European Food Research and Technology最新文献

Extracting valuable physical and chemical information from massive hyperspectral imaging (HSI) data is a pressing challenge for food analysis. In this study, a new and intelligent strategy was developed to identify trace adulterants in the food matrix. The strategy was based on the hierarchical agglomerative clustering analysis of essential information selected by interesting features finder as well as uniform manifold approximation and projection from HSI data (named IFF-UMAP-HAC). Four Raman HSI datasets and four NIR HSI datasets were utilized to verify the accuracy and reliability of the new strategy, and a systematic comparison was conducted among the proposed method, UMAP-HAC, t-distributed stochastic neighborhood embedding with HAC (t-SNE-HAC) as well as IFF-t-SNE-HAC. When the adulterant with a high level was presented in food matrix (i.e. ≥0.1% mass melamine in milk powder), all methods enabled highly accurate identification and well separation of adulterants within HSI datasets. However, only IFF-UMAP-HAC provided satisfactory results for analysis of trace adulterants in food matrix such as samples adulterated with 0.014% melamine in milk powder, ≤ 0.05% melamine in wheat gluten and trace ternary adulterants in milk powder. Moreover, the new strategy did not require prior knowledge of HSI data structures and class information. In summary, the integration of HSI with IFF-UMAP-HAC proved feasible, offering sample preparation-free, nondestructive and green analysis while paving the way for the rapid detection of trace adulterants in food matrix.

The study evaluated the suitability of various commercial yeasts for the production of low-alcohol beers. Four yeast strains were tested: Biodiva, FM56, LA01 and LONA, using US05 strain as a control. The fermentation process was monitored by measuring weight loss of samples. The beers were analysed for alcohol content, residual extract, colour, bitterness, turbidity, pH, metal ion content, and sensory characteristics. The fermentation efficiency of the FM56 and US05 strains was high, resulting in beers with a alcohol content of 3.77–3.83% by volume. In contrast, beers produced with other strains (Biodiva, LA01 and LONA) had a lower alcohol content ranging from 0.8 to 0.83% by volume. Physicochemical analysis revealed significant differences in attenuation, extract utilisation, and turbidity. Sensory evaluation indicated that each strain contributed a distinct flavour profile. The results of the study demonstrate the diverse capabilities of distinct yeast strains in the production of low-alcohol beers that exhibit desirable physicochemical and sensory characteristics.

This work investigated the chemical composition, antioxidant, antimicrobial and insecticidal properties of Cananga odorata essential oil (COEO) in relation to the use of essential oils to extend the shelf life of foods. In addition, microbiological tests were performed on Hokkaido pumpkin sous vide after Salmonella enterica challenge. According to GC/MS analysis, linalool (25.1%) was the main component of COEO. COEO showed its superiority to neutralize the ABTS radical cation (TEAC, 0.018) compared to DPPH radical (TEAC, 0.006). The statistically highest (p ≤ 0.05) antibacterial activity of COEO with disc diffusion method was against Streptococcus constellatus (15.67 mm). In assay used for determination of minimum inhibitory concentrations (MIC) COEO showed the best activity against Escherichia coli (0.230 mg/mL). In situ investigations activity COEO demonstrated its statistically highest (p ≤ 0.05) efficiency against biofilm-forming S. enterica in the cucumber model (86.63%), Candida parapsilosis and in the peach model (85.83%), C. glabrata in the radish model (86.99%) and E. coli in the strawberry model (93.27%). The results of our analyses show that the application of COEO on the sous vide Hokkaido pumpkin model is effective against S. enterica using the highest temperatures and time. The insecticidal activity of COEO was demonstrated at the highest concentrations of EO. Additionally, Bacillus amyloliquefaciens was the most often isolated bacteria found in sous vide Hokkaido pumpkin samples with different treatment. Changes in the protein composition of the biofilm-forming bacteria S. enterica were detected by MALDI-TOF MS. The results of our study suggest that both as a natural pesticide and as a food additive to extend the shelf life of Hokkaido pumpkin, COEO may be a suitable candidate for use in the food sector.

This study investigated the effects of thermal treatments, often required in the processing of chestnut starch (CNS), on its structure, digestive characteristics of chestnut starch and its effect on gut microbiota composition. CNS was treated by dry heat (DH), heat moisture (HM), and high temperature and high pressure (HTHP) to obtain thermal treated CNS (TT-CNS). The results showed that three treatments exhibited different effects on the crystal structure of starch, and HM and HTHP treatment promoted the formation of looser and more poriferous structure of chestnut starch, while DH treatment could enhance the amylose double-helix structure, leading to a denser starch microstructure. Hence, only CNS-DH (31.42%) showed slight resistance during the initial stages of digestion compared to CNS (33.42%), while other groups demonstrated higher digestibility. CNS-DH exhibited a certain resistance to digestion, which may contribute to glycemic stability. In addition, gut microbiota analysis demonstrated that the three thermal treatment groups significantly increased the relative abundance of beneficial bacteria (Parabacteroides and Limosilactobacillus) and reduced the relative abundance of Clostridium sensu stricto and Peptostreptococcaceae, which is conducive to maintaining a healthy gut environment. All these results indicated that thermal treatment might enhance the health benefits of chestnut starch, providing a theoretical guidance for the development of functional foods containing chestnut starch.

This study aims to detect bruises on peaches using deep learning and Near-Infrared (NIR) imaging. The research presents an extensive dataset of NIR images, trains advanced Convolutional Neural Networks (CNN) models, and develops a resilient model for detecting naturally formed bruises on bicolored peaches. The dataset includes images of both healthy and bruised peaches of the “Hale” cultivar. An experimental setup was created for noise-free image acquisition on a black background, and image processing was used to crop the original image. The final dataset split into training, validation, and test images were used to train various state-ofart models. The optimal hyperparameters were selected based on their frequent usage in fruit classification problems. The results show that DenseNet121 outperformed all the other state-of-the-art models by obtaining high accuracy and was selected as a base model for further improvement. The proposed model was crafted by integrating extra layers like the average pooling layer, dropout layer, dense layer, and SoftMax layer into the DenseNet121 architecture. Moreover, to boost its performance, an adaptive learning rate employing an exponential decay schedule was implemented. The results demonstrate promising accuracy in identifying bruise areas on peach fruit, indicating potential for enhancing quality control in the peach industry.

Climatic variations throughout the phenological cycle can accelerate, delay, or cause uneven ripening of coffee cherries. This study evaluated the sensory quality and chemical changes in coffees harvested at different times from the same Coffea arabica L. plantation, which were subjected to five processing methods, including fermentations. We conducted sensory analyses and volatile compound profiling. There were no significant sensory differences in the first harvest across processing methods, but the results indicated that washed coffee performed better in the second harvest. There were also significant differences between harvest times. A Gas chromatography coupled with mass spectrometry (GC–MS) identified classes of volatile compounds such as acids, pyrazoles, alcohols, aldehydes, furans, thiophenes, thiols, pyrroles, phenols, and pyridines. Processing methods and harvest times influenced the volatile composition of the coffee, impacting its final flavor and aroma. The climatic conditions during each period may have influenced the metabolism of compounds, affecting the volatile fraction and, consequently, the coffee’s flavor and aroma.

Graphic abstract

The industrial demand for natural flavourings is increasing, together with green technologies development and diffusion. This article investigates different extraction strategies for flavouring obtainment from Calabrian chili peppers supply chain by-products. Green solvent mixtures were tested on Soxhlet, Ultrasound-Assisted Extraction (UAE) and Supercritical Fluid Extraction (SFE). Extracts were characterized through bioactive compounds quantification and volatile profile monitoring. Preliminary Soxhlet experiments set the matrix/solvent ratio at 1/25 (g/mL). SFE extracts exhibited the highest concentration of less-polar polyphenols, such as flavonoids, total carotenoids, but also alpha-tocopherol and capsaicinoids. On the other hand, the UAE and Soxhlet processes, involving ethanol/water mixtures, allowed the highest yields, the greatest antioxidant activity and a high content of vitamin C (up to 1.27 mg/g). In conclusion, this work underlines the importance of extraction technique choice and could represent an advancement in industrial flavourings extractions, suggesting sustainable methods to obtain value-added extracts from discarded matrices.

The quality and price of wine depend on raw materials, geographical origin, specific production processes, and product aging. The prediction of the age of wine based on chemical composition is laborious and time-consuming, so spectroscopic methods are studied as a faster and cheaper but still reliable alternative. In this study, UV-Vis absorption and synchronous fluorescence (SF) spectral data sets for undiluted and diluted sweet wines were obtained, and each data set was then evaluated separately by partial least squares (PLS) regression to predict the age of the wines, using the entire data set or significant spectral variables selected by forward selection of predictors and Variable Importance in Projection-PLS (VIP-PLS). The VIP-PLS model based on the SF spectral data of diluted samples had the best performance characteristics (coefficient of determination of cross-validation (R²CV) of 0.992, root mean square error of cross-validation (RMSECV) of 0.5 years, coefficient of determination of prediction (R²P) of 0.990, root mean square error of prediction (RMSEP) of 0.8 years, and relative predictive deviation (RPD) of 9.2). To assign the selected spectral variables to chemical compounds, phenolic compounds (catechin, caffeic acid, caftaric acid, gallic acid, protocatechuic acid, and p-coumaric acid) were determined by the HPLC-DAD method, and their contents were correlated with the age of the wine. A high correlation was found between the age of the wine and the content of caftaric acid, catechin, and gallic acid (p < 0.0001). Therefore, and considering their characteristic spectral features, these compounds can be related to some of the selected variables. Overall, SF spectroscopy combined with chemometric methods could establish an advanced analytical tool for wine age prediction that significantly reduces analytical time, cost, and environmental damage compared to chemical and chromatographic methods.

The term ‘freshly roasted’ has long been regarded as a hallmark of coffee quality. This study challenges this assumption by investigating whether (i) a discernible sensory effect and (ii) a perceptible consumer preference exist between ‘freshly roasted’ (1 day post-roast) and ‘post-roast matured’ (145 days) coffee beans. Employing a triangle test (ISO 4120:2021) and a hedonic preference test (ISO 8587:2013) with 60 untrained participants attending coffee-related events, 70% of participants successfully discriminated between samples (p < 0.001). The preference test revealed no significant difference in the proportion of participants selecting either sample (52.4% mature vs. 47.6% fresh, p = 0.875). These findings underscore the potential for a more nuanced approach to post-roast coffee maturation, demonstrating that extended maturation produces perceivable sensory changes without systematic preference disadvantage.

Halal meat products must be free from any ingredients prohibited under Islamic law, such as porcine derivatives. The real-time polymerase chain reaction (qPCR) method is commonly used to detect porcine DNA in food products. Although various genes have been studied to develop porcine-specific primers, the NADH Dehydrogenase 4 L (ND4L) gene remains unexplored. This study aimed to design novel qPCR primers targeting the ND4L gene to detect porcine DNA in commercial food products. The primers were evaluated based on repeatability, sensitivity, and specificity. SYBR Green dye was used for amplification detection, and Ct values were recorded for quantification analysis. Fifty-two commercial food products were tested to assess the method’s robustness. The designed primers had the following sequences: forward, 5′-TCATTATAGCATTCACGATTGCCC-3′, and reverse, 5′-TGTGTGTTTAGGACAATTAGAGTCG-3′, yielding a 133 bp PCR product. These primers effectively amplified porcine DNA (20 ng/µL) with a Ct value of 12.44 ± 0.30 and a melting temperature (Tm) of 78.25 ± 0.05 °C. The limit of detection (LOD) was determined at 5 pg/µL of DNA and 0.001% w/w in a pork-beef binary mixture. The ND4L primers demonstrated high specificity, amplifying only pig and wild boar DNA without cross-reacting with 30 non-target animal species. Among the tested samples, 20 out of 52 were positive for porcine DNA, consistent with their labeling. These findings indicate that ND4L-based primers provide a reliable and sensitive approach for detecting porcine DNA in processed food products.