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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]

Near infrared hyperspectral imaging (NIR‐HSI) with a spectral range of 900 to 1700 nm was for the first time used to predict the changes of sugar content in Lingwu jujube during storage. Monte Carlo method was adopted to detect outliers, and multiple scattering correction (MSC), standard normal variate transformation (SNV), and Baseline were used to optimize modeling. Competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), and interval random frog (IRF) were used to select optimal wavelengths. In addition, partial least square regression (PLSR) and support vector machine (SVM) modeling based on optimal wavelengths were compared. The results showed that 30, 30, and 24 wavelengths were selected by CARS; 106, 87, and 112 feature wavelengths were selected by iVISSA; and 96, 71, and 83 optimal wavelengths were selected by IRF for sucrose, fructose, and glucose, respectively. The CARS–PLSR models provided the best results for fructose and glucose, and iVISSA–SVM model was better for sucrose. The results indicated that NIR–HSI model may be used as a rapid and nondestructive method for the determination of sugar content in jujubes. [ABSTRACT FROM AUTHOR]

This study was conducted to determine the correlation among serum biochemical indices, slaughter traits, texture characteristics and water-holding capacity in Tan sheep. Eighty Tan sheep aged 4–6 months were selected. Blood samples were collected from live animals to analyse serum biochemical indices. The slaughter traits, texture characteristics and water-holding capacity of meat were assessed after slaughter. There were highly significant correlations (p<.01) and high linear regression R values among serum biochemical indices, slaughter traits, meat texture characteristics and water-holding capacity in Tan sheep. Therefore, this study has demonstrated that the meat qualities of Tan mutton may be evaluated by the serum biochemical indices of live Tan sheep. This study was conducted to determine the correlation among serum biochemical indices, slaughter traits, texture characteristics and water-holding capacity in Tan sheep. This study has demonstrated that meat qualities of Tan mutton may be assessed by the serum biochemical indices of live Tan sheep. It is necessary to determine slaughter traits, meat texture characteristics and water-holding capacity of meat, which are major indicators for assessing meat and meat products by consumers. [ABSTRACT FROM AUTHOR]

In this study, the ENVI 4.6 software was used to obtain the spectral reflection value of samples. The outlier samples were eliminated by the Monte Carlo method, and then SPXY (sample set partitioning based on be x–y distances) was used to divide the calibration set and prediction set. The spectral images were pretreated and characteristic wavelengths were extracted. The spectral models of full and pretreated spectra and characteristic bands were established by partial least squares regression (PLSR) and principle component regression (PCR), and the optimal modeling combination was selected. The results showed that the modeling effect of the original spectrum was the best. In full‐PLSR model, the determination coefficient of the calibration set (Rc2), the determination coefficient of prediction set (Rp2), and the determination coefficient of interactive verification set (Rcv2) were 0.8804, 0.7375, and 0.7422, and root‐mean‐square error of calibration set (RMSEC), root‐mean‐square error of prediction (RMSEP), and root mean square error of interactive validation set (RMSECV) were 2.3630, 2.9607, and 3.4209, respectively. PLSR and PCR models were established to obtain the optimal models of CARS‐PLSR and PCR‐PLSR. In the CARS‐PLSR model, the Rc2, Rp2, and Rcv2 were 0.9135, 0.7654, and 0.8171, respectively, while RMSEC, RMSEP, and RMSECV were 2.0275, 2.9306, and 2.9262, respectively. In the iRF‐PCR model, Rc2, Rp2, and Rcv2 were 0.7952, 0.7372, and 0.7280, respectively, while RMSEC, RMSEP, and RMSECV were 3.0207, 2.8278, and 3.4288, respectively. This study has demonstrated that visible and near‐infrared hyperspectral imaging system can rapidly predict the content of metmyoglobin in cooked tan mutton. Practical Application: This study has demonstrated that visible and near‐infrared (Vis/NIR) hyperspectral imaging system can rapidly predict the content of MetMb in cooked tan mutton. With the advantages of nondestructive, rapid, real‐time, Vis/NIR, hyperspectral imaging system can be widely expanded and applied to the detection of myoglobin in meat to evaluate the color of meat. [ABSTRACT FROM AUTHOR]