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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]

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]

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 R2p 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 R2p 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. [ABSTRACT FROM AUTHOR]

• The R spectra were transformed into two spectral units, A and K-M. • IVISSA algorithm was used to select characteristic wavelengths. • PLS-DA and SVM algorithms were used to identify sound and bruised jujubes. • A-Raw-iVISSA-PLS-DA was the optimal classification discriminant model. Bruising is one of the major challenges appearing in the postharvest grading and processing of Lingwu long jujubes, which leads to quality deterioration and microbial infection. In the present work, the spectra of reflectance (R), absorbance (A) and Kubelka-Munk (K-M) in Lingwu were obtained by hyperspectral imaging, and applied to non-destructively detect bruising symptoms. Spectra were preprocessed and characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and the interval variable iterative space shrinkage approach (iVISSA). Classification discriminant models were constructed by partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM). By comparison, the results revealed that the A-raw-iVISSA-PLS-DA model showed the lowest errors in cross-validation, while the number of feature variables was the lowest accounting for 28.8 %, and the accuracies of the calibration and cross validation were 88.9 % and 100.0 %, respectively. In particular, this study demonstrated the feasibility to detect the early bruising degree in Lingwu long jujubes based on absorbance spectrum. Consequently, it also laid a foundation for future studies about detecting early bruising in small fruit with a rapid and non-destructive spectral-optical measurement. [ABSTRACT FROM AUTHOR]