Journal of Food Safety最新文献

In the present study, the ethanolic extracts derived from Hass avocado pulp were observed to exhibit exceptional bioactive qualities and demonstrate bactericidal efficacy against a wide range of microorganisms, encompassing both gram-positive and gram-negative bacteria, as well as fungi. Therefore, this work aimed to develop a biodegradable active film by incorporating the Hass avocado extracts into chitosan/gelatin-based film (HGCF) for the preservation of beef and pork. The study reveals that the chitosan/gelatin-based film (GCF) and HGCF exhibit significant water stability and absorption capabilities. HGCF offered the synergy of antimicrobial properties of Hass avocado extracts and high swelling in water of chitosan/gelatin blend to absorb liquid discharged from fresh meat for prolonged storage. HGCF demonstrated a significant effectiveness in controlling microbial density in comparison to uncoated samples and samples coated with plain gelatin/chitosan film. In detail, HGCF was able to partially eliminate Staphylococcus aureus and Escherichia coli during storage, resulting in the densities after 14 days that were similar to those of uncoated meat samples after 6 days. These results demonstrate the potential of HGCF as active packaging for food preservation and advance the sustainable production and preservation of meat products.

To address the challenges posed by chemical methods for detecting pesticide residues in sorghum, such as complicated sample preparation and prolonged detection periods, this study presents a rapid and nondestructive detection approach based on hyperspectral imaging (HSI) technology. A group of sorghum without pesticide residues and three groups uniformly sprayed with pesticides were used in this study. Firstly, support vector machine (SVM) classification models were built using spectral data preprocessed with Savitzky–Golay (SG), discrete wavelet transform (DWT), and standard normal variate (SNV) methods, respectively, and SNV was determined to be the best preprocessing method. Secondly, the gradient boosting decision tree (GBDT) algorithm, principal component analysis (PCA), and the successive projections algorithm (SPA) were respectively used to extract feature wavelengths. Pesticide residue identification models based on full and feature wavelengths were then respectively established using backpropagation neural network (BPNN), SVM, and partial least squares discriminant analysis (PLS-DA). The results show that the BPNN model developed using the feature wavelengths obtained from GBDT was the best for identification of pesticide residues, with an accuracy of 97.8% for both the training and testing sets. Finally, visualization of pesticide residue species in sorghum was achieved using the optimal model. This study demonstrates that utilizing HSI in conjunction with the GBDT-BPNN model is an effective, rapid, and nondestructive method for identifying pesticide residues in sorghum.

Campylobacter jejuni is a causative agent of gastroenteritis in humans worldwide, and wild and domestic poultry carry of this bacterium in their gastrointestinal tract. Molecular studies to determine the pathogenicity, origin, and epidemiological relationships among C. jejuni isolates from poultry such as chicken, turkey, and goose consumed as human food are important for public health and infection control. This study aimed to investigate the prevalence of virulence genes and Enterobacterial Repetitive Intergenic Consensus (ERIC-PCR) based genotyping of C. jejuni isolates obtained from goose cloacal swab samples. For this purpose, PCR analysis of flaA, racR, dnaJ, pldA, cadF, cdtC, ciaB, cdtB, cdtA, virB11, and wlaN virulence genes and ERIC-PCR analysis of 50 C. jejuni isolates were performed. The emerged genetic profiles and antimicrobial resistance genes regarding the isolates were interpreted with the existing multi-drug resistance (MDR) findings. Virulence gene positivity was detected as 88%, 84%, 82%, 82%, 80%, 80%, 72%, 30%, and 18% for flaA, racR, dnaJ, pldA, cadF, cdtC, ciaB, cdtB, and cdtA, respectively. VirB11 and wlaN genes were not detected among the C. jejuni isolates. Virulence genes-based genotyping revealed that the C. jejuni isolates exhibited 22 profiles (A–V). As a result of ERIC-PCR analysis, the C. jejuni isolates showed heterogeneous distribution, exhibiting 14 different ERIC-PCR profiles (Cluster I [Cl-I]–Cluster XIV [Cl-XIV]). The MDR positivity was detected in 7 (14%) of the C. jejuni isolates. Tetracycline and ciprofloxacin were the antibiotics most frequently included in the MDR profiles. There was no clear correlation between ERIC-PCR profiles, virulence gene profiles, and MDR profiles. However, isolates with triple-MDR resistant to ampicillin, tetracycline, and ciprofloxacin showed significant heterogeneity in both ERIC-PCR profile and virulence gene-based genetic profile, all of which were positive for ciaB and flaA genes. These results indicate that carriage of the C. jejuni isolates with high gene prevalence and MDR profiles by geese may pose a risk for Campylobacter infections in humans.

The study aimed to develop a portable electronic nose system for detecting adulteration with soybean protein isolate (SPI) in chicken meat. The system mainly consisted of three parts: the gas sensor array, the DSP28335 control board, and the upper computer. The DSP28335 control board, developed using C language, included analog to digital converter (ADC) module, digital output (DO) module, pulse width modulation (PWM) module, controller area network (CAN) module, power module, drive circuit, and so forth. The upper computer, developed using LabVIEW, facilitated user interaction with the user by primarily handling CAN configuration and monitoring, displaying and storing sensor data, temperature and flow data, and sending and monitoring electronic nose commands. The feasibility of the proposed electronic nose for characterizing adulterated chicken meat was tested on six classes of chicken meat that had been adulterated with varied quantities of SPI. The mass fractions of SPI were 0%, 5%, 10%, 15%, 20%, and 25%, respectively. On the basis of odor data from the electronic nose, K-nearest neighbor (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) were applied to qualitatively distinguish minced chicken meat with different adulteration ratios. The results showed that the SVM model had the best recognition effect. When the best parameters (c, g) were c = 16 and g = 1, the accuracy of SVM model was 97.22% and 93.75% in the training and testing sets, respectively. These results demonstrated that the portable electronic nose designed in this paper effectively identifies minced chicken meat under various adulteration conditions, enabling rapid and nondestructive detection of chicken meat adulteration.

Agricultural byproducts, often discarded, possess significant nutritional value and technological potential. This study investigates the efficacy of ethanolic-water extracts from avocado (Persea americana Mill.) byproducts, obtained with minimal solvent use through optimized extraction, in enhancing the stability of mechanically separated meat-tambaqui (Colossoma macropomum) burgers, a high-fat Amazonian fish. 2000 ppm of avocado seed and peel extract were applied in shelf-life tests conducted under refrigeration and freezing conditions to evaluate the stability of the burgers. The results demonstrated that the bioactive compounds from avocado byproducts resulted in lower TBARS values, indicating strong antioxidant properties, reduced formation of volatile nitrogen compounds, and color maintenance than sodium erythorbate. Monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) were preserved using bioactive ethanolic-water extracts, and the protein content was improved compared to the control, enhancing nutritional quality. This study highlights the potential of using agri-food byproducts, especially for application in highly perishable items such as fish, promoting the development of clean label products, thus supporting a more efficient and environmentally friendly food industry focusing on the circular economy.