BUSCADOR RECOLECTA

Similarity measures, as studied in the context of fuzzy set theory, have been proven applicable to many different fields. Surely, their primary role is to model the perceived (dis-) similarity between two fuzzy sets or, equivalently, the linguistic terms they represent. However, the richness of the dedicated study makes the similarity measures portable to other contexts in which quantitative comparison plays a key role. In this work we present the application of similarity measures to hyperspectrum comparison in the context of in-lab hyperspectral imaging for bioengineering., This work was supported by the National Institute for Agricultural and Food Research and Technology (INIA), Project RTA2013-00006-C03-03. Also by the Spanish Ministry of Science, Project TIN2016-77356-P (FEDER/UE, AEI).

BACKGROUND: Over the last two decades, the attractive colours and shapes of pigmented tubers and the increasing concern
about the relationship between nutrition and health have contributed to the expansion of their consumption and a specialty
market. Thus, we have quantified the concentration of health promoting compounds such as soluble phenolics, monomeric
anthocyanins, carotenoids, vitamin C, and hydrophilic antioxidant capacity, in a collection of 18 purple- and red-fleshed potato
accessions.
RESULTS: Cultivars and breeding lines high in vitamin C, such as Blue Congo, Morada and Kasta, have been identified. Deep
purple cultivars Violet Queen, Purple Peruvian and Vitelotte showed high levels of soluble phenolics, monomeric anthocyanins,
and hydrophilic antioxidant capacity, whereas relatively high carotenoid concentrations were found in partially yellow coloured
tubers, such as Morada, Highland Burgundy Red, and Violet Queen.
CONCLUSION: The present characterisation of cultivars and breeding lines with high concentrations of phytochemicals is an
important step both to support the consideration of specialty potatoes as a source of healthy compounds, and to obtain new
cultivars with positive nutritional characteristics. Moreover, by using near infrared spectroscopy a non-destructive identification
and classification of samples with different levels of phytochemicals is achieved, offering an unquestionable contribution to the
potato industry for future automatic discrimination of varieties., This work was financed within the frame of INIA’s project
RTA2013-00006-C03 and the Basque Government.

Potato (Solanum tuberosum, L) compounds are generally determined by analytical methods including gasliquid
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. Nearinfrared
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 (R2) above 0.83. Moreover, PLS models obtained for the estimation of HAC could be used for
screening and approximate calibrations., This work was financed within the frame of INIA’s project RTA2013-00006-C03-01-03, the Basque
Government and the Universidad Pública de Navarra through the concession of a predoctoral research grant.

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., The funding of this work has been covered by the Universidad
Pública de Navarra through the concession of both a predoctoral
research grant (Res. 1753/2012) and a mobility grant (Res. 1506/
2013), by the National Institute for Agricultural and Food Research
and Technology (INIA) project: “Mejora genetica de la patata:
caracterizacion reol ogica y por tecnología NIRS del material” RTA2013-00006-C03-03, and by the Agency for Innovation by Science and Technology in Flanders (IWT) through the Chameleon
(SB-100021) project.