基于光谱和高光谱成像技术的海水鱼品质快速无损检测
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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。
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.
(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。
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.
引文
AOAC, Official method 991.36. Association of Official Analytical Chemists, Washington, D.C., 2006.
Armenta S., Coelho N.M.M., Roda R., et al. Seafood freshness determination through vapour phase Fourier transform infrared spectroscopy. Analytica Chimica Acta,2006,580: 216-222.
Baixas-Nogueras S., Bover-Cid S., Veciana-Nogues M.T., et al. Effects of previous frozen storage on chemical, microbiological and sensory changes during chilled storage of Mediterranean hake (Merluccius merluccius) after thawing. European Food Research and Technology,2007,226:287-293.
Bechmann I.E., Jorgensen B.M. Rapid assessment of quality parameters for frozen cod using near infrared spectroscopy. Food Science and Technology-Lebensmittel-Wissenschaft & Technologie,1998,31:648-652.
Benjakul S., Visessanguan W., Thongkaew C., et al. Comparative study on physicochemical changes of muscle proteins from some tropical fish during frozen storage. Food Research International,2003,36:787-795.
Boknaes N., Jensen K.N., Andersen C.M., et al. Freshness assessment of thawed and chilled cod fillets packed in modified atmosphere using near-infrared spectroscopy. Lebensmittel-Wissenschaft Und-Technologie-Food Science and Technology,2002,35: 628-634.
Chen T., Martin E. The impact of temperature variations on spectroscopic calibration modelling: a comparative study. Journal of Chemometrics,2007,21:198-207.
Cheng J.-H., Sun D.-W., Zeng X.-A., et al. Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. Innovative Food Science and Emerging Technologies,2014,21:179-187.
Cozzolino D., Murray I., Chree A., et al. Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy. Lwt-Food Science and Technology,2005,38:821-828.
Cozzolino D., Murray L., Scaife J.R. Near infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish. Aquaculture Nutrition,2002,8:1-6.
Dhanoa M.S., Lister S.J., Sanderson R., et al. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of near Infrared Spectroscopy,1994,2:43-47.
Downey G. Non-invasive and non-destructive percutaneous analysis of farmed salmon flesh by near infra-red spectroscopy. Food Chemistry,1996,55:305-311.
Duflos G., Le Fur B., Mulak V., et al. Comparison of methods of differentiating between fresh and frozen-thawed fish or fillets. Journal of the Science of Food and Agriculture,2002,82: 1341-1345.
ElMasry G., Wold J.P. High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. Journal of Agricultural and Food Chemistry,2008,56: 7672-7677.
Etheridge R.D., Pesti G.M., Foster E.H. A comparison of nitrogen values obtained utilizing the Kjeldahl nitrogen and Dumas combustion methodologies (Leco CNS 2000) on samples typical of an animal nutrition analytical laboratory. Animal Feed Science and Technology, 1998,73:21-28.
FAO. Fisheries and Aquaculture Information and Statistics Service.2013, http://www.fao.org/fishery/statistics/global-aquacultureproduction/en.
Folkestad A., Wold J.P., Rorvik K.A., et al. Rapid and non-invasive measurements of fat and pigment concentrations in live and slaughtered Atlantic salmon (Salmo salar L.). Aquaculture,2008,280:129-135.
Geladi P., Kowalski B.R. Partial least-squares regression-a tutorial. Analytica Chimica Acta, 1986,185:1-17.
Green A.A., Berman M., Switzer P., et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. Ieee Transactions on Geoscience and Remote Sensing,1988,26:65-74.
Gruger E.H., Nelson R.W., Stansby M.E. Fatty acid composition of oils from 21 species of marine fish, freshwater fish and shellfish. Journal of the American Oil Chemists Society, 1964,41:662-667.
Haralick R.M., Shanmuga.K, Dinstein I. Textural features for image classification. Ieee Transactions on Systems Man and Cybernetics,1973, SMC3:610-621.
He H.-J., Wu D., Sun D.-W. Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Science & Emerging Technologies,2013,18:237-245.
He H.-J., Wu D., Sun D.-W. Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets. Journal of Food Engineering,2014,126:156-164.
Isaksson T., Naes T. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Applied Spectroscopy,1988,42:1273-1284.
Isaksson T., Swensen L.P., Taylor R.G., et al. Non-destructive texture analysis of farmed Atlantic salmon using visual/near-infrared reflectance spectroscopy. Journal of the Science of Food and Agriculture,2002,82:53-60.
Johnson R.A. Applied Multivariate Methods for Data Analysis. New York:Duxbury Press, 1998.
Karoui R., Lefur B., Grondin C, et al. Mid-infrared spectroscopy as a new tool for the evaluation of fish freshness. International Journal of Food Science and Technology,2007, 42:57-64.
Karoui R., Thomas E., Dufour E. Utilisation of a rapid technique based on front-face fluorescence spectroscopy for differentiating between fresh and frozen-thawed fish fillets. Food Research International,2006,39:349-355.
Khodabux K., Sophia M., L 'Omelette S., et al. Chemical and near-infrared determination of moisture, fat and protein in tuna fishes. Food Chemistry,2007,102:669-675.
Khojastehnazhand M., Khoshtaghaza M.H., Mojaradi B., et al. Comparison of Visible-Near Infrared and Short Wave Infrared hyperspectral imaging for the evaluation of rainbow trout freshness. Food Research International.2014,56:25-34.
Kimiya T., Sivertsen A.H., Heia K. VIS/NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets. Journal of Food Engineering,2013, 116:758-764.
Li B., Sun D.-W. Novel methods for rapid freezing and thawing of foods-a review. Journal of Food Engineering,2002,54:175-182.
Li H.D., Liang Y.Z., Xu Q.S., et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta,2009, 648:77-84.
Li H.D., Xu Q.S., Liang Y.Z. Random frog:An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification. Analytica Chimica Acta,2012,740:20-26.
Menesatti P., Costa C., Aguzzi J. In Sun D.-W. (Ed.), Hyperspectral Imaging for Food Quality Analysis and Control. London, Burlington, San Diego:Academic Press,2010.
Misimi E., Mathiassen J.R., Erikson U. Computer vision-based sorting of Atlantic salmon (Salmo salar) fillets according to their color level. Journal of Food Science,2007,72: S3O-S35.
Nilsen H., Esaiassen M. Predicting sensory score of cod (Gadus morhua) from visible spectroscopy. Lwt-Food Science and Technology,2005,38:95-99.
Nilsen H., Esaiassen M., Heia K., et al. Visible/near-infrared spectroscopy:A new tool for the evaluation of fish freshness? Journal of Food Science,2002,67:1821-1826.
Nortvedt R., Torrissen O.J., Tuene S. Application of near-infrared transmittance spectroscopy in the determination of fat, protein and dry matter in Atlantic halibut fillet. Chemometrics and Intelligent Laboratory Systems,1998,42:199-207.
Olafsdáttir G., Martinsd6ttir E., Oehlenschla(¨)ger J., et al. Methods to evaluate fish freshness in research and industry. Trends in Food Science & Technology,1997,8:258-265.
Osborne B.G., Fearn T. Near Infrared Spectroscopy in Food Analysis. New York:Longman Scientific & Technical,1986.
Ottestad S., Hoy M., Stevik A., et al. Prediction of ice fraction and fat content in super-chilled salmon by non-contact interactance near infrared imaging. Journal of near Infrared Spectroscopy,2009,17:77-87.
Pink J., Naczk M., Pink D. Evaluation of the quality of frozen minced red hake:use of Fourier transform near-infrared spectroscopy. Journal of Agricultural and Food Chemistry,1999,47 4280-4284.
Quevedo R.A., Aguilera J.M., Pedreschi F. Color of salmon fillets by computer vision and sensory panel. Food and Bioprocess Technology,2010,3:637-643.
Rasco B.A., Miller C.E., King T.L. Utilization of NIR spectroscopy to estimate the proximate composition of trout muscle with minimal sample pretreatment. Journal of Agricultural and Food Chemistry,1991,39:67-72.
Rye M. Prediction of carcass composition in Atlantic salmon by computerized-tomography. Aquaculture,1991,99:35-48.
Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry,1964,36:1627-1639.
Segtnan V.H., Hoy M., Lundby F., et al. Fat distribution analysis in salmon fillets using non-contact near infrared interactance imaging:a sampling and calibration strategy. Journal of near Infrared Spectroscopy,2009a,17:247-253.
Segtnan V.H., Hoy M., Sorheim O., et al. Noncontact salt and fat distributional analysis in salted and smoked salmon fillets using X-ray computed tomography and NIR interactance imaging. Journal of Agricultural and Food Chemistry,2009b,57:1705-1710.
Segtnan V.H., Mevik B.H., Isaksson T., et al. Low-cost approaches to robust temperature compensation in near-infrared calibration and prediction situations. Applied Spectroscopy, 2005,59:816-825.
Shewan J.M., Macintosh R.G., Tucker C.G., et al. The development of a numerical scoring system for the sensory assessment of the spoilage of wet white fish stored in ice. Journal of the Science of Food and Agriculture,1953,4 (6):283-298.
Shimasaki T., Miake K., Tsukamasa Y., et al. Effect of water activity and storage-temperature on the quality and microflora of smoked salmon. Nippon Suisan Gakkaishi,1994,60: 569-576.
Sigernes F., Esaiassen M., Heia K., et al. Assessment of fish (cod) freshness by VIS/NIR spectroscopy. Methods to evaluate fish freshness in research and industry, Proceedings of the final meeting of the concerted action on evaluation of fish freshness AIR CT94-2283, Nantes, France,1998:369-375.
Sikorski Z.E. Protein changes in muscle foods due to freezing and frozen storage. International Journal of Refrigeration,1978,1(3):173-180.
Sivertsen A.H., Kimiya T., Heia K. Automatic freshness assessment of cod (Gadus morhua) fillets by Vis/Nir spectroscopy. Journal of Food Engineering,2011,103:317-323.
Solberg C., Saugen E., Swenson L.P., et al. Determination of fat in live farmed Atlantic salmon using non-invasive NIR techniques. Journal of the Science of Food and Agriculture,2003, 83:692-696.
Sotelo C.G., Pin(~)eiro C, Perez-Martin R.I. Denaturation of fish proteins during frozen storage: role of formaldehyde. Zeitschrift fu(¨)r Lebensmittel-Untersuchung und -Forschung,1995, 200:14-23.
Stien L.H., Manne F., Ruohonene K., et al. Automated image analysis as a tool to quantify the colour and composition of rainbow trout (Oncorhynchus mykiss W.) cutlets. Aquaculture, 2006,261:695-705.
Sun D.-W. Hyperspectral Imaging for Food Quality Analysis and Control. London, Burlington, San Diego:Academic Press,2010.
Suykens J.A.K., Vandewalle, J. Least squares support vector machine classifiers. Neural Processing Letters,1999,9:293-300.
Uddin M., Okazaki E. Classification of fresh and frozen-thawed fish by near-infrared spectroscopy. Journal of Food Science,2004,69:C665-C668.
Uddin M., Okazaki E., Fukushima H., et al. Nondestructive determination of water and protein in surimi by near-infrared spectroscopy. Food Chemistry,2006,96:491-495.
Uddin M., Okazaki E., Turza S., et al. Non-destructive visible/NIR spectroscopy for differentiation of fresh and frozen-thawed fish. Journal of Food Science,2005,70: C506-C510.
Vapnik V. The Nature of Statistical Learning Theory. New York:Springer-Verlag,1995.
Vogt A., Gormley T.R., Downey G., et al. A comparison of selected rapid methods for fat measurement in fresh herring (Clupea harengus). Journal of Food Composition and Analysis,2002,15:205-215.
Wold J.P., Isaksson T. Non-destructive determination of fat and moisture in whole Atlantic salmon by near-infrared diffuse spectroscopy. Journal of Food Science,1997,62:734-736.
Wold J.P., Jakobsen T., Krane L. Atlantic salmon average fat content estimated by near-infrared transmittance spectroscopy. Journal of Food Science,1996,61:74-77.
Wold J.P., Johansen I.R., Haugholt K.H., et al. Non-contact transflectance near infrared imaging for representative on-line sampling of dried salted coalfish (bacalao). Journal of near Infrared Spectroscopy,2006,14:59-66.
Wu D., Sun D.-W. Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. Talanta, 2013a,116:266-276.
Wu D., Sun D.-W. Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta,2013b,111:39-46.
Wu D., Sun D.-W., He Y. Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. Innovative Food Science & Emerging Technologies,2012,16:361-372.
Wu D., Sun D.-W., He Y. Novel non-invasive distribution measurement of texture profile analysis (TPA) in salmon fillet by using visible and near infrared hyperspectral imaging. Food Chemistry,2014,145:417-426.
Xiccato G., Trocino A., Tulli F., et al. Prediction of chemical composition and origin identification of European sea bass (Dicentrarchus labrax L.) by near infrared reflectance spectroscopy (NIRS). Food Chemistry,2004,86:275-281.
Yu X.L., Li X.B., Zhao L., et al. Effects of different freezing rates and thawing rates on the manufacturing properties and structure of pork. Journal of Muscle Foods,2010,21: 177-196.
Zhang H.Z., Lee T.C. Rapid near-infrared spectroscopic method for the determination of free fatty acid in fish and its application in fish quality assessment. Journal of Agricultural and Food Chemistry,1997,45:3515-3521.
Zheng C.X., Sun D.-W., Zheng L.Y. Recent applications of image texture for evaluation of food qualities-a review. Trends in Food Science & Technology,2006,17:113-128.
程旎,李小昱,赵思明等.鱼体新鲜度近红外光谱检测方法的比较研究.食品安全质量检测学报,2013,4(2)：427-432.
褚小立.化学计量学方法与分子光谱分析技术.北京：化学工业出版社,2011.
黄星奕,吴磊,徐富斌.计算机视觉技术在鱼新鲜度检测中的应用研究.计算机工程与设计,2013,34(10)：3562-3567.
李汴生,俞裕明,朱志伟等.QIM和理化指标综合评价南方鲇鱼片冷藏新鲜度.华南理工大学学报(自然科学版),2007,35(12)：126-131.
李民赞,韩东海,王秀.光谱分析技术及其应用.北京：科学出版社,2006.
刘燕,王锡昌,刘源.金枪鱼解冻方法及其品质评价的研究进展.食品科学,2009,30(21)：476-480.
陆婉珍.现代近红外光谱分析技术.北京：中国石化出版社,2006.
栾东磊.近红光谱分析技术在几种水产品中的应用研究[硕士学位论文].中国海洋大学,2009.
马明宇,王桂芸,黄安民等.人工神经网络结合近红外光谱用于木材树种识别.光谱学与光谱分析,2012,32(9)：2377-2381.
史策,崔建云,王航等.反复冷冻-解冻对鲢品质的影响.中国水产科学,2012,19(1)：167-173.
苏翔,周晓琴,王涛等.利用微藻生产ω-3多不饱和脂肪酸的研究进展.重庆工商大学学报(自然科学版),2012,29(5)：100-104.
孙通,徐惠荣,应义斌.近红外光谱分析技术在农产品/食品品质在线无损检测中的应用研究进展.光谱学与光谱分析,2009,29(1)：122-126.
王海鸥,姜松.质构分析(TPA)及测试条件对面包品质的影响.粮油食品科技,2004,12(3)：1-4.
吴迪.微藻综合品质信息快速无损获取技术和方法研究[博士学位论文].浙江大学,2011.
徐富斌,黄星奕,丁然等.基于近红外光谱的大黄鱼新鲜度评价模型.食品安全质量检测学报,2012,3(6)：644-648.
杨燕.基于高光谱成像技术的水稻稻瘟病诊断关键技术研究[博士学位论文].浙江大学,2012.
张璐,侯红漫,伦成成.多宝鱼优势腐败菌生长动态模型和货架期预测.食品科技,2010,35(7)：158-162.
张晓敏.近红外光谱技术快速评估鲈鱼新鲜度的方法研究[硕士学位论文].浙江工商大学,2012.
张玉华,孟一,许丽丹等.基于近红外光谱技术的带鱼新鲜度检测研究.分析检测,2013,34(19)：281-286.
郑岩.基于液位传感器的牛肉系水力检测方法研究[博士学位论文].吉林大学,2009.
Armenta S., Coelho N.M.M., Roda R., et al. Seafood freshness determination through vapour phase Fourier transform infrared spectroscopy. Analytica Chimica Acta,2006,580: 216-222.
Baixas-Nogueras S., Bover-Cid S., Veciana-Nogues M.T., et al. Effects of previous frozen storage on chemical, microbiological and sensory changes during chilled storage of Mediterranean hake (Merluccius merluccius) after thawing. European Food Research and Technology,2007,226:287-293.
Bechmann I.E., Jorgensen B.M. Rapid assessment of quality parameters for frozen cod using near infrared spectroscopy. Food Science and Technology-Lebensmittel-Wissenschaft & Technologie,1998,31:648-652.
Benjakul S., Visessanguan W., Thongkaew C., et al. Comparative study on physicochemical changes of muscle proteins from some tropical fish during frozen storage. Food Research International,2003,36:787-795.
Boknaes N., Jensen K.N., Andersen C.M., et al. Freshness assessment of thawed and chilled cod fillets packed in modified atmosphere using near-infrared spectroscopy. Lebensmittel-Wissenschaft Und-Technologie-Food Science and Technology,2002,35: 628-634.
Chen T., Martin E. The impact of temperature variations on spectroscopic calibration modelling: a comparative study. Journal of Chemometrics,2007,21:198-207.
Cheng J.-H., Sun D.-W., Zeng X.-A., et al. Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. Innovative Food Science and Emerging Technologies,2014,21:179-187.
Cozzolino D., Murray I., Chree A., et al. Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy. Lwt-Food Science and Technology,2005,38:821-828.
Cozzolino D., Murray L., Scaife J.R. Near infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish. Aquaculture Nutrition,2002,8:1-6.
Dhanoa M.S., Lister S.J., Sanderson R., et al. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of near Infrared Spectroscopy,1994,2:43-47.
Downey G. Non-invasive and non-destructive percutaneous analysis of farmed salmon flesh by near infra-red spectroscopy. Food Chemistry,1996,55:305-311.
Duflos G., Le Fur B., Mulak V., et al. Comparison of methods of differentiating between fresh and frozen-thawed fish or fillets. Journal of the Science of Food and Agriculture,2002,82: 1341-1345.
ElMasry G., Wold J.P. High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. Journal of Agricultural and Food Chemistry,2008,56: 7672-7677.
Etheridge R.D., Pesti G.M., Foster E.H. A comparison of nitrogen values obtained utilizing the Kjeldahl nitrogen and Dumas combustion methodologies (Leco CNS 2000) on samples typical of an animal nutrition analytical laboratory. Animal Feed Science and Technology, 1998,73:21-28.
FAO. Fisheries and Aquaculture Information and Statistics Service.2013, http://www.fao.org/fishery/statistics/global-aquacultureproduction/en.
Folkestad A., Wold J.P., Rorvik K.A., et al. Rapid and non-invasive measurements of fat and pigment concentrations in live and slaughtered Atlantic salmon (Salmo salar L.). Aquaculture,2008,280:129-135.
Geladi P., Kowalski B.R. Partial least-squares regression-a tutorial. Analytica Chimica Acta, 1986,185:1-17.
Green A.A., Berman M., Switzer P., et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. Ieee Transactions on Geoscience and Remote Sensing,1988,26:65-74.
Gruger E.H., Nelson R.W., Stansby M.E. Fatty acid composition of oils from 21 species of marine fish, freshwater fish and shellfish. Journal of the American Oil Chemists Society, 1964,41:662-667.
Haralick R.M., Shanmuga.K, Dinstein I. Textural features for image classification. Ieee Transactions on Systems Man and Cybernetics,1973, SMC3:610-621.
He H.-J., Wu D., Sun D.-W. Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Science & Emerging Technologies,2013,18:237-245.
He H.-J., Wu D., Sun D.-W. Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets. Journal of Food Engineering,2014,126:156-164.
Isaksson T., Naes T. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Applied Spectroscopy,1988,42:1273-1284.
Isaksson T., Swensen L.P., Taylor R.G., et al. Non-destructive texture analysis of farmed Atlantic salmon using visual/near-infrared reflectance spectroscopy. Journal of the Science of Food and Agriculture,2002,82:53-60.
Johnson R.A. Applied Multivariate Methods for Data Analysis. New York:Duxbury Press, 1998.
Karoui R., Lefur B., Grondin C, et al. Mid-infrared spectroscopy as a new tool for the evaluation of fish freshness. International Journal of Food Science and Technology,2007, 42:57-64.
Karoui R., Thomas E., Dufour E. Utilisation of a rapid technique based on front-face fluorescence spectroscopy for differentiating between fresh and frozen-thawed fish fillets. Food Research International,2006,39:349-355.
Khodabux K., Sophia M., L 'Omelette S., et al. Chemical and near-infrared determination of moisture, fat and protein in tuna fishes. Food Chemistry,2007,102:669-675.
Khojastehnazhand M., Khoshtaghaza M.H., Mojaradi B., et al. Comparison of Visible-Near Infrared and Short Wave Infrared hyperspectral imaging for the evaluation of rainbow trout freshness. Food Research International.2014,56:25-34.
Kimiya T., Sivertsen A.H., Heia K. VIS/NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets. Journal of Food Engineering,2013, 116:758-764.
Li B., Sun D.-W. Novel methods for rapid freezing and thawing of foods-a review. Journal of Food Engineering,2002,54:175-182.
Li H.D., Liang Y.Z., Xu Q.S., et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta,2009, 648:77-84.
Li H.D., Xu Q.S., Liang Y.Z. Random frog:An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification. Analytica Chimica Acta,2012,740:20-26.
Menesatti P., Costa C., Aguzzi J. In Sun D.-W. (Ed.), Hyperspectral Imaging for Food Quality Analysis and Control. London, Burlington, San Diego:Academic Press,2010.
Misimi E., Mathiassen J.R., Erikson U. Computer vision-based sorting of Atlantic salmon (Salmo salar) fillets according to their color level. Journal of Food Science,2007,72: S3O-S35.
Nilsen H., Esaiassen M. Predicting sensory score of cod (Gadus morhua) from visible spectroscopy. Lwt-Food Science and Technology,2005,38:95-99.
Nilsen H., Esaiassen M., Heia K., et al. Visible/near-infrared spectroscopy:A new tool for the evaluation of fish freshness? Journal of Food Science,2002,67:1821-1826.
Nortvedt R., Torrissen O.J., Tuene S. Application of near-infrared transmittance spectroscopy in the determination of fat, protein and dry matter in Atlantic halibut fillet. Chemometrics and Intelligent Laboratory Systems,1998,42:199-207.
Olafsdáttir G., Martinsd6ttir E., Oehlenschla(¨)ger J., et al. Methods to evaluate fish freshness in research and industry. Trends in Food Science & Technology,1997,8:258-265.
Osborne B.G., Fearn T. Near Infrared Spectroscopy in Food Analysis. New York:Longman Scientific & Technical,1986.
Ottestad S., Hoy M., Stevik A., et al. Prediction of ice fraction and fat content in super-chilled salmon by non-contact interactance near infrared imaging. Journal of near Infrared Spectroscopy,2009,17:77-87.
Pink J., Naczk M., Pink D. Evaluation of the quality of frozen minced red hake:use of Fourier transform near-infrared spectroscopy. Journal of Agricultural and Food Chemistry,1999,47 4280-4284.
Quevedo R.A., Aguilera J.M., Pedreschi F. Color of salmon fillets by computer vision and sensory panel. Food and Bioprocess Technology,2010,3:637-643.
Rasco B.A., Miller C.E., King T.L. Utilization of NIR spectroscopy to estimate the proximate composition of trout muscle with minimal sample pretreatment. Journal of Agricultural and Food Chemistry,1991,39:67-72.
Rye M. Prediction of carcass composition in Atlantic salmon by computerized-tomography. Aquaculture,1991,99:35-48.
Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry,1964,36:1627-1639.
Segtnan V.H., Hoy M., Lundby F., et al. Fat distribution analysis in salmon fillets using non-contact near infrared interactance imaging:a sampling and calibration strategy. Journal of near Infrared Spectroscopy,2009a,17:247-253.
Segtnan V.H., Hoy M., Sorheim O., et al. Noncontact salt and fat distributional analysis in salted and smoked salmon fillets using X-ray computed tomography and NIR interactance imaging. Journal of Agricultural and Food Chemistry,2009b,57:1705-1710.
Segtnan V.H., Mevik B.H., Isaksson T., et al. Low-cost approaches to robust temperature compensation in near-infrared calibration and prediction situations. Applied Spectroscopy, 2005,59:816-825.
Shewan J.M., Macintosh R.G., Tucker C.G., et al. The development of a numerical scoring system for the sensory assessment of the spoilage of wet white fish stored in ice. Journal of the Science of Food and Agriculture,1953,4 (6):283-298.
Shimasaki T., Miake K., Tsukamasa Y., et al. Effect of water activity and storage-temperature on the quality and microflora of smoked salmon. Nippon Suisan Gakkaishi,1994,60: 569-576.
Sigernes F., Esaiassen M., Heia K., et al. Assessment of fish (cod) freshness by VIS/NIR spectroscopy. Methods to evaluate fish freshness in research and industry, Proceedings of the final meeting of the concerted action on evaluation of fish freshness AIR CT94-2283, Nantes, France,1998:369-375.
Sikorski Z.E. Protein changes in muscle foods due to freezing and frozen storage. International Journal of Refrigeration,1978,1(3):173-180.
Sivertsen A.H., Kimiya T., Heia K. Automatic freshness assessment of cod (Gadus morhua) fillets by Vis/Nir spectroscopy. Journal of Food Engineering,2011,103:317-323.
Solberg C., Saugen E., Swenson L.P., et al. Determination of fat in live farmed Atlantic salmon using non-invasive NIR techniques. Journal of the Science of Food and Agriculture,2003, 83:692-696.
Sotelo C.G., Pin(~)eiro C, Perez-Martin R.I. Denaturation of fish proteins during frozen storage: role of formaldehyde. Zeitschrift fu(¨)r Lebensmittel-Untersuchung und -Forschung,1995, 200:14-23.
Stien L.H., Manne F., Ruohonene K., et al. Automated image analysis as a tool to quantify the colour and composition of rainbow trout (Oncorhynchus mykiss W.) cutlets. Aquaculture, 2006,261:695-705.
Sun D.-W. Hyperspectral Imaging for Food Quality Analysis and Control. London, Burlington, San Diego:Academic Press,2010.
Suykens J.A.K., Vandewalle, J. Least squares support vector machine classifiers. Neural Processing Letters,1999,9:293-300.
Uddin M., Okazaki E. Classification of fresh and frozen-thawed fish by near-infrared spectroscopy. Journal of Food Science,2004,69:C665-C668.
Uddin M., Okazaki E., Fukushima H., et al. Nondestructive determination of water and protein in surimi by near-infrared spectroscopy. Food Chemistry,2006,96:491-495.
Uddin M., Okazaki E., Turza S., et al. Non-destructive visible/NIR spectroscopy for differentiation of fresh and frozen-thawed fish. Journal of Food Science,2005,70: C506-C510.
Vapnik V. The Nature of Statistical Learning Theory. New York:Springer-Verlag,1995.
Vogt A., Gormley T.R., Downey G., et al. A comparison of selected rapid methods for fat measurement in fresh herring (Clupea harengus). Journal of Food Composition and Analysis,2002,15:205-215.
Wold J.P., Isaksson T. Non-destructive determination of fat and moisture in whole Atlantic salmon by near-infrared diffuse spectroscopy. Journal of Food Science,1997,62:734-736.
Wold J.P., Jakobsen T., Krane L. Atlantic salmon average fat content estimated by near-infrared transmittance spectroscopy. Journal of Food Science,1996,61:74-77.
Wold J.P., Johansen I.R., Haugholt K.H., et al. Non-contact transflectance near infrared imaging for representative on-line sampling of dried salted coalfish (bacalao). Journal of near Infrared Spectroscopy,2006,14:59-66.
Wu D., Sun D.-W. Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. Talanta, 2013a,116:266-276.
Wu D., Sun D.-W. Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta,2013b,111:39-46.
Wu D., Sun D.-W., He Y. Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. Innovative Food Science & Emerging Technologies,2012,16:361-372.
Wu D., Sun D.-W., He Y. Novel non-invasive distribution measurement of texture profile analysis (TPA) in salmon fillet by using visible and near infrared hyperspectral imaging. Food Chemistry,2014,145:417-426.
Xiccato G., Trocino A., Tulli F., et al. Prediction of chemical composition and origin identification of European sea bass (Dicentrarchus labrax L.) by near infrared reflectance spectroscopy (NIRS). Food Chemistry,2004,86:275-281.
Yu X.L., Li X.B., Zhao L., et al. Effects of different freezing rates and thawing rates on the manufacturing properties and structure of pork. Journal of Muscle Foods,2010,21: 177-196.
Zhang H.Z., Lee T.C. Rapid near-infrared spectroscopic method for the determination of free fatty acid in fish and its application in fish quality assessment. Journal of Agricultural and Food Chemistry,1997,45:3515-3521.
Zheng C.X., Sun D.-W., Zheng L.Y. Recent applications of image texture for evaluation of food qualities-a review. Trends in Food Science & Technology,2006,17:113-128.
程旎,李小昱,赵思明等.鱼体新鲜度近红外光谱检测方法的比较研究.食品安全质量检测学报,2013,4(2)：427-432.
褚小立.化学计量学方法与分子光谱分析技术.北京：化学工业出版社,2011.
黄星奕,吴磊,徐富斌.计算机视觉技术在鱼新鲜度检测中的应用研究.计算机工程与设计,2013,34(10)：3562-3567.
李汴生,俞裕明,朱志伟等.QIM和理化指标综合评价南方鲇鱼片冷藏新鲜度.华南理工大学学报(自然科学版),2007,35(12)：126-131.
李民赞,韩东海,王秀.光谱分析技术及其应用.北京：科学出版社,2006.
刘燕,王锡昌,刘源.金枪鱼解冻方法及其品质评价的研究进展.食品科学,2009,30(21)：476-480.
陆婉珍.现代近红外光谱分析技术.北京：中国石化出版社,2006.
栾东磊.近红光谱分析技术在几种水产品中的应用研究[硕士学位论文].中国海洋大学,2009.
马明宇,王桂芸,黄安民等.人工神经网络结合近红外光谱用于木材树种识别.光谱学与光谱分析,2012,32(9)：2377-2381.
史策,崔建云,王航等.反复冷冻-解冻对鲢品质的影响.中国水产科学,2012,19(1)：167-173.
苏翔,周晓琴,王涛等.利用微藻生产ω-3多不饱和脂肪酸的研究进展.重庆工商大学学报(自然科学版),2012,29(5)：100-104.
孙通,徐惠荣,应义斌.近红外光谱分析技术在农产品/食品品质在线无损检测中的应用研究进展.光谱学与光谱分析,2009,29(1)：122-126.
王海鸥,姜松.质构分析(TPA)及测试条件对面包品质的影响.粮油食品科技,2004,12(3)：1-4.
吴迪.微藻综合品质信息快速无损获取技术和方法研究[博士学位论文].浙江大学,2011.
徐富斌,黄星奕,丁然等.基于近红外光谱的大黄鱼新鲜度评价模型.食品安全质量检测学报,2012,3(6)：644-648.
杨燕.基于高光谱成像技术的水稻稻瘟病诊断关键技术研究[博士学位论文].浙江大学,2012.
张璐,侯红漫,伦成成.多宝鱼优势腐败菌生长动态模型和货架期预测.食品科技,2010,35(7)：158-162.
张晓敏.近红外光谱技术快速评估鲈鱼新鲜度的方法研究[硕士学位论文].浙江工商大学,2012.
张玉华,孟一,许丽丹等.基于近红外光谱技术的带鱼新鲜度检测研究.分析检测,2013,34(19)：281-286.
郑岩.基于液位传感器的牛肉系水力检测方法研究[博士学位论文].吉林大学,2009.