Discrimination of Potato (Solanum tuberosum L.) Accessions Collected in Majella National Park (Abruzzo, Italy) Using Mid-Infrared Spectroscopy and Chemometrics Combined with Morphological and Molecular Analysis

Abstract

Development of local plant genetic resources grown in specific territories requires approaches that are able to discriminate between local and alien germplasm. In this work, three potato (Solanum tuberosum L.) local accessions grown in the area of Majella National Park (Abruzzo, Italy) and five commercial varieties cultivated in the same area were characterized using 22 morphological descriptors and microsatellite (SSR) DNA markers. Analysis of the DNA and of the plant, leaf, flower, and tuber morpho-agronomic traits allowed for a reliable discrimination of the local potato accessions, and provided a clear picture of their genetic relationships with the commercial varieties. Moreover, infrared spectroscopy was used to acquire a fingerprint of the tuber flesh composition. A total of 279 spectra, 70% of which were used in calibration and the remaining 30% for prediction, were processed using partial least squares discriminant analysis. About 97% of the calibration samples and 80% of the prediction samples were correctly classified according to the potato origin. In summary, the combination of the three approaches were useful in the characterization and valorization of local germplasm. In particular, the molecular markers suggest that the potato accession named Montenerodomo, cultivated in Majella National Park, can be considered a local variety and can be registered into the Regional Voluntary GR Register and entered into the foreseen protection scheme, as reported by the Italian regional laws.

Automatic hierarchical model builder

“…Three hundred and fifty‐three saffron samples coming from the main production areas of Italy (Abruzzo, Campania, Friuli‐Venezia Giulia, Sardinia, Sicily, Umbria, Emilia‐Romagna, Tuscany, Basilicata, Liguria, Veneto, Apulia, Latium, and Molise) and belonging to six production years (from 2015 to 2020) were directly provided by the producers of the related Consortia. The saffron stigmas were gently ground in a mortar, and the multispectral imaging of the ground sample was performed using a VideometerLab Instrument (https://videometer.com, Accessed March 7, 2022) measuring 18 specific wavelengths from ultraviolet (430, 450, 470, 505, 565, 590, 630, 645, 660, and 700 nm) to near‐infrared 11 (850, 870, 890, 910, 920, 940, 950, and 970 nm). The mean spectrum was calculated on the region of interest of each multispectral image.…”

“…This dataset contains information about eight different potatoes species ( Solanum tuberosum L.): three of them grown in the area of Majella National Park (Abruzzo, Italy) and five commercial varieties cultivated in the same area 11 . A total of 279 attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectra (in the range of 4000–940 cm −1 ) was used to acquire a fingerprint of the tuber flesh composition.…”

Special Issue “Application of Spectroscopy in Food Analysis”

“…An example of this is the paper published by Di Donato and collaborators [11], where three potato (Solanum tuberosum L.) grown in the area of Majella National Park (Abruzzo, Italy) and five commercial varieties cultivated in the same area were characterized by means of morphological descriptors and microsatellite (SSR) DNA markers. Eventually, samples were also analyzed by infrared spectroscopy and partial least squares discriminant analysis (PLS-DA) [12] was used to classify them according to their origin.…”

Sequential Data Fusion Techniques for the Authentication of the P.G.I. Senise (“Crusco”) Bell Pepper

“…These two instrumental techniques were chosen because they are relatively rapid, non-destructive, and they have demonstrated to be suitable allies against frauds in food matrices [7][8][9][10][11]. On the other hand, the choice of the classifier fell on data fusion (DF) approaches because, when applicable, multi-block methodologies are expected to perform better than the disjoint analysis of the individual data blocks [12][13][14].…”