Eva María Toledo-Martín1, María Carmen García-García2, Rafael Font3, José Manuel Moreno-Rojas4, Pedro Gómez1, María Salinas-Navarro5, Mercedes Del Río-Celestino1. 1. Department of Plant Breeding and Crop Biotechnology, Center IFAPA La Mojonera, Camino San Nicolás, 1, 04745, La Mojonera, Almería, Spain. 2. Department of Crop Production, Center IFAPA La Mojonera, Camino San Nicolás, 1, 04745, La Mojonera, Almería, Spain. 3. Department of Postharvest technology and the Agrifood Industry, Center IFAPA La Mojonera, Camino San Nicolás, 1, 04745, La Mojonera, Almería, Spain. 4. Department of Postharvest technology and the Agrifood Industry, Center IFAPA Alameda del Obispo, 14080, Córdoba, Spain. 5. Department of Applied Biology (Genetic), University of Almería, Edificio CITE II-B, Ctra. Sacramento s/n, La Cañada de San Urbano, 04120, Almería, Spain.
Abstract
BACKGROUND: The characterization of internal (°Brix, pH, malic acid, total phenolic compounds, ascorbic acid and total carotenoid content) and external (color, firmness and pericarp wall thickness) pepper quality is necessary to better understand its possible applications and increase consumer awareness of its benefits. The main aim of this work was to examine the feasibility of using visible/near-infrared reflectance spectroscopy (VIS-NIRS) to predict quality parameters in different pepper types. Commercially available spectrophotometers were evaluated for this purpose: a Polychromix Phazir spectrometer for intact raw pepper, and a scanning monochromator for freeze-dried pepper. RESULTS: The RPD values (ratio of the standard deviation of the reference data to the standard error of prediction) obtained from the external validation exceeded a value of 3 for chlorophyll a and total carotenoid content; values ranging between 2.5 < RPD < 3 for total phenolic compounds; between 1.5 < RPD <2.5 for °Brix, pH, color parameters a* and h* and chlorophyll b; and RPD values below 1.5 for fruit firmness, pericarp wall thickness, color parameters C*, b* and L*, vitamin C and malic acid content. CONCLUSION: The present work has led to the development of multi-type calibrations for pepper quality parameters in intact and freeze-dried peppers. The majority of NIRS equations obtained were suitable for screening purposes in pepper breeding programs. Components such as pigments (xanthophyll, carotenes and chlorophyll), glucides, lipids, cellulose and water were used by modified partial least-squares regression for modeling the predicting equations.
BACKGROUND: The characterization of internal (°Brix, pH, malic acid, total phenolic compounds, ascorbic acid and total carotenoid content) and external (color, firmness and pericarp wall thickness) pepper quality is necessary to better understand its possible applications and increase consumer awareness of its benefits. The main aim of this work was to examine the feasibility of using visible/near-infrared reflectance spectroscopy (VIS-NIRS) to predict quality parameters in different pepper types. Commercially available spectrophotometers were evaluated for this purpose: a Polychromix Phazir spectrometer for intact raw pepper, and a scanning monochromator for freeze-dried pepper. RESULTS: The RPD values (ratio of the standard deviation of the reference data to the standard error of prediction) obtained from the external validation exceeded a value of 3 for chlorophyll a and total carotenoid content; values ranging between 2.5 < RPD < 3 for total phenolic compounds; between 1.5 < RPD <2.5 for °Brix, pH, color parameters a* and h* and chlorophyll b; and RPD values below 1.5 for fruit firmness, pericarp wall thickness, color parameters C*, b* and L*, vitamin C and malic acid content. CONCLUSION: The present work has led to the development of multi-type calibrations for pepper quality parameters in intact and freeze-dried peppers. The majority of NIRS equations obtained were suitable for screening purposes in pepper breeding programs. Components such as pigments (xanthophyll, carotenes and chlorophyll), glucides, lipids, cellulose and water were used by modified partial least-squares regression for modeling the predicting equations.
Authors: María Del Carmen García-García; Emilio Martín-Expósito; Isabel Font; Bárbara Del Carmen Martínez-García; Juan A Fernández; Juan Luis Valenzuela; Pedro Gómez; Mercedes Del Río-Celestino Journal: Sensors (Basel) Date: 2022-05-28 Impact factor: 3.847