Rapid and nondestructive detection of marine fishmeal adulteration by hyperspectral imaging and machine learning
文献类型： 外文期刊
作者： Kong, Dandan 1 ; Sun, Dawei 3 ; Qiu, Ruicheng 1 ; Zhang, Wenkai 1 ; Liu, Yufei 1 ; He, Yong 1 ;
作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China
2.Minist Agr & Rural Affairs, Key Lab Spect Sensing, Hangzhou 310058, Peoples R China
3.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou 310021, Peoples R China
关键词： Fishmeal; Binary adulteration; Processed animal protein; NIR hyperspectral imaging; Support vector machine; Wavelength selection
期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.831； 五年影响因子：4.073 ）
ISSN： 1386-1425
年卷期： 2022 年 273 卷
页码：
收录情况： SCI
摘要： Pure fishmeal (PFM) from whole marine-origin fish is an expensive and indispensable protein ingredient in most aquaculture feeds. In China, the supply shortage of domestically produced PFM has caused frequent PFM adulteration with low-cost protein sources such as feather meal (FTM) and fishmeal from by-products (FBP). The aim of this study was to develop a rapid and nondestructive detection method using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms for the identification of PFM adulterated with FTM, FBP, and the binary adulterant (composed of FTM and FBP). A hierarchical modelling strategy was adopted to acquire a better classification accuracy. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) coupled with four spectral preprocessing methods were employed to construct classification models. The SVM with baseline offset (BO-SVM) model using 20 effective wavelengths selected by successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) achieved classification accuracy of 100% and 99.43% for discriminating PFM from the adulterants (FTM, FBP) and adulterated fishmeal (AFM), respectively. This study confirmed that NIR-HSI offered a promising technique for feed mills to identify AFM containing FTM, FBP, or binary adulterants. (c) 2022 Elsevier B.V. All rights reserved.
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