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United States Department of Agriculture

Agricultural Research Service

Research Project: Quality Based Inspection and Sorting of Specialty Crops Using Imaging and Physical Methods

Location: Healthy Processed Foods Research

Title: Detection of mold-damaged chestnuts by near-infrared spectroscopy

Authors
item Moscetti, Roberto -
item Monarca, Danilo -
item Cecchini, Massimo -
item Haff, Ronald
item Contini, Marina -
item Massantini, Riccardo -

Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 15, 2014
Publication Date: July 3, 2014
Citation: Moscetti, R., Monarca, D., Cecchini, M., Haff, R.P., Contini, M., Massantini, R. 2014. Detection of mold-damaged chestnuts by near-infrared spectroscopy. Postharvest Biology and Technology. 93:83-90.

Interpretive Summary: Mold infection is a significant postharvest problem for processors of chestnuts (Castanea sativa, Miller). Fungal disease causes direct loss of product or reduced value due to the lower-quality grade of the chest-nut lot. In most cases, fungal infection is not detectable using traditional sorting techniques. In this study, the feasibility of using Near-Infrared (NIR) spectroscopy to detect hidden mold infection in chestnut was demonstrated. Classification error rates as low as 2.42% false negative, 2.34% false positive, and 2.38% total error were achieved. The optimal features corresponded to Abs[1118 nm], Abs[1200 nm],Abs[1626 nm], and Abs[1844 nm].The results represent an important step toward the development of a sorting system based on multi-spectral NIR bands, with the potential to rapidly detect and remove chestnuts contaminated by fungi and reduce the incidence of hidden mold in chestnut lots.

Technical Abstract: Mold infection is a significant postharvest problem for processors of chestnuts (Castanea sativa, Miller).Fungal disease causes direct loss of product or reduced value due to the lower-quality grade of the chest-nut lot. In most cases, fungal infection is not detectable using traditional sorting techniques. In this study, the feasibility of using Near-Infrared (NIR) spectroscopy to detect hidden mold infection in chestnut was demonstrated. Using a genetic algorithm for feature selection (from 2 to 6 wavelengths) in combination with image analysis grading and Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis(QDA) or k-Nearest-Neighbors (kNN) routines, classification error rates as low as 2.42% false negative, 2.34% false positive, and 2.38% total error were achieved, with an Area Under the ROC Curve (AUC)value of 0.997 and a Wilk’s lambda of 0.363 (P < 0.001). A Savitzky–Golay first derivative spectral pretreatment with 33 smoothing points was used. The optimal features corresponded to Abs[1118 nm], Abs[1200 nm],Abs[1626 nm], and Abs[1844 nm].The results represent an important step toward the development of a sorting system based on multi-spectral NIR bands, with the potential to rapidly detect and remove chestnuts contaminated by fungi and reduce the incidence of hidden mold in chestnut lots.

Last Modified: 10/22/2014
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