Non-Destructive Measurement of the Internal Quality of Citrus Fruits Using a Portable NIR Device

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Santos, CSP (Author) Other; Cruz, R (Author) Other; Goncalves, DB (Author) Other; Queiros, R (Author) Other; Bloore, M (Author) Other; Kovacs, Z (Author) Other; Hoffmann, I (Author) Other; Susana Casal (Author) FFUP

Journal

Title: Journal of AOAC International

Vol. 104

Pages: 61-67

ISSN: 1060-3271

Publisher: AOAC INTERNATIONAL

Indexing

ISI Web of Knowledge - 12 Citations

Other information

Authenticus ID: P-00T-PBV

DOI: 10.1093/jaoacint/qsaa115

Abstract (EN): The citrus industry has grown exponentially as a result of increasing demand on its consumption, giving it high standing among other fruit crops. Therefore, the citrus sector seeks rapid, easy, and non-destructive approaches to evaluate in real time and in situ the external and internal changes in physical and nutritional quality at any stage of fruit development or storage. In particular, vitamin C is among the most important micronutrients for consumers, but its measurement relies on laborious analytical methodologies. In this study, a portable near infrared spectroscopy (NIRS) sensor was used in combination with chemometrics to develop robust and accurate models to study the ripeness of several citrus fruits (oranges, lemons, clementines, tangerines, and Tahiti limes) and their vitamin C content. Ascorbic acid, dehydroascorbic acid, and total vitamin C were determined by HILIC-HPLC-UV, while soluble solids and total acidity were evaluated by standard analytical procedures. Partial least squares regression (PLSR) was used to build regression models which revealed suitable performance regarding the prediction of quality and ripeness parameters in all tested fruits. Models for ascorbic acid, dehydroascorbic acid, total vitamin C, soluble solids, total acidity, and juiciness showed R-cv(2) = 0.77-0.87, R-cv(2) = 0.29-0.79, R-cv(2) = 0.77-0.86, R-cv(2) = 0.75-0.97, R-cv(2) = 0.24-0.92, and R-cv(2) = 0.38-0.75, respectively. Prediction models of oranges and Tahiti limes showed good to excellent performance regarding all tested conditions. The resulting models confirmed that NIRS technology is a time- and cost-effective approach for predicting citrus fruit quality, which can easily be used by the various stakeholders from the citrus industry.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7