Food Analytical Methods

Niyogi, Arindam; Bhattacharyya, Soumyadeb; Pal, Souvik; Mukherjee, Subhankar; Sarkar, Subrata; Ghosh, Alokesh; Basu, Surupa; Bhattacharya, Arunaloke; Chatterjee, Sirshendu; Das Mohapatra, Pradeep Kumar; Ray, Samit Kumar; Kumar, Akshai; Semwal, Sangeeta

doi: 10.1007/s12161-025-02887-8pmid: N/A

Allergic diseases are the most common unfavorable circumstances encountered by most individuals in their lifespan. The allergens that produce sensitivity reactions in individuals can originate from a food or food component, insects, pollens, etc. The vast food options and the diversity of food habits are the leading sources of differential food-based allergies in the Indian subcontinent. If not appropriately diagnosed and treated accordingly, an allergic reaction can also become life-threatening for an individual, especially for an infant. Numerous kits are available in the market for individual or multiple food-based allergen detection. However, most kits are costly, ELISA-based, and depend on high-end instruments and a skilled workforce. These drawbacks have encouraged modern researchers to develop cheap but highly selective and sensitive biosensors for rapid screening of food allergens. This endeavor delivers a vast knowledge of novel electrochemical, electromechanical, and optical biosensors specific for food allergen detection. The review mainly focuses on the major food allergens prevalent in the Indian subcontinent, their types and classification, and their pathophysiological effects. Finally, the review also encompasses a detailed list of commercially available food allergen testing kits specific to the mentioned allergens.Graphical Abstract[graphic not available: see fulltext]

Shen, Hongyu; Fang, Yi; Li, Xiuxiu; Hu, Dingyu; Cheng, Jinxin

doi: 10.1007/s12161-025-02895-8pmid: N/A

The widespread use of veterinary drugs in livestock production, while beneficial for disease prevention and growth promotion, leads to hazardous residues in animal-derived foods that significantly endanger human health. This review comprehensively examines nanozyme-based detection technologies as a transformative solution, highlighting their superior sensitivity, rapid response, operational simplicity, and cost-effectiveness compared to conventional methods. We systematically analyze the following: (1) major veterinary drug categories and associated health risks; (2) fundamental principles and practical applications of diverse nanozyme sensors; and (3) critical evaluation of current detection platforms for animal-derived foods. And several pivotal performance-enhancement strategies are emphasized: dual-mode detection system development, enzyme cascade catalytic network design, precise nanozyme activity regulation, specialized recognition element incorporation, and innovative sensing platform construction. The review concludes with forward-looking insights on emerging trends and practical recommendations, serving as a valuable reference for advancing next-generation veterinary drug monitoring technologies in food safety applications.

Fitriadi, Bayu Refindra; Ahda, Mustofa; Dewi, Addriani Mardika; Putri, Ayutia Ciptaningtyas

doi: 10.1007/s12161-025-02906-8pmid: N/A

Peanuts are a widely traded agricultural commodity, with Indonesia importing substantial quantities from countries in Africa and Asia. However, peanuts are highly susceptible to fungal growth, particularly when stored and transported under suboptimal conditions. These conditions can promote the production of aflatoxins by Aspergillus flavus and Aspergillus parasiticus, posing serious health risks. Effective monitoring of aflatoxin levels is therefore essential to ensure food safety. Indonesia has set maximum residue limits (MRLs) at 15 µg/kg for aflatoxin B1 and 20 µg/kg for total aflatoxins (sum of aflatoxins B1 + B2 + G1 + G2). In this study, a robust, sensitive, simple, and cost-effective method was developed and validated for the determination of total aflatoxins and aflatoxin B1 in peanuts. The method employs ultrasonic-assisted extraction and ultra-performance liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), and its validation was conducted in accordance with SANTE 11312/2021 v2 guidelines and the Guidance document on identification of mycotoxins and plant toxins in food and feed. The extraction method used was dissolving the sample in acetonitrile, followed by sonication for 1 h and centrifugation at a speed of 3700 rpm for 4 min. This method has the advantages of being cheaper, easier, and faster in the sample preparation process compared to previous methods. The limit of quantification (LOQ) was determined to be 15 µg/kg for total aflatoxins and 6 µg/kg for aflatoxin B1. Calibration curves showed excellent linearity (R2 > 0.999), with residuals ranging from −5 to 19%. The method demonstrated high specificity, as reagent and matrix blanks remained below 30% of the LOQ. Accuracy ranged from 101 to 113% for total aflatoxins and 75 to 109% for aflatoxin B1, with precision values (%RSD) of 4.48% and 16.8%, respectively. Participation in an international proficiency test confirmed the method’s reliability, with all z-scores falling within the satisfactory range (|z|≤ 2). From 2023 to 2024, the method was applied to 40 peanut samples originating from various countries. The results revealed that only 25 samples complied with the legal aflatoxin limits.

Ahmad, Rizwan

doi: 10.1007/s12161-025-02897-6pmid: N/A

This preliminary study aimed to identify and simultaneously quantify four phenolic of gallic acid (GA), scopoletin (SC), rosmarinic acid (RA), and resveratrol (RV) in the fenugreek (Trigonella foenum-graecum) seeds. Ultrasonic-assisted extraction using a dismembrator (20 kHz) was performed using the uncrushed on whole seeds with three green solvents: acetone (ACT), ethanol (EtOH), and water (H₂O). An in-house UHPLC-DAD method was developed, validated, and applied to determine phenolic profiles in 15 seed samples from Egypt, India, Qassim (Saudi Arabia), Yemen, and Iran. Method validation showed high accuracy [GA: 99.77 ± 12.33%, SC: 99.88 ± 7.94%, RA: 100.37 ± 8.02%, RV: 101.39 ± 5.39%] with r2 ≥ 0.9998. Relative SD ranged from 3.04–3.90%, LOD from 10.10–13.85 ppm, and LOQ from 30.60–41.97 ppm. Extract yields (1.2–152.9 mg; mean 24.19 ± 33.12 mg, N = 45) ranked: H₂O (622.8 ± 231.4 mg) > ACT (417.8 ± 61.2 mg) > EtOH (48 ± 5.8 mg). Yemen-origin JY1 (152.9 mg), Egypt-origin JE2 (140.3 mg), and Qassim-origin RQ2 (107.3 mg) gave the highest yields. Total phenolics followed SC (2337.0 ppm) > GA (146.2 ppm) > RV (135.4 ppm) > RA (109.7 ppm). Herein, a solvent specific phenolic recovery ranked as: H₂O (2613.3 ± 502.1 ppm) > EtOH (60.99 ± 27.17 ppm) > ACT (53.6 ± 7.41 ppm), with occurrence patterns: H₂O (SC > GA > RA > RV), EtOH (RV > SC > GA), ACT (GA > SC > RV). Pearson’s correlation confirmed a positive relationship between solvent type and phenolic yield, while K-means clustering identified JQ1 (Qassim) and RE1 (Egypt) as rich in multiple phenolics. These findings offer practical insight of solvent-dependent variation in phenolic content, underlining water as the most effective green solvent for maximizing extraction yield and phenolic recovery.

Nagpal, Simran; P., Chaithra K.; S., Sreelekha; P., Vinod T.

doi: 10.1007/s12161-025-02908-6pmid: N/A

This study utilizes Caesalpinia sappan L., traditionally valued for its culinary and medicinal uses, to develop a colorimetric indicator solution for monitoring milk spoilage. The indicator provides real-time updates on milk freshness through color changes induced by biochemical alterations during spoilage. The color of the indicator solution transitions distinctly from orange-red to orange to yellow as the pH shifts from 7.00 to 5.50 to 3.50, correlating with progressive stages of spoilage. An orange-red color was observed for the fresh stage, orange color for about to be spoilt, and yellow color for the spoilt stage of milk samples. The colorimetric changes are attributed to the presence of Brazelin in Caesalpinia sappan L. Digital images of the indicator solution treated with milk samples were analyzed using RGB (red, green, and blue) indices, with the green chromatic shift serving as a reliable parameter for quantifying color changes, providing reliable assessment of milk spoilage. Findings of this study highlight a simple, accessible, and accurate method for milk quality monitoring that requires no specialized equipment or trained personnel, making it suitable for food safety practices in resource-limited settings.Graphical Abstract[graphic not available: see fulltext]

Roh, Eunyoung; Choi, Kihwan; Hyung, Seok-Won

doi: 10.1007/s12161-025-02898-5pmid: N/A

This study investigated the efficacy of ultra-performance liquid chromatography–isotope dilution–tandem mass spectrometry (UPLC-ID-MS/MS) combined with either liquid–liquid extraction–solid phase extraction (LLE-SPE) or quick, easy, cheap, effective, rugged, and safe (QuEChERS) for the simultaneous determination of six sulfonamides and trimethoprim in beef, pork, and chicken powders. Method performance was comparatively assessed based on chromatographic peak intensities, recoveries, matrix effects, detection limits, and measurement uncertainties. Both methodologies generally achieved recoveries close to 100% for all analytes across meat types, indicating high accuracy and reliability. The only exception was trimethoprim in pork samples processed by LLE-SPE. QuEChERS provided superior matrix cleanup and yielded more consistent results across different meat types. The limits of quantification ranged from 5.4 to 9.8 ng/kg, and measurement uncertainties ranged from 1.6% to 8.0% for both methods. These findings demonstrate that both methods are capable of detecting sulfonamide and trimethoprim residues well below regulatory thresholds, with high precision and generally high recovery across various analytes and matrices. By combining the high separation efficiency and sensitivity of UPLC with the accuracy of IDMS, this approach offers a robust tool for routine monitoring and accurate quantification of these veterinary drug residues in meat products, thereby enhancing food safety oversight.

Ilhan, Esmanur; Ivanova, Mariia; Fuentes, Cristian A.; Solmaz, Hatice Gul; Goksu, Aylin Ozgur; Castillo, Rosario Del. P; Grunin, Leonid; Oztop, Mecit Halil

doi: 10.1007/s12161-025-02903-xpmid: N/A

Pectin, a complex polysaccharide known for its gelling, thickening, and stabilizing properties, is an essential ingredient in various industries, including food, pharmaceuticals, and cosmetics. Its functional characteristics are highly dependent on its molecular structure, which can be influenced by extraction and purification methods. This study investigates the impact of different extraction and purification techniques—specifically ultrafiltration (UF) and alcohol precipitation with and without maltodextrin (MD) addition—on the structural and dynamic properties of pectin. To characterize the molecular dynamics and structural heterogeneity of pectin samples, time-domain nuclear magnetic resonance (TD-NMR) methods including solid echo (SE), double-quantum (DQ) build-up experiment, saturation-recovery (SR), and Goldman-Shen (GS) sequences were employed in combination with Fourier-Transform Infrared (FTIR) spectroscopy. The results demonstrated that the molecular composition of pectins is influenced by the choice of extraction and purification methods. MD treatments result in increased solid content and higher averaged spin–lattice relaxation times, indicative of a more rigid and densely packed structure. The addition of maltodextrin not only enhances the dry matter content but also stabilizes the pectin network through cross-linking and reduced water mobility, which is vital for achieving desired textural properties. The Goldman-Shen sequence provided insights into spin diffusion, revealing that treatments involving isopropanol (IPA) and UF modify the structural domains organization of pectin without significantly altering solid content. This suggests an enhancement in molecular order and flexibility. Correlation analysis of TDQ values with various models (Pake, Abragamian, Gaussian, Polynomial) further elucidates distinct molecular interactions and relaxation behaviors among different pectin samples.

Meenakshi, ; Das, Sathi; Nanda, Omita; Kumari, Anjika; Chhibber, Kushar Dev; Mehta, Dalip Singh; Saxena, Kanchan

doi: 10.1007/s12161-025-02900-0pmid: N/A

Milk has long been a vital nutrient source, but adulteration compromises its quality and introduces harmful substances, posing serious health risks to consumers. This study presents a rapid, and portable method for detecting milk adulterants, such as melamine, dicyanamide (DCD), and ammonium sulfate (AmS), at ultra-trace levels using surface-enhanced Raman spectroscopy (SERS). In this study, we systematically explored two often utilized forms of SERS substrates composed of silver nanoparticles (Ag NPs) of spherical shape—one with colloidal Ag NPs solutions, and another with thin film-based Ag NPs based SERS substrate. A comprehensive analysis showed that colloidal solution-based spherical-shaped Ag NPs provided higher signal intensity than Ag NP films when using R6G as a probe molecule. This difference is due to 3D adhesion and closer contact between the analyte and Ag NPs in the colloidal solution, compared to the Ag NP-based SERS films. This study combines SERS with machine learning (ML) to detect and quantify milk adulteration. SERS spectra, obtained using spherical Ag NPs, were analysed using ML models to classify various adulterants efficiently. The detection limits (LOD) for these adulterants were as low as 0.012 ppm, 0.02 ppm, and 0.13 ppm, respectively, with accuracies of 96%, 98%, and 97%. The optimized SERS substrate, which is cost-effective, enables the detection of low-concentration milk adulteration without the need for sample pretreatment. This method offers a simple approach and shows that the spherical Ag NPs remain stable over time, supporting their extended shelf life for practical food safety use. The key novelty of this work lies in the integration of a scalable, low-cost colloidal SERS substrate with machine learning-based quantification, delivering a rapid and reliable solution for real-time detection of milk adulteration in complex matrices without the need for elaborate sample preparation.

Maia, Lucas Moreira; Reis, Bianca Cristina Carvalho; de Miranda Souza, Maria Regina; Fonseca, Maira Christina Marques; Pinto, Cleide Maria Ferreira; de Oliveira Mendes, Tiago Antônio

doi: 10.1007/s12161-025-02902-ypmid: N/A

Pereskia aculeata Miller, popularly known as ora-pro-nóbis, is a nutritionally valuable Unconventional Food Plant (UFP) with growing interest for agricultural production. However, little is known about how cultivation practices, such as planting density, influence its nutritional composition. This study evaluated the impact of different planting densities on the production of bioactive compounds in two P. aculeata clones (57009 and 58827), cultivated under four densities (1, 8, 16, and 32 plants/m2). The total protein and phenolic contents remained stable across planting densities, with average protein concentrations of 113.09 ± 11.90 mg/g and 130.94 ± 14.79 mg/g of fresh mass in clones 57009 and 58827, respectively. Flavonoid content was significantly higher in clone 57009, with up to 78% more flavonoids than clone 58827 at the lowest planting density. However, flavonoid concentration in clone 57009 decreased by approximately 50% as planting density increased. Clone 58827 showed a 43% increase in carotenoid content at higher densities compared to the lowest density. In contrast, antioxidant activities (ABTS and DPPH), hydrogen peroxide (H₂O₂), and nitric oxide (NO) levels were not significantly affected by planting density. These findings demonstrate the distinct biochemical profiles of P. aculeata clones and highlight how planting density influences the accumulation of specific bioactive compounds.

Zhang, Guiyu; Tang, Yaohong; He, Rutao; Zeng, Xianglin; Li, Gao

doi: 10.1007/s12161-025-02901-zpmid: N/A

The relationships among trace components in Baijiu base liquor are complex and diverse. An evaluation model was developed based on near-infrared (NIR) spectral data to enable rapid and convenient prediction of its quality grade. First, the weighted SPXY (WSPXY) method was employed to comprehensively consider spectral and target variable spaces for training–testing set division. Second, an adaptive multifactor feature evolutionary algorithm (AMFEA) was introduced. By integrating feature selection, variance analysis, and Spearman correlation coefficient tasks with a knowledge transfer strategy, AMFEA screened 83 features from the preprocessed base spirit spectra, which were then used as inputs for the model. Finally, an extreme gradient boosting (XGBoost) model with Bayesian parameter optimization (Optuna) was employed for grade classification of the base liquor. The results indicate that the features extracted by the AMFEA–Optuna–XGBoost algorithm effectively represent the chemical composition of the base liquor, achieving an accuracy, precision, recall, and F1-score of 95.86%, 96.62%, 95.83%, and 96.21%, respectively. The proposed method provides a reference for rapidly detecting Baijiu base liquor grades.