Interpretation and rapid detection of secondary structure modification of actomyosin during frozen storage by near-infrared hyperspectral imaging

Effect of static magnetic field-assisted freezing at different temperatures on the structural and functional properties of pacific white shrimp (Litopenaeus vannamei) myofibrillar protein

The effects of static magnetic field-assisted freezing (MF) on the structural and functional characteristics of Litopenaeus vannamei myofibrillar protein (MP) at various temperatures (-35 ∼ -20 °C) were examined to assess its influence on MP and its energy-saving potential. The results indicated that -35 °C MF (MF-35) exhibited greater solubility and lower turbidity than -35 °C immersion freezing (IF-35), suggesting that MF-35 inhibited MP aggregation. MF-35 prevented the reduction in fluorescence intensity and α-helix content, protecting the MP tertiary and secondary structures. The emulsifying stability and gel strength of MF-35 surpassed those of the other frozen samples, indicating that MF-35 was the most efficient at mitigating the degradation of MP emulsifying and gel properties generated by freezing. No significant differences in solubility, surface hydrophobicity, emulsifying activity, and gel strength were detected between IF-35 and MF-25 (P > 0.05). In conclusion, MF impeded the denaturation of MP and exhibited energy-saving potential.

Advancements in Hyperspectral Imaging for Assessing Nutritional Parameters in Muscle Food: Current Research and Future Trends

Assessing the nutritional value of muscle food (MF) necessitates comprehensive component analysis. Traditional chemical analytical methods are often time-intensive, destructive, and environmentally detrimental, requiring specialized laboratory expertise. Hyperspectral imaging (HSI) emerges as an innovative technique that effectively integrates spectral and spatial information to enable rapid, nondestructive, and multidimensional predictions of nutritional parameters in MF. This Review examines the cutting-edge advancements in HSI technology, elucidating its novel technical and methodological dimensions. It systematically explores the principles and methodologies of HSI, presenting recent research and diverse applications in predicting MF nutritional parameters, and evaluates HSI's significant advantages and current limitations while addressing field-specific challenges and prospective research trends, ultimately positioning HSI as a potentially transformative tool in ensuring meat industry quality and safety.

The impacts of freeze-drying-induced stresses on the quality of meat and aquatic products: Mechanisms and potential solutions to acquire high-quality products

Freeze-drying is a preservation method known for its effectiveness in dehydrating food products while minimizing their deterioration. However, protein denaturation and oxidation during freezing and drying can degrade the quality of meat and aquatic products. Therefore, finding the strategies to ensure the dried products' sensory, functional, and nutritional attributes is crucial. This study aimed to summarize protein denaturation mechanisms and overall quality changes in meat and aquatic products during freezing and drying, while also exploring methods for quality control. Different freeze-drying conditions result in varying levels of oxidation and functionality in meat and aquatic products, leading to changes in quality, such as altered fatty and amino acid compositions, protein digestibility, and sensory attributes. To obtain high-quality dried products by freeze-drying, several parameters should be considered, including sample type, freezing and drying temperatures, moisture content, pulverization effects, and storage conditions.

Prediction of freezing point and moisture distribution of beef with dual freeze-thaw cycles using hyperspectral imaging

The freezing point (FP) is an important quality indicator of the superchilled meat. Currently, the potential of hyperspectral imaging (HSI) for predicting beef FP as affected by multiple freeze-thaw (F-T) cycles was explored. Correlation analysis revealed that the FP had a negative correlation with the proportion of bound water (P21) and a positive correlation with the proportion of immobilized water (P22). Moreover, the optimal wavelengths were selected by principal component analysis (PCA). Principal component regression (PCR) and partial least squares regression (PLSR) models were successfully developed based on the optimal wavelengths for predicting FP with determination coefficient in prediction (RP2) of 0.76, 0.76 and root mean square errors in prediction (RMSEP) of 0.12, 0.12, respectively. Additionally, PLSR based on full wavelengths was established for predicting P21 with RP2 of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P22 with RP2 of 0.87 and RMSEP of 0.66. The results show the potential of hyperspectral technology to predict the FP and moisture distribution of meat as a nondestructive method.

Data fusion of near-infrared and Raman spectroscopy: An innovative tool for non-destructive prediction of the TVB-N content of salmon samples

Total volatile basic nitrogen (TVB-N) serves as a crucial indicator for evaluating the freshness of salmon. This study aimed to achieve accurate and non-destructive prediction of TVB-N content in salmon fillets stored in multiple temperature settings (-20, 0, -4, 20 °C, and dynamic temperature) using near-infrared (NIR) and Raman spectroscopy. A partial least square support vector machine (LSSVM) regression model was established through the integration of NIR and Raman spectral data using low-level data fusion (LLDF) and mid-level data fusion (MLDF) strategies. Notably, compared to a single spectrum analysis, the LLDF approach provided the most accurate prediction model, achieving an R2P of 0.910 and an RMSEP of 1.922 mg/100 g. Furthermore, MLDF models based on 2D-COS and VIP achieved R2P values of 0.885 and 0.906, respectively. These findings demonstrated the effectiveness of the proposed method for precise quantitative detection of salmon TVB-N, laying a technical foundation for the exploration of similar approaches in the study of other meat products. This approach has the potential to assess and monitor the freshness of seafood, ensuring consumer safety and enhancing product quality.

Evaluation of the Quality Changes in Three Commercial Pastourma Samples during Refrigerated Storage Using Physicochemical, Microbiological, and Image Analyses Combined with Chemometrics

Despite the inherent stability of dried and cured products, such as pastourma, appropriate refrigeration remains essential for preserving their optimal characteristics. This study explored quality and safety characteristics in lamb, beef, and buffalo pastourma during 16-day refrigeration storage after package opening. The comprehensive approach employed Attenuated Total Reflection-Fourier-Transform Infrared (ATR-FTIR) spectroscopy, colorimetry, and image analysis, alongside physicochemical and microbiological analyses, to shed light on these alterations. The findings reveal a reduction in textural uniformity and color vibrancy (fading reds and yellows) across all samples during storage, with lamb pastourma exhibiting the most pronounced effects. Notably, image analysis emerged as a powerful tool, enabling the accurate classification of samples based on storage duration. Additionally, significant variations were observed in moisture content, hue angle, firmness, and TBARS levels, highlighting their influence on pastourma quality. The study documented a gradual decrease in lactic acid bacteria and aerobic plate count populations over time. ATR-FTIR spectra's interpretation revealed the presence of lipids, proteins, carbohydrates, and water. Protein secondary structures, demonstrably influenced by the meat type used, exhibited significant changes during storage, potentially impacting the functional and textural properties of pastourma. Overall, the findings contribute to a deeper understanding of pastourma spoilage during storage, paving the way for the development of improved preservation and storage strategies.

Changes in beef protein digestibility in an in vitro infant digestion model with prefreezing temperatures and aging periods

The protein digestibility of beef at three prefreezing temperatures (freezing at -20 °C, F20; freezing at -50 °C, F50; and freezing at -70 °C, F70) and aging periods (4, 14, and 28 days) was investigated using an in vitro infant digestion model. The increased cathepsin B activity in the frozen-then-aged treatments (P < 0.05) resulted in a higher content of 10% trichloroacetic acid-soluble α-amino groups than in the aged-only group on days 14 and 28 (P < 0.05). F50 had the most α-amino groups in the digesta and digested proteins under 3 kDa on day 28 (P < 0.05), with the disappearance of actin band in the digesta electrophoretogram. The secondary and tertiary structures of myofibrillar proteins revealed that F50 underwent irreversible denaturation (P < 0.05), especially in the myosin fraction, while F20 and F70 showed protein renaturation during aging (P < 0.05). In general, prefreezing at -50 °C then aging can improve the in vitro protein digestibility of beef through freezing-induced structural changes.

Freezing-induced denaturation of myofibrillar proteins in frozen meat

Freezing is commonly used to extend the shelf life of meat and meat products but may impact the overall quality of those products by inducing structural changes in myofibrillar proteins (MPs) through denaturation, chemical modification, and encouraging protein aggregation. This review covers the effect of freezing on the denaturation of MPs in terms of the effects of ice crystallization on solute concentrations, cold denaturation, and protein oxidation. Freezing-induced denaturation of MPs begins with ice crystallization in extracellular spaces and changes in solute concentrations in the unfrozen water fraction. At typical temperatures for freezing meat (lower than -18 °C), cold denaturation of proteins occurs, accompanied by an alteration in their secondary and tertiary structure. Moreover, the disruption of muscle cells triggers the release of cellular enzymes, accelerating protein degradation and oxidation. To minimize severe deterioration during the freezing and frozen storage of meat, there is a vital need to use an appropriate freezing temperature below the glass transition temperature and to avoid temperature fluctuations during storage to prevent recrystallization. Such an understanding of MP denaturation can be applied to determine the optimum freezing conditions for meat products with highly retained sensory, nutritional, and functional qualities.

Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR

INTRODUCTION

Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology.

OBJECTIVE

This research aimed to identify the geographical traceability for different parts of W. cocos.

METHODS

The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm^-1 , 6300-5150 cm^-1 and 4450-4050 cm^-1 of original spectra and the second-order derivative (SD) spectra preprocessed.

RESULTS

From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified.

CONCLUSION

Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation.

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.