Bogomolov, Andrey

Kucheryavskiy, Sergey

Melentiyeva, Anastasiya

English

VISIBLE LIGHT SCATTER AS A QUANTITATIVE INFORMATIONSOURCE ON MILK CONSTITUENTSA. Melenteva 1, S. Kucheryavski 2, A. Bogomolov 1,31Samara State Technical University, Molodogvardeyskaya Street 244, 443100 Samara, Russia.2Aalborg University, campus Esbjerg, Niels Bohrs vej 8, 6700 Esbjerg, Denmark.3J&amp;M Analytik AG, Willy-Messerschmitt-Strasse 8, 73457 Essingen, Germany.bogomolov@j-m.deFat and protein are two major milk nutrients that are routinely analyzed in the dairy industry.Growing food quality requirements promote the dissemination of spectroscopic analysis, enabling real-time monitoring of processes and products. Optical analysis is generally performed in near and middle infrared (NIR and MIR) regions and relies on the component absorbance and Beer’s Law. The light scatter effect is therefore considered as a disturbance to be avoided during the measurement or eliminated at the data analysis stage.The region of visible (Vis) light (400-800 nm) is economically attractive, because it offers a range of inexpensive light sources, optics and detectors. At present, however, it is commonly considered useless, because of the light scatter by fat globules (1-10 μm) and protein micelles (80-200 μm) that strongly dominates; therefore, making the classical absorbance analysis hardly applicable.At the same time, diffused light by itself delivers information on the milk composition, specifically, fat content as illustrated in Fig. 1, and can potentially be used for the quantitative analysis. The main task here is to extract individual quantitative information on milk fat and total protein content from spectral data. This is particularly challenging problem in the case of raw natural milk, where the fat globule sizes may essentially differ depending on source.Fig. 1. Spots of light transmitted through homogenized milk samples with different fat content.The preceding research [1] has shown that individual scatter patterns of fat and protein in non-homogenized milk can be distinguished, thus, enabling their quantitative multivariate analysis.In the present study, a representative designed set of raw milk samples with simultaneously varying fat, total protein and particle size distribution has been analyzed in the Vis spectral region.The feasibility of raw milk analysis by PLS regression on spectral data has been proved. The root mean-square errors below 0.10% and 0.04% for fat and protein, respectively, have been obtained. PLS components were interpreted in terms of captured information.The results obtained provide a basis for the replacement of traditional spectroscopy by custom optical analyzers, optimized for the purpose of milk analysis. Preliminary achievements in this new research area are presented and discussed.References:[1] A. Bogomolov, S. Dietrich, B. Boldrini, R.W. Kessler, Food Chemistry (2012),doi:10.1016/j.foodchem.2012.02.077

Melentieva, Anastasiya

Visible light scatter as quantitative information source on milk constituents

    Aalborg UniversitetVisible light scatter as quantitative information source on milk constituentsMelentiyeva, Anastasiya; Kucheryavskiy, Sergey; Bogomolov, AndreyPublication date:2012Document VersionEarly version, also known as pre-printLink to publication from Aalborg UniversityCitation for published version (APA):Melentiyeva, A., Kucheryavskiy, S., & Bogomolov, A. (2012). Visible light scatter as quantitative informationsource on milk constituents. Poster presented at Chemometrics in Analytical Chemistry, Budapest, Hungary.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            ? You may not further distribute the material or use it for any profit-making activity or commercial gain            ? You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: December 25, 2020Visible Light Scatter as a Quantitative Information Source on Milk Constituents A. Melenteva 1, S. Kucheryavski 2 and A. Bogomolov 1,3 1 Samara State Technical University, 244 Molodogvardeyskaya Street, 443100 Samara, Russia, www.samgtu.ru 2 Aalborg University, campus Esbjerg, Niels Bohrs vej 8, 6700 Esbjerg, Denmark, www.esbjerg.aau.dk  3 J&M Analytik AG,  Willy-Messerschmitt-Strasse 8,  73457 Essingen,  Germany,  www.j-m.de Background Multiple light scattering by colloidal fat (1–10 µm) and protein (80–200 nm) particles complicates spectroscopic analysis of milk. Visible light region (400–800 nm) with dominating scatter effect is typically avoided. Our preceding research [1,2] has proved that diffused light by itself delivers quantitative information and can be used for accurate spectroscopic determination of milk fat and total protein in the region 400–1100 nm in the presence of essential variability of fat globule sizes (Fig. 1).   Challenge Replace spectroscopy with a low-end detection technique, such as digital imaging combined with multivariate image analysis. However, visual inspection of the images at different light colors reveals differences of fat, protein and homogenization effects; these are in correspondence with spectral differences in Fig. 1. Beside the overal extinction, some differences in radial intensity distribution are observed using the difference images in Fig. 4. Therefore, spatial distribution of diffusely transmitted light should be taken into account by the feature extraction algorithm converting the raw data into a model input. Results A new feature extraction algorithm, developed for this purpose, calculates average intensity values for N concentric rings built with an equal radius step around the center of a light spot (Fig. 5). N=8 was found to be an optimal resolution for subsequent modeling.  Experimental Two series of 16 samples with varying fat and total protein content were prepared from milk standards of exactly known composition (Table 1) by a pair-wise mixing design (Fig. 2). Vis/SW-NIR transmittance spectra were acquired using a 4-mm cavity. To simulate the variation of fat globule sizes each sample was analyzed 3 times: raw and after two partial homogenizations for 10 and 20 s with an ultrasound processor.  Figure 1 – Spectra of milk samples with different fat and protein content at different homogenization grades  Figure 2 – Pair-wise mixing design for a sample series [1] A. Bogomolov, S. Dietrich, R.W. Kessler, Food Chem. 134 (2012) 412–418 [2] A. Bogomolov, A. Melenteva, Chemometr. Intell. Lab. Syst. (2012) submitted 1.5 % 3.5 %    %Fat %Protein F-series 11 2.37 3.47 22 3.28 3.49 33 4.24 3.57 44 5.46 3.61 P-series 11 3.64 3.00 22 4.00 3.37 33 4.32 3.66 44 4.13 4.09 Table 1 – Composition of milk standards used for the preparation of fat (F) and protein (P) sample series raw 10 s 20 s 11 2212 21233233344344144113312442Each sample was placed in a Petri-dish and sequentially illuminated from below by red, green and blue LEDs through a fiber cable. The photos of diffusely transmitted light spot on the milk surface were acquired by Cannon 400D using bracketing with five exposition steps, thus, giving 15 images per sample. Increase of fat or protein content results in a higher light extinction in a sample: compare spot intensities and sizes in Fig. 3. Sample homogenization produces a similar effect. Figure 3 – Photos of samples with smallest and largest fat content (a), with smallest and largest total protein content (b) and sample with smallest fat content before and after homogenisation. The calculated features were used as predictors in PLS regression modells for of fat and total protein content. The best models, obtained after iPLS variables selection are presented in Fig. 6.  2 2.5 3 3.5 4 4.5 5 5.5 622.533.544.555.56Fat, measuredFat, predicted  calval0 5 10 15 2000.10.20.30.40.50.60.7nLVRMSE  calval2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.42.833.23.43.63.844.24.4Protein, measuredProtein, predicted  calval0 5 10 15 2000.050.10.150.20.250.30.35nLVRMSE  calvalFigure 6 – PLS models for prediction of fat (top, RMSECV = 0.190) and protein (bottom, RMSECV = 0.048) content. Conclusions Multivariate modeling of conventional digital images enables fat and total protein content prediction in raw milk with a reasonable accuracy. The method is resistant to essential variations of fat globule size distribution.  Figure 5 – Scheme of concentric rings based features calculation for N = 8.  Figure 4 – Difference in light intensities for the green images 

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Visible light scatter as quantitative information source on milk constituents

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