Evaluation of quality changes in big‐eye tuna ( Thunnus obesus ) based on near‐infrared reflectance spectroscopy ( NIRS ) and low field nuclear magnetic resonance ( LF‐NMR )

Effect of different freezing conditions on ice crystal formation behavior and ice-growth inhibition by cryoprotectants

BACKGROUND

The formation of ice crystals will have adverse effects on aquatic products, and the key to ensure the long-term preservation and better quality preservations of the product is to evaluate the intercellular ice crystal formation to find suitable refrigeration conditions and cryoprotectants.

RESULTS

The ice crystal formation was successfully captured by using an inverted microscope cryomicroscopic system equipped with a low-temperature stage, the ice crystals formed under different freezing methods between tuna muscle cells were observed directly, the deformation degree of muscle tissue pores during crystallization was evaluated, and the effect of freeze-thaw times on tuna samples was analyzed. The effects of the use of cryoprotectant such as cellobiose and carboxylated cellulose nanofibers on ice-growth inhibition were investigated, and the reliability of the ice crystal observation results was further verified by the determination of physical properties. The results showed that carboxylated cellulose nanofibers had the best ice-growth inhibition effect, they prevented about 50% cell deformation compared with the control group, and also reduced the minimum size of ice crystal formation. In addition, the addition of cellobiose and sodium tripolyphosphate gave the ice crystals a more uniform size and roundness.

CONCLUSION

The experiment proposed a stable and clear observation method for the process of intercellular ice crystal formation, and the accuracy of the observation method was further verified by some physical indicators. This may help in the selection of suitable measurement methods to directly observe ice crystal formation behavior and screen cryoprotectants. © 2024 Society of Chemical Industry.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Carboxymethylation modification, characterization of dandelion root polysaccharide and its effects on gel properties and microstructure of whey protein isolate

In the present study, a native polysaccharide (DP) with sugar content of 87.54 ± 2.01 % was isolated from dandelion roots. DP was chemically modified to obtain a carboxymethylated polysaccharide (CMDP) with DS of 0.42 ± 0.07. DP and CMDP were composed of the same six monosaccharides including mannose, rhamnose, galacturonic acid, glucose, galactose, and arabinose. The molecular weights of DP and CMDP were 108,200 and 69,800 Da, respectively. CMDP exhibited more stable thermal performance and better gelling properties than DP. The effects of DP and CMDP on the strength, water holding capacity (WHC), microstructure, and rheological properties of whey protein isolate (WPI) gels were investigated. Results showed that CMDP-WPI gels had higher strength and WHC than DP-WPI gels. With the addition of 1.5 % CMDP, WPI gel had a good three-dimensional network structure. The apparent viscosities, loss modulus (G"), and storage modulus (G') of WPI gels were increased with the polysaccharide addition, the influence of CMDP was remarkable compared to DP at the same concentration. These findings suggest that CMDP may be used as a functional ingredient in protein-containing food products.

Measuring water holding capacity in pork meat images using deep learning

Water holding capacity (WHC) plays an important role when obtaining a high-quality pork meat. This attribute is usually estimated by pressing the meat and measuring the amount of water expelled by the sample and absorbed by a filter paper. In this work, we used the Deep Learning (DL) architecture named U-Net to estimate water holding capacity (WHC) from filter paper images of pork samples obtained using the press method. We evaluated the ability of the U-Net to segment the different regions of the WHC images and, since the images are much larger than the traditional input size of the U-Net, we also evaluated its performance when we change the input size. Results show that U-Net can be used to segment the external and internal areas of the WHC images with great precision, even though the difference in the appearance of these areas is subtle.

Identification of Illicit Conservation Treatments in Fresh Fish by Micro-Raman Spectroscopy and Chemometric Methods

In the field of food control for fresh products, the identification of foods subjected to illicit conservation treatments to extend their shelf life is fundamental. Fresh fish products are particularly subjected to this type of fraud due to their high commercial value and the fact that they often have to be transported over a long distance, keeping their organoleptic characteristics unaltered. Treatments of this type involve, e.g., the bleaching of the meat and/or the momentary abatement of the microbial load, while the degradation process continues. It is therefore important to find rapid methods that allow the identification of illicit treatments. The study presented here was performed on 24 sea bass samples divided into four groups: 12 controls (stored on ice in the fridge for 3 or 24 h), and 12 treated with a Cafodos-like solution for 3 or 24 h. Muscle and skin samples were then characterized using micro-Raman spectroscopy. The data were pre-processed by smoothing and taking the first derivative and then PLS-DA models were built to identify short- and long- term effects on the fish's muscle and skin. All the models provided the perfect classification of the samples both in fitting and cross-validation and an analysis of the bands responsible for the effects was also reported. To the best of the authors' knowledge, this is the first time Raman spectroscopy has been applied for the identification of a Cafodos-like illicit treatment, focusing on both fish muscle and skin evaluation. The procedure could pave the way for a future application directly on the market through the use of a portable device.