Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)
Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)
复制标题
偏最小二乘回归与高光谱数据处理相结合用于鲤鱼鳞鳞率的无损检测
DOI：
10.3390/foods9040500
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发表时间：
2020-04
期刊：
影响因子：
5.2
通讯作者：
Tan Mingqian
中科院分区：
文献类型：
--
作者：
Wang Huihui;Wang Kunlun;Zhu Xinyu;Zhang Peng;Yang Jixin;Tan Mingqian
鲤鱼的鳞片率是制约鲤鱼加工自动化、智能化水平的重要因素之一。为了解决常用的人工检测方法的不足，本论文旨在研究高光谱技术(400-1024.7 nm)在鲤鱼鳞片率检测中的应用潜力。由于不同区域的光谱响应不同，整个鱼体被分成三个区域(腹部、背部和尾部)进行分析。不同的预处理方法，包括Savitzky-Golay(SG)、一阶导数(FD)、多元散射校正(MSC)和标准正态变量(SNV)用于光谱预处理。然后，分别应用连续投影算法(SPA)、回归系数(RC)和二维相关光谱(2D-COS)选择特征波长(CWS)。建立了全波长(FWS)和CWS标化率检测的偏最小二乘回归(PLSR)模型。根据建模结果，确定FD-RC-PLSR、SNV-SPA-PLSR和SNV-RC-PLSR是预测背面结垢率的最优模型(校正集决定系数(RC2)=96.23%，预测集决定系数(RP2)=95.55%，校正均方根误差(RMSEC)=6.20%，预测均方根误差(RMSEP)=7.54%，相对偏差(RPD)=3.98)，腹部(RC2=93.44%，RP2=90.81%，RMSEC=8.05%，RMSEP=9.13%，RPD=3.07)和Tail区(RC2=95.34%，RP2=93.71%，RMSEC=6.66%，RMSEP=8.37%，RPD=3.42)。可以看出，PLSR与特定的预处理和降维方法相结合，在不同鲤鱼地区的鳞片率检测中具有很大的潜力。这些结果证实了利用高光谱技术对鲤鱼鳞片率进行无损检测的可能性。
The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400–1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky–Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (RC2) = 96.23%, the coefficient of determination in prediction set (RP2) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (RC2 = 93.44%, RP2 = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (RC2 = 95.34%, RP2 = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.
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DOI：
10.1109/igarss.2000.861716
发表时间：
2000-07
期刊：
IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120)
影响因子：
--
作者：
M. Lennon;M. Mouchot;G. Mercier;L. Hubert‐Moy
通讯作者：
M. Lennon;M. Mouchot;G. Mercier;L. Hubert‐Moy
影响因子：
3.4
作者：
Toktam Mohammadi-Moghaddam;S. Razavi;M. Taghizadeh;B. Pradhan;A. Sazgarnia;Ahmad Shaker-Ardekani
通讯作者：
Toktam Mohammadi-Moghaddam;S. Razavi;M. Taghizadeh;B. Pradhan;A. Sazgarnia;Ahmad Shaker-Ardekani
DOI：
10.1007/s11694-019-00180-x
发表时间：
2019-12-01
影响因子：
3.4
作者：
Wang，Qingqing;Liu，Yunhong;Yu，Huichun
通讯作者：
Yu，Huichun
影响因子：
5.6
作者：
Wu, Di;Wang, Songjing;Yao, Jiansong
通讯作者：
Yao, Jiansong
基于一阶导数光谱的高光谱指数密切追踪沙漠植物的冠层蒸腾作用
影响因子：
5.1
作者：
Jin, Jia;Wang, Quan
通讯作者：
Wang, Quan