Non-destructive assessment of the myoglobin content of Tan sheep using hyperspectral imaging

Lipidomics analysis reveals the effects of Schizochytrium sp. supplementation on the lipid composition of Tan sheep meat

Schizochytrium sp. (SZ) can potentially be employed in nutritional strategies for producing high-quality sheep meat. However, the effects of SZ on the lipid composition of sheep meat are insufficiently understood. In this study, the effects of SZ supplementation on the lipid profile of Tan sheep meat were evaluated using non-targeted lipidomic techniques. Lipidomics analysis revealed 383 differential lipids (DLs) between the SZ and control groups, and there were six metabolic pathways associated with lipids, including glycerophospholipid metabolism, glycerolipid metabolism, α-linolenic acid metabolism, linoleic acid metabolism, glycine, serine and threonine metabolism, and arachidonic acid metabolism (P < 0.05). Glycerophospholipid metabolism was the core pathway of DLs; we found that phosphatidylcholine, phosphatidylserine, and lysophosphatidylcholine were the crucial lipid metabolites of this pathway. Dietary supplementation with SZ increased n-3 polyunsaturated fatty acid (PUFA), C22:6n-3, and C20:5n-3 (P < 0.05), while it decreased C18:0, saturated fatty acid (SFA), and SFA/PUFA (P < 0.05). These results indicate that SZ supplementation induces positive alterations in the lipid profile of Tan sheep meat, which is beneficial to meat quality and sheds valuable insights into the future development of functional lipids in sheep meat.

Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish

The polyunsaturated fatty acids (PUFAs), particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are critical determinants of the nutritional quality of fish. To rapidly and non-destructively determine the muscular PUFAs in living fish, an accuracy technique is urgently needed. In this study, we combined skin hyperspectral imaging (HSI) and machine learning (ML) methods to assess the muscular PUFAs contents of common carp. Hyperspectral images of the live fish skin were acquired in the 400-1000 nm spectral range. The spectral data were preprocessed using Savitzky-Golay (SG), multivariate scattering correction (MSC), and standard normal variable (SNV) methods, respectively. The competitive adaptive reweighted sampling (CARS) method was applied to extract the optimal wavelengths. With the skin spectra of fish, five ML methods, including the extreme learning machine (ELM), random forest (RF), radial basis function (RBF), back propagation (BP), and least squares support vector machine (LS-SVM) methods, were used to predict the PUFAs and EPA + DHA contents. With the spectral data processed with the SG, the RBF model achieved outstanding performance in predicting the EPA + DHA and PUFAs contents, yielding coefficients of determination (R2 P) of 0.9914 and 0.9914, root mean square error (RMSE) of 0.3352 and 0.3346, and mean absolute error (MAE) of 0.2659 and 0.2660, respectively. Finally, the visualization distribution maps under the optimal model would facilitate the direct determination of the fillet PUFAs and EPA + DHA contents. The combination of skin HSI and the optimal ML method would be promising to rapidly select living fish having high muscular PUFAs contents.

A new comprehensive quantitative index for the assessment of essential amino acid quality in beef using Vis-NIR hyperspectral imaging combined with LSTM

The quality of beef is usually predicted by measuring a single index rather than a comprehensive index. To precisely determine the essential amino acid (EAA) contents in 360 beef samples, the feasibility of optimized spectral detection techniques based on the comprehensive EAA index (CEI) and comprehensive weight index (CWI) constructed by factor analysis was explored. Two-dimensional correlation spectroscopy (2D-COS) was used to analyse the mechanisms of spectral peak shifts in complex disturbance systems with CEI and CWI contents, and 15 sensitive feature variables were extracted to establish a quantitative analysis model of a long short-term memory network (LSTM). The results indicated that 2D-COS had good predictive performance in both CEI-LSTM (R2P of 0.9095 and RPD of 2.76) and CWI-LSTM (R2P of 0.8449 and RPD of 2.45), which reduced data information by 88%. This indicates that utilizing 2D-COS can eliminate collinearity and redundant information among variables while achieving data dimensionality reduction and simplification of calibration models. Furthermore, a spatial distribution map of the comprehensive EAA content was generated by combining the optimal prediction model. This study demonstrated that the comprehensive index method furnishes a new approach to rapidly evaluate EAA content.

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

Multi-task convolutional neural network for simultaneous monitoring of lipid and protein oxidative damage in frozen-thawed pork using hyperspectral imaging

Lipid and protein oxidation are the main causes of meat deterioration during freezing. Traditional methods using hyperspectral imaging (HSI) need to train multiple independent models to predict multiple attributes, which is complex and time-consuming. In this study, a multi-task convolutional neural network (CNN) model was developed for visible near-infrared HSI data (400-1002 nm) of 240 pork samples treated with different freeze-thaw cycles (0-9 cycles) to evaluate the feasibility of simultaneously monitoring lipid oxidation (thiobarbituric acid reactive substance content) and protein oxidation (carbonyl content) in pork. The performance of the commonly used partial least squares regression (PLSR) model based on the spectra after pre-processing (Standard normal variate, Savitzky-Golay derivative, and Savitzky-Golay smoothing) and feature selection (Regression coefficients) and single-output CNN model was compared. The results showed that the multi-task CNN model achieved the optimal prediction accuracies for lipid oxidation (R²p = 0.9724, RMSEP = 0.0227, and RPD = 5.2579) and protein oxidation (R²p = 0.9602, RMSEP = 0.0702, and RPD = 4.6668). In final, the changes of lipid and protein oxidation of pork in different freeze-thaw cycles were successfully visualized. In conclusion, the combination of HSI and multi-task CNN method shows the potential of end-to-end prediction of pork oxidative damage. This study provides a new, convenient and automated technique for meat quality detection in the food industry.

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the R p 2 , R c 2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the R p 2 , R c 2 , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near-infrared hyperspectral imaging (NIR-HSI) combined with two-dimensional correlation spectroscopy (2D-COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D-COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH₂ in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136-1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D-COS analysis, and provided excellent results (R²_C of 0.8141, R²_P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D-COS combined with NIR-HSI could be used as an effective method to monitor Ala content in beef.

Evaluation of key aroma compounds and protein secondary structure in the roasted Tan mutton during the traditional charcoal process

The traditional charcoal technique was used to determine the changes in the key aroma compounds of Tan mutton during the roasting process. The results showed that the samples at the different roasting time were distinguished using GC-MS in combination with PLS-DA. A total of 26 volatile compounds were identified, among which 14 compounds, including (E)-2-octenal, 1-heptanol, hexanal, 1-hexanol, heptanal, 1-octen-3-ol, 1-pentanol, (E)-2-nonenal, octanal, 2-undecenal, nonanal, pentanal, 2-pentylfuran and 2-methypyrazine, were confirmed as key aroma compounds through the odor activity values (OAV) and aroma recombination experiments. The OAV and contribution rate of the 14 key aroma compounds were maintained at high levels, and nonanal had the highest OAV (322.34) and contribution rate (27.74%) in the samples after roasting for 10 min. The content of α-helix significantly decreased (P < 0.05), while the β-sheet content significantly increased (P < 0.05) during the roasting process. The content of random coils significantly increased in the samples roasted for 0-8 min (P < 0.05), and then no obvious change was observed. At the same time, β-turn content had no obvious change. Correlation analysis showed that the 14 key aroma compounds were all positively correlated with the content of α-helix and negatively correlated with the contents of β-sheet and random coil, and also positively correlated with the content of β-turn, except hexanal and 2-methypyrazine. The results are helpful to promoting the industrialization of roasted Tan mutton.

Predictability of carcass traits in live Tan sheep by real-time ultrasound technology with least-squares support vector machines

This study aimed to investigate the performance of least-squares support vector machines to predict carcass characteristics in Tan sheep using noninvasive in vivo measurements. A total of 80 six-month-old Tan sheep (37 rams and 43 ewes) were examined. Back fat thickness and eye muscle area between the 12th and 13th ribs were measured using real-time ultrasound in live Tan sheep. All carcasses were dissected to hind leg, longissimus dorsi muscle, lean meat, fat, and bone to determine carcass composition. Multiple linear regression (MLR), partial least squares regression (PLSR), and least-squares support vector machines (LSSVM) were applied to correlate the live Tan sheep characteristics with carcass composition. The results showed that the LSSVM model had a better efficacy for estimating carcass weight, longissimus dorsi muscle weight, lean meat weight, fat weight, lean meat, and fat percentage in live lambs (R = 0.94, RMSE = 0.62; R = 0.73, RMSE = 0.02; R = 0.86, RMSE = 0.47; R = 0.78, RMSE = 0.63; R = 0.73, RMSE = 0.02; R = 0.65, RMSE = 0.03, respectively). LSSVM algorithm was a potential alternative to the conventional MLR method. The results demonstrated that LSSVM model might have great potential to be applied to the evaluation of sheep with superior carcass traits by combining with real-time ultrasound technology.

Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration

Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models' performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin.

Prediction of carcass composition and meat and fat quality using sensing technologies: A review

Consumer demand for high-quality healthy food is increasing, &nbsp;thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.

UHPLC-Q-Orbitrap HRMS-based quantitative lipidomics reveals the chemical changes of phospholipids during thermal processing methods of Tan sheep meat

Thermal processing affects the lipid compositions of meat products. The study determined the effects of boiled, steamed and roasted processing methods on the lipidomics profiles of Tan sheep meat with a validated UPLC-Q-Orbitrap HRMS combined lipid screening strategy method. Combined with sphingolipid metabolism, the boiled approach was the suitable choice for atherosclerosis patients for more losses of sphingomyelin than ceramide in meat. The similarly less losses of phosphatidylcholine and lysophosphatidylcholine showed in glycerophospholipid metabolism implied that steamed Tan sheep meat was more suitable for the populations of elderly and infants. Furthermore, a total of 90 lipids with significant difference (VIP > 1) in 6 lipid subclasses (sphingomyelin, ceramide, lysophosphatidylcholine, phosphatidylcholine, phosphatidylethanolamines, triacylglycerol,) were quantified among raw and three types of thermal processed Tan sheep meat, further providing useful information for identification of meat products with different thermal processing methods (LOD with 0.14-0.31 μg kg^-1, LOQ with 0.39-0.90 μg kg^-1).

Development of a novel quantitative function between spectral value and metmyoglobin content in Tan mutton

This study was aimed to establish a quantitative function between spectral reflectance values and metmyoglobin (MetMb) content in Tan mutton during refrigeration. Near-infrared hyperspectral data combined with generalized two-dimensional correlation spectroscopy (G2D-COS) method to identify characteristic bands and investigate the sequence of chemical waveband changes. Characteristic wavebands identified by G2D-COS analysis had the best performance in predicting the content of MetMb, with a high R²p of 0.849, a low RMSEP of 2.695 and a high RPD of 2.786. The results showed that the G2D-COS may be a powerful tool for describing intensity changes of MetMb band. The partial least square regression method was used to develop the relationships between the spectral values and MetMb content in Tan mutton meat for predicting MetMb content. This study has provided a convenient and rapid non-destructive quantitative method for assessing the color of Tan mutton meat.

Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on laboratory measures and are often subject to human error. Therefore, this study proposes novel hyperspectral imaging (400–1000 nm) based non-destructive isos-bestic myoglobin (Mb) spectral features for minced meat classification. A total of 60 minced meat spectral cubes were pre-processed using true-color image formulation to extract regions of interest, which were further normalized using the Savitzky–Golay filtering technique. The proposed pipeline outperformed several state-of-the-art methods by achieving an average accuracy of 88.88%.