Hazelnuts classification by hyperspectral imaging coupled with variable selection methods

G. Bonifazi; G. Capobianco; R. Gasbarrone; S. Serranti

doi:10.1117/12.2588287

12 April 2021 Hazelnuts classification by hyperspectral imaging coupled with variable selection methods

G. Bonifazi, G. Capobianco, R. Gasbarrone, S. Serranti

Proceedings Volume 11754, Sensing for Agriculture and Food Quality and Safety XIII; 117540Q (2021) https://doi.org/10.1117/12.2588287
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

The increasing normative requirements and market competitiveness lead the agricultural sector and the food industry to constantly look for new fast and non-destructive classification logics that can be applied for product sorting applications and/or quality control actions. With reference to hazelnut production, the dried fruits must be sorted from unwanted foreign bodies or inedible hazelnuts that can negatively affect the quality of the final product. In this perspective, the utilization of HyperSpectral Imaging (HSI) can be applied to set-up a novel hazelnuts quality control. Hazelnuts and contaminants were acquired by a push-broom hyperspectral device working in the Short-Wave InfraRed (SWIR: 1000-2500 nm) region. A PLSDA model was set up in order to identify 3 classes of products (i.e. edible hazelnuts, hazelnut shells and rotten hazelnuts) with the highest level of efficiency in full spectrum mode (Precision = 0.92, Accuracy = 0.94, Efficiency = 0.94). Subsequently, different variable selection methods (i.e. Interval PLSDA, Selectivity Ratio and Variable Importance in Projection score methods) were adopted in order to identify the fundamental bands to recognize the 3 classes and evaluate which of the variable selection methods shows efficiency values close to the values obtained by the full spectrum mode. VIP score-based classification showed the best performance, with Precision, Accuracy and Efficiency values equal to those based on full spectrum PLSDA. Classification results suggest that this methodological approach can be powerful to develop and implement hazelnut sorting and/or quality control strategies. Moreover, the variable selection approach allows to increase processing speed , compared to that in full spectrum mode, making possible online applications directly at plant scale.

Conference Presentation

Citation Download Citation

G. Bonifazi, G. Capobianco, R. Gasbarrone, and S. Serranti "Hazelnuts classification by hyperspectral imaging coupled with variable selection methods", Proc. SPIE 11754, Sensing for Agriculture and Food Quality and Safety XIII, 117540Q (12 April 2021); https://doi.org/10.1117/12.2588287

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available