Food Control最新文献

To establish a rapid and simple method for predicting the content of key non-volatile compounds in rosemary, compounds from rosemary were analyzed using an electronic nose (E-nose) with 18 sensors (S1-S18), headspace solid phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and liquid chromatography-mass spectrometry (LC-MS). The data were analyzed by cluster analysis, principal component analysis (PCA). A total of 161 volatile compounds were detected using GC-MS, including 40 alcohols, 2 aromatic hydrocarbons, 5 phenolic compounds, 2 furan compounds, 1 sulfur compound, 6 ethers, 6 aldehydes, 2 acids, 5 terpene, 19 ketones, 16 esters, and 57 other compounds. The content of caffeic acid, nepetin, luteolin, apigenin, diosmetin, rosmarinic acid, carnosic acid, and rosmanol in rosemary samples was determined using LC-MS. The odor profile of rosemary was analyzed using the E-nose. The PCA indicated using the E-nose for discriminating the quality of rosemary was feasible. Meanwhile, the partial least squares (PLS) and artificial neural networks (ANN) model for predicting the content of key non-volatile compounds in rosemary was established using E-nose. In comparison with the PLS model, the constructed ANN model possessed greater predictive capability. Predicting the content of non-odors from rosemary using odor detector was feasible. This provides a basis for the rapid detection method for rosemary using an E-nose.

The identification of white tea mainly relies on sensory evaluation and mass spectrometry methods, which have drawbacks such as strong subjectivity and complex analysis. Thus, this study established a novel approach based on colorimetric indicator displacement assay (IDA) sensor to achieve rapid and accurate white tea identification. Cost-effective dyes and phenylboronic acids with different substituents were selected as the indicator and receptor, respectively. A color-signal-responsive IDA sensor array was constructed to enable the rapid and sensitive identification of white tea. Additionally, three supervised pattern recognition algorithms were utilized to process image data. The results demonstrate that combining the IDA sensor with the random forest algorithm accurately differentiates white tea samples by origin and type, achieving a 100% recognition accuracy in both training set (n = 84) and prediction set (n = 56) when ntree = 1000. This work offers an economical and efficient method for the rapid identification of white tea products.

Patulin (PAT) contamination in fruits, particularly apples and their products, poses a significant health hazard. This research aimed to eliminate PAT from simulated juice solution and actual apple juice using live and ultrasonically deactivated (D) probiotic yeasts, specifically Saccharomyces cerevisiae (S.c) and Saccharomyces boulardii (S.b) yeasts enriched with selenium (Se). The Se-enriched yeasts were analyzed using various techniques including Fourier transform infrared spectrometer (FT-IR), energy-dispersive X-ray analysis (EDX), scanning electron microscopy (SEM), and Inductively coupled plasma–optical emission spectroscopy (ICP-OES). The study assessed the effects of yeast type, contact time (2–18 h), ultrasound treatment, and initial concentration of PAT (50–150 μg.L⁻¹) on PAT adsorption. The most effective treatment involved the Se-S.b-D yeast strain, with an initial PAT concentration of 50 μg.L⁻¹ for 10 h, exhibiting notable toxin removal percentages in both the apple juice simulator (98.27% reduction equivalent to 49.135 μg.L⁻¹) and real apple juice (87.19%). The detoxification outcomes were consistent with pseudo-second-order kinetic and Langmuir isotherm models, indicating that Se-S.b-D is a biocompatible and low-cost strain with high efficiency to remove PAT in aqueous solutions.

Moldy core is a common internal disease in apples, with current visible/near-infrared (Vis/NIR) spectroscopy-based detection methods often suffering from inaccuracies due to variations in fruit diameter and soluble solids content (SSC). To address this issue, an online detection system was developed that integrates spectral corrections for both diameter and SSC information. The spectra were first corrected using a hyperbolic sine function based on fruit diameter, followed by a second correction utilizing the spectral features of SSC through the Variational Mode Decomposition (VMD) algorithm. This two-step correction process effectively mitigated the influence of diameter and SSC variations on the spectral data. A Partial Least Squares Discriminant Analysis (PLS-DA) model was subsequently constructed using the corrected spectra and moldy core labels. Compared to the uncorrected model, the improved model exhibited substantial enhancements in accuracy, recall, and precision, achieving 94.44%, 92.59%, and 96.15%, respectively. Additionally, online validation with independent samples demonstrated an accuracy of 88.33%, highlighting the system's stability and robustness for efficient online detection of apple moldy core.

In food and beverage production, maintaining cleanliness is paramount, despite the emphasis placed on it, food safety incidents due to incomplete CIP still occur frequently. This study used the cleaner production of a beverage (healthy water) as a model. An intelligent CIP system employs advanced technologies such as automation, big data, and digital twin to standardize CIP-related data through modeling. Comprehensive management of the CIP process at all levels—from operators to supervisors and managers—is achieved through access control. The digital twin system ensures effective CIP execution by comparing real-time data from automated operations with standard model data. Full traceability and post-event review of CIP activities are realized through CIP order management, while big data analysis quickly identifies problem areas for continuous improvement. This approach ensures the entire CIP process is error-free, fully transparent, traceable, continuously optimized, and conducive to cleaner production.

The co-occurrence of various mycotoxins in food, resulting from fungal infections, can lead to increased toxicity and enhanced harm through synergistic effects. In this work, a fluorescent assay was developed for simultaneous detection of zearalenone (ZEN), ochratoxin A (OTA) and aflatoxin B1 (AFB₁) to address the issue of multiple mycotoxins co-contamination. Specific DNA hairpin-based aptamers were employed to recognize the mycotoxins and form a double-stranded DNA structure. Exonuclease III was introduced for the DNA cleavage, releasing single-stranded DNA triggers that could activate the assembling of DNA quadrilateral template. Copper nanoparticles were generated on the frame of DNA quadruplex template and produced fluorescent detection signals, while two hybridization chain reactions occurring on the different branches of the template and provided two additional detection signals. Inadequately reacted DNA chains were efficiently adsorbed and eliminated by Fe₃O₄@graphene oxide through magnetic separation. The limits of detection and linear ranges for ZEN, AFB₁ and OTA were determined as 0.008 ng/mL, 0.003 ng/mL, 0.006 ng/mL and 0.010–100.000 ng/mL, 0.010–100.000 ng/mL, 0.010–50.000 ng/mL, respectively. Moreover, successful verification for real samples yielded recoveries from 99.2% to 102.7%, with a relative standard deviation of <5.23%. Artificially prepared toxic fungus infected samples were also analyzed. This method has demonstrated practical implications for the monitoring of food contaminants.

Microfluidics has emerged as a potentially valuable technique in food analysis. The major benefits include miniaturization, automation, quick throughput, and reduced sample and reagent volumes. The miniaturization of food processing systems enabled by microfluidics can lead to reduced energy consumption, improved food safety, and lower waste outputs. In microfluidic devices, sensors and actuators are frequently utilized to visualize and control pertinent factors. Microfluidics could have a significant impact in pathogen identification, allergen testing, quality control, flavor analysis, safety monitoring, adulteration detection, and sensory analysis. Microfluidic devices enable rapid separation and identification of volatile components by gas chromatography. By detecting and measuring features like pH, temperature, and the substances present in food samples, microfluidic sensors can monitor and optimize the quality of food production. Microfluidics simulations can be used to study the sensory analysis of food by investigating the interaction of food ingredients with taste receptors in microfluidic channels that mimic the oral cavity. This review paper outlines current status of microfluidic technologies in food processing and discusses the potential applications and challenges in the maintaining the food quality.

Red deer (Cervus elaphus) meat is highly regarded as an exclusive gourmet food due to its sensory quality, nutritional value and premium price, thus being susceptible to adulteration practices by its substitution with inferior meat species. This study proposes a novel real-time PCR method targeting the troponin I gene for detecting and quantifying red deer meat in processed food products. The assay showed acceptable performance parameters, achieving absolute and relative limits of quantification of 0.01 ng of DNA and 0.5% (w/w) of red deer meat in both raw and thermally processed pâté. The assay parameters, using raw and autoclaved reference mixtures, regarding PCR efficiency (89.3% and 100.0%, respectively), slope of the calibration curve (−3.6067 and −3.3224, respectively) and R² (0.9889 and 0.9893, respectively) highlight the high performance of the calibration models for the determination of red deer meat. The method was successfully validated using blind mixtures and applied to 46 commercial meat products from Poland, Portugal and Spain. Data suggested the adulteration of 48% of the samples by the total or partial substitution of red deer species and the suitability of the method for the routine testing of game meat products in quality control laboratories.

The high protein content in sufu provides a substrate for microorganisms to produce biogenic amines (BAs), thereby posing a potential risk to food safety. The aim of this study was to identify the main BAs produced by different types of sufu (red, white and grey sufu) and investigate the relationship between bacterial diversity and BA production using high-throughput sequencing. The results revealed significant differences in the total BA content of the three types of sufu at the post-fermentation stage, with grey sufu exhibiting the highest total BA content (ranging from 6389.00 mg/kg to 7255.49 mg/kg). The main BAs found in all three types of sufu were tyramine, putrescine and cadaverine. Enterococcus genus produced high levels of tyramine and phenylethylamine in all three types of sufu. The predominant cadaverine-producing bacteria in red sufu was Enterobacter genus, whereas Klebsiella pneumoniae was the primary producer of cadaverine in white and grey sufu. Putrescine was primarily associated with the Bacillus and Enterobacter genera in red and grey sufu. Acinetobacter, Enterococcus and Enterobacter were the common dominant genera during the post-fermentation stage of the three kinds of sufu, which were positively correlated with the production of various BAs. These findings will help to understand the mechanism of BAs production by bacterial communities in different types of sufu to ensure safe production.

The effects of postharvest pre-treatments with para-coumaric acid (PCA) and cinnamic acid (CA) against brown blotch disease in Agaricus bisporus mushrooms caused by Pseudomonas tolaasii were investigated. PCA and CA at the concentration of 300 mg L⁻¹ provided the highest inhibition ratio of 28.9% and 20.9%, respectively, on the in vitro growth of P. tolaasii. In addition, PCA and CA pre-treatments dramatically reduced the P. tolaasii-caused brown blotch disease in postharvest A. bisporus. Biochemical analysis revealed that PCA and CA inhibited P. tolaasii-induced activation of polyphenol oxidase and melanin production, increased activities of β-N-acetylglucosaminidase, β-1,3-glucanase and chitinase, promoted phenylpropanoid biosynthetic enzyme activities and metabolite production, enhanced antioxidant enzyme activities, and decreased malondialdehyde content in A. bisporus mushrooms. Collectively, these results demonstrated that PCA and CA can induce resistance to postharvest brown blotch disease in A. bisporus mushrooms, and have good prospects for disease control in edible mushrooms.