Journal of AOAC International最新文献

Background: High-performance thin-layer chromatography (HPTLC) is a widely used and accepted technique for identification of botanicals. Current best practices involve subjective comparison of HPTLC-generated images between test samples and certified botanical reference materials based on specific bands.

Objective: This research was designed to evaluate the potential of cutting-edge machine vision-based machine learning techniques to automate identification of botanicals using native HPTLC image data.

Method: HPTLC images from ginger and its closely related species and common adulterants were used to create large, synthetic datasets using a deep conditional generative adversarial network. This synthetic dataset was used to train and validate a deep convolutional neural network capable of automatically identifying new HPTLC image data. Performance of both neural networks was evaluated over time using appropriate loss functions as an indicator of their progress during learning. Validation of the overall system was measured via the accuracy of the learned model when applied to real HPTLC data.

Results: The machine vision system was able to generate realistic synthetic HPTLC images that were successfully used to train a deep convolutional neural network. The resulting learned model achieved high-accuracy identification from HPTLC images corresponding to ginger and six other related species.

Conclusions: A proof-of-concept HPTLC image-based machine vision system for the identification of botanicals was proven to be feasible, and a fully working prototype was validated for several species related to ginger.

Highlights: This use of an autonomous machine-vision system for botanical identification removed the subjectivity inherent to human-based evaluation. The learned model also accurately evaluated botanical HPTLC images significantly faster than its human counterpart, which could save both time and resources.

Background: Food contamination with mycotoxins has become a concern in recent years. Cyclopiazonic acid (CPA) is a mycotoxin found in foods, such as peanuts, corn, and cheese, which poses health hazards to humans.

Objective: To develop and validate an enzyme-linked immunosorbent assay (ELISA) for detecting CPA in various food matrixes using appropriate pretreatment methods, with liquid chromatography/ultraviolet detection (LC/UV) and/or LC/time of flight mass spectrometry (TOF-MS).

Methods: Pretreatment of food was optimized using liquid-liquid extraction and solid-phase dispersive extraction (SPDE), and validated across liquid and solid foods. Indirect competitive ELISA was performed. The data from recovery experiments were analyzed using a one-way analysis of variance.

Results: All food specimens added at high concentrations yielded satisfactory ELISA results and exhibited a strong correlation with the LC/UV measurements. In the case of moderate-concentration additions, satisfactory accuracy was obtained for foods other than spices. At low concentrations, the results of ELISA and instrumental analyses (LC/UV and LC-TOF/MS) were comparable for sake, soy sauce, noodle soup, corn, and miso. However, elevated values were observed for the ELISA of cheese, peanuts, and spices.

Conclusions: Although the low recovery levels of CPA in spices suggests the need for a more effective cleanup method, the developed ELISA is suitable as a rapid screening method for CPA in various foods, with LC confirmation recommended in complex matrixes.

Highlights: The use of SPDE for CPA analyses resulted in effective cleanup, which subsequently enabled the application of ELISA for screening.

Background: Micro- and nanoplastics from textiles, rubber, cleaning products, and other sources present in living and working environments can release toxic compounds that are added to plastics to enhance their properties.

Objective: This study explores the presence of plastic additives in settled dust from domestic and workplace indoor environments.

Methods: A Standard Reference Material was used to verify the validity of the analytical method. Settled dust from two occupational environments (a sail loft and a tire shop), and from the home of a worker in the sail loft, was extracted, obtaining two fractions to be injected in GC-MS and in HPLC-MS/MS to detect and quantify 32 plastic additives. Results from working environments were also compared with those from a treating plant of waste electrical and electronic equipment (WEEE).

Results: After a cleanup procedure and the use of matrix-matched calibration curves, the method proved to be reliable.Significant differences among the concentrations of analytes extracted from the workplace settled dust were not found except for higher values of Bis-2-ethylhexyl adipate (DEHA) and Bis-2-ethylhexyl phthalate (DEHP) in the tire shop and of dimethyl phthalate (DMP) and diisodecyl phthalate (DiDP) in the sail loft. Comparing the results from house and work environments, higher concentrations of additives were found at home. The results from the present workplaces compared with those from a WEEE treating plant showed that the latter was a much more polluted environment.

Conclusions: These preliminary results about the presence of plastic additives in the settled dust from living and working environments suggest that the WEEE treating plant deserves more attention than others. Furthermore, the home environment hides some dangers for the presence of material that can release toxic compounds.

Highlights: Optimization of a method for detecting plastic additives in environmental settled dust. NIST SRM 2585 analysis of phthalates, adipates, phosphates, citrates, trimellitate, benzoate, sebacate, dicarboxylate, benzo-triazole, -triazine demonstrated that exposure to plastic additives occurs both in workplace and living environments.

Background: The development of analytical methods in food science has grown parallel with consumers' concern about food composition. There is a desire to better understand the phytochemicals present in foods and feeds. Comprehensive screening of nutrients and bioactive compounds can be challenging due to the vast differences in molecular properties of the compounds.

Objective: To develop a sensitive liquid chromatography-mass spectrometry (LC-MS) analytical method to detect, identify, and quantify hundreds of compounds from a broad range of molecular categories in a single method, from hydrophilic (e.g., organic acids) to hydrophobic (e.g., sterols) nutrients.

Method: The chromatographic separation was tailored for a balanced detection of hydrophilic and hydrophobic compounds, with attention towards the separation of isomeric polyphenols. The chromatographic separation was performed in 45 min on a C18-PFP stationary phase, which provided enhanced resolution of isomers. The method employs two columns in series to improve the resolution, which requires ultrahigh-pressure instrumentation (1000 bar). The addition of an in-line ultraviolet (UV) diode-array detector allowed for spectral profile confirmation of the identities of many targeted and unknown compounds.

Results: The developed method was applied in this work to measure different types of bioactives recognized to have health benefits: flavonoids, phenolic acids, vitamins, tocochromanols, phytosterols, sugars, organic acids, lipids, amines, nucleic acids, and many other small molecules. Four chocolate products were analyzed as a demonstration of the range of the method.

Conclusions: This method has enabled discoveries of new commercial value in the food processing industry by capturing information on a wider range of analytes than previous individual methods, and these data can be leveraged to promote the health and well-being of consumers.

Highlights: This new LC-UV-MS methodology provides a more comprehensive analysis of a broad range of molecularly diverse bioactive constituents in raw materials and finished products in the food and feed industry.

Background: Cranberry (Vaccinium macrocarpon Ait.) is a highly consumed fruit found in foods and supplements and grown throughout northern North America. Its tart flavor makes it a common food ingredient, rather than being directly consumed as fresh fruit.

Objective: Cranberry fruit samples of 15 genotypes (cultivars and accessions) grown in 16 locations in 4 states (MA, NJ, OR, and WI) and a Canadian province (British Columbia) were analyzed by mass spectrometry. Data were analyzed using chemometric methods to determine the correlation of composition with geographic location.

Method: 214 cranberry samples were analyzed by nontargeted fuzzy chromatography-direct injection mass spectrometry. Data were collected for 206 ions and analyzed by multifactorial multivariate-analysis of variance-principal component analysis (MFMV-ANOVA-PCA).

Results: Sample composition varied statistically (P < 0.001) with respect to the major experimental factors (state/province, growing location, genotype, and analytical batch) and cross factors (genotype-state/province and genotype-growing location). PCA score plots verified a systematic variation with respect to 42 genotype-state/province pairs and 82 genotype-growing location pairs. PCA variable loadings identified major ions that varied with each of the major factors and cross factors and 56 ions were annotated. The location-ion count matrix was transposed and analyzed by hierarchical cluster analysis (HCA) producing dendrograms that grouped ions with respect to metabolic pathways for either the genotype-state/province or genotype-growing location pairs. Annotation of the ions in the hierarchical clusters allowed evaluation of the impact of genetics and location on compounds of interest. Ions expected to correlate with fruit quality measurements (brix, titratable acid, total anthocyanins, and total pro-anthocyanidins) were identified.

Conclusions: This study demonstrates that mass spectral data coupled with chemometric analysis is a valuable tool for predicting the composition of specific genotypes for specific growing locations.

Highlights: The general design of this study can be used as a model for other food plants.

Background: Batches of Pigment Red 4 (PR4) submitted to the U.S. Food and Drug Administration (FDA) for certification as D&C Red No. 36 (R36) must comply with specifications listed in the U.S. Code of Federal Regulations (CFR). Currently, a tedious thin-layer chromatography (TLC) method is used to enforce limiting specifications for four subsidiary colors: Pigment Orange 5 (PO5, CAS 3468-63-1) and Yellow-1-Naphthol (Y1N, CAS 36265-89-1), each ≤0.5%, and Pigment Red 1 (PR1, CAS 6410-10-2) and Pigment Red 6 (PR6, CAS 6410-13-5), each ≤0.3%.

Objective: To develop improved analytical methods for determination of the subsidiary colors in samples from batches submitted for certification as R36.

Methods: High-performance liquid chromatography (HPLC) and ultrahigh-performance liquid chromatography (UHPLC) were the techniques used for development of the new methods. Reference materials for PO5, PR1, and PR6 were purchased, and a standard for Y1N was synthesized, with its identity confirmed by HRMS and NMR. Calibration curves were prepared for quantification of the subsidiary colors by each method.

Results: Both newly developed methods enabled baseline separation of the four subsidiary colors and their identification based on defined retention times, elution sequence, and UV-Vis spectra. Linearity was demonstrated for both methods, with R2 values >0.999 for each of the analytes. Ranges of other validation data included: LOD, 0.006-0.018%; LOQ, 0.007-0.05%; recoveries, 85.5 ± 7.4% - 101.8 ± 2.0%. The new methods were implemented to quantify the analytes in 24 surveyed batches of R36. They yielded identical or nearly identical values for the analyzed subsidiary colors. Obtained levels in all but two samples were below CFR limits.

Conclusions: For routine batch-certification analyses of subsidiary colors in R36, either the HPLC or UHPLC method developed in this study can replace the outdated TLC procedure.

Highlights: The R36 subsidiary colors PO5, Y1N, PR1, and PR6 can be accurately quantified using the newly developed HPLC and UHPLC methods.

Background: Pogostemon cablin (P. cablin) is a valuable medicinal plant used in traditional medicine and the fragrance industry, but QC is challenging due to inconsistent stem-to-leaf ratios and frequent essential oil adulteration.

Objective: This study compares volatile components in different parts (aerial parts, stems, and leaves) and essential oil of P. cablin to support QC (not less than 20% leaf) and its rational use.

Methods: Volatile components in 21 batches of aerial parts, stems, and leaves, and 13 batches of essential oils, were analyzed using GC-MS. Multivariate curve resolution-alternating least-squares (MCR-ALS) was used for resolving co-eluted peaks, and chemical fingerprinting with chemometric techniques including hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least-squares discrimination analysis (OPLS-DA) were applied.

Results: Volatile profiling identified 56, 47, 28, and 45 components in the aerial parts, leaves, stems, and essential oil of P. cablin, respectively. MCR-ALS resolved 10 major volatile compounds to create chemical fingerprints for each analytical sample type. Quantitative analysis showed higher patchouli alcohol in leaves (12.47 mg/g) compared to stems (2.05 mg/g), while stems had more pogostone (2.71 mg/g versus 1.40 mg/g in leaves). Aerial parts and essential oil showed significant compositional differences. Based on the results of qualitative and quantitative analysis, chemometric methods, including HCA, PCA, PLS-DA, and OPLS-DA, clearly differentiated the four types of P. cablin analytical samples.

Conclusion: Significant differences in volatile components across P. cablin parts and its essential oil support QC (not less than 20% leaf) and rational use.

Highlights: This study is the first to use MCR-ALS and other chemometrics for qualitative and quantitative analysis of P. cablin parts and essential oils, aiding QC.

Background: The growing need to increase the efficiency of routine analyses in the QC laboratories of agricultural fertilizer manufacturers underscores the importance of automating official methods or developing validated alternatives to enhance analytical throughput. Phosphorus, a key nutrient in fertilizers, demands precise and rapid analytical techniques that comply with current regulatory requirements.

Objective: This study aimed to evaluate flow injection analysis (FIA) methods with molecular absorption spectrophotometric detection-based on official protocols-and compare them to a more advanced technique, microwave-induced plasma optical emission spectrometry (MIP-OES), for the quantification of elemental phosphorus.

Methods: Three methodologies were assessed: molecular absorption spectrophotometry based on the formation of yellow vanadophosphomolybdate, the formation of molybdenum blue, and MIP-OES. These methods were applied to fertilizer samples extracted using water and neutral ammonium citrate (NAC), as prescribed by official procedures.

Results: FIA methods provided a sampling throughput of up to 120 analyses/h, while MIP-OES enabled up to 240 analyses/h. Both spectrophotometric methods had LOQ near 1.0 mg/L. However, the yellow vanadophosphomolybdate method showed better linearity, up to 40 mg/L, compared to 20 mg/L for the molybdenum blue method. MIP-OES exhibited the highest sensitivity (LOQ <0.2 mg/L) and the broadest linear range. All methods showed acceptable accuracy and precision when tested with certified reference materials.

Conclusion: FIA systems are advantageous over conventional methods due to their simplicity and low cost, making them suitable for routine laboratory settings. MIP-OES, while requiring greater instrumental investment, offers higher throughput and eliminates the need for reagent preparation.

Highlights: Considering their respective strengths-FIA for accessibility and MIP-OES for performance-it is recommended that both methods are included as official methods of analysis by the Brazilian Ministry of Agriculture (MAPA) and other international regulatory bodies.

Background: Qinghai-Tibet Plateau (QTP) rape honey, recognized as a Protected Geographical Indication (PGI) product in China, has faced significant challenges due to fraudulent mislabeling of its origins in the market. To ensure the authenticity of PGI honey products and uphold market integrity, it is crucial to develop a rapid, precise, and efficient geographical traceability technology.

Objective: This study investigated the stable isotope signatures of rape honey from the Qinghai-Tibet Plateau (QTP) and the southern region (SR) for identifying key geographical indicators for the origin traceability of rape honey products in QTP. The research compared isotopic differences and elucidated their formation mechanisms across bulk honey, saccharides, and proteins. Finaly, multivariate discrimination models were established for specifically identifying QTP-origin rape honey, with optimized parameters to improve discrimination accuracy.

Methods: A total of 208 honey samples were collected from QTP (n = 71) and five provinces in the southern region (SR, n = 137) of China. Stable isotope ratios (δ13C, δ15N, δ2H, and δ18O) of bulk honey, endogenous proteins, and saccharides (glucose, fructose, and sucrose) were measured. One-way analysis of variance (ANOVA) was employed to analyze regional differences among the variables. Partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) models were constructed based on stable isotopic data to discriminate honey sample origins.

Results: ANOVA indicated the geospatial differences (P < 0.05) in δ2H and δ18O of bulk honey, as well as all four ratios of honey protein, are significant between QTP and SR. LDA exhibited superior discrimination performance, with leave-one-out cross-validation accuracies of 87.3% for QTP and 89.1% for SR.

Conclusions: An integrated strategy combining stable isotope ratios analysis with multivariate modeling provides an accurate and effective verification method for geographical origin traceability of high-value honey from QTP. This approach provides a reliable tool to address the issue of fraudulent mislabeling of PGI rape honey.

Highlights: Stable isotopic signatures of QTP rape honey were discussed. Bulk and component-specific isotopic ratios were informative geospatial indicators. Machine learning algorithms significantly enhanced honey origin discrimination. LDA accuracy for QTP honey samples reached up to 87.3%. This strategy was developed to combat origin mislabeling and ensure food integrity.