Journal of AOAC International最新文献

{"title":"Method Development for Determining β-Nicotinamide Mononucleotide (NMN) in Cosmetics Using m-PFC-HPLC.","authors":"Tao Lan, Wenyu Zhang, Hongtao Chu, Zhenyu Yun, Bin Qu","doi":"10.1093/jaoacint/qsaf017","DOIUrl":"10.1093/jaoacint/qsaf017","url":null,"abstract":"<p><strong>Background: </strong>As a new cosmetic ingredient, NMN is widely used in cosmetics production, but due to the lack of a detection method, QC of related products cannot be achieved.</p><p><strong>Objective: </strong>This study will develop a detection method for NMN in three matrixes (facial mask essence, emulsion, and cream) for QC of related cosmetics.</p><p><strong>Methods: </strong>Given the high ester content in facial emulsions and creams, which can hinder the detection of trace substances, a novel multi-plug filtration clean-up (m-PFC) purification column packed with multi-walled carbon nanotubes (MWCNTs) was employed to purify these matrixes. An HPLC method for NMN in three matrixes (facial mask essence, emulsion, and cream) was established. Methodological verification was conducted.</p><p><strong>Results: </strong>Results demonstrated a good linear relationship within a range of 5.0-500 μg/mL, with an LOQ of 5.0 mg/kg. The RSD of the precision experiment was less than 3%, and the RSD for six repeated experiments ranged from 1.2 to 5.3%, indicating the method's stability, reliability, and good repeatability. Recovery rates in the three cosmetic matrixes were between 93.9 and 109.4%, with an RSD below 3.7%. This method was applied to detect NMN content in seven cosmetics purchased from an e-commerce platform; NMN was not detected in some products claiming to contain NMN.</p><p><strong>Conclusion: </strong>This method had the advantages of simple operation, high sensitivity, and good accuracy, and provides technical support for cosmetic regulation.</p><p><strong>Highlights: </strong>Through this study, we should raise awareness and supervision of NMN cosmetics by establishing relevant standards.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":"489-496"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Neutral Detergent Fiber Analysis Methods for Feed Ingredients, Diets, and Feces of Pigs.","authors":"Yoojin Koh, Jeonghyeon Son, Beob Gyun Kim","doi":"10.1093/jaoacint/qsaf030","DOIUrl":"10.1093/jaoacint/qsaf030","url":null,"abstract":"<p><strong>Background: </strong>An accurate determination of fiber concentrations in feeds and feces is critical for the measurement of fiber digestibility in pigs. The method of AOAC INTERNATIONAL for determining amylase-treated neutral detergent fiber (aNDF; Method 2002.04) has been widely used for pig diets. To overcome the complexity of the AOAC procedure, the Ankom method is also available for determining aNDF. Although these two methods have been compared for ruminant diets and feces, a comparison of the methods for pig diets and feces has not been documented.</p><p><strong>Objective: </strong>The objective was to compare aNDF values determined by the AOAC (aNDFAOAC) and the Ankom methods (aNDFAnkom) of ingredients, diets, and feces for pigs.</p><p><strong>Methods: </strong>A total of 255 test samples, consisting of 26 feed ingredients, 39 diets, and 190 feces of pigs, were analyzed for aNDF. To compare the AOAC Method 2002.04 and Ankom methods for aNDF, regression analyses were performed with the aNDFAnkom minus the mean aNDFAnkom as an independent variable and the aNDFAOAC minus the aNDFAnkom as a dependent variable.</p><p><strong>Results: </strong>The aNDFAnkom were greater than the aNDFAOAC by 2.90% (standard error = 0.63; P < 0.001) on average for ingredients and by 2.56% (standard error = 0.34; P < 0.001) on average for diets. For feces, the aNDFAnkom were greater than the aNDFAOAC by 1.30% (standard error = 0.32; P < 0.001) on average. The differences between the aNDFAnkom and aNDFAOAC were not consistent across the data ranges represented by a linear bias (slope = -0.16; standard error = 0.04; P < 0.001) in feces.</p><p><strong>Conclusion: </strong>ANDF concentrations determined by the Ankom method were greater than from the AOAC method in pig feeds and feces.</p><p><strong>Highlights: </strong>Despite convenience, the Ankom method yields greater aNDF values compared with the AOAC method.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":"648-651"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Reproducibility of HPTLC Analysis for Cranberry Supplements through Digitization and Chemometric Preprocessing.","authors":"Mengliang Zhang, Jianghao Sun, Elizabeth Corwin, James M Harnly","doi":"10.1093/jaoacint/qsaf063","DOIUrl":"https://doi.org/10.1093/jaoacint/qsaf063","url":null,"abstract":"<p><strong>Background: </strong>High-performance thin-layer chromatography (HPTLC) is widely used for the identification and quality assessment of botanical supplements. However, traditional interpretation methods are subjective, and variability between plates hinders reproducibility and inter-plate comparisons.</p><p><strong>Objective: </strong>This study aimed to enhance the reproducibility and analytical utility of HPTLC by digitizing chromatograms and applying chemometric preprocessing to cranberry dietary supplement analysis.</p><p><strong>Method: </strong>Cranberry supplements of diverse dosage forms were extracted and analyzed using a standardized HPTLC protocol. Plates were derivatized with natural products and anisaldehyde reagents and imaged under multiple lighting conditions. Digital chromatograms were processed using normalization and retention factor (RF) alignment. Chemometric methods, including principal component analysis (PCA) and analysis of variance principal component analysis (ANOVA-PCA), were applied to assess variability and improve classification.</p><p><strong>Results: </strong>The digitization and preprocessing workflow significantly reduced plate-related variability while enhancing classification accuracy. RF alignment lowered between plate variance from 23% to 11%, while increasing sample-type variance from 59% to 79%. Combining data from multiple derivatization and imaging conditions improved chemical fingerprinting and enabled tighter clustering in PCA models.</p><p><strong>Conclusions: </strong>The integration of digitized HPTLC data with chemometric preprocessing modernizes the analytical workflow, improves reproducibility, and enables more robust and interpretable botanical fingerprinting. This approach supports improved quality control of botanical products and aligns with emerging standards for data transparency and reusability.</p><p><strong>Highlights: </strong>Digitization and alignment reduce HPTLC variability and enhance reproducibility. Combined profiles from multiple derivatization conditions improve sample classification. Chemometric analysis enables better interpretation and data-driven quality control and assessment for botanicals.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation Study of the Petricore™ Aerobic Count (AC) for the Enumeration of Mesophilic Aerobic Bacteria in a Broad Range of Foods and Select Environmental Samples: AOAC Performance Tested MethodSM 032502.","authors":"Wonkee Sung, Young-Hee Cho, Sujeong Moon, Kateland Lanzit, M Joseph Benzinger, Benjamin Bastin, Erin Crowley","doi":"10.1093/jaoacint/qsaf064","DOIUrl":"https://doi.org/10.1093/jaoacint/qsaf064","url":null,"abstract":"<p><strong>Background: </strong>The Petricore™ Aerobic Count (AC) method is used for enumeration of mesophilic bacterial colony counts in a broad range of food and environmental samples. The plate is a ready-to-use dry rehydratable film media composed of modified Standard Methods nutrients, water-soluble-gelling agents and a tetrazolium indicator on the adhesive sheets, and transparent cover film.</p><p><strong>Objective: </strong>The purpose of this study is to validate the Petricore™ AC for AOAC Performance Tested Methods  SM (PTM) certification.</p><p><strong>Methods: </strong>Matrix studies were conducted on a broad range of foods and select environmental samples; fresh raw ground beef, fresh raw ground pork, raw bacon, raw shrimp, raw salmon, frozen raw tuna, frozen sliced mushrooms, frozen avocado, frozen blueberries, bacon-lettuce-tomato sandwich, frozen pizza (margherita), cooked sausage (fish and chicken breast), Romaine lettuce, cabbage, fresh green juice, stainless steel surface, plastic surface, and lettuce wash water. Petricore™ AC results were compared to standard method plating procedure results appropriate to each matrix type. Product consistency and stability testing was performed on three production lots of Petricore™ AC, and robustness experiments examined the allowable range of three parameters: culture temperature, incubation time, and inoculum amount.</p><p><strong>Results: </strong>In the matrix study, equivalent results were observed between the Petricore™ AC method and reference methods for all matrices evaluated. The mean log10 differences between Candidate method and Reference methods were within the ranged from -0.23 to 0.35 log10 within the acceptable range of -0.50 to 0.50 log10. The range of standard deviation values of the candidate method (0.01-0.88 log10) and the reference method (0.02-0.91 log10) were similar in all matrices evaluated. The range of a correlation factor of R2 was between 0.9539 and 0.9981, indicating strong correlation between two methods. In the product consistency/stability study, the Petricore™ AC plate was proven to be equivalent across production lots, and the shelf-life was established at 1 year. Small differences in method parameters did not affect the Petricore™ AC results in robustness testing.</p><p><strong>Conclusions: </strong>The Petricore™ AC plate is an accurate method for the enumeration of mesophilic aerobic bacteria in the matrices evaluated.</p><p><strong>Highlights: </strong>The data were reviewed by the AOAC PTM Program and approval was granted for certification of Petricore™ AC as PTM 032502.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of the OnSite® Gluten Test Kit for Detection of Gluten in Selected Foods and Environmental Surfaces: AOAC Performance Tested MethodSM 012501.","authors":"Meiting Wu, Lora Benoit, Cesar Nadala, David P Cox, Bryan Ndiritu, Anna M Willie, Kissi Yesu, Mansour Samadpour","doi":"10.1093/jaoacint/qsaf060","DOIUrl":"https://doi.org/10.1093/jaoacint/qsaf060","url":null,"abstract":"<p><strong>Background: </strong>The OnSite® Gluten Test Kit is a qualitative immunochromatographic assay designed for the detection of gluten in foods and on environmental surfaces.</p><p><strong>Objective: </strong>To validate the performance of the OnSite Gluten Test Kit in rice flour, oat flour, spice mix, and bread, and on stainless steel surface.</p><p><strong>Methods: </strong>The kit was assessed for cross-reactivity, interference, detection capability of spiked and incurred gluten, recovery from stainless steel surfaces, lot-to-lot consistency, robustness, and stability.</p><p><strong>Results: </strong>Testing revealed no cross-reactivity or interference from a panel of gluten-free food items, or susceptibility to high analyte concentration. Analysis of selected spiked or incurred test materials at various estimated concentrations of gluten supported claimed detection capabilities (CDC) ranging from 5 to 20 mg/kg gluten depending on the aliquot volume and food matrix. The method detected wheat gluten present on stainless steel at a contamination level of 11 µg gluten/100 cm2 (POD 0.95, CI 0.76-1.00). Testing results also indicated consistency, robustness, and kit stability. Independent laboratory testing of select food matrixes supported the findings of the candidate laboratory.</p><p><strong>Conclusions: </strong>The performance of the OnSite Gluten Test Kit was validated on gluten spiked or incurred into rice flour, oat flour, spice mix, and bread, and on stainless steel surfaces.</p><p><strong>Highlights: </strong>The OnSite Gluten Test Kit offers an easy-to-use assay method to detect gluten in select foods and on stainless steel surfaces. The testing format affords the user flexibility in their choice of gluten detection thresholds.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outlier identification method based on multi-model weighted consensus in conjunction with Monte Carlo Cross-Validation.","authors":"Yujing Wang, Zhengguang Chen, Jinming Liu, He Wang","doi":"10.1093/jaoacint/qsaf061","DOIUrl":"https://doi.org/10.1093/jaoacint/qsaf061","url":null,"abstract":"<p><strong>Background: </strong>The accurate identification and removal of outliers are fundamental to the development of a robust model.</p><p><strong>Objective: </strong>Nevertheless, relying solely on a single model for outlier identification may prove inadequate for accurately identifying all outliers, potentially leading to false positives, false negatives, and model dependence.</p><p><strong>Methods: </strong>This study introduces a method termed Monte Carlo cross-validation in conjunction with multiple models of Weighted Consensus for outlier identification (MCWC). The proposed method integrates Monte Carlo random sampling with three distinct modeling methods: Partial Least Squares Regression (PLSR), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). This integration allows for the amalgamation of predictions from each model, facilitating the identification of outliers effectively.</p><p><strong>Results: </strong>This study employed a dataset comprising 305 sorghum samples as the experimental foundation. The predictive model for sorghum protein was built using the data after removing outliers using the single model method and the MCWC method, respectively. The experimental results indicate that the dataset, which was obtained by removing outliers using a single modeling method, is appropriate for further modeling with the same method. However, it is not suitable for modeling with other methods due to issues related to model dependence. After applying the MCWC method to remove outliers, the average R2 of the model prediction set was found to be 0.8525. In contrast, the average R2 of the model prediction set, obtained by applying the Monte Carlo method combined exclusively with PLSR for outlier removal, is 0.8037.</p><p><strong>Conclusion: </strong>The MCWC method exhibits superior accuracy in identifying outliers and effectively addresses challenges such as false positive, false negative, and model dependence in the process of identifying near-infrared spectral outliers. This enhances the overall predictive performance of the calibration model for spectral quantitative analysis.</p><p><strong>Highlights: </strong>A multi-model dynamic weighted consensus outlier identification for NIRS data was proposed. This dynamic weighting method effectively addresses the biases that can occur with simple averaging. The data after removing outliers using consensus methods is more suitable for modeling with a wider range of models.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of Selenoneine in Seafood and Seafood-Derived Products.","authors":"Eri Matsumoto, Takuya Seko, Yumiko Yamashita, Michiaki Yamashita","doi":"10.1093/jaoacint/qsaf062","DOIUrl":"https://doi.org/10.1093/jaoacint/qsaf062","url":null,"abstract":"<p><strong>Background: </strong>Selenoneine exhibits antioxidant properties and is thus expected to become a new functional ingredient. Accurately determining selenoneine levels in foods is therefore critical.</p><p><strong>Objective: </strong>This study investigated and validated extraction methods for selenoneine in seafood and seafood-derived products.</p><p><strong>Method: </strong>Selenoneine was extracted from seafood and seafood-derived products by sonication for 1 h and incubation at 37 °C for 24 h in a solution of 50 mmol/L dithiothreitol (DTT). The concentration of selenoneine was then determined using liquid chromatography-inductively coupled plasma mass spectrometry (LC-ICP-MS) and size exclusion column chromatography using a mobile phase of 0.1 mmol/L ammonium acetate with 0.1% IGEPAL®.</p><p><strong>Results: </strong>The method was validated using a dithiothreitol (DTT) solution that effectively extracts selenoneine. The limit of detection (0.020-0.030 mg/kg), limit of quantitation (0.067-0.099 mg/kg), repeatability (3.4-8.9%), intermediate precision (4.1-8.9%), and trueness (recovery of 94-109% based on spiked samples) of the proposed method were satisfactory for determining selenoneine in seafood and seafood-derived products. Selenoneine was detected in migratory fish and processed migratory fish products obtained in Japan. Particularly large amounts of selenoneine were detected in dark muscle of bluefin tuna.</p><p><strong>Conclusions: </strong>Using a reagent reactive to thiol groups, selenoneine was effectively extracted from seafood and seafood-derived products. The results of method validation analyses were satisfactory. Selenoneine was detected in processed products prepared from migratory fish, indicating that selenoneine remains even after processing. Water-soluble selenoneine was found to be extracted in the liquid.</p><p><strong>Highlights: </strong>Selenoneine could be effectively extracted using DTT, and determination of selenoneine in various seafood was possible using LC-ICP-MS.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a K Value Method for Freshness Evaluation of Bony Fish Using High-Performance Liquid Chromatography: An Interlaboratory Study.","authors":"Takeya Yoshioka, Tomoko Nishimura, Kanako Hashimoto, Kenji Ishihara, Masashi Kadokura, Tomoaki Moriyama, Yuko Murata, Kunihiko Konno, Toshiyuki Suzuki","doi":"10.1093/jaoacint/qsaf058","DOIUrl":"https://doi.org/10.1093/jaoacint/qsaf058","url":null,"abstract":"<p><strong>Background: </strong>Freshness is one of the most important qualities of fish and has a significant impact on the utilization and commercial value. As global production and trade volumes of fishery products increase, standardization of scientific methods for evaluating fish freshness is required.</p><p><strong>Objective: </strong>K value based on the ratio of ATP derivative contents is widely recognized as a scientific freshness index of fish. The aim of this study was to standardize the K value analysis method that would be useful for fair commercial transactions of bony fish.</p><p><strong>Methods: </strong>Conventional methods for analyzing K values of fish were modified. An interlaboratory study was conducted to evaluate the reproducibility of the method. The 11 participating laboratories analyzed 10 test samples (five pairs of blind duplicates of material) using high-performance liquid chromatography (HPLC).</p><p><strong>Results: </strong>For five test materials with a K value of 6.12-83.4%, the range of reproducibility relative standard deviation (RSDR) was 1.3-3.5%. The reproducibility standard deviation (sR) was 0.21-1.2%.</p><p><strong>Conclusions: </strong>Considering the rate of increase of K value in typical bony fish species and storage conditions, the target value of reproducibility standard deviation was set to sR ≤1.25%, so as detecting a difference of 5% in K values. The results of the interlaboratory study met this criterion.</p><p><strong>Highlights: </strong>The precision of the method was found to be acceptable.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144277069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolomics Analysis on Different Parts of Ligustrum Lucidum Ait Based on UPLC-Q-TOF-MS.","authors":"Lijie Zuo, Xiaojin Ge, Qingmei Qiao, Huifang Lv, Zhikun Xu, Shuya Xu, Lihong Li","doi":"10.1093/jaoacint/qsaf057","DOIUrl":"https://doi.org/10.1093/jaoacint/qsaf057","url":null,"abstract":"<p><strong>Background: </strong>Ligustri Lucidi Fructus (LLF), the dried fruit of Ligustrum lucidum Ait (LLA), is a traditional Chinese medicine used for nourishing liver and kidney.</p><p><strong>Objective: </strong>To chemically characterize and compare medicinal and non-medicinal plant parts of LLA to potentially improve biomass utilization.</p><p><strong>Method: </strong>The metabolite profiles of three different plant parts were evaluated by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS). PCA (principal components analysis) and PLS-DA (partial least squares discriminant analysis) were used to compare the chemical composition of the leaf, stem, and fruit of LLA. Differential metabolites were analyzed via the Pathway Analysis module of MetaboAnalyst 5.0 for pathway enrichment.</p><p><strong>Results: </strong>A total of 37 compounds were identified from three different plant parts by UPLC-QTOF-MS/MS combined with UNIFI v1.8.1 software. Significant metabolic differences were observed among the leaf, stem, and fruit of LLA using PCA and PLS-DA. Eleven compounds were identified as markers. The content of loganate, secologanoside, nuzhenal C, Luteolin, iso-oleonuezhenide, dammarenediol-II was so much higher in the fruit than those in the leaf and stem. The content of oleanolic acid was higher in the fruit, stem than that in the leaf. Metabolic pathway analysis revealed that triterpenoids (dammarenediol-II, oleanolic acid, β-Amyrin) exhibited significantly higher abundance in the fruit and stem than in the leaf.</p><p><strong>Conclusion: </strong>The stem of LLA may processed as a source of oleanolic acid in the future. This study laid the foundation for the rational utilization of non-medicinal LLA resources.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Genetics and Environment on Cranberry Fruit Metabolites.","authors":"James Harnly, Ping Geng, James Polashock, Pei Chen, Jennifer Johnson, Nicholi Vorsa","doi":"10.1093/jaoacint/qsaf056","DOIUrl":"https://doi.org/10.1093/jaoacint/qsaf056","url":null,"abstract":"<p><p>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 non-targeted fuzzy chromatography-direct injection mass spectrometry (FC-DIMS). The data collected for 206 ions were analyzed by multifactorial multivariate analysis of variance-principal component analysis (MFMV-ANOVA-PCA). MFMV-ANOVA-PCA showed that sample composition varied statistically (p < 0.001) with respect to the major factors (state/province, growing location, genotype, and analytical batch) and cross factors (genotype-state/province and genotype-growing location). MFMV-ANOVA-PCA score plots verified a systematic variation with respect to 42 genotype-state/province pairs and 82 genotype-growing location pairs. MFMV-ANOVA-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 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 proanthocyanidins) were identified. 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. The general design of this study can be used as a model for other food plants.</p>","PeriodicalId":94064,"journal":{"name":"Journal of AOAC International","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}