臺灣博碩士論文加值系統

摘要

本論文研究探討利用近紅外光反射光譜儀(Near Infrared Spectroscopy)分析魚粉中組織胺的可行性，其先搜集176件魚粉的光譜圖，利用苯甲醯氯衍生以高效能液相層析法分析求得其分析值，大部份樣本分佈在10.6至109.6之間，100 ppm以下計125件，100-500 ppm共計44件，高於500 ppm的有7件，實驗中利用WinISIⅡ軟體解析光譜值與化學分析的相關性，原始光譜經二次微分可解析出較多的資訊，且相關度提高至+ 0.5及-0.5以上。
本次實驗中利用修正部份最小平方迴歸(Modified Partial Least Square Regression, MPLSR)、主成分迴歸( Principal Component Regression, PCR)和類神經網路模式(Artificial Neural Network, ANN)模式，探討全波段400-1098、1100-2498及部份波段700-900、1200-1300、1600-1700、2000-2200，在不同的數學處理模式 (原始光譜、一次微分、二次微分)及不同因子，找出最佳預測模式，在之波段檢量模式中以部份波段MPLSR經二次迴歸、因子數5 ，rc=0.95、SEC=22.67 ppm，rp=0.85、SEP=36.21、RPD=1.88最佳。
非線性之波段檢量模式中，全波段中以ANN(PLSR)隱藏層Linear輸出層Sigmoid、二次微分之表現最佳rc=0.99、SEC=9.24 ppm，rp=0.98、SEP=12.69 ppm、RPD=5.51，在ANN部份波段以ANN隱藏層Linear輸出層Sigmoid表現最佳其因子數為3 ，rc=0.97、SEC=16.57 ppm，rp=0.88、SEP=33.72 ppm、RPD=3.06。
利用研究所得最佳預測模式，全波段ANN(PLSR)隱藏層Linear輸出層Sigmoid、光譜二次微分所得之方程式，進行驗証組光譜預測，由其預測值與化學分析值之相關性可以發現，組織胺含量100 ppm以下樣本群組共53件，其rc=0.56，SEC=20.48 ppm，另一樣本組為組織胺含量100-350 ppm共計16件，其預測值與化學分析值相關係數rc=0.95，SEC=13.87 ppm，由此可以得知高濃度的相關性比低濃度高。

Abstract

The purpose of this research is to describe the probability of using near infrared spectroscopy to analyze histamine spectrogram of the unit of the fish meal. 176 samples were collected and analyzed. The value through HPLC for the analysis of benzoylated amines is 10.6-109.6 ppm. There were 125 samles under 100 ppm, 44 samples between 100-500 ppm, and 7 samples beyond 500 ppm. WinISI Ⅱwas used to analyze the correlation between histamine contents and absorption spectrums of fish meal measured by near infrared spectroscopy. The 2nd derivative treatments of absorption spectrum have more information and the absolute value of correlation increased to more than 0.5.
The Modified Partial Least Square Regression (MPLSR), Principal Component Regression (PCR) and Artificial Neural Network (ANN) were used in discussion in the whole bands of 400-1098 and 1100-2498, and the partial bands of 700-900, 1200-1300, 1600-1700, and 2000-2200 under different mathematic models (original, first derivative and second derivative) and factors to find out the best prediction model. The MPLSR shows the best result in linear regression partial bands (factor = 5, rc =0.95, SEC=22.67 ppm, rp =0.85, SEP=36.21, RPD=1.88).
In non-linear model, ANN shows the best result in both bands of study. (In the whole bands: rc =0.99, SEC=9.24 ppm, rp =0.98, SEP=12.69 ppm, RPD=5.51; in the partial band: best factor is 3 , rc=0.97, SEC=16.57 ppm, rp =0.88, SEP=33.72 ppm, RPD=3.06).

The best prediction model, ANN shows the relationship between the values of prediction and chemical analysis. There are 53 samples whose contents of histamines are under 100 ppm ( rc =0.56, SEC=20.48 ppm ). There are 16 samples in another group, whose contents of histamine are between 100 and 350 ppm. The relative coefficient between the values of prediction and chemical analysis are rc =0.95, SEC=13.87 ppm. The results show the groups with higher concentration have higher relationship.

目 錄
誌謝……………………………………………………..……… Ⅰ
摘要……….………………………………………...…… Ⅱ
Abstract……………………………………………………… Ⅳ
第一章 前言………………….…………….…………………. 1
1.1 研究背景和動機…………………………………………... 1
1.2 研究目的…………………………………………...… 1
第二章 文獻探討………………………………………. 2
2.1組織胺來源………......…………………………… 2
2.2組織胺的生理功能…………………………………………… 2
2.3組織胺引起的毒……………………………………………… 3
2.3.1組織胺對人體的毒…………………………………………. 3
2.3.2 組織胺對動物的毒………………………………………… 3
2.4 組織胺的檢驗標準…………………………………..….… 4
2.5 組織胺的分析方法………………………………………….. 5
2.5.1呈色法……………………………………………………… 5
2.5.2 層析法……………………………………………………… 5
2.5.3 毛細管電泳………………………………………………… 6
2.5.4 免疫酵素連結法…………………………………………… 7
2.6近紅外光理論及吸收原理…………………………………… 7
2.7近紅外光分析相關應用……………………………………… 10
2.7.1石油化學工業……………………………………………… 10
2.7.2高分子工業……………………………….………………… 10
2.7.3製藥工業…………………………………………………… 11
2.7.4農產食品工業…………………………………….……… 11
第三章 材料與方法……………………………………………. 25
3.1 化學分析量測……………………………………...……… 25
3.1.1 實驗樣品…………………………………………………… 25
3.1.2 化學藥品…………………………………………………… 25
3.1.3 生物胺標準溶液之調製…………………………………… 26
3.1.4 實驗方法…………………………………………………… 26
3.1.5 HPLC分析條件……………………………………………… 26
3.2 近紅外光光譜檢測……………………………………..…… 27
3.2.1 實驗室型近紅外光譜分析儀……………………………… 27
3.2.2 光譜分析軟體……………………………………………… 28
3.2.3 實驗步驟…………………………………………………… 28
3.2.4魚粉光譜資料庫的建立……………………………………. 28
3.3 光譜處理及分析方法…………………………..………… 29
3.3.1光譜處理………………………………….……………… 29
3.3.2光譜微分處理…………………………………………… 30
3.3.3光譜雜訊分析……………………………………………… 31
3.3.4光譜平滑化處理…………………………………. 31
3.3.5 最佳波段之挑選…………...…………………… 32
3.3.6 交叉驗證模式………………………………………… 33
3.3.7樣本分組……………………………………………………. 33
3.3.8刪除無效樣本………………………………………………. 34
3.3.9 校正組與預測組之分群…………………………………… 34
3.4. 光譜分析模式……………………………………………… 35
3.4.1 MPLSR 模式分析…………………………………………… 37
3.4.2 MLR多重線性迴歸………………………………………… 38
3.4.3 ANN類神經網路模式分析……………………………………39
3.4.4 PCA主成份分析……………………………………… 41
3.5.光譜分析流程及檢量線判斷………………………………… 41
第四章結果與討論………………………….…………………. 57
4.1化學分析量測……………………………………………… 57
4.2 光譜分析…………………………………………….……… 57
4.3 線性之波段檢量模式-MPLSR分析結果……………………… 58
4.4線性之波段檢量模式-PCR分析結果……………………… 59
4.5非線性之波段檢量模式-ANN 分析結果…………….…………60
第五章結論…………………………………..………………… 92
參考文獻……………………………………….….…………… 93

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