Search Results (116)

The Australian pork industry has been seeking a rapid and non-destructive way to predict pork chemical components and eating quality. In this study, near-infrared spectroscopy (NIRS) and nuclear magnetic resonance (NMR) were applied to fresh pork Longissimus thoracis et lumborum (LTL) and Semimembranosus (SM) with the aim to build prediction models for intramuscular fat (IMF) content, collagen content and solubility, pH, and sensory attributes, namely tenderness, juiciness, liking of flavor and overall liking as well as investigate the effects of chemical components on pork eating quality. Results showed that the NIRS output, which was a predicted IMF content calibrated for the IMF of lamb, correlated with the chemically analyzed IMF content across both muscles. In LTL, NMR parameter p_2f was weakly correlated with IMF and pH. For the LTL, NMR parameters p₂₁ and p₂₂ were related to sensory tenderness, while T₂₂ was correlated with the liking of flavor. In both muscles, the collagen content and pH were related to all sensory attributes, and IMF was related to the liking of flavor. The chemical properties of SM were weakly correlated with those of LTL. The NIRS and NMR weakly predicted the pork chemical components and sensory properties, but more studies are required to improve the accuracy. Full article

This study systematically analyzed the multidimensional effects of Lactobacillus fermentation on Pueraria lobata (PL) and investigated the potential mechanisms underlying its hypolipidemic activity. Results indicated that fermentation significantly increased the total acid content from 1.02 to 3.48 g·L⁻¹, representing a 2.41-fold increase. Although slight reductions were observed in total flavonoids (8.67%) and total phenolics (6.72%), the majority of bioactive components were well preserved. Other antioxidant capacities were retained at >74.71% of baseline, except hydroxyl radical scavenging. Flavor profiling showed increased sourness and astringency, accompanied by reduced bitterness, with volatile compounds such as β-pinene and trans-2-hexenyl butyrate contributing to a distinct aromatic profile. Untargeted metabolomics analysis revealed that fermentation specifically enhanced the abundance of low-concentration isoflavone aglycones, including daidzein and genistein, suggesting a compositional shift that may improve hypolipidemic efficacy. Integrated network pharmacology and computational modeling predicted that eight key components, including genistein, could stably bind to ten core targets (e.g., AKT1 and MMP9) primarily through hydrogen bonding and hydrophobic interactions, potentially regulating lipid metabolism via the PI3K-AKT, PPAR, and estrogen signaling pathways. This study reveals the role of Lactobacillus fermentation in promoting the conversion of isoflavone glycosides to aglycones in PL and constructs a multi-dimensional “components-targets-pathways-disease” network, providing both experimental evidence and a theoretical foundation for further research on the lipid-lowering mechanisms of fermented PL and the development of related functional products. Full article

Wine quality is closely linked to sensory attributes such as aroma, taste, and mouthfeel, all of which are influenced by grape variety, “terroir”, and vinification practices. Among these, aroma is particularly important for consumer preference, and it results from a complex interplay of numerous volatile compounds. Conventional sensory methods, such as descriptive analysis (DA) performed by trained panels, offer valuable insights but are often time-consuming, resource-intensive, and subject to individual variability. Recent advances in sensor technologies—including electronic nose (E-nose) and electronic tongue (E-tongue)—combined with chemometric techniques and machine learning algorithms, offer more efficient, objective, and predictive approaches to wine aroma profiling. These tools integrate analytical and sensory data to predict aromatic characteristics and quality traits across diverse wine styles. Complementary techniques, including gas chromatography (GC), near-infrared (NIR) spectroscopy, and quantitative structure–odor relationship (QSOR) modeling, when integrated with multivariate statistical methods such as partial least squares regression (PLSR) and neural networks, have shown high predictive accuracy in assessing wine aroma and quality. Such approaches facilitate real-time monitoring, strengthen quality control, and support informed decision-making in enology. However, aligning instrumental outputs with human sensory perception remains a challenge, highlighting the need for further refinement of hybrid models. This review highlights the emerging role of predictive modeling and sensor-based technologies in advancing wine aroma evaluation and quality management. Full article

China’s liquor industry continues to steadily expand and develop. The industry is currently transforming, shifting its focus from scale to quality and efficiency. This transformation is significantly increasing the demand for quality and safety testing. Currently, the testing system relies mainly on manual operation or traditional mechanical equipment. Technical bottlenecks include low testing efficiency, a significant imbalance in the cost–benefit ratio, and difficulty meeting the modern industry’s dual technical index requirements of testing accuracy and systematicity. In this context, the innovative research and development of new detection technology is key to promoting technological upgrades in the liquor industry. Near-infrared (NIR) spectroscopy is a core, competitive analytical method for non-destructive wine quality testing due to its technical advantages, such as non-destructive analysis, real-time online detection, and the absence of sample pretreatment requirements. This study systematically elaborates on the optical principle and detection mechanism of NIR spectroscopy and explores the application paradigm of chemometrics in spectral data analysis. This study covers the quantitative analysis of alcoholic strength, the determination of main ingredient content (sugar, acidity, esters, etc.), the construction of trace flavor substance fingerprints, the authentication and origin tracing of alcoholic products, and the monitoring of wine aging quality dynamics, among other key technology areas. Additionally, we review the fusion and innovation trends of artificial intelligence and big data technology, the R&D progress of miniaturized testing equipment, and the technical bottlenecks of spectral modeling and algorithm optimization. We also make scientific predictions about the evolution path of this technology and its industrial application prospects. Full article

Blueberries are valued for their flavor and health benefits, but inconsistent ripeness at harvest complicates post-harvest food processing such as sorting and quality control. To address this, we propose a lightweight convolutional neural network (CNN) to detect blueberry ripeness in complex field environments, supporting efficient and automated food processing workflows. To meet the low-power and low-resource demands of embedded systems used in smart processing lines, we introduce a Grouped Large Kernel Reparameterization (GLKRep) module. This design reduces computational cost while enhancing the model’s ability to recognize ripe blueberries under complex lighting and background conditions. We also propose a Unified Adaptive Multi-Scale Fusion (UMSF) detection head that adaptively integrates multi-scale features using a dynamic receptive field. This enables the model to detect blueberries of various sizes accurately, a common challenge in real-world harvests. During training, a Semantics-Aware IoU (SAIoU) loss function is used to improve the alignment between predicted and ground truth regions by emphasizing semantic consistency. The model achieves 98.1% accuracy with only 2.6M parameters, outperforming existing methods. Its high accuracy, compact size, and low computational load make it suitable for real-time deployment in embedded sorting and grading systems, bridging field detection and downstream food-processing tasks. Full article

This study was designed to systematically identify novel umami peptides in lager beer, clarify their molecular interactions with the T1R1/T1R3 receptor, and determine their specific effects on multidimensional sensory attributes. The peptides were characterized by LC-MS/MS combined with de novo sequencing, and 906 valid sequences were obtained. Machine-learning models (UMPred-FRL, Tastepeptides-Meta, and Umami-MRNN) predicted 76 potential umami peptides. These candidates were docked to T1R1/T1R3 with the CDOCKER protocol, producing 57 successful complexes. Six representative peptides—KSTEL, DELIK, DIGISSK, IEKYSGA, DEVR, and PVPL—were selected for 100 ns molecular-dynamics simulations and MM/GBSA binding-energy calculations. All six peptides stably occupied the narrow cleft at the T1R1/T1R3 interface. Their binding free energies ranked as DEVR (−44.09 ± 5.47 kcal mol⁻¹) < KSTEL (−43.21 ± 3.45) < IEKYSGA (−39.60 ± 4.37) ≈ PVPL (−39.53 ± 2.52) < DELIK (−36.14 ± 3.11) < DIGISSK (−26.45 ± 4.52). Corresponding taste thresholds were 0.121, 0.217, 0.326, 0.406, 0.589, and 0.696 mmol L⁻¹ (DEVR < KSTEL < IEKYSGA < DELIK < PVPL < DIGISSK). TDA-based sensory validation with single-factor additions showed that KSTEL, DELIK, DEVR, and PVPL increased umami scores by ≈21%, ≈22%, ≈17%, and ≈11%, respectively, while DIGISSK and IEKYSGA produced marginal changes (≤2%). The short-chain peptides thus bound with high affinity to T1R1/T1R3 and improved core taste and mouthfeel but tended to amplify certain off-flavors, and the long-chain peptides caused detrimental impacts. Future formulation optimization should balance flavor enhancement and off-flavor suppression, providing a theoretical basis for targeted brewing of umami-oriented lager beer. Full article

Flavor, as one of the primary factors that attracts consumers, has always been a crucial indicator for evaluating the quality of food. From processing to final consumption, the conditions that affect consumers’ perception of the aroma of aquatic products can be divided into three stages: aroma formation, release, and signal transmission. Currently, there are few reviews on the formation, release, and perception of aroma in aquatic products, which has affected the product development of aquatic products. This review summarizes aroma formation pathways, the effects of processing methods, characteristic volatile compounds, various identification techniques, aroma-release influencing factors, and the aroma perception mechanisms of aquatic products. The Maillard reaction and lipid oxidation are the main pathways for the formation of aromas in aquatic products. The extraction, identification, and quantitative analysis of volatile compounds reveal the odor changes in aquatic products. The composition of aquatic products and oral processing mainly influence the release of odorants. The characteristic odorants perceived from the nasal cavity should be given more attention. Moreover, the relationship between various olfactory receptors (ORs) and the composition of multiple aromatic compounds remains to be understood. It is necessary to clarify the relationship between nasal cavity metabolism and odor perception, reveal the binding and activation mode of ORs and odor molecules, and establish an accurate aroma prediction model. Full article

Milk jujube has become an increasingly popular tropical fruit. The sugar content, which is commonly represented by the soluble solid content (SSC), is a key indicator of the flavor, internal quality, and market value of milk jujubes. Traditional methods for assessing SSC are time-consuming, labor-intensive, and destructive. These methods fail to meet the practical demands of the fruit market. A rapid, stable, and effective non-destructive detection method based on visible/near-infrared (Vis/NIR) spectroscopy is proposed here. A Vis/NIR reflectance spectroscopy system covering 340–1031 nm was constructed to detect SSC in milk jujubes. A structured spectral modeling framework was adopted, consisting of outlier elimination, dataset partitioning, spectral preprocessing, feature selection, and model construction. Comparative experiments were conducted at each step of the framework. Special emphasis was placed on the impact of outlier detection and dataset partitioning strategies on modeling accuracy. A data-augmentation-based unsupervised anomaly sample elimination (DAUASE) strategy was proposed to enhance the data validity. Multiple data partitioning strategies were evaluated, including random selection (RS), Kennard–Stone (KS), and SPXY methods. The KS method achieved the best preservation of the original data distribution, improving the model generalization. Several spectral preprocessing and feature selection methods were used to enhance the modeling performance. Regression models, including support vector regression (SVR), partial least squares regression (PLSR), multiple linear regression (MLR), and backpropagation neural network (BP), were compared. Based on a comprehensive analysis of the above results, the DAUASE + KS + SG + SNV + CARS + SVR model exhibited the highest prediction performance. Specifically, it achieved an average precision (AP_p) of 99.042% on the prediction set, a high coefficient of determination (R_P²) of 0.976, and a low root-mean-square error of prediction (RMSEP) of 0.153. These results indicate that Vis/NIR spectroscopy is highly effective and reliable for the rapid and non-destructive detection of SSC in milk jujubes, and it may also provide a theoretical basis for the practical application of rapid and non-destructive detection in milk jujubes and other jujube varieties. Full article

Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. However, the prediction of pineapple quality by hyperspectral data applied to ML is not known. The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. Three inputs were used for each model: leaf reflectance, peel reflectance, and fruit reflectance. The machine learning model SVM, stood out for its best results, demonstrating good generalization capacity and effectiveness in predicting these attributes, reaching accuracy values above 0.7 for Brix and ratio, using fruit reflectance. In terms of the overall efficiency of the input variables, peel and fruit were the most informative, with peel standing out for the estimation of secondary metabolism compounds, while the fruit showed excellent performance in predicting flavor-related attributes, such as acidity, °Brix and ratio, as mentioned previously, above 0.7. These results highlight the potential of using spectral data and machine learning in the non-destructive assessment of pineapple quality, enabling advances in monitoring and selecting fruits with better sensors. Full article

Grapes, highly nutritious and flavorful fruits, require adequate chlorophyll to ensure normal growth and development. Consequently, the rapid, accurate, and efficient detection of chlorophyll content is essential. This study develops a data-driven integrated framework that combines hyperspectral imaging (HSI) and convolutional neural networks (CNNs) to predict the chlorophyll content in grape leaves, employing hyperspectral images and chlorophyll a + b content data. Initially, the VGG16-U-Net model was employed to segment the hyperspectral images of grape leaves for leaf area extraction. Subsequently, the study discussed 15 different spectral preprocessing methods, selecting fast Fourier transform (FFT) as the optimal approach. Twelve one-dimensional CNN models were subsequently developed. Experimental results revealed that the VGG16-U-Net-FFT-CNN1-1 framework developed in this study exhibited outstanding performance, achieving an R² of 0.925 and an RMSE of 2.172, surpassing those of traditional regression models. The t-test and F-test results further confirm the statistical robustness of the VGG16-U-Net-FFT-CNN1-1 framework. This provides a basis for estimating chlorophyll content in grape leaves using HSI technology. Full article

Flavor is one of the crucial factors that influences the quality and consumer acceptance of baijiu. In this study, we analyzed the volatile organic compound (VOC) profiles of five different quality grades of Nongxiangxing baijiu (NXB), fermented during the summer of 2024, using 2D gas chromatography time-of-flight mass spectrometry (GC×GC–TOF-MS). We employed machine-learning (ML)-based classification and prediction models to evaluate the flavor. For TW, the scores of the sensory evaluation for coordination and overall evaluation were the highest. TW contained the highest concentration of ethyl caproate; we detected 965 VOCs in total, including several pyrazine compounds with potential health benefits. Principal component analysis (PCA) combined with orthogonal partial least squares discriminant analysis (OPLS-DA) enabled us to distinguish different samples, with eight VOCs emerging as primary contributors to the aroma of the samples, possessing variable importance in projection (VIP) values > 1. Furthermore, we tested eight ML models; random forest (RF) demonstrated the best classification performance, effectively discriminating samples based on their VOC profiles. The key VOC contributors that showed quality-grade specificity included 1-butanol, 3-methyl-1-butanol, and ethyl 5-methylhexanoate. The results elucidate the flavor-based features of NXB and provide a valuable reference for discriminating and predicting baijiu flavors. Full article

Background: There is a continuous demand to create new, superior sensory food experiences. In the food industry, yeast-derived flavor products (YPs) are often used as ingredients in foods to create new aromas and taste qualities that are appreciated by consumers. Methods: Chicken bouillon samples containing diverse YPs were chemically and sensorially characterized using statistical multivariate analyses. The sensory evaluation was performed using quantitative descriptive analysis (QDA) by trained panelists. Thirty-four sensory attributes were scored, including odor, flavor, mouthfeel, aftertaste and afterfeel. Untargeted metabolomic profiles were obtained using stir bar sorptive extraction (SBSE) coupled to GC-MS, RPLC-MS and targeted HILIC-MS. Results: In total, 261 volatiles were detected using GC-MS, from chemical groups of predominantly aldehydes, esters, pyrazines and ketones. Random Forest (RF) modeling revealed volatiles associated with roast odor (2-ethyl-5-methyl pyrazine, 2,3,5-trimethyl-6-isopentyl pyrazine) and chicken odor (2,4-nonadienal, 2,4-decadienal, 2-acetyl furan), which could be predicted by our combined model with R² > 0.5. In total, 2305 non-volatiles were detected for RPLC-MS and 34 for targeted HILIC-MS, where fructose-isoleucine and cyclo-leucine-proline were found to correlate with roast flavor and odor. Furthermore, a list of metabolites (glutamate, monophosphates, methionyl-leucine) was linked to umami-related flavor. This study describes a straightforward data-driven approach for studying foods with added YPs to identify flavor-impacting correlations between molecular composition and sensory perception. It also highlights limitations and preconditions for good prediction models. Overall, this study emphasizes a matrix-based approach for the prediction of food taste, which can be used to analyze foods for targeted flavor design or quality control. Full article

Navigating cross-cultural food choices is complex, influenced by cultural nuances and various factors, with flavor playing a crucial role. Understanding cultural flavor preferences helps individuals make informed food choices in cross-cultural contexts. We examined flavor differences across China, the US, and Germany, as well as consistent flavor preference patterns using online recipes from prominent recipe portals. Distinct from applying traditional food pairing theory, we directly mapped ingredients to their individual flavor compounds using an authorized database. This allowed us to analyze cultural flavor preferences at the molecular level and conduct machine learning experiments on 25,000 recipes from each culture to reveal flavor-based distinctions. The classifier, trained on these flavor compounds, achieved 77% accuracy in discriminating recipes by country in a three-class classification task, where random choice would yield 33.3% accuracy. Additionally, using user interaction data on appreciation metrics from each recipe portal (e.g., recipe ratings), we selected the top 10% and bottom 10% of recipes as proxies for appreciated and less appreciated recipes, respectively. Models trained within each portal discriminated between the two groups, reaching a maximum accuracy of 66%, while random selection would result in a baseline accuracy of 50%. We also explored cross-cultural preferences by applying classifiers trained on one culture to recipes from other cultures. While the cross-cultural performance was modest (specifically, a max accuracy of 54% was obtained when predicting food preferences ofthe USusers with models trained on the Chinesedata), the results indicate potential shared flavor patterns, especially between Chinese and US recipes, which show similarities, while German preferences differ. Exploratory analyses further validated these findings: we constructed ingredient networks based on co-occurrence relationships to label recipes as savory or sweet, and clustered the flavor profiles of compounds as sweet or non-sweet. These analyses showed opposing trends in sweet vs. non-sweet/savory appreciation between US and German users, supporting the machine learning results. Although our findings are likely to be influenced by biases in online data sources and the limitations of data-driven methods, they may still highlight meaningful cultural differences and shared flavor preferences. These insights offer potential for developing food recommender systems that cater to cross-cultural contexts. Full article

This study investigated the production process of ready-to-eat crayfish, focusing on changes in sensory quality, pH, total volatile base nitrogen (TVB-N), total viable count (TVC), acid value (AV), springiness, and hardness during storage at 4 °C, 25 °C, and 37 °C. A shelf-life prediction model was developed using the Arrhenius model. The optimal crayfish formula was determined to be 0.12% spices, 0.80% salt, and a stewing time of 70 min, which achieved the highest sensory score of 9.25 points. This combination resulted in shrimp meat with an intact texture, a soft and smooth taste, and rich spicy and briny flavors. A Pearson correlation analysis showed significant correlations among TVB-N, TVC, AV, springiness, and hardness. When fitting each indicator with zero-order, first-order, and second-order kinetics, TVB-N, AV, and springiness aligned more closely with the zero-order kinetics model, while TVC and hardness fit better with the first-order kinetics model. The Arrhenius equation-based shelf-life model demonstrated an error margin of 9.1% between predicted and actual quality indicators, confirming its feasibility for predicting the quality and shelf life of spicy crayfish. These findings provide a crucial theoretical basis for the intelligent prediction of storage and distribution conditions for ready-to-eat crayfish. Full article

The use of buffalo meat in fermented sausage production represents a sustainable and innovative approach to enhancing the value of underutilized meat cuts. However, its high heme content and specific fatty acid composition makes the meat particularly sensitive to lactic fermentation with lipid oxidation phenomena and sensory character decay. Therefore, buffalo meat requires tailored fermentation strategies to ensure product stability. The aim of this study was to optimize fermentation strategies by exploring milder acidification processes and the fortification of buffalo meat with vegetable oils to reduce oxidation while maintaining microbiological quality. In particular, the effect of adding or omitting glucose and fortifying with pumpkin seed oil in Napoli-style buffalo salami was studied and the impact on the main quality parameters was evaluated. Pumpkin seed oil (0.5%) was selected for its antimicrobial and antioxidant properties and evaluated for its interaction with starter cultures through Minimum Inhibitory Concentration (MIC) tests and predictive microbiology models. Based on the findings, its use was validated in Napoli-style salami, produced with and without glucose. Microbial dynamics, physicochemical changes over time, oxidation indices, and sensory attributes were assessed. Results indicated that the sugar-free formulations combined with pumpkin seed oil achieved optimal sensory and safety attributes. The addition of glucose facilitated rapid lactic acid bacterial growth (about 2.5 ∆ log CFU/g), enabling pH reduction to safe levels (<5.2) and the effective inhibition of Enterobacteriaceae and coliforms. However, acidification in the control batch, as demonstrated by multiple variable regression analyses, induced pre-oxidative conditions, increasing lipid oxidation markers (TBARSs > 0.7 mg MAD/Kg), which negatively impacted flavor and color stability. The use of pumpkin seed oil confirmed its antimicrobial and antioxidant potential, making it a promising fortifying ingredient for producing slow-fermented, mildly acidified (pH > 5.4) buffalo meat salami, offering a novel strategy for improving the nutritional, sensorial, and safety quality of dry fermented meat. Full article