Search

This study aimed to develop simplified models for rapid and nondestructive monitoring myoglobin contents (DeoMb, MbO 2 and MetMb) during refrigerated storage of Tan sheep based on a hyperspectral imaging (HSI) system in the spectral range of 400-1000 nm. Partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) were applied to correlate the spectral data with the reference values of myoglobin contents measured by a traditional method. In order to simplify the LSSVM models, competitive adaptive reweighted sampling (CARS) and Interval variable iterative space shrinkage approach (iVISSA) were used to select key wavelengths. The new CARS-LSSVM models of DeoMb and MbO 2 yielded good results, with R 2 p of 0.810 and 0.914, RMSEP of 1.127 and 2.598, respectively. The best model of MetMb was new iVISSA-CARS-LSSVM, with an R 2 p of 0.915 and RMSEP of 2.777. The overall results from this study indicated that it was feasible to predict myoglobin contents in Tan sheep using HSI., Competing Interests: Declaration of Competing Interest There are no conflicts of interest., (Copyright © 2019. Published by Elsevier Ltd.)

Summary: The study investigated the feasibility of using a combination of near‐infrared hyperspectral imaging (NIR‐HSI) with two‐dimensional correlation (2DCOS) analysis for rapid and non‐destructive determination of the content of biogenic amines in mutton during refrigerated storage. Total contents of biogenic amines (TBA) were used as the perturbation. By analysing the synchronous and asynchronous two‐dimensional correlation spectra, sensitive variables that were closely related to TBA contents were obtained. The results showed that the wavelengths in the spectra range of 1002–1335 nm were the research area for the detection of TBA contents in mutton. The least‐squares support vector machines (LSSVM) model based on effective wavelengths selected by competitive adaptive reweighted sampling (CARS) from 2DCOS analysis showed excellent results, with correlation coefficient in prediction (Rp) of 0.91, root mean square error in prediction (RMSEP) of 1.67 mg kg−1 and the ratio of performance deviation (RPD) of 2.76. The research demonstrated that the combination of NIR‐HSI and 2DCOS could be used as an effective method for monitoring the content of biogenic amines in mutton. [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]

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]

Hyperspectral imaging (HSI), a non-destructive method combining image and spectral techniques to obtain image and spectral information of target objects, and qualitative and quantitative analyses using spectral data, has been widely used in agricultural product detection. This study was conducted to assess the feasibility of using HSI (900–1700 nm) for rapid determination of myoglobin contents (DeoMb, OxyMb and MetMb) in nitrite-cured mutton during refrigerated storage. The data were analyzed using evolutionary computing methods, including partial least squares regression (PLSR) and least squares support vector machines (LSSVM). Corresponding feature wavelengths were then selected by using competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and interval variable iterative space shrinkage approach (iVISSA) respectively. The simplified iVISSA-PLSR model showed the best performance for determining DeoMb with R p of 0.9017 and RMSEP of 2.3509. In addition, the best model for OxyMb was CARS-PLSR, with R p of 0.9661 and RMSEP of 2.3762. Moreover, the CARS-LSSVM model showed a better performance with a higher R p (0.8931) and a lower RMSEP (3.2743) for MetMb. Furthermore, the overall performance of wavelength selection methods in terms of CARS (R pDeoMb ≥ 0.8943, R pOxyMb ≥ 0.9607, R pMetMb ≥ 0.8910) and iVISSA (R pDeoMb ≥ 0.8976, R pOxyMb ≥ 0.9568, R pMetMb ≥ 0.8880) were equivalent and slightly better than VCPA (R pDeoMb ≥ 0.8711, R pOxyMb ≥ 0.9573, R pMetMb ≥ 0.8808). The results demonstrated that effective wavelength selection method can improve the performance of HSI system for the determination of myoglobin contents in comparison to full wavelength. • Feature wavelengths were selected by CARS, VCPA, iVISSA and iVISSA-CARS. • PLSR and LSSVM models were developed to measure myoglobin contents (DeoMb, OxyMb and MetMb) of nitrite-cured mutton. • The correlation coefficient of simpled model is acceptable for measuring myoglobin contents. [ABSTRACT FROM AUTHOR]

• Make a quantitative damage device for Lingwu long jujube. • Spectral of samples was scanned by VIS/NIR hyperspectral system. • Using SPA, UVE, CARS, iVISSA and 2D-COS to select the characteristic wavelength. • Establish the PLS-DA classification models. Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube. [ABSTRACT FROM AUTHOR]