López Maestresalas, Ainara

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López Maestresalas

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Ainara

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Ingeniería

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IS-FOOD. Research Institute on Innovation & Sustainable Development in Food Chain

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0000-0001-6636-0752

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750902

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810716

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2013 - 20162
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End date: 2019 × Subject: Solanum tuberosum L. ×

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Now showing 1 - 2 of 2
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    PublicationOpen Access
    A review of the application of near-infrared spectroscopy for the analysis of potatoes
    (American Chemical Society, 2013) López Maestresalas, Ainara; Arazuri Garín, Silvia; García Ruiz, Ignacio; Mangado Ederra, Jesús; Jarén Ceballos, Carmen; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Gobierno de Navarra / Nafarroako Gobernua
    Potato (Solanum tuberosum L.) is one of the most important crops in the world being considered as a staple food in many developing countries. The potato industry like other vegetable and fruit industries is subject to the current demand of quality products. In order to meet this challenge, the food industry is relying on the adoption of nondestructive and environmentally friendly techniques to determine quality of products. Near-infrared spectroscopy (NIRS) is currently one of the most advanced nondestructive technologies regarding instrumentation and application, and it also complies with the environment requirements as it does not generate emissions or waste. This paper reviews research progress on the analysis of potatoes by NIRS both in terms of determination of constituents and classification according to the different constituents of the tubers. A brief description of the fundamentals of NIRS technology and its advantages over other quality assessment techniques is included. Finally, future prospects of the development of NIRS technology at the industrial level are explored.
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    PublicationOpen Access
    Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging
    (Elsevier, 2016) López Maestresalas, Ainara; Keresztes, Janos C.; Goodarzi, Mohammad; Arazuri Garín, Silvia; Jarén Ceballos, Carmen; Saeys, Wouter; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    Blackspot is a subsurface potato damage resulting from impacts during harvesting. This type of bruising represents substantial economic losses every year. As the tubers do not show external symptoms, bruise detection in potatoes is not straightforward. Therefore, a nondestructive and accurate method capable of identifying bruised tubers is needed. Hyperspectral imaging (HSI) has been shown to be able to detect other subsurface defects such as bruises in apples. This method is nondestructive, fast and can be fully automated. Therefore, its potential for non-destructive detection of blackspot in potatoes has been investigated in this study. Two HSI setups were used, one ranging from 400 to 1000 nm, named VisibleNear Infrared (Vis-NIR) and another covering the 1000e2500 nm range, called Short Wave Infrared (SWIR). 188 samples belonging to 3 different varieties were divided in two groups. Bruises were manually induced and samples were analyzed 1, 5, 9 and 24 h after bruising. PCA, SIMCA and PLS-DA were used to build classifiers. The PLS-DA model performed better than SIMCA, achieving an overall correct classification rate above 94% for both hyperspectral setups. Furthermore, more accurate results were obtained with the SWIR setup at the tuber level (98.56 vs. 95.46% CC), allowing the identification of early bruises within 5 h after bruising. Moreover, the pixel based PLS- DA model achieved better results in the SWIR setup in terms of correctly classified samples (93.71 vs. 90.82% CC) suggesting that it is possible to detect blackspot areas in each potato tuber with high accuracy.