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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. [ABSTRACT FROM AUTHOR]

A combined hydroxyl-functionalized and NH 4 Cl-assisted etching strategy is developed to effectively promote the yield of edge-hosted single-atomic Cu-N4 sites. The developed CuSAs@DCSs renders outstanding ORR activity (E onset =1.02 V, E 1/2 = 0.90 V), and excellent stability. CuSAs@DCSs-based Zn-air battery achieves ultralong cycle life and large power density. Rich edge-hosted active sites are responsible for the remarkable ORR performance. [Display omitted] • A hydroxyl-functionalized and NH 4 Cl-assisted etching strategy is developed to promote the yield of edge-hosted Cu-N4 sites. • Rich accessible edge-hosted Cu-N4 sites are responsible for the remarkable ORR performance. • CuSAs@DCSs exhibits extraordinarily ORR activity and excellent stability in both alkaline and acid solution. • CuSAs@DCSs exhibits superb performance when used as air cathode for Zn–air battery. • This strategy can be extended to fabricate other single atoms catalyst with controlled electronic structure. Single-atom electrocatalysts with metal-nitrogen-carbon (M N C SACs) moieties in carbon support display outstanding electrocatalytic performance towards oxygen reduction reaction (ORR) and have received widespread attentions. Only active sites on the edges of pores in carbon support are electrochemically accessible and contribute to ORR. Herein, we report a combined hydroxyl-functionalized and NH 4 Cl-assisted etching strategy to effectively promote the yield of edge-hosted Cu SAs. Thus, well-defined SAs with Cu-N 4 configuration are generated into the defect of carbonaceous nanospheres (CuSAs@DCSs). Impressively, the obtained SACs renders outstanding electrocatalytic ORR activity with onset, half-wave potentials of 1.02 V, 0.90 V, and extremely high stability, which transcends the noble-metals and most of the previously reported catalysts. When used in rechargeable Zn-air batteries, CuSAs@DCSs achieves ultralong cycle life at the large current density of 10 mA cm−2 (over 260 h) with low charge–discharge potential gap. Our study demonstrates that the creation of abundant micropores enriches the electrochemically accessible SAs, which locate at the edge-defects and are responsible for the remarkable ORR performance. This work sheds a facile strategy for designing and developing efficient electrocatalyst for various energy-related electrocatalytic reactions. [ABSTRACT FROM AUTHOR]

• The NIR-HSI was used for the first time to evaluate the lipid oxidation in mutton. • G2D-CS was used to determine the sequence of the changes in chemical bonds. • The CARS-LSSVM model showed good result to assess TBARS content in mutton (R p 2 = 0.83). This study aimed to investigate the feasibility of a rapid and non-destructive detecting method for lipid oxidation degree in mutton by near-infrared hyperspectral imaging (NIR-HSI). The hyperspectral images of the samples were collected in the spectral range of 900–1700 nm based on line-scanning. Partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) were established to correlate the full spectra with measured TBARS values. To reduce the complexity of the calibration models, eight new PLSR and LSSVM models based on the four sets of key variables extracted by interval variable iterative space shrinkage approach (iVISSA), interval random frog (IRF), variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were developed and compared. The optimized LSSVM model based on the feature wavebands selected by CARS methods was found to be good in predicting TBARS content, with R p 2 of 0.83, RMSEP of 0.11 mg kg−1, RPD of 2.82 and RER of 10.36. In summary, this study showed that it was feasible to non-destructively and rapidly evaluate lipid oxidation degree in mutton using NIR-HSI. [ABSTRACT FROM AUTHOR]