Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Huihui Wang; Kunlun Wang; Xinyu Zhu; Peng Zhang; Jixin Yang; Mingqian Tan

doi:10.3390/foods9040500

Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Foods. 2020 Apr 16;9(4):500. doi: 10.3390/foods9040500.

Authors

Huihui Wang^{1

2

3

4}, Kunlun Wang^{1

2

3

4}, Xinyu Zhu^{1

2

3

4}, Peng Zhang^{1

2

3

4}, Jixin Yang^{1

2

3

4}, Mingqian Tan^{2

3

4}

Affiliations

¹ School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian 116034, China.
² School of Food Science and Technology, National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian 116034, China.
³ Collaborative Innovation Center of Seafood Deep Processing, Dalian 116034, China.
⁴ Engineering Research Center of Seafood of Ministry of Education of China, Dalian 116034, China.

Abstract

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 (R_C²) = 96.23%, the coefficient of determination in prediction set (R_P²) = 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 (R_C² = 93.44%, R_P² = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (R_C² = 95.34%, R_P² = 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.

Keywords: carp; hyperspectral; partial least square regression; preprocessing; scaling rate.

Abstract

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