Abstract
Non-destructive determination of TVB-N content in beef using hyperspectral imaging (HSI) technique was evaluated. In order to create a robust model to predict the TVB-N content in beef, partition of sample set, spectral pretreatment, and the optimum wavelength selection were discussed. After the beef sample set was parted by concentration gradient (CG) algortithm, and the spectra of beef samples were preprocessed by standard normalized variate (SNV) combined with auto scale(AS), the partial least square regression (PLSR) model was established using the full spectral range, which had the best prediction abilities with R cv 2 of 0.9124, R p 2 of 0.8816, RMSECV of 1.5889, and RMSEP of 1.7719, respectively. After the optimum wavelengths which is closely related to the TVB-N content of beef samples was obtained using the competitive adaptive re-weighted (CARS) algorithm, a new PLSR model was established using the optimum wavelengths, which had outstanding prediction abilities with R cv 2 of 0.9235, R p 2 of 0.9241, RMSECV of 1.4881, and RMSEP of 1.4882, respectively.The study showed that HSI is a powerful technique to predict the TVB-N content in beef by a nondestructive way.
Highlights
Due to the role of enzymes, microorganisms and other functions, beef is prone to spoilage during storage, transportation and processing. spoiled beef can not meet the needs of people's taste and nutrition, and endanger people's health, cause disease
Put the minimum and maximum of Total volatile basic-nitrogen (TVB-N) content all into the calibration set.Table 1 indicates that the mean value 17.6931 of the calibration set and the mean value 17.5910 of the test set obtained by concentration gradient (CG) algorithm were all closest to the mean value 17.6680 of all samples
This study aimed to investigate the aptitude of hyperspectral imaging (HSI) technique in predicting the TVB-N content in beef
Summary
Due to the role of enzymes, microorganisms and other functions, beef is prone to spoilage during storage, transportation and processing. spoiled beef can not meet the needs of people's taste and nutrition, and endanger people's health, cause disease. Due to the role of enzymes, microorganisms and other functions, beef is prone to spoilage during storage, transportation and processing. The detection of beef freshness has important significance in public health. It is an important index to evaluate the degree of beef freshness[1]. The detection of beef freshness mainly includes sensory detection, physical and chemical detection, microbial detection, and so on[2,3,4,5,6]. These traditional detection methods are not conducive to the rapid detection of beef freshness in the product circulation
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