【作者】 朱逢乐；

【导师】 何勇； Michael Ngadi；

【作者基本信息】 浙江大学 ， 生物系统工程， 2014， 博士

【摘要】 鱼肉历来作为易消化、营养价值高的食物广泛受到人们的青睐,但鱼肉水分含量较高,肉质细嫩,容易在酶和微生物的综合作用下发生腐败变质,因此其品质和安全的检测很重要。传统的物理化学测量分析方法耗时、繁琐、有损,且污染环境,已经不能满足现代数字渔业快速、无损、实时的发展要求。本文以海水鱼为研究对象,应用光谱和高光谱成像技术,并结合多种化学计量学方法、图像处理算法、数据挖掘技术,研究了新鲜与冷冻-解冻大菱鲆鱼肉的鉴别,大菱鲆冷藏存储时间的快速准确预测和分布可视化,大菱鲆质构参数和系水力的快速检测,大西洋鲑脂肪和水分含量的快速预测及空间分布可视化,为鱼肉的精细生产和数字化管理提供理论和技术支撑。研究主要内容和成果有：(1)采用可见／短波近红外高光谱成像鉴别新鲜与冷冻-解冻的大菱鲆鱼肉。冷冻处理分为不同的冷冻温度(快速冷冻-700C、慢速冷冻-20℃)以及不同的解冻次数(冷冻-解冻1次、2次)。提取高光谱图像的平均光谱并进行预处理,应用新型的竞争性自适应重加权算法(CARS)提取能表征鱼肉鲜度和冷冻状态的特征波长,同时对高光谱图像进行主成分分析(PCA)并提取主成分图像的纹理变量,最后分别基于光谱特征波长、纹理变量、特征波长+纹理变量建立线性(PLSDA)和非线性(BP-ANN、LS-SVM)的鉴别模型。对于新鲜、快速冷冻-解冻、慢速冷冻-解冻鱼肉的鉴别,最佳总体正确判别率为94.44%；对于新鲜、冷冻-解冻1次、冷冻-解冻2次鱼肉的鉴别,最佳总体正确判别率为100%。(2)应用可见/短波近红外高光谱成像实现了大菱鲆冷藏存储时间的快速准确预测和可视化。提取高光谱图像的平均光谱并建立PLSR. BP-ANN和LS-SVM的冷藏时间检测模型。线性PLSR模型精度较高,预测集Rp为0.9849,RMSEP为0.6799。采用PLSR对预测集样本图像上每个像素点的冷藏时间进行预测,结合IDL软件的图像编程技术将不同的时间用不同的颜色表示,以伪彩图的形式实现了冷藏时间的可视化,形象、直观地展示出鱼肉的新鲜度状态和分布情况。(3)应用可见／短波近红外光谱快速检测大菱鲆的质构参数和系水力。质构参数的检测与冷藏相结合,选取粘性、弹性和内聚性三个参数；系水力的检测与冷冻相结合,采用滴水损失描述。优选最佳光谱预处理方法,采用新型的Random frog算法提取光谱特征波长,并建立PLSR, BP-ANN和LS-SVM参数检测模型。粘性、弹性、内聚性、滴水损失最优预测Rp分别为0.9094、0.8754、0.8462、0.8678。(4)采用长波近红外高光谱成像实现了大西洋鲑脂肪和水分含量的快速无损检测及其分布可视化。对提取的平均光谱和脂肪、水分含量建立PLSR、BP-ANN和LS-SVM校正模型,线性PLSR模型精度较高,脂肪预测集Rp为0.9263,RMSEP为1.2405；水分预测集Rp为0.9366,RMSEP为1.0579。再将PLSR模型应用于预测集样本图像上的所有像素点,最终以伪彩图的形式展示脂肪和水分在大西洋鲑鱼片的含量分布。此外,还探索了该技术用于整鱼片成分含量成图的可行性。更多还原

【Abstract】 Fish has always been regarded as a kind of popular food because it is easily-digestible and has high nutritional value. Fish is succulent with high moisture content, and spoilage occers easily in fish due to the activity of catabolic enzymes and microorganisms. Therefore the detection of fish quality and safety is significant. Traditional physical and chemical analytical methods are time-consuming, lengthy, destructive, and producing hazardous pollutant, thus they can no longer meet the requirements of rapid, non-destructive and real-time measurement for modern digital fishery development. This dissertation focuses on the application of spectroscopy and hyperspectral imaging technique, combined with chemometrics, image processing methods and data mining technologies to inspect the quality and safety of marine fish, providing technical support for digital fishery. Four parts are included:the discrimination of fresh and frozen-thawed turbot, the detection and visualization of the chilling storage time for turbot, the fast determination of Texture Profile Analysis (TPA) properties and water holding capacility for turbot, the mapping of fat and moisture contents in Atlantic salmon. The main research contents and results are as follows:(1) Fresh and frozen-thawed turbot samples were discriminated using visible and short-wave near-infrared hyperspectral imaging. Frozen treatments cover the different freezing temperature (fast frozen-70℃and slow frozen-20℃) and the different thawing times (frozen-thawed once and twice). Mean spectra were extracted from the hyperspectral images to be preprocessed by different pretreatment methods, then a novel method called competitive adaptive reweighted sampling (CARS) was employed to select the effective wavelengths from the full-spectrum. Meanwhile, principal component analysis (PCA) was conducted on the hyperspectral images, and textural variables were extracted from the first three principal component (PC) images. Finally, the linear method of partial least squares discriminant analysis (PLSDA), non-linear methods of back-propagation artificial neural network (BP-ANN) and least-square support vector machine (LS-SVM) were applied for discrimination based on the effective wavelengths, textural variables, combined effective wavelengths and textural variables, respectively. For the differentiation of fresh, fast frozen-thawed, slow frozen-thawed fish, the best overall correct classification rate was94.44%; for the differentiation of fresh, frozen-thawed once, frozen-thawed twice fish, the best overall correct classification rate was100%. (2) The detection and visualization of the chilling storage time for turbot was achieved by visible and short-wave near-infrared hyperspectral imaging. Mean spectra were extracted from the hyperspectral images to be correlated with the chilling storage time using partial least squares regression (PLSR), BP-ANN and LS-SVM. Good results were obtained by the linear PLSR model with correlation coefficient (Rp) of0.9849and root mean square error of prediction (RMSEP) of0.6799. Then the chilling storage time of each pixel in the hyperspectral images for all prediction samples was predicted by PLSR and displayed in different colors for visualization based on pseudo-color images with the aid of an IDL program, displaying fish freshness status and distribution vividly.(3) The Texture Profile Analysis (TPA) properties and water holding capacility for turbot flesh was determined based on visible and short-wave near-infrared spectroscopy. Three TPA properties of adhesiveness, springiness and cohesiveness was measured for turbot under chilling storage, and drip loss was used to describe the water holding capacility of frozen turbot. The best spectral pretreatment method was selected, and a novel method called Random frog was employed to select the effective wavelengths from the full-spectrum. Then PLSR, BP-ANN and LS-SVM models were established based on the effective wavelengths for determining the different physical parameters. The highest prediction Rp for adhesiveness, springiness, cohesiveness and drip loss were0.9094,0.8754,0.8462and0.8678, respectively.(4) Mapping of fat and moisture distribution in Atlantic salmon was achieved using long-wave near-infrared hyperspectral imaging. The extracted mean spectra from the hyperspectral images were correlated with their corresponding fat and moisture contents using PLSR, BP-ANN and LS-SVM. High performances were obtained by the linear PLSR models with Rp of0.9263, RMSEP of1.2405for fat prediction, and Rp of0.9366, RMSEP of1.0579for moisture prediction. Then the PLS models were applied pixel-wise to the hyperspectral images of the prediction samples to produce pseudo-color chemical images for visualizing fat and moisture distribution along the whole fillet. Besides, the feasibility of using the proposed technique to map fat and moisture distribution for whole fillets was also explored.更多还原