Abstract
Dielectric spectroscopy is usually used to obtain dielectric properties of materials. It not only has the advantages of high speed and little or no sample preparation but can also be used in in-line or in situ quality monitoring. To provide a quick and convenient method for milk fat concentration determination using dielectric spectroscopy, the dielectric spectra of 143 fresh raw cow milk samples were obtained from 20 to 4500 MHz. All samples were divided into calibration set and prediction set by using joint x-y distances sample set partitioning method. One hundred twenty-three, 9, and 40 dielectric variables were selected as effective variables (EVs) by applying uninformative variable elimination, successive projection algorithm, and stability competitive adaptive reweighted sampling methods, respectively. The models of partial least squares regression (PLSR), extreme learning machine (ELM), and least squares-supporting vector machine (LS-SVM) were built when the full dielectric spectra and selected EVs were used as inputs. The results showed that the PLSR model developed with full dielectric spectra was the best model on fat concentration determination with the root-mean-squares error of prediction set of 0.168%. The study tells that dielectric spectra have potential in exploring a milk fat concentration sensor used for in-line or in situ detection.
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References
Aernouts B, Polshin E, Lammertyn J, Saeys W (2011) Visible and near-infrared spectroscopic analysis of raw milk for cow health monitoring: reflectance or transmittance? J Dairy Sci 94(11):5315–5329. https://doi.org/10.3168/jds.2011-4354
Araújo MCU, Saldanha TCB, Galvao RKH, Yoneyama T, Chame HC, Visani V (2001) The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom Intell Lab Syst 57(2):65–73. https://doi.org/10.1016/S0169-7439(01)00119-8
Bogomolov A, Dietrich S, Boldrini B, Kessler RW (2012) Quantitative determination of fat and total protein in milk based on visible light scatter. Food Chem 134(1):412–418. https://doi.org/10.1016/j.foodchem.2012.02.077
Centner V, Massart DL, de Noord OE, de Jong S, Vandeginste BM, Sterna C (1996) Elimination of uninformative variables for multivariate calibration. Anal Chem 68(21):3851–3858. https://doi.org/10.1021/ac960321m
Dong J, Guo W, Zhao F, Liu D (2017) Discrimination of “hayward kiwifruits” treated with forchlorfenuron at different concentrations using hyperspectral imaging technology. Food Anal Methods 10(2):477–486. https://doi.org/10.1007/s12161-016-0603-6
El-Abassy RM, Eravuchira PJ, Donfack P, von der Kammer B, Materny A (2010) Fast determination of milk fat content using Raman spectroscopy. Vib Spectrosc 56(1):3–8
Feng X, Su R, Xu N, Wang X, Yu A, Zhang H, Cao Y (2013) Portable analyzer for rapid analysis of total protein, fat and lactose contents in raw milk measured by non-dispersive short-wave near-infrared spectrometry. Chem Res Chin Univ 29(1):15–19. https://doi.org/10.1007/s40242-013-2191-y
Galvão RKH, Araujo MCU, José GE, Pontes MJC, Silva EC, Saldanha TCB (2005) A method for calibration and validation subset partitioning. Talanta 67(4):736–740. https://doi.org/10.1016/j.talanta.2005.03.025
Guo W, Fang L, Liu D, Wang Z (2015) Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy. Comput Electron Agric 117:226–233. https://doi.org/10.1016/j.compag.2015.08.012
Guo W, Gu J, Liu D, Shang L (2016) Peach variety identification using near-infrared diffuse reflectance spectroscopy. Comput Electron Agric 123:297–303. https://doi.org/10.1016/j.compag.2016.03.005
Guo W, Lin B, Liu D, Zhu X (2017) A novel technique on determining water content in milk using radio-frequency/microwave dielectric spectroscopy and chemometrics. Food Anal Methods 10(12):3781–3789. https://doi.org/10.1007/s12161-017-0946-7
He H, Sun D, Wu D (2016) Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis. Food Res Int 62:476–483
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Jiang H, Zhang H, Chen Q, Mei C, Liu G (2015) Identification of solid state fermentation degree with FT-NIR spectroscopy: comparison of wavelength variable selection methods of CARS and SCARS. Spectrochim Acta A Mol Biomol Spectrosc 149:1–7. https://doi.org/10.1016/j.saa.2015.04.024
Kawasaki M, Kawamura S, Tsukahara M, Morita S, Komiya M, Natsuga M (2008) Near-infrared spectroscopic sensing system for on-line milk quality assessment in a milking robot. Comput Electron Agric 63(1):22–27. https://doi.org/10.1016/j.compag.2008.01.006
Kucheryavskiy S, Melenteva A, Bogomolov A (2014) Determination of fat and total protein content in milk using conventional digital imaging. Talanta 121:144–152. https://doi.org/10.1016/j.talanta.2013.12.055
Kudra T, Raghavan GSV, Akyel C, Bosisio R, van de Voort F (1992) Electromagnetic properties of milk and its constituents at 2.45 MHz. J. Microw. Power Electromagn. Energy 27(4):199–204. https://doi.org/10.1080/08327823.1992.11688191
Lawton BA, Pethig R (1993) Determining the fat-content of milk and cream using ac conductivity measurements. Meas Sci Technol 4(1):38–41. https://doi.org/10.1088/0957-0233/4/1/007
Li X, Feng F, Gao R, Wang L, Qian Y, Li C, Zhou G (2016) Application of near infrared reflectance (NIR) spectroscopy to identify potential PSE meat. J Sci Food Agric 96(9):3148–3156. https://doi.org/10.1002/jsfa.7493
Lu Y, Du C, Yu C, Zhou J (2014) Fast and nondestructive determination of protein content in rapeseeds (Brassica napus L.) using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS). J Sci Food Agric 94(11):2239–2245. https://doi.org/10.1002/jsfa.6548
Mabrook MF, Petty MC (2003) Effect of composition on the electrical conductance of milk. J Food Eng 60(3):321–325. https://doi.org/10.1016/S0260-8774(03)00054-2
Manganiello L, Rios A, Valcarcel M, Ligero A, Tena T (2000) Automatic determination of fat in milk by use of a flow injection system with a piezoelectric detector. Anal Chim Acta 406(2):309–315. https://doi.org/10.1016/S0003-2670(99)00775-8
Melfsen A, Hartung E, Haeussermann A (2012a) Accuracy of in-line milk composition analysis with diffuse reflectance near-infrared spectroscopy. J Dairy Sci 95(11):6465–6476. https://doi.org/10.3168/jds.2012-5388
Melfsen A, Hartung E, Haeussermann A (2012b) Accuracy of milk composition analysis with near infrared spectroscopy in diffuse reflection mode. Biosyst Eng 112(3):210–217. https://doi.org/10.1016/j.biosystemseng.2012.04.003
Nicolai BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118. https://doi.org/10.1016/j.postharvbio.2007.06.024
Nunes AC, Bohigas X, Tejada J (2006) Dielectric study of milk for frequencies between 1 and 20 GHz. J Food Eng 76(2):250–255. https://doi.org/10.1016/j.jfoodeng.2005.04.049
Povolo M, Contarini G (2009) Fast gas chromatography: applications in milk fat analysis. Lipid Technol 21(4):88–90. https://doi.org/10.1002/lite.200900018
Purnomoadi A, Batajoo KK, Ueda K, Fuminori T (1999) Influence of feed source on determination of fat and protein in milk by near-infrared spectroscopy. Int Dairy J 9(7):447–452. https://doi.org/10.1016/S0958-6946(99)00050-3
Sasic S, Ozaki Y (2001) Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least squares regression and band assignment. Anal Chem 73(1):64–71. https://doi.org/10.1021/ac000469c
Spanos GA, Schwartz SJ, van Breemen RB, Huang CH (1995) High-performance liquid chromatography with light-scattering detection and desorption chemical-ionization tandem mass spectrometry of milk fat triacylglycerols. Lipids 30(1):85–90. https://doi.org/10.1007/BF02537046
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300. https://doi.org/10.1023/A:1018628609742
Tsenkova R, Atanassova S, Itoh K, Ozaki Y, Toyoda K (2000) Near infrared spectroscopy for biomonitoring: cow milk composition measurement in a spectral region from 1,100 to 2,400 nanometers. J Anim Sci 78(3):515–522. https://doi.org/10.2527/2000.783515x
Tsenkova R, Atanassova S, Toyoda K, Ozaki Y, Itoh K, Fearn T (1999) Near-infrared spectroscopy for dairy management: measurement of unhomogenized milk composition. J Dairy Sci 82(11):2344–2351. https://doi.org/10.3168/jds.S0022-0302(99)75484-6
Urban C, Schurtenberger P (1999) Application of a new light scattering technique to avoid the influence of dilution in light scattering experiments with milk. Phys Chem Chem Phys 1(17):3911–3915. https://doi.org/10.1039/a903906f
Woo YA, Terazawa Y, Chen JY, Iyo C, Terada F, Kawano S (2002) Development of a new measurement unit (MilkSpec-1) for rapid determination of fat, lactose, and protein in raw milk using near-infrared transmittance spectroscopy. Appl Spectrosc 56(5):599–604. https://doi.org/10.1366/0003702021955150
Xin Q, Zhi LH, Jian LT, Zhu Y (2006) The rapid determination of fat and protein content in fresh raw milk using the laser light scattering technology. Opt Lasers Eng 44(8):858–869. https://doi.org/10.1016/j.optlaseng.2005.02.007
Zang H, Li L, Wang F, Yi Q, Dong Q, Sun C, Wang J (2012) A method for identifying the origin of chondroitin sulfate with near infrared spectroscopy. J Pharm Biomed Anal 61:224–229. https://doi.org/10.1016/j.jpba.2011.12.011
Zheng K, Li Q, Wang J, Geng J, Cao P, Sui T, Wang X, Du Y (2012) Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra. Chemom Intell Lab Syst 112(6):48–54. https://doi.org/10.1016/j.chemolab.2012.01.002
Zhu X, Guo W, Liang Z (2015) Determination of the fat content in cow's milk based on dielectric properties. Food Bioprocess Technol 8(7):1484–1494
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The study was financially supported by the project of National Natural Science Foundation of China (No. 31671935).
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Xinhua Zhu declares that he has no conflict of interest. Wenchuan Guo declares that he has no conflict of interest. Dayang Liu declares that he has no conflict of interest. Kang Fei declares that he has no conflict of interest.
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All experiments involving animals were conducted according to the principles of the Chinese Academy of Agricultural Sciences Animal Care and Use Committee (Beijing, China), which approved the study protocols.
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Zhu, X., Guo, W., Liu, D. et al. Determining the Fat Concentration of Fresh Raw Cow Milk Using Dielectric Spectroscopy Combined with Chemometrics. Food Anal. Methods 11, 1528–1537 (2018). https://doi.org/10.1007/s12161-017-1140-7
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DOI: https://doi.org/10.1007/s12161-017-1140-7