Detection of Mold-Damaged Chestnuts by Near-Infrared Spectroscopy

• NIR spectroscopy is a feasible method for the detection of mold damage in chestnut.
• QDA allows reliable detection of infected chestnut.
• Best results depend on the side of the chestnut analyzed.
• Classification accuracy depends on the severity of infection.
• The suggested approach provides the basis for a rapid online detection system.

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 chestnut 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 λ 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.