From land to glass: An integrated approach for quality and traceability assessment of top Italian wines

Conti, Marcelo Enrique; Rapa, Mattia; Simone, Cristina; Calabrese, Mario; Bosco, Gerardo; Canepari, Silvia; Astolfi, Maria Luisa

doi:10.1016/j.foodcont.2023.110226

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…They selected 10 elements, including Ni and Cs, from 45 elements as the main information variables for origin tracing. Combined with chemometric classification methods, they were able to accurately classify Piemonte and Sicily samples 6 .…”

Section: Introductionmentioning

confidence: 99%

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

Wang,

Ta,

Wei

et al. 2024

Sci Rep

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To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000–1400 nm and 1500–1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

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Section: Introductionmentioning

confidence: 99%

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

Wang,

Ta,

Wei

et al. 2024

Sci Rep

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Determination of Some Element’s Migrants in Aqueous Simulant from Plastic Food Contact Products by Inductively Coupled Plasma Mass Spectrometer

Ghuniem

2024

Food Anal. Methods

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Various chemicals present at different stages in the food supply chain can lead to the leaching of heavy metals. These metals can accumulate in the human body through the consumption of contaminated food. Consequently, it is necessary to validate an analytical technique for the quantification chemical that could contaminate food. This study presents a rapid, straightforward, and efficient analytical method for the direct quantification of some potentially toxic elements in aqueous simulants from plastic food contact products using an inductively coupled mass spectrometer (ICP-MS). The method’s validation encompassed the study of the estimated detection limits, practical quantification limits, linearity, accuracy, and measurement uncertainty of aluminium (Al), antimony (Sb), arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn) under optimized ICP-MS conditions. The estimated detection limits ranged from 7.5 × 10−4 to 0.074 mg/kg, while practical quantification limits spanned from 0.02 to 0.8 mg/kg. The average recoveries ± standard deviations at different spiking levels were varied between 85.7 ± 1.51 and 115.6 ± 0.88% with coefficients of variation between 0.42 and 5.85%. The method trueness was verified by using references materials (test material in aqueous acetic acid) purchased from Food Chemistry Proficiency Testing and Analysis (FAPAS) yielding satisfactory results within acceptable recovery and Z-score values. The method precision, in terms of relative standard deviation (RSD), was being below 4.22%. The method uncertainty expressed as expanded uncertainty of all validated elements was found to be ≤ 21.9%. Validated method was employed to determine specific elements in aqueous simulants of thirty commercial plastic food packaging samples, representing three distinct types of plastic polymers. The results showed that the mean concentrations, in mg/kg, were as follows: 2.04 (Al), 0.02 (As), 0.02 (Cd), 0.02 (Co), 0.06 (Cr), 0.41 (Cu), 1.55 (Fe), 0.09 (Mn), 0.15 (Ni), 0.07 (Pb), 0.05 (Sb), and 0.81 (Zn). Furthermore, 30% of analyzed samples exceeding the maximum permissible limits of Al for plastic materials and articles intended to come into contact with food.

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Authentication of selected white wines by geographical origin using ICP spectrometric and chemometric analysis

Temerdashev,

Khalafyan,

Abakumov

et al. 2024

Heliyon

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From land to glass: An integrated approach for quality and traceability assessment of top Italian wines

Cited by 5 publications

References 35 publications

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

Determination of Some Element’s Migrants in Aqueous Simulant from Plastic Food Contact Products by Inductively Coupled Plasma Mass Spectrometer

Authentication of selected white wines by geographical origin using ICP spectrometric and chemometric analysis

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