A combined metabolomics and peptidomics approach to discriminate anomalous rind inclusion levels in Parmigiano Reggiano PDO grated hard cheese from different ripening stages

Rocchetti, Gabriele; Michelini, S.; Pizzamiglio, Valentina; Masoero, Francesco; Lucini, Luigi

doi:10.1016/j.foodres.2021.110654

Cited by 28 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quality control (QC) samples (prepared by pooling same aliquots of each sample) were acquired in a data-dependent (TOP N = 3) MS/MS mode, and the Top N ions were selected for fragmentation under stepped (10, 20, 40 eV) Normalized Collisional Energy. The HESI parameters were previously optimized by Rocchetti et al ( Rocchetti et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…The raw spectral data were processed using MS-DIAL software (version 4.80) ( Tsugawa et al, 2015 ) for post-acquisition and data filtering procedures. The MS-DIAL parameters were adapted from previously published works on LC-MS untargeted metabolomics-based analysis ( Rocchetti et al, 2021 ). The mass features were searched in the mass range of 80–1200 m / z , having a minimum peak height of 10,000 cps.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Untargeted metabolomics and conventional quality characterization of rowanberry pomace ingredients in meatballs

et al. 2023

Self Cite

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Untargeted metabolomics and conventional quality characterization of rowanberry pomace ingredients in meatballs

et al. 2023

Self Cite

View full text Add to dashboard Cite

“…The best markers of rind inclusion of >18% in grated Parmigiano Reggiano, including 2‐hydroxyadenine and the amino acid derivatives argininic acid and 5‐hydroxyindole acetaldehyde were detected by using a combined metabolomics and peptidomics approach (Rocchetti et al . 2021). Afshari et al .…”

Section: Dairy Quality Evaluation Based On Metabolomicsmentioning

confidence: 99%

Review of the applications of metabolomics approaches in dairy science: From factory to human

Zhang¹,

Zheng²,

Liu³

et al. 2023

Int J of Dairy Tech

View full text Add to dashboard Cite

Dairy products are nutritious and are increasingly consumed as an important dietary component in China. Exploring the factors that affect the nutritional quality of dairy products and ensuring their safety have become the main focus of dairy research. The composition of metabolites in dairy products is large and complex. The levels and types of metabolites vary according to various factors in the process from factory to human dining table. Therefore, metabolites might be used to assess the nutritional value, traceability and authenticity, and physiological function of dairy products. This review's main goal is to introduce the most recent developments and applications of metabolomics as an efficient tool for comprehensively characterising the composition and dynamic changes of metabolites in the area of food science and nutrition research in the process of getting dairy products from factory to human. The examples are taken from the most relevant metabolomics work published from 2018 to 2022, focusing on potential marker metabolites and metabolic mechanisms related to dairy product quality, authenticity/traceability and dairy intake monitoring. The future direction of metabolomics in the field of dairy science was also discussed. This information will provide a reference for the further application of metabolomics technology to Chinese dairy products to develop their quality, safety and nutritional value.

show abstract

“…In metabolomics studies, OPLS-DA can be applied to data classi cation with one or more classes (Cassago et Additionally, the OPLS-DA model validation parameters (goodness-of-t -R 2 Y -together with goodness-ofprediction -Q 2 Y) were checked. Thereafter, the OPLS-DA model was checked for outliers, CV-ANOVA p-value threshold < 0.05 for model signi cance, and a permutation testing (N > 200) was performed to exclude model over tting (Rocchetti et al, 2021). The permutation plot shows the correlation coe cient between the original Y-variable and the permuted Y-variable on the X-axis versus the cumulative R 2 and Q 2 on the Y-axis, and then originates a regression curve, where the intercept is the measure of the over tting.…”

Section: Data Pre-processing and Multivariate Analysismentioning

confidence: 99%

Untargeted Metabolomic Approach Based on UHPL-ESI-HRMS to Investigate Metabolic Profiles of Different Coffea Species and Terroir

Artêncio,

Cassago,

da Silva

et al. 2023

Rev. Bras. Farmacogn.

View full text Add to dashboard Cite

Coffee (Coffea spp) has evolved from an agricultural commodity to a specialty beverage, regarding the product's trading, appreciation, philosophies, and purposes of consumption. Consequently, part of the coffee industry has focused on the sensory complexion and high-quality to meet engaged consumers. To evaluate the chemical pro les and distinctiveness of natural products from plants, metabolomics has emerged as a valuable tool. In this work, we carried out an untargeted metabolomic approach based on reversed-phase liquid chromatography coupled with mass spectrometry, followed by multivariate statistical analysis to obtain the metabolic ngerprints of 21 coffee samples belonging to two species and ve botanical varieties, as follows: C. arabica (var. yellow catuai, yellow bourbon, and yellow obata) and C. canephora (var. conilon, and robusta). The samples were obtained in the 2022 Edition of the "Brazilian International Conference of Coffee Tasters", state of Rondônia, Brazil. Principal Component Analysis and Orthogonal Projections Latent Structures Discriminant Analysis were performed using the metabolomic data, resulting in the discrimination of coffee samples based on their chemical pro les. Caffeine, DIMBOA-Gl, roemerine, and cajanin were determined as chemical markers for C. canephora samples, and toralactone, cnidilide, LysoPC(18:2(9Z,12Z)), Lysophosphatidylcholine(16:0/0:0), and 2,3-Dehydrosilybin for C. arabicasamples. In addition to the genetic variability, our results show the possible in uence of a terroir factor in the production of secondary metabolites of coffee samples, mainly for individuals of C. canephora.

show abstract

A combined metabolomics and peptidomics approach to discriminate anomalous rind inclusion levels in Parmigiano Reggiano PDO grated hard cheese from different ripening stages

Cited by 28 publications

References 35 publications

Untargeted metabolomics and conventional quality characterization of rowanberry pomace ingredients in meatballs

Untargeted metabolomics and conventional quality characterization of rowanberry pomace ingredients in meatballs

Review of the applications of metabolomics approaches in dairy science: From factory to human

Untargeted Metabolomic Approach Based on UHPL-ESI-HRMS to Investigate Metabolic Profiles of Different Coffea Species and Terroir

Contact Info

Product

Resources

About