Application of Vis/NIR spectroscopy for predicting sweetness and flavour parameters of 'Valencia' orange (Citrus sinensis) and 'Star Ruby' grapefruit (Citrus x paradisi Macfad). (January 2017)

• Record Type: Journal Article
• Authors: Ncama, Khayelihle; Opara, Umezuruike Linus; Tesfay, Samson Zeray; Fawole, Olaniyi Amos; Magwaza, Lembe Samukelo
• Is Part Of: Journal of food engineering. Volume 193(2017)
• Page Start: 86
• Page End: 94
• Publication Date: 2017-01
• Subjects: BrimA (Brix minus Acids) -- Total soluble solids (TSS) -- Maturity index -- Citrus flavour -- Near infrared spectroscopy -- Sensory quality; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2016.08.015
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Sweetness and flavour are desirable attributes used for quality control and assurance of citrus fruit, which are largely determined by total soluble solids (TSS), titrable acidity (TA) and TSS: TA ratio. However, the accuracies of TSS, TA and TSS: TA as flavour indices have been recently criticised. BrimA (Brix minus acids), on the other hand, is an accurate organoleptic parameter that has been shown to be highly related to sweetness and flavour of citrus fruit. In this study, the ability of visible to near infrared spectroscopy (Vis/NIRS), in reflectance mode, to non-destructively quantify BrimA, TSS, TA and TSS: TA ratio of 'Valencia' orange and 'Star Ruby' grapefruit was evaluated. Vis/NIR spectral data was acquired using a laboratory bench-top monochromator NIR Systems. Reference measurements and spectral datasets were subjected to partial least square (PLS) regression analysis. The best prediction models were observed for BrimA of 'Valencia' oranges with the coefficient of determination (R 2 ) = 0.958; root mean square error of prediction (RMSEP) = 0.006 and residual predictive deviation (RPD) = 3.96, followed by TSS: TA ratio (R 2 = 0.958; RMSEP = 0.605; RPD = 4.92). Good models for predicting flavor of grapefruit were also attained, with TSS having the best model (R 2 = 0.896, RMSEP = 0.308 and RPD = 2.94), followed by BrimA (R 2 = 0.858; RMSEP = 0.429; RPD = 2.45). These results demonstrated the ability of Vis/NIRS to non-destructively predict sweetness and flavour attributes of oranges and grapefruit. Vis/NIRS was recommended as a possible fast and accurate technique to be used for fruit discrimination based on flavour parameters during packing and for pricing of fruit in the market. Highlights: Vis/NIRS quantified sweetness and flavour of 'Valencia' oranges and 'Star Ruby' grapefruit. Partial least square models accurately predicted TSS, TA, TSS:TA ratio and BrimA. Results demonstrated importance of integrating multi-seasonal dataset in PLS models. Vis/NIRS can be used for bench marking sweetness and flavour of individual citrus fruit.