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

Giacomo Bedini [Department for Innovation in Biological, Agro-food and Forest System (DIBAF), University of Tuscia]
Swathi Sirisha Nallan Chakravartula, Giorgia Bastianelli, Romina Caccia [Department for Innovation in Biological, Agro-food and Forest System (DIBAF), University of Tuscia]
Mario Contarini [Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia]
Carmen Morales-Rodríguez [Department for Innovation in Biological, Agro-food and Forest System (DIBAF), University of Tuscia]
Luca Rossini, Stefano Speranza [Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia]
Andrea Vannini, Roberto Moscetti, Riccardo Massantini [Department for Innovation in Biological, Agro-food and Forest System (DIBAF), University of Tuscia]

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.

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