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Gennari F, Pagano M, Toncelli A, Lisanti MT, Paoletti R, Roversi PF, Tredicucci A, Giaccone M. Terahertz imaging for non-invasive classification of healthy and cimiciato-infected hazelnuts. Heliyon 2023; 9:e19891. [PMID: 37809509 PMCID: PMC10559270 DOI: 10.1016/j.heliyon.2023.e19891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 10/10/2023] Open
Abstract
The development of new non-invasive approaches able to recognize defective food is currently a lively field of research. In particular, a simple and non-destructive method able to recognize defective hazelnuts, such as cimiciato-infected ones, in real-time is still missing. This study has been designed to detect the presence of such damaged hazelnuts. To this aim, a measurement setup based on terahertz (THz) radiation has been developed. Images of a sample of 150 hazelnuts have been acquired in the low THz range by a compact and portable active imaging system equipped with a 0.14 THz source and identified as Healthy Hazelnuts (HH) or Cimiciato Hazelnut (CH) after visual inspection. All images have been analyzed to find the average transmission of the THz radiation within the sample area. The differences in the distribution of the two populations have been used to set up a classification scheme aimed at the discrimination between healthy and injured samples. The performance of the classification scheme has been assessed through the use of the confusion matrix on 50 samples. The False Positive Rate (FPR) and True Negative Rate (TNR) are 0% and 100%, respectively. On the other hand, the True Positive Rate (TPR) and False Negative Rate (FNR) are 75% and 25%, respectively. These results are relevant from the perspective of the development of a simple, automatic, real-time method for the discrimination of cimiciato-infected hazelnuts in the processing industry.
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Affiliation(s)
- Fulvia Gennari
- Dipartimento di Fisica “E. Fermi”, Università di Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy
| | - Mario Pagano
- Institute of Research on Terrestrial Ecosystems (IRET), National Research Council (CNR), Via Madonna del Piano 10, 50019, Sesto Fiorentino, Italy
| | - Alessandra Toncelli
- Dipartimento di Fisica “E. Fermi”, Università di Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy
- Centro per l’Integrazione della Strumentazione dell’Università di Pisa (CISUP), Lungarno Pacinotti 43/44, 56126, Pisa, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy
- Istituto Nanoscienze – CNR, Piazza S. Silvestro 12, 56127, Pisa, Italy
| | - Maria Tiziana Lisanti
- Università degli Studi di Napoli Federico II, Dipartimento di Agraria, Sezione di Scienze della Vigna e del Vino, viale Italia 60, 83100, Avellino, Italy
| | - Riccardo Paoletti
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy
- Dipartimento di Scienze Fisiche, della Terra e dell’Ambiente, Sezione di Fisica, Università di Siena, via Roma 56, 53100, Siena, Italy
| | - Pio Federico Roversi
- CREA, Research Centre for Plant Protection and Certification, 50125, Firenze, Italy
| | - Alessandro Tredicucci
- Dipartimento di Fisica “E. Fermi”, Università di Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy
- Centro per l’Integrazione della Strumentazione dell’Università di Pisa (CISUP), Lungarno Pacinotti 43/44, 56126, Pisa, Italy
- Istituto Nanoscienze – CNR, Piazza S. Silvestro 12, 56127, Pisa, Italy
| | - Matteo Giaccone
- Institute for Mediterranean Agricultural and Forestry Systems, National Research Council, 80055 P.le Enrico, Fermi 1 - Loc. Porto del Granatello, 80055, Portici, Naples, Italy
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Squara S, Caratti A, Fina A, Liberto E, Spigolon N, Genova G, Castello G, Cincera I, Bicchi C, Cordero C. Artificial Intelligence decision-making tools based on comprehensive two-dimensional gas chromatography data: the challenge of quantitative volatilomics in food quality assessment. J Chromatogr A 2023; 1700:464041. [PMID: 37150088 DOI: 10.1016/j.chroma.2023.464041] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/18/2023] [Accepted: 04/28/2023] [Indexed: 05/09/2023]
Abstract
Effective investigation of food volatilome by comprehensive two-dimensional gas chromatography with parallel detection by mass spectrometry and flame ionization detector (GC×GC-MS/FID) gives access to valuable information related to industrial quality. However, without accurate quantitative data, results transferability over time and across laboratories is prevented. The study applies quantitative volatilomics by multiple headspace solid phase microextraction (MHS-SPME) to a large selection of hazelnut samples (Corylus avellana L. n = 207) representing the top-quality selection of interest for the confectionery industry. By untargeted and targeted fingerprinting, performant classification models validate the role of chemical patterns strongly correlated to quality parameters (i.e., botanical/geographical origin, post-harvest practices, storage time and conditions). By quantification of marker analytes, Artificial Intelligence (AI) tools are derived: the augmented smelling based on sensomics with blueprint related to key-aroma compounds and spoilage odorant; decision-makers for rancidity level and storage quality; origin tracers. By reliable quantification AI can be applied with confidence and could be the driver for industrial strategies.
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Affiliation(s)
- Simone Squara
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, Torino 10125, Italy
| | - Andrea Caratti
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, Torino 10125, Italy
| | - Angelica Fina
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, Torino 10125, Italy
| | - Erica Liberto
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, Torino 10125, Italy
| | - Nicola Spigolon
- Soremartec Italia Srl, Piazzale Ferrero 1, Alba, Cuneo 12051, Italy
| | - Giuseppe Genova
- Soremartec Italia Srl, Piazzale Ferrero 1, Alba, Cuneo 12051, Italy
| | | | - Irene Cincera
- Soremartec Italia Srl, Piazzale Ferrero 1, Alba, Cuneo 12051, Italy
| | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, Torino 10125, Italy
| | - Chiara Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, Torino 10125, Italy.
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Mycotoxin Contamination in Hazelnut: Current Status, Analytical Strategies, and Future Prospects. Toxins (Basel) 2023; 15:toxins15020099. [PMID: 36828414 PMCID: PMC9965003 DOI: 10.3390/toxins15020099] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023] Open
Abstract
Hazelnuts represent a potential source of mycotoxins that pose a public health issue due to their increasing consumption as food ingredients worldwide. Hazelnuts contamination by mycotoxins may derive from fungal infections occurring during fruit development, or in postharvest. The present review considers the available data on mycotoxins detected in hazelnuts, on fungal species reported as infecting hazelnut fruit, and general analytical approaches adopted for mycotoxin investigation. Prompted by the European safety regulation concerning hazelnuts, many analytical methods have focused on the determination of levels of aflatoxin B1 (AFB1) and total aflatoxins. An overview of the available data shows that a multiplicity of fungal species and further mycotoxins have been detected in hazelnuts, including anthraquinones, cyclodepsipeptides, ochratoxins, sterigmatocystins, trichothecenes, and more. Hence, the importance is highlighted in developing suitable methods for the concurrent detection of a broad spectrum of these mycotoxins. Moreover, control strategies to be employed before and after harvest in the aim of controlling the fungal contamination, and in reducing or inactivating mycotoxins in hazelnuts, are discussed.
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Ortega-Gavilán F, Squara S, Cordero C, Cuadros-Rodríguez L, Bagur-González MG. Application of chemometric tools combined with instrument-agnostic GC-fingerprinting for hazelnut quality assessment. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Cordero C, Birch C. Multidimensional Gas Chromatography Moves Forward. LCGC EUROPE 2022. [DOI: 10.56530/lcgc.eu.bj8777l1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, leading figures from the separation science community explore contemporary trends in separation science and identify possible future developments.
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