BUSCADOR RECOLECTA

The determination of reducing sugars in potatoes is important due to their impact on product quality during industrial processing. The significant variability of these compounds between genotypes presents a challenge to the development of accurate predictive models. This study evaluated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of reducing sugars in potatoes. For this, a wide range of genotypes (n=92) from two seasons (2020-2021) was selected. Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR) methods were used to build the prediction models. Furthermore, interval PLS (iPLS), recursive weighted PLS (rPLS), Genetic Algorithm (GA) and Competitive Adaptive Reweighted Sampling (CARS) were used for relevant wavelength identification to develop less computationally complex models. The best full spectrum model (SNV-PLSR) achieved coefficient of determination and root mean square error values of 0.88 and 0.053% and 0.86 and 0.057%, for calibration and external validation, respectively. Variable selection algorithms successfully reduced the dimensionality of the data without compromising the performance of the models. Robust predicted models were built with only 2.65% (CARS-PLSR) and 3.57% (iPLS-SVMR) of the total wavelengths. Finally, a pixel-wise prediction was performed on the validation set and chemical images were built to visualise the spatial distribution of reducing sugars. This study demonstrated that NIR-HSI is a feasible technique for predicting reducing sugars in several potato genotypes., This work was supported by the Ministerio de Ciencia, Innovación y Universidades (MICIU/AEI /10.13039/501100011033), (Spain), project: PID2019-109790RR-C22 and the predoctoral grant (PRE2020-094533) associated to it.

La patata es uno de los alimentos más importante del mundo y una de las formas más habituales de consumirla es como patatas fritas. Al freírla a altas temperaturas, los azúcares reductores y la asparagina de la patata pueden dar lugar a acrilamidas, por medio de la reacción de Maillard. La acrilamida está clasificada como sustancia probablemente cancerígena para el ser humano. Por eso es importante que las patatas destinadas a fritura tengan un bajo contenido en azúcares reductores. Este contenido depende de factores genéticos, medioambientales, culturales y condiciones de almacenamiento. Por ello, en este trabajo se pretende analizar algunos de esos factores en una variedad rica en azúcares reductores como es Jaerla. Los factores analizados fueron el estrés hídrico durante el cultivo, dos temperaturas de almacenamiento (8 y 13ºC) y tiempo de almacenamiento en las anteriores temperaturas, desde 0 hasta 13 semanas. Las muestras de patatas de cada uno de los tratamientos se liofilizaron y se determinó su contenido en azúcares: glucosa, fructosa y sacarosa. Los datos fueron analizados con R-Studio. Solo se encontraron diferencias significativas en el factor temperatura de conservación para los tres azúcares, obteniéndose los valores más altos en las patatas conservadas a 8ºC., Potato is one of the most important foods in the world and one of the ways to consume it is as crisps. When fried at high temperatures, the reducing sugars and the asparagine in potatoes may contribute to the formation of acrylamide through the Maillard reaction. Acrylamide is classified as a substance that is probably carcinogenic to humans. It is therefore important that potatoes that are suitable for frying have a low content of reducing sugars. This content depends on genetic factors, environmental and cultural factors and storage conditions. Therefore, this work aims to analyse some of these factors in a variety rich in reducing sugars such as Jaerla. The factors analysed were water stress during cultivation, two storage temperatures (8 and 13ºC) and storage time at those temperatures, from 0 to 13 weeks. Potato samples from each of the treatments were freeze-dried and their sugar content was determined: glucose, fructose (reducing sugars) and sucrose. The data were analysed with R-Studio. Significant differences were found only in the storage temperature factor for the three sugars, where the highest values were obtained for potatoes stored at 8ºC., Este trabajo ha sido financiado por el MICINN: PID2019-109790RR-C22.

The potato (Solanum tuberosum L.) is the world's fifth most important staple food with high socioeconomic relevance. Several potato cultivars obtained by selection and crossbreeding are currently on the market. This diversity causes tubers to exhibit different behaviors depending on the processing to which they are subjected. Therefore, it is interesting to identify cultivars with specific characteristics that best suit consumer preferences. In this work, we present a method to classify potatoes according to their cooking or frying as crisps aptitude using NIR hyperspectral imaging (HIS) combined with a Partial Least Squares Discriminant Analysis (PLS-DA). Two classification approaches were used in this study. First, a classification model using the mean spectra of a dataset composed of 80 tubers belonging to 10 different cultivars. Then, a pixel-wise classification using all the pixels of each sample of a small subset of samples comprised of 30 tubers. Hyperspectral images were acquired using fresh-cut potato slices as sample material placed on a mobile platform of a hyperspectral system in the NIR range from 900 to 1,700 nm. After image processing, PLS-DA models were built using different pre-processing combinations. Excellent accuracy rates were obtained for the models developed using the mean spectra of all samples with 90% of tubers correctly classified in the external dataset. Pixel-wise classification models achieved lower accuracy rates between 66.62 and 71.97% in the external validation datasets. Moreover, a forward interval PLS (iPLS) method was used to build pixel-wise PLS-DA models reaching accuracies above 80 and 71% in cross-validation and external validation datasets, respectively. Best classification result was obtained using a subset of 100 wavelengths (20 intervals) with 71.86% of pixels correctly classified in the validation dataset. Classification maps were generated showing that false negative pixels were mainly located at the edges of the fresh-cut slices while false positive were principally distributed at the central pith, which has singular characteristics., This work was funded by the Ministerio de Ciencia e Innovación (Spanish Ministry of Science and Innovation) (project PID2019-109790RR-C22).

El cambio climático es uno de los mayores retos de la agricultura moderna. El
aumento del rendimiento de los cultivos en el futuro sólo será posible si pueden hacer
frente a las consecuencias del cambio climático causado por el aumento de CO2 en la
atmósfera. En el cultivo de la patata es muy probable que los estreses abióticos se
incrementen considerablemente comprometiendo la sostenibilidad de su producción.
A largo plazo, las condiciones de elevado CO2 podrían alterar la toma y transporte
de nutrientes, particularmente del nitrógeno (N). Esto conlleva la necesidad de
seleccionar cultivares que por sus características genéticas, fisiológicas y agronómicas
se adapten mejor a las condiciones del cambio climático global, particularmente a la
eficiencia en el uso del N. Para ello, en este estudio, se ha empleado la tecnología de
imágenes hiperespectrales con el objetivo de desarrollar modelos de clasificación de
variedades más eficientes en el uso del N. Se han muestreado plantas de dos campos
experimentales: control y con una reducción del 75% de aporte de N. Se han
adquirido imágenes hiperespectrales de 120 hojas de las plantas control y 120 de
plantas sometidas a una reducción del 75% de aporte de N. Se han aplicado métodos
multivariantes de clasificación para comprobar el potencial de las imágenes
hiperespectrales en la identificación de cultivares de patata mejor adaptados a una
deficiencia de N, con resultados prometedores. Además, para evaluar la respuesta de
las plantas a las diferentes dosis de N, se analizará el contenido total de N, lo que
permitirá evaluar la eficiencia en el uso del N en función de la productividad, así como
la concentración de metabolitos nitrogenados., Este trabajo ha sido financiado en el marco del Proyecto GENIRPAT 'Mejora
genética integrada de patata: incorporación de la espectroscopia NIR para estreses abióticos
y calidad de procesado' (PID2019-109790RR-C22) por la Agencia Estatal de Investigación
(AEI) del Ministerio de Ciencia, Innovación y Universidades en la convocatoria 2020-
2023.

This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of
acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in
two seasons and processed under the same frying conditions were analysed. Partial Least Square Regression
(PLSR) and Support Vector Machine Regression (SVMR), combined with a logarithmic transformation of the
acrylamide levels, were applied to develop predictive models. The most optimal outcomes for PLSR yielded R2
p:
0.85, RMSEP: 201 μg/kg and RPD: 2.53, while for SVMR yielded R2
p: 0.80, RMSEP: 229 μg/kg and RPD: 2.22.
Furthermore, the selection of significant wavelengths enabled an 87.95 % reduction in variables without
affecting the model’s accuracy. Finally, spatial mapping of acrylamide content was conducted on all chips in the
external validation set. This method provides both quantification and visualization capabilities, thus enhancing
quality control for acrylamide identification in processed potatoes., This work was supported by the Ministerio de Ciencia, Innovación y Universidades (MICIU/AEI /10.13039/501100011033), Spain, project: PID2019-109790RR-C22 and the predoctoral grant (PRE2020-094533) associated to it.

The determination of reducing sugars in potatoes is important due to their impact on product quality during industrial processing. The significant variability of these compounds between genotypes presents a challenge to the development of accurate predictive models. This study evaluated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of reducing sugars in potatoes. For this, a wide range of genotypes (n=92) from two seasons (2020-2021) was selected. Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR) methods were used to build the prediction models. Furthermore, interval PLS (iPLS), recursive weighted PLS (rPLS), Genetic Algorithm (GA) and Competitive Adaptive Reweighted Sampling (CARS) were used for relevant wavelength identification to develop less computationally complex models. The best full spectrum model (SNV-PLSR) achieved coefficient of determination and root mean square error values of 0.88 and 0.053% and 0.86 and 0.057%, for calibration and external validation, respectively. Variable selection algorithms successfully reduced the dimensionality of the data without compromising the performance of the models. Robust predicted models were built with only 2.65% (CARS-PLSR) and 3.57% (iPLS-SVMR) of the total wavelengths. Finally, a pixel-wise prediction was performed on the validation set and chemical images were built to visualise the spatial distribution of reducing sugars. This study demonstrated that NIR-HSI is a feasible technique for predicting reducing sugars in several potato genotypes., This work was supported by the Ministerio de Ciencia, Innovación y Universidades (MICIU/AEI /10.13039/501100011033), (Spain), project: PID2019-109790RR-C22 and the predoctoral grant (PRE2020-094533) associated to it.

El objetivo de este trabajo fue evaluar el potencial de las imágenes hiperespectrales para clasificar tubérculos sometidos a estreses abióticos controlados., Este trabajo ha sido financiado en el marco del Proyecto GENIRPAT 'Mejora genética integrada de la patata: incorporación de la espectroscopi NIR para estreses abióticos y calidad de procesado' (PID2019-109790RR-C22) por la Agencia Estatal de Investigación (AEI) del Ministerio de Ciencia, Innovación y Universidades en la convocatoria 2020-2023.

The application of hyperspectral imaging (HSI) has gained signifcant importance in
the past decade, particulary in the context of food analysis, including potatoes. However, the current literature lacks a comprehensive systematic review of the application of this technique in potato cultivation. Therefore, the aim of this work was to
conduct a systematized review by analysing the most relevant compounds, diseases
and stress factors in potatoes using hyperspectral imaging. For this purpose, scientifc studies were retrieved through a systematic keyword search in Web of Science
and Scopus databases. Studies were only included in the review if they provided
at least one set of quantitative data. As a result, a total of 52 unique studies were
included in the review. Eligible studies were assigned an in-house developed quality
scale identifying them as high, medium or low risk. In most cases the studies were
rated as low risk. Finally, a comprehensive overview of the HSI applications in potatoes was performed. It has been observed that most of the selected studies obtained
better results using linear methods. In addition, a meta-analysis of studies based on
regression and classifcation was attempted but was not possible as not enough studies were found for a specifc variable., The funding of this work
has been covered by the Ministry of Science and Innovation (Spain) project: PID2019-109790RR-C22
and the predoctoral grant (PRE2020-094533) associated to it. The Open Access funding was provided by
Universidad Pública de Navarra.