Food Control最新文献

Contamination by aflatoxins and ochratoxin A of traditional Korean fermented soybean paste (Doenjang) is a serious food safety issue. In this study, we investigated the inhibitory effects of commercial Koji, a microbial starter culture used for fermentation, on mycotoxin contamination during Doenjang production at in vitro and scaled-up fermentation levels. A commercial Koji dominated by Aspergillus oryzae JI-4 exhibited strong antifungal and antimycotoxigenic activities against aflatoxigenic Aspergillus flavus and ochratoxigenic Penicillium verrucosum both in vitro and in situ. Furthermore, co-inoculation with Koji JI and mycotoxigenic fungi in the early stage of Doenjang fermentation significantly suppressed aflatoxins and ochratoxin A production by more than 93% (p < 0.05) and improved the nutritional properties and sensory qualities of the final product. These results suggest that the addition of commercial Koji could be an effective and practical approach to reduce aflatoxins and ochratoxin A contamination during the fermentation of soybean paste.

Chia oil has high commercial value due to polyunsaturated fatty acids (PUFAs), especially α-linolenic acid (ALA), and suffers from tampering. Traditional adulterant detection in oils applies gas chromatography, but this approach has disadvantages such as time consumption. The development of fast analytical methods like infrared spectroscopy is important to detect oil fraud. The study aims to employ mid-infrared (FTIR) and chemometrics to detect adulteration in chia oil. Chia oil was extracted by cold pressing and adulterated with sunflower, corn, and soybean oils. FTIR-ATR spectra were obtained using a Fourier transform infrared spectrophotometer and horizontal attenuated reflectance accessory (HATR). Partial least square (PLS) models were adjusted to predict the adulteration content in chia oil and to predict the fatty acid content, including ALA. Gas chromatography was the reference method for the fatty acid content, and the adulteration content was known. The model obtained for adulteration content in chia oil had a high predictive capacity with r² = 0.9868 for the prediction set and a low limit of detection (1.47%) and limit of quantification (4.40%). The models for fatty acid content also had good prediction capabilities (0.90 < r², RMSE <21 mg g⁻¹, RSD <6.5%, LOD <12 mg g⁻¹, and LOQ <36 mg g⁻¹). The results indicate that it is possible to quantify fraud in chia oil even using different adulterants when analyzing FTIR-ATR spectra in tandem with PLS. The proposed method is an important, fast, low-cost alternative for monitoring adulterations in vegetable oils.

Vibrations during transportation inevitably lead to mechanical damage, endangering grape freshness and directly impacting their economic worth. While adequate packaging serves as a viable solution, current studies on packaging efficacy lack depth. Moreover, conventional methods for forecasting fruit freshness fail to accommodate the varying freshness levels of grapes across different packaging techniques. Consequently, a novel approach for predicting fruit freshness leveraging multi-sensing technology and machine learning algorithms is introduced. By reasonably evaluating packaging performance, the automation, intelligence, and accuracy of fruit freshness prediction are enhanced. Initially, critical control points in grape supply chain logistics were scrutinized using the HACCP method to identify key environmental parameters (vibration, temperature, and humidity) and their interaction with grape freshness. Subsequently, an environmental monitoring platform was devised for the grape supply chain, facilitating environmental surveillance under distinct packaging types (corrugated carton, foam box, plastic box, and inflatable package). Through a blend of environmental monitoring outcomes and physical-chemical indicators, the protective efficacy of diverse transport packaging was meticulously analyzed and appraised alongside finite element analysis. Notably, environmental data proved capable of characterizing grape freshness in lieu of quality data, with vibration metrics exhibiting strong correlations with quality metrics. Machine learning models were developed to predict grape freshness based on environmental cues, yielding prediction accuracies of 92.512% (SVM) and 94.334% (GA-ANN). The automated, non-destructive data acquisition and novel machine learning approaches offer a fresh avenue for evaluating packaging, predicting freshness, and managing food quality within grape logistics operations.

This study aims to explore the presence of mycotoxins in total mixed rations (TMR) employed for feeding dairy cattle and their potential carry-over to milk. A total of 87 TRM samples were collected in farms from the north of Portugal from 2019 to 2022. A method based on a QuEChERS extraction followed by UHPLC-MS/MS detection was employed for sample analysis. The method was in-house validated in terms of matrix effect, linearity, sensitivity, accuracy and precision. The most frequently detected regulated toxins were fumonisins, with a positivity rate of 74%, while deoxynivalenol and zearalenone were found in approximately 35% of the samples. Among the emerging toxins, beauvericin and enniatins exhibited the highest detection rates. In addition, milk samples were collected from 21 farms, providing insights into the carry-over of these toxins and roquefortine to milk, with an estimated rate ranging between 2% and 10%.

Queen bee larvae (QBL), a by-product of royal jelly production, have nutritional value and confer health benefits. The commercialization of QBL is hindered because of the lack of evidence for its nutrition and safety parameters. Thus, this study was conducted to investigate the microbial dynamics, nutritional composition, and antioxidant activity of QBL, aiming to assess its potential as a food product. Lyophilized QBL powder samples were sterilized through gamma irradiation and high-pressure processing at varying doses and pressures. Gamma irradiation at 10 and 15 kGy effectively reduced the total aerobic count to 0, whereas high-pressure processing led to no significant reduction. Regarding the microbial profile of QBL, 16S rDNA gene sequencing unveiled five bacteria—Acinetobacter pittii, Bacillus thuringiensis, Enterococcus mundtii, Enterococcus faecalis, and Fructobacillus fructosus—but no major foodborne pathogens. Lactobacillus, Bombella, and Fructobacillus emerged as the predominant genera in QBL, with low abundances of Staphylococcus and Escherichia. Proximate composition analysis indicated no significant postirradiation changes in macronutrient contents; notably, protein was the predominant component (approximately 48.0%). Sugar and amino acid analyses revealed essential nutrients meeting human dietary requirements. Upon fatty acid analysis, palmitic and oleic acids emerged as predominant fatty acids. Gamma irradiation at 10 kGy exerted no significant effect on the nutritional composition or antioxidant activity of QBL. Our study provides valuable insights into the microbial safety and nutritional value of QBL, indicating its potential as a dietary protein source with favorable microbiota.

Excessive consumption of flour products containing excessive azodicarbonamide (ADA) has been associated with some adverse effects in humans. Thus, developing a facile method to monitor the amount of ADA in flour is extremely significant. Herein, a graphene quantum dot (GQD) sensitized upconversion nanoparticles (GQD-UCNPs) was designed to monitor the amount of ADA in flour with high specificity and sensitivity. Surface coating of UCNPs with GQD by coordination interaction significantly enhances the upconversion luminescence (UCL) intensity and the water solubility of UCNPs. Copper ions (Cu²⁺) can quench the UCL intensity of GQD-UCNPs through electron transfer processes. After introduction of glutathione (GSH), the UCL intensity of GQD-UCNPs gradually increases because of the interaction between GSH and Cu²⁺, accompanied by the generation of Cu⁺. The sulfydryl group of GSH can be oxidized to disulfide bond by ADA, which hinders the interaction between GSH and Cu²⁺, preventing the recovery of UCL intensity. Thus, the presence of ADA decreases the UCL intensity of GQD-UCNPs + Cu²⁺ + GSH system, allowing for the quantitative detection of ADA. The nanosensor realized a highly sensitive and specific ADA detection with a high detection limit down to 0.055 μM. Furthermore, excellent recovery rates in spiked real samples were obtained, indicating that the designed method possesses good prospects for practical application. Notably, this method broadens the application prospects of upconversion technology in the field of food safety.

Multi-species mushroom-based products (MPs) sold in Italy were authenticated by metabarcoding (MB). One degenerated primer pair was projected for the ITS-1 region amplification. The final data were interpreted using positive controls and extraction blanks. Sequences of macrofungi, microfungi and bacteria were detected in 100%, 90.9% and 77.3% of the MPs, respectively. Not edible and toxic macrofungi were also found, although in low sequence amount. The match between species declared on MPs label and species detected by MB was 59.1%. Cases of voluntary species substitution cannot be excluded. Methods to authenticate these products, still poorly investigated, should be further employed, and MB protocol should be standardized to be used in the context of both official control and companies’ self-control. Alternative approaches should be considered for canned and frozen MPs, in virtue of the observed DNA fragmentation.

In this study, we investigated the additive patterns observed in near-infrared (NIR) diffuse reflectance spectra within the context of food and pharmaceutical product formulation. Employing the Kubelka–Munk theory, we examined the linear correlation between spectra and concentration in polymer materials and tobacco powder samples. Our findings confirm the principle of spectral additivity in diffuse reflectance spectra, demonstrating a linear relationship when the sample scattering coefficient remains constant. Moreover, our results validate the feasibility of substituting actual mixed spectra with NIR additive spectra in tobacco leaf systems. This approach can potentially enhance formulation design, thereby improving efficiency and accuracy while expanding the scope and combination of formulation materials. Furthermore, this study offers a rapid, information-rich, and environmentally friendly alternative to traditional methods, with significant implications for the future of product formulation.

During the cold chain, fruits are exposed to variations in temperature and relative humidity, which will contribute to reducing the quality and commercial value of these perishable products. Aiming to extend the shelf life, reduce losses and increase profits from the trade of climacteric fruits, this work estimated the commercial value of ‘Pedro Sato’ guavas treated or not with hydrogen peroxide during the cold chain and evaluated the feasibility of replacing the management system of stock F.I.F.O. (first In, first Out) by F.E.F.O. (first expired, first out) The commercial value of guavas was determined by combining the values of weight loss, tonality and firmness, which were estimated by semi-empirical models. The experimental data showed that peroxide positively influenced the quality of guavas, quality assessments demonstrated that weight loss was the attribute that limited the shelf life of guavas and flow simulations, along the cold chain, confirmed that the implementation of a F.E.F.O. management system can increase retailer's profits by up to 13 %.

The effect of UV-C light-activated gallic acid (AGA) alone or combined with ultrasound (US) on the inactivation of E. coli and S. Typhimurium inoculated in aqueous solution and lettuce was studied. The contact time (0–30 min) and AGA concentration (0.05–0.25 M) were evaluated against microorganisms inoculated in aqueous solutions. At selected conditions, the combined effect of AGA and US process was evaluated against microorganisms inoculated in lettuce leaves; samples were taken from washing peptone water and the surface of lettuce using a cotton swab. The microbial inactivation of AGA in lettuce was described using the Monod model, which adequately reproduces the inactivation kinetics (R > 0.96). In aqueous solutions, 10 min of contact time and 0.15 M and 0.25 M of AGA were required to reduce more than 5 log reduction of S. Typhimurium and E. coli, respectively. In lettuce, the E. coli showed higher resistance than S. Typhimurium to the AGA treatment, obtaining a maximum microbial reduction (4.33 ± 0.17 log reduction) using washing peptone water as a sampling method and AGA plus US process. Moreover, the AGA treatment combined with the US process presented a higher microbial inactivation compared to a common disinfectant (colloidal silver). Therefore, it is possible to indicate that AGA combined with the US may be an adequate alternative to reduce enteropathogens in lettuce.