Browsing by Author "Oblitas, Jimy"

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Application of Machine Learning in the Discrimination of Citrus Fruit Juices: Uses of Dielectric Spectroscopy.
(Institute of Electrical and Electronics Engineers, 2020-10) Chuquizuta, Tony Steven; Oblitas, Jimy; Arteaga, Hubert; Castro, Wilson Manuel
Nowadays, process control in the juice industry requires fast, safe and easily applicable methods. In this regard, the use of dielectric spectroscopy is being coupled to statistical methods such as machine learning in order to develop new methods to identify adulteration. However, there is a small number of scientific reports above the application of the aforementioned methods when citric fruit juices is being identified. Therefore, the objective of this research was to evaluate dielectric spectroscopy and four different classification techniques (Support Vector Machine - SVM, K-nearest neighbor-KNN, Linear Discriminat -LD and Quadratic Discriminat-QD) to discriminate between three citrus juices. For this purpose, samples of Citrus limetta, Citrus limettioides and Citrus reticulata were evaluated; obtaining its dielectric spectral profiles in the range of 5 to 9 GHz. Then from the spectral profiles the loss factor (e”) was calculated using the reflection coefficient. Next e” value was pretreated, reducing noise through a savitzky golay filter, and new variables created through Principal Component Analysis (PCA). Finally, the models for classification were constructed with the previously mentioned techniques and the principal components. The results shown that using four components the variance can be explained in 97%; likewise, the discrimination values vary between 88.9 and 100.0%, with SVM, LD and QD the best discrimination techniques all successfully at 100.0 %. Therefore; It is concluded that the technique of dielectric spectroscopy and machine learning presents potential for the discrimination of citrus fruit juices.
Dielectric Spectral Profiles for Andean Tubers Classification: A Machine Learning Techniques Application.
(Institute of Electrical and Electronics Engineers, 2021-09) Chuquizuta Trigoso, Tony Steven; Oblitas, Jimy; Arteaga, Hubert; Yarlequé Medina, Manuel A.; Castro, Wilson Manuel
Currently, the agri-food industry prioritizes the development of non-destructive methods, such as dielectric spectroscopy, for quality control. The obtained dielectric spectral properties can be coupled to multivariate statistical methods as "machine learning" when identification of attributes is wanted. However, these techniques have not been applied to andean tubers classification. Therefore, the objective of the present investigation is to evaluate the possibility of discriminating four andean tubers using dielectric spectra properties and machine learning techniques (Support Vector Machine - SVM, K-Nearest Neighbors-KNN, and Linear Discriminat - LD). For this purpose, samples of Tropaeolum tuberosum (Killu isañu), Solanum tuberosa (yellow) and two varieties of Oxalis tuberosa (Puka kamusa and Lari oqa) were acquired, 30 units per tuber. The dielectric spectral profile was extracted twice for each tubers sample, in the range from 2 to 8 GHz. Then, the dielectric constant (e') were calculated, and its dimensionality was reduced using principal component analysis. Finally, models for classification were built by employing KNN, SVM and LD techniques. The results showed that three components can explain the variance at 99.6 %. Likewise, the accuracy in the discrimination values varied between 79.17 - 83.04, being SVM the best discrimination technique. Consequently, it is concluded that the technique of dielectric spectroscopy and machine learning presents potential for andean tuber discrimination.
Dielectric spectroscopy for the prediction of pork quality during the post-mortem time
(Elsevier, 2025-08) Chuquizuta Trigoso, Tony Steven; Peralta, Magaly; Medina, Sideli; Arteaga, Hubert; Oblitas, Jimy; Chavez, Segundo G.; Castro, Wilson Manuel; Castro-Giraldez, Marta; Fito, Pedro Juan
Dielectric spectroscopy was used in this study to predict and classify pork quality during the post-mortem time. Eighty ~1 kg- longissimus dorsi muscles were collected and stored at 4 ± 1 ◦C and pH, instrumental color, and dielectric properties (ε’ and ε’’) were subsequently determined in the microwave range (0.5–9 GHz) at 3, 4, 5, 6, 7, 8, 9, 10 and 24 h post-mortem (hpm), as well as moisture at 8 hpm and drip weight loss at 24 hpm. Of the 80 pork samples, two types of meat were found. RFN (33) and DFD (47) between males and females. Quality parameters: RFN (pH=5.708–5.714; L*=43.341–43.692; moisture (%) = 68.857–69.604; drip loss = 1.655–1.833) and DFD (pH=6.154–6.177; L*=40.152–41.91; moisture (%) = 69.032–69.9; drip loss = 1.129–1.693). Quality parameter predictions during muscle-to-meat transformation showed R² of 0.743 (pH), 0.811 (L*) and 0.603 (C*) for DFD meats with PLSR (full) and R2 of 0.359 (pH), 0.558 (L*) and 0.284 (C*) for RNF meats with PLSR (optimized) from male pigs. R2 cv of 0.412–0.637 for pH, L* and c* for RFN and DFD meats from female pigs with PLSR (optimized). Dielectric spectroscopy predicts pork quality moderately well, but models that are more robust are needed to improve predictions of internal pork quality.
Predicción de atributos de calidad de leche fresca no pasteurizada mediante espectroscopia dieléctrica acoplada a herramientas quimiométricas.
(Institute of Electrical and Electronics Engineers, 2022-06) Chuquizuta Trigoso, Tony Steven; Colunche, Y.; Rubio, M.; Oblitas, Jimy; Arteaga, Hubert; Castro, Wilson Manuel
El objetivo de esta investigación es predecir los atributos de calidad de la leche fresca no pasteurizada mediante espectroscopia dieléctrica acoplada a herramientas quimiométricas. Para ello, se trabajó con leche fresca no pasteurizada de la raza Pardo Suizo, obtenida del establo “La Lechera”. Se obtuvieron diluciones de agua y leche fresca del 70 al 100 %.25∘do, seguida de la caracterización fisicoquímica (densidad, sólidos totales, punto de congelación, sólidos grasos, proteínas y agua añadida) y las propiedades dieléctricas en el rango de 0,5 a 9 GHz mediante una sonda coaxial de extremo abierto (N1501A-001), conectada a un Analizador de Redes Vectoriales, modelo N9915A-Keysight Technologies. Asimismo, se empleó la regresión de mínimos cuadrados parciales para correlacionar las propiedades fisicoquímicas con las propiedades dieléctricas. Los resultados obtenidos en la predicción del punto de congelación, las proteínas, los sólidos grasos y el agua añadida de leche fresca no pasteurizada presentaron un coeficiente de determinación y un error cuadrático medio en el rango de [0,95-0,98] y [2]..57 ×10− 7− 7,46 ×10− 2]En consecuencia, se concluye que la técnica de espectroscopia dieléctrica y aprendizaje automático presenta potencial para la predicción de las características fisicoquímicas de la leche fresca no pasteurizada, pudiendo implementarse en las líneas de producción para evaluar de forma rápida y fiable la calidad de la leche de vaca.
The frequency range in THz spectroscopy and its relationship to the water content in food: A first approach.
(Universidad Nacional de Trujillo, 2021-12) Arteaga, Hubert; León-Roque, Noemí; Oblitas, Jimy
The objective of this review is to report on the progress made so far in the development of THz spectroscopy technology with application in the food industry, as well as, to evaluate the range of frequencies used by this technology in relation to the water content of food, to find patterns in which the physicochemical characterization of food samples is most effective. From the literature reviewed, it has been found that THz spectroscopy is still in constant development, both in the physical part of the equipment and in the data processing techniques. Despite these advances, the frequency ranges in which the identification of compounds are influenced by the interference of the water composition of food have not been clearly identified, even molecular behavior of water in the frequency ranges corresponding to the spectral band of THz is still little known. When performing a meta-analysis of the data specifying the frequency ranges in relation to the water content of food samples, reported in the literature, two intervals have been identified, where the action of THz waves have a better response in terms of the quantification of water, as well as of other compounds, which are mainly evidenced in lower water content, explained by the mechanisms of water relaxation in response to the interaction of THz waves. This result suggests that the influence of water content on the quantification of compounds should be considered, as it may be under or overestimated.