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ARS Home » Pacific West Area » Albany, California » Western Regional Research Center » Healthy Processed Foods Research » Research » Publications at this Location » Publication #296058
Title: Nondestructive detection of infested chestnuts based on NIR spectroscopy

Author
item MOSCETTI, ROBERTO - University Of Tuscia
item Haff, Ronald - Ron
item SARANWONG, SIRINNAPA - Bruker Optics Kk
item MONARCA, DANILO - University Of Tuscia
item CECCHINI, MASSIMO - University Of Tuscia
item MASSANTINI, RICCARDO - University Of Tuscia

Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/13/2013
Publication Date: 1/3/2014
Citation: Moscetti, R., Haff, R.P., Saranwong, S., Monarca, D., Cecchini, M., Massantini, R. 2014. Nondestructive detection of infested chestnuts based on NIR spectroscopy. Postharvest Biology and Technology. 87:88-94.

Interpretive Summary: Insect feeding is a significant postharvest problem for processors of Chestnuts. In most cases, damage from insects is 'hidden', i.e. not visually detectable on the fruit surface. Consequently, traditional sorting techniques, including manual sorting, are generally inadequate for the detection and removal of chestnuts with hidden damage. For the most part, the only method currently used by processors is the floatation system, in which chestnuts are placed in salt water and those that float are discarded. Flotation is unreliable, and a more effective method for detection of insect damage would benefit industry and consumer alike. In this study, the feasibility of using NIR spectroscopy to detect hidden insect damage is demonstrated. Using statistical software to analyze the NIR spectra and classify the nuts as either infested or not infested, classification error rates as low as 16.81% false negative, 0.00% false positive, and 8.41% total error were achieved. These results represent an average of 55.3% improvement over a traditional floatation sorting system.

Technical Abstract: Insect feeding is a significant postharvest problem for processors of Chestnuts (Castanea sativa, Miller). In most cases, damage from insects is 'hidden', i.e. not visually detectable on the fruit surface. Consequently, traditional sorting techniques, including manual sorting, are generally inadequate for the detection and removal of chestnuts with hidden damage. For the most part, the only method currently used by processors is the floatation system, in which chestnuts are placed in salt water and those that float are discarded. Flotation is unreliable, and a more effective method for detection of insect damage would benefit industry and consumer alike. In this study, the feasibility of using NIR spectroscopy to detect hidden insect damage is demonstrated. Using a genetic algorithm for feature selection (from 2 to 6 wavelengths) in combination with a linear discriminant analysis routine, classification error rates as low as 16.81% false negative, 0.00% false positive, and 8.41% total error were achieved, with an AUC value of 0.952 and an Wilk's ' of 0.403 (P < 0.001). A Savitzky-Golay first derivative spectral pretreatment with 13 smoothing points was used. The optimal features corresponded to Abs[1582 nm], Abs[1900 nm], and Abs[1964 nm]. These results represent an average of 55.3% improvement over a traditional floatation sorting system.