This study was carried out in three phases. During the first stage, the physicochemical properties of French sauce prepared from different blends of soybean and olive oils were evaluated. In the second stage, the oxidative stability of the optimum sauce sample from the first stage enriched with various amounts of olive leaf polyphenolic extract (OLE) (obtained via ultrasound-assisted extraction) was investigated over 90 days of storage. During the third stage, the microbiological and sensory properties of the samples containing the optimum amounts of OLE, as a substitution for the synthetic preservatives, were studied. According to the results, addition of olive oil at higher levels (75 and 100%) could affect the physicochemical properties of the sauce as compared to the control sample. It was also found that the addition of olive oil (up to 50%) would not significantly impact the sauce properties. Regarding the OLE enrichment in the samples, it was found that high levels of OLE could improve the oxidative stability of the samples. Based on the results of the experiments in the third phase, it was found that OLE could be used as a preservative instead of the commercial ones. Overall, this study suggests the potential use of olive oil and olive leaf extract in the preparation of French sauce to boost its nutritional value and its stability.

Lactobacillus plantarum has been observed to play a crucial role in shaping the sensory properties of chili sauce. However, the specific taste-active metabolites responsible for the desirable flavor profile of chili sauce remain inadequately characterized. This study employed a combination of metabolomics and web-based computational tools analysis to investigate the dynamic changes in taste-active metabolites during chili sauce fermentation. Initially, metabolites were rapidly annotated using a feature-based molecular network, leading to the tentative annotation of 206 metabolites, of which a significant proportion had not been previously reported. Subsequently, the VirtualTaste tool identified dihydrosphingosine, lactic acid, isoleucine, phytosphingosine, and gluconic acid as potential taste markers for quality control. Finally, pathway enrichment analysis revealed that these components were primarily associated with amino acid tRNA, phenylalanine, tyrosine, and tryptophan biosynthesis, as well as sphingolipid metabolism. This study provides valuable insights into the mechanisms underlying the formation of the distinctive flavor of chili sauce.

The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.