Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: Current state-of-the-art research advances

Real‐time prediction of pre‐cooked Japanese sausage color with different storage days using hyperspectral imaging

“…The R 2 values of the calibration model using spectral in the range 400–1000 nm ( R c 2 = 0.96) were reported to be higher compared to those obtained in the range 900–1700 nm ( R c 2 = 0.85) using the same models for color prediction . It was demonstrated that the range 400–1000 nm exceeded the range 900–1700 nm as a result of the wide availability and low cost of CCD detectors , …”

“…Hyperspectral imaging (HSI) is an emerging and promising platform method 16 that integrates spectroscopy and imaging to obtain the spectral and spatial information from a subject simultaneously. It has gained wide recognition for grading and classfying meat products, 17,18 categorising and authenticating meat, [19][20][21][22][23] and predicting the quality attributes of meat products. 24,25 However, to the best of our knowledge, color prediction in cooked sausages using HSI with the range 380-1000 nm, has not yet been exploited.…”

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions

“…In this way, the uninformative wavelengths were eliminated (Xu and others ; Feng and others ), computing speed enhanced (Cheng and others , ) and the spectral dimension was reduced to a large extent (Wu and others ; Jia and others ). Furthermore, the feature wavelengths selection will facilitate the design an optimized online multispectral imaging system (Kamruzzaman and others , ; Feng and others ). In current study, the optimum wavelengths for pH were selected according to the weighted regression coefficients resulting from the best PLSR model.…”

“…1st D and 2nd D are applied to remove background noise, baseline drift and enhance small spectral features (Shen and others ). For regression algorithms (RAs), partial least squares regression (PLSR), least‐squares support vector machine (LS‐SVM), stepwise multiple linear regression (SMLR), backpropagation neural network (BPNN), and support vector machine (SVM) are commonly employed to interpret complex relationship between spectra and measured attributes (Feng and others ; Qi and others ). Among those RAs, PLSR is a useful and powerful multivariate data method to analyse data with numerous and strongly collinear variables in the independent variables (X) and dependent variables (Y) (Jia and others ; Qiao and others ; Wang and others ).…”

“…Besides the meat processing techniques for cooling (Feng and others , , , , , , ; Feng and Sun ; Feng and Li ), drying (Başlar and others ), and packaging (Saenmuang and others ) to enhance the products’ quality, rapid and reliable detection methods are also highly desirable for the fast‐paced production in the processing environment (Kamruzzaman and others ). Hyperspectral imaging (HSI), as an innovative technology, can simultaneously obtain both spatial and spectral information from a sample (Kamruzzaman and others ; Feng and others ). It offers numerous advantages over traditional analytical methods, with the most significant being, without doubt, its accurate prediction ability.…”