臺灣博碩士論文加值系統

現今，越來越多食品標示不全之案例接二連三地爆發，其中包含標示錯誤產地及原料等，然而現在仍然沒有一套完整的方法來區分醬油的豆種及地理來源。市場上大多數醬油都以大豆和小麥等為主要原料，利用麴菌將原料分解，下缸後經酵母菌及乳酸菌發酵而成；有的則以黑豆為原料製造，被稱為黑豆醬油，又稱為蔭油。在台灣，黑豆釀造的醬油十分有名，如西螺醬油，它用傳統古法釀造，滋味與一般不同，有著濃而綿長的醬香，推測黑豆醬油具有特殊物質。醬油含有多種揮發性物質，不同的醬油有不一樣的揮發性成分，而揮發性成分會隨著原料的品種、原料的品質、使用不同菌種以及釀造方式而有差異，因此這些物質或許可用來作為種源鑑別的依據。而檢測揮發性物質常使用的儀器為氣相層析質譜儀 (gas chromatography-mass spectrometry, GC-MS)，可搭配頂空固相微萃取法 (head space-solid phase micro extraction, HS-SPME)，增強分析物之訊號。為了準確地了解醬油的揮發性化合物，我們開發了一種簡單、快速和有效的方法，使用以 HS-SPME 搭配 GC-MS，進行揮發物之定性、定量分析。在本研究中，共鑑定出 400 種揮發性化合物。在主成分分析 (Principal Components Analysis, PCA) 和正交偏最小二乘判别分析 (Orthogonal Projections to Latent Structures Discriminant Analysis, OPLS-DA) 的結果中顯示不僅可以區分摻混的醬油樣品，還可以區分醬油的產地來源。這些特徵揮發性化合物將有助於政府和工業界鑑定摻假的醬油和產國，期望於未來可提升國內消費者信心，增加傳統釀製廠商銷售，保障契作供應小農收益。

Nowadays, there are more and more cases of incomplete food labeling causes the real contents of foods are inaccurate. However, there is still not a complete set of methods to distinguish the geographical origins of soy sauce now. Most of the soy sauces on the market is based on soybeans, wheat and so on as the main raw materials, which are decomposed by Aspergillus oryzae, and then fermented by yeast and lactic acid bacteria. However, volatile compounds may be modified with the quality and type of raw materials, the use of different strains, and the different brewing ways1. Therefore, these volatile compounds may be used as a basis for the identification of provenance. Commonly, gas chromatography-mass spectrometry (GC-MS) is suitable to determine high-boiling-point compounds, such as volatile substances, combining with headspace solid-phase microextraction (HS-SPME) to enhance the signal strength of the analytes. To understand accurately the volatile compounds of soy sauce, we develop a simple, rapid and effective method by using HS-SPME coupled with GC-MS for qualitative and quantitative analysis of volatiles. In this study, a total of 400 volatile compounds were identified. In the results of Principal Components Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) identified the soy sauce adulterated with bean species and its origin. These soy sauce volatile signatures will help the government and industry for the identification of soy sauce adulterated and producing country source.

中文摘要 i
Abstract ii
目錄 iii
圖表目次 vii
第一章 緒論 1
第一節 前言 1
第二節 醬油簡介 2
2.1 醬油的種類 3
2.1.1 依使用之原料分類 3
2.1.2 依製程分類 3
2.2 傳統醬油 4
2.3 日式醬油 4
2.4 蔭油和醬油不同之處 5
第三節 固相微量萃取 5
3.1 固相微量萃取簡介 5
3.2 固相微量萃取之纖維塗層種類 6
3.3 頂空固相微萃取 (HS-SPME) 7
3.4 頂空固相微量萃取操作方式 7
3.5 影響頂空固相微萃取法效率的實驗因子 8
第四節 氣相層析質譜儀 12
第五節 即時直接分析法 (Direct Analysis in Real Time, DART) 13
第六節 主成分分析 (Principal Component Analysis, PCA) 14
6.1 PCA 簡介 14
6.2 PCA 基本原理 14
6.3 PCA 在質譜上的應用 15
第七節 前人研究 18
第八節 研究動機與目標 19
第九節 研究流程 20
第二章 材料與方法 21
第一節 醬油樣品 21
第二節 揮發性化合物分析 21
第三節 氣相層析管柱最佳化測試 22
第四節 頂空固相微萃取最佳化測試 22
4.1 稀釋測試 22
4.2 離子強度測試 23
4.3 不同纖維塗層之測試 23
4.4 吸附時間測試 23
4.5 萃取和吸附溫度之測試 23
4.6 熱脫附時間之測試 23
第五節 氣相層析溫度梯度最佳化測試 24
第六節 監控儀器狀態與操作手法之內標準品添加量測試 26
第七節 多變量統計分析 26
7.1 不同運算方式之比較 27
第八節 盲測分析 27
第九節 未知醬油樣品之豆源分析 27
第十節 即時直接分析法 (Direct Analysis in Real Time, DART) 28
10.1 實驗儀器與設備 28
10.2 醬油樣品清單 28
10.3 分析軟體 28
10.4 實驗方法 28
10.5 圖譜分析之方法 28
10.6 PCA 統計之方法 28
第三章 結果與討論 29
第一節 氣相層析管柱之優化選擇 29
第二節 頂空固相微萃取 (HS-SPME) 條件之優化結果 29
2.1 樣品稀釋稀釋之影響 29
2.2 離子強度 (Ion strength) 之優化 29
2.3 纖維塗層 (HS-SPME fiber coating) 之優化 30
2.4 吸附時間 (Adsorption time) 之優化 31
2.5 萃取及吸附溫度 (Extraction & adsorption temperature) 之優化 32
2.6 脫附時間 (Desorption time) 之優化 32
第三節 氣相層析溫度梯度最佳化測試 33
第四節 內標準品添加量測試 33
第五節 多變量統計分析 (Multivariate Statistical Analysis) 34
5.1 醬油豆源之區分 35
5.1.1 不同運算方式之比較 35
5.1.2 盲測分析 35
5.1.3 未知醬油樣品之豆源分析 36
5.1.4 黃黑豆醬油混樣測試 36
5.1.5 區分黃黑豆醬油豆源之化合物探討 37
5.2 黃豆醬油產地之區分 37
5.2.1 區分日本和台灣醬油之化合物探討 38
第六節 以 DART 搭配多變量統計分析之結果 39
6.1 醬油豆源之區分 39
6.2 日本和台灣黃豆醬油之產地區分 39
6.3 以 DART 結果驗證 GC-MS 結果 40
6.3.1 區分黃黑豆醬油豆源之 49 個化合物 40
6.3.2 區分台灣及日本黃豆醬油產地之 16 個化合物 40
第四章 結論 41
參考文獻 42
圖表目次 47

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