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Peraza Alemán, Carlos Miguel

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https://academica-e.unavarra.es/handle/2454/53844

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Peraza Alemán

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Carlos Miguel

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Ingeniería

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

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0000-0002-8104-5213

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751454

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812402

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Now showing 1 - 5 of 5
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    PublicationEmbargo
    Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
    (Elsevier, 2025-03-14) Peraza Alemán, Carlos Miguel; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Barandalla, Leire; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
    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.
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    PublicationEmbargo
    Predicting the spatial distribution of reducing sugars using near-infrared hyperspectral imaging and chemometrics: a study in multiple potato genotypes
    (Elsevier, 2025-03-27) 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.
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    PublicationOpen Access
    Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
    (Frontiers Media, 2022) López Maestresalas, Ainara; López Molina, Carlos; Oliva Lobo, Gil Alfonso; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Peraza Alemán, Carlos Miguel; Arazuri Garín, Silvia; Ingeniaritza; Estatistika, Informatika eta Matematika; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Estadística, Informática y Matemáticas
    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.
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    PublicationOpen Access
    Influencia de factores de cultivo y conservación en el contenido en azúcares reductores en patata
    (Universidad de Sevilla, 2023) Jarén Ceballos, Carmen; Peraza Alemán, Carlos Miguel; Mangado Ederra, Jesús; López Maestresalas, Ainara; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
    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.
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    PublicationOpen Access
    A systematized review on the applications of hyperspectral imaging for quality control of potatoes
    (Springer, 2024) Peraza Alemán, Carlos Miguel; López Maestresalas, Ainara; Jarén Ceballos, Carmen; Rubio Padilla, Niuton; Arazuri Garín, Silvia; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    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.