近幾年，食品安全是一個備受矚目的問題，食品中的有毒物質造成龐大的經濟損失和公共衛生風險。2017年，發現雞蛋中含有有毒農藥質芬普尼(Fipronil)，這項食品安全事件影響了超過15個國家包括台灣。在此研究，我們使用表面增強拉曼光譜法(SERS)來分別檢測雞蛋中的芬普尼及芬普尼代謝物(Fipronil sulfone)，而不是使用傳統的定量方法，例如:質譜儀或液相色譜法。
　　在這項研究中，我們製備了奈米金棒(AuNR)與氧化石墨烯(GO)結合成的奈米複合材料，將其材料疊加組成三維(3D)奈米金棒-氧化石墨烯基板，並用羅丹明6G(R6G)做為優化基板的分析物。實驗發現，將R6G加入疊加四層的奈米金棒與氧化石墨烯(比率為100:1)基板能產生最大的增強效應。我們使用此優化後的SERS基板分別在丙酮及蛋液中分別檢測芬普尼(0.001~0.000000001 M)及其代謝物(0.0001~0.000000001 M)。表面增強拉曼光譜法對丙酮和雞蛋中的芬普尼及其代謝物的檢測極限為0.00000001 M (4.4 ppb)，其結果低於台灣最大殘留極限(10 ppb)。此外，我們分別測量含有芬普尼(0.0000001, 0.001M)及其代謝物(0.00000001, 0.0001M)低與高濃度加標雞蛋樣品的SERS強度，並使用芬普尼及其代謝物的校準曲線計算結果，而結果落在實際/預期強度的20%以內。然而，三維的奈米金棒-氧化石墨烯基板的相對標準偏差(RSD)落在5~20%之間，此結果對於標準的SERS感測器是合理的。本研究提供了一個使用表面增強拉曼光譜能快速篩檢到食品級蛋中芬普尼及其代謝物的新方法。

Food safety is one glaring issue where repeated incidents of intentional toxic contamination had resulted in huge economic losses, and public health risks. The scandal of Fipronil contaminated “poisonous eggs” made news in 2017 which influenced more than fifteen countries. Instead of conventional quantitative techniques such as mass spectrometry or liquid chromatography we have used Surface Enhanced Raman Spectroscopy (SERS) to detect Fipronil and Fipronil sulfone in pure form and in spiked food grade eggs.
In this study, we used plasmonic gold nanorods (AuNRs) with graphene (GO) nanocomposite, and fabricated a layer-by-layer assembled three dimensional (3D) AuNRs-GO substrate. The substrate was optimized, using Rhodamine 6G, to yield the maximum response for the 3D substrate with a layer number of 4 and using AuNR:GO=100:1. We have used this optimized SERS substrate to detect Fipronil (0.001~0.000000001 M) /Fipronil sulfone (0.0001~0.000000001 M) in acetone, and in egg, respectively. The limit of detection, by SERS, for the Fipronil/Fipronil sulfone in acetone, and in egg are 0.00000001 M (equivalent to 4.4 ppb), which is below the maximum residue limit (10 ppb) in Taiwan. Low and high spiked egg samples, with Fipronil (0.0000001, 0.001 M), and Fipronil sulfone (0.00000001, 0.0001 M) were measured for the SERS intensities which fell within 20 % of the target /expected intensity from the calibration curve for both Fipronil and Fipronil sulfone. The relative standard deviation (RSD) for the 3D AuNRs-GO substrate is between 5 – 20 % and is reasonable for a standard SERS sensor. This study demonstrates a quick screening method for detection of Fipronil/Fipronil sulfone in real food grade egg by SERS.

Table of Contents…………………………………………………………...………i
Acknowledgements……………………………………………………...................v
Abstract……………………………………………………………………………..vii
摘要………………………………………………………………………………….ix
List of Figures……………………………………………………………………...x
List of Tables………………………………………………………………….…..xxi
Chapter 1. Global Food Safety and Health Issues………………………….1
1.1 Global Food Safety problems…………………………………………………1
1.2 Toxic Additives…………………………………………………………………3
　1.3 Toxin Egg Issues……………………………………………………………….5
1.3.1 Dioxin in Egg Issue.......................................................................................5
1.3.2 Fipronil in Egg Issue……………………………………………………….6
1.3.3 Brief Introduction of Fipronil and Fipronil Metabolite…………………….9
Chapter 2. Pesticide Analysis………………...……………………………..….12
　2.1 Biosensors……………………………………………………………………..12
2.2 Mass–sensitive Spectrometry………………………………………………..15
2.2.1 Mass Spectrometry (MS)…………………………………………………15
2.2.2 High Performance Liquid Chromatography (HPLC) .................................17
2.2.3 Gas Chromatography (GC) ........................................................................18
2.3 Surface Plasmon Resonance (SPR)………………………………………….19
2.3.1 Background of SPR ....................................................................................19
2.4 Raman Spectroscopy and SERS…………………………………………….25
2.4.1 Background of Raman Spectroscopy .........................................................25
2.4.2 Background of Surface Enhanced Raman Spectroscopy (SERS)………...29
2.4.3 SERS Substrates..........................................................................................33
2.4.4 Fipronil analysis by Raman Spectroscopy using AuNRs-GO SERS Substrate………………………………………………………………………...39
Chapter 3. Experimental………………………………………………..41
　3.1 Materials……………………………………………………………………...41
3.1.1 Synthesis of graphene oxide (GO)………………………………………..41
3.1.2 Synthesis of Gold nanorods (AuNRs): CTAB-coated AuNRs……………42
3.1.3 Synthesis of AuNRs-GO nanocomposites………………………………...43
3.2 Methods……………………………………………………………………….44
3.2.1 Fabrication and optimization the SERS substrate………………………...44
　3.2.1.1 Drop coated of different ratios of AuNRs with GO………………….44
3.2.1.2 Layer-by-layer assembly of 3D AuNRs-GO mediated by the air-liquid interface……………………………………………………………....………45
3.2.2 Transmission Electron Microscope (TEM) ................................................47
3.2.3 Scanning Electron Microscope (SEM)……………………………………48
3.2.4 Ultraviolet-visible Spectroscopy.................................................................49
3.2.5 Raman and SERS........................................................................................50
3.2.6 Detection Fipronil and Fipronil sulfone using AuNRs-GO SERS Substrate………………………………………………………………………...51
3.2.6.1 Detection Fipronil/Fipronil sulfone in liquid sample………………...51
3.2.6.2 Detection Fipronil/Fipronil sulfone spiked in food grade Egg……….52
3.2.6.3 Spike-and recovery…………………………………………………...54
Chapter 4. Results & Discussion I………………………………………….....56
4.1 Morphological study of AuNRs, GO, and AuNRs-GO…………………….56
4.2 Optical properties of AuNRs, GO, and AuNRs-GO……..…………………57
4.2.1 Optimization and Purification of AuNRs………………………………....57
4.2.2 UV-vis spectra of GO, AuNRs, and AuNRs-GO………………………….59
4.2.3 Raman spectra of AuNRs, GO and AuNRs-GO…………………………..61
4.3 SERS of R6G for substrate optimization……………………………..….....62
4.3.1 Optimization of AuNRs-GO ratio………………………………………...62
4.3.2 SERS of R6G using layer by layer AuNRs-GO substrate………………...64
4.3.2.1 Morphological study of 3D AuNRs substrate………………………..64
4.3.2.2 Optimization of number of AuNRs-GO layers……………………….65
4.4 Raman and SERS spectra of Fipronil, and Fipronil sulfone…………………70
4.4.1 Raman of Fipronil, and Fipronil sulfone powder…………………………70
4.4.2 Reproducibility of the 3D AuNRs-GO SERS substrate for different concentrations of Fipronil, and Fipronil sulfone………………………………..72
4.4.3 Raman and SERS of concentration dependent Fipronil, and Fipronil sulfone solution………………………………………………………………………….76
Chapter 5. Results & Discussion II……………………………...……..……..85
5.1 Detection of Fipronil and Fipronil sulfone in food grade Eggs………....…85
5.1.1 Raman spectra of egg white, egg yolk, and whole egg…………………...86
5.2 Raman and SERS spectra of Fipronil and Fipronil sulfone spiked in food grade egg………………………………………………………………………….87
5.2.1 Reproducibility of the 3D AuNRs-GO SERS substrate for different concentrations of Fipronil, and Fipronil sulfone in egg………………………….87
5.2.2 Raman and SERS of concentration dependent Fipronil, and Fipronil sulfone spiked in egg……………………………………………………………………..90
5.2.3 Detection of Fipronil/Fipronil sulfone from unknown spiked liquid egg….99
Chapter 6. Conclusion & Future Work .........................................................104
Chapter 7. References……….…………………………………………………106

List of Figures
Chapter 1. Global Food Safety and Health Issues
Figure 1-1 Schematic of Fipronil, and its metabolite………………………………..11
Chapter 2. Pesticides Analysis
Figure 2-1 Biosensors: A combination of interdisciplinary research areas…………...13
Figure 2-2 Schematic illustration of the basic configuration of a sensor……………...14
Figure 2-3 Schematic of the HPLC operation process………………………………..18
Figure 2-4 Schematic plasmon oscillation for a metal sphere induced by the electric field of incident light………………………………………………………………….21
Figure 2-5 Extinction (black), absorption (red), and scattering (blue) spectra calculated for Ag nanoparticles of different shapes: (a) a sphere displaying a single dipole resonance peak and (b) a cube, (c) a tetrahedron, (d) an octahedron, (e) a triangular plate, and (f) Extinction spectra of rectangular bars with aspect ratios of 2 (black), 3 (red), and 4 (blue). Note that the nonspherical particles typically exhibit multiple, red-shifted resonance peaks………………………………………………………………23
Figure 2-6 Typical set-up for an SPR biosensor. Surface plasmon resonance (SPR) detects changes in the refractive index in the immediate vicinity of the surface layer of a sensor chip. SPR is observed as a sharp shadow in the reflected light from the surface at an angle that is dependent on the mass of material at the surface. The SPR angle shifts (from 1 to 2 in the above right-hand diagram) when biomolecules bind to the surface and change the mass of the surface layer. This change in resonant angle can be monitored noninvasively in real time as a plot of resonance signal (proportional to mass change) versus time…………………………………………………………………..25
Figure 2-7 Photograph of Chandrasekhara Venkata Raman and the band diagram of Raman scattering phenomenon. (a) Light incident on molecule which scatters it. More than 99% of the incident photons will scatter (Rayleigh scattering) without a change in their frequency. Only few of the incident photons will undergo Raman scattering (Stokes and Anti-Stokes) with a change in frequency. (b) The electronic transitions responsible for Rayleigh, Raman Stokes and Raman Anti-Stokes scattering. (c) Schematic of the Rayleigh, Stokes and Anti-Stokes line as a function of wavelength (nm), wavenumber (〖cm〗^(-1)) and Raman shift (〖cm〗^(-1)). It is the Raman shift which is independent of the incident wavelength and used as a measure of the vibrational energy difference……………………………………………………………………………..27
Figure 2-8 Surface plasmon absorption spectra of different metal nanoparticles as a function of wavelength. The absorption frequency depends on the nanoparticles material and size. For resonance, the corresponding incident light wavelength should be tuned to match the absorption peaks to obtain highest SERS signal……………….32
Figure 2-9 SEM images of generally different type of direct sensing SERS substrates. (a) Inverted pyramids in KlariteTM substrates. (b) Silicon nanowire decorated with Ag nanoparticles. (c) Cicada wing decorated by silver nanoparticles. (d) iFyberTM membranes coated by Au/Ag nanoparticles. (e) Inkjet printed Au nanoparticles paper. (f) PVA nanofiber mat with silver nanoparticles………………………………………36
Figure 2-10 TEM image of (a) hybrid AuNRs/GO, (b) 20 nm AuNPs deposited onto GO sheets, (c) GO/AuNRs, (d) GO/PDDA/AgNPs, (e) Ag@GO, and (f) GO/AgNPs..38
Figure 2-11 (a) Schematic illustration of LSPR excitation for GNSs. (b) A typical LSPR absorption band of GNSs……………………………………………………………..39
Figure 2-12 The Zeta potential of AuNRs, GO, Dox and PEG-coated AuNRs-GO-Dox…………………………………………………………………………………...40
Chapter 3. Experimental
Figure 3-1 Schematic of experimental steps for the synthesis of CTAB-coated AuNRs………………………………………………………………………………..43
Figure 3-2 Schematic illustration of the interaction of AuNRs–GO. Positively charge CTAB coated AuNRs interact electrostatically with the negatively charged GO to generate the composite AuNRs-GO…………………………………………………..44
Figure 3-3 Schematic illustration of experimental steps for fabrication different ratios of AuNRs with GO substrates, and optimization ratio of AuNRs with GO by R6G Raman probe………………………………………………………………………….45
Figure 3-4 Schematic illustration of experimental steps for the fabrication of layer-by-layer self-assembled AuNRs-GO substrates, and optimization by Raman intensity and RSD of the data using R6G as Raman probe………………………………………….47
Figure 3-5 Photograph of the JEM-2000EX transmission electron microscope, JEOL Co., JAPAN…………………………………………………………………………..48
Figure 3-6 Photograph of the HR FE-SEM, JSM-6700F, JEOL, scanning electron microscope…………………………………………………………………………...49
Figure 3-7 Photograph of the V-770, UV-VIS spectrophotometer, JASCO Co., JAPAN………………………………………………………………………………..50
Figure 3-8 Photograph of the JOBIN YVON HR800, Raman spectrometer………….51
Figure 3-9 Schematic of the experimental steps for the Raman (and SERS) detection of Fipronil/Fipronil sulfone solution (in acetone). 10 µL of Fipronil (〖10〗^(-3)~〖10〗^(-9) M) /Fipronil sulfone (〖10〗^(-4)~〖10〗^(-9) M) solution was dropped on the substrates. Raman scattering data (weak, on the left) were obtained from bare silicon, and SERS data (strong, on the right) from the substrate having AuNRs-GO………….........................52
Figure 3-10 Schematic of the steps to detect Fipronil/Fipronil sulfone from real food grade eggs. Eggs were beaten and mixed with Fipronil/Fipronil sulfone in acetone solution. The resultant is ultra-centrifuged. The supernatant is collected. 10 µL of the supernatant was dropped on the silicon and on the AuNRs-GO substrate for conventional Raman, and SERS spectra measurement, respectively…………………54
Chapter 4. Results & Discussion I
Figure 4-1 Morphology of AuNRs, GO, and their nanocomposite. Transmission Electron Microscopy (TEM) images of (a) Graphene oxide (GO), (b) AuNRs, and (C) AuNRs-GO nanocomposite. (d) Magnified view of the marked area (red box) in Figure 2c. The AuNRs-GO nanocomposite was made with a mixing ratio of AuNRs:GO = 100:1………………………………………………………………………………….57
Figure 4-2 (a) Optical image of purified AuNRs having different aspect ratios. (b) Optical absorption of 6 different AuNRs samples (1 to 6) having different aspect
ratios………………………………………………………………………………….59
Figure 4-3 UV-Vis absorption spectra of (i) GO (red solid line), (ii) AuNRs (black solid line), and (iii) AuNRs-GO (AuNRs:GO = 100:1) nanocomposites (blue dash line). Different absorption bands (225, 288, and 700 nm ) are marked…………………......61
Figure 4-4 Raman scattering spectra of (i) AuNRs (black solid line), (ii) GO (red solid line), and (iii) AuNRs-GO (AuNRs:GO = 100:1) nanocomposites (blue solid line).
The samples were dispersed on silicon substrate, and the peak at 520 〖cm〗^(-1) is from Silicon………………………………………………………………………………...62
Figure 4-5 The comparison of SERS spectra of 〖10〗^(-6) M of R6G loaded on different SERS substrates having different mixing ratios (wt. %) of AuNRs and GO in the nanocomposites. The spectrum for ‘only R6G’ indicate a conventional Raman spectrum showing only the Si 520 〖cm〗^(-1) peak as no AuNRs was involved……………………64
Figure 4-6 SEM images of the 3D AuNRs substrates with NL of 4 and 6 respectively. Cross-section (top) and top-view (bottom) SEM images of the 3D AuNRs produced using NL=4 (left), and NL=6 (Right). The thickness of the 3D layer is marked with RED dashed line……………………………………………………………………...65
Figure 4-7 SERS spectra of 〖10〗^(-6) M of R6G, measured at 5 different spots on the 3D AuNRs-GO (100:1) substrates with number of layers from 1-20 (mentioned in each panel). The relative standard deviation (RSD) is shown at the bottom of each panel (in black)……………………………………………………………………..…………..67
Figure 4-8 Variation of the 612 cm-1 signal (normalized with respect to the Si 520 〖cm〗^(-1) signal) from R6G as a function of the layer numbers (1-20) of the 3D AuNRs-GO SERS substrate. The error bar in each data shows the scatter in the signal measured over 5 spots on the sample. The data is fitted according to the Langmuir adsorption isotherm (red line)…………………………………………………………………….70
Figure 4-9 Conventional Raman spectrum of Fipronil powder……………………...71
Figure 4-10 Conventional Raman spectrum of Fipronil sulfone powder……………72
Figure 4-11 SERS spectra of (a) 〖10〗^(-3), (b) 〖10〗^(-4), (c) 〖10〗^(-5), (d) 〖10〗^(-6), (e) 〖10〗^(-7), (f) 〖10〗^(-8), and (g) 〖10〗^(-9) M of Fipronil (in acetone), measured at 5 different spots on the 4 layers AuNRs-GO (100:1) SERS substrate. The relative standard deviation (RSD) is shown at the bottom of each panel (in black) ...……………………………………….74
Figure 4-12 SERS spectra of (a) 〖10〗^(-4), (b) 〖10〗^(-5), (c) 〖10〗^(-6), (d) 〖10〗^(-7), (e) 〖10〗^(-8), and (f) 〖10〗^(-9) M of Fipronil sulfone (in acetone), measured at 5 different spots on the 4 layers AuNRs-GO (100:1) SERS substrate. The relative standard deviation (RSD) is shown at the bottom of each panel (in black)………………………………………….75
Figure 4-13 Concentration dependent background subtracted (a) Raman and (b) SERS spectra of Fipronil (〖10〗^(-3), 〖10〗^(-4), 〖10〗^(-5), 〖10〗^(-6),〖10〗^(-7), 〖10〗^(-8), and 〖10〗^(-9) M in acetone) on plain silicon substrate, and the optimized 3D SERS substrate (4 layers AuNRs-GO (100:1)), respectively…………………………………………………………………77
Figure 4-14 Concentration dependent background subtracted (a) Raman and (b) SERS spectra of Fipronil sulfone (〖10〗^(-4), 〖10〗^(-5), 〖10〗^(-6),〖10〗^(-7), 〖10〗^(-8), and 〖10〗^(-9) M in acetone) on plain silicon substrate, and the optimized 3D SERS substrate (4 layers AuNRs-GO (100:1)), respectively…………………………………………………………………78
Figure 4-15 Raman of Fipronil solid powder and SERS of Fipronil solution (〖10〗^(-2) M) showing specific peak shifts. Same colours indicate the same peak in the two spectra. The colors used from left to right are orange, purple, yellow, dark blue, bright blue, pink, and bright green………………………………………………………………...79
Figure 4-16 Raman of Fipronil sulfone solid powder and SERS of Fipronil sulfone solution (〖10〗^(-2) M) showing specific peak shifts. Same colours indicate the same peak in the two spectra. The colors used from left to right are orange, purple, yellow, dark blue, bright blue, pink, and bright green…………………………………………………....80
Figure 4-17 Variation of the (a) 1215, (b) 1337, and (c) 1359 〖cm〗^(-1) Fipronil Raman (•) and SERS () signals as a function of Fipronil concentration (〖10〗^(-3)~〖10〗^(-9) M). The error bars indicate the total scatter in the data, and the symbol specifies the mean of five measurements. The line joining the data points represent a linear fit. The Raman data were not fitted because of vanishingly small signals over most of the low concentration regime………………………………………………………………………………...82
Figure 4-18 Variation of the (a) 748, (b) 888, and (c) 1289 〖cm〗^(-1) Fipronil sulfone Raman (•) and SERS () signals as a function of Fipronil sulfone concentration (〖10〗^(-4)~〖10〗^(-9) M). The error bars indicate the total scatter in the data, and the symbol specifies the mean of five measurements. The line joining the data points represent a linear fit. The Raman data were not fitted because of vanishingly small signals over most of the low concentration regime………………………………………………...84
Chapter 5. Results & Discussion II
Figure 5-1 Raman spectra of (i) egg white (black solid line), (ii) egg yolk (red solid line), and (iii) whole egg (blue solid line). Essential band positions are marked. The samples were dispersed on silicon substrates (520 〖cm〗^(-1))…………………………...87
Figure 5-2 SERS spectra of (a) 〖10〗^(-3), (b) 〖10〗^(-4), (c) 〖10〗^(-5), (d) 〖10〗^(-6), (e) 〖10〗^(-7), (f) 〖10〗^(-8), and (g) 〖10〗^(-9) M of Fipronil (in liquid egg), measured at 5 different spots on the 4 layers AuNRs-GO (100:1) SERS substrate. The relative standard deviation (RSD) is shown at the bottom of each panel (in black)………………………………………….88
Figure 5-3 SERS spectra of (a) 〖10〗^(-4), (b) 〖10〗^(-5), (c) 〖10〗^(-6), (d) 〖10〗^(-7), (e) 〖10〗^(-8), and (f) 〖10〗^(-9) M of Fipronil sulfone (in liquid egg), measured at 5 different spots on the 4 layers AuNRs-GO (100:1) SERS substrate. The relative standard deviation (RSD) is shown at the bottom of each panel (in black)………………………………………….89
Figure 5-4 Concentration dependent background subtracted (a) Raman and (b) SERS spectra of Fipronil (〖10〗^(-3), 〖10〗^(-4), 〖10〗^(-5), 〖10〗^(-6),〖10〗^(-7), 〖10〗^(-8), and 〖10〗^(-9) M in liquid whole egg) dispersed on plain silicon substrate, and the optimized 3D SERS substrate (4 layers AuNRs-GO (100:1)), respectively…………………………………………..91
Figure 5-5 Concentration dependent background subtracted (a) Raman and (b) SERS spectra of Fipronil sulfone (〖10〗^(-4), 〖10〗^(-5), 〖10〗^(-6),〖10〗^(-7), 〖10〗^(-8), and 〖10〗^(-9) M in liquid whole egg) dispersed on plain silicon substrate, and the optimized 3D SERS substrate (4 layers AuNRs-GO (100:1)), respectively…………………………………………..92
Figure 5-6 Variation of the intensity of the (a) 1215, (b) 1337, and (c) 1359 〖cm〗^(-1) signals in Raman (•) and SERS () measurements as a function of Fipronil concentration (〖10〗^(-3)~〖10〗^(-9) M) in liquid egg (spiked). The error bars indicate the total scatter in the data, and the symbol specifies the mean of five measurements. The line joining the data points represent a linear fit…………………………………………...94
Figure 5-7 Variation of the intensity of the (a) 748, (b) 888, and (c) 1289 〖cm〗^(-1) signals in Raman (•) and SERS () measurements as a function of Fipronil sulfone concentration (〖10〗^(-4)~〖10〗^(-9) M) in liquid egg (spiked). The error bars indicate the total scatter in the data, and the symbol specifies the mean of five measurements. The line joining the data points represent a linear fit…………………………………………...95
Figure 5-8 Comparison of the variation of the SERS intensities of (a) 1215, (b) 1337, and (c) 1359 〖cm〗^(-1) Fipronil signal in acetone (), and in liquid egg (•) as a function of Fipronil concentration (〖10〗^(-3)~〖10〗^(-9) M). The error bars indicate the total scatter in the data, and the symbol specifies the mean of five measurements. The line joining the data points represent a linear fit……………………………………………………………97
Figure 5-9 Comparison of the variation of the SERS intensity of the (a) 748, (b) 888, and (c) 1289 〖cm〗^(-1) Fipronil sulfone signals in acetone (), and in liquid egg (•) as a function of Fipronil sulfone concentration (〖10〗^(-4)~〖10〗^(-9) M). The error bars indicate the total scatter in the data, and the symbol specifies the mean of five measurements. The line joining the data points represent a linear fit………………………………………98
Figure 5-10 SERS spectra of Fipronil (in 4 layers AuNRs-GO substrate) in spiked liquid egg sample (a) S1, and (b) S2 having unknown concentration of Fipronil. The variation of I1359 () with known Fipronil concentration (as in Figure 5-8c). The yellow bar intersection with the calibration curve indicate the actual S1 (low spike) and S2 (high spike) concentrations, and their expected I1359 values. The blue, and green bars intersection with the calibration curve indicate the measured S1 (low spike) and S2 (high spike) concentrations, and their measured I1359 values. The line joining the data points in (c) is a linear fit. The error bar in each data (c) shows the scatter in the signal measured over 5 spots on the sample………………………………………………...100
Figure 5-11 SERS spectra of Fipronil sulfone (in 4 layers AuNRs-GO substrate) in spiked liquid egg sample (a) S3, and (b) S4 having unknown concentration of Fipronil sulfone. (c) The variation of I748 () with known Fipronil sulfone concentration (as in Figure 5-9a). The yellow bar intersection with the calibration curve indicate the actual S3 (low spike) and S4 (high spike) concentrations, and their expected I748 values. The red, and purple bars intersection with the calibration curve indicate the measured S3(low spike) and S4 (high spike) concentrations, and their measured I748 values. The line joining the data points in (c) is a linear fit. The error bar in each data (c) shows the scatter in the signal measured over 5 spots on the sample……………………………102


List of Tables
Chapter 1. Global Food Safety and Health Issues
Table 1-1 Large scale global food safety issues in the past……………………………..2
Table 1-2 Large scale economic loss due to food safety issues in USA………………...3
Table 1-3 The MRL of Fipronil in eggs in various countries………………………......9
Chapter 5. Result & Discussion II
Table 5-1 Measured (IS1, and IS2), and expected I1359 values for low, and high spiked samples, S1, and S2 measured over 5 sets. SERS measurements done on optimized 3D AuNRs-GO substrate with five spiked liquid egg samples………………………….101
Table 5-2 Measured (IS3, and IS4), and expected I748 values for low and high spiked samples, S3, and S4 measured over 5 sets. SERS measurements done on optimized 3D AuNRs-GO substrate with five spiked liquid egg samples…………………………..103

1 Akhtar, S., Sarker, M. R. & Hossain, A. Microbiological food safety: a dilemma of developing societies. Critical Reviews in Microbiology 40, 348-359, doi:10.3109/1040841X.2012.742036 (2014).
2 Kirk, M. D. et al. World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseases, 2010: A Data Synthesis. PLOS Medicine 12, e1001921, doi:10.1371/journal.pmed.1001921 (2015).
3 Dewey-Mattia, D., Manikonda, K., Hall, A. J., Wise, M. E. & Crowe, S. J. Surveillance for Foodborne Disease Outbreaks - United States, 2009-2015. Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002) 67, 1-11, doi:10.15585/mmwr.ss6710a1 (2018).
4 Fukuda, K. Food safety in a globalized world. Bulletin of the World Health Organization 93, 212-212, doi:10.2471/BLT.15.154831 (2015).
5 Guillén, M. D., Sopelana, P. & Palencia, G. Polycyclic Aromatic Hydrocarbons and Olive Pomace Oil. Journal of Agricultural and Food Chemistry 52, 2123-2132, doi:10.1021/jf035259q (2004).
6 Pei, X. et al. The China melamine milk scandal and its implications for food safety regulation. Food Policy 36, 412-420, doi:https://doi.org/10.1016/j.foodpol.2011.03.008 (2011).
7 Kupferschmidt, K. Dioxin scandal triggers food debate in Germany. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne 183, E221-E222, doi:10.1503/cmaj.109-3801 (2011).
8 Deng, W. et al. [Quick determination of fipronil and its metabolites in eggs by gas chromatography-triple quadrupole mass spectrometry]. Se Pu 36, 547-551, doi:10.3724/sp.j.1123.2017.09043 (2018).
9 van Larebeke, N. et al. The Belgian PCB and dioxin incident of January-June 1999: exposure data and potential impact on health. Environmental Health Perspectives 109, 265-273, doi:10.1289/ehp.01109265 (2001).
10 Hussain, M. A. & Dawson, C. O. Economic Impact of Food Safety Outbreaks on Food Businesses. Foods (Basel, Switzerland) 2, 585-589, doi:10.3390/foods2040585 (2013).
11 McBride, D. L. Safety Concerns About Food Additives and Children's Health. Journal of Pediatric Nursing 45, 76-77, doi:https://doi.org/10.1016/j.pedn.2018.09.008 (2019).
12 Gu, C., Lan, T., Shi, H. & Lu, Y. Portable Detection of Melamine in Milk Using a Personal Glucose Meter Based on an in Vitro Selected Structure-Switching Aptamer. Analytical Chemistry 87, 7676-7682, doi:10.1021/acs.analchem.5b01085 (2015).
13 Hu, Y. et al. Detection of melamine in milk using molecularly imprinted polymers–surface enhanced Raman spectroscopy. Food Chemistry 176, 123-129, doi:https://doi.org/10.1016/j.foodchem.2014.12.051 (2015).
14 Wu, C.-F. et al. Interaction of melamine and di-(2-ethylhexyl) phthalate exposure on markers of early renal damage in children: The 2011 Taiwan food scandal. Environmental Pollution 235, 453-461, doi:https://doi.org/10.1016/j.envpol.2017.12.107 (2018).
15 Tsai, H.-J. et al. Intake of phthalate-tainted foods and microalbuminuria in children: The 2011 Taiwan food scandal. Environment International 89-90, 129-137, doi:https://doi.org/10.1016/j.envint.2016.01.015 (2016).
16 Piskorska-Pliszczynska, J. et al. Pentachlorophenol from an old henhouse as a dioxin source in eggs and related human exposure. Environmental Pollution 208, 404-412, doi:https://doi.org/10.1016/j.envpol.2015.10.007 (2016).
17 Vries, M. D., Kwakkel, R. P. & Kijlstra, A. Dioxins in organic eggs: a review. NJAS - Wageningen Journal of Life Sciences 54, 207-221, doi:https://doi.org/10.1016/S1573-5214(06)80023-0 (2006).
18 G Stafford, E. et al. Consequences of fipronil exposure in egg-laying hens. Journal of the American Veterinary Medical Association 253, 57-60, doi:10.2460/javma.253.1.57 (2018).
19 European Food Safety, A., Reich, H. & Triacchini, G. A. Occurrence of residues of fipronil and other acaricides in chicken eggs and poultry muscle/fat. EFSA Journal 16, e05164, doi:10.2903/j.efsa.2018.5164 (2018).
20 Ling, S., Zhang, J., Hu, L. & Zhang, R. Effect of fipronil on the reproduction, feeding, and relative fitness of brown planthopper, Nilaparvata lugens. Applied Entomology and Zoology 44, 543-548, doi:10.1303/aez.2009.543 (2009).
21 Wang, N. et al. Determination of fipronil and its metabolites in eggs by UPLC-QqLIT-MS/MS with multistage mass spectrometry mode. Journal of Liquid Chromatography & Related Technologies 41, 544-551, doi:10.1080/10826076.2018.1485041 (2018).
22 Hingmire, S., Oulkar, D. P., Utture, S. C., Ahammed Shabeer, T. P. & Banerjee, K. Residue analysis of fipronil and difenoconazole in okra by liquid chromatography tandem mass spectrometry and their food safety evaluation. Food Chemistry 176, 145-151, doi:https://doi.org/10.1016/j.foodchem.2014.12.049 (2015).
23 Duhan, A., Kumari, B. & Duhan, S. Determination of Residues of Fipronil and Its Metabolites in Cauliflower by Using Gas Chromatography-Tandem Mass Spectrometry. 94, 260-266, doi:10.1007/s00128-014-1447-7 (2015).
24 Vasylieva, N., Ahn, K. C., Barnych, B., Gee, S. J. & Hammock, B. D. Development of an Immunoassay for the Detection of the Phenylpyrazole Insecticide Fipronil. Environmental Science & Technology 49, 10038-10047, doi:10.1021/acs.est.5b01005 (2015).
25 S. Ratra, G., Kamita, S. & Casida, J. Role of Human GABAA Receptor β3 Subunit in Insecticide Toxicity. Toxicology and applied pharmacology 172, 233-240, doi:10.1006/taap.2001.9154 (2001).
26 Gupta, R. C. et al. Chapter 26 - Insecticides. Biomarkers in Toxicology (Second Edition), 455-475, doi:https://doi.org/10.1016/B978-0-12-814655-2.00026-8 (2019).
27 Eiden, A. L. et al. Determination of metabolic resistance mechanisms in pyrethroid-resistant and fipronil-tolerant brown dog ticks. Medical and Veterinary Entomology 31, 243-251, doi:10.1111/mve.12240 (2017).
28 Carithers, D., Crawford, J., Everett, W. & Gross, S. Efficacy and speed of kill of a combination of fipronil/(S)-methoprene/pyriproxyfen against Ctenocephalides felis flea infestations on dogs from day 2 to day 30 post-treatment, compared with a combination of fipronil/(S)-methoprene. International Journal of Applied Research in Veterinary Medicine 15, 108-115, doi:10.1186/s13071-019-3512-x (2017).
29 Kaur, R., Mandal, K., Kumar, R. & Singh, B. Analytical Method for Determination of Fipronil and its Metabolites in Vegetables Using the QuEChERS Method and Gas Chromatography/Mass Spectrometry. 98, 464-471, doi:10.5740/jaoacint.13-066 (2015).
30 Peng, X.-T., Li, Y.-N., Xia, H., Peng, L.-J. & Feng, Y.-Q. Rapid and sensitive detection of fipronil and its metabolites in edible oils by solid-phase extraction based on humic acid bonded silica combined with gas chromatography with electron capture detection. Journal of Separation Science 39, 2196-2203, doi:10.1002/jssc.201501250 (2016).
31 Gupta, R. C. & Anadón, A. Chapter 42 - Fipronil. Veterinary Toxicology (Third Edition), 533-538, doi:https://doi.org/10.1016/B978-0-12-811410-0.00042-8 (2018).
32 Lee, S.-J. et al. Acute illnesses associated with exposure to fipronil-surveillance data from 11 states in the United States, 2001-2007. Clinical toxicology (Philadelphia, Pa.) 48, 737-744, doi:10.3109/15563650.2010.507548 (2010).
33 Kim, Y. A. et al. Distribution of fipronil in humans, and adverse health outcomes of in utero fipronil sulfone exposure in newborns. International Journal of Hygiene and Environmental Health 222, 524-532, doi:https://doi.org/10.1016/j.ijheh.2019.01.009 (2019).
34 Hodgson, E. Chapter 1 - Introduction to Pesticide Biotransformation and Disposition. Pesticide Biotransformation and Disposition, 1-3, doi:https://doi.org/10.1016/B978-0-12-385481-0.00001-0 (2012).
35 Meshram, B. D., Agrawal, A., Adil, S., Ranvir, S. & Sande, K. K. Biosensor and its Application in Food and Dairy Industry: A Review. 7, 3305-3324, doi:10.20546/ijcmas.2018.702.397 (2018).
36 AlKahtani, R. N. The implications and applications of nanotechnology in dentistry: A review. The Saudi Dental Journal 30, 107-116, doi:https://doi.org/10.1016/j.sdentj.2018.01.002 (2018).
37 Myszka, D. G. Improving biosensor analysis. Journal of Molecular Recognition 12, 279-284, doi:10.1002/(SICI)1099-1352(199909/10)12:5<279::AID-JMR473>3.0.CO;2-3 (1999).
38 Kumar, H. & Rani, N. Enzyme-based electrochemical biosensors for food safety: a review. 29, doi:10.2147/NDD.S64847 (2016).
39 Thévenot, D. R., Toth, K., Durst, R. A. & Wilson, G. S. Electrochemical biosensors: recommended definitions and classification1International Union of Pure and Applied Chemistry: Physical Chemistry Division, Commission I.7 (Biophysical Chemistry); Analytical Chemistry Division, Commission V.5 (Electroanalytical Chemistry).1. Biosensors and Bioelectronics 16, 121-131, doi:https://doi.org/10.1016/S0956-5663(01)00115-4 (2001).
40 Berggren, C., Bjarnason, B. & Johansson, G. Capacitive Biosensors. 13, 173-180, doi:10.1002/1521-4109(200103)13:3<173::AID-ELAN173>3.0.CO;2-B (2001).
41 Hoffmann, E. d. Mass Spectrometry. Kirk‐Othmer Encyclopedia of Chemical Technology, doi:10.1002/0471238961.1301191913151518.a01.pub2 (2005).
42 Beavis, R. C. & Chait, B. T. Rapid, sensitive analysis of protein mixtures by mass spectrometry. Proceedings of the National Academy of Sciences 87, 6873, doi: 10.1073/pnas.87.17.6873 (1990).
43 Domon, B. & Aebersold, R. Mass Spectrometry and Protein Analysis. Science 312, 212, doi:10.1126/science.1124619 (2006).
44 Van Gyseghem, E. et al. Determining orthogonal chromatographic systems prior to the development of methods to characterise impurities in drug substances. Journal of Chromatography A 988, 77-93, doi:https://doi.org/10.1016/S0021-9673(02)02012-5 (2003).
45 R. Deans, D. A New Technique in Heart Cutting in Gas Chromatography. 1, 18-22, doi:10.1007/BF02259005 (1968).
46 de Zeeuw, J., de Nijs, R. C. M. & Henrich, L. T. Adsorption Chromatography on PLOT (Porous-Layer Open-Tubular) Columns: A New Look at the Future of Capillary GC. Journal of Chromatographic Science 25, 71-83, doi:10.1093/chromsci/25.2.71 (1987).
47 Wesseler, E. P., Iltis, R. & Clark, L. C. The solubility of oxygen in highly fluorinated liquids. Journal of Fluorine Chemistry 9, 137-146, doi:https://doi.org/10.1016/S0022-1139(00)82152-1 (1977).
48 Atay, T., Song, J.-H. & Nurmikko, A. V. Strongly Interacting Plasmon Nanoparticle Pairs:  From Dipole−Dipole Interaction to Conductively Coupled Regime. Nano Letters 4, 1627-1631, doi:10.1021/nl049215n (2004).
49 Lyon, L. A., Musick, M. D. & Natan, M. J. Colloidal Au-Enhanced Surface Plasmon Resonance Immunosensing. Analytical Chemistry 70, 5177-5183, doi:10.1021/ac9809940 (1998).
50 Dodekatos, G., Schünemann, S. & Tüysüz, H. Surface Plasmon-Assisted Solar Energy Conversion. 371, doi:10.1007/128_2015_642 (2015).
51 Losic, D., Mitchell, J. G. & Voelcker, N. H. Fabrication of gold nanostructures by templating from porous diatom frustules. New Journal of Chemistry 30, 908-914, doi:10.1039/B600073H (2006).
52 Sherry, L. J. et al. Localized Surface Plasmon Resonance Spectroscopy of Single Silver Nanocubes. Nano Letters 5, 2034-2038, doi:10.1021/nl0515753 (2005).
53 Pradhan, N., Das Adhikari, S., Nag, A. & Sarma, D. D. Luminescence, Plasmonic, and Magnetic Properties of Doped Semiconductor Nanocrystals. Angewandte Chemie International Edition 56, 7038-7054, doi:10.1002/anie.201611526 (2017).
54 Sharma, B., Frontiera, R. R., Henry, A.-I., Ringe, E. & Van Duyne, R. P. SERS: Materials, applications, and the future. Materials Today 15, 16-25, doi:https://doi.org/10.1016/S1369-7021(12)70017-2 (2012).
55 Mitsushio, M., Miyashita, K. & Higo, M. Sensor properties and surface characterization of the metal-deposited SPR optical fiber sensors with Au, Ag, Cu, and Al. Sensors and Actuators A: Physical 125, 296-303, doi:https://doi.org/10.1016/j.sna.2005.08.019 (2006).
56 Roy, P. k., Huang, Y.-F. & Chattopadhyay, S. Detection of melamine on fractals of unmodified gold nanoparticles by surface-enhanced Raman scattering. 19, 11002, doi:10.1117/1.JBO.19.1.011002 (2014).
57 Sajanlal, P. R., Sreeprasad, T. S., Samal, A. K. & Pradeep, T. Anisotropic nanomaterials: structure, growth, assembly, and functions. Nano Rev 2, 10.3402/nano.v3402i3400.5883, doi:10.3402/nano.v2i0.5883 (2011).
58 Narayanan, R. & El-Sayed, M. A. Shape-Dependent Catalytic Activity of Platinum Nanoparticles in Colloidal Solution. Nano Letters 4, 1343-1348, doi:10.1021/nl0495256 (2004).
59 Cui, C. et al. Octahedral PtNi Nanoparticle Catalysts: Exceptional Oxygen Reduction Activity by Tuning the Alloy Particle Surface Composition. Nano Letters 12, 5885-5889, doi:10.1021/nl3032795 (2012).
60 Shankar, S. S. et al. Biological synthesis of triangular gold nanoprisms. Nature Materials 3, 482-488, doi:10.1038/nmat1152 (2004).
61 Nagarajan, S. & Arumugam Kuppusamy, K. Extracellular synthesis of zinc oxide nanoparticle using seaweeds of gulf of Mannar, India. Journal of Nanobiotechnology 11, 39, doi:10.1186/1477-3155-11-39 (2013).
62 Kato, K., Ono, A., Inami, W. & Kawata, Y. Plasmonic nanofocusing using a metal-coated axicon prism. Opt. Express 18, 13580-13585, doi:10.1364/OE.18.013580 (2010).
63 Yuan, Y. & Smalyukh, I. I. Topological nanocolloids with facile electric switching of plasmonic properties. Opt. Lett. 40, 5630-5633, doi:10.1364/OL.40.005630 (2015).
64 Wu, C. & Xu, Q.-H. Stable and Functionable Mesoporous Silica-Coated Gold Nanorods as Sensitive Localized Surface Plasmon Resonance (LSPR) Nanosensors. Langmuir 25, 9441-9446, doi:10.1021/la900646n (2009).
65 Billot, L. et al. Surface enhanced Raman scattering on gold nanowire arrays: Evidence of strong multipolar surface plasmon resonance enhancement. Chemical Physics Letters 422, 303-307, doi:https://doi.org/10.1016/j.cplett.2006.02.041 (2006).
66 Zhang, H., Zhou, J., Zou, W. & He, M. Surface plasmon amplification characteristics of an active three-layer nanoshell-based spaser. Journal of Applied Physics 112, 074309, doi:10.1063/1.4757416 (2012).
67 Rodríguez-Lorenzo, L., Romo-Herrera, J. M., Pérez-Juste, J., Alvarez-Puebla, R. A. & Liz-Marzán, L. M. Reshaping and LSPR tuning of Au nanostars in the presence of CTAB. Journal of Materials Chemistry 21, 11544-11549, doi:10.1039/C1JM10603A (2011).
68 Petryayeva, E. & Krull, U. J. Localized surface plasmon resonance: Nanostructures, bioassays and biosensing—A review. Analytica Chimica Acta 706, 8-24, doi:https://doi.org/10.1016/j.aca.2011.08.020 (2011).
69 Karlsson, R. & Stahlberg, R. Surface Plasmon Resonance Detection and Multispot Sensing for Direct Monitoring of Interactions Involving Low-Molecular-Weight Analytes and for Determination of Low Affinities. Analytical Biochemistry 228, 274-280, doi:https://doi.org/10.1006/abio.1995.1350 (1995).
70 Matsui, J. et al. SPR Sensor Chip for Detection of Small Molecules Using Molecularly Imprinted Polymer with Embedded Gold Nanoparticles. Analytical Chemistry 77, 4282-4285, doi:10.1021/ac050227i (2005).
71 Pollet, J. et al. Fiber optic SPR biosensing of DNA hybridization and DNA–protein interactions. Biosensors and Bioelectronics 25, 864-869, doi:https://doi.org/10.1016/j.bios.2009.08.045 (2009).
72 Dykman, L. & Khlebtsov, N. ChemInform Abstract: Gold Nanoparticles in Biomedical Applications: Recent Advances and Perspectives. 41, 2256-2282, doi:10.1039/c1cs15166e (2012).
73 Campion, A. & Kambhampati, P. Surface-enhanced Raman scattering. Chemical Society Reviews 27, 241-250, doi:10.1039/A827241Z (1998).
74 Bumbrah, G. S. & Sharma, R. M. Raman spectroscopy – Basic principle, instrumentation and selected applications for the characterization of drugs of abuse. Egyptian Journal of Forensic Sciences 6, 209-215, doi:https://doi.org/10.1016/j.ejfs.2015.06.001 (2016).
75 Singh, R. & Riess, F. The 1930 Nobel Prize for Physics: A close decision? Notes and Records of the Royal Society of London 55, 267-283, doi:10.1098/rsnr.2001.0143 (2001).
76 Ember, K. J. I. et al. Raman spectroscopy and regenerative medicine: a review. npj Regenerative Medicine 2, 12, doi:10.1038/s41536-017-0014-3 (2017).
77 Kim, Y.-i. et al. Endoscopic imaging using surface-enhanced Raman scattering. European Journal of Nanomedicine 9, 91, doi:10.1515/ejnm-2017-0005 (2017).
78 Szymanski, H. A. Spectroscopy and Molecular Structure (King, Gerald W.). Journal of Chemical Education 41, 692, doi:10.1021/ed041p692.1 (1964).
79 Thygesen, L. G., Løkke, M. M., Micklander, E. & Engelsen, S. B. Vibrational microspectroscopy of food. Raman vs. FT-IR. Trends in Food Science & Technology 14, 50-57, doi:https://doi.org/10.1016/S0924-2244(02)00243-1 (2003).
80 Fleischmann, M., Hendra, P. J. & McQuillan, A. J. Raman spectra of pyridine adsorbed at a silver electrode. Chemical Physics Letters 26, 163-166, doi:https://doi.org/10.1016/0009-2614(74)85388-1 (1974).
81 Haynes, C. L. Surface-Enhanced Raman Scattering:  Physics and Applications Edited by Katrin Kneipp (Harvard University Medical School, Boston), Martin Moskovits (University of California, Santa Barbara), and Harald Kneipp (Harvard University Medical School). Springer:  Berlin, Heidelberg, New York. 2006. xviii + 464 pp. $219.00. ISBN 3-540-33566-8. Journal of the American Chemical Society 129, 2197-2198, doi:10.1021/ja069825k (2007).
82 Ding, S.-Y., You, E.-M., Tian, Z.-Q. & Moskovits, M. Electromagnetic theories of surface-enhanced Raman spectroscopy. Chemical Society Reviews 46, 4042-4076, doi:10.1039/C7CS00238F (2017).
83 Nuntawong, N. et al. Surface-enhanced Raman scattering substrate of silver nanoparticles depositing on AAO template fabricated by magnetron sputtering. Vacuum 84, 1415-1418, doi:https://doi.org/10.1016/j.vacuum.2009.12.020 (2010).
84 Pitsillides, C. M., Joe, E. K., Wei, X., Anderson, R. R. & Lin, C. P. Selective Cell Targeting with Light-Absorbing Microparticles and Nanoparticles. Biophysical Journal 84, 4023-4032, doi:https://doi.org/10.1016/S0006-3495(03)75128-5 (2003).
85 Garcia-Vidal, F. & B. Pendry, J. Collective Theory of Surface Enhanced Raman Scattering. 77, 1163-1166, doi:10.1103/PhysRevLett.77.1163 (1996).
86 Taleb, A., Russier, V., Courty, A. & Pileni, M. P. Collective optical properties of silver nanoparticles organized in two-dimensional superlattices. Physical Review B 59, 13350-13358, doi:10.1103/PhysRevB.59.13350 (1999).
87 Haynes, C. L. et al. Nanoparticle Optics:  The Importance of Radiative Dipole Coupling in Two-Dimensional Nanoparticle Arrays. The Journal of Physical Chemistry B 107, 7337-7342, doi:10.1021/jp034234r (2003).
88 Liu, S. et al. Large-scale fabrication of polymer/Ag core–shell nanorod array as flexible SERS substrate by combining direct nanoimprint and electroless deposition. 115, doi:10.1007/s00339-013-7917-7 (2013).
89 Park, M. et al. Highly stretchable electric circuits from a composite material of silver nanoparticles and elastomeric fibres. Nature Nanotechnology 7, 803, doi:10.1038/nnano.2012.206
https://www.nature.com/articles/nnano.2012.206#supplementary-information
(2012).
90 Fan, Z. et al. A Three-Dimensional Carbon Nanotube/Graphene Sandwich and Its Application as Electrode in Supercapacitors. Advanced Materials 22, 3723-3728, doi:10.1002/adma.201001029 (2010).
91 Liao, W.-J., Roy, P. k. & Chattopadhyay, S. An ink-jet printed surface enhanced Raman scattering paper for food screening. 4, doi:10.1039/C4RA04821K (2014).
92 Christian, P. & Bromfield, M. Preparation of small silver, gold and copper nanoparticles which disperse in both polar and non-polar solvents. Journal of Materials Chemistry 20, 1135-1139, doi:10.1039/B920301J (2010).
93 Huh, Y. S., Chung, A. J. & Erickson, D. Surface enhanced Raman spectroscopy and its application to molecular and cellular analysis. Microfluidics and Nanofluidics 6, 285, doi:10.1007/s10404-008-0392-3 (2009).
94 Mortensen, G. K., Main, K. M., Andersson, A.-M., Leffers, H. & Skakkebæk, N. E. Determination of phthalate monoesters in human milk, consumer milk, and infant formula by tandem mass spectrometry (LC–MS–MS). Analytical and Bioanalytical Chemistry 382, 1084-1092, doi:10.1007/s00216-005-3218-0 (2005).
95 Yoshioka, N., Akiyama, Y. & Teranishi, K. Rapid simultaneous determination of o-phenylphenol, diphenyl, thiabendazole, imazalil and its major metabolite in citrus fruits by liquid chromatography-mass spectrometry using atmospheric pressure photoionization. Journal of Chromatography A 1022, 145-150, doi:https://doi.org/10.1016/j.chroma.2003.09.021 (2004).
96 Ekman, E. et al. Determination of 5-hydroxythiabendazole in human urine as a biomarker of exposure to thiabendazole using LC/MS/MS. Journal of Chromatography B 973, 61-67, doi:https://doi.org/10.1016/j.jchromb.2014.10.003 (2014).
97 Qin, L. et al. Designing, fabricating, and imaging Raman hot spots. 103, 13300-13303, doi:10.1073/pnas.0605889103 %J Proceedings of the National Academy of Sciences (2006).
98 Chattopadhyay, S., Lo, H.-C., Hsu, C.-H., Chen, L.-C. & Chen, K.-H. Surface-Enhanced Raman Spectroscopy Using Self-Assembled Silver Nanoparticles on Silicon Nanotips. Chemistry of Materials 17, 553-559, doi:10.1021/cm049269y (2005).
99 Huang, Y.-F., Chen, C.-Y., Chen, L.-C., Chen, K.-H. & Chattopadhyay, S. Plasmon management in index engineered 2.5D hybrid nanostructures for surface-enhanced Raman scattering. Npg Asia Materials 6, e123, doi:10.1038/am.2014.67
https://www.nature.com/articles/am201467#supplementary-information (2014).
100 Jiwei, Q. et al. Large-area high-performance SERS substrates with deep controllable sub-10-nm gap structure fabricated by depositing Au film on the cicada wing. 8, 437, doi:10.1186/1556-276x-8-437 (2013).
101 Tanahashi, I. & Harada, Y. Naturally inspired SERS substrates fabricated by photocatalytically depositing silver nanoparticles on cicada wings. Nanoscale Research Letters 9, 298, doi:10.1186/1556-276X-9-298 (2014).
102 Guo, L. et al. Cicada wing decorated by silver nanoparticles as low-cost and active/sensitive substrates for surface-enhanced Raman scattering. 115, 213101, doi:10.1063/1.4880956 (2014).
103 He, D., Hu, B., Yao, Q.-F., Wang, K. & Yu, S.-H. Large-Scale Synthesis of Flexible Free-Standing SERS Substrates with High Sensitivity: Electrospun PVA Nanofibers Embedded with Controlled Alignment of Silver Nanoparticles. ACS Nano 3, 3993-4002, doi:10.1021/nn900812f (2009).
104 Hankus, M. E., Stratis-Cullum, D. N. & Pellegrino, P. M. Surface enhanced Raman scattering (SERS)-based next generation commercially available substrate: physical characterization and biological application. SPIE NanoScience + Engineering 8099 (2011).
105 Matulaitienė, I. et al. Potential dependence of SERS spectra of reduced graphene oxide adsorbed on self-assembled monolayer at gold electrode. Chemical Physics Letters 590, 141-145, doi:https://doi.org/10.1016/j.cplett.2013.10.068 (2013).
106 Hu, C. et al. Fabrication of a graphene oxide–gold nanorod hybrid material by electrostatic self-assembly for surface-enhanced Raman scattering. Carbon 51, 255-264, doi:https://doi.org/10.1016/j.carbon.2012.08.051 (2013).
107 Ling, X. et al. Can Graphene be used as a Substrate for Raman Enhancement? Nano Letters 10, 553-561, doi:10.1021/nl903414x (2010).
108 Xu, W. et al. Graphene-Veiled Gold Substrate for Surface-Enhanced Raman Spectroscopy. 25, 928-933, doi:10.1002/adma.201204355 (2013).
109 Ling, X. & Zhang, J. First-Layer Effect in Graphene-Enhanced Raman Scattering. 6, 2020-2025, doi:10.1002/smll.201000918 (2010).
110 Caires, A. J., Alves, D. C. B., Fantini, C., Ferlauto, A. S. & Ladeira, L. O. One-pot in situ photochemical synthesis of graphene oxide/gold nanorod nanocomposites for surface-enhanced Raman spectroscopy. RSC Advances 5, 46552-46557, doi:10.1039/C4RA17207H (2015).
111 Huang, J. et al. Nanocomposites of size-controlled gold nanoparticles and graphene oxide: Formation and applications in SERS and catalysis. Nanoscale 2, 2733-2738, doi:10.1039/C0NR00473A (2010).
112 Ren, W., Fang, Y. & Wang, E. A Binary Functional Substrate for Enrichment and Ultrasensitive SERS Spectroscopic Detection of Folic Acid Using Graphene Oxide/Ag Nanoparticle Hybrids. ACS Nano 5, 6425-6433, doi:10.1021/nn201606r (2011).
113 Chen, S., Li, X., Zhao, Y., Chang, L. & Qi, J. Graphene oxide shell-isolated Ag nanoparticles for surface-enhanced Raman scattering. Carbon 81, 767-772, doi:https://doi.org/10.1016/j.carbon.2014.10.021 (2015).
114 Qian, Z., Cheng, Y., Zhou, X., Wu, J. & Xu, G. Fabrication of graphene oxide/Ag hybrids and their surface-enhanced Raman scattering characteristics. Journal of Colloid and Interface Science 397, 103-107, doi:https://doi.org/10.1016/j.jcis.2013.01.049 (2013).
115 Zhang, L., Jiang, C. & Zhang, Z. Graphene oxide embedded sandwich nanostructures for enhanced Raman readout and their applications in pesticide monitoring. Nanoscale 5, 3773-3779, doi:10.1039/C3NR00631J (2013).
116 Liu, Z. et al. Fe3O4@Graphene Oxide@Ag Particles for Surface Magnet Solid-Phase Extraction Surface-Enhanced Raman Scattering (SMSPE-SERS): From Sample Pretreatment to Detection All-in-One. ACS Applied Materials & Interfaces 8, 14160-14168, doi:10.1021/acsami.6b02944 (2016).
117 Cao, J., Sun, T. & Grattan, K. T. V. Gold nanorod-based localized surface plasmon resonance biosensors: A review. Sensors and Actuators B: Chemical 195, 332-351, doi:https://doi.org/10.1016/j.snb.2014.01.056 (2014).
118 Liu, X., Cao, L., Song, W., Ai, K. & Lu, L. Functionalizing Metal Nanostructured Film with Graphene Oxide for Ultrasensitive Detection of Aromatic Molecules by Surface-Enhanced Raman Spectroscopy. ACS Applied Materials & Interfaces 3, 2944-2952, doi:10.1021/am200737b (2011).
119 Bamrungsap, S. et al. Visual colorimetric sensing system based on the self-assembly of gold nanorods and graphene oxide for heparin detection using a polycationic polymer as a molecular probe. Analytical Methods 11, 1387-1392, doi:10.1039/C8AY02129E (2019).
120 Kao, F.-H. et al. In Vivo and in Vitro Demonstration of Gold Nanorod Aided Photothermal Presoftening of B16F10 Melanoma for Efficient Chemotherapy Using Doxorubicin Loaded Graphene Oxide. ACS Applied Bio Materials 2, 533-543, doi:10.1021/acsabm.8b00701 (2019).
121 Willets, K. A. & Van Duyne, R. P. Localized Surface Plasmon Resonance Spectroscopy and Sensing. Annual Review of Physical Chemistry 58, 267-297, doi:10.1146/annurev.physchem.58.032806.104607 (2007).
122 Nikoobakht, B. & El-Sayed, M. A. Preparation and Growth Mechanism of Gold Nanorods (NRs) Using Seed-Mediated Growth Method. Chemistry of Materials 15, 1957-1962, doi:10.1021/cm020732l (2003).
123 Liu, S.-Y., Tian, X.-D., Zhang, Y. & Li, J.-F. Quantitative Surface-Enhanced Raman Spectroscopy through the Interface-Assisted Self-Assembly of Three-Dimensional Silver Nanorod Substrates. Analytical Chemistry 90, 7275-7282, doi:10.1021/acs.analchem.8b00488 (2018).
124 Thermo scientific. Spike-and-recovery and linearity-of-dilution assessment, <https://www.thermofisher.com/tw/zt/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/overview-elisa/spike-recovery-linearity-assessment.html> (2007).
125 Thompson, J., Crossley, A., Nellist, P. D. & Nicolosi, V. Single-step exfoliation and chemical functionalisation of graphene and hBN nanosheets with nickel phthalocyanine. Journal of Materials Chemistry 22, 23246-23253, doi:10.1039/C2JM34854C (2012).
126 Ricciardella, F., Massera, E., Polichetti, T., Miglietta, M. L. & Di Francia, G. A calibrated graphene-based chemi-sensor for sub parts-per-million NO2 detection operating at room temperature. Applied Physics Letters 104, 183502, doi:10.1063/1.4875557 (2014).
127 Wang, S. et al. Three-photon luminescence of gold nanorods and its applications for high contrast tissue and deep in vivo brain imaging. Theranostics 5, 251-266, doi:10.7150/thno.10396 (2015).
128 Daniel, M.-C. & Astruc, D. Gold Nanoparticles: Assembly, Supramolecular Chemistry, Quantum-Size-Related Properties, and Applications Toward Biology, Catalysis, And Nanotechnology. 104, 293-346, doi:10.1021/cr030698+ (2004).
129 Gole, A. & Murphy, C. J. Seed-Mediated Synthesis of Gold Nanorods:  Role of the Size and Nature of the Seed. Chemistry of Materials 16, 3633-3640, doi:10.1021/cm0492336 (2004).
130 Li, J.-J., An, H.-Q., Zhu, J. & Zhao, J.-W. Improve the surface enhanced Raman scattering of gold nanorods decorated graphene oxide: The effect of CTAB on the electronic transition. Applied Surface Science 347, 856-860, doi:https://doi.org/10.1016/j.apsusc.2015.04.194 (2015).
131 Gurunathan, S., Han, J. W., Eppakayala, V. & Kim, J. H. Biocompatibility of microbially reduced graphene oxide in primary mouse embryonic fibroblast cells. Colloids Surf B Biointerfaces 105, 58-66, doi:10.1016/j.colsurfb.2012.12.036 (2013).
132 Huang, X., Neretina, S. & El-Sayed, M. A. Gold nanorods: from synthesis and properties to biological and biomedical applications. Adv Mater 21, 4880-4910, doi:10.1002/adma.200802789 (2009).
133 Wang, L.-P., Kuo, S.-C., Jeng, U. S. & Lai, Y.-H. Adsorption of p-nitrothiophenol on mesostructured polyoxometalate–silicate–surfactant composites containing Au nanoparticles: study of surface-enhanced Raman scattering activity. RSC Advances 5, 37323-37329, doi:10.1039/C5RA04754D (2015).
134 Zhu, Y. et al. Graphene and graphene oxide: synthesis, properties, and applications. Adv Mater 22, 3906-3924, doi:10.1002/adma.201001068 (2010).
135 Zhu, J., Gao, J., Li, J.-J. & Zhao, J.-W. Improve the surface-enhanced Raman scattering from rhodamine 6G adsorbed gold nanostars with vimineous branches. Applied Surface Science 322, 136-142, doi:https://doi.org/10.1016/j.apsusc.2014.10.095 (2014).
136 Zhang, Y., Ahn, J., Liu, J. & Qin, D. Syntheses, Plasmonic Properties, and Catalytic Applications of Ag–Rh Core-Frame Nanocubes and Rh Nanoboxes with Highly Porous Walls. Chemistry of Materials 30, 2151-2159, doi:10.1021/acs.chemmater.8b00602 (2018).
137 Metzger, M., Leibowitz, G., Wainstein, J., Glaser, B. & Raz, I. Reproducibility of Glucose Measurements Using the Glucose Sensor. Diabetes Care 25, 1185, doi:10.2337/diacare.25.7.1185 (2002).
138 Jarvis, R. M. et al. Towards quantitatively reproducible substrates for SERS. Analyst 133, 1449-1452, doi:10.1039/B800340H (2008).
139 Pandey, S. K. & Kim, K.-H. The Relative Performance of NDIR-based Sensors in the Near Real-time Analysis of CO₂ in Air. Sensors (Basel, Switzerland) 7, 1683-1696, doi:10.3390/s7091683 (2007).
140 Yang, Z., Suzuki, H., Sasaki, S., Karube, I. J. A. M. & Biotechnology. Disposable sensor for biochemical oxygen demand. 46, 10-14, doi:10.1007/s002530050776 (1996).
141 Tu, Q. et al. A simple and rapid method for detecting the pesticide fipronil on egg shells and in liquid eggs by Raman microscopy. Food Control 96, 16-21, doi:https://doi.org/10.1016/j.foodcont.2018.08.025 (2019).
142 Ly, N. H., Nguyen, T. H., Nghi, N. Đ., Kim, Y.-H. & Joo, S.-W. Surface-Enhanced Raman Scattering Detection of Fipronil Pesticide Adsorbed on Silver Nanoparticles. 19, 1355, doi:https://doi.org/10.3390/s19061355 (2019).
143 Ozaki, Y., Cho, R., Ikegaya, K., Muraishi, S. & Kawauchi, K. Potential of Near-Infrared Fourier Transform Raman Spectroscopy in Food Analysis. Appl. Spectrosc. 46, 1503-1507, doi:10.1366/000370292789619368 (1992).
144 Cluff, K. et al. Determination of yolk contamination in liquid egg white using Raman spectroscopy. 95, pew095, doi:10.3382/ps/pew095 (2016).
145 Nevin, A. et al. Raman Spectra of Proteinaceous Materials Used in Paintings:  A Multivariate Analytical Approach for Classification and Identification. Analytical Chemistry 79, 6143-6151, doi:10.1021/ac070373j (2007).
146 Vandenabeele, P. et al. Analysis with micro-Raman spectroscopy of natural organic binding media and varnishes used in art. Analytica Chimica Acta 407, 261-274, doi:https://doi.org/10.1016/S0003-2670(99)00827-2 (2000).
147 Carlesi, S., Becucci, M. & Ricci, M. Vibrational Spectroscopies and Chemometry for Nondestructive Identification and Differentiation of Painting Binders. 2017, 1-10, doi:10.1155/2017/3475659 (2017).