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In order to establish an effective mechanism for predicting spoilage of chilled goose breast meat, the microfloral succession was investigated by high-throughput sequencing and the dominant spoilage bacteria of chilled goose breast meat were determined. Meanwhile, the effects of three dominant spoilage bacteria on the volatile flavor substances of chilled goose breast meat were characterized by using gas chromatography-ion mobility spectrometry (GC-IMS) and solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS). The results showed that the diversity of microflora was richer during the early and late storage periods. The dominant phylum was Amoeba, and the dominant spoilage bacterium was Pseudomona. Pseudomona began to grow significantly starting from day 3 and reached a peak with a relative abundance of 96.79% on day 9. Contamination with the dominant spoilage bacteria significantly affected the composition of volatile components in goose breast at the end of storage, resulting in higher proportions and contents of esters and ketones than fresh goose breast. Orthogonal partial least squares discrimination analysis (OPLS-DA) showed that methyl palmitoleate, methyl palmitate, ethyl oleate, methyl linoleate, ethyl linoleate, ethyl palmitoleate, 3-hydroxy-2-butanone, methyl trans-oleate, ethyl trans-oleate and neopentyl hexanoate were the major contributors to the volatile flavor of spoiled goose breast.

The traditional hulling of Camellia oleifera fruit is mainly manual, with high work intensity, high labor cost and low production efficiency. Mechanized hulling is the inevitable trend of modernization of Camellia oleifera industry. The existing hulling device of Camellia oleifera fruit has poor universality, low hulling rate and high seed kernel breakage rate, which affects the popularization of mechanized hulling technology of Camellia oleifera. By comparing the existing hulling methods of Camellia oleifera, the structure and working principle of each hulling device were analyzed, their advantages and disadvantages were summarized, and their development direction was prospected.

Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two-dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi-scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University's (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology. [ABSTRACT FROM AUTHOR]

In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings. [ABSTRACT FROM AUTHOR]