In the meat industry, it is essential to monitor and identify meat freshness grades due to its impact on the safety of human diets. This study aimed to identify premium, sub-fresh, and spoiled lamb ...samples using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) in the range of 400–1000 nm coupled with chemometrics methods. The two-dimensional correlation spectroscopy (2D-CS) was utilized to select effective wavelengths for simplifying the model and increasing the calculation speed. The capabilities of the four models including partial least squares discriminant analysis (PLS-DA), soft independent modelling of class analogy (SIMCA), back propagation neuron network (BP), decision tree (DT) and random forest (RF) were compared to select the best identification model. The results showed that the RF model generated excellent performance, the accuracies of the training and test sets were 93% and 91%, respectively. In summary, this study showed that it was feasible to rapidly and non-destructively identify and evaluate the freshness grades of lamb using Vis-NIR.
•Vis-NIR spectroscopy and chemometrics were used to determine freshness grades of lamb.•Spectral preprocessing and wavelength selection were applied to optimize the models.•The 2D-CS-RF model provided fast recognition with 91% accuracy on the testing set.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The discolorations or abnormal colors in meat and meat products during processing and storage have negative effects on their commercial value. In this study, myoglobin content (MetMb and OxyMb) in ...Tan mutton was rapidly detected using near-infrared hyperspectral imaging (NIR-HSI) system (900–1700 nm), and built predictive models with full wavebands (FW) based on partial least squares regression (PLSR), least-squares support vector machines (LSSVM), and back propagation neuron network (BP). To reduce the computational complexity of calibration models, feature bands were obtained by bootstrapping soft shrinkage (BOSS), variable combination population analysis coupled with iteratively retains informative variables (VCPA-IRIV), and competitive adaptive reweighted sampling (CARS), respectively. The optimized BP model based on feature wavebands with BOSS method selection displayed the best capability for predicting MetMb level (
R
2
C
= 0.8340,
R
2
P
= 0.8253
RMSEC
= 3.1592,
RMSEP
= 3.2918). In addition, the simplified VCPA-IRIV-BP model was significant in predicting OxyMb content with
R
2
C
,
R
2
P
,
RMSEC
, and
RMSEP
values of 0.8024, 0.8680, 3.4676, and 2.7605, respectively. Results provided a theoretical reference for rapid evaluation of myoglobin content in other animal products via NIR-HSI.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Particle swarm optimization (PSO) is an intelligent evolutionary method, which is widely used to search the global optimal solution. However, in the early stage of the algorithm, the rapid flight of ...particle swarm to the current optimal solution may lead to premature convergence, while in the later stage of the algorithm, the convergence of most particles will lead to the decrease of particle swarm velocity. In this paper, the advantages and principles of IPSOA are discussed, and the insulation problem of mixed gas is discussed. By comparing the standard PSOA with the improved PSOA, the results show that the calculation result of the improved PSOA is close to the optimal value of the function itself, which proves that the improved PSOA has better optimization ability.
