Contents lists available at ScienceDirect Food Chemistry journal homepage: www.elsevier.com/locate/foodchem Combining near-infrared hyperspectral imaging with elemental and isotopic analysis to discriminate farm-raised pacific white shrimp from high-salinity and low-salinity environments Dawei Sun a,b , Haiyong Weng a,b , Xiantao He a,b , Li Li c , Yong He a,b , Haiyan Cen a,b, ⁎ a College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China b Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China c Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, PR China ARTICLE INFO Keywords: Shrimp Litopenaeus vannamei Salinity Spectrology Stable isotope analysis ABSTRACT White shrimp (Litopenaeus vannamei) raised in low-salinity farm are considered inferior to those in seawater. In order to develop a rapid discrimination method for the food industry, we investigated the potential of using near- infrared hyperspectral imaging to discriminate shrimp muscle samples from freshwater and seawater farms. We constructed 3 different discrimination models with 4 optimal wavelength selection methods and compared the performance of each model. The results showed that sequential forward selection combined with partial least squares discriminant analysis (SFS-PLS-DA) generated the best discrimination performance with an overall ac- curacy of 99.2%. The elemental and isotopic analysis indicated a high correlation between 918 and 925 nm region (which was selected by SFS) and 13 C concentration. This agrees with the fact that there is more 13 C in shrimp of salty water compared to those of freshwater. The results demonstrated (hyperspectral imaging) HSI is promising to discriminate L. vannamei raised in fresh and seawater environments. 1. Introduction Shrimp are one of the most important aquaculture products (Yu, Tang, Wu, & Lu, 2018) due to their delicacy and high value in nutrition such as protein, unsaturated fatty acids, and minerals (Li, Ren, Dong, & Feng, 2018). Given the excellent growth performance and euryhaline nature of white shrimp (Litopenaeus vannamei), which can tolerate a wide range of salinity (from 0.5 to 45 g L -1 )(Roy et al., 2010), they quickly become one of three major commercially farmed species worldwide together with Penaeus monodon and Fenneropenaeus chinensis (Liu et al., 2014). Food and Agriculture Organization (FAO) of the United Nations reported that the total production of L. vannamei reached around 3.9 million tons in the year of 2015 alone (FAO, 2018). Despite the ability to be cultured in low-salinity brackish water, white shrimp raised in low-salinity farm are considered inferior to those grown in seawater, in terms of crude protein content, free amino acids, moisture, pH and flavor (Liang, Wang, Wang, Chang, & Mai, 2010). The difference between the quality, especially the nutritional value and off- flavor of shrimp from low-salinity water and salty water will cause customers’ preference thus a price difference. In addition, food fraud, mislabeling, and food safety are becoming a serious issue worldwide (Puertas & Vázquez, 2019), especially in counterfeiting, substation and adulteration of shrimp products. Therefore, in order to guarantee that customers are provided with the right information about the origin of white shrimp on the market, there is an urgent need for the dis- crimination of shrimp cultured in low and high salinities. Currently, the major method for detecting the origin of aquaculture shrimp is che- mical analysis, such as elemental profiling and stable isotope analysis (Li et al., 2018; Li, Han, Dong, & Boyd, 2019). Although this method can provide a relatively precise discrimination, it is time-consuming, labor intensive, destructive and costly. In addition, the processing procedure is complex, which requires the use of various reagents and chemical solvents, which could pose harm to operators and environ- ment. Based on these characteristics, it could be very difficult to be implemented on-line in a rapid manner. The technique of near-infrared (NIR) hyperspectral imaging (HSI) has been widely used in the food industry due to its nature of non- destructive, rapid, online application, and minimum human interven- tion. It combines the advantages of digital imaging technologies for spatial information about the spatial distribution of chemical https://doi.org/10.1016/j.foodchem.2019.125121 Received 7 March 2019; Received in revised form 1 June 2019; Accepted 2 July 2019 ⁎ Corresponding author at: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China. E-mail addresses: dzs0015@zju.edu.cn (D. Sun), hyweng@zju.edu.cn (H. Weng), hxt@zju.edu.cn (X. He), l_li@ouc.edu.cn (L. Li), yhe@zju.edu.cn (Y. He), hycen@zju.edu.cn (H. Cen). Food Chemistry 299 (2019) 125121 Available online 02 July 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved. T