Detection of Pesticide Residues in Food Using Surface-Enhanced

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Review

Detection of Pesticide Residues in Food Using Surface-Enhanced Raman Spectroscopy: A Review Menglei Xu, Yu Gao, Xiao Xia Han, and Bing Zhao J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b02504 • Publication Date (Web): 20 Jul 2017 Downloaded from http://pubs.acs.org on July 21, 2017

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Journal of Agricultural and Food Chemistry

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Detection of Pesticide Residues in Food Using

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Surface-Enhanced Raman Spectroscopy: A Review

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Meng-Lei Xu†, Yu Gao‡, Xiao Xia Han†*, Bing Zhao†*

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†

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Changchun 130012, PR China

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‡

State Key Laboratory of Supramolecular Structure and Materials, Jilin University,

College of Agriculture, Jilin Agricultural University, Changchun 130118, PR China.

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*Corresponding authors: [email protected]; [email protected]

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ACS Paragon Plus Environment


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ABSTRACT: Pesticides directly pollute the environment and contaminate foods

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ultimately being absorbed by the human body. Their residues contain highly toxic

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substances that have been found to cause serious problems to human health even at

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very low concentrations. The gold standard method, gas/liquid chromatography

15

combined with mass spectroscopy, has been widely used for the detection of pesticide

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residues. However, these methods have some drawbacks such as complicated

17

pre-treatment

18

surface-enhanced Raman spectroscopy (SERS) has promoted the creation of

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alternative detection techniques. SERS is a useful detection tool with ultrasensitivity

20

and simpler protocols. Present SERS-based pesticide residue detection often uses

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standard solutions of target analytes in conjunction with theoretical Raman spectra

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calculated by density functional theory (DFT), and actual Raman spectra detected by

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SERS. SERS is quite a promising technique for the direct detection of pesticides at

24

trace levels in liquid samples, or on the surface of solid samples following simple

25

extraction to increase the concentration of analytes. In this review, we highlight recent

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studies on SERS-based pesticide detection, including SERS for pesticide standard

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solution detection and for pesticides in/on food samples. Moreover, deep analysis of

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pesticide chemical structures, structural alteration during food processing, interaction

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with SERS substrates, and selection of SERS-active substrates are involved.

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KEYWORDS: SERS, food, pesticide residue, detection, semiconductor

and

cleanup

steps.

Recent

technological

31


advancements

of

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INTRODUCTION

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Pesticides are natural or synthetic compounds, which are used to prevent, destroy,

34

or control diseases, pests and weeds, or to adjust plants and insect growth. In fact, it

35

has been estimated that less than 0.1% of the pesticides applied to crops actually

36

reaches the target pest.(1) The rest enters the environment gratuitously, which may

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directly pollute the environment after application, and then enters into the food chain.

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Pesticide residues are any particular substance, which are found in food, agricultural

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products, or animal feed due to the use of pesticides, such as their transformation

40

products, metabolites, reaction products and impurities to be of toxicological

41

significance.(2) Pesticide residues may be absorbed by humans via the digestive and

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respiratory systems or through the skin. Eating pesticide-contaminated food is one of

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the main ways of contacting pesticides. However, food poisoning incidents caused by

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pesticide residues in food may also happen occasionally.

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Detection of pesticide residues in food is an essential step in regulating and

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monitoring the levels of pesticides. Since the 1970s, most routine pesticide residue

47

determination has been conducted by gold standard chromatographic methods,

48

including gas/liquid chromatography combined with mass spectroscopy, however,

49

there is now a clear demand for an increase in the number of rapid detection

50

methodologies.(3)

51

electrophoresis are common fast detection methods, and their advantages relative to

52

chromatographic techniques have been widely discussed in other reviews.(4) However,

53

these methods still suffer from several inherent defects, such as solution instability

Immunoassays,

electrochemical

detection,


and

capillary


54

and short storage time.

55

SERS is an advanced Raman technique that enhances the vibrational spectrum of

56

molecules adsorbed on or in the vicinity of metal particles and/or surfaces, which was

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firstly observed in 1970s.(5) This technique gives analytical identity information since

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it can provide characteristic vibrational fingerprints of molecules with nondestructive

59

testing. Its excellent sensitivity to the detection of a wide range of pesticides and

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single-molecule pesticides has promoted its use as an alternative detection technique

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for rapid pesticide analysis.(6) Since 1987, increasing studies have used SERS for fast

62

detection of pesticide residues in food.(7) Food sample which may contain pesticide

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residues should be pretreated by extraction and clean-up before SERS measurements.

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A small amount of sample should subsequently be put into colloidal substrates, or

65

dropped onto solid substrates. At last, Raman spectra are collected and analyzed.

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In this review, we first focus on the detection of pesticide standard solutions by

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SERS, and then review pesticide residue detection in food samples. Lastly, promising

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future trends and perspectives are also discussed.

69 70

Development and application of SERS for pesticide standard solution detection.

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The establishment of an analytical method usually begins with the study of standard

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products, and the same is true for SERS studies on pesticide residues in food. It

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includes the theoretical Raman spectra of analytes calculated by density functional

74

theory (DFT) theory, in combination with the actual Raman spectra measured absent

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or present by SERS detection, and enhancement factor (EF) calculated at last.(9)


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DFT simulations of pesticide Raman spectra. DFT calculations using the post

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self-consistent field method have been used extensively to predict spectral

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information and molecular conformation. DFT calculations are carried out by

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assuming that the energy of a molecule is a function of the electron density. The

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energy is then minimized with respect to the density, and an optimized structure may

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then be obtained. This method can calculate the structural information of a molecule,

82

which contains molecular bond length, bond angle and the size of the entire

83

molecule.(10) The theoretical Raman spectra could be investigated by DFT calculations,

84

and given reasonable explanations, which are optimized with the Gaussian suite of

85

programs.(11) For pesticide standard solution, its theoretical spectra can be obtained by

86

caculating the states of molecules in pure solutions. Several studies about molecular

87

vibration modes and the Raman characteristic peaks have been reported, such as

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organophosphorus, organochlorine, carbamate, pyrethroid pesticides and so on.(12)-(15)

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DFT has become an accurate and computationally economical alternative approach to

90

quantum mechanical calculations.(16) However, the real-world Raman spectral or

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SERS signals are so different from the theoretical ones, the reason could be due to

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different molecular structure of pesticide or substrates, and their interaction. Thus,

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further studies are needed to investigate real-world Raman spectral signals.(17)

94

Chemical structure of SERS-active pesticides. Based on chemical structures,

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pesticides can be divided into inorganic and organic species. Inorganic pesticides

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include calcium arsenate, arsenate of lead, aluminum phosphide, lime sulphur, copper

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sulfate etc. Organic pesticides can either be natural (usually extracted from plants or



98

bacteria) or synthetic. Synthetic pesticides can be classified into organochlorine (e.g.

99

chlorobenzenes), organophosphorus (Fig. 1a), carbamate (Fig. 1b), pyrethroid

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pesticides (Fig. 1c).(18)

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SERS substrates selection. SERS can enhance Raman signals 104-105 times, when

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the target analyte is placed on an active substrate, thus, sensitive substrates are found

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to be a very influential factor in SERS detection.

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Different types of SERS substrates were reported, such as rough metal electrodes,

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noble metal, transition metal, semiconductor nanomaterials and composites. Rough

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metal electrodes were the earliest SERS substrate, but the whole electrode process is

107

not controllable; thus, this substrate is not suitable for theoretical study. Metal

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colloid-based substrates, such as gold (Au) and silver (Ag) colloids, are widely used

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as substrates for their low cost, simple preparation and favorable enhancement

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compared to other substrates. There are also two typical ways to improve SERS

111

signals effectively with colloid-based substrates. One is optimizing the physical

112

properties of nanomaterials including shapes, sizes, and components.(19)(20) The other

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is improving the reproducibility and stability of the SERS-active substrate by using

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self-assembled structures of NPs, such as fractal-like, elastomeric templates, and

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colloidal silica crystals.(21)-(23) However, these noble metal colloid are stabilized by

116

electrostatic repulsion, and once this stable state is broken, the colloid no longer has

117

SERS activity, and therefore requires a more stable substrate.

118

SERS-active nanomaterials have extended from noble metals, transition metals to

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semiconductor materials with the development of SERS for more than 40 years.


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Compared with metals, semiconductor materials have more controllable properties,

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such as band gap, photoluminescence, stability and resistance to degradation.(24)

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In recent years, some semiconductors (TiO2 and CdTe) were found to display a

123

rather large enhancement (above 106) under optimized conditions.(25) (26) Moreover,

124

metal-semiconductor hybrid nanomaterials were found to be capable of displaying

125

higher enhancement than pure metal substrates ascribed to the synergetic contribution

126

of metal and semiconductor to SERS. Pesticides with certain functional groups, such

127

as carboxyl and thiol can strongly bind to substrates. Thus, such kind of SERS-active

128

substrates have great potential for highly robust SERS sensing in pesticides.

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For solid surface-based substrates, more attention is paid to their positioning, in

130

order to achieve more rapid and efficient extraction of targets from complex surfaces.

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There are two types of SERS substrates: “hard” and “flexible.” In “hard” SERS

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substrates, small SERS-active building blocks are self-assembled onto “rigid”

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substrates or films using various methods such as the Langmuir-Blodgett technique,

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layer-by-layer assembly, spin-casting and DNA assisted assembly.(27)-(31) However,

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these complicated processes require long fabrication times and sophisticated

136

equipment.(32) “Flexible” SERS substrates are made of polymers, papers, plastics,

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sandpaper, carbon fibers, and adhesive tape.(33)-(40) These reported flexible substrates

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are effective for SERS detection as they can swab the complex surface of a diverse

139

array of actual analytes. Nevertheless, the opacity of the above-mentioned flexible

140

substrates prevents the incident light from direct excitation of SERS on the opposite

141

side.



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To select appropriate SERS-active substrates for pesticide detection, the following

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three key points should be considered before Raman measurements. To select

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appropriate SERS-active substrates for pesticide detection, the following three key

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points should be considered before Raman measurements.(41)-(43)

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1) The enhancement ability of the SERS-active materials. Noble metals (Ag and Au)

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are most commonly used substrates for universal analyte detection due to their

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relatively high enhancement ability. In recent years, metal-semiconductor hybrid

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nanomaterials were found to be capable of displaying higher enhancement than

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pure metal substrates.

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2) The affinity of target molecules to SERS-active materials. The surface selection

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rule of SERS determines selective enhancement for the absorbed molecules close

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to (within 10 nm) the substrate surface. In general, the interaction types between

154

target molecules and substrates includes electrostatic, hydrophobic and covalent

155

bindings.

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3) Resonance Raman effect. Resonance Raman scattering is an unique Raman effect

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and it occurs when the incident laser frequency is close in energy to an electronic

158

transition of a compound, which can lead to remarkably (>102) enhanced intensity

159

of the Raman scattering. Thus, surface-enhanced resonance Raman scattering

160

(SERS) would exhibit further enhancement based on SERS, allowing

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single-molecule detection under optimized experimental condition.

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Interaction of SERS substrates and pesticide molecules. For present pesticide

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detection, Ag and Au nanoparticles (Au NPs) are the most common SERS substrate.(44)


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There are several reports about the interaction between Au or Ag NPs and pesticide

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molecules, such as chlorpyrifos, malathion, paraquat, and tricyclazole.(45)-(47) In recent

166

years, a variety of semiconductor-metal nanocomposites were synthesized and found

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to show stronger Raman signal enhancement and multifunctionality for pesticide

168

detection. Li et al.(48) and Yang et al.(49) reported a kind of Au-coated TiO2 nanotube

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arrays with dual functions of SERS substrate and photocatalytic property. In this case,

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2,4-D and methyl-parathion can be degraded into clean inorganic molecules by this

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substrate with UV-irradiation after detection. Ngan et al. synthesised silver

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nanodendrites on silicon, which could be used as SERS substrates to detect

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pyridaben.(50)

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The possible interactions between pesticide molecules and nanostructures can occur

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through the pesticides adsorbed on the substrates surfaces. Pesticides with certain

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functional groups, such as carboxyl, hydroxyl, thiol, amine that can bind to Au or Ag

177

substrates strongly are also good targets.(51), (52) While, pesticide molecules without

178

these functional groups could be coupled by some probe molecules (e.g., 4-MBA,

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PATP) and bind to the substrates indirectly .(19) For intrinsic vibrations, molecules

180

with conjugated double bond systems and symmetric vibrational modes are more

181

active than molecules without these characteristics.

182

The ideal Raman signal of pesticide molecules can be obtained by SERS.

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Comparing theoretical Raman spectra calculated with DFT theory, we can get

184

information on the chemical bond vibration, and interaction of SERS substrates and

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pesticide molecules. For pesticide molecules, its standard solution is usually dissolved



186

in ultra-pure water or highly pure organic solvents. Thus, we can get ideal Raman

187

spectra and develop standard data.

188 189

SERS for detection of pesticides in food samples

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Detection of pesticides in real-world food samples is sometimes different from

191

detection in standard solutions due to the complexity of food matrices. Food can be

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classified in different groups: grains, oil-producing plants, vegetables, fruits, nuts,

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sugars, beverages, edible fungi, flavorings, medicinal plants, foods with animal

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origins. Pesticide residues’ fate in food include their transformation products,

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metabolites, reaction products and impurities which are of toxicological

196

significance.(1) Accordingly, we discuss SERS-based pesticide detection by food

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category and their properties, pesticide residues’ fate in food, and distribution of

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pesticide residues.

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In different food category. In terms of food types, there are more reports in

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beverages, fruits, or vegetables than other groups on the detection of pesticide

201

residues using SERS.(40) (53)-(88) Hou et al. developed an in situ SERS method to detect

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and discriminate four pesticides on plant surfaces directly without extracting.(53)

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Wijaya et al. detected acetamiprid without pre-treatment of apple juice samples by

204

using Ag NPs as substrates.(61) He et al. combined a surface swab capture method and

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SERS for recovery and quantitative detection of thiabendazole on apple surfaces.(69)

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SERS is an ideal alternative method to detect chemical pesticides in liquid food

207

samples, and also considered to be very powerful analytical method especially for


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field test after a simple extract process. For liquid food samples, pesticide residues

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and their transformation products may transfer into juice and thus become a potential

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risk to consumer health. Their uniform matrices are favorable for pesticide residue

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detection based on SERS (Figure 2a). For in situ SERS studies, pesticide residues are

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often distributed on the surface of these food-types, and could be detected by SERS.

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However, most foods are solid or solid liquid mixtures (Figure 2b).

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However, besides these three types of food, vegetables, fruits, or beverages, they

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are less reports on pesticides detection in grains, oil plants, animal origin food or other

216

types.(61), (89)-(93) In additional, there are also few studies on pesticides detection inside

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solid food were reported. The reason is that, a extraction process is needed in

218

detection to increase the analyte concentration to achieve a better SERS signal (Figure

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2c). At present, commonly used extraction technologies include solvent extraction and

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SPE.(85)-(89) Xie et al. weighed and grinded vegetable samples to dry powder with

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Na2SO4, added activated carbon and acetone, and then shook and filtered the

222

solution.(88) The filtrate was concentrated to 5 mL, and the solution was finally passed

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through 0.45 µm membranes for SERS detection. Luo et al. applied the QuEChERS

224

method as an extraction and purification technology in order to detect phosmet and

225

thiabendazole in apples.(94)

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In different pesticide species. Based on their chemical structures, pesticides can

227

be divided into inorganic pesticides and organic pesticides. Organic pesticides can

228

either be natural (usually extracted from plants or bacteria) or synthetic. Synthetic

229

pesticides can be classified into organophosphorus, organochlorine, pyrethroid,



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carbamate pesticides. Generally, SERS-based methods are not suitable for analysis of

231

inorganic pesticides with relatively low Raman cross-section. Botanical pesticides are

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environmentally friendly, and also highly bio-degradable, so they become inactive

233

within hours or a few days. While, many microbial pesticides are also believed to be

234

low toxic and safe. On the other hand, increasing numbers of new pesticide molecules

235

have been developed to control diseases or pests in agriculture. Shen et al. detected

236

six pyrazole fungicides, which are a new group of pesticides with strong antifungal

237

activities in grape wine by solid-phase extraction and gas chromatography-tandem

238

mass spectrometry.(96) However, among numbers of pesticides, only a fraction of these

239

molecules were detected with full peak assignments by Raman spectroscopy,

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especially for organic synthetic pesticides.

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Pesticides can also be classified according to their physiological effects, such as

242

contact, stomach toxicity, systemic effects etc. Systemic pesticides or herbicides are

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able to penetrate into the plant tissues and translocate from one site to other parts of

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the plant.(97)-(99) Here in this review, besides synthetic pesticides we also introduced

245

non-systemic pesticides distributed on the surface of food. Kim et al. developed a

246

portable sensor system composed of high performance and reliable Au nanofinger

247

sensor strips to detect chlorpyrifos on apple skin.(64) Jiang et al. provided Ag NP

248

SERS substrates for the identification and detection of triphenyltin residues on apple

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peels without the requirement of complicated sample pretreatments.(72)

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In food processing. Concerns about the impact of pesticides on environment has

251

prompted studies on the fate of these agents.1 Pesticide residues or their


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transformation products, metabolites, reaction products and impurities are all of

253

toxicological significance. However, the behavior of pesticides in food processing is

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different from that in plants or animals. Several factors such as high temperature for

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cooking or blanching, pH alteration, and decrease in moisture by drying, may cause

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the increase or decrease of pesticide concentration. In addition, pesticide residues

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might metabolize to new chemical substrances during food processing.(100) Regueiro

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et al. reported pesticides residues in food could influence fermentative microbiota,

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and then influence the sensory quality.(101) Although degradation of pesticides in

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environmental have been well documented, studies about the development of

261

breakdown products and metabolite identification during cooking or processing food

262

are scarce due to low concentration and complex structure.(102),

263

approach has a higher sensitivity and selectively for target analytes than traditional

264

chromatographic methods, which could have a great potential in the application of

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trace pesticide derivative identification during or after food processing.

(103)

SERS-based

266

A separation and purification technique, for example SPE, is still needed before

267

pesticide residue detection by SERS in solid food samples becomes more widespread.

268

The scope of testing using SERS still needs to be expanded due to the complexity of

269

food matrices. At the same time, an increasing variety of pesticides are used in

270

agriculture and absorbed by humans. However, detection based on SERS methods are

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still only focused on a few pesticide residues. In addition, pesticide residues might

272

metabolize to new chemical substances during food processing, however, studies

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about the development of breakdown products and metabolite identification during



274

cooking or processing food are scarce. Thus, more pesticides and their transformation

275

products, metabolites, reaction products and impurities need to be regarded as

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analytes in SERS detection. Pesticide residues in different food matrices detected by

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SERS methods are summarized in Table 1.

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FUTURE TRENDS AND PERSPECTIVES

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SERS detection methods have a high potential as rapid technique for the

281

determination of pesticide residues in food. SERS has many advantages such as

282

ultrasensitive detection, simpler protocols, and reduced cost. A spectral library of

283

pesticide molecules needs to be established. Additionally, pesticide residues might

284

metabolize to new chemical substances during food processing, however, there are

285

only few studies on their transformation products, metabolites, reaction products and

286

impurities during cooking or food-processing. In future studies, more attention should

287

be paid to the detection them in food by SERS. For different crops, registered

288

pesticides, which are allowed to be applied, are different. In real-world detection, the

289

unregistered pesticides should also be studied. Ag or Au NPs are often the preferred

290

substrate for pesticide detection. However, there is still lack of cost-effective

291

commercial SERS substrates. Semiconductor-metal hybrid substrates have great

292

potential for highly robust SERS sensing in pesticides with high enhancement ability,

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good reproducibility, acceptable stability and reusability. Food matrices are complex

294

such that background interference is a big challenge for analysis of pesticides by

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SERS. Although SERS is quite a promising technique for direct detection of


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pesticides at trace levels in liquid samples, or on the surface of solid samples (with a

297

simple extraction to increase the analyte concentration at the outer surface), it is still

298

very challenging to apply SERS for the detection of internalized pesticides in complex

299

solid food matrices. Non-uniform distributions of pesticide molecules increase the

300

difficulty of detection. As SERS is not a separation method, analysis of internalized

301

pesticides still requires extraction and purification to avoid interference from

302

non-target components in food samples. Future study should expand the variety of

303

pesticides and food matrices studied, and extend substrates from metals to

304

semiconductor materials.

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ACKNOWLEDGMENTS

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This work is supported by Key Laboratory of Integrated Pest Management on

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Crops in Northeast Open Project Fund (No. DB201505KF03) and the National

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Natural Science Fund (No. 31401486).

310

CONFLICTS

311

The authors declare no competing financial interest.

312 313

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Page 30 of 37

Page 31 of 37


641

Table 1 Pesticide residues in different food matrices detected by SERS

642 Food groups

Grains

food matrices

Analyte

Substrates

Separation technique

Paddy Rice Rice Rice

Tricyclazole Chlorpyriphos Carbofuran Thiram, methyl parathion

Ag NPs Au NPs Ag NPs

SPE SPE Ethanol

Au-MBA@void@Au

Ethyl alcohol

Pepper, cucumber peels

Beverages

LOD

Ref.

0.002 mg/L 0.506 mg/L 0.446 mg/L 8 nM, 1.5 µM

(89) (90) (91) (92)

Tea leaves

Isocarbophos, phorate, imidacloprid, deltamethrin

Au NPs

None

0.25~0.50 mg/kg

(53)

Tea leaves surface

Thiram

Au NPs grafted on dendritic a-Fe2O3

None

5×10-6 M

(54)

Ultrapure water, tap water

Prometryn

Ag NPs

None

5×10-12, 5×10-9 mol/L

(55)

water

Methyl parathion

None

1 nM

(56)

Grape juice

Thiram

None

0.1 µM

(57)

Grape juice

Thiram

None

0.1 µM

(58)

Apple juice

Thiram

None, ethanol

10-7 M

(59)

Tap water, apple juice, vegetable juice

Thiram, ferbam

None

1.78~87.01 nM

(60)

Au/Ag-NPs@Al2O3-layer@Ag -nanoparticles Au@Ag NPs/GO/Au@Ag NPs sandwich structure Ag dendritic NPs TiO2 supported silver nanoparticles Au nanoparticle



Food groups

Fruits

Page 32 of 37

food matrices

Analyte

Substrates


Orange, grapefruit juice

Carbaryl

Standing AuNR Arrays

Centrifugation

Apple juice

Acetamiprid

Swab sticks

None

Apple juice Drinking water

Carbaryl Chlorpyrifos, thiabendazole

Standing AuNR Arrays Au nanofingers

Apple, orange peels

Parathion-methyl, thiram, chlorpyrifos

Apple peels

LOD

Ref.

50 ppb 3 µg/mL

(61)

None Water

2.5 ppm 35 ppt

(63) (64)

“Paste and peel off” Au NPs

None

2.6, 0.24, 3.51 ng/cm2

(40)

Isocarbophos, phorate, imidacloprid, deltamethrin

Au NPs

None

0.01~0.02 mg/kg

(53)

Apple peels

Thiram

Au NPs grafted on dendritic a-Fe2O3

None

5×10-6 M

(54)

Apple peels

Thiram

TiO2 supported silver nanoparticles

None, ethanol

240 ng/cm2

(59)

Apple surfaces

Acetamiprid

Swab sticks

None

0.125 µg/cm2

(62)

Apple, grape, mango, pear, peach peels

Thiram, chlorpyrifos, methyl parathion

Au@Ag NPs

Ethanol

0.025~7.23 ng/cm2

(65)

Apple peels

Thiram

Ag nanoshells (Ag NSs)

None

38 ng/cm2

(66)

Apple skin

Thiabendazole

Au nanostar/ polydimethylsiloxane(PDMS) film

None

20 ppb

(67)

Apple peels

Thiram

Fe3O4@NR

None

10-7 M

(68)

Apples (Gala)

Thiabendazole

Ag dendrites

None

5 ppm µg/g per weight

(69)


(62)

Page 33 of 37


Food groups

food matrices

Analyte

Substrates


LOD

Apple, pear skin

Paraquat

Ag NPs

None

10-9 M

Apples, banana peels Apple peels

Thiram, paraoxon Triphenyltin

Ag NPs-decorated filter paper Ag NPs

None

Ref. (70) 2

7.2 ng/cm

None

6.25 ng/cm

(72)

-7

(73)

Apple peels

Chlorpyrifos

Ag2O@Ag NPs

None

10 M

Apples (Fuji) surface

Chlorpyrifos

Au NPs

None

0.13 mg/kg

Apple peels

(71)

2

9

−

(74) 2

Thiram

AgNRs embedded PDMS

Ethanol

2.4×10 g/cm

(75)

Oranges

Phosmet, disulfoton

Au NPs decorated glycidyl methacrylate-ethylene dimethacrylate material

None

8.25, 39.7 mg/kg

(76)

Orange

Methyl parathion

Ag film

None

10-6 M

(77)

Orange surface

Thiram, 4-polychlorinated biphenyl, Methyl Parathion

Ag-NC@PE composite film

None

10 nM, 1 µM, 10 nM

(78)

Pear surface

Triazophos

Ag-coated 3000 meshsandpaper

None

53.3 pM/cm2

(79)

Pear surface

Chlorpyrifos

Au nanopopcorn

SPE

0.35 mg/kg

(80)

Navel orange skin

Phosme, chlorpyrifos

Ag NPs

Acetonitrile

1.23 mg/L ,1.26 mg/L

(81)

Apples (Fuji)

Carbaryl

Au-coated Klarite

SFE

0.5 µg/g

(82)

Apple

Azinphosmethyl, phosmet, carbaryl

Au NPs

Acetonitrile, H2O mixed solvent

4.51~6.66 ppm

(83)

Apple

Thiabendazole

Cellulose nanofibers coated with Ag NPs

Acetonitrile-water solution

5 ppm

(84)



Food groups

Page 34 of 37

food matrices

Analyte

Substrates


LOD

Ref.

Cucumbers, rape peels

Parathion-methyl, thiram, chlorpyrifos

“Paste and peel off” Au NPs

None

2.6, 0.24, 3.51 ng/cm2

(40)

Cucumber peels

Thiram, methyl parathion, malachite green

Ethanol

1.6~10 ng/cm2

(85)

Cabbage

Carbaryl

Ag NPs on the 3D poly(dimethylsiloxane) PDMS nanotentacle array Standing AuNR Arrays

Acetonitrile-water

2.5 ppm

(63)

Tomatoes

Carbaryl, phosmet, azinphos-methyl

Au NPs

Acetonitrile, H2O mixed solvent

(83)

Pak choi Red bell pepper Red amaranthus, little cabbage, Chinese cabbage, Leek, Spinach, Chinese little greens

Difenoconazole Thiophanate-methyl, carbendazim

Au NPs Ag NPs

SPE None

5.35, 2.91, 2.94 ppm 0.4143 mg/L 8 mg/kg

Methamidophos

Ag NPs

Acetone

0.01 µg/mL

(88)

Oil plants

Soybean

Carbofuran

Ag NPs

Ethanol

0.520 ppm

(91)

Animal origin food

Milk Honey

Carbaryl Dimethoate

Standing AuNR Arrays KlariteTM Au NPs

Centrifugation None

50 ppb 2 ppm

(61) (93)

Vegetables

643


(86) (87)

Page 35 of 37


644

Figure 1 R1

X P

R2

(c) Z3

(a)

N

Z1

(b)

O

R3

C

Z2

R4 O Z4 O O

645

R1, R2 = C2H5O-, CH3OR3, R4 = H-, CH3-, C2H5-, C3H7X = S or O Z1, Z2, Z3, Z4 = subsitituent groups

646

Figure 1. Chemical structure of pesticides, organophosphorus (a), carbamate (b),

647

pyrethroid pesticides (c)(18).

648



649

Figure 2

650 651

Figure 2. Pesticide residues detection in liquid (a), on the surface (b) or inside solid

652

foods by SERS.

653


Page 36 of 37

Page 37 of 37


654

Topic of Content

655


Detection of Pesticide Residues in Food Using Surface-Enhanced

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