Deep learning in a sensor array system based on the distribution of volatile compounds from meat cuts using GC–MS analysis

Quality characterization of tobacco flavor and tobacco leaf position identification based on homemade electronic nose

A set of nine unique tobacco extract samples was analyzed using a self-developed electronic nose (E-nose) system, a commercial E-nose, and gas chromatography-mass spectrometry (GC-MS). The evaluation employed principal component analysis, statistical quality control, and soft independent modeling of class analogies (SIMCA). These multifaceted statistical methods scrutinized the collected data. Subsequently, a quality control model was devised to assess the stability of the sample quality. The results showed that the custom E-nose system could successfully distinguish between tobacco extracts with similar odors. After further training and the development of a quality control model for accepted tobacco extracts, it was possible to identify samples with normal and abnormal quality. To further validate our E-nose and extend its use within the tobacco industry, we collected and accurately classified the flavors of different tobacco leaf positions, with a remarkable accuracy rate of 0.9744. This finding facilitates the practical application of our E-nose system for the efficient identification of tobacco leaf positions.

Flavor formation based on lipid in meat and meat products: A review

Meat product is popular throughout the world due to its unique taste. Flavor is one of the most important quality characteristics of meat products and also is a key influencing factor in the overall acceptability of meat products. The flavor of meat products is formed by precursors undergoing a series of complex reactions. During meat product processing, lipids are hydrolyzed by lipase to produce flavor precursors such as free fatty acid, then further oxidized to form volatile flavor compounds. This review summarizes lipolysis, lipid oxidation, and interaction of lipid with Maillard reaction and amino acid during meat products processing and storage as well as influencing factors on lipid degradation including raw meat (source of meat, feeding pattern, and castration), processing methods (thermal processing, nonthermal processing, salting, and fermentation) and additives. Meanwhile, the volatile compounds produced by lipids in meat products including aldehydes, alcohols, ketones, and hydrocarbons are summed up. Analytical methods of volatile compounds and the application of lipidomics analysis in mechanisms of flavor formation of meat products are also reviewed. PRACTICAL APPLICATIONS: Flavor is one of the most important quality characteristics of meat products, which influences the acceptability of meat products for consumption. Lipids play an important role in the flavor formation of meat products. Understanding the relationship between flavor compounds and changes in lipid compositions during the processing and storage of meat products will be helpful to control the quality of meat products.

Predictive Modeling of Changes in TBARS in the Intramuscular Lipid Fraction of Raw Ground Beef Enriched with Plant Extracts

The aim of the study was to develop and compare the predictive models of lipid oxidation in minced raw beef meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary and thyme) expressed as value changes of TBARS (thiobarbituric acid reactive substances) in various time/temperature conditions. Meat samples were stored at the temperatures of 4, 8, 12, 16 and 20 °C. The value changes of TBARS in samples stored at 12 °C were used as the external validation dataset. Lipid oxidation increased significantly with storage time and temperature. The rate of this increase varied depending on the addition of the plant extract and was the most pronounced in the control sample. The dependence of lipid oxidation on temperature was adequately modeled by the Arrhenius and log-logistic equation with high average R2 coefficients (≥0.98) calculated for all extracts. Kinetic models and artificial neural networks (ANNs) were used to build the predictive models. The obtained result demonstrates that both kinetic Arrhenius (R2 = 0.972) and log-logistic (R2 = 0.938) models as well as ANN (R2 = 0.935) models can predict changes in TBARS in raw ground beef meat during storage.