Italian tomato-based products authentication by multi-element approach: A mineral elements database to distinguish the domestic provenance

Fragni, Rosaria; Trifiró, A.; Nucci, Ada; Seno, Andrea; Allodi, Alessia; Rocco, M. Di

doi:10.1016/j.foodcont.2018.06.002

Cited by 33 publications

(14 citation statements)

References 31 publications

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“…Sample powders were digested in 65% nitric acid by microwave digestion system (MDS‐6G, SINEO), and the content of mineral elements was determined by inductively coupled plasma‐mass spectrometry (ICP‐MS, iCAP‐RQ, Thermo Fisher Scientific) using multielement standard solution for ICP analysis as the standard (0.1–10,000 ng/ml, GNM‐M301629‐2013) (Fragni et al., 2018; Jia et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Consumers' attention on identification, nutritional compounds, and safety in heavy metals of Canadian sea cucumber in Chinese food market

Song

Wen

et al. 2020

Food Science & Nutrition

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Sea cucumbers, also called bêche-de-mer, belong to the class Holothuroidea (phylum Echinodermata), which are usually processed into dried product and valued as important seafood delicacies particularly in China, Japan, and South Asia (Correia-da-Silva, Sousa, Pinto, & Kijjoa, 2017). Based on nutritional view, sea cucumbers are considered to be ideal tonics due to their profile of high-valued nutrients such as protein, polysaccharides, saponins, amino acids, and min

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Section: Methodsmentioning

confidence: 99%

Consumers' attention on identification, nutritional compounds, and safety in heavy metals of Canadian sea cucumber in Chinese food market

Song

Wen

et al. 2020

Food Science & Nutrition

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“…Among other definitions, ML is defined as the scientific field that gives machines the ability to learn without being strictly programmed [ 1 ]. Year by year, ML applies in more and more scientific fields including, for example, bioinformatics [ 2 , 3 ], biochemistry [ 4 , 5 ], medicine [ 6 , 7 , 8 ], meteorology [ 9 , 10 , 11 ], economic sciences [ 12 , 13 , 14 ], robotics [ 15 , 16 ], aquaculture [ 17 , 18 ], and food security [ 19 , 20 ], and climatology [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Review

Λιάκος

Busato

Moshou

et al. 2018

Sensors

1,887

827

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Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

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“…Further, to the authors' knowledge, this is the first application of the unique full-spectra capabilities single particle ICP-TOF-MS analysis to detect and characterize NP in plant tissues. Older generations of ICP-TOF-MS technologies (without single particle capabilities) have been used to quantify co-occurrence of analytes in samples, including plant tissue-based samples [75][76][77], and this type of analysis is extended here. While ICP-TOF-MS was useful for the evaluation of the method, the lack of association of plant material with the NP indicates that TOF-MS is not essential to characterize the single-metal NP types tested here.…”

Section: Resultsmentioning

confidence: 99%

Methanol-based extraction protocol for insoluble and moderately water-soluble nanoparticles in plants to enable characterization by single particle ICP-MS

et al. 2020

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The detection and characterization of soluble metal nanoparticles in plant tissues are an analytical challenge, though a scientific necessity for regulating nano-enabled agrichemicals. The efficacy of two extraction methods to prepare plant samples for analysis by single particle ICP-MS, an analytical method enabling both size determination and quantification of nanoparticles (NP), was assessed. A standard enzyme-based extraction was compared to a newly developed methanol-based approach. Au, CuO, and ZnO NPs were extracted from three different plant leaf materials (lettuce, corn, and kale) selected for their agricultural relevance and differing characteristics. The enzyme-based approach was found to be unsuitable because of changes in the recovered NP size distribution of CuO NP. The MeOH-based extraction allowed reproducible extraction of the particle size distribution (PSD) without major alteration caused by the extraction. The type of leaf tissue did not significantly affect the recovered PSD. Total metal losses during the extraction process were largely due to the filtration step prior to analysis by spICP-MS, though this did not significantly affect PSD recovery. The methanol extraction worked with the three different NPs and plants tested and is suitable for studying the fate of labile metal-based nano-enabled agrichemicals.

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Italian tomato-based products authentication by multi-element approach: A mineral elements database to distinguish the domestic provenance

Cited by 33 publications

References 31 publications

Consumers' attention on identification, nutritional compounds, and safety in heavy metals of Canadian sea cucumber in Chinese food market

Consumers' attention on identification, nutritional compounds, and safety in heavy metals of Canadian sea cucumber in Chinese food market

Machine Learning in Agriculture: A Review

Methanol-based extraction protocol for insoluble and moderately water-soluble nanoparticles in plants to enable characterization by single particle ICP-MS

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