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Title: Feasibility of NIR Spectroscopy to detect olive fruit infested by Bactrocera oleae

Author
item MOSCETTI, ROBERTO - University Of Tuscia
item Haff, Ronald - Ron
item STELLA, ELIZABETTA - University Of Tuscia
item CONTINI, MARINA - University Of Tuscia
item MONARCA, DANILO - University Of Tuscia
item CECCHINI, MASSIMO - University Of Tuscia
item MASSANTINI, RICARDO - University Of Tuscia

Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/24/2014
Publication Date: 8/15/2014
Citation: Moscetti, R., Haff, R.P., Stella, E., Contini, M., Monarca, D., Cecchini, M., Massantini, R. 2014. Feasibility of NIR Spectroscopy to detect olive fruit infested by Bactrocera oleae. Postharvest Biology and Technology. 99:58-62.

Interpretive Summary: Olive fruit fly infestation is a significant problem for the milling process. In most cases, damage from insects is ‘hidden’, i.e. not visually detectable on the fruit surface. Consequently, traditional visual sorting techniques can’t detect insect damage. In this study, the feasibility of using NIR spectroscopy to detect hidden insect damage is demonstrated. Using a computer program to select from 2 to 6 appropriate wavelengths in combination with classification algorithms, classification error rates as low as 0.00% false negative, 12.50% false positive, and 6.25% total error were achieved. Multiplicative Scatter Correction, Savitzky-Golay spectral pretreatment with 13 smoothing points and Mean Centering spectral pretreatments were used. The optimal features corresponded to Abs[1100 nm], Abs[1232 nm], Abs[1416 nm], Abs[1486 nm] and Abs[2148 nm].

Technical Abstract: Olive fruit fly infestation is a significant problem for the milling process. In most cases, damage from insects is ‘hidden’, i.e. not visually detectable on the fruit surface. Consequently, traditional visual sorting techniques are generally inadequate for the detection and removal of olives with insect damage. 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 Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) or k-Nearest-Neighbors (kNN) routines, classification error rates as low as 0.00% false negative, 12.50% false positive, and 6.25% total error were achieved, with an AUC value of 0.9766 and a Wilk’s lambda of 0.3686 (P < 0.001). Multiplicative Scatter Correction, Savitzky-Golay spectral pretreatment with 13 smoothing points and Mean Centering spectral pretreatments were used. The optimal features corresponded to Abs[1100 nm], Abs[1232 nm], Abs[1416 nm], Abs[1486 nm] and Abs[2148 nm].