Search

The amount and chemical state of myoglobin are often relevant to meat color, which is one of the important characteristics of meat freshness and wholesomeness. To precisely determine OxyMb and MetMb contents in mutton, the feasibility of optimization detection technology of spectral image fusion was explored. Three methods were used to select characteristic wavelengths while textural features of muscle tissue were derived. Compared among all models, LSSVM models coupled with spectra and textural parameters all performed better than these PLSR models. The iVISSA-COR-LSSVM model for OxyMb prediction achieved a good performance of R2 C = 0.9525, RMSEC = 2.2955, R2 P = 0.8956, and RMSEP = 3.0036. The more effective prediction performances emerged from VCPA-IRIV-COR-LSSVM model with R2 C = 0.8809, RMSEC = 2.2009, R2 P = 0.8353, and RMSEP = 3.2096 for MetMb. In final, the pseudo color maps of OxyMb and MetMb distribution were generated. This study demonstrated that the data fusion method furnishes a new approach to rapidly evaluate myoglobin content. • Better results were obtained by iVISSA and VCPA-IRIV identified wavebands. • Data fusion was used for enhancing hyperspectral prediction performance of myoglobin value. • LSSVM models built for predicting OxyMb and MetMb contents showed good results. • Distribution maps of OxyMb and MetMb values were visualized. [ABSTRACT FROM AUTHOR]

The detection of meat texture is of great value because it is the key factor that drives consumer purchasing decisions. In this study, a hyperspectral imaging (HSI) system was utilized to determine the texture parameters of Tan mutton. In order to observe the influence of mutton spectra during different refrigeration periods for modeling, hyperspectral images of the Tan mutton samples were collected in the 900–1700 nm spectral range, and the correction models of Tan mutton texture parameters were established. The four machine learning algorithms, such as partial least squares regression (PLSR), least squares support vector machine (LSSVM), random forest (RF), and decision trees (DT), were developed to establish the spectral models based on the characteristic bands selected by different extraction strategies including interval variable iterative space shrinkage approach (iVISSA), competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and variable combination population analysis (VCPA). The results showed that the LSSVM-iVISSA-CARS models exhibited excellent performance in predicting hardness and gumminess with root-mean-square errors (RMSEP) of 5.259 and 3.051 as well as the coefficient of determination for the prediction data set (R p 2) of 0.986 and 0.984 respectively. Good performances were achieved with R p 2 of 0.987 and RMSEP of 4.970 with the LSSVM-iVISSA-SPA model for chewiness, respectively. Therefore, HSI has potential for the evaluation and prediction of texture parameters in Tan mutton. • Spectra at 900–1700 nm was applied for detecting texture parameters of Tan mutton. • The OSC and MC methods were both utilized to preprocess X-block and Y-block. • Informative wavelengths were selected using iVISSV-CARS and iVISSV-SPA approaches. • The LSSVM model yielded the best result in predicting hardness, gumminess and chewiness of Tan mutton. [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]

[Display omitted] • BOSS exhibited better superiority in wavelength selection compared with SCARS. • Texture features entropy, inertia, and gradient mean square error were highly correlated with the bruise over time (|r|>0.7). • Spectral data combined with textural features with the highest correlations improved prediction accuracy. • The 1D-CNN model had the advantage of handling large datasets. The timely detection and classification of bruised Lingwu long jujube is crucial for reducing economic losses in the agricultural industry. Traditional machine learning methods had been effectively used for bruise time classification with limited samples while the challenges were presented when the amount of data expanded. Deep learning methods can offer a solution. The hyperspectral image of the jujube sample was obtained using near-infrared hyperspectral imaging system (900–1700 nm). The optimal input variables were obtained by comparing the performance of the partial least squares-discriminant analysis, artificial neural network, and one-dimensional convolutional neural network (1D-CNN) models based on the spectral data and the fusion data of spectral and textural features. The optimal variable was the fusion data that could improve the model performance (from 92.68% to 96.10%). The above-mentioned three classifiers were used to establish models based on fusion data for two different dataset sizes consisting of large datasets (LD) (820) and small datasets (SD) (200) to explore the influence of dataset size on model performance. As the sample size increased, the 1D-CNN had good robustness with a classification rate of 94.00% for SD and 96.10% for LD in the prediction sets for all samples. The classification rate of prediction sets for 0 h after bruising increased from 90.00% to 92.68%, and from 80.00% to 90.24% in the class of 24 h after bruising. The findings provided a new approach to building a large spectral library and developing an online classification and detection system, thereby providing new ideas for the development of grading systems for Lingwu long jujube. [ABSTRACT FROM AUTHOR]