Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review

Effect of l-cysteine on functional properties and fibrous structure formation of 3D-printed meat analogs from plant-based proteins

The development of three-dimensional (3D) printed meat analogs with fiber, texture, and sensory resembling meat remains challenging. This study investigated the effect of l-cysteine on functionality enhancement and fibrous structure formation in mixtures of mung bean protein isolate (MBPI) and wheat gluten (WG) for meat analog production. 3D printing was used to construct fibrous filaments. Raw MBPI-WG mixtures decreased rheological properties when increasing l-cysteine contents (0.0%-0.6%), promoting ink extrudability. The cys-0.4% ink exhibited the highest printing resolution and structural stability, correlated with its higher mechanical strength and increased disulfide cross-links. After cooking, the cys-0.4% sample showed a pronounced fibrousness in agreement with its microstructure image. This meat analog displayed a muscle-meat-like structure, improved texture, and reduced beany odor and bitter taste. Excessive cysteine contents (0.5%-0.6%) negatively affected the functionality of meat analogs. This study provides guidance for optimizing the amount of l-cysteine in meat analogs to improve product quality.

Inclusion of Beef Heart in Ground Beef Patties Alters Quality Characteristics and Consumer Acceptability as Assessed by the Application of Electronic Nose and Tongue Technology

Consumer purchasing of beef is often driven by the trinity of flavor, palatability, and convenience. Currently, beef patties in the United States are manufactured with fat and lean trimmings derived from skeletal muscles. A reduction in total beef supply may require the use of animal by-product utilization such as variety meats to achieve patty formulations. The current study aimed to assess textural, color, and flavor characteristics in addition to volatile compounds through electronic technology, e-nose and e-tongue, of ground beef patties formulated with beef heart. Ground beef patties were manufactured with 0%, 6%, 12%, or 18% beef heart, with the remainder of the meat block being shoulder clod-derived ground beef. Patties (n = 65/batch/treatment) within each batch (n = 3) with each treatment were randomly allocated to cooked color (n = 17/batch/treatment), Allo-Kramer shear force (AKSF; n = 17/batch/treatment), texture profile analysis (TPA; n = 6/batch/treatment), cooking loss (n = 17/batch/treatment), consumer panel (n = 3/batch/treatment), e-nose (n = 1/batch/treatment), and e-tongue (n = 1/batch/treatment) analysis groups. Patties containing beef heart did not require additional cooking time (p = 0.1325) nor exhibit greater cooking loss (p = 0.0803). Additionally, inclusion rates of beef heart increased hardness (p = 0.0030) and chewiness values (p = 0.0316) in TPA, were internally redder (p = 0.0001), and reduced overall liking by consumer panelists (p = 0.0367). Lastly, patties containing beef heart exhibited greater red-to-brown (p = 0.0003) and hue angle (p = 0.0001) values than control patties. The results suggest that beef heart inclusion does alter ground beef quality characteristics and consumer acceptability.

Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging

In the food industry, quality and safety issues are associated with consumers' health condition. There is a growing interest in applying various noninvasive sensorial techniques to obtain quickly quality attributes. One of them, hyperspectral/multispectral imaging technique has been extensively used for inspection of various food products. In this paper, a stacking-based ensemble prediction system has been developed for the prediction of total viable counts of microorganisms in beef fillet samples, an essential cause to meat spoilage, utilizing multispectral imaging information. As the selection of important wavelengths from the multispectral imaging system is considered as an essential stage to the prediction scheme, a features fusion approach has been also explored, by combining wavelengths extracted from various feature selection techniques. Ensemble sub-components include two advanced clustering-based neuro-fuzzy network prediction models, one utilizing information from average reflectance values, while the other one from the standard deviation of the pixels' intensity per wavelength. The performances of neurofuzzy models were compared against established regression algorithms such as multilayer perceptron, support vector machines and partial least squares. Obtained results confirmed the validity of the proposed hypothesis to utilize a combination of feature selection methods with neurofuzzy models in order to assess the microbiological quality of meat products.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Characterization and Discrimination of Volatile Compounds in Chilled Tan Mutton Meat during Storage Using HiSorb-TD-GC-MS and E-Nose

Chilled Tan mutton is currently the mainstream of Tan mutton production and consumption in China, but the reports on chilled meat quality evaluation and shelf-life discrimination by volatiles are limited. This study aimed to investigate the changes of volatile compounds in chilled Tan mutton at four storage stages (1d, 3d, 5d, 7d) in order to differentiate the various storage stages. An analysis protocol was established for the characterization and discrimination of the volatiles in chilled Tan mutton based on high capacity sorptive extraction-thermal desorption-gas coupled with chromatography-mass spectrometry (HiSorb-TD-GC-MS), electronic nose (E-nose), and multivariate statistical analysis. A total of 96 volatile compounds were identified by HiSorb-TD-GC-MS, in which six compounds with relative odor activity value >1 were screened as the key characteristic volatiles in chilled Tan mutton. Four storage stages were discriminated by partial least squares discriminant analysis, and nine differential volatile compounds showed a variable importance for the projection score >1, including octanoic acid, methyl ester, decanoic acid, methyl ester, acetic acid, heptanoic acid, methyl ester, propanoic acid, 2-hydroxy-, methyl ester, (ñ)-, hexanoic acid, propanoic acid, butanoic acid, and nonanoic acid. With the volcano plot analysis, hexadecanoic acid, methyl ester, was the common volatile marker candidate to discriminate chilled stages of Tan mutton. Meanwhile, E-nose could discriminate chilled Tan mutton at different storage stages rapidly and efficiently using linear discriminant analysis. Furthermore, E-nose sensors could obtain comprehensive volatile profile information, especially in esters, acids, and alcohols, which could confirm the potential of E-nose for meat odor recognition. Thus, this analysis protocol could characterize and discriminate the volatiles in chilled Tan mutton during storage.