Sodium nitrite is commonly used as a multifunctional curing ingredient in the processing of prepared dishes, especially meat products, to impart unique color, flavor and to prolong the shelf life of such products. However, the use of sodium nitrite in the meat industry has been controversial due to potential health risks. Finding suitable substitutes for sodium nitrite and controlling nitrite residue have been a major challenge faced by the meat processing industry. This paper summarizes possible factors affecting the variation of nitrite content in the processing of prepared dishes. New strategies for controlling nitrite residues in meat dishes, including natural pre-converted nitrite, plant extracts, irradiation, non-thermal plasma and high hydrostatic pressure (HHP), are discussed in detail. The advantages and limitations of these strategies are also summarized. Raw materials, cooking techniques, packaging methods, and storage conditions all affect the content of nitrite in the prepared dishes. The use of vegetable pre-conversion nitrite and the addition of plant extracts can help reduce nitrite residues in meat products and meet the consumer demand for clean labeled meat products. Atmospheric pressure plasma, as a non-thermal pasteurization and curing process, is a promising meat processing technology. HHP has good bactericidal effect and is suitable for hurdle technology to limit the amount of sodium nitrite added. This review is intended to provide insights for the control of nitrite in the modern production of prepared dishes.
Vegetable sauerkraut is a traditional fermented food. Due to oxidation reactions that occur during storage, the quality and flavor in different periods will change. In this study, the quality evaluation and flavor characteristics of 13 groups of vegetable sauerkraut samples with different storage time were analyzed by using physical and chemical parameters combined with electronic nose. Photographs of samples of various periods were collected, and a convolutional neural network (CNN) framework was established. The relationship between total phenol oxidative decomposition and flavor compounds was linearly negatively correlated. The vegetable sauerkraut during storage can be divided into three categories (full acceptance period, acceptance period and unacceptance period) by principal component analysis and Fisher discriminant analysis. The CNN parameters were fine-tuned based on the classification results, and its output results can reflect the quality changes and flavor characteristics of the samples, and have better fitting, prediction capabilities. After 50 epochs of the model, the accuracy of three sets of data namely training set, validation set and test set recorded 94%, 85% and 93%, respectively. In addition, the accuracy of CNN in identifying different quality sauerkraut was 95.30%. It is proved that the convolutional neural network has excellent performance in predicting the quality of Szechuan Sauerkraut with high reliability.