Feasibility of Using Electrical Impedance Spectroscopy for Assessing Biological Cell Damage during Freezing and Thawing

AI-based R&D for frozen and thawed meat: Research progress and future prospects

Frozen and thawed meat plays an important role in stabilizing the meat supply chain and extending the shelf life of meat. However, traditional methods of research and development (R&D) struggle to meet rising demands for quality, nutritional value, innovation, safety, production efficiency, and sustainability. Frozen and thawed meat faces specific challenges, including quality degradation during thawing. Artificial intelligence (AI) has emerged as a promising solution to tackle these challenges in R&D of frozen and thawed meat. AI's capabilities in perception, judgment, and execution demonstrate significant potential in problem-solving and task execution. This review outlines the architecture of applying AI technology to the R&D of frozen and thawed meat, aiming to make AI better implement and deliver solutions. In comparison to traditional R&D methods, the current research progress and promising application prospects of AI in this field are comprehensively summarized, focusing on its role in addressing key challenges such as rapid optimization of thawing process. AI has already demonstrated success in areas such as product development, production optimization, risk management, and quality control for frozen and thawed meat. In the future, AI-based R&D for frozen and thawed meat will also play an important role in promoting personalization, intelligent production, and sustainable development. However, challenges remain, including the need for high-quality data, complex implementation, volatile processes, and environmental considerations. To realize the full potential of AI that can be integrated into R&D of frozen and thawed meat, further research is needed to develop more robust and reliable AI solutions, such as general AI, explainable AI, and green AI.

Bioimpedance as an alternative tool for subjective, visual scoring of a prevalent ham quality defect

The detection of meat quality defects can involve both subjective and objective methods. PSE-like meat is linked to a common pork defect and can be caused by rapid post-mortem damage of muscle fibers. This damage can again be linked to various factors, such as a low ultimate pH or a higher slaughter weight. PSE-like defects are characterized by discoloration, structural damage, and excessive moisture loss. However, the lack of suitable instrument-based methods makes the detection of PSE-like defects difficult, and subjective methods typically suffer from poorer reproducibility. The objective of this study was to establish how subjective visual evaluation correlates with electrical impedance spectroscopy and with traditional quality parameters. To do so, visual scoring was performed together with measurements of bioimpedance, color, and pH in two ham muscles (Adductor, Semimembranosus) for 136 animals 24-hours post-mortem. When comparing with visual scoring, Pearson correlation analysis shows the strongest correlation for bioimpedance (Py , r = -0.46, R2 = 21%), followed by pHu (r = 0.44, R2 = 19%). When using all five quality measures, i.e., Py , pHu, and CIELAB L * a * b *, the multivariate regression model had a prediction error of 0.76 for the visual scores. This was close to the error describing the subjective bias of visual scoring, more specifically the prediction error between the two observers (0.85). In all, Py showed the strongest correlation among instrument-based quality tests and alone may be used for predicting pork ham structural defects, i.e., as an instrument-based alternative for subjective, visual scoring. However, an instrument that combines Py with pH and/or L*a*b* would improve the prediction of PSE-like quality defects.

Beyond standard PSE testing: An exploratory study of bioimpedance as a marker for ham defects

During post-mortem conversion from muscle to meat, diverse quality anomalies can emerge. Recent pork defects are often accompanied by deteriorating fibre structure. Here we investigate how bioimpedance response, an indicator of structural disintegration, can help in detecting quality defects. We, first, measured the relationship between standard meat quality variables (pHu, CIELAB, drip loss) and bioimpedance (BI) response. To screen for defect-biomarkers that are linked to aberrant bioimpedance and physicochemical indicators of quality decline, we performed LC-MS/MS proteomic analysis on samples, classified with a multivariate-based separation into good versus poor quality. We found that BI correlated significantly with, e.g., colour and drip loss. Proteomics revealed eleven proteins to be unique for either, good or poor ham quality groups, and maybe linked to structural degradation. In all, our data supports a wider integration of BI testing in pork quality testing to assess structural disintegration, which can render ham unsuitable for, e.g., costly curing.

Bioimpedance-based Authentication of Defrosted Versus Fresh Pork at the End of Refrigerated Shelf Life

Correct food labeling is a legal requirement and helps consumers to make informed purchasing choices. Mislabeling defrosted meat as fresh is illegal in the EU. However, there are no standardized technologies to authenticate fresh versus defrosted meat. We address this by testing if bioimpedance-based measurements can separate defrosted meat from refrigerated-only meat at the end of shelf life, i.e., when also fresh meat shows deterioration. Pork sirloin samples from 20 pigs were first tested at 12 days postmortem ('fresh group'). This time point was chosen to represent a typical use-by date for refrigerated storage of fresh pork. Then, all samples were transferred to a -24°C freezer for 3 days and thawed for 2 days before final testing ('frozen-thawed group'). Bioimpedance analyses (BIA) were done in a frequency range of [10²-10⁶ Hz]. Weight, pH and electrode positioning were assessed to test for potential confounding effects. Statistics for treatment dependent differences were based on the established P_y parameter and phase angle, which were extracted from the BI spectra. We found that using bioimpedance testing with tetrapolar electrodes, P_y and phase angle allowed almost complete separation of fresh and previously frozen samples. However, within the whole sample population, there was some overlap between the spectra of fresh and frozen samples. Yet, based on P_y, only one fresh sample (5% of N_total=20) fell in the lowest P_y class with all the frozen samples. We used a multifactorial design that allowed to test the effects of potential confounding factors, such as electrode positioning and meat quality parameters. We found a relatively low explained variance for the P_y parameter, indicating that confounding effects from other factors or quality defects in fresh pork may affect the detection capacity of bioimpedance-based authentication of fresh pork. Our data, therefore, suggest that reliable fresh-label authentication with bioimpedance testing should be based on testing a small number of samples to represent a specific lot of pork that is to be inspected.