【作者】 姜训鹏；

【导师】 韩鲁佳；

【作者基本信息】 中国农业大学 ， 农业工程， 2014， 博士

【摘要】 开展饲料中不同肉骨粉快速分析方法的研究,可为保障饲料安全提供必要的技术支撑,具有重要的现实意义。近红外显微成像是一种联用技术,结合了近红外光谱学与显微成像方法,具有快速、无损、无污染、高分辨、微区化等优势。挖掘与融合近红外显微图像中光谱信息和空间信息,有助于实现饲料中肉骨粉的准确识别。因此,本文开展了基于光谱/空间信息的饲料中不同肉骨粉近红外显微成像分析方法的研究。研究结果为进一步提高饲料中不同肉骨粉的检测精度奠定了方法学基础。论文取得的主要创新成果有：1.以精料补充料、鱼粉、肉骨粉为研究对象,通过四氯乙烯富集获取沉淀颗粒,采用近红外显微成像系统扫描样本沉淀颗粒,结合主成分分析和K均值聚类方法分析成像光谱,实现了精料补充料与肉骨粉,鱼粉与肉骨粉的快速区分,并利用聚类方法进一步细化了精料补充料中样本类别。2.以不同种属肉骨粉(猪源、禽源、反刍源)为研究对象,采用近红外显微成像技术,快速获取并优选具有代表性的沉淀颗粒光谱,构建不同种属肉骨粉近红外标准光谱库。设计开发了基于Matlab的近红外显微图像颗粒光谱自动提取软件,从显微成像数据阵中获取单条颗粒光谱,以自动化批处理方式提高了光谱的提取效率,颗粒光谱准确提取率为96.4%。基于标准光谱库,结合偏最小乘判别分析与支持向量机判别分析方法,构建不同种属肉骨粉定性判别模型,模型的实际应用结果良好,对猪源、禽源和反刍源的灵敏度分别为0.896,0.949和0.918,特异度分别为0.963,0.969和0.950。3.基于近红外显微图像中空间信息与光谱信息的同步提取和融合,提出并采用了Markov随机场模型方法构建饲料中肉骨粉定性判别模型与鱼粉中肉骨粉定性判别模型。该方法通过支持向量机判别分析获得初始类别属性,通过主成分分析获得特征向量,采用条件迭代模型算法结合初始类别属性和特征向量建立Markov随机场模型。以饲料与肉骨粉、鱼粉与肉骨粉颗粒相互叠加的样本为对象,采用Markov随机场模型处理近红外显微图像,获取标记肉骨粉与饲料、肉骨粉与鱼粉类别的分类图像,实现对饲料中肉骨粉、鱼粉中肉骨粉的识别。结果表明识别饲料覆盖的肉骨粉的分类准确度可达86.59%,Kappa系数可达0.68,鱼粉覆盖的肉骨粉的分类准确度可达86.22%,Kappa系数可达0.66。与传统近红外图像处理方法比较,新方法有助于对叠加样本的识别,有效提高了饲料中肉骨粉的近红外显微成像识别精度。4.以掺杂不同肉骨粉质量分数的饲料样本为研究对象,采用Markov随机场模型方法建立了饲料中肉骨粉近红外显微定量分析模型,并与采用PLSR方法和SVMDA方法构建的模型相比较。结果显示：基于PLSR方法、SVMDA方法和MRF方法构建的定量分析模型,对肉骨粉质量分数低于0.10样本的决定系数(R2)分别为0.52、0.56、0.62,平均绝对误差(AAE)分别为0.89%、0.97%、0.60%；对全部样本的R2分别为0.99、0.96、0.98,AAE分别为0.46%、0.59%、0.42%。与传统近红外显微图像处理方法比较,MRF模型方法能够实现成像数据中的空间信息与光谱信息同步提取和融合,有助于饲料中肉骨粉的近红外成像定量分析。更多还原

【Abstract】 Meat and bone meal (MBM) is one of the main prion-contaminated animal feed ingredients responsible for BSE, generally called "mad cow disease". As many countries have legislated the banning of MBM as a feedstuff or feed ingredient to prevent the transmission of this disease among cattle and other livestock, techniques to detect MBM are required. By combining near-infrared (NIR) spectroscopy and microscopy, NIR microscopic imaging records a spectrum per site (pixel) of sample surface and finally forms an image of the sample. This new advanced analytical technique is a rapid, non-destructive, non-polluted, high-accuracy and micro-visualization method in characterizing complex mixtures. This method will be enhanced for the identification of MBM by extracting spectral information combined with spatial information from NIR spectroscopic images. This study investigated the appropriate approaches for integrating spatial with spectral information from NIR microscopic image. The research work has important scientific and practical value in enriching rapid detection method of MBM and preventing the occurrence of BSE disease.1. Samples of MBM, dairy concentrate supplement and fish meal (FM) were collected, sedimented and arranged on polytetrarruoroethene (PTFE) background plate for NIR microscopic imaging. Both principal component analysis (PCA) and K-means clustering analysis were used to extract and present relevant information from NIR microscopic images. The results showed that MBM could be distinguished from dairy concentrate supplement/FM by the scores from principal component analysis, and samples were subdivided by using K-means clustering based on the PCA analysis. It is demonstrated that NIR microscopic imaging approach is one of most promising methods for the detection of MBM.2. To test the performance of NIR microscopic imaging to species identify MBM, a rapid method to construct NIR microscopic standard spectra database is demonstrated. For the study, bone fragments of three different species MBM (porcine origin, avian origin and ruminant origin) were analyzed on NIR microscopic imaging system, and both VIS and NIR images were acquired at the same size. To extract and mark the position of every single bone fragment in visible image, a graphical user-friendly interface, based on marker-controlled watershed segmentation method, written in Matlab for extracting and marking bone fragments has been developed in this paper. By the position information in VIS image, NIR microscopic image was decompounded to spectrum of each bone fragment. The determination model is then constructed by two methods (PLSDA and SVMDA) with different spectral preprocessing. The sensitivity of the best discrimination model is0.896,0.949and0.918, respective for three species. The specificity is0.963,0.969and0.950.3. This study introduces an innovative approach to analyzing NIR microscopic images: an Markov random field-based approach has been developed using the ICM (Iterative conditional mode) algorithm, integrating initial labeling derived results from SVMDA and observation data derived from the results of PCA. The results showed that MBM covered by feed can be successfully recognized with an overall accuracy of86.59%and a Kappa coefficient of0.68. MBM covered by FM can be successfully recognized with an overall accuracy of86.22%and a Kappa coefficient of0.66.Compared with conventional methods, the MRF-based approach is capable of extracting spectral information combined with spatial information from NIR microscopic images. This new approach enhances the identification of MBM using NIR microscopic imaging.4. Feed containing between0%-100%(w/w) MBM are prepared to test the quantification ability of NIR microscopic imaging. Three different strategies are demonstrated to construct a quantification model, including PLSR, SVMDA and MRF-based approach. The results showed that the determination coefficients (R2) of three quantification models for the low level content samples (0%MBM-10%MBM) is0.52,0.56and0.62with an average absolute error (AAE)0.89%,0.97%and0.60%, respectively. The determination coefficients (R2) of three quantification models for all samples is0.99,0.96and0.98with an AAE0.46%,0.59%and0.42%, respectively.更多还原