| 基于近红外光谱技术的淡水鱼品种快速鉴别 |
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| 引用本文： | 徐文杰,刘 茹,洪响声,熊善柏.基于近红外光谱技术的淡水鱼品种快速鉴别[J].农业工程学报,2014,30(1):253-261. |
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| 作者姓名： | 徐文杰 刘 茹 洪响声 熊善柏 |
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| 作者单位： | 1. 华中农业大学食品科学技术学院，武汉 430070；2. 国家大宗淡水鱼加工技术研发分中心《武汉》，武汉 430070;1. 华中农业大学食品科学技术学院，武汉 430070；2. 国家大宗淡水鱼加工技术研发分中心《武汉》，武汉 430070;3. 华中农业大学水产学院，武汉 430070;1. 华中农业大学食品科学技术学院，武汉 430070；2. 国家大宗淡水鱼加工技术研发分中心《武汉》，武汉 430070 |
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| 基金项目： | 国家现代农业产业技术体系专项基金（CARS-46-23）；国家科技支撑计划（2013BAD19B10）；中央高校基本科研业务费专项资金（2013PY1085） |
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| 摘 要： | 为探索淡水鱼品种的快速鉴别方法,该文应用近红外光谱分析技术,结合化学计量学方法,对7种淡水鱼品种的判别分类进行了研究。采集了青、草、鲢、鳙、鲤、鲫、鲂等7种淡水鱼,共665个鱼肉样品的近红外光谱数据,经过多元散射校正(multiplicative scatter correction,MSC)、正交信号校正(orthogonal signal correction,OSC)、数据标准化(standardization,S)等20种方法预处理,在1 000~1 799 nm范围内分别采用偏最小二乘法(partial least square,PLS)、主成分分析(principal component analysis,PCA)和BP人工神经网络技术(back propagation artificial neural network,BP-ANN)、偏最小二乘法和BP人工神经网络技术对7种淡水鱼原始光谱数据进行了鉴别分析。结果表明,近红外光谱数据,结合主成分分析和BP人工神经网络技术建立的淡水鱼品种鉴别模型最优,模型的鉴别准确率达96.4%,对未知样本的鉴别准确率达95.5%。模型具有较好的鉴别能力,采用该方法能较为准确、快速地鉴别出淡水鱼的品种。
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| 关 键 词： | 近红外光谱，主成分分析，鱼，偏最小二乘，人工神经网络，品种，鉴别 |
| 收稿时间： | 2013/8/27 0:00:00 |
| 修稿时间： | 2013/11/14 0:00:00 |
Discrimination of freshwater fish varieties based on near-infrared spectra |
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| Xu Wenjie,Liu Ru,Hong Xiangsheng and Xiong Shanbai.Discrimination of freshwater fish varieties based on near-infrared spectra[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(1):253-261. |
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| Authors: | Xu Wenjie Liu Ru Hong Xiangsheng and Xiong Shanbai |
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| Institution: | 1. College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China2. National Research and Development Branch Center For Conventional Freshwater Fish Processing , Wuhan 430070, China;1. College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China2. National Research and Development Branch Center For Conventional Freshwater Fish Processing , Wuhan 430070, China;3. College of Fisheries, Huazhong Agricultural University, Wuhan 430070, China;1. College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China2. National Research and Development Branch Center For Conventional Freshwater Fish Processing , Wuhan 430070, China |
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| Abstract: | Abstract: A new method of discriminating varieties of freshwater fish was developed based on the near infrared spectroscopy technology. 665 freshwater fishes (silver carp 100, herring 100, grass carp 100, bighead carp 100, gurnard 76, carp 89, crucian carp 100) were collected to calibrate the model from different sites in Hubei province of China. The freshwater fishes in this study were obtained from pedlars' market, specialized aquatic research laboratory and aquaculture base. Some of samples were bred in ecological circulating water. Others were bred in non-recycled pool. In addition, freshwater fishes were collected for different seasons. A variety of samples were collected for the model calibration. The near infrared spectra of seven different varieties of 665 freshwater fish samples were analyzed. The discrimination of freshwater fish was conducted with near infrared spectral technology combined with chemometric method. The preprocessing of the spectra can reduce error of prediction, background optical noise and light scattering effects. The development of mathematical models is critical for near infrared spectroscopy. The preprocessing methods used in this study included multiplicative scatter correction (MSC), savitzky-golay (S-G) smoothing, savitzky-golay (S-G) derivative, differential derivation (DD), no spectral data preprocessing (NONE), standard normal variate (SNV), standardization(S), net analyte signal (NAS), orthogonal signal correction (OSC), de-trend (DT). The optimal spectra bands were chosen once determining the preprocessing method. All spectra statistical calculations were conducted using the RIMP software. Samples were randomly divided into two groups, 532 samples for the preparation of the model calibration and the remaining samples for the model validation. The near infrared discrimination models were developed based on partial least square (PLS) regression, principal component analysis (PCA) combined with back propagation artificial neural network (BP-ANN), partial least square combined with back propagation artificial neural network with 1000~1799nm, respectively. The model performance was evaluated by coefficient of determination of calibration (RC), standard error of calibration (SEC), correlation coefficient of validation (RP), the bias-corrected standard error prediction (SEP) and the standard error of cross validation (SECV). The lower typical errors (SEC, SECV and SEP), and higher correlation coefficient indicates that the model is robust. The results showed that discrimination model obtained by PCA combined with BP-ANN was the best. The standardization was the best preprocessing method for discrimination model. The best spectral bands were 1000~1200 and 1300~1450nm of model for the optimal preprocessing method. The linear equation of the model was y = 0.922637x + 0.333081. This equation represents the relationship between NIR spectroscopy predicted and observed values. High correlation coefficients were reached. The accuracy of discrimination rates of samples were 0.9644 and 0.9553 for the model calibration and validation set, respectively. The model performance demonstrates acceptable accuracy and predictive ability. The results indicate that the near infrared spectroscopy has the potential to become a valuable rapid screening method for discriminating freshwater fish. |
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| Keywords: | near infrared spectroscopy (NIRS) principal component analysis (PCA) fish partial least square (PLS) artificial neural network (ANN) variety identification |
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