食品安全衛生近年來是大眾所關注的議題，而如何有效製作出快速檢測出有毒物質一直是業界與學界所想達成的目標，為此表面拉曼增益技術（Surface Enhance Raman Scattering, SERS）正是一個可以達到此目標的一項檢測技術。
    本研究利用SERS檢測二硫代氨基甲酸酯 (Dithiocarbamate, DTC) 當中的得恩地 (thiram) 、富爾邦 (ferbam)與福美鋅(ziram)，其方法是透過電化學中的循環伏安法製備SERS基板，並且直接將農藥滴上SERS基板直接做檢測，其中發現當使用1 M的硫酸作為電解質並且進行掃描六圈時會有最佳的SERS效果，同時在SEM的表面觀察中也可以發現奈米粒子成長的非常具有均勻性，並且使用該條件的SERS基板分別檢測到了5 ppm的得恩地、10 ppm的富爾邦與5 ppm的福美鋅。
     由於可見該二硫代氨基甲酸酯類農藥的拉曼光譜類似，本研究透過了人工智慧的輔助，將拉曼光譜結合人工智慧與機器學習的演算法進行分析，分別達到96.7%的預測準度與95%的預測準度，證明了SERS基板搭配人工智慧的可行性，開發了一套能夠快速、準確與便宜的農藥檢測系統。

In recent years, food safety have been a topic of public issues. How to effectively and quickly detect toxic substances that has always been the goal of the industry and academia. For the reason, Surface Enhance Raman Scattering (SERS) is detection technology that can achieve this goal.
The study used SERS to detect dithiocarbamate (DTC) of thiram, ferbam and ziram. The SERS substrate is prepared by cyclic voltammetry of electro-chemistry. Afterwards, the pesticide directly drop on the SERS substrate. When 1 M sulfuric acid and CV6 have best SERS effect. At the same time, The SEM image can find that the nanoparticle growth is very uniform. And the SERS substrate using this condition detected 5 ppm of Thiram, 10 ppm of Fer-bam, and 5 ppm of Ziram.
Since it can be seen that the Raman spectra of the dithiocarbamate pesticides are similar, so the study uses SERS combined with artificial intelligence and machine learning. In the result, artificial intelligence and machine learning achieves prediction accuracy of 96.7% and 95%, respectively. The result 
proved that the SERS substrate with artificial intelligence, have fast, accurate and cheap pesticide detection system.

目錄
致謝	I
摘要	II
目錄	V
圖目錄	VII
表目錄	IX
 第一章  緒論	1
1.1 前言	1
1.2 研究動機與目的	4
 第二章  理論基礎	5
2.1 SERS基板與技術發展	5
2.2 現今農藥的檢測方式	9
2.3 SERS應用於農藥發展	13
 第三章  實驗方法與步驟	17
3.1 實驗材料	17
3.2 實驗裝置與原理	20
3.2.1 電化學分析儀	20
3.2.2 拉曼光譜儀	22
3.2.3 掃描式電子顯微鏡與能量色散X射線譜	23
3.2.4 可見光微型光譜儀	25
3.3 實驗步驟	26
 第四章  實驗結果與討論	31
4.1 金與銅複合材料奈米結構SERS基板之電化學討論	31
4.2 金與銅複合材料奈米結構SERS基板之表面形貌分析	36
4.3 金與銅複合材料奈米結構SERS基板之吸收光譜	40
4.3 金與銅複合材料奈米結構SERS基板之R6G結果	42
4.3.1 實驗條件與SERS效益分析與討論	42
4.3.2 SERS基板增益效果計算與分析	44
4.4 以SERS基板檢測微量農藥之探討分析	46
4.4.1 以SERS基板檢測微量得恩地結果與探討	46
4.4.2 以SERS基板檢測微量富爾邦結果與探討	49 
4.4.3 以SERS基板檢測微量福美鋅結果與探討	51
4.5 利用機器學習結合SERS系統分析農藥與討論	53
4.5.1 以主成分分析法(PCA)進行數據分析	53
4.5.2 以隨機森林 (Random Forest) 預測模型之分析	55
4.5.3 以卷積神經網絡（CNN）預測模型之分析	57
 第五章  結論	61
 第六章  參考文獻	63
圖目錄
圖 2 1得恩地(thiram)、富爾邦 (ferbam) 與福美鋅 (ziram)結構圖	10
圖 2 2二硫代胺基甲酸鹽類斷裂與貴金屬鍵結示意圖 	11
圖 2 3金/銅奈米粒子的生成示意圖	14
圖 3 1電化學CHI 6113E分析儀	21
圖 3 2拉曼光譜儀設備	22
圖 3 3掃描式電子顯微鏡設備	24
圖 3 4可見光微型光譜儀設備	25
圖 3 5網版印刷電極 (a) 剖面圖 (b)結構圖	27
圖 3 6 R6G的檢測實驗步驟圖	28
圖 3 7得恩地、富爾邦與福美鋅的實驗流程步驟	30
圖 4 1 SERS基板之完整的循環伏安圖	35
圖 4 2 SERS基板之第一圈與第二圈的循環伏安圖	35
圖 4 3一萬倍下的SERS基板之SEM圖	36
圖 4 4六萬倍下的SERS基板之SEM圖	37
圖 4 5 SERS基板之元素分析的位置	38
圖 4 7一號位置之能量色散X射線譜	39
圖 4 8 二號位置之能量色散X射線譜	39
圖 4 8金奈米粒子與銅奈米粒子形成之SERS結構示意圖	40
圖 4 9 SERS基板之反射光譜圖	41
圖 4 10 SERS基板之吸收光譜圖	41
圖 4 11 10-6 M的R6G訊號在不同圈數的SERS基板	43
圖 4 12 10-6 M的R6G訊號在不同電解質中濃度的SERS基板	44
圖 4 13 增益後與增益前10-6 M的R6G拉曼光譜圖	45
圖 4 14 二硫代胺基甲酸鹽類與SERS基板接枝之示意圖	47
圖 4 15得恩地 (Thiram) 各濃度在SERS基板上之光譜	48
圖 4 16得恩地 (Thiram) 對拉曼訊號之關係圖	48
圖 4 17富爾邦 (Ferbam) 各濃度在SERS基板上之光譜	50
圖 4 18 富爾邦 (Ferbam) 對拉曼訊號之關係圖	50
圖 4 19福美鋅 (Ziram) 各濃度在SERS基板上之光譜	52
圖 4 20 福美鋅 (Ziram) 對拉曼訊號之關係圖	52
圖 4 21各農藥之主成分分析圖	55
圖 4 22以隨機森林預測之分析	56
 
表目錄
表 2 1利用SERS檢測之文獻整理一覽	7
表 2 1利用SERS檢測之文獻整理一覽(續)	8
表 2 2利用其他方法檢測之二硫代氨基甲酸酯類文獻整理一覽	12
表 2 3利用SERS進行農藥檢測之文獻一覽表	15
表 2 3利用SERS進行農藥檢測之文獻一覽表(續)	16
表 3 1循環伏安法固定設置參數	28
表 3 2循環伏安法變因參數	29
表 4 1以9成數據訓練累積學習之分析	59
表 4 2以5成數據訓練累積學習之分析	59
表 4-3以9成數據訓練模型預測之分析	59
表 4-4以5成數據訓練模型預測之分析	59

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