臺灣博碩士論文加值系統

中文摘要
近紅外光分析儀運用於稻米成分分析工作時，其測定時間短，測定後樣本粉末可再重複利用於其它特性分析，但分析原理與方法有異於一般的分析工具，其需要校正線。物體的近紅外光光譜之構成因子，除了各組成份所引起的吸收外，還有干擾吸收（interference absorption），與基線飄移（baseline shift）等因子，導致預測效能降低，可利用各種數據處理方法降低干擾。600nm碘呈色度可被用來預測稻米的食味，但它是一利用近紅外光光譜預測表現不佳的性狀。蛋白質含量亦可被用來預測稻米的食味，而糙米粒蛋白質含量之預測效能不如糙米粉，但糙米粒因可減少研磨之時間進而降低成本。故本研究利用各種數據處理方法對稻米樣本之光譜值先進行處理，再利用淨最小平方法進行校正，以期能達到增加解析度及減少干擾的功能，進而得到較佳的建模效能及預測效能。
在600nm碘呈色度使用之稻米材料，包括秈稻155品種、稉稻120品種及糯稻61品種共計336品種；在蛋白質含量使用之稻米材料，共計932品種糙米粒和671品種糙米粉，將樣品進行NIR光譜掃瞄及化學測定後，對原光譜值再進行各種數據前處理，再利用淨最小平方法對此樣本作校正，求得校正統計指標值，再將驗證組樣本作驗證，求得驗證統計指標值。
在600nm碘呈色度所有的各種數據前處理中，以一次差分（segment=1，gap=0）加上正規化處理之預測效能表現最佳，選擇14個成分來建構最適模型，其預測效果最佳，模型的 、 、 、 與 等效能指標分別為0.897、0.0331、0.807、0.0420與1.718， 為26.895。一次差分（segment=1，gap=0）加上正規化處理後表現較原光譜12個成分所建構之最適模型表現佳，其預測效能提昇。其 值亦較原光譜小，表示逢機干擾降低。
在糙米粒進行各種數據前處理的預測效能表現中，以平滑化及未將反射光譜值作 轉換只作正規化處理表現較佳，此二種數據前處理方法是糙米粒蛋白質含量預測表現最佳的，推測可能是因其可降低雜波干擾。此外，一次差分較二次差分及三次差分表現佳，差分時segment及gap設定值大於1的表現亦較佳。但各種數據處理對糙米粒預測效能之改善皆不如糙米粉表現佳，或許是因這些數據處理方法，無法將粒徑差異所導致的光譜散射問題有效改善，所以將來可以嘗試將光譜作MSC處理後，再進行差分處理，以達有效改善糙米粒預測效能之目的。

ABSTRACT
Near-infrared reflectance spectroscopy (NIRS) play an important role on analysising components of rice. It spend less time in predicting the quality of rice samples and repeat using them. But it is necessary to develop the calibration equation. Near-infrared reflectance spectra consist of components reflectance spectra, interference absorption and baseline shift which decrease ability of prediction. Pretreatments could decrease noise to increase ability of prediction. 600 nm Starch-iodine blue value of residual liquid (BV) could be used to predict the palatability of rice. But the performance of model-building and prediction demonstrated that the calibration for BV by NIRS is less accurate. Protein content could also be used to predict the palatability of rice. But the performance of model-building and prediction demonstrated that the calibration for protein content in brown rice is less accurate than that in brown rice flour. However, brown rice samples could spend less time and money. The objective of this study was to improve the performance of model-building and prediction in developing the calibration equation using partial least squares regression (PLSR) by derivative, smooth and normalization pretreatments which could increase resolution and decrease noise, respectively.
A total of 336 rice samples, including 155 indica, 120 japonica, and 61waxy rices, were employed in analyzing the BV. A total of 932 brown rice samples and 671 brown rice flour samples, were employed in analyzing the protein content of rice. The absorbance of the BV was measured with a colorimeter at the wavelength of 600nm. A Bran + Luebbe InfraAlyzer 500 was used to collect spectrum measurement for each sample. The performance of model-building and prediction was evaluated using PLSR by derivative, smooth and normalization pretreatments.
The PLSR model of BV with 14- components by first derivative (segment =1, gap=0) + normalization pretreatment gave the highest correlation coefficient and the lowest standard error of prediction. The , , , , and of the model were 0.897, 0.0331, 0.807, 0.0420, 1.718 and 26.895, respectively. The performance of model-building and prediction was better than that in PLSR with 12 components of raw data. The IRV value of the pretreatment model was smaller than that of raw data model, suggesting that less random noise was involved in the pretreatment model.
The PLSR model of brown rice by smooth and only normalization pretreatments which decrease noise gave the highest correlation coefficient and the lowest standard error of prediction. The PLSR model by first derivative was better than that by second and third derivative, and segment and gap which over 1 of derivative was better. The performance of model-building and prediction in brown rice flour was better than that in brown rice with pretreatments. The derivative, smooth and normalization pretreatments couldn’t decrease the effect of multiplicative scatter led by particle size. So it is suggested that using multiplicative scatter correction (MSC) which delete multiplicative scatter and then derivative which increase resolution to get better performance of model-building and prediction in the future.

目 錄
頁次
表目錄………………..……………………………………………….. Ⅰ
圖目錄………………………………………………………………… Ⅲ
縮寫字………………………………………………………………… Ⅷ
中文摘要……………………………………………………………… ⅰ
英文摘要……………………………………………………………… ⅲ
壹、緒言……………………………………………………………….. 1
貳、前人研究………………………………………………………….. 4
一、影響稻米品質之幾種因素…………………………………... 4
二、近紅外光分析儀……………………………………………... 9
三、各種數據前處理……………....……………………………... 21
參、各種數據處理對600nm碘呈色度預測能力之改善…………….. 36
一、材料與方法………………………………………………... 36
二、結果………………………………………………………….. 40
三、討論………………………………………………………….. 83
肆、各種數據處理對糙米粒蛋白質含量預測能力之改善………….. 95
一、材料與方法…………………………………………………... 95
二、結果………………………………………………………….. 99
三、討論………………………………………………………….. 123
伍、綜合討論……..…………………………………………………… 131
陸、參考文獻………………………………………………………….. 134
附錄一………………………………………………………………… 140
附錄二………………………………………………………………… 145

陸、參考文獻
宋勳。1978。稻米品質劃分之可能性。台中區農業改良場研究彙報 新2:26-31。
宋勳。1986。稻米品質分級與改良。四十年來台灣地區稻作生產改進研討會專輯。p.109-125 黃正華先生農學獎學金基金會出版，台北，台灣。
宋勳、洪梅珠、許愛娜。1991。台灣稻米品質之研究。台中區農業改良場特刊第24號，台中，台灣。
宋勳、劉瑋婷。1996。稻米品質的影響因素與分級。稻作生產改進策略研討會專刊。台灣省農業試驗所特刊第59號 p.133-153。
河野澄夫著 陳一心譯。1995。日本應用非破壞性品質檢驗技術在稻米研究之現況。穀物非破壞性檢驗研討論文集第二冊 p.1-20 台大農機系，台北，台灣。
許愛娜。1994。稻米品質理化性質之研究。博士論文，國立中興大學農藝學研究所，台中，台灣。
陳一心。1995。近紅外光分析儀在省產稻米成分分析應用現況。穀物非破壞性檢驗研討論文集第二冊p.67-70 台大農機系，台北，台灣。
陳鈴霓。1994。台灣稻米支鏈澱粉之微細結構與理化性質之相關性。碩士論文，國立中興大學食品科學研究所，台中，台灣。
魚住 純著 葉詩鈴譯。1984。近紅外線分析與日本食品及飼料工業品質管制自動化。食品工業　16：13-26。
楊珮雅。1999。利用近紅外光分析儀檢測稻米碘呈色度與黏度特性。碩士論文，國立中興大學農藝學研究所，台中，台灣。
鄒虎生、洪瑞良。1990。如何製備近紅外線分析儀之檢量線。近紅外線分析儀在各種農業產品品質管制上之應用研討會。亞洲蔬菜研究發展中心。台南，台灣。
鄭心嫻、張為憲。1995。不同溶劑依序抽取米蛋白質區分條件之探討。中國農業化學會誌33:570-578。
蘇堃綺。2000。淨最小平方法的原理及其在近紅外光譜分析上的應用。碩士論文， 國立中興大學農藝學研究所，台中，台灣。
劉慧瑛、林禮輝、宋勳、洪梅珠。1988。不同稻米品種之食用品質與化學性質之關係。p.76-89。稻米品質。台灣省台中區農業改良場編印，台中，台灣。
Bank, W. and C. T. Greenwood. 1975. Starch and its components. PP.45. Einburgh University Press.
Biliaderis, C. G., D. R. Grant and J. R. Vose. 1981. Structural characterization of legume starches. Ⅰ. Amylopectin and beta-limit dextrins. Cereal Chem. 58:496-502.
Bran+Luebbe. 1996. Sesame Software Operation Manual. Bran+Luebbe GmbH, Norderstedt, Germany.
Bull, C. R. 1991. Compensation for particle size effects in near infrared reflectance. The Analyst 116:781-787.
Butler, W. L., and D. W. Hopkins. 1970. High derivatives analysis of complex absorption spectra, Photochem. Photobiol. 12:439-450.
Chen, H., B. P. Marks, and T. J. Siebenmorgen. 1997. Quantifying surface lipid content of milled rice via visible/near-infrared spectroscopy. Cereal Chem. 74:826-831.
Cura, J. A., P. Jansson and C. R. Krisman. 1995. Amylose is not strictly linear. Starch 47:207-209.
Delwiche, S. R., K. S. McKenzie, and B. D. Webb. 1996. Quality characteristics in rice by near-infrared reflectance analysis of whole-grain milled samples. Cereal Chem. 73:257-263.
Delwiche, S. R., M. M. Bean, and R. E. Miller. 1995. Apparent amylose content of milled rice by near-infrared reflectance spectrophotometry. Cereal Chem. 72:182-187.
Demetriades-Shah, T. H., M. D. Steven, and J. A. Clark. 1990. High resolution derivative spectra in remote sensing. Remote Sens Environ. 33:55-64.
Full, A. F., and G. Smith. 1982. High derivative methods in ultraviolet-visible and infrared spectrophotometry, Anal. Proc. 54:28-32.
Geladi, P., D. MacDougall, and H. Martens. 1985. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl. Spectrosc. 39:491-500.
Ilari, J. L., H. Martens, and T. Isaksson. 1988. Determination of particle size in powders by scatter correction in diffuse near-infrared reflectance. Appl. Spectrosc. 42:722-728.
Isaksson, T. and T. Nas. 1988. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Appl. Spectrosc. 42:1273-1284.
Hruschka, W. R. 1990. Data analysis: wavelength selection methods. In “Near-Infrared Technology in the Agricultural and Food Industries”, eds. by P. C. Williams and K. H. Norris, p.35-40. Am. Assoc. Cereal Chem., St. Paul, MN., U.S.A.
Juliano, B. O. 1972a. The rice caryopsis and its composition. In: Rice Chemistry and Technology. ed. by B. O. Juliano., p.16-74. Am. Assoc. Cereal Chem., St. Paul, MN., U.S.A.
Juliano, B. O. 1985b. Polysaccharides, proteins and lipids. In:Rice Chemistry and Technology. ed. by B. O. Juliano., p.59-174. Am. Assoc. Cereal Chem., St. Paul, MN., U.S.A.
Li, W. S. and J. T. Shaw. 1997. Determining the fat acidity of rough rice by near-infrared reflectance spectroscopy. Cereal Chem. 74:556-560.
Lii, C. Y., S. M. Chang and H. L. Yang. 1986. Correlation between the physicochemical properties and eating quality of milled rice in Taiwan. Bull. Inst. Chem. Academia Sinica. 33:55-62.
Lii, C. Y., T. W. Chiou and Y. L. Chu. 1987. The degree of branching in amylose from tuber and ligume starches. Proc. Natl. Sci. Counc. ROC 11:341.
Manners, D. J. 1985. Some aspects of the structure of starch. Cereal Foods World 30:461-467.
Matsue, Y., K. Odahara and M. Hiramatsu. 1994. Differences in protein content, amylose and palatability in relation to location of grains within rice panicle. Jpn. J. Crop Sci. 63:271-277.
Murray, I., C. Jessiman, and H. Keley. 1981. Use of near-infrared reflectance (NIR) spectrocomputer for forage evaluation, presented at the 12th Annual Meeting of the European Society of Nuclear Methods in Agriculture, Aberdeen, 28 September-3 October.
Nagato, K., M. Ebata and M. Ishikawa. 1972. Protein content of developing and mature rice grain. Jpn. J. Crop Sci. 41:472-479.
Norris, K. H. and P. C. Williams. 1984. Optimization of mathematical of raw near-infrared signal in the measurement of protein in Hard Red spring wheat. I. Influence of particle size. Cereal Chem. 61:158-165.
Norris, K. H., R. F. Barnes, J. E.Moore, and J. S. Shenk. 1976. Predicting forage quality by infrared reflectance spectroscopy. J. Animal Sci. 43:889-897.
Ogawa, M., T. Kumamaru., H. Satoh., T. Omura., T. Park., K. Shintaku and K. Baba. 1989. Mutants for rice storage protein. 2. Isolation and characterization of protein bodies from rice mutants. Theor. Appl. Genet. 78:305-310.
O’Havers, T. C. 1982. Derivative Spectroscopy and its applications in analysis: Derivative spectroscopy: Theoretical aspects. Plenary lecture, Anal. Proc. 54:22-28.
Osborne, B. G. and T. Fearn.1986. Near infrared spectroscopy in food analysis. Longman scientific and technical. P.20-115.
Padhye, V. W. and D. K. Salunkhe. 1979. Extraction and characterization of rice proteins. Cereal Chem. 56:389-393.
Reddy, K. R., S. Z. Ali and K. R. Bhattaccharya. 1993. The fine structure of rice starch amylopectin and its relation to the texture of cooked rice. Carbohydr. Polym. 22:267-276.
Takeda, Y. and S. Hizukuri. 1987. Structures of rice amylopectins with low and high affinities for iodine. Carbohydr. Res. 168:79-88.
Tashiro, T. and I. F. Watdlaw. 1991. The effect of high temperature on the accumulation of drying matter, carbon and nitrogen in the kernel of rice. Aust. J. Plant Physiol. 18:259-265.
Webb, B. D. 1985. Criteria of rice quality in the United States. In: Rice Chemistry and Technology. ed. by B. O. Juliano., p.403-427. Am. Asso. Cereal Chem. St. Paul, MN., U.S.A.
Wehling, R. L., D. S. Jackson, and B. R. Hamaker. 1996. Prediction of corn dry-milling quality by near-infrared spectroscopy. Cereal Chem. 73:543-546.
Wessman, C. A., J. D. Aber, D. L. Peterson, and J. M.Melillo. 1988. Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature 335:154-156.
Williams, P. C. 1975. Application of near-infrared reflectance spectroscopy to analysis of cereal grains and oilseeds. Cereal Chem. 52:561.
Williams, P. C., and B. N. Thompson. 1978. Influence of whole meal granularity on analysis of HRS wheat for protein and moisture by near-infrared reflectance spectroscopy (NIRS). Cereal Chem. 55:1014.
Williams, P. C., K. R. Preston, K. H. Norris, and P. M. Starkey. 1984.Determination of amino acids in wheat and barley by near-infrared spectroscopy. J. Food Sci. 49:17-20.