Application of multi-element (C, N, H, O) stable isotope ratio analysis for the traceability of milk samples from China

Journal Pre-proofs of multi-element (C, N, H, O) stable isotope ratio analysis for the traceability of milk samples from China Shanshan Zhao, Yan Zhao, Karyne M. Rogers, Gang Chen, Ailiang Chen, Shuming Yang PII: DOI: Reference:

S0308-8146(19)31960-0 https://doi.org/10.1016/j.foodchem.2019.125826 FOCH 125826

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Food Chemistry

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17 May 2019 28 October 2019 28 October 2019

Please cite this article as: Zhao, S., Zhao, Y., Rogers, K.M., Chen, G., Chen, A., Yang, S., of multi-element (C, N, H, O) stable isotope ratio analysis for the traceability of milk samples from China, Food Chemistry (2019), doi: https://doi.org/10.1016/j.foodchem.2019.125826

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Application of multi-element (C, N, H, O) stable isotope ratio analysis

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for the traceability of milk samples from China

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Shanshan Zhao1,2, Yan Zhao1,2＊ , Karyne M. Rogers3, Gang Chen1,2, Ailiang Chen1,2,

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Shuming Yang1,2

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Academy of Agricultural Sciences, Beijing 100081, China

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Rural Affairs, Beijing 100081, China

Institute of Quality Standard & Testing Technology for Agro-Products, Chinese

Key Laboratory of Agro-product Quality and Safety, Ministry of Agriculture and

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Zealand

National Isotope Centre, GNS Science, 30 Gracefield Road, Lower Hutt 5040, New

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Corresponding author

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Yan Zhao, Ph. D

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Tel.: +86-010-8210-6558

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Fax: +86-010-8210-6560

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E-mail: [email protected]

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ABSTRACT

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Cow milk samples from various provinces in China were collected, and the effects of

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lactation stage, sampling time, and geographic origin on the samples were studied by

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elemental analysis-isotope ratio mass spectrometry (EA-IRMS). Traceability accuracy

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was determined using δ13C, δ15N, δ2H and δ18O values to specifically assign

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geographic origin. Stable isotope ratios of C, N, H and O were not significantly

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different among three lactation stages; however the δ13C, δ15N, and δ18O values of

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milk were influenced by sampling time. Furthermore, there were highly significant

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regional differences in the mean δ13C and δ15N values of milk. In summary, the

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lactation stage had no effect on the traceability of milk, whereas sampling time and

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geographic origin did affect milk traceability. Different geographic locations with a

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separation distance greater than 0.7 km can be distinguished using multi-element (C,

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N, H, O) stable isotope ratio analysis.

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Key words: Cow milk; Isotope ratio mass spectrometry (IRMS); Lactation; Sampling

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time; Geographic origin; Traceability

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1. Introduction

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Cow’s milk has extremely high nutritional value. The main proteins in milk are casein,

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albumin, globulin, and lactoprotein. Dairy contains all 20 amino acids and the eight

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essential amino acids. Freedom of trade in the global market has led to increasing

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incidents of fraud regarding milk. For example, some milk powder manufacturers

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increase the protein content of product by adding low-cost ingredients (such as

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nitrogen-rich materials) to raise the price of low-quality dairy products. In addition,

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many bakeries add plant butter, which is easy to shape and affordable but harmful to

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human health (Ma, Yang, Yang, Zhang, & Xiao, 2017). Thus, the traceability of cow's

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milk is important.

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In most countries, cow's milk and its products have been prominent in the dairy

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market. In China, the dairy industry started late, but has progressed steadily. However,

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the Chinese dairy industry suffered after the melamine incident of 2008. Since that

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time, consumers have paid attention to the geographic origin of milk. Thus, milk

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source control is important to both consumers and manufacturers. A variety of dairy

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products from specific preferred geographic origins are marketed at higher prices than

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traditional products making them prime targets for counterfeiting. For instance, Inner

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Mongolia is a major grassland area, and its rich forage makes desirable milk (Luo,

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Dong, Luo, Xian, Guo, & Wu, 2015). Therefore, having reliable tools for tracing the

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source of cow’s milk can protect the safety and quality of the products.

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In the field of food source analysis, stable isotope ratio analysis is a relatively new

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technology that has been introduced within the European wine industry to ensure the

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authenticity of wine provenance and detect food adulteration (Almeida &

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Vasconcelos, 2001). Stable isotope ratio analysis is an effective method in the fields

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of geographic origin detection and adulteration analysis. It has been successfully

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applied to beef (Zhao, Zhang, Chen, Chen, Yang, & Ye, 2013), chicken (Lv & Zhao,

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2016), pork (Zhao, Yang, & Wang, 2016), honey (Cabanero, Recio, & Ruperez, 2006),

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fruit juice (Guyon et al., 2014), and essential oil (Rossmann, 2001). Thus, stable

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isotope ratio analysis techniques are an effective means for distinguishing the

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geographical origin of food products. Stable isotope ratio analysis has been used to

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determine the geographic origin of milk in the United States (Bostic, Hagopian, &

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Jahren, 2018) and New Zealand (Ehtesham, Hayman, Van Hale, & Frew, 2015). This

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method has also been applied to the traceability of milk from Australia and New

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Zealand, Germany and France, the USA, and China (Luo et al., 2015). The

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traceability of other products from adjacent geographic regions using stable isotope

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ratio analysis have been previously reported. For example, Valenti, et al., (2017)

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demonstrated that the variability in carbon, hydrogen, oxygen, nitrogen and sulphur

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stable isotope ratios can discriminate between cheeses produced in nearby regions

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within a Protected Designation of Origin (PDO) area; the sulphur and nitrogen stable

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isotope ratios offered the best discrimination (97.2% correct classification of the

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cheeses). There are no reports, however, as to whether this method can distinguish

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between milk samples from adjacent or nearby geographic regions in China.

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Factors such as sampling time may influence the traceability of milk’s geographical

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origin. Garbaras et al. (2018) investigated the traceability of Belarusian milk with

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regard to sampling time. Carbon, nitrogen and oxygen stable isotope ratios were

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measured in milk sampled in Brest, Gomel, Grodno, Minsk, and Mogilev regions in

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Belarus during the summer and winter seasons. The δ13C values in the milk were

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found to be different for the summer and winter seasons.

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Milk traceability can also be affected by lactation changes. Lactation is the most

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energetically expensive aspect of mammalian reproduction. It involves the export of

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significant quantities of maternal nutrients to supply offspring demands (Oftedal,

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1993; Oftedal, 2008). Magdas et al. (2013) observed that that cow milk collected in

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the second and third months of lactation had the heaviest oxygen and hydrogen

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isotope ratios. Therefore, changes in these isotopic signatures of milk may have a

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significant effect on the interpretation of the milk source analysis data.

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It was previously noted that the use of single variable isotope for geographical

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discrimination is usually insufficient to provide unequivocal origin assignment (Luo

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et al., 2015). Thus, milk samples from different provinces in China were collected to

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study the effect of sampling time and lactation stage on the traceability of milk using

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multi-element (C, N, H, O) stable isotope ratio analysis in this study. Cross-validation

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was also performed using δ13C, δ15N, δ2H, and δ18O values to distinguish milk from a

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smaller geographic region such as within one province based on individual farm

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traceability. Distances between two farms in each province ranged from 0.7 km to

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62.8 km.

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2. Materials and methods

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2.1 Sample Information

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2.1.1 Experiment 1: Lactation stage

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Cow’s milk was sampled from two dairy farms selected from four Chinese provinces

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(Hebei, Ningxia, Shaanxi and Inner Mongolia) in July 2014 (Fig. 1a). A total of 120

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milk samples were collected to ensure a representative data set (15 milk samples from

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each farm) (Table S1). Milk samples were also collected during three different

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lactation stages: early (30–90 days of lactation), middle (120–180 days of lactation),

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and late (210–270 days of lactation) (Fig. S1).

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2.1.2 Experiment 2: Sampling time

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Milk samples were collected in March, July and November from two dairy farms in

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each of the four provinces (Tianjin, Hebei, Jiangsu and Inner Mongolia) (Fig. 1b).

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Five replicate samples were collected from each storage tank during each sampling

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event (a total of 120 samples) (Table S2; Fig. S1).

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2.1.3 Experiment 3: Geographic origin

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In order to study the effect of geographic origin on milk traceability, 160 milk

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samples were measured for stable isotopes (120 milk samples from Hebei, Ningxia,

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Shaanxi and Inner Mongolia in experiment 1, and 40 milk samples from July from

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Tianjin, Hebei, Jiangsu, and Inner Mongolia in experiment 2) (Fig. S1). These regions

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were classified as either a pastoral or agricultural according to the type of cattle and

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feeding technique practiced at each location. The pastoral regions included Ningxia

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and Inner Mongolia. The agricultural regions included Hebei, Shaanxi, Tianjin, and

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Jiangsu. After collection, milk samples were frozen at –20°C and then transported to

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the laboratory at –20°C.

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2.2 Preparation of samples

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Milk samples were kept frozen at –20°C until processing. A 50 g (fresh weight)

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sample was freeze-dried for 24 h before being powdered in a ball mill.

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Chloroform:methanol (2:1, v/v) was added to the sample in a centrifuge tube at a 1:5

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(sample:solution) ratio, and the lid was tightly closed. Samples were agitated in a

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vortex mixer operating for 10 minutes. The samples were then centrifuged at 5000

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rpm for 5 minutes. The supernatant was removed and discarded, and the solvent wash

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was repeated two additional times. The samples were then sealed using parafilm with

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a small hole inserted and lyophilized overnight to dry the samples. The defatted dry

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mass (DDM) was then weighed and prepared for isotope analysis.

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2.3 Sample Analysis

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International reference materials were used for two-point calibrations of the isotope

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ratios, USGS43 (Indian Hair, δ13C = –21.28‰, δ15N = 8.44‰), USGS40 (L-glutamic

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acid, δ13C = –26.39‰, δ15N = –4.5‰), CBS (Caribou Hoof, δ2H = –197.0‰, δ18O =

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3.8‰), KHS (Kudu Horn, δ2H = –54.1‰, δ18O = 20.3‰) developed by the United

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States Geological Survey, B2159 (Sorghum flour, δ13C = –13.68‰, δ15N = 1.58‰),

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and protein B2205 (EMA P2, δ2H = –87.80‰, δ18O = 26.90‰) developed by

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Elemental Microanalysis.

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2.3.1 δ13C and δ15N analysis

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For δ13C and δ15N analysis, the DDM as well as the international reference materials

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were weighed into tin capsules and introduced sequentially into an elemental analyzer

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(Flash 2000, Thermo Finnigan, Germany). The reactor packings contained 0.85–1.7

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mm granular chromium oxide, 0.85–1.7 mm silvered granular cobaltous oxide, and 4

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x 0.5 mm fine copper wires. The helium gas flow rate was 100 mL/min. The oxygen

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injection velocity was 175 mL/min. The oxygen injection time was 3 s, and the

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oxygen injection volume was 8.75 mL. The helium dilution pressure was 0.6 bar, the

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CO2 reference gas pressure was 0.6 bar, and the N2 reference gas pressure was 1.0

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bar. The samples were combusted at 960°C, and the resulting CO2 and N2 gases were

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separated by a GC column at 50°C. The gases were then transferred to a Conflo IV

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(Thermo Finnigan, Germany) interface and into an isotope-ratio mass spectrometer

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(Delta V Advantage, Thermo Finnigan, Germany). Two-point normalization

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(stretching) was adopted to ensure accurate isotope ratio measurements via

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international reference materials (Brand, Coplen, Vogl, Rosner, & Prohaska, 2014;

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Coplen et al., 2006; Paul, Skrzypek, & Forizs, 2007; Schimmelmann, Albertino,

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Sauer, Qi, Molinie, & Mesnard, 2009; Werner & Brand, 2001). The international

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reference materials were analyzed sequentially with the DDM. For the δ13C values of

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the DDM, USGS40 and B2159 standards were used for two-point normalization. The

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USGS43 standard was used for quality control (QC). For the δ15N values, the

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USGS43 and USGS40 standards were used for two-point normalization, and the

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B2159 standard was used for QC. Blanks consisting of an empty tin capsule were

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included, and corrections were applied to the results.

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2.3.2 δ2H and δ18O analysis

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For δ2H and δ18O analysis, the DDM and the international reference materials were

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weighed into silver capsules and introduced sequentially into an elemental analyzer

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(Flash 2000, Thermo Finnigan, Germany). The reactor packing consisted of a glassy

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carbon tube reactor and silver wool. The helium gas flow rate was 100 mL/min. The

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helium dilution pressure was 0.6 bar, the CO reference gas pressure was 0.4 bar, and

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the H2 reference gas pressure was 0.4 bar. Samples were combusted at 1380°C, and

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the resulting CO and H2 gases were separated by a GC column maintained at 65°C.

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The gases were then transferred to a Conflo IV (Thermo Finnigan, Germany)

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interface and into an isotope-ratio mass spectrometer (Delta V Advantage, Thermo

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Finnigan, Germany). International reference materials were analyzed sequentially

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with the DDM. CBS and KHS standards were used for a two-point normalization of

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the DDM δ2H value, and B2205 standard was used for QC. CBS and B2205 standards

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were used for two-point normalization of the δ18O values; the KHS standard was used

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for QC. Blanks consisting of an empty silver capsule were included, and corrections

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were applied to the results.

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The δ13C, δ15N, δ2H, and δ18O isotope values are reported in δ-notation in per mil (‰)

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relative to the accepted international standards: Vienna Pee Dee Belemnite (VPDB),

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air, and Vienna Standard Mean Ocean Water (VSMOW). The values were calculated

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as follows:

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where

is the corrected stable isotope ratio of the sample (‰, per mil),

is

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the true stable isotope ratio of the international reference material 1,

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stable isotope ratio of the international reference material 2,

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stable isotope ratio of the sample,

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international reference material 1, and

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the international reference material 2.

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2.4 Statistical analysis

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Statistical analysis of the data was undertaken using SPSS 22.0 package for Windows.

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A post hoc Duncan's test was performed to determine significant differences (p <

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0.05). The cross-validation accuracy was determined using a three-dimensional partial

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least square discriminate analysis (PLSDA) to distinguish the geographic origin of

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each milk sample.

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3. Results

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3.1 Effects of lactation stage on the isotope values

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Figure 2 shows the results of (a) δ13C, (b) δ15N, (c) δ2H, and (d) δ18O in milk samples

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at different lactation stages in experiment 1. The δ13C values of milk samples were –

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17.32 ± 1.21‰ at the early lactation stage, –17.95 ± 1.26‰ at the middle lactation

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stage, and –17.56 ± 1.08‰ at the late lactation stage. The δ15N values in milk samples

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were 3.72 ± 0.48‰ at the early lactation stage, 3.82 ± 0.56‰ at the middle lactation

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stage, and 3.81 ± 0.65‰ at the late lactation stage. The δ2H values in milk samples

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were –94.62 ± 20.48‰ at the early lactation stage, –101.72 ± 24.52‰ at the middle

is the true is the measured

is the measured stable isotope ratio of the is the measured stable isotope ratio of

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lactation stage, and –101.51 ± 18.26‰ at the late lactation stage. The δ18O values in

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milk samples were 17.31 ± 2.99‰ at the early lactation stage, 16.85 ± 2.77‰ at the

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middle lactation stage, and 17.22 ± 2.68‰ at the late lactation stage. The δ13C, δ15N,

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δ2H and δ18O values in milk samples from the three lactation stages showed no

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significant differences according to a post hoc Duncan's test. Therefore, the lactation

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stage was found to have no effect on the traceability of the milk samples.

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3.2 Effects of sampling time on the isotope values

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The (a) δ13C, (b) δ15N, (c) δ2H, and (d) δ18O values in milk samples at different

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sampling time points from experiment 2 are shown in Table 1. Overall, the four stable

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isotope ratios in milk from most provinces were different at variable sampling time

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points. Compared to the δ2H and δ15N stable isotope ratios, the differences for the

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δ13C and δ18O stable isotope ratios were greater at different sampling time points. For

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example, the δ13C and δ18O stable isotope ratios in milk samples from Inner Mongolia

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had significant differences at different sampling times (March, July and November),

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while the δ2H and δ15N values showed little difference.

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The δ13C values of milk samples from Hebei and Inner Mongolia sampled in

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November were significantly higher than milk sampled in March and July (Table 1a).

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The δ13C values of milk samples from Tianjin and Jiangsu were unchanged from July

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to November. The δ15N values in milk samples from most provinces were also

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different according to sampling time (Table 1b). The δ15N values of milk sampled in

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July from Tianjin and Jiangsu were lower than milk sampled in March and November.

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The δ2H values of milk from most provinces showed no obvious trends. This was

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especially noticeable with milk samples from Tianjin (Table 1c). For the δ18O values,

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milk samples from most provinces showed significant differences at different

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collection times, with the δ18O values of milk sampled in July from most provinces

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being higher than in November (Table 1d). Therefore, the traceability of milk was

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found to be affected by sampling time effects.

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3.3 Effects of geographic origin on the isotope values

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Experiment 3 studied the effects of geographic origin on the traceability of milk.

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Figure 3 shows the (a) δ13C, (b) δ15N, (c) δ2H, and (d) δ18O values of milk sample in

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July from the eight dairy farms in experiment 2. The δ13C values of milk samples

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from two dairy farms in Inner Mongolia were –29.42 ± 0.38 and –30.33 ± 0.64, which

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were significantly lower than milk samples from Tianjin, Hebei, and Jiangsu (Fig. 3a).

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The most positive δ13C values of milk were sampled from Tianjin and Hebei. The

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δ15N values of milk samples from two dairy farms in Inner Mongolia were 5.57 ± 0.07

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and 5.83 ± 0.18, which were significantly higher than milk samples from other

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regions (Fig. 3b). The lowest δ15N values of milk were also sampled from Tianjin and

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Hebei. There was no change in the δ2H values and δ18O values of milk samples from

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different regions (Fig. 3c, d). The highest δ2H and δ18O values of milk were sampled

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from two dairy farms in Tianjin.

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3.4 Discriminant analysis results

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Figure 4a shows the three-dimensional PLSDA of the four regions from July in

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experiment 2. The cross-validation accuracy between these four regions was 92.11%.

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Figure 4b shows the three-dimensional PLSDA of the four regions (Hebei, Ningxia,

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Shaanxi, and Inner Mongolia) for the milk samples from experiment 1. The

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cross-validation accuracy between these four regions was 71.55%. Not only was the

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cross-validation accuracy calculated between provinces, but it was also determined

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for different dairy farms in the same province (Table 2). The lowest cross-validation

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accuracy (70%) was found for two farms in Inner Mongolia from experiment 2.

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Figure 4c shows the three-dimensional PLSDA of the two farms in Hebei for milk

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sampled from experiment 1. The cross-validation accuracy between the two farms in

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Hebei was 100%. Three-dimensional PLSDA for milk sampled from the two farms in

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each of the other provinces along with the stable isotopic data is given in Figures S2–

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S8. These findings suggested that the discrimination of milk samples from two nearby

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farms in the same region had satisfactory accuracy.

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4. Discussion

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In experiment 1, no significant change was found in the stable isotope ratios of

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carbon, nitrogen, hydrogen, and oxygen of milk sampled in the early, middle, and late

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lactation stages. Therefore, lactation stage may have no effect on the traceability of

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milk when using light stable isotopes. This differs from a report showing that stable

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isotope variations of milk can be associated with lactation stage (Magdas et al., 2013).

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One reason for this difference may be the sampling times reported in the study was

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different from those used in our experiments. In addition, cattle breed may also be

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different. In a previous study, we also found that lactation stage had no effect on fatty

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acid composition, vitamin A, and oxidative stability (Liang et al., 2017).

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The impact of sampling time on the traceability of milk was also studied (experiment

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2). The δ13C value in milk is highly dependent on diet composition—particularly with

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regard to the proportion of C3 and C4 plants consumed (Garbariene et al., 2016;

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Krivachy, Rossmann, & Schmidt, 2015; Nečemer, Potočnik, & Ogrinc, 2016;

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Piliciauskas, Jankauskas, Piliciauskiene, & Dupras, 2017). Camin et al., (2008)

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observed that changing a C3 diet to a C4 diet increased the δ13C value in milk. The

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δ13C values of milk samples from Hebei and Inner Mongolia increased significantly

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from July to November. This may be due to high rainfall and temperature in July,

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which led to the growth of C3 grass, and cattle feed is mainly based on C3 grasses.

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Subsequently, in November, maize is the main forage crop fed to cows in Hebei and

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Inner Mongolia. Maize is a C4 plant that increases the δ13C value of milk. The δ13C

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values of milk samples from Tianjin and Jiangsu remained constant from July to

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November. This is likely due to the low summer rainfall experienced in the year of

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sampling, which caused the cattle to be fed with supplementary feed of both C3

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grasses (hay) and maize over this period. The δ15N values of milk samples from most

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provinces increased significantly from July to November. This may be due to the fact

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that in July, leguminous plants are the main fed to cows, leguminous plants are

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nitrogen-fixing plants with low δ15N values (Sponheimer et al., 2003). Another

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possible reason is that in November, cows urinate and defecate on the grass, which

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then denitrifies, ensuring the grass has higher δ15N values. This is similar to the

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change of δ15N in Korean organic milk with sampling time (Chung et al., 2018).

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The δ2H, and δ18O values of milk reflect the input of drinking water, food, and

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respiration (Hobson & Koehler, 2015). In July, δ18O values of milk in Jiangsu and

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Inner Mongolia were higher than those in March and November. This may be because

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most of the oxygen and hydrogen in milk is derived from drinking water. In warmer

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temperatures, drinking water sources (ponds, streams or water troughs may become

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evaporatively enriched (more positive δ18O and δ2H values) in July, or be sourced

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from snow and glacier meltwater in the highlands which supplement the local

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groundwater or rainfall. In general, there was no significant difference between the

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δ2H values of milk samples collected in July and November except for those from

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Hebei. Therefore, the δ2H values appear to be less sensitive to the influences of

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sampling time. This may be the result of a change in the correlation between δ2H to

307

δ18O (Boner & Forstel, 2004). This correlation exists in the water (Dunbar & Wilson,

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1983) and may be transmitted to the animal. Therefore, the sampling time can affect

309

the traceability of milk.

310

We studied the influence of geographic origin on the traceability of milk in

311

experiment 3. The δ13C values of milk sampled from Inner Mongolia were the lowest

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as Inner Mongolia has large areas of C3 pasture available as the primary fodder

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source. The other regions are better developed for agriculture, with more C4 plants

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grown locally that are included in the feed, e.g., maize. These dietary composition

315

differences likeiy result in higher δ13C values in the milk samples from Ningxia,

316

Shaanxi and Hebei. These data agreed well with previous research on beef (Zhao,

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Zhang, Chen, Chen, Yang, & Ye, 2013).The δ15N values for milk from two dairy

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farms in Inner Mongolia are significantly higher than those in milk samples from

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other regions. The lower δ15N values in samples from Tianjin and Hebei may be due

320

to chemical fertilizer application used to grow crops (the δ15N values of most

321

fertilizers are near 0 ‰), which are subsequently fed to the cows (Zhao, Zhang, Guo,

322

Wang, & Yang, 2016).

323

The δ2H and δ18O values of milk reflects the input of drinking water to the cow’s diet.

324

However, there is no regular change in the δ2H values and δ18O values of milk

325

sampled from different regions even though these isotopes are typically affected by

326

geographic location, altitude and distance from the ocean among other factors

327

(Bowen, Ehleringer, Chesson, Stange, & Cerling, 2007). Collectively, these results

328

may indicate that the hydrogen isotopic signature of milk is insufficient to provide an

329

unequivocal origin assignment. When tracing milk, multiple stable isotopes should be

330

considered, and should be combined with other chemo-specific analyses (Rutkowska,

331

Bialek, Adamska, & Zbikowska, 2015).

332

The cross-validation accuracy across the four regions from experiment 1 (71.55%) is

333

lower than that of the four regions from experiment 2 (92.11%). This may be because

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the study sites (Shaanxi, Inner Mongolia, and Ningxia) from experiment 1 are

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adjacent to each other and have similar crops and climate (Guo, Wei, Pan, & Li,

336

2010). Jiangsu, in experiment 2, is located in southern China, and its climatic

337

conditions are different from those of the other three provinces (Zhao et al., 2013).

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The distance between each of the two farms from the four regions in experiment 1

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were analyzed relative to cross-validation accuracy. The cross-validation accuracy is

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positively correlated to the distance between the farms. The cross-validation accuracy

341

between the two farms in Ningxia is the lowest because the distance between these

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two farms is the closest, and therefore harder to distinguish characteristic differences.

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In addition, Ningxia is located in a pastoral region, and the animal feed at these two

344

farms is similar. The cross-validation accuracy between the two farms in Hebei is the

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highest because the distance between these two farms is the furthest. Hebei is an

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agricultural region, and each farm has significantly different feed formulations. The

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feed in HB-TH is imported from other provinces and included silage, alfalfa, leymus

348

grass, and hay. The feed at HB-BH is a locally grown:imported ratio of 3:7. The

349

locally grown products included premixed feed, corn, soybean meal, other

350

miscellaneous meal, and bran. The imported feed included various straws. This

351

resulted in significantly different carbon and nitrogen isotope values in the milk from

352

these two farms and increased the cross-validation accuracy. The cross-validation

353

accuracy between the two farms in Hebei in experiment 2 was also high because of

354

the large distance between the two farms. Different feed formulations were also used

355

at the two farms in Jiangsu, which made the cross-validation accuracy between these

356

two farms higher than that between the two farms in Inner Mongolia although the

357

distance between the farms in Jiangsu was smaller than the farms in Inner Mongolia.

358

Thus, it is necessary to consider not only distance between farms, but also the feed

359

composition when using stable isotopes to trace milk. Overall, the feed together with

360

regional climatic differences play an influential role in the traceability of milk,

361

although it must be noted that feed differences may change annually according to feed

362

availability, price and faming practice.

363

5. Conclusion

364

Cow milk collected from various regions in China produced distinct isotopic

365

fingerprints confirming the potential of multi-element (C, N, H, O) stable isotope

366

analysis to trace the origin of milk. Characteristic stable isotope signatures for carbon,

367

nitrogen, hydrogen, and oxygen were used to differentiate the geographic origin of

368

milk from four provinces in China. Sampling time as well as geographic origin

369

influence the stable isotope ratios. The traceability of milk samples using

370

multi-element stable isotope ratio analysis within provinces and countrywide is

371

possible, meanwhile farming practices, feed composition and climatic factors should

372

also be considered.

373

374

Acknowledgement

375

This work was funded by the National Key Research and Development Program of

376

China (2017YFC1601703).

377

References

378

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515 516

FIGURE CAPTIONS:

517

Fig. 1 Regional farm location information for milk samples from experiments 1 (a) and 2 (b).

518

Fig. 2 Values of (a) δ13C, (b) δ15N, (c) δ2H, and (d) δ18O of milk samples at different lactation

519

stages. Values are means ± SD. Means are not significantly different according to the post-hoc

520

Duncan's test.

521

Fig. 3 Values of (a) δ13C, (b) δ15N, (c) δ2H, and (d) δ18O of milk samples from the eight dairy

522

farms sampled in July. Values are means ± SD. Superscript letters are significantly (p < 0.05)

523

different with respect to the row for the eight farms (post-hoc Duncan's test, n = 5).

524

Fig. 4 Three-dimensional PLSDA of the four regions of milk samples from experiment 2 with

525

stable isotope data: Tianjin, Hebei, Jiangsu, Inner Mongolia (a); three-dimensional PLSDA of the

526

four regions of milk sampled from experiment 1 with stable isotope data: Hebei, Ningxia,

527

Shaanxi, Inner Mongolia (b); three-dimensional PLSDA of milk sampled from two farms in Hebei

528

from experiment 1 (c).

529

Figure 1

530

(a)

531 532

533

(b)

534

Figure 2

535

(a)

536 537

538

(b)

539

(c)

540 541 542

543

(d)

544

Figure 3

545

(a)

546 547

548

(b)

549

(c)

550 551

552

553

(d)

554

Figure 4

555

(a)

556 557 558

Cross-validation accuracy between the four regions is 92.11%. (b)

559 560

Cross-validation accuracy between the four regions is 71.55%.

561

562 563

(c)

Cross-validation accuracy between the two farms is 100%.

564

Table 1 Mean (a) δ13C, (b) δ15N, (c) δ2H, and (d) δ18O values and standard deviations of milk

565

samples. Numbers with different superscripts are significantly (p < 0.05) different with respect to

566

each row for the three sampling times: post-hoc Duncan's test (n=5).

567

(a) March

July

November

Location Mean (‰)

SD (‰)

Mean (‰)

SD (‰)

Mean (‰)

SD (‰)

TJ-NK

−18.95

0.45

−16.98

3.01

−17.27

0.60

TJ−FH

−17.75a

0.34

−16.56b

0.07

−16.72b

0.30

HB−LM

−21.61a

0.71

−20.10b

0.18

−19.04c

0.05

HB−ZL

−20.62a

0.66

−21.08a

0.55

−17.64b

0.27

JS−ZX

−22.60a

0.83

−20.86b

0.56

−21.64b

0.22

JS−DX

−23.32a

1.46

−21.33b

1.38

−20.94b

0.25

IM−LM

−27.37b

0.70

−29.42a

0.38

−25.71c

0.59

IM−XC

−22.65b

0.43

−30.33a

0.64

−21.20c

0.16

568 569

(b)

Location

March

July

November

Mean (‰)

SD (‰)

Mean (‰)

SD (‰)

Mean (‰)

SD (‰)

TJ−NK

4.22b

0.27

3.67a

0.14

4.14b

0.12

TJ−FH

4.13c

0.08

3.02a

0.16

3.89b

0.13

HB−LM

3.66

0.22

3.58

0.32

3.56

0.12

HB−ZL

3.27a

0.21

3.34a

0.39

4.21b

0.21

JS−ZX

5.25b

0.16

4.54a

0.36

5.18b

0.17

JS−DX

5.14b

0.60

4.47a

0.25

6.20b

0.33

IM−LM

5.18a

0.17

5.57a

0.07

6.62b

0.72

IM−XC

6.15b

0.26

5.83b

0.18

5.44a

0.35

571

(c)

Location

March

July

November

Mean (‰)

SD (‰)

Mean (‰)

SD (‰)

Mean (‰)

SD (‰)

TJ−NK

−63.81

6.29

−63.48

14.06

−73.32

3.08

TJ−FH

−65.67

7.74

−58.26

3.44

−65.80

3.90

HB−LM

−66.59b

2.94

−70.05ab

9.36

−77.26a

3.43

HB−ZL

−78.24a

4.11

−81.31a

4.26

−60.12b

9.28

JS−ZX

−61.82b

8.23

−78.28a

10.23

−81.37a

10.44

JS−DX

−74.20

18.98

−87.80

8.20

−86.53

8.67

IM−LM

−124.59a

10.49

−77.70b

23.56

−74.90b

18.14

IM−XC

−81.85

4.09

−99.08

8.92

−85.55

18.41

572 573

(d) March

July

November

Location

574 575

Mean (‰)

SD (‰)

Mean (‰)

SD (‰)

Mean (‰)

SD (‰)

TJ−NK

17.41

0.68

18.30

0.83

18.16

0.57

TJ−FH

17.07a

0.36

18.78b

0.23

18.99b

0.52

HB−LM

18.87b

0.63

18.41b

1.14

17.31a

0.40

HB−ZL

14.43b

0.93

13.26a

0.34

17.11c

1.05

JS−ZX

18.73c

0.33

14.83b

0.60

12.17a

0.80

JS−DX

12.71b

0.83

11.80b

1.10

9.45a

0.30

IM−LM

3.87a

0.63

15.77c

4.21

11.45b

0.34

IM−XC

10.18a

0.54

16.55c

1.11

11.58b

0.18

576

577

Table 2 Cross-validation accuracy between different farms from the same region. Different farms from the same region from

Different farms from the same region from

Experiment 1

Experiment 2

Region

Accuracy

Region

Accuracy

Hebei

100%

Tianjin

87.50%

Ningxia

71.43%

Hebei

100%

Shaanxi

86.67%

Jiangsu

90.00%

Inner Mongolia

86.67%

Inner Mongolia

70.00%