Food Analytical Methods最新文献

Hypochlorous acid (HClO) plays a pivotal role in both biological systems and food industrial processes, making the development of fluorescent probes for its sensitive and selective detection an enduring research focus. In this study, we designed and synthesized a fluorescent probe (HClO-1) derived from Hantzsch ester (a 1,4-dihydropyridine derivative) for HClO-sensitive detection. This probe exhibits a selective addition reaction with HClO, leading to the quenching of its intrinsic blue fluorescence. Notably, HClO-1 demonstrates high sensitivity, achieving detection limits of 67.7 nM for fluorometric assay and 20.1 nM for colorimetric assay. Based on these findings, we developed a fluorescence-based quantitative method to assess the HClO scavenging capacity of flavonoids, using half maximal inhibitory concentration (IC₅₀) as the key evaluation parameter. More encouragingly, this fluorescent probe successfully enabled the direct evaluation of flavonoid antioxidants’ ability to scavenge HClO. In conclusion, HClO-1 provides a reliable and feasible method for both the detection of HClO in the environment and the assessment of flavonoid antioxidant capacity.

Graphical Abstract

Sustainability has driven an increasing concern for affordable, renewable, and biodegradable alternatives in food packaging. Bio-based and biodegradable packaging incorporating biosensors and indicators has attracted considerable attention for its ability to both protect food and convey freshness information. This study presents a sustainable, biodegradable packaging material that acts as a smart ammonia and food spoilage indicator. The composition includes a blend of polyvinyl alcohol (P) and starch (S) combined with Ghangaru (Pyracantha crenulata) anthocyanins (G-ATH), fabricated via a casting and drying method with glycerol as a crosslinking agent. For comparison, pH-sensitive films were developed using polyvinyl alcohol, chitosan, and starch with the incorporation of G-ATH to monitor chicken meat freshness. The G-ATH film exhibited distinct color changes and UV–visible spectra across various pH levels (2 to 12). The reversible molecular transformations of the infused anthocyanin (G-ATH) in response to pH are responsible for the color-changing behavior of the film. Among the formulations, the S/P/G-ATH film demonstrated superior antioxidant activity (30.45%). On the other hand, S/C/G-ATH showed high moisture retention (14.69%) and high opacity (10.72), and C/P/G-ATH performed better in water vapor barrier properties (32.68 WVP (10^–11 gm⁻¹ s⁻¹ Pa⁻¹) and WCA (60.79°). As a proof of concept, the film was evaluated for monitoring the freshness of chicken meat stored at 25 °C. Notably, the film changed color from dark red to brown within 36 h as the total volatile basic nitrogen (TVB-N) level approached the rejection limit of 30 mg/100 g and pH reached 8.42 because spoilage of meat produces nitrogenous substances having alkaline properties, which increases the pH. The results of this study suggest that G-ATH-enriched films have the potential to be advanced packaging materials for detecting food spoilage.

Graphical Abstract

This research aims to develop a reliable and versatile methodology for authenticating clove (Eugenia caryophyllata) oil (CLO). Key variables included total antioxidant capacity (TAC), total phenolic content (TPC), quantitative analyses and/or individual antioxidant capacity of fingerprint markers including eugenol (EUG) and tocopherol isomers (α-T, γ-T, and δ-T) using reversed-phase liquid-chromatography (RP-HPLC) with post-column detection. A total of 28 commercial CLO samples (certified as 100% pure and other commercial samples), potential adulterants like vegetable oils (sunflower oil (SFO) and corn oil (CO)), and 14 synthetically adulterated CLO samples blended with varying proportions (ranging from 5 to 50%) of SFO and CO were analyzed. The EUG content and antioxidant capabilities of each marker were ascertained by online RP-HPLC analysis with post column detection utilizing CUPRAC (cupric reducing antioxidant capacity). EUG content in commercial CLO ranged from 1.46 to 98.06 mg g⁻¹, while TAC ranged between 18.54 and 748.46 µmol g⁻¹ Trolox equivalents (TE), as determined by the online HPLC-CUPRAC method. Depending on the type and ratio of adulterating oils, a considerable decrease in the TAC values of virgin CLO was recorded. Classification of the commercial and synthetically adulterated CLOs (total of 42 samples) was performed using partial least square discrimination analysis (PLS-DA) and hierarchical cluster analysis (HCA). Adulteration levels above > 5% were successfully detected with 95% confidence. Thus, the proposed chemometric strategy combining selected chemical markers and TAC data demonstrated high potential for authenticating CLO. This technique provides a more focused investigation alternative for determining the authenticity and quality of commercial CLO.

Graphical Abstract

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.

Graphical Abstract

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.

Graphic Abstract

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.