Food Analytical Methods最新文献

The objective of this study was to develop a high-performance liquid chromatography-diode array detector (HPLC-DAD) method for the quantitative determination of nitrosyl-heme pigment (NO-heme) in cured meat products, to be compared with the classic spectrophotometric method based on absorbance values at 540 nm. NO-heme was extracted in 80% acetone from 48 cured meats purchased from local supermarkets and it was quantified using a lab-made NO-heme standard. The validation procedures for the linearity (r² = 0.997) and relative standard deviation percentage of the response (RSD = 2.91%) in the range 1.63–13.04 mg/L, the limit of detection (LOD = 0.40 mg/L) and quantification (LOQ = 1.21 mg/L) of the HPLC-DAD method were performed. Repeatability (range 0.1–1.1%), intermediate precision (range 1.9–2.6%) and recovery percentage (range 81.6–101.4%) were also evaluated. Fractions of Fe protoporphyrin IX chloride (hemin) and sometimes Zn protoporphyrin IX (ZnPP) were co-extracted in the 80% acetone solution, increasing the overall absorbance at 540 nm. Therefore, the spectrophotometric method overestimated NO-heme, especially in the case of ripened and high-fat cured meats, while it was appropriate for the determination of NO-heme in cooked ham. The co-extracted hemin and ZnPP concentrations, determined by HPLC-DAD and -FLD, respectively, were used to clean the absorbance value at 540 nm, and obtain comparable NO-heme values with the two methods. The nitrosylation degree resulted by far more efficient in cooked than in ripened meat products.

The exact estimation of protein content is required in the biochemical, medical, and food sectors. The current technique, Kjeldahl procedure is time-consuming and uses hazardous reagents; therefore, the search for an easier, safer and time-saving method is of paramount importance. This study aimed to develop and validate a novel spectrophotometric method for the quantification of protein in animal-derived food products by measuring the absorbance of ammonium ions released after acid digestion. Bovine Serum Albumin (BSA) was used as a standard reference protein. Acid digestion of BSA was performed and the samples were scanned using UV–Vis spectrophotometry (200–800 nm), revealing a λ_max at 260 ± 1 nm. A linear calibration graph of BSA was established for protein quantification, and the results were compared with those determined by the Kjeldahl method and other spectrophotometric assays. The method was then applied to various animal food matrices. The proposed method achieved a λ_max at 260 ± 1 nm and demonstrated > 90% protein recovery across diverse food samples, including fresh and frozen veal, chicken, egg albumin and yolk, and beef hotdogs. Statistical analysis (p > 0.01) showed no significant difference between this method and the Kjeldahl and other spectrophotometric methods. The principal UV-absorbing compound was conclusively identified, after distillation and collection of the digested proteins in three different acid solutions (boric, hydrochloric, and sulfuric) without a dye indicator. The collected solutions were scanned revealing a λ_max of 255 ± 2 nm, conclusively representing ammonium ions as the primary absorbing compound, since the solutions are solely composed of ammonium ions and the acids. This method provides a rapid, accurate, and reliable alternative to the Kjeldahl method for protein quantification in animal products, bypassing the other stages used in Kjeldahl, and offers comparable analytical performance.

Chilies have long been valued for their culinary uses, vibrant flavors, and their bioactive compounds that contribute to significant health benefits. Recent advancements in liquid chromatography-mass spectrometry (LC–MS) based metabolomics have provided an in-depth exploration of the chemical diversity within plant samples. This study utilized LC–MS-QTOF (quadrupole time-of-flight) analysis to identify and profile metabolites across 32 samples of three different species of Capsicum (Capsicum annuum L., Capsicum frutescens L., Capsicum chinense Jacq.) from Northeast India. Compounds detected by LC-QTOF-MS with a confidence score above 75 were used for all analyses, including evaluation of chemical diversity among the chili samples. The results obtained through PCA were similar to the hierarchical clustering based on the Jaccard distance. Most chili samples clustered tightly around each other indicating similar metabolic profiles except samples CH3, CH4 of C. annuum var. longum and CH12, CH14, CH16, CH17, and CH30 from C. chinense as distinct outliers, suggesting marked differences in their chemical composition. Pathway analysis using MetaboAnalyst 6.0 revealed that common metabolites predominantly mapped to phenylpropanoid, anthocyanin, and fatty acid biosynthesis pathways, reflecting core metabolic functions related to pigmentation, flavor, and defense. In contrast, unique metabolites impacted arginine, glucosinolate, and ascorbate metabolic pathways, indicating accession-specific variations that contribute to differences in nutritional and adaptive traits. The comprehensive exploration of metabolites highlighted rich diversity of compounds such as capsaicinoids, flavonoids, phenolic, and other bioactive compounds. The study enlists the untapped chili resources of NER suggesting their potential as a valuable basis for future exploration.

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The complexity of supply chains and the inherent vulnerability of meat products make them susceptible to food fraud. Several studies have applied DNA-based methods to authenticate these products; nonetheless, their targeted nature and quantitative issues remain significant challenges. This study aimed to develop a COI metabarcoding approach (COI-MBC) to authenticate meat products marketed in México. We designed a pair of primers that flank a 201-bp region used as a COI barcode for a metabarcoding analysis of DNA mixtures and laboratory-simulated hams (LSH). The COI-MBC method successfully identified the main meat species consumed in México and several potential adulterants present at as low as 1%. In addition, the analysis of LSH demonstrated high performance in estimating species proportions in poultry samples. However, for pork-turkey mixtures, a quantitative bias was observed; therefore, in order to enhance quantitative reliability for these types of mixtures, we recommend the use of a dual set of universal COI primers to specific taxonomic groups (mammals and poultry), better aligning the method with current Mexican regulatory standards such as NOM-158-SCFI-2003. Overall, our results indicated the strong potential of COI-MBC as a tool for both the meat industry and regulatory agencies particularly for monitoring food authenticity and detecting fraud. Future studies should focus on improving the quantification accuracy of COI-MBC to expand its application in routine analysis.

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Representative schematic of the experimental design of the COI-MBC method applied to laboratory-simulated hams and DNA mixtures.

Turmeric, valued for its culinary and medicinal properties, has increasingly become vulnerable to harmful adulteration, posing serious health risks. Among these, ferrous sulfate heptahydrate is particularly concerning due to its potential to cause iron overload and oxidative stress. This study intended to develop a rapid, cost-effective, and non-destructive technique for finding such adulteration using Fourier Transform Infrared (FTIR) spectroscopy in Attenuated Total Reflectance (ATR) mode. Fresh turmeric samples collected from five locations in Tamil Nadu, India, were mixed with ferrous sulfate heptahydrate at concentrations of 0%, 5%, 10%, 15%, 20%, 25%, 30%, and 100%, yielding a total of 36 samples. FTIR spectra revealed distinct peaks for turmeric (1630 cm⁻¹, 1745 cm⁻¹, 2930 cm⁻¹, 3720 cm⁻¹) and for the adulterant (1060 cm⁻¹), providing clear spectral markers for adulteration detection. The spectral dataset was analyzed using Multiple linear regression (MLR), random forest (RF), K-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost), and Artificial neural networks (ANN). Among these, XGBoost achieved the peak overall performance, through an R² of 0.9968, mean squared error (MSE) of 0.0007, root mean squared error (RMSE) of 0.0265 (dimensionless), and very low mean absolute error (MAE). ANN also produced comparable accuracy, confirming the robustness of nonlinear approaches. These findings demonstrate that combining FTIR with advanced machine learning, particularly XGBoost, provides a powerful framework for rapid, reliable, and non-destructive adulteration detection in turmeric, with potential applicability to other food powders.

A less toxic analytical method was developed for benzene, toluene, ethylbenzene, and xylenes by replacing halogenated solvents to achieve green analytical chemistry goals. Dispersive liquid–liquid extraction using acetonitrile and n-hexane followed by analysis using gas chromatography-flame ionization detector was developed. Dispersive liquid–liquid extraction addresses the limitations of dispersive micro liquid–liquid extraction by allowing the use of larger volumes of extraction solvent. This makes it more suitable for samples with high analyte concentrations or greater volumes. By employing a larger volume of extraction solvent (i.e., 1000 µL), dispersive liquid–liquid extraction improved extraction efficiency and recovery rates. This technique was optimized based on the type of extraction and dispersion solvents used, volume ratio of extractant-to-dispersant, and extraction time. Under the optimum conditions (i.e., n-hexane:acetonitrile, 1:1 ratio and 30 s, in respect to optimized parameters above), the extraction recoveries were > 102%. A good linear range (0.35–12.50 µg/mL, R² = 0.9975–0.9997) and good limit of detection values (0.164–0.677 µg/mL) were obtained for all analytes. The evaluated mean accuracy and relative standard deviations % were 98.0–126.4% and 0.5–9.8%, respectively. Eco-Scale score value for the developed method was 52, which indicates a green-oriented analytical method. The method was applied to the fruit drink sample and was reasonable for the analysis of targeted analytes.

The objective of the present study is to optimize the extraction method for maximum retention of nutritional and bioactive compounds from bitter gourd waste (pomace). Earlier used thermal technology like steam blanching results in loss of nutritional quality due to rise in temperature during heating. But novel non-thermal technologies like ultrasonication and cold plasma offer significant advantages over conventional thermal technologies like retaining nutrients, texture and microbial safety. Also, currently, no data is available on the processing of bitter gourd waste through cold plasma and ultrasonication treatments. So, in this study, novel non-thermal technologies such as cold plasma and ultrasonication are compared with conventional thermal technology i.e. steam blanching. The effects of these technologies as pre-treatments were examined on bitter gourd pomace to monitor the changes in the total phenolic content, total flavonoid content, protein content, along with their total antioxidant activity and microbial load. In the present study, cold plasma emerged as the most effective method for enhancing the total phenolic content (18.52 mg GAE/g), reducing microbial load (6.29 *10² CFU/ml), and retaining protein content (4.96%). However, ultrasonication method proved to be superior for maximizing total flavonoid content (10.83 mg quercetin equivalent/g), while steam blanching offered the highest total antioxidant activity (45.91% inhibition) in the bitter gourd waste. Overall, the results suggested that cold plasma technology was found to be most effective pre-treatment for retaining maximum nutritional and bioactive compounds. Collectively, these findings are valuable for advancing food processing technologies, promoting sustainable practices, and optimizing nutrient retention.

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Conventional protein extraction methods often face challenges such as low yields, long processing times, and environmental concerns. Natural deep eutectic solvents (NADES), composed of natural hydrogen-bond donors and acceptors, offer a sustainable and efficient alternative. This review highlights traditional extraction techniques and their limitations then focuses on the advantages of NADES and explores the key factors that affect protein solubility and extraction efficiency. Various NADES components are evaluated for their roles in enhancing protein yield. NADES-extracted proteins exhibit high techno-functional properties beneficial for food systems. Life cycle analyses indicate carbon footprint reductions of 60–75% and energy savings of 40–65% relative to traditional extraction modes, while toxicological studies suggest that food-grade NADES formulations are safe. Recent advancements in applying NADES for protein recovery from oilseeds, legumes, and food by-products are also discussed. Despite their potential, issues like high viscosity and challenges in protein recovery and scalability remain. Current applications include sustainable food protein production, nutraceutical component manufacturing, and therapeutic pharmaceutical protein isolation, together with new opportunities with cellular agriculture and alternative protein technology. Future applications of work involve the computational-guided design of the NADES system, utilizing a Conductor-like Screening Model for Real Solvents (COSMO-RS) modeling to optimize continuous processing and to gain regulatory approval for applications in food-grade use. NADES technology embodies an important shift towards environmentally sustainable protein isolation that maximizes product quality, minimizes environmental impact, and is economically viable for multiple industries. The review concludes by outlining future research needs to improve NADES applications for green, high-performance protein extraction.

Metal–organic frameworks (MOFs) have attracted attention in electrochemical sensing due to their adjustable structures, relatively large surface areas, and the presence of multiple potential active sites. Herein, Ni/CoMOFs were successfully prepared by one-step hydrothermal method and immobilized on glassy carbon electrode (GCE). A non-enzymatic electrochemical sensor for the detection of histamine (HA) was constructed. Owing to the synergistic catalytic effects of Ni and Co centers together with the intrinsic porosity of the MOF, outstanding electrocatalytic activity was displayed by the resulting Ni/CoMOFs/GCE. The sensor exhibited a linear response within the concentration range of 18–333 µM, with a detection limit of 0.55 µM. After 28 days, 92.11% of the initial current response was maintained, and the relative standard deviation (RSD) was less than 5%. These results indicate that the sensor possesses satisfactory stability, reproducibility, and selectivity. Finally, the sensor successfully quantified histamine in wine samples, and the recovery rate was up to 98.13%, demonstrating its practical applicability.

Molecularly imprinted polymers, also known as artificial antibodies, have the potential to develop customized imprints of particular template molecules within a solidified polymeric environment. Analyte-specific molecular recognition is considered a crucial aspect of sensor performance. The effectiveness of sensors is dependent upon selectivity and mass transfer at the sites where analyte recognition occurs. Consequently, it is crucial to take into account the factors influencing mass transfer, such as structural characteristics, stability, and morphological features. This has led to the development of advanced molecularly imprinted probes aimed at enhancing sensor performance. Fiber optic sensors have become increasingly utilized because of their beneficial characteristics. The SPR technique, utilizing the Kretschmann configuration, has gained significant attraction in biosensing applications owing to its rapid and label-free detection capabilities. MIP-based electrochemical sensors employing nanotubes, graphene oxide, and metal oxide nanoparticles and magnetic composites present a promising option for the development of electrochemical sensors, as their distinctive magnetic properties facilitate rapid separation and easy preparation. This review provides the recent developments and research progress made in electrochemical and fiber optical sensors in the framework of MIP-integrated sensors for food security. The progress in surface imprinting and the benefits, including rapid mass transfer and removal of template molecules, are also discussed.

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