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A combination of near‐infrared hyperspectral imaging with two‐dimensional correlation analysis for monitoring the content of biogenic amines in mutton
Author(s) -
Fan Naiyun,
Liu Guishan,
Wan Guoling,
Ban Jingjing,
Yuan Ruirui,
Sun Yourui,
Li Yue
Publication year - 2021
Publication title -
international journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.831
H-Index - 96
eISSN - 1365-2621
pISSN - 0950-5423
DOI - 10.1111/ijfs.14950
Subject(s) - hyperspectral imaging , correlation coefficient , chemistry , partial least squares regression , near infrared spectroscopy , content (measure theory) , analytical chemistry (journal) , mathematics , chromatography , artificial intelligence , computer science , statistics , physics , mathematical analysis , quantum mechanics
Summary The study investigated the feasibility of using a combination of near‐infrared hyperspectral imaging (NIR‐HSI) with two‐dimensional correlation (2DCOS) analysis for rapid and non‐destructive determination of the content of biogenic amines in mutton during refrigerated storage. Total contents of biogenic amines (TBA) were used as the perturbation. By analysing the synchronous and asynchronous two‐dimensional correlation spectra, sensitive variables that were closely related to TBA contents were obtained. The results showed that the wavelengths in the spectra range of 1002–1335 nm were the research area for the detection of TBA contents in mutton. The least‐squares support vector machines (LSSVM) model based on effective wavelengths selected by competitive adaptive reweighted sampling (CARS) from 2DCOS analysis showed excellent results, with correlation coefficient in prediction (Rp) of 0.91, root mean square error in prediction (RMSEP) of 1.67 mg kg −1 and the ratio of performance deviation (RPD) of 2.76. The research demonstrated that the combination of NIR‐HSI and 2DCOS could be used as an effective method for monitoring the content of biogenic amines in mutton.