【摘要】：本文自行設(shè)計(jì)搭建一個(gè)顯微高光譜成像系統(tǒng),其融合了高光譜成像技術(shù)和顯微成像技術(shù),通過對(duì)灘羊肉樣本光譜成像,獲取樣本的顯微圖像及光譜信息,初步研究了貯藏過程中羊肉組織結(jié)構(gòu)變化,為羊肉貯藏過程中品質(zhì)變化機(jī)理的研究提供理論依據(jù)。主要研究?jī)?nèi)容如下:(1)系統(tǒng)搭建及優(yōu)化:以分立單元成像光譜儀、顯微鏡、數(shù)據(jù)采集卡等搭建顯微高光譜成像系統(tǒng),分析顯微高光譜成像系統(tǒng)的成像原理。對(duì)系統(tǒng)的關(guān)鍵技術(shù)進(jìn)行了研究,給出系統(tǒng)的技術(shù)指標(biāo)。最后,對(duì)顯微高光譜成像系統(tǒng)進(jìn)行優(yōu)化。(2)對(duì)羊肉貯藏過程中的pH、肉色、菌落總數(shù)、TVB-N含量和水分含量的變化規(guī)律進(jìn)行了研究,并對(duì)各品質(zhì)指標(biāo)與貯藏時(shí)間及各品質(zhì)指標(biāo)間的相關(guān)性進(jìn)行了分析,結(jié)果表明:水分含量、菌落總數(shù)和TVB-N含量與冷藏時(shí)間極顯著相關(guān)(p0.01),相關(guān)系數(shù)分別為-0.992、0.995、0.991。進(jìn)一步探討了水分含量、菌落總數(shù)和TVB-N含量與冷藏時(shí)間之間的關(guān)系,建立水分含量、菌落總數(shù)和TVB-N含量與冷藏時(shí)間之間的曲線回歸模型,進(jìn)行擬合分析。得到回歸方程分別為 Y=-2.604X2+0.064X+68.623,Y=0.179X2+0.015X+4.359,Y=1.031X2+0.108X+7.448。(3)以羊肉為研究對(duì)象,以貯藏過程中羊肉品質(zhì)指標(biāo)水分含量、菌落總數(shù)和TVB-N含量為評(píng)價(jià)指標(biāo),采用4種不同的光譜預(yù)處理方法進(jìn)行光譜預(yù)處理優(yōu)選最佳光譜預(yù)處理方法,最后結(jié)合不同的建模方法分別建立水分含量、羊肉菌落總數(shù)和TVB-N含量與冷藏時(shí)間的預(yù)測(cè)模型,優(yōu)選最佳模型。結(jié)果顯示:光譜數(shù)據(jù)經(jīng)過正交信號(hào)校正后的光譜建立水分含量、菌落總數(shù)和TVB-N含量的預(yù)測(cè)模型效果較好,其Rc分別為0.9426、0.9696和0.9695,RP分別為0.9122、0.9201和0.9069高于其他光譜預(yù)處理模型。通過不同建模方法的比較,建模效果較好的是PLSR方法,其Rc分別為0.9195、0.9067和0.9147,Rp分別為0.8795、0.8743和0.8802,均優(yōu)于PCR和SVR模型。因此,采用高光譜成像技術(shù)可實(shí)現(xiàn)羊肉品質(zhì)指標(biāo)的定量分析。(4)對(duì)羊肉貯藏過程中組織結(jié)構(gòu)變化進(jìn)行分析研究。首先獲取羊肉樣本的顯微高光譜圖像,并結(jié)合顯微鏡對(duì)羊肉不同貯藏時(shí)間的顯微結(jié)構(gòu)圖進(jìn)行觀察分析;通過主成分分析法對(duì)圖像進(jìn)行降維處理,篩選617nm、622nm、632nm、767nm、875nm和966nm六個(gè)波長(zhǎng),作為特征波長(zhǎng);對(duì)這些特征波長(zhǎng)下的顯微圖像進(jìn)行分析,發(fā)現(xiàn)羊肉組織結(jié)構(gòu)隨著貯藏天數(shù)的增加,破壞程度也增加。研究結(jié)果表明:運(yùn)用顯微高光譜成像技術(shù),可以對(duì)羊肉貯藏過程中的組織結(jié)構(gòu)變化進(jìn)行分析。本研究采用菌落總數(shù)對(duì)羊肉新鮮度進(jìn)行表征,提取羊肉顯微高光譜圖像信息的紋理特征,運(yùn)用SVM和LDA兩種方法對(duì)羊肉的新鮮度等級(jí)進(jìn)行劃分,其校正集判別率分別為98.33%和91.67%,預(yù)測(cè)集判別率分別為93.33%、93.33%,SVM法判別效果較好。因此,顯微高光譜成像技術(shù)結(jié)合適合的算法,可實(shí)現(xiàn)羊肉貯藏過程中新鮮度等級(jí)分類判別,為羊肉貯藏過程中的品質(zhì)變化機(jī)理研究奠定了基礎(chǔ)。
[Abstract]:In this paper, a micro-hyperspectral imaging system is designed and built, which combines hyperspectral imaging technology with microscopic imaging technology. Through spectral imaging of mutton samples, the microscopic images and spectral information of the samples are obtained. The changes of tissue structure of mutton during storage were studied, which provided theoretical basis for studying the mechanism of mutton quality change during storage. The main contents are as follows: (1) system construction and optimization: the micro-hyperspectral imaging system is constructed by discrete unit imaging spectrometer, microscope, data acquisition card and so on, and the imaging principle of micro-hyperspectral imaging system is analyzed. The key technology of the system is studied and the technical index of the system is given. Finally, the microscopic hyperspectral imaging system was optimized. (2) the changes of pH, color, total colony count, TVB-N content and water content of mutton during storage were studied. The correlation of each quality index with storage time and quality index was analyzed. The results showed that water content, total colony count and TVB-N content were significantly correlated with cold storage time (p0.01), and the results showed that water content, colony count and TVB-N content were significantly correlated with cold storage time (p0.01). The correlation coefficients were-0.992,0.995,0.991.The correlation coefficients were-0.992,0.995,0.991. Furthermore, the relationship among water content, total colony count and TVB-N content and storage time was discussed. The curve regression model of water content, total colony count and TVB-N content and cold storage time was established, and the fitting analysis was carried out. The regression equations were Y=-2.604X2 0.064X 68.623, Y = 0.179X2 0.015X 4.359, Y = 1.031X2 0.108X 7.448. (3) the water content of mutton quality index during storage was studied, and the regression equation was Y=-2.604X2 0.064X 68.623, Yx0.179X2 0.015X 4.359, Y = 1.031X2 0.108X 7.448 respectively. The total number of colonies and the content of TVB-N were the evaluation indexes. Four different spectral pretreatment methods were used to optimize the optimum spectral pretreatment methods. Finally, the water content was established by combining different modeling methods. The best model was selected to predict the total colony count, TVB-N content and cold storage time of mutton. The results showed that after the spectral data were corrected by orthogonal signal, the prediction model of total colony count and TVB-N content was better, and its Rc were 0.9426,0.9696 and 0.9695, respectively. The RP values were 0.9122, 0.9201 and 0.9069, respectively, which were higher than those of other spectral pretreatment models. Through the comparison of different modeling methods, the better modeling effect is PLSR method, whose Rc is 0.9195, 0.9067 and 0.9147, respectively, and RP is 0.8795, 0.8743 and 0.8802, which are better than PCR and SVR model. Therefore, the quantitative analysis of mutton quality indexes can be achieved by using hyperspectral imaging technique. (4) the changes of tissue structure of mutton during storage were analyzed and studied. Firstly, the microscopic hyperspectral images of mutton samples were obtained, and the microstructure of mutton at different storage times was observed and analyzed with microscope. Six wavelengths of 617 nm, 622 nm, 632 nm, 767 nm, 875 nm and 966 nm were selected by principal component analysis. By analyzing the microscopic images at these characteristic wavelengths, it was found that the damage degree of mutton tissue structure increased with the increase of storage days. The results show that the microstructure changes of mutton during storage can be analyzed by micro-hyperspectral imaging technique. In this study, the freshness of mutton was characterized by the total number of colonies, the texture characteristics of mutton micro-hyperspectral image were extracted, and the grade of freshness of mutton was classified by SVM and LDA. The calibration set discrimination rate is 98.33% and 91.67% respectively, and the predictive set discrimination rate is 93.33% and 93.33%, respectively. SVM method has a better discriminant effect. Therefore, micro-hyperspectral imaging combined with suitable algorithms can be used to classify and distinguish freshness of mutton during storage, which lays a foundation for studying the mechanism of mutton quality change during storage.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TS251.53;O657.3

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