Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system

Abstract

In this study, the ENVI 4.6 software was used to obtain the spectral reflection value of samples. The outlier samples were eliminated by the Monte Carlo method, and then SPXY (sample set partitioning based on be x–y distances) was used to divide the calibration set and prediction set. The spectral images were pretreated and characteristic wavelengths were extracted. The spectral models of full and pretreated spectra and characteristic bands were established by partial least squares regression (PLSR) and principle component regression (PCR), and the optimal modeling combination was selected. The results showed that the modeling effect of the original spectrum was the best. In full‐PLSR model, the determination coefficient of the calibration set (Rc2), the determination coefficient of prediction set (Rp2), and the determination coefficient of interactive verification set (Rcv2) were 0.8804, 0.7375, and 0.7422, and root‐mean‐square error of calibration set (RMSEC), root‐mean‐square error of prediction (RMSEP), and root mean square error of interactive validation set (RMSECV) were 2.3630, 2.9607, and 3.4209, respectively. PLSR and PCR models were established to obtain the optimal models of CARS‐PLSR and PCR‐PLSR. In the CARS‐PLSR model, the Rc2, Rp2, and Rcv2 were 0.9135, 0.7654, and 0.8171, respectively, while RMSEC, RMSEP, and RMSECV were 2.0275, 2.9306, and 2.9262, respectively. In the iRF‐PCR model, Rc2, Rp2, and Rcv2 were 0.7952, 0.7372, and 0.7280, respectively, while RMSEC, RMSEP, and RMSECV were 3.0207, 2.8278, and 3.4288, respectively. This study has demonstrated that visible and near‐infrared hyperspectral imaging system can rapidly predict the content of metmyoglobin in cooked tan mutton.Practical ApplicationThis study has demonstrated that visible and near‐infrared (Vis/NIR) hyperspectral imaging system can rapidly predict the content of MetMb in cooked tan mutton. With the advantages of nondestructive, rapid, real‐time, Vis/NIR, hyperspectral imaging system can be widely expanded and applied to the detection of myoglobin in meat to evaluate the color of meat.

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

“…Regarding color in red meat, high predictability with benchtop lab HSI systems was reported for L*, a*, and b* values in combined beef, lamb, and pork samples using MLR (R 2 p = 0.97, 0.84, and 0.82, respectively; Kamruzzaman et al, 2016b) and in beef using PLSR (R 2 = 0.98, 0.92, and 0.95, respectively; Yu et al, 2020). Metmyoglobin content is known to influence meat color stability, and many studies recently found high HSI predictability for different metmyoglobin content variables in lambs using benchtop lab HSI systems and different prediction approaches including PLSR and least-squares support vector machines (LSSVM) (R 2 p = 0.85 and 0.91, respectively; Cheng et al, 2020), competitive adaptive reweighted sampling-LSSVM (R 2 p = 0.81 to 0.91; Yu et al, 2020), NIR-HSI data combined with generalized 2D correlation spectroscopy method (R 2 p = 0.85; Cheng et al, 2021), and competitive adaptive reweighted sampling-PLSR (R 2 p = 0.77; Yuan et al, 2020). Few studies have recently applied HSI to predict WHC in meat.…”

Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data

“…Hence, the acquired hyperspectral images need to be corrected with black and white references before extracting the spectral data. 30 The images were corrected by eqn (4):…”

Detection Storage Time of Mild Bruise’s Loquats Using Hyperspectral Imaging

“…In fact, this method is not suitable for use in rapid, automated, and inline sorting in a large loquat production environment, so the model is necessary to be optimized. e variable ltering methods are used to reduce the dimensionality of spectral data, including uninformative variable elimination (UVE), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and genetic algorithm (GA) [23]. In this paper, the LS-SVM model is optimized by UVE, SPA, CARS, and GA, respectively, the number of spectral variables is reduced from 176 to 38, 10, 30, and 4, respectively, and the results are shown in Figure 6.…”