroscopy in the UV region. Sensors Actuators B Chem 154:226–231
Martens H, Martens M (2000) Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Qual. Prefer. 11:5–16
Meza-Márquez OG, Gallardo-Velázquez T, Osorio-Revilla G (2010) 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 86:511–519
Muhammed MA, Bindu BSC, Jini R, Prashanth KVH, Bhaskar N (2015) Evaluation of different DNA extraction methods for the detection of adulteration in raw and processed meat through polymerase chain reaction—restriction fragment length polymorphism (PCR-RFLP). J Food Sci Technol 52:514–520
Myers S, Yamazaki H (1997) Immunological detection of adulteration of ground meats by meats of other origins. Biotechnol Tech 11:533–535
Pannen F, Adler CP, Sandritter W (1973) Protein und myoglobin in hypertrophierten und dilatierten Menschenherzen : quantitative ultraviolett-zytophotometrische Untersuchungen. Beitr. Pathol. 149:70–83
Saeys W, Mouazen AM, Ramon H (2005) Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosyst Eng 91:393–402
Schmutzler M, Beganovic A, Böhler G, Huck CW (2015) Methods for detection of pork adulteration in veal product based on FT-NIR spectroscopy for laboratory, industrial and on-site analysis. Food Control 57:258–267
Snee RD (1977) Validation of regression models: methods and examples. Technometrics 19:415–428
Tang X, Niu L, Xu Y, Peng Y, Ma S, Tian X (2013) Nondestructive determination of water content in beef using visible/near-infrared spectrosco. Trans Chin Soc Agric Eng 29:248–254
Wang L-G, Shi Q-H (2010) Parameters analysis of artificial fish swarm algorithm. Comput Eng 36:169–171
Xin C, Fei L (2013) Application of ant colony optimization algorithm in wavelength selection for analysis of sugar content of apples by near infrared spectroscopy Chinese. Chin. J. Anal. Lab. 32:50–54
Zeng L, He Z (2006) Study on the application of genetic algorithm for synchronous selection of wavelength and spectral data pretreatment method in near-infrared spectrometric analysis. Anal Instrum 24:23–26
Zhao M, Downey G, O’Donnell CP (2014) Detection of adulteration in fresh and frozen beefburger products by beef offal using mid-infrared ATR spectroscopy and multivariate data analysis. Meat Sci 96:1003–1011
Funding
This study was funded by Key Program of Hubei Natural Science Foundation (No. 2015CFA106) and the Fundamental Research Funds for the Central Universities (No. 2015BQ018 and No. 2015PY078).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Wei Chen declares that he has no conflict of interest. Yao-Ze Feng declares that he has no conflict of interest. Gui-Feng Jia declares that he has no conflict of interest. Hai-Tao Zhao 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
Informed consent was obtained from all individual participants included in the study.
Rights and permissions
About this article
Cite this article
Chen, W., Feng, YZ., Jia, GF. et al. Application of Artificial Fish Swarm Algorithm for Synchronous Selection of Wavelengths and Spectral Pretreatment Methods in Spectrometric Analysis of Beef Adulteration. Food Anal. Methods 11, 2229–2236 (2018). https://doi.org/10.1007/s12161-018-1204-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12161-018-1204-3