Feasibility of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging for sorting unsound chestnuts

Abstract

Authors explored the potential use of Vis/NIR hyperspectral imaging (HSI) and Fourier-transform Near-Infrared (FT-NIR) spectroscopy to be used as in-line tools for the detection of unsound chestnut fruits (i.e. infected and/or infested) in comparison with the traditional sorting technique. For the intended purpose, a total of 720 raw fruits were collected from a local company. Chestnut fruits were preliminarily classified into sound (360 fruits) and unsound (360 fruits) batches using a proprietary floating system at the facility along with manual selection performed by expert workers. The two batches were stored at 4 ± 1 °C until use. Samples were left at ambient temperature for at least 12 h before measurements. Subsequently, fruits were subjected to non-destructive measurements (i.e. spectral analysis) immediately followed by destructive analyses (i.e. microbiological and entomological assays). Classification models were trained using the Partial Least Squares Discriminant Analysis (PLS-DA) by pairing the spectrum of each fruit with the categorical information obtained from its destructive assay (i.e., sound, Y = 0; unsound, Y = 1). Categorical data were also used to evaluate the classification performance of the traditional sorting method. The performance of each PLS-DA model was evaluated in terms of false positive error (FP), false negative error (FN) and total error (TE) rates. The best result (8% FP, 14% FN, 11% TE) was obtained using Savitzky-Golay first derivative with a 5-points window of smoothing on the dataset of raw reflectance spectra scanned from the hilum side of fruit using the Vis/NIR HSI setup. This model showed similarity in terms of False Negative error rate with the best one computed using data from the FT-NIR setup (i.e. 15% FN), which, however, had the lowest global performance (17% TE) due to the highest False Positive error rate (19%). Finally, considering that the total error rate committed by the traditional sorting system was about 14.5% with a tendency of misclassifying unsound fruits, the results indicate the feasibility of a rapid, in-line detection system based on spectroscopic measurements.

Impact of ‘brown rot’ caused by Gnomoniopsis castanea on chestnut fruits during the post‐harvest process: critical phases and proposed solutions

“…The effectiveness of product sterilization may be improved by selectively removing unsound chestnut fruits from the stock entering the handling plant using non‐destructive methods based on near infrared (NIR) spectroscopy 41,42 or X‐ray computed tomography 43 . In fact, recent studies on the feasibility of Fourier transform near infrared (FT‐NIR) spectroscopy or visible‐near infrared hyperspectral imaging in detecting ‘brown rot’ of chestnut fruits demonstrated the feasibility of the approach 44 and the need for an upscale from prototype to commercial equipment. A molecular tool for the sensitive detection and quantification of G. castaneae in symptomatic and asymptomatic fruits has been developed to quantify the G. castaneae inoculum 45 .…”

Gnomoniopsis castaneae: An emerging plant pathogen and global threat to chestnut systems

“…Several such methods that use nondestructive imaging approaches, including terahertz (Di Girolamo et al, 2021), Fourier‐transform near‐infrared (FT‐NIR) and Vis/NIR hyperspectral imaging (HSI; Bedini et al, 2020), have been examined for use in the commercial chestnut industry. While these show promise for sorting sound and unsound nuts, they are not specific in identifying G. castaneae and therefore would not detect changes in incidence of the pathogen.…”