Role of Lanthanides in the Traceability of the Milk Production Chain

May 5, 2017 - In fact, the distribution is maintained nearly unaltered along the production chain of milk, apart from the passage into the stomachs of...
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Role of Lanthanides in the Traceability of the Milk Production Chain Maurizio Aceto,*,† Davide Musso,† Elisa Calà,† Fabio Arieri,‡ and Matteo Oddone§ †

Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy ‡ Centrale del latte di Alessandria e Asti, Viale Ennio Massobrio 10/12, 15121 Alessandria, Italy § Thermo Scientific, Strada Rivoltana, 20090 Rodano, Milan, Italy S Supporting Information *

ABSTRACT: The traceability and authentication of milk were studied using trace and ultratrace elements as chemical markers. Among these variables, the group of lanthanides resulted in being particularly useful for this purpose as a result of their homogeneous distribution inside milk, which showed on the contrary to be intrinsically inhomogeneous from the elemental point of view. Using in this pilot study milk samples from a factory in Piedmont (Italy), we demonstrated that the distribution of lanthanides can be used as a fingerprint to put into relation the soil of the pasture land on which cows graze and the bottled milk produced in the factory. In fact, the distribution is maintained nearly unaltered along the production chain of milk, apart from the passage into the stomachs of the cows. Using the same variables, it was possible to discriminate between milk produced in the factory and milk samples taken from the large-scale retail trade. KEYWORDS: milk, traceability, ICP−MS, production chain, homogeneity

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INTRODUCTION Milk is among the most important foods, and many scientific studies have been devoted to the characterization of its composition. From the market point of view, its importance cannot be underestimated because, if taken singularly, its value cannot compete with other expensive foodstuffs, such as truffles, spices, wines, etc., and on the other side, the volume marketed every year is represented in exceedingly high numbers. Consequently, it is not surprising that it has been the subject of several studies concerning its authentication and traceability. It is important to point out the difference between traceability and authentication, which rather than being synonyms are complementary concepts. Traceability is any procedure aiming at finding a link among a foodstuff and the soil where its raw matters come from, while authentication is any procedure aiming at highlighting differences among foodstuffs of the same type differing on a geographical, varietal, or technological basis. 1 The sum of traceability and authentication could be expressed with the term classification. Usually classification studies aim at highlighting the differences among milk of various animal species2 or milk produced in different countries3,4 or even different farms inside the same country,5−7 with the purpose of promoting typical or particularly valuable milk productions; one example is the possibility of discriminating between organic and conventional milk productions.8 These studies can sometimes be extended to valuable cheese productions, such as those labeled with protected designation of origin (PDO) trademark.9,10 Most of these studies exploit trace and ultratrace elements (thereafter “trace elements”) as markers for classification. This is due to the fact that some of these elements act as geochemical tracers, potentially reflecting the features of the grazing land on which cattle stand. We have verified, though, © XXXX American Chemical Society

that an aspect almost totally ignored is the homogeneity of the milk composition from the elemental point of view. In this study, we have evaluated the possibility of using trace elements determined with inductively coupled plasma mass spectrometry (ICP−MS) as markers of the milk production chain and as variables for authentication of milk produced at Centrale del latte di Alessandria e Asti (ClAA) with respect of milk samples produced in other countries. The milk produced at ClAA is obtained after collection from 12 local providers of raw milk located inside the area of Alessandria province (Piedmont, Italy) and transformed into pasteurized milk in three typologies: whole, partially skimmed, and skimmed milk. Before this, we have studied the distribution of trace elements in milk, to assess its grade of homogeneity. The main topics of the present study are, therefore, the following: (1) assessment of the homogeneity of milk, (2) assessment of daily variations of milk, (3) traceability of the milk production chain, and (4) authentication of milk. For the last point, pattern recognition methods were used to verify the possibility of discriminating the milk samples of ClAA providers, who are all located inside Alessandria province, from milk samples of large-scale retail trade produced in other regions or outside Italy; these samples were purchased in a supermarket in the town of Alessandria. Principal component analysis (PCA) was used for this purpose.

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MATERIALS AND METHODS

Reagents. High-purity water (HPW) from a Milli-Q (Milford, MA, U.S.A.) apparatus and ultrapure TraceSelect hydrogen peroxide, nitric Received: Revised: Accepted: Published: A

February 27, 2017 April 24, 2017 May 5, 2017 May 5, 2017 DOI: 10.1021/acs.jafc.7b00916 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry acid, and hydrochloric acid (Sigma-Aldrich, Milan, Italy) were used throughout the work. Polypropylene and polystyrene vials used for sample storage and analysis with the autosampler system, respectively, were kept in 1% nitric acid and then rinsed with HPW upon request. Porcelain capsules of 30 mL volume were used for microwave dry ashing. Element stock solutions (Inorganic Ventures, Lakewood, NJ, U.S.A.) were used to perform external calibration and internal standardization. Sample Collection. Milk, cream, soil, and feed samples were provided by ClAA. Milk samples from large-scale retail trade were purchased in a supermarket in Alessandria. Milk and cream samples were kept frozen at −18 °C before analysis. Soil and feed samples were kept at +4 °C before analysis. Milk, Cream, and Feed Dry Ashing Sample Treatment. To develop a simple and contaminant-free procedure, we applied dry ashing to milk, cream, and feed samples. Nearly 15 g of milk or cream and 15 g of each type of feed were weighted in a porcelain capsule. Milk and cream samples were added to 150 μL of 1 mg/L indium standard solution as an internal standard of the whole process. The samples were subjected to heat treatment in a Pyro 260 microwave ashing system (Milestone, Sorisole, Italy), with a gradient temperature program of 850 °C. The resulting ash was dissolved in 2.0 mL of ultrapure 69% nitric acid. Sample solutions were then taken up to 25 mL with HPW. Milk Acid Digestion Sample Treatment. Acid digestion in a microwave oven was used as an alternative sample treatment method for milk. A total of 4 g of milk was put in a closed polytetrafluoroethylene (PTFE) vessel with 6 mL of ultrapure 69% nitric acid and 2 mL of 30% hydrogen peroxide; the vessel was subjected to microwave irradiation in a Start D microwave oven system (Milestone, Sorisole, Italy) at a temperature of 150 °C for 15 min. The resulting solution was taken up to 25 mL with HPW. Soil Treatment. Samples of soil were treated following a standardized procedure, which involves extraction of metal ions with aqua regia. A proper amount of soil was dried at 105 °C for 24 h, after which 1 g was sieved (ø of 0.2 mm) and extracted with 2 mL of 30% hydrogen peroxide and 8 mL of aqua regia in a closed PTFE vessel in a Start D microwave oven system (Milestone, Sorisole, Italy) at a temperature of 150 °C for 15 min. The resulting mixture was centrifuged at 9000 rpm for 10 min, and then the supernatant was taken to 50 mL with HPW. ICP−MS Analysis. Elemental analyses were performed with a Thermo Fisher Scientific (Waltham, MA, U.S.A.) XSeries 2 model inductively coupled plasma mass spectrometer. The instrument is equipped with a quartz torch with a silver PlasmaScreen device, a quadrupole mass analyzer, a lens ion optics based on a hexapole design with a chicane ion deflector, and a simultaneous detector with realtime multichannel analyzer electronics, operating in either analogue signal mode or pulse-counting mode. The inlet system included an ESI PC3 Peltier Chiller (Elemental Scientific, Omaha, NE, U.S.A.) set at +2 °C, incorporating a perfluoroalkoxy (PFA) microflow concentric nebulizer and a cyclonic spray chamber, and an ESI SC-FAST model automated sample introduction system. Instrument and accessories are personal computer (PC)-controlled by PlasmaLab, version 2.6.2.337, software provided by Thermo Fisher Scientific. Instrument parameters are shown in Table 1. Measurement parameters were as follows: dual mode detection with peak jumping, dwell time of 25 ms, 30 sweeps, three replicates for a total acquisition time of 134 s, and isotopes used: 45Sc-CCT, 47TiCCT, 51V-CCT, 52Cr-CCT, 55Mn-CCT, 56Fe-CCT, 59Co-CCT, 60NiCCT, 63Cu-CCT, 64Zn-CCT, 75As-CCT, 78Se-CCT, 85Rb, 88Sr, 89Y, 90 Zr, 111Cd, 115In, 118Sn, 121Sb, 138Ba, 139La, 140Ce, 141Pr, 144Nd, 147Sm, 153 Eu, 158Gd, 159Tb, 163Dy, 165Ho, 166Er, 169Tm, 172Yb, 175Lu, 201Hg, 208 Pb, 232Th, and 238U. Isotopes labeled as CCT indicate that they were analyzed in cell collision technology−kinetic energy discriminator (CCT−KED) mode to significantly reduce interferences by polyatomic cations having the same m/z ratio of the analytes. To do this, an 8/92% H2/He gas mixture was introduced before the quadrupole mass analyzer at a flow of 5.00 mL/min. Interferences were evaluated as follows: CeO+/Ce+ < 2%, and Ba2+/Ba+ < 3%. A

Table 1. ICP−MS Instrumental Parameters extraction STDa CCTb STDa CCTb

STDa CCTb a

−129 −129 pole bias

lens 1

lens 2

lens 3

focus

−1150 −1150 hexapole bias

−83.9 −83.9 D1

−190 −190 D2

12.0 −10.4 DA

−2.3 −17.8 CCT gas flow

−4.2 −19.8 sampling depth

−43.1 −57.3

−140 −140

horizontal

vertical

−37.0 −39.2 detector voltage

0.0 5.0

125 125

140 140

600 600

3210 3210

Standard mode. bCCT−KED mode.

stability and performance test was performed before each analysis session by monitoring 7Li, 59Co, 115In, and 238U masses and the 59 Co/51ClO ratio of >15 for CCT−KED mode. Background signals were monitored at m/z 5 and 220 to perform a sensitivity test on the above-reported analyte masses. The limits of detection (LODs) and limits of quantification (LOQs), calculated as 3 and 10 times the standard deviation of blank measurements, respectively, are shown in Table 2.

Table 2. LODs and LOQs for the Isotopes Determined isotope

LOD (ng/L)

LOQ (ng/L)

Sca Tia 51 a V 52 a Cr 55 Mna 56 a Fe 59 Coa 60 a Ni 63 Cua 64 Zna 75 a As 78 a Se 85 Rb 88 Sr 89 Y 90 Zr 111 Cd 118 Sn 121 Sb

5.9 58,5 3,8 4.6 6.1 5.5 1.7 19.5 8.1 186.3 9.2 42.9 1.1 0.4 0.2 1.8 1.6 17.0 1.3

19.6 195.5 12.7 15.4 20.2 18.4 5.8 65.1 26.7 622.3 30.6 143.0 3.7 1.5 0.7 6.1 5.3 56.6 4.2

45 47

a

isotope 138

Ba La 140 Ce 141 Pr 144 Nd 147 Sm 153 Eu 158 Gd 159 Tb 163 Dy 165 Ho 166 Er 169 Tm 172 Yb 175 Lu 201 Hg 208 Pb 232 Th 238 U 139

LOD (ng/L)

LOQ (ng/L)

0.5 0.5 0.2 0.1 0.6 0.6 0.1 0.4 0.1 0.3 0.1 0.1 0.1 0.3 0.1 5.6 0.3 0.3 0.4

1.5 1.6 0.8 0.4 2.0 1.9 0.3 1.3 0.3 1.2 0.4 0.4 0.3 1.0 0.2 18.7 1.4 0.9 1.3

Analyte determined in CCT−KED mode.

External calibration was employed for quantification, using multielement standards prepared at six concentration levels in the range of 0.1−50.0 μg/L, by diluting multi-element stock solutions (100 mg/L) in 1% nitric acid solution. Isotope responses were corrected by dedicated internal standards using 69Ga, 103Rh, 115In, and 193Ir nuclides. Analysis of Certified Samples. To check the performance and recovery of the proposed sample treatment for soil, SRM 2586 (trace elements in soil containing lead from paint) certified material from the National Institute of Standards and Technology (NIST) was analyzed according to the described treatment. Chemometric Analysis. For PCA, XLSTAT, version 2012.2.02, statistical add-in for Microsoft Excel (Addinsoft, Paris, France) was used in a Windows 7 64-bit environment. B

DOI: 10.1021/acs.jafc.7b00916 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry

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RESULTS AND DISCUSSION In the following paragraphs, the classification of milk based on trace element distribution will be discussed in terms of both traceability and authentication. Assessment of the Homogeneity of Milk. Before studying the classification of milk, we wanted to assess the homogeneity of the matrix for what concerns the trace element distribution. This aspect is of course of the highest importance to establish the reliability of data obtained by means of ICP− MS analysis. Usually milk samples are subjected to pretreatment: should untreated milk be directly analyzed with plasma techniques, apart from blockage of cones, deposition of organic matter in the injector tube of the torch, and different types of interferences, a major consequence would be a variation in the degree of atomization/ionization during droplet residence in the plasma, depending upon the chemical form of the analyte, therefore causing poor precision and accuracy. For this reason, methods such as acid digestion in a microwave system, dry ashing, hot-plate digestion, dilution with specific reagents,11,12 or precipitation of proteins13 have been proposed. It must be considered that most of the studies involving the determination of trace elements rely on the accuracy of their data upon analysis of certified materials that are not true milk: in fact, usually powdered milk formulas are used, such as NIST SRM 1549 (non-fat milk powder), SRM 1549a (whole milk powder), and SRM 1849a (infant/adult nutritional formula), JRC ERM-BD150 (skimmed milk powder) and ERM-BD151 (skimmed milk powder), and NCS ZC73015 (milk powder). Upon analysis, these materials can easily yield accurate and precise results, but indeed, they do not definitely reflect the complex composition of milk. For example, they completely miss the micellar structure of most proteins and the fat globule macrostructure. Considering this, it is not surprising to find that the aspect of elemental homogeneity has rarely been considered in previous studies on milk, as if it had been taken for granted. After all, as far as milk macro components are concerned, there are several studies reporting large variations in composition as a result of a wide range of causes;14 the largest variations are known to occur in the fat content, then protein, lactose, and finally the mineral content. It is reported also that, within a single milling of one cow, fat globules and casein micelles can vary significantly in composition. In addition, it must be considered that milk is not a system in equilibrium. Given that, we could only expect that the elemental content had large variability, in particular for trace elements. Despite this, the variation of elemental features in milk has been discussed with concern to environmental conditions, nutrition of the animal, stage of lactation, animal breed, or other parameters but rarely considering the intrinsic homogeneity. Nantapo and Muchenje evaluated the mineral composition of milk produced by cows fed under a pasture-based dairy system, verifying significant differences in most elements (up to 250%) between winter and spring milk samples.15 More specifically, Krachler et al. analyzed milk powders produced in Austria and found that the concentrations of trace elements varied considerably from not only one commercial formula to another but also batch to batch, with high-to-low concentration ratios ranging from 1.1 to 4.8.16 To verify specifically the topic of intrinsic homogeneity of milk with a focus on trace elements, we carried out different experiments: (1) six aliquots of milk taken from the same bottle

were subjected to dry ashing and analyzed; (2) six aliquots of milk taken from the same bottle were subjected to acid digestion in a microwave oven and analyzed; (3) six aliquots of milk taken from the same bottle were added with 1 μg/L of In as the internal standard, subjected to dry ashing, and analyzed; and (4) one aliquot of milk was subjected to dry ashing; ash was dissolved in concentrated HNO3, diluted to 1% HNO3 with HPW; and the resulting solution was divided into six aliquots, which were then analyzed. Data obtained from the four experiments are shown in Table S1 of the Supporting Information. Data are in micrograms per liter on milk. The first experiment highlighted a wide variation in the results. Relative standard deviation (RSD) values calculated on six aliquots ranged between 3.57% (Sr) and 92.87% (Zr), with an average RSD value, calculated on all elements, of 33.90%. The very low repeatability can hardly be due to the pretreatment; it must be considered that, because dry ashing causes partial or total volatilization of elements with a low boiling point (e.g., As, Cd, Hg, etc.), only non-volatile elements were taken into consideration in this experiment. The second experiment gave even worst results. Even if with acid digestion in a microwave oven more elements can be determined, including the volatile elements, the limited amount of sample allowed for digestion in a PTFE bomb (4 mL maximum) caused lower concentrations of analytes in the resulting solutions. Therefore, many elements occurred at ultratrace level concentrations or below the detection limits of the instruments used. RSD values on six aliquots ranged between 9.88% (Zn) and 244.95% (Tb), with an average RSD value of 61.52%. The third experiment (samples added with In and then subjected to pretreatment) was performed to evaluate whether the wide variability observed in the previous two experiments was caused partially or totally by the pretreatment procedures. This can result by comparing the repeatability for the elements determined to the repeatability for In. RSD values on six aliquots ranged between 9.25% (Pb) and 82.46% (Th), with an average RSD value of 42.56%. In comparison of this value, calculated on all elements, to the RSD value of In, which resulted to be 14.46%, it appears clearly that the dry ashing procedure cannot be the main procedure responsible for the low repeatability of the results. Finally, we wanted to test the repeatability of our ICP−MS instrument by analyzing simplified samples. The fourth experiment, in fact, involved a single sample of milk subjected to dry ashing, therefore losing its entire organic matrix and the particular structural features of fats and proteins. The ash obtained after heating at 850 °C was dissolved in 1% HNO3, after which the resulting solution was divided into six aliquots, which were analyzed as separate samples. Not surprisingly, RSD values on six aliquots ranged between 2.01% (Cu) and 22.71% (Tm), with an average RSD value of 5.52%. It is apparent that the lack of the organic matter strongly improved the repeatability of the results. Also, these results can be considered similar to those yielded by analysis of the certified materials cited before: in fact, even if those materials still contain a significant amount of organic matter, the fatty and protein macro components are not present anymore in the original structure but rather in an artificially homogenized form, which inaccurately favors the repeatability. These experiments highlighted clearly that a major issue exists in the elemental analysis of milk: the matrix is intrinsically inhomogeneous. Causes for this inhomogeneity could be easily C

DOI: 10.1021/acs.jafc.7b00916 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry related to the complex nature of milk, in which compounds can be present partially in solution, emulsion, or dispersion. After all, the relatively poor homogeneity of some macro components themselves has already been verified. Lopez highlighted the heterogeneity of the size of milk fat globules, whose diameters vary in the range of 0.1−20 μm; also, their composition was variable in terms of triacylglycerols and minor compounds.17 Weiss et al.18 showed a strong negative relationship between the milking interval and milk fat content. Rico et al.19 noted that, as a result of the well-known changes in milk fat, sampling at different times during milking can have a major effect on the concentration of milk fat, while there is a modest decrease in the milk protein and lactose contents. Thus, problems such as malfunctioning meters and insufficient sample agitation can potentially result in inaccurate determinations of milk fat. Because metal ions are partially associated with macro components, such as fats or proteins,20 the chemical form of each element may be critical and the relative variation in macro component amounts can only negatively influence the repeatability in the determination of trace elements. This is not entirely the case, though, for lanthanides. In fact, in all of the experiments carried out, the RSD values for lanthanides were high, as for all other elements, but their distribution was maintained surprisingly unaltered. This can be appreciated in Figure 1, in which the distributions of lanthanides are shown as obtained in the first (a), third (b), and fourth (c) experiments; data from the second experiment were not considered because heavy lanthanides were mostly below detection limits. All concentration data were normalized to Ce according to the formula [lanthanidenorm] = [lanthanideoriginal]/[Ceoriginal]

Note that a logarithmic scale is used rather than a linear scale, to highlight the differences on the heavy lanthanides that could not be properly appreciated under a linear scale. In the figure shown, we chose to leave an empty place for prometium (Pm), to make apparent how the lanthanide distribution follows strictly the Oddo−Harkins rule; i.e., every even-numbered nuclide is more abundant than the previous and the subsequent odd-numbered nuclides. From Figure 1, it is apparent how the lanthanide distribution is unaltered in all six replicates in the three experiments. Note that, according to the Oddo−Harkins rule, the concentration of Eu should be lower than those of Sm and Gd but this element suffers from positive interference from Ba as a result of the formation of oxides in the plasma (135Ba16O+ interferes with 151 Eu+, while 137Ba16O+ interferes with 153Eu+), which is nonnegligible though kept under 0.6% with the instrumental setup used. This interference could be easily addressed by calculating the respective factors and subtracting the contribution of Ba oxides, but we preferred to keep in the graphic the Eu concentration uncorrected to show the bad repeatability of an element external to the lanthanide group (Ba) under the three experiments. The same choice was made for all of the subsequent graphics. Assessment of Daily Variations of Milk. After assessing the intrinsic inhomogeneity of milk but the homogeneous behavior of lanthanides, we wanted to evaluate the daily variation in elemental composition. A survey was made on the milk of a single provider, collecting a daily sample along 30 days; the survey was made in a period in which the diet of cows was constant. Dry ashing was the method used for milk

Figure 1. Distribution of lanthanides in the six replicates of the (a) first, (b) third, and (c) fourth experiments.

pretreatment. Table 3 reports the concentration ranges, averages, and relative standard deviations for all of the elements determined. Data are in micrograms per liter on milk. In addition, Table 4 reports the variation ranges for the following macro components: total fat content, total protein content, lactose, dry residue, and calcium. All elements varied widely in concentration along the 30 days, most likely as a consequence of the daily variation in macro components, with fats above all as suggested before,18−20 to which the previously shown intrinsic inhomogeneity was added. In fact, RSD values ranged from 14.18% for Sr to 106.51% for Cu. The average RSD value calculated among all elements was 45.32%. The distributions of data are shown in Figure 2. Concentration data were normalized to Ce according to the formula [element norm] = [elementoriginal]/[Ceoriginal]

and represented on a logarithmic scale. Apparently, though, lanthanides showed a different behavior. Their concentrations had wide variations (the average RSD value among the 13 elements was 44.73%), but their distribution did not. In fact, the lanthanide distribution was D

DOI: 10.1021/acs.jafc.7b00916 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry Table 3. Elemental Data from the 30 Day Survey on Milk from a Single Provider

a

element

minimum

maximum

average

RSD

Sc Ti V Cr Mn Fe Co Ni Cu Zn Rb Sr Y Zr Sn Sb Ba La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Pb Th U

0.842 63.2 1.35 2.95 25.5 313.1 0.347 8.37 20.8 994.5 51.8 423.2 0.301 0.632 0.532 0.016 378.7 1.11 2.58 0.234 0.846 0.138 0.363 0.129 0.016 0.064 0.008 0.025 a 0.024 a 32.16 0.668 0.04

1.55 157.1 4.98 13.4 60.9 1594.3 2.02 69.4 652.7 2201.4 404.7 939.0 1.63 13.47 4.03 0.191 1934.8 8.53 17.2 1.86 6.822 1.27 2.15 1.12 0.117 0.453 0.067 0.19 0.025 0.157 0.017 162.7 5.75 0.294

1.17 106.5 3.18 6.43 45.1 777.3 0.966 28.9 169.5 1640.7 188.6 727.9 0.928 5.48 2.10 0.074 943.6 4.10 9.31 0.905 3.22 0.578 1.28 0.552 0.056 0.236 0.039 0.105 0.012 0.083 0.011 88.4 2.22 0.153

15.9 26.9 37.2 34.8 22.1 61.9 36.0 65.2 106.5 21.7 46.7 14.2 43.4 84.6 42.5 62.7 43.1 43.5 39.8 42.2 43.7 45.9 41.8 43.4 43.3 40.9 45.6 44.6 51.7 48.9 50.7 39.9 51.9 52.2

Figure 2. Distribution of elements in the 30 samples of milk.

Table 5. Pearson Correlation Coefficients in the 30 Day Survey Dataset

Below detection limits.

Table 4. Macro Component Data from the 30 Day Survey on Milk from a Single Provider macro component

minimum (g/L)

maximum (g/L)

average (g/L)

RSD (%)

total fat total protein lactose dry residue Ca

35.7 30.2 42.3 112.0 1.10

42.4 36.5 53.7 137.8 1.35

38.8 33.3 49.0 125.8 1.20

9.34 9.37 9.87 8.99 7.10

element

element

similarity

La Pr La Nd La Nd Y Sm Nd Pr La Pr Pr Gd La Sm Nd Ho Sm Pr Sm Y La Y Er Tb

Nd Nd Pr Sm Sm Sm Er Gd Gd Sm Sm Sm Gd Tb Gd Gd Tb Er Tb Tb Tb Yb Tb Ho Yb Dy

0.998 0.997 0.996 0.995 0.994 0.993 0.993 0.991 0.991 0.991 0.990 0.988 0.988 0.987 0.984 0.983 0.981 0.979 0.979 0.978 0.977 0.976 0.973 0.969 0.964 0.959

distribution (not their absolute concentration values) could be used for the classification of milk. This could be true for at least two reasons: (1) the lanthanide distribution data arising from elemental analysis have shown to be more reliable than other elements, considering the intrinsic inhomogeneity of milk, and (2) previous studies demonstrated the role of the lanthanide distribution in the classification of foodstuffs.21,22 To verify this hypothesis, we tested the potential of lanthanide distribution in two scenarios: (a) the traceability of the milk production chain and (b) the authentication of milk produced at ClAA. Traceability of the Milk Production Chain. For the traceability study, we analyzed samples taken from the various stages of the milk production chain. Two milk providers of

far more constant than the distribution of the other elements, as appreciated from the inset highlighted in the figure. In fact, from the calculation of the correlation among elements inside the 30 day dataset with the Pearson correlation coefficient (Table 5), the highest scores of similarity resulted to be for couples of lanthanides or couples of a lanthanide and yttrium, which, not by chance, is a rare earth element. This behavior, which was to be expected considering the well-known chemical similarity inside this group, suggests that the lanthanides are distributed in milk in a very similar way among themselves. Potential of Lanthanide Distribution in the Classification of Milk. Having ascertained the role of lanthanides inside the composition of milk, it is apparent that their E

DOI: 10.1021/acs.jafc.7b00916 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Journal of Agricultural and Food Chemistry ClAA were taken into consideration. Samples were from the following stages of the whole chain: (1) soil from the grazing land, (2) samples of the plants grown in the grazing land and used by the providers as fodder for their cows, together with samples of additional artificial feed from external sources, given by the provider to cows to integrate their diet (provider a used a diet richer in local plants, while provider b used more external feeds), (3) raw milk before the pasteurization treatments, (4) samples of pasteurized milk with different levels of fat (whole, partially skimmed, and skimmed), (5) samples of milk cream with different levels of fat (derived from whole, partially skimmed, and skimmed milk). The suitability of the method used for soil analysis was checked with a certified soil material, SRM 2586 from NIST. Analysis gave the results reported in Table 6, showing a good agreement between certified and found values. Table 6. Analysis of NIST SRM 2586 Certified Soil Material element

certified values (mg/kg)

uncertainty

found (mg/kg)

standard deviation

La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu As Ba Cd Co Cr Cu Fe Mn Ni Pb Sc Se Sr Th Ti V Y Zn

29.7a 58a 7.3b 26.4a 6.1b 1.5b 5.8b 0.9b 5.4b 1.1b 3.30b 0.5b 2.64a c 8.7 413a 2.71 35b 301 81b 51610a 1000a 75b 432 24b 0.6b 84.1a 7b 6050a 160b 21b 352a

4.8a 8a c 2.9a c c c c c c c c 0.51a c 1.5 18a 0.54 c 45.0 c 890a 18a c 17.0 c c 8.0a c 660a c c 16a

25.4 54.0 6.9 25.2 5.6 1.2 5.2 0.69 3.6 0.66 1.9 0.25 1.6 0.10 8.9 258 2.74 30.6 184 73 37393 878 60 474 16 0.65 51.0 9.7 2693 127 16.3 345

1.2 2.0 0.3 1.1 0.4 0.08 0.3 0.04 0.20 0.04 0.14 0.02 0.12 0.01 0.40 22.0 0.11 1.1 13.0 7.0 2312 64 3.0 6.0 2.0 0.02 7.0 0.7 586 12 1.3 9.0

Figure 3. Distribution of lanthanides in the soil of grazing land and in plants and external feeds used for fodder by providers a and b. Solid lines, local plants; dashed lines, external feeds.

distribution in plants could reflect the fingerprint of soil has already been suggested by many works in the scientific literature.23−25 Significantly, the most relevant differences are between soil and external feeds “nucleo unifeed 31” in the case of provider a and corn, cotton, dark pellet, dry grass hay, integral corn mash, short straw, soybean flour, and soybean hulls in the case of provider b, which are made of components produced outside the areas. Then, a comparison was made between soils and raw milks. The relative lanthanide distributions (Ce-normalized data on a logarithmic scale) are shown in Figure 4. In both cases, the distributions of lanthanides seem to be slightly fractionated when passing from soil to raw milk, but this could be easily due to the contribution of the external feeds used in the diet, which bring different fingerprints from the respective soils of origin; moreover, the possible effect of animal metabolism must be taken into consideration. In fact, the production of milk by cows involves the passage into no less than four stomachs. The anomalous behavior of Eu is due to the fact that the soil sample showed a higher Eu/Ba ratio than milk (in soil, Eu/Ba is ca. 1:10; in milk, Eu/Ba is ca. 1:100); therefore, the Eu content in soil was less interfered by Ba, and the lanthanide distribution follows more correctly the Oddo−Harkins rule. The following stage is the whole of the treatments carried out at the ClAA factory. Milk is supplied by 12 different providers located in the Alessandria province. The sequence of

a c

Reference mass fraction value. bInformative concentration value. Not indicated in the NIST report.

First at all, soil was compared to plants growing on the grazing land and with external feeds. The relative lanthanide distributions (Ce-normalized data in a logarithmic scale) are shown in Figure 3. The results are very similar between the two providers: in both cases, there is a good constancy in the distributions in soil and in plants, as expected. The fact that the lanthanide F

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(1) raw milk, (2) raw milk after storage, (3) preheated raw milk, (4) totally skimmed raw milk, (5) pasteurized whole milk, (6) pasteurized partially skimmed milk, (7) pasteurized skimmed milk, (8) bottled whole milk, (9) bottled partially skimmed milk, (10) bottled skimmed milk, (11) whole milk cream, (12) partially skimmed milk cream, and (13) skimmed milk cream. All of the samples cited above have been analyzed with ICP− MS after dry ashing treatment. Figure 6a (Ce-normalized data

Figure 4. Distribution of lanthanides in the soils of grazing land and in raw milks of providers a and b.

treatments is illustrated in the flowsheet diagram of the processes involved in the transformation of raw milk into the products marketed by ClAA (Figure 5). Raw milk is first

Figure 6. Distribution of (a) lanthanides and (b) all elements in the milk samples after pasteurization and separation of cream.

on a logarithmic scale) shows the lanthanide distribution in the different samples taken along this sector of the production chain, while Figure 6b (same features) extends the view on the distributions of the other elements determined. Again, there is a good correlation among the lanthanide distribution in the different samples. Neither the pasteurization nor the separation of cream from milk seems to significantly alter the initial distribution as measured in raw milk. The same does not hold true for the other elements: apparently, the treatments introduce fractionation in the original distributions, particularly for first transition series metals. Finally, it is possible to evaluate the extreme points of the whole milk chain, that is, soil and bottled milk. In Figure 7, the lanthanide distribution is shown as determined in soil, bottled whole milk, bottled partially skimmed milk, and bottled skimmed milk. Apparently, there is no difference among the various typologies of milk, despite the significant difference in

Figure 5. Flowsheet diagram of the processes used at the ClAA factory for transformation of raw milk into products.

preheated at 55 °C and separated from cream to obtain a totally skimmed base; after that, cream is titrated to determine the fat content and remixed with skimmed milk in a ratio determined by the typology of the product desired (whole milk with 3.70% fat content, partially skimmed milk with 1.65% fat content, and skimmed milk with 0.25% fat content) and then come homogenization, pasteurization, cooling, and bottling. A total of 13 milk and cream samples were withdrawn inside the sequence of treatments. The typologies were the following: G

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Figure 7. Distribution of lanthanides in soil and various typologies of bottled milk. Figure 9. PC1 versus PC2 plot of Ce-normalized data from ClAA and large-scale retail trade milk samples.

fat amount (from 3.70% in whole milk to 0.25% in totally skimmed milk); the same result was obtained with the various typologies of milk creams (data not shown). However, a slight degree of fractionation is introduced along the chain from soil to bottled milks, most likely as a consequence of the metabolism of cows. Fractionation is particularly significant on heavy lanthanides. It must also be considered that these analytes are determined at very low concentrations (tens of nanorams per liter), while data for light lanthanides (hundreds of nanograms per liter) are more reliable. In the end, the lanthanide distribution represents a good way to follow the production chain of milk. In particular, it is possible to demonstrate that the original fingerprint is maintained from soil to fodder plants and from raw milk to the different bottled milk types and creams. The intervention of the metabolism of cows introduces some fractionation on heavy lanthanides. Authentication of Milk. The last aspect in milk classification is assessing the possibility of discriminating milk produced at ClAA from milk marketed in the large-scale retail trade. Milk samples from 10 providers of ClAA were compared to 10 milk samples taken from the large-scale retail trade in the town of Alessandria (labeled as LSRT in Figures 8 and 9); these

samples were produced in other Italian regions or in foreign countries (France, Germany, and Austria). The commercial samples were all ultra-high-temperature (UHT) pasteurized; some were partially skimmed milks (labeled as “ps” in graphs). In one case, a sample was collected in Tetrapak and glass bottle typologies (“-tp” and “-b”, respectively, in Figure 9). First, a classification was attempted by taking into consideration all of the elements determined by means of ICP−MS. On the basis of 34 variables (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Sn, Sb, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Pb, Th, and U), a fairly good discrimination was obtained between ClAA and LSRT samples, as shown in Figure 8, where the first principal component (PC1) versus the second principal component (PC2) plot is shown. The percentage of variance contained in the plot was 78.83%. Two ClAA samples were discarded because their principal component (PC) scores were too high. A second PCA was performed using only Ce-normalized data of lanthanides. In this case, all samples were included; the resulting PC1 versus PC2 plot is shown in Figure 9, together with the loadings plot, where the variables are indicated with arrows. The percentage of variance contained in the plot was 90.33%. Once again, the selection of lanthanides as variables and the transformation into Ce-normalized data were successful in obtaining a good classification, in this case allowing us to distinguish ClAA milk samples from milk samples produced in other countries. The loadings plot indicated that the lanthanide content was generally higher in LSRT samples than in ClAA samples, with no particular distinction between light and heavy lanthanides. It is apparent that the group of lanthanide metals behaves differently from other elements in milk. While many elements are distributed in various phases (i.e., associated with protein micelles or fats, as free ions, etc.), lanthanides seem to act as a single element because their distribution is maintained nearly unaltered along the production chain of milk, apart from the passage into the stomachs of cows. This is coherent with their chemical similarity, but it has been verified in this study on milk for the first time. Also, the distribution of lanthanides offers a way to verify the traceability of the dairy production because the fingerprint of

Figure 8. PC1 versus PC2 plot of data from ClAA and large-scale retail trade milk samples. H

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Monitoring the authenticity of Brazilian UHT milk: A chemometric approach. Food Chem. 2011, 124, 692−695. (8) Erich, S.; Schill, S.; Annweiler, E.; Waiblinger, H. U.; Kuballa, T.; Lachenmeier, D. W.; Monakhova, Y. B. Combined chemometric analysis of 1H NMR, 13C NMR and stable isotope data to differentiate organic and conventional milk. Food Chem. 2015, 188, 1−7. (9) Brescia, M. A.; Monfreda, M.; Buccolieri, A.; Carrino, C. Characterisation of the geographical origin of buffalo milk and mozzarella cheese by means of analytical and spectroscopic determinations. Food Chem. 2005, 89, 139−147. (10) Sacco, D.; Brescia, M. A.; Sgaramella, A.; Casiello, G.; Buccolieri, A.; Ogrinc, N.; Sacco, A. Discrimination between Southern Italy and foreign milk samples using spectroscopic and analytical data. Food Chem. 2009, 114, 1559−1563. (11) Nóbrega, J. A.; Gélinas, Y.; Krushevska, A.; Barnes, R. M. Direct determination of major and trace elements in milk by Inductively Coupled Plasma Atomic Emission and Mass Spectrometry. J. Anal. At. Spectrom. 1997, 12, 1243−1246. (12) Azcarate, S. M.; Savio, M.; Smichowski, P.; Martinez, L. D.; Camiña, J. M.; Gil, R. A. Single-step solubilization of milk samples with N,N-dimethylformamide for inductively coupled plasma−mass spectrometry analysis and classification based on their elemental composition. Talanta 2015, 143, 64−70. (13) Husákova, L.; Urbanová, I.; Šramková, J.; Konečná, M.; Bohuslavová, J. Multi-element analysis of milk by ICP-oa-TOF-MS after precipitation of calcium and proteins by oxalic and nitric acid. Talanta 2013, 106, 66−72. (14) Walstra, P.; Wouters, J. T. M.; Geurts, T. J. Dairy Science and Technology, 2nd ed.; CRC Press: Boca Raton, FL, 2006; pp 98−108. (15) Nantapo, C. T. W.; Muchenje, V. Winter and spring variation in daily milk yield and mineral composition of Jersey, Friesian cows and their crosses under a pasture-based dairy system. S. Afr. J. Anim. Sci. 2013, 43, S17−S21. (16) Krachler, M.; Rossipal, E.; Irgolic, K. J. Trace elements in formulas based on cow and soy milk and in Austrian cow milk determined by inductively coupled plasma mass spectrometry. Biol. Trace Elem. Res. 1998, 65, 53−74. (17) Lopez, C. Milk fat globules enveloped by their biological membrane: Unique colloidal assemblies with a specific composition and structure. Curr. Opin. Colloid Interface Sci. 2011, 16, 391−404. (18) Weiss, D.; Hilger, M.; Meyer, H. H. D.; Bruckmaier, R. M. Variable milking intervals and milk composition. Milchwissenschaft 2002, 57, 246−249. (19) Rico, D. E.; Marshall, E. R.; Choi, J.; Kaylegian, K. E.; Dechow, C. D.; Harvatine, K. J. Within-milking variation in milk composition and fatty acid profile of Holstein dairy cows. J. Dairy Sci. 2014, 97, 4259−4268. (20) Neville, M. C.; Zhang, P.; Allen, J. C. Minerals, ions, and trace elements in milk: A. Ionic interactions in milk. In Handbook of Milk Composition; Jensen, R. G., Ed.; Academic Press: London, U.K., 1995; Chapter 7, pp 577−592, DOI: 10.1016/B978-012384430-9/50025-1. (21) Bettinelli, M.; Spezia, S.; Baffi, C.; Beone, G. M.; Rocchetta, R.; Nassisi, A. ICP−MS determination of REEs in tomato plants and related products: A new analytical tool to verify traceability. Atom. Spectrosc. 2005, 26, 41−50. (22) Oddone, M.; Aceto, M.; Baldizzone, M.; Musso, D.; Osella, D. Authentication and traceability study of hazelnuts from Piedmont, Italy. J. Agric. Food Chem. 2009, 57, 3404−3408. (23) Brown, P. H.; Rathjen, A. H.; Graham, R. D.; Tribe, D. E. Rare earth elements in biological systems. In Handbook on the Physics and Chemistry of Rare Earths; Gschneider, K. A., Eyring, L., Eds.; Elsevier: Amsterdam, Netherlands, 1990, Vol. 13, Chapter 92, pp 423−452, DOI: 10.1016/S0168-1273(05)80135-7. (24) Tyler, G. Rare earth elements in soil and plant systems: A review. Plant Soil 2004, 267, 191−206. (25) Liang, T.; Ding, S.; Song, W.; Chong, Z.; Zhang, C.; Li, H. A review of fractionations of rare earth elements in plants. J. Rare Earths 2008, 26, 7−15.

the raw milk at the entrance of the production block is wellmaintained up to the final product, that is, bottled milk; the same fingerprint can be found in cream. This work can be considered as a first approach to the study of milk traceability through the determination of the distribution of lanthanides. It is, however, necessary to improve the research in this field by performing more studies in different geographic areas to confirm the hypothesis that lanthanides are proper markers for classification of milk.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.7b00916. Data from assessment of homogeneity of milk (Table S1) (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Maurizio Aceto: 0000-0001-6360-3632 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank Centrale del latte di Alessandria e Asti and their milk providers for supplying samples of milk and cream. ABBREVIATIONS USED ClAA, Centrale del latte di Alessandria e Asti; CCT−KED, cell collision technology−kinetic energy discriminator; ICP−MS, inductively coupled plasma mass spectrometry

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REFERENCES

(1) Aceto, M. The use of ICP−MS in food traceability. In Advances in Food Traceability Techniques and Technologies: Improving Quality Throughout the Food Chain; Espiñeira, M., Santaclara, F. J., Eds.; Woodhead Publishing: Sawston, U.K., 2016; Woodhead Publishing Series in Food Science, Technology and Nutrition, Chapter 8, pp 137−164, DOI: 10.1016/B978-0-08-100310-7.00008-9. (2) Necemer, M.; Potocnik, D.; Ogrinc, N. Discrimination between Slovenian cow, goat and sheep milk and cheese according to geographical origin using a combination of elemental content and stable isotope data. J. Food Compos. Anal. 2016, 52, 16−23. (3) Luo, D. H.; Dong, H.; Luo, H. Y.; Xian, Y. P.; Guo, X. D.; Wu, Y. L. Multi-Element (C, N, H, O) Stable Isotope Ratio Analysis for determining the geographical origin of pure milk from different regions. Food Anal. Meth. 2016, 9, 437−442. (4) Zain, S. M.; Behkami, S.; Bakirdere, S.; Koki, I. B. Milk authentication and discrimination via metal content clusteringA case of comparing milk from Malaysia and selected countries of the world. Food Control 2016, 66, 306−314. (5) Osorio, M. T.; Koidis, A.; Papademas, P. Major and trace elements in milk and Halloumi cheese as markers for authentication of goat feeding regimes and geographical origin. Int. J. Dairy Technol. 2015, 68, 573−581. (6) Magdas, D. A.; Dehelean, A.; Feher, I.; Cristea, G.; Puscas, R.; Dan, S. D.; Cordea, D. V. Discrimination markers for the geographical and species origin of raw milk within Romania. Int. Dairy J. 2016, 61, 135−141. (7) Souza, S. S.; Cruz, A. G.; Walter, E. H. M.; Faria, J. A. F.; Celeghini, R. M. S.; Ferreira, M. M. C.; Granato, D.; Sant’Ana, A. D. I

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