Ruiz de Galarreta, José Ignacio

Profile Picture

Email Address

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/49922

Birth Date

Job Title

Last Name

Ruiz de Galarreta

First Name

José Ignacio

person.page.departamento

Derecho Privado

person.page.instituteName

ORCID

person.page.observainves

1174673

person.page.upna

571

Name

Filters

20252
Reset filters

Settings

Author: Arazuri Garín, Silvia × Subject: Regression ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    Predicting the spatial distribution of reducing sugars using near-infrared hyperspectral imaging and chemometrics: a study in multiple potato genotypes
    (Elsevier, 2025-08-01) Peraza Alemán, Carlos Miguel; Arazuri Garín, Silvia; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Barandalla, Leire; López Maestresalas, Ainara; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
    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.
  • Thumbnail Image
    PublicationOpen Access
    Intergenotypic prediction of reducing sugars in intact potatoes using near-infrared spectroscopy and multivariate analysis
    (Elsevier, 2025-12-01) Peraza Alemán, Carlos Miguel; Arazuri Garín, Silvia; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Barandalla, Leire; López Maestresalas, Ainara; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    Potatoes (Solanum tuberosum L.) are among the most widely consumed foods worldwide and are used in various culinary preparations. As a result, their production has increased in recent decades, prompting the potato industry to place greater emphasis on quality control measures for this food. In this context, reducing sugars stand out as being directly linked to the formation of acrylamide, a recognized carcinogen. Although Near Infrared Spectroscopy (NIRS) has been successfully used to predict reducing sugar content in this crop, the applicability of models across different potato cultivars remains limited due to genotypic variability. This study aimed to assess the potential of NIRS (1200¿2200 nm) to predict reducing sugar content across a diverse set of potato genotypes (n = 114). Excellent outcomes were obtained for both full spectrum and selected wavelength models. The results demonstrated high predictive accuracy with an R2 of 0.89 and an RMSE of 0.061 % for calibration, while external predictions in new genotypes yielded an R2 of 0.91 and RMSE of 0.065 % for SVMR model. These findings highlight the feasibility of using NIRS for rapid, real-time and non-destructive assessment of reducing sugars in untested potato genotypes, offering a valuable tool for industry applications.