基于顯微高光譜成像技術(shù)的灘羊肉品質(zhì)檢測研究
發(fā)布時間：2019-04-27 03:22
【摘要】：本文自行設(shè)計搭建一個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)變化機理的研究提供理論依據(jù)。主要研究內(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。對系統(tǒng)的關(guān)鍵技術(shù)進行了研究,給出系統(tǒng)的技術(shù)指標(biāo)。最后,對顯微高光譜成像系統(tǒng)進行優(yōu)化。(2)對羊肉貯藏過程中的pH、肉色、菌落總數(shù)、TVB-N含量和水分含量的變化規(guī)律進行了研究,并對各品質(zhì)指標(biāo)與貯藏時間及各品質(zhì)指標(biāo)間的相關(guān)性進行了分析,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。進一步探討了水分含量、菌落總數(shù)和TVB-N含量與冷藏時間之間的關(guān)系,建立水分含量、菌落總數(shù)和TVB-N含量與冷藏時間之間的曲線回歸模型,進行擬合分析。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含量為評價指標(biāo),采用4種不同的光譜預(yù)處理方法進行光譜預(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含量與冷藏時間的預(yù)測模型,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)可實現(xiàn)羊肉品質(zhì)指標(biāo)的定量分析。(4)對羊肉貯藏過程中組織結(jié)構(gòu)變化進行分析研究。首先獲取羊肉樣本的顯微高光譜圖像,并結(jié)合顯微鏡對羊肉不同貯藏時間的顯微結(jié)構(gòu)圖進行觀察分析;通過主成分分析法對圖像進行降維處理,篩選617nm、622nm、632nm、767nm、875nm和966nm六個波長,作為特征波長;對這些特征波長下的顯微圖像進行分析,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:運用顯微高光譜成像技術(shù),可以對羊肉貯藏過程中的組織結(jié)構(gòu)變化進行分析。本研究采用菌落總數(shù)對羊肉新鮮度進行表征,提取羊肉顯微高光譜圖像信息的紋理特征,運用SVM和LDA兩種方法對羊肉的新鮮度等級進行劃分,其校正集判別率分別為98.33%和91.67%,預(yù)測集判別率分別為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,可實現(xiàn)羊肉貯藏過程中新鮮度等級分類判別,為羊肉貯藏過程中的品質(zhì)變化機理研究奠定了基礎(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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS251.53;O657.3
本文編號：2466650
[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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS251.53;O657.3
【參考文獻】
相關(guān)期刊論文 前10條
1 陳玉峰;吳燕燕;李來好;楊賢慶;鄧建朝;林婉玲;胡曉;榮輝;;腌干魚貯藏過程生物胺的變化及其貨架期研究[J];核農(nóng)學(xué)報;2016年08期
2 呂日琴;黃星奕;辛君偉;蔣飛燕;穆麗君;顧菲菲;姚麗婭;管超;韓方凱;;魚新鮮度檢測方法研究進展[J];中國農(nóng)業(yè)科技導(dǎo)報;2015年05期
3 李志成;傅忙娟;岳田利;白連社;李猛;胡海梅;;羊肉新鮮度與其揮發(fā)性有機化合物之間的關(guān)系研究[J];現(xiàn)代食品科技;2015年09期
4 姜沛宏;張玉華;錢乃余;張長峰;陳東杰;;基于機器視覺技術(shù)的肉新鮮度分級方法研究[J];食品科技;2015年03期
5 劉洪英;李慶利;顧彬;王依婷;薛永祺;;新型分子高光譜成像系統(tǒng)性能分析及數(shù)據(jù)預(yù)處理[J];光譜學(xué)與光譜分析;2012年11期
6 楊晨龍;袁大林;牟定榮;孟昭宇;孫彥琳;喬丹娜;湯建國;;近紅外顯微成像技術(shù)及其應(yīng)用進展[J];光譜實驗室;2012年05期
7 劉燕德;張光偉;;高光譜成像技術(shù)在農(nóng)產(chǎn)品檢測中的應(yīng)用[J];食品與機械;2012年05期
8 何小亢;劉樹楠;陳小軍;曾立波;吳瓊水;丁毅;;水稻花粉雄性不育光譜成像細(xì)胞學(xué)定量技術(shù)研究[J];光散射學(xué)報;2012年03期
9 趙杰文;張燕華;陳全勝;黃林;許慧;;光譜和成像融合技術(shù)檢測豬肉中揮發(fā)性鹽基氮[J];激光與光電子學(xué)進展;2012年06期
10 李江波;饒秀勤;應(yīng)義斌;;農(nóng)產(chǎn)品外部品質(zhì)無損檢測中高光譜成像技術(shù)的應(yīng)用研究進展[J];光譜學(xué)與光譜分析;2011年08期
相關(guān)博士學(xué)位論文 前3條
1 黎靜;大豆源蛋白飼料原料中三聚氰胺/三聚氰酸的近紅外顯微成像分析方法研究[D];中國農(nóng)業(yè)大學(xué);2014年
2 黃林;基于單一技術(shù)及多信息融合技術(shù)的豬肉新鮮度無損檢測研究[D];江蘇大學(xué);2013年
3 韓劍眾;豬肉生鮮品質(zhì)的控制與評價方法研究[D];浙江工商大學(xué);2008年
相關(guān)碩士學(xué)位論文 前3條
1 陳蓮蓮;基于紅外顯微成像的小麥種子性狀檢測研究[D];西安電子科技大學(xué);2012年
2 姜曉文;肌肉水分分布、抗氧化性與生鮮豬肉持水性的關(guān)系[D];浙江工商大學(xué);2009年
3 劉建偉;波爾山羊肌肉組織學(xué)性狀與肉品理化性狀的研究[D];河北農(nóng)業(yè)大學(xué);2007年
，本文編號：2466650
本文鏈接：http://www.sikaile.net/kejilunwen/huaxue/2466650.html
最近更新
教材專著
