Guangzhi Wang,Yuchen Guo,Yang Yu,Yan Shi,Yuxiang Ying,Hong Men
Pork freshness is crucial for flavour, nutrition and consumer health. The current colorimetric sensor array (CSA) detection systems face challenges related to high sensor development costs, low recognition accuracy and limitations in the platform use. Herein, we developed a CSA and ColorNet framework to detect pork freshness. The 53-point CSA was designed by selecting sensitised pH indicators and aldehyde/ketone indicators. To optimize the sensor, the Euclidean distance method was used to identify 24 array points with dyes that exhibited more sensitive responses. The ColorNet captured the color information of pork freshness, allowing real-time detection with a 99.5 % accuracy. For practical deployment and mobile applications, a refined 12-point CSA was developed using gradient activation mapping, maintaining a 99 % recognition rate, which is comparable with the 24-point CSA. The proposed CSA and model ensure consumer health and safety, providing strong technical support for quality monitoring and control in the pork industry.
Aims This study aimed to design the Chinese Modified Olfactory Identification (CMOI) test based on the Sniffin' Sticks Olfactory Identification (SSOI) test by changing unfamiliar distractors and odors for more familiar ones for the Chinese population. Materials and methods We recruited 200 healthy volunteers (103 males and 97 females, aged 18–65 years, mean age 35.04 years, SD 10.96); in a survey, 100 volunteers rated their familiarity with 121 odors, including all the SSOI test odor descriptors and common odors in Chinese daily life. The SSOI test was modified according to the survey results. The other 100 volunteers were tested three times using the SSOI test, the Modified Distractors Olfactory Identification (MDOI) test established by modifying distractors in the SSOI test, and the CMOI test developed by using familiar unpleasant odors to displace the odors with low correct recognition rates in the MDOI test. We compared the test scores of the volunteers during the modification process. Results Volunteers were unfamiliar with 31 odor descriptors in the SSOI test; 23 distractors with low familiarity were displaced with more familiar distractors. The three odors with the lowest correct recognition rate in the MDOI test (apple, leather, and pineapple) were displaced with familiar unpleasant odors. The test scores were significantly higher in the CMOI test than in others ( p < 0.0001); the correct recognition rate in the CMOI test was significantly higher than in the SSOI test ( p < 0.01). Conclusion The test scores in the CMOI test were significantly improved; it prevented choosing wrongly due to unfamiliarity with an odor and its distractors.
Yuan Pan,Hui-Wen Gu,Yi Lv,Xiao-Li Yin,Ying Chen,Wanjun Long,Haiyan Fu,Yuanbin She
Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines.
危险品化学品经营许可证（带存储）