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.
This cross-sectional study was conducted to examine the most effective strategies for managing malodorous and infected wounds in patients who have been diagnosed with advanced cervical cancer. The research was conducted in Liupanshui, China. The study specifically examined demographic profiles, wound characteristics and effectiveness of wound management approaches. The study incorporated the heterogeneous sample of 289 participants who fulfilled the inclusion criteria. Data collection was conducted via structured questionnaires and medical record evaluations. Descriptive statistics and statistical analyses, such as regression analysis, were utilized to evaluate demographic attributes, wound profiles and effects of different approaches to wound management. The findings unveiled the heterogeneous demographic composition of patients, encompassing differences in socioeconomic standing, educational attainment and age. A wide range of wound characteristics were observed, as 65.7% of lesions during the acute phase with diameter between 2 and 5 centimetres, while 41.5% of lesions had this range. The most prevalent types of infections were those caused by fungi (48.4%), followed by bacterial infections lacking resistance (38.1%). A moderate degree of odour intensity was prevalent, affecting 45.0% of the cases. With maximal odour reduction of 80%, a mean healing time of 25 days and patient satisfaction rating of 4.5 out of 5, Negative Pressure Wound Therapy demonstrated itself to be the most efficacious treatment method. Additional approaches, such as photodynamic therapy and topical antibiotic therapy, demonstrated significant effectiveness, as evidenced by odour reductions of 70% and 75%, respectively, and patient satisfaction ratings of 4.3 and 4.2. Thus, the study determined challenges associated with management of malodorous and infected lesions among patients with advanced cervical cancer. The results underscored the significance of individualized care approaches, drew attention to efficacious wound management techniques and identified critical determinants that impacted patient recuperation. The findings of this study hold potential for advancing palliative care for individuals diagnosed with advanced cervical cancer.