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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳世銘(Suming Chen) | |
dc.contributor.author | Yung-Kun Chuang | en |
dc.contributor.author | 莊永坤 | zh_TW |
dc.date.accessioned | 2021-05-16T16:22:10Z | - |
dc.date.available | 2018-08-01 | |
dc.date.available | 2021-05-16T16:22:10Z | - |
dc.date.copyright | 2013-08-23 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-07-24 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6161 | - |
dc.description.abstract | 本論文使用獨立成分分析法為唯一之核心演算法,應用於三種生物材料之近紅外光定量分析,包含蓮霧(Syzygium samarangense Merrill & Perry)、藥用植物龍膽(Gentiana scabra Bunge)及白米之研究,亦對不同型態樣本(蔗糖水溶液、蓮霧完整果、龍膽乾燥粉末及白米粒)進行分析探討。第一部分研究結合獨立成分分析法與近紅外光光譜於蓮霧糖度之快速定量分析,結合JADE演算法、線性迴歸及光譜前處理方法,分別對蓮霧與蔗糖溶液之近紅外光光譜進行分析。相較於其他多變量分析方法,獨立成分分析法可提供更完整之蓮霧糖度資訊,其最佳光譜檢量模式使用一次微分光譜搭配正規化處理,光譜範圍為600∼700 nm與900∼1098 nm,Rc = 0.988,SEC = 0.243°Brix,SEV = 0.381°Brix,顯示獨立成分分析法可快速準確地擷取蓮霧光譜中之糖度資訊,並建立具高預測能力之光譜檢量模式,更有效地定量蓮霧糖度。第二部分研究應用獨立成分分析法於龍膽指標成分龍膽苦苷與當藥苦苷之近紅外光分析,對94個組織培養瓶苗與68個植株樣本(包含68個地上部與68個地下部)進行探討。選擇與兩種指標成分高度相關之獨立成分後,組織培養瓶苗、植株地上部及植株地下部清楚分佈於獨立成分空間之三個位置,可觀察龍膽苦苷與當藥苦苷含量之變化趨勢。龍膽苦苷之最佳光譜檢量模式使用二次微分光譜,光譜範圍為600∼700 nm、1600∼1700 nm及2000∼2300 nm,其Rc = 0.847,SEC = 0.865%,SEV = 0.909%;當藥苦苷之最佳光譜檢量模式使用一次微分光譜,光譜範圍為600∼800 nm與2200∼2300 nm,其Rc = 0.948,SEC = 0.168%,SEV = 0.216%,皆具有良好之預測能力。本研究成功建立龍膽苦苷與當藥苦苷之定性與定量關係,可針對不同生長時期之龍膽進行兩種指標成分含量之檢測,作為快速且準確之龍膽品質評估工具。第三部分研究應用獨立成分分析法於稻米新鮮度之快速定性與定量分析,新鮮度為決定稻米品質之重要指標,稻米貯藏時間會影響其外觀、食味及營養價值。本研究對六個收穫時期(95年第一期、96年第一期、97年第一期、98年第一期、99年第一期及99年第二期)之白米進行探討,結果顯示不同新鮮度之白米清楚分佈於三維之獨立成分空間中,對酸鹼度pH值所建立之光譜檢量模式亦具有高預測能力,其Rc = 0.939,SEC = 0.202,SEP = 0.233,表示結合獨立成分分析法與近紅外光光譜可有效評估稻米之新鮮度,且pH值與脂肪酸度較年份期別為更合適之評量指標。結合獨立成分分析法與近紅外光光譜可快速且正確地評估生物材料之內部成分,獨立成分分析法提供近紅外光光譜於生物材料內部成分定量分析一項快速可靠之工具,應用於評估生物材料內部品質具有重大貢獻。 | zh_TW |
dc.description.abstract | In this study, independent component analysis (ICA) was first adopted as the sole tool in conducting NIR quantitative analyses of biomaterials, including wax jambu fruit (Syzygium samarangense Merrill & Perry), medicinal plant Gentiana scabra Bunge, and milled white rice, to evaluate the applicability of this method. The influence due to various types of samples (sucrose solution, intact fruit, dry powder of Gentiana scabra Bunge, and rice kernel) was also studied. In the first part, ICA was integrated with near infrared (NIR) spectroscopy for rapid quantification of sugar content in wax jambu. The JADE algorithm (Joint Approximate Diagonalization of Eigenmatrices) and linear regression with spectral pretreatments were incorporated to analyze the NIR spectra of wax jambu as well as sucrose solutions. Unlike other multivariate approaches, ICA enabled comprehensive quantification of sugar content in wax jambu. In the present study, ICA was used as the sole tool to build the NIR calibration model of internal quality of intact wax jambu without any other assisted multivariate analysis methods. The best spectral calibration model of wax jambu (600 to 700 nm and 900 to 1098 nm) yielded Rc = 0.988, SEC = 0.243 °Brix, and SEV = 0.381 °Brix using the normalized first derivative spectra. Thus, ICA can quickly identify and effectively quantify the sugar contents in wax jambu with calibration models achieving high predictability. In the second part, ICA was applied to NIR spectroscopy on the analysis of gentiopicroside and swertiamarin, the two bioactive components of Gentiana scabra Bunge. Independent components (ICs) that are highly correlated to the two bioactive components were selected for the analysis of tissue cultures, shoots and roots, which were found to distribute in three different positions within the domain (2D and 3D) constructed by the ICs. This setup could be used for quantitative determination of respective contents of gentiopicroside and swertiamarin within the plants. For gentiopicroside, the spectral calibration model based on the 2nd derivative spectra produced the best effect in the wavelength ranges of 600 to 700 nm, 1600 to 1700 nm, and 2000 to 2300 nm (Rc = 0.847, SEC = 0.865 %, and SEV = 0.909 %). For swertiamarin, spectral calibration model based on the 1st derivative spectra gave the best effect in the wavelength ranges of 600 to 800 nm and 2200 to 2300 nm (Rc = 0.948, SEC = 0.168 %, and SEV = 0.216 %). Both models showed a satisfactory predictability. This study successfully established qualitative and quantitative correlations for gentiopicroside and swertiamarin with NIR spectra, enabling rapid and accurate inspection on the bioactive components of Gentiana scabra Bunge at different growth stages. Furthermore, determination of freshness is an important issue for rice quality. The storage time of rice has an enormous effect on its appearance, flavor, and quality of the nutrients. A total of 180 white rice samples were collected from 6 crop seasons (2nd crop of 2010, 1st crop of 2010, 1st crop of 2009, 1st crop of 2008, 1st crop of 2007 and 1st crop of 2006) for the purpose of developing an ICA NIR based procedure for rice freshness as quantified by pH. Freshness of white rice could be distinguished either visually by a 3-dimensional diagram composed from ICs 2, 3 and 4, or statistically by a calibration model (Rc = 0.939, SEC = 0.202, and SEP = 0.233). The results showed that ICA with NIR has the potential to be a useful tool for evaluating rice freshness. Compared to harvest time, pH value and fat acidity were more appropriate to serve as indicators of rice freshness. By combining ICA with NIR spectroscopy, fast and accurate evaluation of constituents in biomaterials could be achieved. ICA offers a rapid and reliable tool for quantitative analyses of constituents in biomaterials by NIR spectroscopy. The obtained results contribute substantially to identify multiple constituents of biomaterials and evaluate their concentrations. | en |
dc.description.provenance | Made available in DSpace on 2021-05-16T16:22:10Z (GMT). No. of bitstreams: 1 ntu-102-F94631005-1.pdf: 2139964 bytes, checksum: e41716b54cab1f64cf2e6cf6b36735b9 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 誌 謝 i
中文摘要 ii ABSTRACT iv CONTENTS vii LIST OF FIGURES xii LIST OF TABLES xv CHAPTER 1. GENERAL INTRODUCTION 1 1.1 INTRODUCTION 1 1.1.1 WAX JAMBU 4 1.1.2 GENTIANA SCABRA BUNGE 5 1.1.3 RICE 7 1.2 GENERAL OBJECTIVE 8 1.3 DISSERTATION ORGANIZATION 9 CHAPTER 2. INTEGRATION OF INDEPENDENT COMPONENT ANALYSIS WITH NEAR INFRARED SPECTROSCOPY FOR RAPID QUANTIFICATION OF SUGAR CONTENT IN WAX JAMBU 10 2.1 INTRODUCTION 10 2.2 MATERIALS AND METHODS 14 2.2.1 SAMPLE PREPARATION 14 2.2.2 NIR SPECTRA AND SUGAR CONTENT MEASUREMENT 14 2.2.3 DATA ANALYSIS 16 2.3 RESULTS AND DISCUSSION 22 2.3.1 SUCROSE SOLUTION 22 2.3.2 WAX JAMBU 35 2.4 CONCLUSIONS 46 CHAPTER 3. QUANTIFICATION OF BIOACTIVE GENTIOPICROSIDE IN A MEDICINAL PLANT GENTIANA SCABRA BUNGE BY NEAR INFRARED SPECTROSCOPY 48 3.1 INTRODUCTION 48 3.2 MATERIALS AND METHODS 50 3.2.1 G. SCABRA BUNGE SAMPLE PREPARATION 50 3.2.2 NIR SPECTRA AND HPLC MEASUREMENT 50 3.2.3 DATA ANALYSIS 51 3.3 RESULTS AND DISCUSSION 56 3.3.1 GENTIOPICROSIDE CONCENTRATION AND DISTRIBUTION IN G. SCABRA BUNGE 56 3.3.2 CORRELATION BETWEEN NIR SPECTRA AND GENTIOPICROSIDE CONTENT 57 3.3.3 GENTIOPICROSIDE QUANTIFICATION USING SPECIFIC WAVELENGTH RANGES 61 3.3.4 GENTIOPICROSIDE QUANTIFICATION USING CCD CAMERA WAVELENGTH SPECTRA 67 3.4 CONCLUSIONS 75 CHAPTER 4. INTEGRATION OF INDEPENDENT COMPONENT ANALYSIS WITH NEAR INFRARED SPECTROSCOPY FOR ANALYSIS OF BIOACTIVE COMPONENTS IN A MEDICINAL PLANT GENTIANA SCABRA BUNGE 76 4.1 INTRODUCTION 76 4.2 MATERIALS AND METHODS 78 4.2.1 GENTIANA SCABRA BUNGE SAMPLE PREPARATION 78 4.2.2 NIR SPECTRA AND HPLC MEASUREMENT 79 4.2.3 DATA ANALYSIS 80 4.3 RESULTS AND DISCUSSION 85 4.3.1 DISTRIBUTIONS OF THE TARGET CONSTITUENTS IN GENTIANA SCABRA BUNGE 85 4.3.2 CORRELATION BETWEEN NIR SPECTRA AND TARGET CONSTITUENTS’ CONTENTS 86 4.3.3 NIR SPECTRA DECOMPOSITION AND ICA ANALYSIS OF THE TARGET CONSTITUENTS 91 4.4 CONCLUSIONS 99 CHAPTER 5. INTEGRATION OF INDEPENDENT COMPONENT ANALYSIS WITH NEAR INFRARED SPECTROSCOPY FOR EVALUATION OF RICE FRESHNESS 101 5.1 INTRODUCTION 101 5.2 MATERIALS AND METHODS 103 5.2.1 SAMPLE PREPARATION 103 5.2.2 NIR SPECTRA AND PH VALUE MEASUREMENT 104 5.2.3 DATA ANALYSIS 106 5.3 RESULTS AND DISCUSSION 108 5.3.1 RELATIONSHIP BETWEEN FAT ACIDITY AND PH VALUE 108 5.3.2 DISTRIBUTIONS OF THE PH VALUE IN RICE 110 5.3.3 NIR SPECTRA DECOMPOSITION AND ICA ANALYSIS OF THE PH VALUE 112 5.4 CONCLUSIONS 117 CHAPTER 6. GENERAL CONCLUSIONS 119 6.1 GENERAL DISCUSSION 119 6.2 RECOMMENDATIONS FOR FUTURE RESEARCH 122 REFERENCES 123 | |
dc.language.iso | en | |
dc.title | 應用獨立成分分析法於生物材料之近紅外光分析 | zh_TW |
dc.title | Near Infrared Analysis of Biomaterials Using Independent Component Analysis | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 盧福明(Fu-Ming Lu),盛中德(Chung-Teh Sheng),謝廣文(Kuang-Wen Hsieh),邱奕志(Yi-Chich Chiu) | |
dc.subject.keyword | 近紅外光光譜,獨立成分分析法,蓮霧,糖度,龍膽,龍膽苦苷,當藥苦苷,稻米新鮮度, | zh_TW |
dc.subject.keyword | Near infrared spectroscopy,Independent component analysis,Wax jambu,Sugar content,Gentiana scabra Bunge,Gentiopicroside,Swertiamarin,Rice freshness, | en |
dc.relation.page | 136 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2013-07-24 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
顯示於系所單位: | 生物機電工程學系 |
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