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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 #309582
Title: Nondestructive inspection of nuts for food quality and safety using NIRS (abstract)

Author
item Haff, Ronald - Ron
item MOSCETTI, ROBERTO - University Of Tuscia
item MASSINTINI, RICARDO - University Of Tuscia

Submitted to: US-Japan Coop Pgm on Dev and Util of Natural Products Abstracts Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 9/11/2014
Publication Date: 10/19/2014
Citation: Haff, R.P., Moscetti, R., Massintini, R. 2014. Nondestructive inspection of nuts for food quality and safety using NIRS (abstract). In: Proceedings of the US-Japan Cooperative Program in Natural Resources, October 19-23, 2014, Athens, Georgia. p. 1-8.

Interpretive Summary: Mold infection and insect infestation are significant postharvest problems for processors of nuts. 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 and insect infestation are significant postharvest problems for processors of nuts. 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 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.