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Single-Drop Raman Imaging Exposes the Trace Contaminants in Milk Zong Tan, Tingting Lou, Zhixuan Huang, Jing Zong, Kexin Xu, Qifeng Li, and Da Chen J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b01814 • Publication Date (Web): 10 Jul 2017 Downloaded from http://pubs.acs.org on July 13, 2017

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Journal of Agricultural and Food Chemistry

Single-Drop Raman Imaging Exposes the Trace Contaminants in Milk Zong Tan1, Ting-ting Lou2, Zhi-xuan Huang1,3, Jing Zong1,3, Ke-xin Xu1, Qi-feng Li*,2 and Da Chen*,1 1

State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China

2

School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China

3

Zhejiang Bigdata Co., Ltd. 3-17 Feiyue Innovation Park, Jiaojiang District, Taizhou City, Zhejiang Province 318000, China

Corresponding Author: Prof. Da Chen, State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China. Fax: 86-022-27401233

Tel: 86-136-6203-4255

Email: [email protected]

Prof. Qifeng Li, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China. Fax: 86-022-87402130

Tel: 86-150-2279-9638

Email: [email protected]

1

ACS Paragon Plus Environment


1

ABSTRACT: Better milk safety control can offer important means to promote public

2

health. However, few technologies can detect different types of contaminants in milk

3

simultaneously. In this regard, the present work proposes a single-drop Raman

4

imaging (SDRI) strategy for semi-quantitation of multiple hazardous factors in milk

5

solutions. By developing SDRI strategy that incorporates coffee-ring effect (a natural

6

phenomenon often presents in a condensed circle pattern after a drop evaporated) for

7

sample pretreatment and discrete wavelet transform for spectra processing, the

8

method serves well to expose typical hazardous molecular species in milk products,

9

such as melamine, sodium thiocyanate and lincomycin hydrochloride, with little

10

sample preparation. The detection sensitivity for melamine, sodium thiocyanate and

11

lincomycin hydrochloride are 0.1 mg·kg-1, 1 mg·kg-1 and 0.1 mg·kg-1, respectively.

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Theoretically, we establish that the SDRI represents a novel and environment-friendly

13

method that screens the milk safety efficiently, which could be well extend to

14

inspection of other food safety.

15 16

KEYWORDS: milk contaminants, single-drop Raman imaging, coffee-ring effect,

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discrete wavelet transform, melamine, sodium thiocyanate, lincomycin hydrochloride

18 19 20 21 22 2


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INTRODUCTION

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Milk and milk-derived products represent one of the most completely nutritious foods

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that are consumed by much of the world’s population.1, 2 However, contaminants in

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milk solutions, such as antibiotic residues, preservative, illegal additives, etc. have

27

raised great threat not only to public health but also to livestock and dairy industry.3

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The maximum residue limits (MRLs) of these contaminant components were thus set

29

by World Health Organization (WHO), the US Food & Drug Administration (FDA)

30

and many other countries.3 This creates an urgent demand to screen the milk

31

contaminants for public health, especially for infants and children.

32

Analytical approaches for screening contaminants in milk are mainly based on

33

HPLC,4, 5 UPLC,6 LC-MS/MS,7, 8 CE-MS,9 GC-MS10, etc. These methods require

34

complicated wet-chemical steps of derivatization, enrichment, separation and linkage

35

analysis in combination with mass spectrometry or ECD detectors, as well as

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laborious work by highly skilled personnel.11 Besides, no single method mentioned

37

above can simultaneously detect different types of contaminants in milk because of

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the diverse requirements for experimental conditions. Unfortunately, the huge

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worldwide production of dairy products at a level of billion tons presents a serious

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challenge to current analytical methods, requiring versatile and simple methods for

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high-throughput screening of contaminant components in milk.

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A potential solution to this challenge is Raman spectroscopy, which presents unique

43

advantages to measure multiple hazardous components.12, 13 With the combination of

44

microscopy systems, Raman imaging technology comes into being with capability of 3



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high-throughput screening as well as microanalysis of multiple materials, which has

46

been successfully applied in many fields.14-18 However, highly overlapped bands and

47

uncontrolled matrix variance combine to limit the further applications of Raman

48

methodologies.19 This creates an urgent need to develop highly efficient

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pre-enrichment methods as well as tailored chemometrics methods for isolating the

50

interest analysts in Raman signals, thus enabling the high-throughput screening of

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trace substances at a reasonable sensitivity level.

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For enrichment strategy, a novel method named “coffee-ring” effect, in which a drop

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of solution is deposited on a suitable substrate followed by solvent evaporation

54

leaving a stacking pattern with edge enrichment,20 is proposed for the further Raman

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imaging analysis. Coffee-ring effect has attracted a large amount of interests because

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it is totally a natural separation and enrichment procedure requiring no extra

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processing or external forces.21, 22 During the evaporation, there is a self-assembly of

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solutes, enforcing materials from the inner center of the drop to the edge that is driven

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by the evaporation velocity difference between edge and center.23, 24 In sequence, a

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circle pattern arises with condensed materials distributing along radical dominantly

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due to the differences in particle size.25 With the combination of coffee-ring effect, the

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sensitivity of Raman imaging would be improved as a result of the enhancement of

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interested analytes in certain regions of the coffee-ring pattern.

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Another effective strategy to improve the sensitivity of Raman imaging is known as

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discrete wavelet transform (DWT), a powerful chemometrics tool for improving the

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signal-to-noise ratio (SNR).26, 27 DWT is capable of isolating weak Raman bands of 4


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interest embedded in the overwhelming and highly overlapped Raman signals through

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removing low-frequency background and high-frequency noise information. It is

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expected that the DWT will efficiently improve the capacity of Raman imaging for

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screening the multiple trace contaminants in milk.

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The present study introduces a novel strategy, named as single-drop Raman imaging

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(SDRI), which integrates the enrichment effect of coffee-ring with the microanalysis

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of Raman imaging for simultaneous detection of trace contaminants in milk. SDRI

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requires little sample preparation and chemical reagents. In SDRI, the Raman imaging

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signals are then processed by DWT-based algorithms for isolating the interested

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analytes in the presence of complex matrix. To provide a controlled demonstration of

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SDRI, we utilized a milk solution with three typical hazardous contaminants of

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different molecular species: melamine,28,

79

hydrochloride,31 as a model to yield representative challenges encountered in common

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milk analysis. The results illustrate how well SDRI serves to expose the trace

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contaminants in milk, providing a new insight into screening milk safety at a single

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drop level.

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MATERIALS AND METHODS

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Chemicals

29

sodium thiocyanate30 and lincomycin

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Melamine (AR) was purchased from Aladdin (Shanghai, China), sodium thiocyanate

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(AR) and concentrated HCl (AR) were purchased from Jiangtian Chemical

87

Technology Co., Ltd (Tianjin, China), and lincomycin hydrochloride (>850 µg·mg-1)

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was purchased from Meilun Biotechnology Co., Ltd (Dalian, China). Control milk 5



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samples (whole milk) were provided by Tianjin Entry-Exit Inspection and Quarantine

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Bureau, which was excluded the contamination of these chemicals by HPLC

91

measurement. Deionized water with a resistivity of 15 MΩ·cm was prepared by a

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Merck Millipore Elix 5 water purification system (Boston, USA). Aluminum sheet,

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stainless steel plate and Mo mirror were purchased from Aidahengsheng Technology

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Co., Ltd (Tianjin, China), and gold coated slide was purchased from Thermo Fisher

95

Scientific Inc (Waltham, USA).

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Sample Preparation

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Standard mixture solutions of melamine, sodium thiocyanate and lincomycin

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hydrochloride were prepared with each solute in concentrations ranging from 3000

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mg·kg-1 to 0.2 mg·kg-1. Then, milk solutions with these three analytes in

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concentrations ranging from 1500 mg·kg-1 to 0 mg·kg-1 were prepared by mixing

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control milk samples with the standard mixture solutions mentioned above at the mass

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ratio of 1:1. Afterwards, 0.1 M HCl obtained by diluting concentrated HCl was used

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to cause denaturation and precipitation of most milk proteins and fats at a pH of

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4.6±0.1 (the isoelectric point of protein), following by centrifuging in 5 mL

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centrifugal tubes at speed of 10000 rpm for 3 min to get the supernatant. The most of

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fats adhering to the casein were also removed because of the good affinity between

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casein and fat.32 All solutions were prepared daily using deionized water.

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Coffee-ring Formation

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Coffee-ring stain concentrates particles at its edge, and the formation principles of

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coffee-ring was previously described by Filik et al.22 In practice, supernatant droplets 6


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(with an approximate volume of 5 µL) were vertically dropped onto the surface of

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horizontally arranged substrates using a pipette, evaporating under standard

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environmental conditions (temperature of 20 °C and relative humidity of about 40%).

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Triple parallel experiments were conducted at the same condition to validate

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repeatability of coffee-ring formation.

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Substrates present another importance source that affects the coffee-ring formation.

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Here, gold coated slide was chosen as the optimal substrate for the analysis of

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coffee-ring stains. Other substrates, such as aluminum sheet, stainless steel plate and

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Mo mirror, etc. were also explored, which was found to offer no advantage over the

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gold coated slide. The coffee-ring patterns on the gold coated slide possessed a size of

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approximately 3 mm in diameter.

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Raman Imaging Collection

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After the formation of coffee-ring stain, we recorded Raman Imaging using a

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Renishaw Raman microscopy imaging system (Gloucestershire, UK). This system

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integrated a microscope system (high precision motorized XYZ mapping and sample

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stage) with a 785 nm diode laser (~250 mW) and a 1200 groove mm-1 grating. In this

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work, the ×50 LWD Leica objective (Wetzlar, Germany) was used to collect Raman

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imaging. Raman spectra were acquired in extended mode with a laser power of 250

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mW, an integration time of 10 s and a spectrum range from 280 to 2200 cm−1.

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Using the Renishaw system, we recorded 250 spectra of each coffee-ring deposit for

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reconstructing of Raman imaging. Specifically, 4 typical circle areas (circle 1, circle 2,

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circle 3, center) with an interval of about 1/4 radius distance of each coffee-ring 7



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deposit were selected for Raman scanning. In order to guarantee the representative

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sampling, 90 points for circle1, 70 points for circle2, 50 points for circle3 and 40

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points for center area were taken to conduct Raman imaging, respectively. As a result,

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the distribution of target analytes in each region was obtained by Raman imaging. The

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Raman imaging were then transmitted for the subsequent off-line chemomtrics

138

processing.

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Data Analysis through DWT and Raman imaging reconstruction

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Before Raman imaging reconstruction, the collected data of Raman imaging was

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proceeded by the DWT-based algorithms, which was written in MATLAB using the

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Wavelet Toolbox. In detail, the signals were split into different frequency components

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by the DWT-based algorithms, and then the low-frequency background and the

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high-frequency noise components were removed from the raw signals simultaneously.

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Through calculating a large amount of Raman spectra of coffee-ring deposits, the

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Daubechies wavelet filter (db10, scale=7) was selected as the optimal wavelet filter to

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provide the sharpest peaks associated with the interest analytes. This would definitely

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enhance the capability of discriminating the trace contaminants in milk from the

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matrix interference. Afterwards, the DWT filtered signals were transferred for Raman

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imaging reconstruction. The DWT-based algorithms and the program of image

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reconstruction were coded and operated with MATLAB R2012b.

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The core steps of SDRI can be described as follows: (1) Sample preparation, (2)

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Coffee-ring formation, (3) Raman imaging collection, (4) data analysis through DWT

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and Raman image reconstruction, where each step is performed according to the 8


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details of corresponding sections mentioned above. With these four steps, the SDRI

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strategy was successfully adopted for simultaneous detection of trace contaminants in

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milk.

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RESULTS AND DISCUSSIONS

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Chosen of Substrate

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Substrate plays a primary role in generating the coffee-ring patterns that determined

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the enrichment factors of target analytes, and it is therefore extremely important to

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investigate the effects of different substrates on coffee ring shapes. Here we adopted 4

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typical substrates, i.e. aluminium sheet, stainless steel plate, Mo mirror and gold

164

coated slide for investigating the coffee-ring patterns of milk supernatants.

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Figure 1 shows the photomicrographs of coffee-ring patterns on the four substrates

166

and Raman spectra of each substrate, respectively. As shown in Figure 1a~1d, surface

167

roughness of four substrates are notably different, making the size and shape of

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coffee-ring patterns in different substrates vary significantly. It is clear that the pattern

169

forming on the gold coated slide (Figure 1a) possesses the relatively smaller size with

170

regular distribution, while the size of pattern on the Mo mirror (Figure 1b) is the

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largest and most irregular. One might expect that the more regular distribution, the

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more reproducibility. Accordingly, the coffee-ring shape on the gold coated slide is the

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most regular circle rather than on other substrates.

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The size of coffee-ring pattern presents another important parameter, because

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smaller size means better concentration. The difference in size of coffee-ring pattern

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can be attributed to the hydrophobicity and solid-liquid-gas interfacial tensions of 9



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different substrates.33 As a result, the shape of coffee-ring pattern is related to the

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isotropy of the substrate surface material directly, and a regular circle will facilitate

179

the subsequent scanning process proceeding in circle mode. Meanwhile, the Raman

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spectral background of four substrates were also collected, which was illustrated in

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Figure 1e. It is clear that the gold coated slide possesses the lowest Raman

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background signal with high SNR as illustrated in bottom line. In summary, the gold

183

coated slide was thus chosen as the optimal substrate in this work.

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Raman Spectra of Target Materials

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During sample preparation, 0.1 M HCl was used to make casein precipitated out

186

from milk samples, as well as most of fat was also removed together. However, the

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HCl may also react with target molecules. It is therefore very important to investigate

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the effects of HCl on SDRI results. Hence, we collected photomicrographs and

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Raman spectra of coffee-ring patterns obtained from standard mixture solutions with a

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pH of 7 and 4.6 (with HCl) to investigate the effects of HCl. The results were

191

illustrated in Figure 2.

192

As shown in Figure 2(a1) and Figure 2(a2), the crystal profiles are quite disparate

193

since the aggregate state of mix-analytes molecules varied in different pH, and the

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details of the two crystal states can be seen from the partial magnification in the left

195

bottom. This illustrates the change of Raman bands caused by HCl, i.e. characteristic

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peaks of melamine, sodium thiocyanate and lincomycin hydrochloride shift from 676

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cm-1, 2067 cm-1 and 980 cm-1 (Figure 2(b1)) to 691 cm-1, 2065 cm-1 and 982 cm-1

198

(Figure 2(b2)), respectively. The red shift from 676 cm-1 to 691 cm-1 can be ascribed 10


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to the formation of hydrogen-bond among melamine molecules in acidic condition

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that affected the in-plane bending vibration of N-C-N in melamine molecule, while

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the shift from 2067 cm-1 to 2065 cm-1 and 980 cm-1 to 982 cm-1 can be due to an

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influence of pH to the stretch vibration of C≡N in sodium thiocyanate and breathing

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vibration of pyrrolidine ring in lincomycin hydrochloride.34-37 The spectra of pure

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analyte in acid solutions (pH of 4.6) are presented in supporting information, which

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also indicates the attribution of Raman bands shown in Figure 2(b2). Hence, in the

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subsequent analysis of milk samples, the peaks at 691 cm-1, 2065 cm-1 and 982 cm-1

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were treated as diagnostic peaks of melamine, sodium thiocyanate and lincomycin

208

hydrochloride, respectively.

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Raman Imaging Analysis of Coffee-ring Patterns

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The raw Raman signals contain the information of both target analytes and matrix,

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hence signal procession for suppressing the matrix interference is necessary to

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improve the sensitivity of this method before the reconstruction of Raman imaging.

213

Here we adopted the Daubechies wavelet filter of db10 with a scale of 7 to conduct

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the DWT filtering. The comparison of Raman spectra before and after DWT

215

processing was shown in Figure 3 (a) and (b), respectively, indicating that the matrix

216

effect was suppressed effectively. Thereafter, Raman imaging analysis of coffee-ring

217

patters proceeded.

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Coffee-ring patterns derived from milk supernatants with mixed-components of

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melamine, sodium thiocyanate and lincomycin hydrochloride, were shown in Figure 4,

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whose concentrations ranging from 1500 mg·kg-1 to 0 mg·kg-1. It is clear that the 11



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concentrations of the mixture analytes have a distinct effect on the morphology of

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coffee-ring patterns, while the coffee-ring sizes remain approximately constant. The

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reason is being that the viscosity of milk supernatants remains relatively high though

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most of proteins have been removed.38 During the evaporation process, the migration

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of analytes molecules was restricted by the high viscosity matrix and thus wrapped by

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the sticky substances. As for the size, the changing of mix-analytes concentration has

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little to do with the viscosity of supernatant, and the size of coffee-ring patterns keeps

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relatively constant due to the interfacial tension.33 This also facilitates the

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repeatability of further coffee-ring analysis.

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For each coffee-ring deposit, 4 circle areas (named circle 1, circle 2, circle 3 and

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center) from edge to the center were collected for Raman imaging analysis. We

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divided the milk supernatants with different concentrations into two groups to reveal

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the SDRI results: (1) high concentrations ranging from 1500 mg·kg-1 to 20 mg·kg-1,

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and (2) low concentrations ranging from 10 mg·kg-1 to 0 mg·kg-1. It is of great interest

235

to find that the concentrations of contaminants affect the distribution of coffee-ring

236

patterns significantly.

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As for the high concentration group, the Raman imaging of coffee-ring patterns

238

obtained from milk supernatants are shown in the supplement document. It is of great

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interest to find that the coffee-ring patterns depend on not only the concentrations of

240

the analytes, but also the molecular species, i.e. each analyte may have its own unique

241

distribution in coffee-ring patterns.

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As for the low concentration group, Raman imaging analysis of coffee-rings was 12


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also carried out, representing the practical detection of trace contaminants in milk. In

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order to evaluate the limitation of detection, 10 Raman spectra of blank samples were

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collected to calculate the deviation at 691 cm-1, 2065 cm-1 and 982 cm-1, which was

246

shown in Figure 5. When the peak intensity was three times higher than the

247

corresponding deviation, it was regarded as a successful detection of the target analyte.

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The spot was then lightened. Here, the bright spots in colors of red, green and yellow

249

represent the successful detection of melamine, sodium thiocyanate and lincomycin

250

hydrochloride, respectively. The brighter the spot, the higher the intensity. The results

251

of reconstructed Raman imaging were demonstrated in Figure 6. The spectra listed in

252

the right side of Figure 6 showed the contrasts of bright spots to black spots, which

253

could indicate the enhancement of target signals derived from coffee-ring effect.

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As shown in Figure 6, a small number of red spots could be observed until

255

concentration decreased to 0.1 mg·kg-1, green spots were visible until concentration

256

decreased to 1 mg·kg-1, and sporadic yellow spots still existed when the concentration

257

was 0.1 mg·kg-1. It appears that the minimum detectable concentration of melamine,

258

sodium thiocyanate and lincomycin hydrochloride in milk was 0.1 mg·kg-1, 1 mg·kg-1

259

and 0.1 mg·kg-1, which meets the MRLs of all these three materials in milk well. In

260

practice, the MRLs for melamine, sodium thiocyanate and lincomycin hydrochloride

261

in milk are 0.15 mg·kg-1,39 10~14 mg·kg-1 40, 41 and 0.15 mg·kg-1,42 respectively. With

262

the SDRI results, the relations between Raman imaging and target analytes were

263

further explored for semi-quantitative analysis.

264

Statistical semi-quantitative relationship between SDRI results and the concentration 13



265

of three analytes in milk supernatants were established by accumulating the signal

266

intensity of bright spots in reconstructed Raman imaging, which were shown in Figure

267

7. As shown as in Figure 7, the statistical intensity relates the concentrations of

268

analytes well in the range of 0 mg·kg-1 to 10 mg·kg-1. The equations for estimating

269

concentrations of these three components were listed in the supplement document. As

270

a result, the relationship models between SDRI results and concentrations of analytes

271

were estimated semi-quantitatively, providing an efficient tool for high-throughput

272

screening of dairy safety.

273

This study presented a single-drop Raman imaging strategy that we regard as an

274

approach for high-throughput screening of multiple trace contaminants in milk. With

275

the combination of coffee-ring effect for sample pretreatment and DWT for spectra

276

analysis, the SDRI strategy was successfully applied in exposing different three

277

hazardous molecular species in milk. The detection sensitivity of melamine, sodium

278

thiocyanate and lincomycin hydrochloride are 0.1 mg·kg-1, 1 mg·kg-1 and 0.1 mg·kg-1,

279

respectively, which presents 103~104 enhancement factor than that of normal Raman

280

approach. The screening results satisfied the MRLs of these three contaminants in

281

milk very well. In practice, the SDRI requires little sample preparation and reagents

282

consumption, which serves well for semi-quantitation of trace contaminants in milk at

283

a single drop level. This methodology can be also extended to screen trace

284

contaminants in other foods as well, presenting a great application prospect in the

285

future.

286

ABBREVIATIONS USED 14


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SDRI, single-drop Raman imaging; HPLC, high performance liquid chromatography;

288

UPLC,

289

chromatography-tandem mass spectrometry; CE-MS, capillary electrophoresis-mass

290

spectrometry; GC-MS, gas chromatography-mass spectrometry; ECD, electronic

291

catcher detector; DWT, discrete wavelet transform; SNR, signal-to-noise ratio; MRLs,

292

maximum residue limits.

293

FUNDING

ultra

performance

liquid

chromatography;

LC-MS/MS,

liquid

294

This work is supported by the National Natural Science Foundation of China

295

(61378048, 21305101, 21273159), Tianjin Research Program of Application

296

Foundation and Advanced Technology (14JCZDJC34700), National Key Research

297

and Development Program of China (2017YFC0803600), the Open Funding of State

298

Key Laboratory of Precision Measuring Technology and Instruments (PIL1605), the

299

Program for New Century Excellent Talents in University (NCET-11-0368), and the

300

111 Project (B07014).

301

Supporting Information. Figure S1. Raman spectra of supernatants obtained from

302

skim milk and whole milk; Figure S2. Raman spectra of pure melamine, lincomycin

303

hydrochloride and sodium thiocyanate in HCl solutions with pH of 4.6; Figure S3.

304

Raman imaging of coffee-ring patterns obtained from supernatants with melamine,

305

lincomycin hydrochloride and sodium thiocyanate in concentration range of 1500

306

mg·kg-1 to 20 mg·kg-1, where red, green and yellow colors represent the intensity of

307

signal at 691 cm-1, 2065 cm-1 and 982 cm-1, respectively; Figure S4. Average Raman

308

spectra of points in 4 regions of coffee-ring patterns obtained from the milk 15



309

supernatants with three analytes in concentrations of (a) 1500mg·kg-1, (b) 1000

310

mg·kg-1, (c) 500 mg·kg-1, (d) 200 mg·kg-1, (e) 100 mg·kg-1, (f) 50 mg·kg-1 and (g) 20

311

mg·kg-1; Table S1. Summary of nonlinear fitting equations of statistical intensity in

312

SDRI results vs concentrations of melamine, sodium thiocyanate and lincomycin

313

hydrochloride in range of 10 mg·kg-1 to 0 mg·kg-1.

314 315

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FIGURE CAPTIONS

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Figure 1. Photomicrographs of coffee-ring patterns on the four substrates: (a) gold

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coated slide, (b) Mo mirror, (c) stainless steel plate, (d) aluminium sheet, and (e)

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Raman spectra of each substrate.

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Figure 2. Photomicrographs of coffee-ring patterns obtained from mixture analytes in

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(a1) neutral and (b1) acid solutions; and the corresponding Raman spcetra (a2) and

428

(b2).

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Figure 3. Comparation of Raman spectra (a) before and (b) after treatment by DWT.

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Figure 4. Photomicrographs of coffee-ring patterns obtained from supernatants of

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milk samples with melamine, lincomycin hydrochloride and sodium thiocyanate in

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concentrations ranging from 1500 mg·kg-1 to 0 mg·kg-1.

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Figure 5. The contrasts of effective signals at (a) 691 cm-1, (b) 2065 cm-1 and (c) 982

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cm-1 to deviations obtained from 10 spectra of blank samples.

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Figure 6. Raman imaging of coffee-ring patterns obtained from milk supernatants

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with melamine, lincomycin hydrochloride and sodium thiocyanate in concentration

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range of 10 mg·kg-1 to 0 mg·kg-1, where red, green and yellow colors represent the

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intensity of signal at 691 cm-1, 2065 cm-1 and 982 cm-1, respectively.

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Figure 7. Semi-quantitative relations of accumulate intensity of Raman imaging to the

440

concentration of three target analyts in milk supernatants. Error bars indicate the

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deviation of the triple parallel experiments.

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FIGURES

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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TOC graphic

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Single-Drop Raman Imaging Exposes the Trace ... - ACS Publications

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