Effects of umami substances as taste enhancers on salt reduction in meat products: A review.

Sodium is one of the essential additives in meat processing, but excessive sodium intake may increase risk of hypertension and cardiovascular disease. However, reducing salt content while preserving its preservative effect, organoleptic properties, and technological characteristics poses challenges. In this review, the mechanism of salt reduction of umami substances was introduced from the perspective of gustation-taste interaction, and the effects of the addition of traditional umami substances (amino acids, nucleotides, organic acids(OAs)) and natural umami ingredients (mushrooms, seaweeds, tomatoes, soybeans, tea, grains) on the sensory properties of the meat with reduced-salt contents were summarized. In addition, the impacts of taste enhancers on eating quality (color, sensory, textural characteristics, and water-holding capacity (WHC)), and processing quality (lipid oxidation, pH) of meat products (MP) and their related mechanisms were also discussed. Among them, natural umami ingredients exhibit distinct advantages over traditional umami substances in terms of enhancing quality and nutritional value. On the basis of salt reduction, natural umami ingredients improve the flavor, texture, WHC and antioxidant capacity. This comprehensive review may provide the food industry with a theoretical foundation for mitigating salt consumption through the utilization of umami substances and natural ingredients.

Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review.

Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.

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.

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

This study aimed to develop simplified models for rapid and nondestructive monitoring myoglobin contents (DeoMb, MbO2 and MetMb) during refrigerated storage of Tan sheep based on a hyperspectral imaging (HSI) system in the spectral range of 400-1000 nm. Partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) were applied to correlate the spectral data with the reference values of myoglobin contents measured by a traditional method. In order to simplify the LSSVM models, competitive adaptive reweighted sampling (CARS) and Interval variable iterative space shrinkage approach (iVISSA) were used to select key wavelengths. The new CARS-LSSVM models of DeoMb and MbO2 yielded good results, with R2p of 0.810 and 0.914, RMSEP of 1.127 and 2.598, respectively. The best model of MetMb was new iVISSA-CARS-LSSVM, with an R2p of 0.915 and RMSEP of 2.777. The overall results from this study indicated that it was feasible to predict myoglobin contents in Tan sheep using HSI.