CHEMICAL ENGINEERING TRANSACTIONS VOL. 58, 2017 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Remigio Berruto, Pietro Catania, Mariangela Vallone Copyright © 2017, AIDIC Servizi S.r.l. ISBN 978-88-95608-52-5; ISSN 2283-9216 Prediction of Main Potato Compounds by NIRS Ainara López-Maestresalas*, Claudia Pérez, Roberto Tierno, Silvia Arazuri, Jose Ignacio Ruiz de Galarreta, Carmen Jarén Department of Projects and Rural Engineering, Universidad Pública de Navarra, Campus de Arrosadia 31006, Navarra, Spain ainara.lopez@unavarra.es Potato (Solanum tuberosum, L) compounds are generally determined by analytical methods including gas- liquid chromatography (GLC), HPLC and UV-VIS spectrophotometry. These methods require a lot of time and are destructive. Therefore, they seem to be not suitable for in-line applications in the food industry. Near- infrared spectroscopy (NIRS) is a technique that presents some advantages over reference methods for quantitative analysis of agricultural and food products since it is fast, reliable and non-destructive. For this reason, in this study, quantitative analyses were carried out to determine main compounds in potatoes using NIRS. Potato tubers grown in two consecutive years were used for the analyses. NIR spectral acquisition was acquired on lyophilized samples. In year 1, a total of 135 samples were used while 228 samples were used in year 2. Lyophilized samples were also scanned by NIRS, two replicates per samples were acquired and the mean spectrum of each sample was used for the analysis. Different chemical analyses were carried out each year. Thus, in year 1 the following parameters were quantified: reducing sugars (RS) and nitrogen (N), whereas in year 2, total soluble phenolics (TSP) and hydrophilic antioxidant capacity (HAC) were extracted and quantified. Then, chemometric analyses were performed using Unscrambler X (version 10.3, CAMO software AS, Oslo, Norway) to correlate wet chemical analysis with spectral data. Quantitative analyses based on PLS regression models were developed in order to predict the above chemical compounds of tubers in a non-destructive manner. Good PLS regression models were obtained for the prediction of nitrogen and TSP with coefficients of determination (R 2 ) above 0.83. Moreover, PLS models obtained for the estimation of HAC could be used for screening and approximate calibrations. 1. Introduction Potato (Solanum tuberosum, L.) is one of the most important crops in the world, occupying the fifth position in terms of production. Despite of being a highly appreciated product, potato industry faces the continuously growing demand of quality products from consumers and regulatory bodies. The acceptance of these products in the market depends on several factors as the general aspect, texture and internal quality. The analytical methods commonly employed to determine the main compounds of potatoes in order to control their quality require a lot of time and are destructive. These include gas-liquid chromatography (GLC), HPLC and UV-VIS spectrophotometry. Therefore, they seem not to be suitable for in-line applications in the food industry (Chen et al., 2010). In this respect, near-infrared spectroscopy (NIR) presents some advantages over reference methods for quantitative analysis of agricultural and food products. For these reasons, the aim of this study is to estimate potato compounds by NIR spectroscopy in a wide range of samples. 2. Materials and methods 2.1 Vegetal material A total of 363 tubers were used in this study. Tubers were selected from potato accessions (Potato Germplasm Collection, NEIKER) grown during the years 2012 and 2013 in a precise field trial in Arkaute (Álava) in the north-east of Spain. Some of the varieties included in this work are currently undergoing DOI: 10.3303/CET1758065 Please cite this article as: Lopez-Maestresalas A., Perez C., Tierno R., Arazuri S., Ruiz De Galarreta J.I., Jaren C., 2017, Prediction of main potato compounds by nirs, Chemical Engineering Transactions, 58, 385-390 DOI: 10.3303/CET1758065 385