Food Analytical Methods最新文献

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.

Sacha inchi (Plukenetia volubilis L.) seed oil (SIO), recognized for its richness in omega-3 fatty acids content, has gained economic significance in the global fats and oils industry. The authenticity and quality of sacha inchi seed oil (SIO) are critical due to concerns about edible oil adulteration, including the potential mixing of SIO with lower-grade or cheaper oils, which can compromise its nutritional value and safety. This research employs Fourier transform infrared (FTIR) spectroscopy in conjunction with chemometric to detect and measure the adulteration practice in SIO. The study highlights the significance of spectral pre-processing techniques, including signal corrections, normalization, data scaling, and spectral derivatization, in improving data quality and the performance of chemometric models. Due to its rapid and non-destructive nature, FTIR spectroscopy presents a highly promising analytical tool for routine authentication of SIO against lower-grade adulterants. The viability demonstrated in this study highlights its potential integration into industrial regulatory frameworks, including routine analysis, and may stimulate further research toward establishing standardized methods for edible oil authentication. SIO can be differentiated from other edible oils by C-H bending of -CH₂ = CH (cis) functional groups observed at 721 cm⁻¹. This functional group is a characteristic of unsaturated fatty acids such as linoleic acid, oleic acid, palmitoleic acid, and alpha-linolenic acid. Partial least squares (PLS) calibration model, utilizing first derivative FTIR spectra without signal corrections within the wavenumber range of 3008–712 cm⁻¹, could accurately estimate the concentration of virgin coconut oil (VCO) as an adulterant in SIO. The model produces root mean square error of calibration (RMSEC) of 1.07, root mean square error of prediction (RMSEP) of 1.28, and R² values for the correlation between actual value and FTIR predicted values of 0.9994 (in calibration model) and 0.9992 (in validation model). Furthermore, discriminant analysis successfully classified 100% of the samples into their appropriate categories of pure and contaminated samples. This method illustrates the potential use of FTIR spectroscopy combined with chemometrics for quality control and assurance processes of SIO from VCO adulterant, and this procedure can be replicated to other oil adulterants.

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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.

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.

Burkholderia gladioli pv. cocovenenans is an emerging foodborne pathogen commonly found in foodstuffs such as mushrooms and rice noodles. It produces bongkrekic acid, a highly lethal and thermostable toxin, posing a serious threat to food safety. Therefore, the development of rapid, cost-effective, and highly sensitive detection methods is urgently needed. In this study, Si–OH magnetic beads were employed for nucleic acid extraction, followed by loop-mediated isothermal amplification (LAMP) for the amplification of target DNA. This method effectively distinguishes B. gladioli pv. cocovenenans from non-toxic B. gladioli strains. The LAMP products were detected using fluorescence signals and visualized through colorimetric analysis, assisted by color analysis software integrated into a smartphone application. Results were classified based on hue, saturation, and lightness (HSL) values: orange (HSL ≦ 44.1) indicated a negative result, while green (HSL ≧ 63.8) indicated a positive result. The entire process can be completed in under 50 min. The detection sensitivity reached 2.5 pg/μL for purified pathogen DNA and 6.68 × 10² CFU/25 g for artificially contaminated rice noodle samples. This method is 10 times more sensitive than conventional polymerase chain reaction (PCR), providing a robust and efficient platform for the rapid detection of B. gladioli pv. cocovenenans in food safety monitoring.

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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.

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The fish processing industry frequently disposes of byproducts such as skin, scales, and bones, viewing them as waste. However, these byproducts are rich in protein, particularly collagen, which can be transformed into gelatin. This study seeks to determine the optimal conditions for extracting gelatin from the discarded skin of longtail butterfly rays (Gymnura poecilura). To achieve this, a central composite design featuring four factors and five levels was used to analyze the influence of pretreatment time in acid and alkali solution (A), skin-to-water ratio (B), extraction temperature (C), and extraction time (D) on yield (Y). The most critical factors impacting the yield were identified as pretreatment time, extraction temperature, and extraction time. The optimized conditions for the maximum gelatin yield (22.7%) were 30 min of pretreatment time followed by 5 h of extraction time at 60 °C extraction temperature. The optimized gelatin exhibited a protein content of 90.25%, along with 6.30% moisture and 0.23% ash content. Fat was not detected in ray gelatin. Analysis of the ray gelatin indicates that its proximate composition meets acceptable standards in relation to commercial porcine gelatin. The ray gelatin manifested a higher gel strength (288 g) and viscosity, 7 cP, than commercial porcine gelatin (p < 0.05). The FTIR spectra of ray gelatin reveal absorption bands predominantly located in the amide band region, specifically amide A, amide B, amide I, amide II, and amide III, which are comparable to those found in commercial gelatin. This investigation successfully refined the gelatin extraction process by pinpointing the best conditions for maximizing the yield. The results demonstrate that ray fish skin can be an excellent alternative source for gelatin production, contributing to a sustainable and beneficial application for marine byproducts.

This study examines the efficacy of natural deep eutectic solvents (NADES) in conjunction with ultrasound-assisted extraction (UAE) for extracting polyphenols from Vernonia amygdalina leaves. Optimization via central composite design (CCD) identified a NADES formulation (1:1.413 molar ratio of choline chloride to glucose with 79.125% water) that maximized total phenolic content (TPC) to 163.50 mg GAE/mL. This environmentally sustainable extraction technique considerably surpassed traditional ethanol-based extraction. The characterization using LC-HRMS, TEM, FTIR, and DSC validated a diverse phenolic profile, spherical particle shape, stable hydrogen-bonding interactions, and thermal stability of the optimized extract. The antioxidant evaluation revealed significant efficacy, achieving up to 89.27% DPPH inhibition at a 20% concentration. The anti-inflammatory properties evaluated in LPS-induced RAW 264.7 cells demonstrated a decrease in nitric oxide, reactive oxygen species, and proinflammatory cytokine production, highlighting the extract’s therapeutic potential. This work emphasizes the combined advantages of sustainable green extraction techniques and the potential biological activity of V. amygdalina, facilitating its use in natural therapies and nutraceuticals.

Honey consumption is increasing worldwide due to its natural qualities, but its safety and authenticity are not always assured. Honey adulteration through mislabeling and fraudulent production significantly damages consumer trust and poses health risks. This study aimed to identify the botanical makeup of Kazakh and imported Honey to assess whether it matches with the declared labeling using melissopalynological analysis and DNA metabarcoding. During our research, melissopalynological analysis showed that 64.7% of Kazakh Honey samples and 62.5% of imported Honey matched the declared labeling. In 6 samples of domestic Honey and 6 samples of imported honey, the plant source differed from what was indicated on the label. According to DNA metabarcoding data, the botanical origin of honey by the ITS2 marker was confirmed in 58.8% of Kazakh and 31.25% of imported honey samples. Using the rbcL marker, only 29.4% of domestic and 31.25% of imported honey samples matched the declared plant origin. Agreement between melissopalynology and ITS2 marker data was observed in 10 samples, and between melissopalynology and rbcL in 8 samples. Eight samples also matched the labeling based on both genetic markers. Thus, combining DNA metabarcoding with melissopalinological analysis enables a more accurate identification of the honey's botanical origin, thereby strengthening product authenticity control, helping to detect counterfeits, facilitating access to international markets, and increasing consumer confidence. Overall, the results highlight the importance of strict monitoring and verification methods to protect consumers and maintain market integrity.