Objective To explore the mineral element differences in different visceral by-products of Simmental beef cattle in Ningxia. Methods Using post-slaughter by-products from Ningxia Simmental cattle (beef heart, beef liver, beef tripe, beef intestines) as the research subject, the content differences of 42 kinds of mineral elements in the 4 kinds of by-products were detected and analyzed. And verified through discriminant analysis. Results The results indicated that there were significant differences in the content of 38 kinds of and 41 kinds of mineral elements respectively in the 4 kinds of beef cattle by-products from Guyuan and Wuzhong regions (P>0.05). The content of Pb, Cd, Hg, As, Cr in all beef cattle by-products is below the limit standards. Beef tripe has a strong adsorption and accumulation effect on heavy metals such as Ni and Cr, rare earth elements Eu, Gd, Nd and Sm, as well as other elements included Nb, Sb and Sn; beef liver had a strong absorption and enrichment effect on heavy metals like Cd, trace elements Cu and Mo. Based on mineral elements, effective differentiation of various by-products and samples from different origins could be achieved. Selected Cs, Na, Li, Cd, Tl, Rb, Cr, U, Pt, Ta, Co, Sc, Mo, Ni, Zn, Ir, Sn and Fe as the key characteristic elements for distinguishing between cow stomach, cow liver, cow heart, and cow intestine. Eleven key elements had been identified to distinguish the origin of beef by-products from Guyuan and Wuzhong, included Cs, Cr, Rb, As, Sn, Nb, Ni, Li, Sb, Ti and Hg. Conclusion Beef by-products have their own elemental characteristics, and different by-products and samples from different origins can be effectively distinguished by the content of mineral elements. The research findings will help establish a quality database for beef by-products, providing data support for the development of new products by enterprises.

Objective To establish a fingerprint spectrum for the volatile compounds of A brand ready-to-eat bird's nest by solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS), and conduct differential analysis on different brands and types of ready-to-eat bird's nest products. Methods Using commercially available ready-to-eat bird's nest as the research object, SPME-GC-MS was used to analyze the volatile substances in 37 batches of A brand ready-to-eat bird's nest samples with different storage periods. The volatile substances were analyzed through the database of NIST 20 and the method of peak area normalization. The “TCM chromatographic fingerprint similarity evaluation system” was used to establish the volatile substances fingerprint spectrum of A brand ready-to-eat bird's nest. Results Under the service of NIST 20 database, 26 kinds of characteristic volatile components were identified in brand A's ready-to-eat bird's nest, mainly including unsaturated compounds such as 5-hydroxymethylfurfural, nonanol, lauryl alcohol, tetrahydro lavender alcohol, benzaldehyde, and so on. A fingerprint spectrum of the volatile substances in this ready-to-eat bird's nest product was established. The evaluation criteria were based on a total of 26 matching peaks, the similarity was greater than or equal to 65.8%, which could be well applied to evaluate the quality of different commercially available ready-to-eat bird's nests. Conclusion By analyzing the composition of volatile substances in ready-to-eat bird's nest and establishing a fingerprint and similarity evaluation method of volatile components for ready-to-eat bird's nest, it is possible to effectively distinguish different quality levels of ready-to-eat bird's nest, providing a scientific basis for quality control and authenticity identification of ready-to-eat bird's nest.

In the face of global population growth and agricultural production pressure, as well as the serious challenges of agricultural product quality and safety issues, the traditional methods of agricultural product integrity monitoring and risk prediction have become insufficient. The rapid development of machine learning technology provides new solution ideas for agricultural product integrity monitoring and risk prediction. This paper systematically summarized the applications of machine learning technology in agricultural product safety risk monitoring (including physical, chemical and biological risks), agricultural product authenticity and traceability assurance, and agricultural product risk assessment prediction based on historical data. Machine learning technology undoubtedly improves the efficiency of agricultural product integrity monitoring effectively, realizes early detection and prevention of risks, and provides new solution for constructing a safer and more reliable food supply chain provides new solutions. Although these applications show great promise, there are still challenges to artificial intelligence in the field of agricultural produce integrity monitoring and risk prediction. Based on summarizing the literature, this paper further explored the prospects and directions of this trend, and presented the importance of machine learning model interpretability and trust issues, as well as problems in data acquisition and use, to improve the application of machine learning in agricultural product integrity monitoring and risk prediction.

Objective To develop a rapid and sensitive detection technology for pathogenic microorganisms in food packaging based on the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 13a (Cas13a) system. Methods The method leveraged the specific recognition and signal amplification characteristics of the CRISPR-Cas13a technology. Multiple guide RNAs were designed to target different sites on pathogenic RNA, enabling a single RNA molecule to activate multiple Cas13a enzymes simultaneously. This cascade triggered trans-cleavage of RNA reporter molecules in the reaction mixture, generating a detectable fluorescent signal. The tandem design synergistically enhanced signal amplification efficiency, thereby improving detection sensitivity. Results The limits of detection of this method for pathogenic and RNA were 800 copies/mL and 18.7 pmol/L, respectively, and the detection could be completed in a short time, accurately distinguishing different pathogens. Testing food packaging samples contaminated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudvirus showed recovery rates ranging from 89.71% to 102.56%. Conclusion This study presents a rapid, cost-effective and easy-to-operate method for detecting pathogenic microbial contamination on food packaging surfaces, indicating high accuracy, offering potential application value for monitoring microbial risks in foodborne transmission pathways.

Objective To study the preparation and physicochemical properties of polypeptide-selenium chelate from Juglans regia L.. Methods Juglans regia L. protein was extracted by alkali-soluble acid precipitation method, and Juglans regia L. polypeptide was prepared by alkaline protease. Juglans regia L. polypeptide reacted with sodium selenite to produce Juglans regia L. polypeptide-selenium chelate. The preparation technology was optimized by single factor test and orthogonal test. Results When the mass ratio of polypeptide to sodium selenite was 3:1, the chelating temperature was 45 ℃, the chelating time was 50 min, and the pH was 8, the chelating rate was 58.99%±1.44%. The chelate was resistant to heat, acid and alkali, had good digestive stability and high bioavailability of selenium. Compared with Juglans regia L. polypeptide, Juglans regia L. polypeptide-selenium chelate had stronger scavenging ability of 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical, hydroxyl free radical and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) cation free radical. Conclusion In this study, Juglans regia L. polypeptide-selenium chelate is successfully prepared through optimized synthesis conditions. The resulting complex demonstrates favorable physicochemical properties and significant antioxidant capacity, providing a potential raw material reference for developing selenium-enriched nutritional supplements.

Objective To establish a rapid visual detection method for Aspergillus niger in Zea mays L. by loop-mediated isothermal amplification (LAMP) technology. Methods Based on LAMP technology, specific primers targeting the key ochratoxin A (OTA) biosynthesis gene OTAhal were designed. Through screening of the primers and optimization of conditions such as primer sequence, primer ratio, reaction temperature and visual dye, the optimal detection conditions were determined. Using the total DNA of Aspergillus niger and artificially contaminated corn samples as templates, the sensitivity of the detection method and the feasibility of its practical application were validated. Results The optimal primer ratio was 8:4:1, the optimal reaction temperature was 64 °C, and the method could complete the amplification of target DNA within 30 minutes. The color of the positive sample changed from violet to blue-purple. This method could detect DNA concentrations as low as 6.87×10⁻³ ng/μL. When applied to actual Zea mays L. sample testing, it achieved a detection limit as low as 10¹ spores/mL. Conclusion This method is simple to operate, highly sensitive, and does not require sophisticated instruments. It can be applied to the detection of OTA in actual Zea mays L. samples, indicating broad application prospects.

Objective To optimize the extraction process of total polyphenols in Artemisia capillaris and study the content of it in Henan Province. Methods A single factor optimization combined with response surface optimization was used to establish the optimal method for extracting total polyphenols from Artemisia capillaris, mainly examining the effects of solid-liquid ratio, ethanol volume fraction, extraction time and extraction temperature on the extraction of total polyphenols from Artemisia capillaris. The samples of Artemisia capillaris were taken from 18 cities in Henan Province, and the total polyphenol content in Artemisia capillaris was analyzed based on soil type and distribution area. Results The best values of 4 factors were as follows: Solid-liquid ratio 1:30 (m:V), ethanol concentration 50%, extraction time 72 min, extraction temperature 63 ℃, and the best extraction rate of total polyphenols was 1.85%. With the best values of 4 factors, the total polyphenols in Artemisia capillaris from 18 regions in Henan was determined and analyzed. The highest value appears in Anyang and the lowest value appears in Zhengzhou. According to the types of soil, the tide soil had the highest value of total polyphenols, which was 7.51 mg/g, and the paddy soil had the lowest value of total polyphenols, which was 6.15 mg/g. Conclusion The method optimized by this study is stable, reliable and economy. The model established can predict the extraction rate of total polyphenols well. The content of total polyphenols in Artemisia capillaris from different regions in Henan is discrepant. But the discrepancy is not significant according to the soil types.

Liquid fermentation technology has garnered significant attention since the mid-20th century and continued to advance with the evolution of biotechnology. Its advantages lie in shortening culture cycles, enhancing strain activity and purity, and reducing production costs, among other benefits. As a critical component of liquid fermentation technology, edible fungus liquid fermentation enables efficient production of mycelium and bioactive metabolites through optimization of medium components, fermentation parameters and metabolic regulation pathways. This paper reviewed the research progress of edible fungus liquid fermentation technology, with a focus on its advantages, challenges and application prospects in production processes. Through liquid fermentation, not only can the production efficiency of edible fungus mycelium be improved, but also bioactive components such as polysaccharides, terpenoids and peptides can be effectively extracted. These components possess diverse biological activities, including anti-tumor, antioxidant and immunomodulatory effects, holding significant importance for the food and pharmaceutical industries. In addition, the application of liquid fermentation technology for edible fungi in producing fungal metabolites and flavor substances has also demonstrated remarkable potential, providing the food industry with abundant natural ingredients. Based on current research achievements, this paper analyzed the key factors, development trends and major challenges faced by liquid fermentation technology in the edible fungi industry, and prospected the application directions and prospects of this technology in food, pharmaceuticals, and other fields. This review provides a theoretical reference for the efficient production of mycelia and active metabolites, and facilitates the translational application of edible fungus liquid fermentation in various fields.

Objective To develop an ultra performance liquid chromatography-tandem mass spectrometry method for the determination of Nisin residues in various foods. Methods The samples were extracted with 0.1% formic acid solution+acetonitrile (80:20, V:V), delipid with n-hexane, eluted with 0.1% formic acid solution and acetonitrile as mobile phase gradient, and separated by ZORBAX Eclipse Plus C₁₈ (100 mm×2.1 mm, 1.8 μm). Electrospray ion source and positive ion multi-reaction monitoring mode were used for detection, and matrix matching standard curve external standard method was used for quantification. Results The linear relationship between Nisin A and Nisin Z was good in the range of 0.5 to 50.0 μg/mL, and the correlation coefficient was greater than 0.995. The limits of detection and quantitation were 0.05 to 0.30 mg/kg and 0.15 to 1.00 mg/kg, respectively. The recoveries ranged from 88.8% to 106.2% and the relative standard deviations were 1.32% to 8.85% at the 3 added levels of 1.0, 5.0 and 200.0 mg/kg. Conclusion The established method is fast, efficient, repeatable, sensitive and accurate, and can be used for the determination of Nisin A and Nisin Z in batch food samples.

Objective To establish a method for the simultaneous determination of 19 kinds of common preservatives, sweeteners and synthetic colorants in beverages by high performance liquid chromatography (HPLC). Methods HPLC was employed with an ACE Excel 5 C₁₈ column (4.6 mm×250 mm, 5 μm) at a flow rate of 1.0 mL/min. Gradient elution was performed using 20 mmol/L ammonium acetate (pH 7.5) and methanol as the mobile phases, and detection was carried out using a diode array multi-wavelength detector. Results Effective separation of all 19 kinds of food additives was achieved. The linear range of the method was 0.5-50.0 μg/mL, with correlation coefficients above 0.9998. The average recovery rates ranged from 92.26% to 104.59%, with relative standard deviations (RSDs) between 0.45% and 1.67%. The limits of detection were 0.07-0.29 mg/kg, and the limits of quantitation were 0.23-0.96 mg/kg, both of which were below the national standards and met the detection requirements. Conclusion This method is simple to operate, requires short pre-treatment time, provides good separation efficiency, accurate data, and high sensitivity, making it suitable for the simultaneous determination of 19 kinds of food additives in beverages.