Research progress on mutton origin tracing and authenticity

Effects of sampling time and location on the geographical origin traceability of protected geographical indication (PGI) Hongyuan yak milk: Based on stable isotope ratios

Hongyuan yak milk is a protected geographical indication (PGI) product of rich nutritional value, which is popular among consumers. Stable isotope ratio analysis (SIRA) is an effective way to protect the authenticity of the geographical origin of PGI products, and it is crucial to study the factors affecting stable isotopes. Firstly, we proved that the SIRA could be used to identify the geographical origin of Hongyuan yak milk, and that the identification accuracy in combination with δ13C and δ18O was 100 %. Secondly, we analyzed the effect of sampling selection on the stable isotopes of Hongyuan yak milk in practical applications, which showed that sampling time influenced the δ13C, δ2H, and δ18O, while the sampling locations did not. There were interactions between the effect of sampling time and location on δ2H and δ18O. These results provide a reliable method for identifying PGI products and also provide new guidance on sampling models.

An innovative molecular approach towards the cost-effective entomological authentication of honey

Honey authentication and traceability are crucial not only for economic purposes but also for ensuring safety. However, the widespread adoption of cutting-edge technologies in practical applications has been hampered by complex, time-consuming sample pre-treatment processes, the need for skilled personnel, and substantial associated expenses. This study aimed to develop a simple and cost-effective molecular technique to verify the entomological source of honey. By utilizing newly designed primers, we successfully amplified the mitochondrial 16S ribosomal RNA gene of honey bees from honey, confirming the high quality of the extracted DNA. Employing RFLP analysis with AseI endonuclease, species-specific restriction patterns were generated for honey derived from six closely related honey bees of the Apis genus. Remarkably, this method was proven equally effective in identifying heat-treated and aged honey by presenting the same RFLP profiles as raw honey. As far as we know, this is the initial research of the simultaneous differentiation of honey from closely related honey bee species using the restriction endonuclease AseI and mitochondrial 16S rRNA gene fragments. As a result, it holds tremendous potential as a standardized guideline for regulatory agencies to ascertain the insect origins of honey and achieve comprehensive traceability.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Transcriptomics and metabolomics reveal improved performance of Hu sheep on hybridization with Southdown sheep

Consumers are increasingly demanding high-quality mutton. Cross breeding can improve meat quality and is widely used in sheep breeding. However, little is known about the molecular mechanism of cross breeding sheep meat quality. In this study, male Southdown and female Hu sheep were hybridized. The slaughter performance and longissimus dorsi quality of the 6-month-old hybrid offspring were measured, and the longissimus dorsi of the hybrid offspring was analyzed by transcriptomics and metabolomics to explore the effect of cross breeding on meat quality. The results showed that the production performance of Southdown × Hu F1 sheep was significantly improved, the carcass fat content was significantly decreased, and the eating quality of Southdown × Hu F1 sheep were better. Compared with the HS group (Hu × Hu), the NH group (Southdown × Hu) had 538 differentially expressed genes and 166 differentially expressed metabolites (P < 0.05), which were significantly enriched in amino acid metabolism and other related pathways. Up-regulated genes METTL21C, PPARGC1A and down-regulated gene WFIKKN2 are related to muscle growth and development. Among them, the METTL21C gene, which is related to muscle development, was highly correlated with carnosine, a metabolite related to meat quality (correlation > 0.6 and P < 0.05). Our results provide further understanding of the molecular mechanism of cross breeding for sheep muscle growth and meat quality optimization.

Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion

To achieve accurate detection the content of multiple parts pork adulterated in mutton under the effect of mutton flavor essence and colorant by RGB images, the improved CBAM-Invert-ResNet50 network based on the attention mechanism and the inversion residual was used to detect the content of pork from the back, front leg, and hind leg in adulterated mutton. The deep features of different parts extracted by the CBAM-Invert-ResNet50 were fused by feature, stitched, and combined with transfer learning, and the content of pork from mixed parts in adulterated mutton was detected. The results showed that the R2 of the CBAM-Invert-ResNet50 for the back, front leg, and hind leg datasets were 0.9373, 0.8876, and 0.9055, respectively, and the RMSE values were 0.0268 g·g-1, 0.0378 g·g-1, and 0.0316 g·g-1, respectively. The R2 and RMSE of the mixed dataset were 0.9264 and 0.0290 g·g-1, respectively. When the features of different parts were fused, the R2 and RMSE of the CBAM-Invert-ResNet50 for the mixed dataset were 0.9589 and 0.0220 g·g-1, respectively. Compared with the model built before feature fusion, the R2 of the mixed dataset increased by 0.0325, and the RMSE decreased by 0.0070 g·g-1. The above results indicated that the CBAM-Invert-ResNet50 model could effectively detect the content of pork from different parts in adulterated mutton as additives. Feature fusion combined with transfer learning can effectively improve the detection accuracy for the content of mixed parts of pork in adulterated mutton. The results of this study can provide technical support and a basis for maintaining the mutton market order and protecting mutton food safety supervision.

Marker-Free Isoelectric Focusing Patterns for Identification of Meat Samples via Deep Learning

Isoelectric focusing (IEF) is a powerful tool for resolving complex protein samples, which generates IEF patterns consisting of multiplex analyte bands. However, the interpretation of IEF patterns requires the careful selection of isoelectric point (pI) markers for profiling the pH gradient and a trivial process of pI labeling, resulting in low IEF efficiency. Here, we for the first time proposed a marker-free IEF method for the efficient and accurate classification of IEF patterns by using a convolutional neural network (CNN) model. To verify our method, we identified 21 meat samples whose IEF patterns comprised different bands of meat hemoglobin, myoglobin, and their oxygen-binding variants but no pI marker. Thanks to the high throughput and short assay time of the microstrip IEF, we efficiently collected 1449 IEF patterns to construct the data set for model training. Despite the absence of pI markers, we experimentally introduced the severe pH gradient drift into 189 IEF patterns in the data set, thereby omitting the need for profiling the pH gradient. To enhance the model robustness, we further employed data augmentation during the model training to mimic pH gradient drift. With the advantages of simple preprocessing, a rapid inference of 50 ms, and a high accuracy of 97.1%, the CNN model outperformed the traditional algorithm for simultaneously identifying meat species and cuts of meat of 105 IEF patterns, suggesting its great potential of being combined with microstrip IEF for large-scale IEF analyses of complicated protein samples.

ZnSnO3 based gas sensors for pyridine volatile marker detection in rice aging during storage

This study reports the development of ZnSnO₃ based gas sensors for pyridine detection in rice aging. Pyridine is one of heterocyclic markers formed via Maillard reaction and lipid oxidation. Herein, graphitic carbon nitride (g-C₃N₄) decorated ZnSnO₃ microstructures were obtained through a template-free approach. And the sensing results reveal that 5 wt%g-C₃N₄ decorated ZnSnO₃ exhibited a high sensitivity (47.9), a short response/recovery time (14/120 s) and a low detection limit (0.45 ppm), which is due to the catalysis of g-C₃N₄ nanosheets, the decorated microstructure and the formation of heterojunctions. Meanwhile, the practical experiment demonstrates that the sensitivity towards volatiles generated from Japonica rice aging is 48.7, which is around 4 and 2.5 times higher than those of Indica rice and Polished Glutinous rice, indicating that the sensor has anticipated application in the development of a high-performance E-nose for the quality inspection of rice and other products.

Non-genetic factors affecting the meat quality and flavor of Inner Mongolian lambs: A review

The Inner Mongolia Autonomous Region ranks first among the five major pastoral areas in terms of lamb breeding of China. The Inner Mongolia Autonomous Region has a vast territory, with many famous grasslands and thousands of forage plants and multiple local high-quality lamb breeds. After hundreds of years of artificial breeding and improvement, Mongolian sheep have developed many varieties. Different diets, feeding and treatment methods have effects on the production performance, lipid deposition and flavor composition of mutton sheep. Therefore, understanding the relationship among Inner Mongolian lamb, meat quality, and flavor will improve the production of high-quality mutton. The regulation of meat quality and flavor will have a profound impact on the deep processing and income-generating capabilities of mutton. Non-genetic factors affect the quality and flavor of mutton, which are more intuitive than genetic factors. In this review, we cover the contributions made by scientists to explore and improve the quality and flavor of Inner Mongolia lambs through non-genetic means, compare the differences between grazing and drylot-feeding in detail, and summarize some feed additives. We hope that based on our review, we can provide some inspiration to improve the meat quality of Mongolian sheep.