Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system

Development and characterization of a new potato starch/watermelon peel pectin composite film loaded with TiO2 nanoparticles and microencapsulated Lycium barbarum leaf flavonoids and its use in the Tan mutton packaging

Reinforced edible film with active nanoparticles has been in increasing demand as a new technology to improve the quality and extend the shelf-life of muscle foods. The study aimed to fabricate and characterize a novel potato starch (Pst)/watermelon peel pectin (Wpp) composite film with the microencapsulated Lycium barbarum leaf flavonoids (MLF) and nano-TiO2 (Pst/Wpp/MLF/TiO2) and further apply the film in Tan mutton preservation. The moisture content, thickness and water vapor permeability (WVP) of the composite film were relatively increased with increasing the percentage of MLF, while nano-TiO2 had slight influence on the thickness, but leaded to a significantly decreased the moisture content and WVP. Also, the SEM images showed that the roughness and porosity were created on the film surface by adding MLF and nano-TiO2. FTIR revealed electrostatic and hydrogen bond interactions between the components in the film system. Meanwhile, MLF and nano-TiO2 effectively enhanced the mechanical strength, UV-barrier, controlled-release, thermal stability, antimicrobial and antioxidation properties of the Pst/Wpp film. Also, the composite film containing MLF and nano-TiO2 significantly inhibited the growth of microorganisms and chemical deterioration of mutton samples, which suggested that such film has potential as a prospective active packaging for preserving Tan mutton.

A rapid extraction process monitoring of Swertia mussotii Franch. With near infrared spectroscopy

Swertia mussotii Franch. (SMF), a traditional Tibetan medicine, which has miraculous effect on treating hepatitis diseases. However, there is no research on its entire production process, and invisible production process has seriously hindered the SMF modern development. In this study, principal component analysis (PCA), subtractive spectroscopy, and two-dimensional correlation spectroscopy (2D-COS) were used to explain changes of characteristic groups in the extraction process. Four main characteristic peaks at 1884 nm, 1944 nm, 2246 nm and 2308 nm were identified to describe the changes of molecular structure information of total active components in SMF extraction process. In addition, multi critical quality attributes (CQAs) models were established by near-infrared spectroscopy (NIRS) combined with the total quantum statistical moment (TQSM). The coefficients of determination (R²_eval and R²_ival) were both greater than 0.99. The ratios of the standard deviation of validation to the standard error of the prediction (RPD_e and RPD_i) were greater than five. The quantitative model of AUC_T could save time on primary data measurement by not requiring determination of indicator components compared with others. In conclusion, these results demonstrated that it was feasible to understand the SMF extraction process through AUC_T and characteristic groups. These could realize the visual digital characterization and quality stability of the SMF extraction process.

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.

Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka-Munk spectral data

Impact damage is one of the main forms of damage during the postharvest transportation and processing of yellow peaches. Thus, a quantitative prediction of the impact damage degree of yellow peaches is significant for their postharvest grading. In the present study, mechanical parameters such as the damage area, absorbed energy and maximum force were obtained based on a single pendulum collision device and an intelligent data acquisition system. The reflection spectra (R) of the damaged areas of yellow peaches were collected by a hyperspectral imaging system and transformed into absorbance (A) spectra and Kubelka-Munk (K-M) spectra. The R, A and K-M spectra were preprocessed by standard normal variables (SNV), moving average (MA) and Gaussian filtering (GF). Partial least squares regression (PLSR) models and support vector regression (SVR) models based on original and preprocessed spectra were established, respectively. By comparative analysis, the spectral data with better prediction performance (raw or preprocessed spectra) were selected from all spectra, and the characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The PLSR and SVR models based on characteristic wavelengths were established. The results revealed that the prediction performance of the K-M-GF-CARS-PLSR model is the best. For the damage area, absorbed energy and maximum force, the R _P ² and RMSEP of the K-M-GF-CARS-PLSR model were 0.870 and 77.865 mm², 0.772 and 1.065 J, 0.895 and 47.996 N, respectively. Furthermore, the values of their RPD were 2.700, 1.768 and 3.050, respectively. The characteristic wavelengths of the model were 18.8%, 10.2% and 21.6%, respectively. The results of this study showed that there was a strong correlation between the mechanical parameters and K-M spectrum, which demonstrates the feasibility of quantitatively predicting the damage degree of yellow peaches based on the K-M spectrum. Therefore, the results of this work not only provide theoretical guidance for the postharvest grading of fruits, but also enrich the theoretical system of biomechanics.

The application of computer vision systems in meat science and industry - A review

Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisition devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.

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

Consumer demand for high-quality healthy food is increasing,  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.

NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness

In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.