This study elucidates the structure and antioxidant activity of a galactomannan (EGSP) from the unique Chinese endemic species Gleditsia japonica var. delavayi. Structural analysis characterized EGSP as a polysaccharide (1.054 × 106 Da) with a backbone of →4)-β-D-Manp-(1 → residues, bearing side chains of α-D-Galp-(1 → 6) at the O-6 position, consistent with its Man:Gal:Glc molar ratio of 70.74:28.28:0.35. In vitro assays demonstrated its potent, concentration-dependent scavenging activity against DPPH•, ABTS•+, and •OH radicals, as well as considerable reducing power. Moreover, in a cellular model, EGSP protected RAW 264.7 macrophages from H2O2-induced oxidative damage by significantly reducing reactive oxygen species (ROS) and malondialdehyde (MDA) levels, while enhancing the activities of superoxide dismutase (SOD) and catalase (CAT). Mechanistic studies further associated this cytoprotective effect with the activation of the Nrf2 antioxidant signaling pathway. The well-defined structure-activity relationship established herein positions EGSP as a promising natural antioxidant candidate for functional foods and therapeutics.

Near-infrared (NIR) spectroscopy has emerged as a pivotal non-destructive analytical technique within the cheese industry, offering rapid and precise insights into the chemical composition and quality attributes of various cheese types. This review explores the evolution of NIR spectral sensors, highlighting key technological advancements and their integration into cheese production processes as well as final products already in markets. In addition, the review discusses challenges such as calibration complexities, the influence of sample heterogeneity and the need for robust data and interpretation models through spectroscopy coupled with AI methods. The future potential of NIR spectral sensors, including real-time in-line monitoring and the development of portable devices for on-site analysis, is also examined. This review aims to provide a critical assessment of current NIR spectral sensors and their impact on the cheese industry, offering insights for researchers and industry professionals aiming to enhance quality control and innovation in cheese production, as well as authenticity and fraud studies. The review concludes that the integration of advanced NIR spectroscopy with AI represents a transformative approach for the cheese industry, enabling more accurate, efficient and sustainable quality assessment practices that can strengthen both production consistency and consumer trust.

Sampling plays a pivotal role in the analytical process, particularly when employing non-destructive spectroscopic sensors (NDSS). This review bridges Theory of Sampling (ToS) and Design of Experiments (DoE) to address sampling challenges in NDSS agri-food applications. Sampling quality is the primary driver of overall uncertainty, often significantly surpassing laboratory and instrumental errors. Non-destructive spectroscopic setups inherently sample through their optical configurations. We highlight the importance of replication methods to determine sources of variance, particularly in physical sampling procedures, and provide practical guidelines for achieving representative sampling. Additionally, the review briefly discusses computational augmentation and resampling techniques. Practical considerations and case studies from food and feed applications illustrate the constraints and solutions for effective sampling, providing insights for researchers and industry aiming to optimize NDSS measurements.

The reformulation of processed foods to eliminate industrial trans fats and reduce saturated fat content has created an urgent need for alternative lipid structuring technologies that preserve functionality without compromising health or sustainability. Oleogelation, particularly via the emulsion-template method, offers a promising route to immobilize liquid oils within biopolymer networks, yielding semi-solid materials suitable for fat replacement. This study investigates the physicochemical properties of oleogels prepared using hydroxypropyl methylcellulose (HPMC) and xanthan gum (XG) across six vegetable oils with diverse fatty acid profiles (unrefined palm oil: UPO, refined palm oil: RPO, coconut oil: CCO, high oleic sunflower oil: HOS, extra virgin olive oil: EVO) and almond oil: PAO). Microstructural analysis revealed that palm oil-based oleogels formed crystalline domains due to saturated fat segregation, while high-oleic oils such as extra virgin olive oil (EVO), high-oleic sunflower oil (HOS), and almond oil (PAO) maintained emulsion-like dispersion. Oil-binding capacity (OBC) tests showed superior retention in HOS-based oleogels, while coconut oil (CCO) exhibited the highest oil loss. The study advances the understanding of oil–hydrocolloid interactions and supports the development of tailored oleogel systems as viable, healthier alternatives to conventional solid fats in food applications.

Hyperspectral Imaging (HSI) is an advanced remote sensing tool for rapid, non-destructive food analysis, where spectral information from a large area is acquired with applications for products with heterogeneous surfaces and distribution that are traditionally analysed with difficulty. The robustness of HSI acquired data, however, requires comprehensive exploration in the case of very thin food materials. This paper aims to examine the robustness of HSI measurements for thinly sliced food products using ham products as a model system. Three replicates of 1, 2, 3, 4, and 5 mm slices of a Spanish ham were analysed against two types of background using two HSI-NIR camera sensors (400-1000 and 900-1700 nm), in both their original form (3- dimensional) and in their average spectra and pixel spectra (2- dimensional). Several methods were used to analyse and visualise the HSI data structures: the average spectra were undergone statistical analysis for standard derivation (SD) value; Hyperspectral Imaging- Root Mean Square (HSI- RMS) values were calculated using pixel spectra to reveal the internal differences based on the state of the sample; Principal Component Analysis (PCA) and Self- Organising Map (SOM) were applied to the 3D form. When focusing on the average or pixel spectra, low-quality HSI data (thin ham samples) have broader variation of SD value for average spectra and HSI- RMS values for pixel spectra, showing that the HSI sensors simultaneously capture the information of the background together with the sample. Conversely, both SOM and PCA are a useful approach for maintaining the information of the original form, which allow further machine learning approaches and automation. Overall, this study shows the effectiveness of HSI in capturing chemical information of thin food surfaces while exploring measurement limitations related to the penetration depth and the background which can serve as a feasibility study on how to obtain more robust HSI data in the future.

Multivariate calibration methods have enabled the use of non-destructive spectral sensors in a wide range of applications but carry a risk of overfitting to the available training samples. For this reason, the prediction of unseen samples plays a vital role both in tuning the prediction algorithm and in assessing its performance, two activities that need to be carefully distinguished. Methods employed include data-splitting, cross-validation, and the use of genuinely independent sets of data. These approaches are described and some common issues with them are identified. The focus is on food applications but the methods discussed are widely used in other areas.

Honey is a valuable and nutritious food product, but it is at risk to fraudulent practices such as the addition of cheaper syrups including corn, rice, and sugar beet syrup. Honey authentication is of the utmost importance, but current methods are faced with challenges due to the large variations in natural honey composition (influenced by climate, seasons and bee foraging), or the incapability to detect certain types of plant syrups to confirm the adulterant used. Molecular methods such as DNA barcoding have shown great promise in identifying plant DNA sources in honey and could be applied to detect plant-based sugars used as adulterants. In this work DNA barcoding was successfully used to detect corn and rice syrup adulteration in spiked UK honey with novel DNA markers. Different levels of adulteration were simulated (1 - 30%) with a range of different syrup and honey types, where adulterated honey was clearly separated from natural honey even at 1% adulteration level. Moreover, the test was successful for multiple syrup types and effective on honeys with different compositions. These results demonstrated that DNA barcoding could be used as a sensitive and robust method to detect common sugar adulterants and confirm syrup species origin in honey, which can be applied alongside current screening methods to improve existing honey authentication tests.

Currently, the identification and quantification of complex adulteration of high-value vegetable oils are still challenging. In this study, the extreme vertex design method was adopted to design representative multivariate adulterated camellia oil samples. Thereafter, 11 characteristic lipid species were identified by considering the statistically significant difference and categorical contribution. A discriminant method and linear regression model were established based on 11 key lipids. The accuracy rate of the model was 100%, which could correctly discriminate the camellia oil with an adulteration ratio as low as 2.5 %. The root mean square error (RMSE) of the regression equation was close to 0, and the coefficient of determination (R2P) was 0.9054. This method has been successfully applied to commercially available samples for validation purposes, detecting 27.3 % of camellia oil adulteration. Overall, the results indicate that the discriminating method and content prediction model can provide a potential strategy for authentication of multivariate vegetable oils.

Meat offers essential nutrients and protein, with some vitamins and minerals rare in plant-based diets. Its colour, an essential quality indicator, influences consumer choices, shelf life, and economic aspects of meat products. Conventional measurements include an objective description of instrumental meat colour (CIELAB) and evaluation of myoglobin profiles, which are usually resource intensive and time consuming. This study aimed to expand the use of spectral techniques as a screening tool for efficient evaluation of colour and myoglobin profile of beef products. NIR spectroscopy (NIRS) was used to evaluate colour related meat quality parameters of beef products over long storage days, including CIELAB colour (L*, a*, b*, ΔE), total myoglobin content (mg/g), and three myoglobin forms (Deoxymyoglobin - DeoMb, Oxymyoglobin - MbO2, and Metmyoglobin - MetMb). Results have shown that the use of NIR spectroscopy for evaluating colour parameters in beef products shows great promise as a reliable and efficient method. At the validation stage, the RPD values following PLS-R modelling of these quality parameters (L*, a*, b*, ΔE, Total Myoglobin, DeoMb, MbO2, MetMb) were 7.03, 7.03, 6.84, 1.12, 7.79, 4.18, 7.09, 25.38, and 16.27, respectively. This study demonstrates that the NIR spectroscopy coupled with chemometrics methods is a promising approach for rapid quantitative analysis of colour and myoglobin parameters in meat products.

The increasing popularity of plant-based milk alternatives (PBMAs) necessitates effective safety and authentication measures to ensure food product integrity and maintain consumer trust. This review aims to offer a comprehensive overview of potential contaminants, allergens, and adulterants in PBMAs, and the analytical methodologies employed for their detection and quantitation. It details the advantages and limitations of widely employed testing techniques, such as chromatography, spectroscopy, immunoassays and PCR. In addition, it explores recent advancements in portable detection methods based on novel technologies such as CRISPR and biosensor systems that offer new opportunities for rapid and precise analysis. Despite these technological innovations, important challenges remain, particularly in optimizing sample preparation protocols and improving DNA-based methods efficiency. The integration of multiple detection strategies and the development of rapid, cost-effective analytical tools are critical steps towards enhancing both industry compliance and consumer confidence. Furthermore, green analytical methods - such as solvent-free extraction, AI-driven spectroscopy, and sustainable sample preparation techniques - pave the way toward eco-friendly and more efficient PBMA safety testing.

Monitoring veterinary drug residues in food is one key link to ensuring food safety. In recent years, non-targeted screening technology based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) has attracted widespread attention. However, the development and application of non-targeted screening methods still face many challenges due to the constantly updated variety of veterinary drugs, the complexity of sample matrices, the large amount of data, and the cumbersome data identification process. To address the above challenges, this review systematically proposes a workflow for non-targeted screening, focusing on the research progress in sample preparation, instrument analysis, data preprocessing, and recognition strategy. In addition, it summarizes molecular formula prediction models, chemical structure prediction models, and retention time validation models, discusses the latest application of this technique in detecting veterinary drug residues in food, providing new insights to meet the challenges of non-targeted screening.

Rancidity in infant formula rice flour (IFRF) is a common challenge with measurable impact on the dairy industry development. This study proposed a real-time chromogenic film based on PVA/Schiff’s reagent that targets rancidity. The results demonstrated that the developed ten-point, five-grade sensory scale method exhibited excellent linearity (R2 = 0.998) between the sensory grades and the rank sum of rancid odour intensity. Orthogonal partial least squares discriminant analysis (OPLS-DA) analysis confirmed a positive linear correlation between the contents of IFRF aldehyde marker and the observed rancidity levels. By optimising the film preparation process, the best processing parameters were determined to be 83 ℃, 18 min, and 0.5 Hz for the chromogenic film preparation. Validation using the sensory evaluation showed an identification accuracy exceeding 88.67

Heather honey is an important honey type produced in the UK, valued for its unique flavour, thixotropic texture and health-promoting properties. Botanical authentication can be challenging due to the natural variability in honey composition and typical pollen analysis relies heavily on expert knowledge. As an alternative, real-time PCR (qPCR) can be a rapid and robust method to identify floral species in honey. In this work, species-specific markers for Calluna vulgaris and Erica cinerea were developed and used to quantify 266 honey samples relative to the plant trnL P6 loop. The method classed 96% of 234 heather honeys as containing > 3% heather DNA, with 68% classified as dominant (> 45%) for ling heather origin. Moreover, high specificity was achieved with negligible amplification in the 32 non-heather honeys. Our qPCR method offered comparable results to melissopalynology, DNA metabarcoding, and digital PCR, showing potential as an alternative and accessible method for botanical authentication of heather honeys.

Fibres from different agri-food by products have varying physicochemical and functional properties that require further investigation in the context of sustainable food production to fundamentally understand the relationship between composition, structure and function. In this study, physicochemical and rheological properties of dietary fibres (DF) extracted from nine different sources namely apple pomace, wheat straw, hemp fibres, oat hulls, oat bran, pumpkin seeds, mushrooms compost, and coffee silverskin were compared to methylcellulose. The hydration, emulsifying, structural, and rheological properties were characterised. Hemp fibre (69.63 g/100g) and apple pomace (19.52 g/100g) had the relatively highest and lowest DF contents. Onion peels dietary fibre (OPDF) exhibited the highest water binding (0.94 g/g) and swelling capacities (13.85 mL/g). Methylcellulose (MC), acid-extracted apple pomace dietary fibre (AC-APDF) had the highest water holding capacities (12.74 g/g). MC was the only sample to exhibit excellent emulsification property with no serum layer observed. All DF samples and MC exhibited shear thinning behaviours with increasing shear rates. The spectral analysis showed that all DF samples contained characteristic peaks of polysaccharides. The findings correlate with results from PCA analysis and indicate the potential of DF from onion peels and apple pomace to act as a functional ingredient in the food industry.

Rice bran, a primary by-product from the rice processing industries, containing 10-15% oil, attracts significant attention from consumers due to its many health-promoting effects. The extraction methodology used is one of the most critical factors affecting the quality and yield of oil from rice bran. Using solvents is the current commercial process for rice bran oil extraction, which has its setbacks. It is challenging and expensive, and there is a risk of traces of solvent residue in the oil. Emerging combination extraction technologies offer zero to minimal solvent residues or chemical deformation while considering increasing environmental and energy footprint. Emerging combination processing technologies include new-age methods like supercritical fluid extraction, sub-critical fluid extraction, ultrasound-assisted enzymatic extraction, ohmic heating, and microwave-assisted extraction. These techniques have been reported to extract oil from rice bran, improving extraction efficiency and quality. These techniques demonstrate solid prospects for future applications. The present review discusses and compares these emerging technologies for oil extraction from rice bran commercially.

The aim of this study was to create rapid and sustainable instrumental methods for screening virgin olive oils (VOOs) to support the Panel test. The Panel test is the official sensory method used in EU regulations to determine the commercial category of VOOs. The Panel test is based on a time-consuming and expensive approach, so reducing the number of samples to be analysed is crucial. Spectroscopy offers a potential solution for quickly determining VOOs composition and predicting their quality grade. In this context, three spectroscopic techniques were explored: Near-Infrared (NIR), Fourier-Transform Infrared (FT-IR), and Raman spectroscopy. A dataset of 100 VOOs samples, categorized into the three official grades (extra virgin, EVOO, virgin, VOO and lampante, LOO) established in EU, based on the Panel test results, was analysed. An initial analysis of all spectra revealed typical for triacylglycerols molecular vibrations and not good variability between types of samples, indicating low specificity. However, FT-IR data paired with two different Partial Least Squares-Discriminant Analysis (PLS-DA) models - one differentiating LOO from non-LOO (VOO and EVOO) and another distinguishing LOO from VOO - yielded promising results. Cross-validation indicated successful sample classification with percentages ranging from 81% to 96%, in which LOO vs. no-LOO model showed the highest performance. These findings suggest that FT-IR coupled with chemometric analysis holds promise, particularly for discriminating LOO (inedible) from the higher-quality grade VOO and EVOO categories. Further research efforts are needed to possibly make the herein developed models more robust and potentially extend their application to differentiate all three VOO quality grades.