Microbiological feasibility of microwave processing of coconut water

Pinto, Raquel O.M.; Nascimento, Renata B. do; Jermolovicius, Luiz Alberto; Jurkiewicz, Cynthia; Gut, Jorge Andrey Wilhelms; Pinto, Uelinton Manoel; Landgraf, Mariza

doi:10.1016/j.lwt.2021.111344

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…Tender coconut water (CW), the main edible part from the nuts of Cocos nucifera L., has gained immense popularity as a health drink due to its pleasant flavor [ 1 ], rich nutritional components [ 2 ], and therapeutic properties [ 2 , 3 ]. However, the organoleptic and functional characteristics of CW can vary based on factors, such as postharvest storage time, coconut cultivar, and maturity [ 4 , 5 ], posing challenges for industrial producers in utilizing it as raw material.…”

Section: Introductionmentioning

confidence: 99%

Potential of Near-Infrared Spectroscopy (NIRS) for Efficient Classification Based on Postharvest Storage Time, Cultivar and Maturity in Coconut Water

Shen

Wang

Wei

et al. 2023

Foods

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Coconut water (CW) is a popular and healthful beverage, and ensuring its quality is crucial for consumer satisfaction. This study aimed to explore the potential of near-infrared spectroscopy (NIRS) and chemometric methods for analyzing CW quality and distinguishing samples based on postharvest storage time, cultivar, and maturity. CW from nuts of Wenye No. 2 and Wenye No. 4 cultivars in China, with varying postharvest storage time and maturities, were subjected to NIRS analysis. Partial least squares regression (PLSR) models were developed to predict reducing sugar and soluble sugar contents, revealing moderate applicability but lacking accuracy, with the residual prediction deviation (RPD) values ranging from 1.54 to 1.83. Models for TSS, pH, and TSS/pH exhibited poor performance with RPD values below 1.4, indicating limited predictability. However, the study achieved a total correct classification rate exceeding 95% through orthogonal partial least squares discriminant analysis (OPLS-DA) models, effectively discriminating CW samples based on postharvest storage time, cultivar, and maturity. These findings highlight the potential of NIRS combined with appropriate chemometric methods as a valuable tool for analyzing CW quality and efficiently distinguishing samples. NIRS and chemometric techniques enhance quality control in coconut water, ensuring consumer satisfaction and product integrity.

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