本文選題：傅里葉變換紅外光譜　切入點(diǎn)：葡萄果苗　出處：《云南師范大學(xué)》2015年碩士論文

【摘要】：葡萄是我國(guó)云南省主要的經(jīng)濟(jì)作物之一,以其香甜味美、營(yíng)養(yǎng)價(jià)值高而受大眾喜愛(ài)。葡萄種類繁多、種質(zhì)資源豐富,一般很難通過(guò)外觀對(duì)其區(qū)分。本文運(yùn)用傅里葉變換紅外光譜(FTIR)技術(shù)結(jié)合化學(xué)計(jì)量學(xué)統(tǒng)計(jì)分析方法對(duì)葡萄果苗進(jìn)行分類鑒別研究。葡萄果苗的紅外光譜主要由蛋白質(zhì)、多糖和脂類等吸收帶組成;各種葡萄果苗的紅外光譜差異不大;但在1800~750 cm-1波數(shù)范圍內(nèi)差異顯著,用1800~750cm-1范圍二階導(dǎo)數(shù)光譜進(jìn)行主成分分析和系統(tǒng)聚類分析;前3個(gè)主成分累計(jì)方差貢獻(xiàn)率達(dá)到94.9%,分類正確率達(dá)100%;系統(tǒng)聚類分析的正確率達(dá)到96%,能夠很好地鑒別五個(gè)不同品種的葡萄果苗。將傅里葉變換紅外光譜(FTIR)與Morlet小波主成分分析和偏最小二乘法判別分析(PLS-DA)相結(jié)合對(duì)紅地球和皇家秋天進(jìn)行了研究。原始光譜整體相似,僅在1800~750 cm-1范圍有微小差異。選取該波段第7尺度的Morlet小波系數(shù)和原始光譜進(jìn)行主成分分析;以及對(duì)該波段進(jìn)行20尺度的一維連續(xù)小波變換,并將變換結(jié)果用于偏最小二乘法判別(PLS-DA)。結(jié)果表明Morlet小波主成分分析和PLS-DA都能很好地鑒別兩個(gè)品種的葡萄果苗,其中Morlet小波主成分分析的正確率為100%;PLS-DA在隱含潛變量為9時(shí),紅地球和皇家秋天果苗的分類正確率均達(dá)到100%。研究結(jié)果表明,傅里葉變換紅外光譜結(jié)合化學(xué)計(jì)量學(xué)統(tǒng)計(jì)分析方法能夠準(zhǔn)確地分類鑒別不同品種的葡萄果苗,為葡萄果苗的分類鑒別研究提供了快速和準(zhǔn)確的方法。
[Abstract]:Grape is one of the main cash crops in Yunnan Province of China. It is loved by the public for its sweet taste and high nutritional value. There are many kinds of grapes and abundant germplasm resources. It is difficult to distinguish grape fruit by its appearance. In this paper, Fourier transform infrared spectroscopy (FTIR) and chemometrics were used to classify and identify grape fruit seedlings. The absorption bands of polysaccharides and lipids were not different from each other, but the difference was significant in the range of 1800,750 cm-1 wave number. Principal component analysis and systematic cluster analysis were carried out by using second-derivative spectra in 1800~750cm-1 range. The cumulative variance contribution rate of the first three principal components was 94.9%, the classification accuracy was 100%, and the correct rate of systematic cluster analysis was 96%, which could be used to identify the grape fruit seedlings of five different varieties. Fourier transform infrared spectroscopy (FTIR) and Morlet wavelet were used to identify grape fruit plantlets. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to study red earth and royal autumn. There is only a slight difference in the range of 1800 ~ 750 cm-1. The Morlet wavelet coefficients and the original spectrum of the seventh scale of the band are selected for principal component analysis, and the one-dimensional continuous wavelet transform of 20 scales is carried out on the band. The results show that Morlet wavelet principal component analysis and PLS-DA can identify the grape fruit plantlets of two varieties well, and the correct rate of Morlet wavelet principal component analysis is 100% and the latent variable is 9%. The classification accuracy of red earth and royal autumn fruit seedlings is 100. The results show that Fourier transform infrared spectroscopy combined with chemometrics statistical analysis method can accurately classify and identify grape fruit seedlings of different varieties. It provides a rapid and accurate method for the classification and identification of grape fruit seedlings.
【學(xué)位授予單位】：云南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：S663.1;TN219

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本文編號(hào)：1689141