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

【摘要】：葡萄是我國云南省主要的經濟作物之一,以其香甜味美、營養(yǎng)價值高而受大眾喜愛。葡萄種類繁多、種質資源豐富,一般很難通過外觀對其區(qū)分。本文運用傅里葉變換紅外光譜(FTIR)技術結合化學計量學統(tǒng)計分析方法對葡萄果苗進行分類鑒別研究。葡萄果苗的紅外光譜主要由蛋白質、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)范圍內差異顯著,用1800~750cm-1范圍二階導數(shù)光譜進行主成分分析和系統(tǒng)聚類分析;前3個主成分累計方差貢獻率達到94.9%,分類正確率達100%;系統(tǒng)聚類分析的正確率達到96%,能夠很好地鑒別五個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)相結合對紅地球和皇家秋天進行了研究。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)和原始光譜進行主成分分析;以及對該波段進行20尺度的一維連續(xù)小波變換,并將變換結果用于偏最小二乘法判別(PLS-DA)。結果表明Morlet小波主成分分析和PLS-DA都能很好地鑒別兩個品種的葡萄果苗,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,紅地球和皇家秋天果苗的分類正確率均達到100%。研究結果表明,傅里葉變換紅外光譜結合化學計量學統(tǒng)計分析方法能夠準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.
【學位授予單位】：云南師范大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S663.1;TN219

【參考文獻】

相關博士學位論文 前1條

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本文編號：1689141