【摘要】：水果組織光學(xué)特性參數(shù)是反映水果組織自身化學(xué)成分、物理結(jié)構(gòu)和生理、病理狀態(tài)的重要參數(shù)。水果組織光學(xué)特性參數(shù)的測(cè)量,對(duì)于研究組織內(nèi)部結(jié)構(gòu)成像規(guī)律和光子的傳輸特點(diǎn),分析組織化學(xué)特性和物理結(jié)構(gòu),建立組織內(nèi)部品質(zhì)(狀態(tài))檢測(cè)和評(píng)價(jià)模型等具有重要意義。本文基于MC仿真模擬了光在單層水果組織中的傳輸現(xiàn)象,并實(shí)現(xiàn)了光學(xué)特性參數(shù)的逆向求解;其次利用高光譜散射成像系統(tǒng)采集了組織模擬液的高光譜散射圖像,結(jié)合建立的非線性反演回歸模型,實(shí)現(xiàn)了組織模擬液光學(xué)特性參數(shù)的反演求解;并在此研究基礎(chǔ)上,研究了蘋果組織對(duì)光譜的吸收和散射情況,建立了光譜特征與蘋果硬度和可溶性固體含量之間的預(yù)測(cè)模型。論文的主要工作如下:1.針對(duì)漫射模型與MC模擬在近光源處存在較大誤差,提出了一種基于迭代反演的輸運(yùn)平均自由程估計(jì)及光源-檢測(cè)器最小距離確定方法。該方法利用迭代估計(jì)思想,自適應(yīng)地評(píng)估出輸運(yùn)平均自由程的值,并改變光源-檢測(cè)器最小距離,從而獲得較為合理的用于光學(xué)特性參數(shù)反演的數(shù)據(jù)區(qū)間。結(jié)果表明:與傳統(tǒng)經(jīng)驗(yàn)估計(jì)方法相比,迭代反演方法能夠減少近光源處誤差的引入,有效地提高水果組織光學(xué)特性參數(shù)的反演準(zhǔn)確度。在無(wú)噪聲的條件下,吸收系數(shù)μ_a反演的平均相對(duì)誤差為7.17%;有效散射系數(shù)μ_s反演的平均相對(duì)誤差為5.73%。在加入一定信噪比噪聲的情況下,迭代反演方法仍然能獲得較高的光學(xué)特性參數(shù)反演準(zhǔn)確度。2.由于光學(xué)近似模型存在各種限制,研究中采用機(jī)器學(xué)習(xí)方法建立光學(xué)特性參數(shù)μ_a和μ_s的預(yù)測(cè)模型。利用基于穩(wěn)態(tài)空間分辨技術(shù)的高光譜散射成像系統(tǒng)獲取組織模擬液530-900nm波段范圍內(nèi)的散射圖像,結(jié)合傅里葉分解和最小二乘支持向量機(jī)算法建立光學(xué)特性參數(shù)的非線性反演回歸模型。結(jié)果表明:基于實(shí)驗(yàn)數(shù)據(jù)的傅里葉分解和最小二乘支持向量機(jī)的建模方法能獲得更好的預(yù)測(cè)結(jié)果,μ_a和μ_s反演的平均相對(duì)誤差分別為11.03%和7.16%。3.研究了蘋果的硬度和可溶性固體含量(SSC)預(yù)測(cè)模型。在線高光譜散射成像系統(tǒng)用于采集2009 年和 2010 年的'Golden Delicious,(GD), 'Jonagold,(JG)和'Delicious'(RD)蘋果樣本500-1000nm波段范圍內(nèi)的散射圖像。利用光學(xué)特性參數(shù)方法、矩方法和傅里葉分解方法分析高光譜散射圖像并提取光譜特征,結(jié)合偏最小二乘和最小二乘支持向量機(jī),建立蘋果硬度和SSC的預(yù)測(cè)模型。結(jié)果表明:融合后的光譜特征(光學(xué)特性參數(shù)μ_a和μ_s、零階矩和一階矩、傅里葉系數(shù))相比于單一光譜特征,能提供更多有關(guān)于散射曲線的信息,從而提高了蘋果硬度和SSC的預(yù)測(cè)精度。
[Abstract]:The optical properties of fruit tissue are important parameters reflecting the chemical composition, physical structure, physiological and pathological state of fruit tissue. The measurement of the optical characteristic parameters of fruit tissue, for studying the imaging law of the inner structure of the tissue and the characteristics of photon transmission, analyzing the histochemical characteristics and physical structure, It is of great significance to establish internal quality (state) detection and evaluation model. In this paper, the transmission of light in single layer fruit tissue is simulated based on MC, and the inverse solution of optical characteristic parameters is realized. Secondly, the hyperspectral scattering images of tissue simulated fluid are collected by hyperspectral scattering imaging system, and the inversion solution of the optical characteristic parameters of tissue simulation fluid is realized by combining with the nonlinear inversion regression model. On the basis of this study, the absorption and scattering of apple tissue were studied, and the prediction model between the spectral characteristics and the hardness and soluble solid content of apple was established. The main work of this paper is as follows: 1. In view of the large errors between the diffuse model and the MC simulation near the light source, an iterative inversion based method for estimating the mean free path of transport and determining the minimum distance between the light source and the detector is proposed. The method adaptively evaluates the mean free path of transport by using iterative estimation idea and changes the minimum distance between light source and detector to obtain a more reasonable data interval for the inversion of optical characteristic parameters. The results show that compared with the traditional empirical estimation method, the iterative inversion method can reduce the near-light source error and improve the retrieval accuracy of the optical characteristic parameters of fruit tissue. Under the condition of no noise, the average relative error of absorption coefficient 渭 _ a inversion is 7.17 and the average relative error of effective scattering coefficient 渭 _ s inversion is 5.73. In the case of certain SNR noise, the iterative inversion method can still obtain higher inversion accuracy of optical characteristic parameters. 2. Due to the various limitations of the optical approximation model, the prediction models of the optical characteristic parameters 渭 _ a and 渭 _ s are established by using the machine learning method. The hyperspectral scattering imaging system based on steady-state spatial resolution technique is used to obtain the scattering images in the 530-900nm band range of tissue simulation fluid. The nonlinear inverse regression model of optical parameters is established by combining Fourier decomposition and least squares support vector machine (LS-SVM) algorithm. The results show that the method of Fourier decomposition and least squares support vector machine based on experimental data can obtain better prediction results. The average relative errors of 渭 _ a and 渭 _ s inversion are 11.03% and 7.16.3 respectively. The (SSC) prediction model of apple hardness and soluble solid content was studied. The online hyperspectral scattering imaging system is used to collect the scattering images of 'Golden Delicious, (GD),' Jonagold, (JG) and 'Delicious' (RD) apple samples in 500-1000nm band range from 2009 to 2010. The hyperspectral scattering images were analyzed and extracted by optical characteristic parameter method, moment method and Fourier decomposition method. The prediction model of apple hardness and SSC was established by combining partial least squares and least squares support vector machine. The results show that the fused spectral features (optical parameters 渭 _ a and 渭 _ s, zero-order moments and first-order moments, Fourier coefficients) can provide more information about the scattering curves than the single spectral features. Thus, the prediction accuracy of apple hardness and SSC is improved.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S66;TP391.41

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