Chestnut quality classification by THz Time-Domain Hyperspectral Imaging combined with unsupervised learning analysis

Year: 2025

Authors: Martinez A., Di Sarno V., Maddaloni P., Rocco A., Paturzo M., Ruocco M., Paparo D.

Autors Affiliation: Univ Napoli Federico II, Scuola Super Meridionale, Naples, Italy; Ist Nazl Ott INO CNR, Consiglio Nazl Ric, Pozzuoli, Italy; CNR, Inst Appl Sci & Intelligent Syst, ISASI, Pozzuoli, Italy; CNR, Ist Protez Sostenibile Piante, IPSP, Portici, Italy.

Abstract: Chestnut crops are threatened by fungal pathogens such as Gnomoniopsis castaneae, , which cause significant degradation of quality. Early detection of such infections is crucial to maintain the quality of chestnuts in the food industry. This study explores the application of Terahertz Time-Domain Hyperspectral Imaging (THzTDHIS) combined with unsupervised learning techniques to identify fungal infections in chestnuts. Unlike conventional methods that rely on light attenuation, this approach leverages the unique spectral signatures of infected tissues. By employing Principal Component Analysis, K-Means Clustering, and Agglomerative Clustering, we effectively differentiate between healthy and infected portions of chestnuts. Our findings indicate that spectral features, rather than just intensity variations, provide more reliable markers for infection. In addition, we demonstrate that these methods enable the quantification of the degree of infection in chestnuts. The robustness of these unsupervised learning methods in handling large and heterogeneous data sets further underscores their potential in agricultural applications. This integrated THz-TDHIS and machine learning approach presents a promising solution to ensure chestnut quality and safety.

Journal/Review: FOOD CONTROL

Volume: 168      Pages from: 110878-1  to: 110878-8

More Information: This research was funded by the project PSR Campania 2014/2020 Misura 16 -Tipologia di intervento 16.1 -Azione 2 Sostegno ai Progetti Operativi di Innovazione (POI) -Progetto ’Migliorcast’ (CUP B78H19005230008) . The TeraHz ASOPS system was acquired through SHINE project funding (Strengthening the Italian Nodes of E-RIHS, Avviso 424/2018 dell’Azione II.1 PON R&I & I 2014-2020, DD n. 461 del 14-03-2019, PIR01_00016, CUP B27E19000030007) . We also extend our gratitude to Mr. Mario De Angioletti from CNR-IPCB for his assistance in slicing the chestnuts.
KeyWords: THz-TDS; Machine learning; THz imaging; Principal components analysis; K-means clustering; Agglomerative clustering
DOI: 10.1016/j.foodcont.2024.110878