Evidence map›Paper›PMID 36080972›Full record

ArticleSensors (Basel, Switzerland)2022

A Monitoring Framework with Integrated Sensing Technologies for Enhanced Food Safety and Traceability.

Antonio Vincenzo Radogna, Maria Elena Latino, Marta Menegoli, Carmela Tania Prontera, Gabriele Morgante, Diamantea Mongelli, Lucia Giampetruzzi, Angelo Corallo, Andrea Bondavalli, Luca Francioso

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Antonio Vincenzo RadognaInstitute for Microelectronics and Microsystems, National Research Council of Italy (CNR-IMM), Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy.ORCID 0000-0002-7305-5614
Maria Elena LatinoDepartment of Innovation Engineering, University of Salento, Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy.ORCID 0000-0001-8686-5109
Marta MenegoliDepartment of Innovation Engineering, University of Salento, Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy.ORCID 0000-0002-6771-509X
Carmela Tania PronteraInstitute for Microelectronics and Microsystems, National Research Council of Italy (CNR-IMM), Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy.ORCID 0000-0002-5934-7733
Gabriele MorganteResiltech s.r.l., Piazza N. Iotti, 25, 56025 Pontedera, Italy.
Diamantea MongelliResiltech s.r.l., Piazza N. Iotti, 25, 56025 Pontedera, Italy.
Lucia GiampetruzziInstitute for Microelectronics and Microsystems, National Research Council of Italy (CNR-IMM), Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy.ORCID 0000-0001-5385-2133
Angelo CoralloDepartment of Innovation Engineering, University of Salento, Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy.ORCID 0000-0001-5216-5366
Andrea BondavalliResiltech s.r.l., Piazza N. Iotti, 25, 56025 Pontedera, Italy.ORCID 0000-0001-7366-6530
Luca FranciosoInstitute for Microelectronics and Microsystems, National Research Council of Italy (CNR-IMM), Campus Ecotekne, Via per Monteroni s.n., 73100 Lecce, Italy.ORCID 0000-0002-4500-0421

Funding

Regione Puglia CUP: B37H17005120007
6 · The paper itself

Abstract

A novel and low-cost framework for food traceability, composed by commercial and proprietary sensing devices, for the remote monitoring of air, water, soil parameters and herbicide contamination during the farming process, has been developed and verified in real crop environments. It offers an integrated approach to food traceability with embedded systems supervision, approaching the problem to testify the quality of the food product. Moreover, it fills the gap of missing low-cost systems for monitoring cropping environments and pesticides contamination, satisfying the wide interest of regulatory agencies and final customers for a sustainable farming. The novelty of the proposed monitoring framework lies in the realization and the adoption of a fully automated prototype for in situ glyphosate detection. This device consists of a custom-made and automated fluidic system which, leveraging on the Molecularly Imprinted Polymer (MIP) sensing technology, permits to detect unwanted glyphosate contamination. The custom electronic mainboard, called ElectroSense, exhibits both the potentiostatic read-out of the sensor and the fluidic control to accomplish continuous unattended measurements. The complementary monitored parameters from commercial sensing devices are: temperature, relative humidity, atmospheric pressure, volumetric water content, electrical conductivity of the soil, pH of the irrigation water, total Volatile Organic Compounds (VOCs) and equivalent CO2. The framework has been validated during the olive farming activity in an Italian company, proving its efficacy for food traceability. Finally, the system has been adopted in a different crop field where pesticides treatments are practiced. This has been done in order to prove its capability to perform first level detection of pesticide treatments. Good correlation results between chemical sensors signals and pesticides treatments are highlighted.

Indexed as

PesticidesFood SafetySoilTechnologyWaterPesticidesSoilWaterAgriculture 4.0food traceabilityglyphosate low-cost detectionICT for sensor networksIndustry 4.0Internet of Thingsmodel based system engineering

Identifiers

PMID36080972
PMCPMC9459684

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.