Non-invasive spectroscopic methods to estimate orange firmness, peel thickness, and total pectin content

Abstract

Orange firmness, peel thickness, and total pectin content are associated with fruit quality and denote important parameters for the food industry. These attributes are usually determined through destructive methods that can be time-consuming and also unable to monitor fruit quality over time. Therefore, non-invasive methods such time-domain nuclear magnetic resonance (TD-NMR), near-infrared (NIR), and mid-infrared (MIR) spectroscopies may represent efficient alternatives to evaluate these quality attributes. In this work, partial least square regression (PLSR) models of TD-NMR relaxometry as well as NIR and MIR spectroscopic data were used to predict firmness, peel thickness, and total pectin content of fresh Valencia oranges. Principal component analyses (PCA) were applied to explain the correlations of orange ripening stage, flowering, and crop season with its physicochemical parameters. Data obtained through standard destructive methods were used to calibrate and validate the PLSR models. NIR and MIR showed the best PLSR models for orange firmness, with Pearson correlation coefficients (r) of 0.92 and 0.84 and squared errors of prediction (SEP) equal to 6.22 and 9.05 N, respectively. Orange peel thickness PLSR model was validated only by TD-NMR (r = 0.72; SEP = 0.49 mm). TD-NMR and NIR also presented potential to predict total pectin orange in orange (r = 0.76 and 0.70; SEP = 5.76% and 5.04%, respectively). Therefore, NIR presented a higher potential to predict orange firmness than MIR and TD-NMR. On the other hand, TD-NMR showed a higher prediction power concerning peel thickness than NIR and MIR. Both NIR and TD-NMR methods showed similar prediction powers for total pectin content.

Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy

“…Both juice pH and vitamin C also seem easily assessed by devices operating in the Vis-SWNIRS range devices [55,94,95], but TA has been shown to require wavelengths range > 1000 nm [13,96]. Additionally, calibration models for firmness have been difficult to obtain, although there are a few exceptions reported for several orange varieties, in the reflectance mode and in the ranges 500-1690 nm [67], and 1000-2500 nm [73]. Of course, the calibration models for specific compounds, such as sugars, acids or antioxidants, require in most cases the longer NIR spectral range [12,69,74].…”

“…External validation means validation through a dataset with a different origin (spatial or temporal) relatively to the datasets used in calibration. Nevertheless, there are some clear examples of this approach, such as previously reported for mandarin [53,56,[97][98][99], orange [12,56,65,66,70,72,73,97], and grapefruit [12,70]. Without the effective external validation, it is not possible to know exactly how well these models would work in real conditions due to the large variability within the trees, orchards, sites and harvest seasons.…”

Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

“…This subsection compares the accuracy of the proposed method with the results reported by other authors that also estimate the firmness of different fruits using spectral information. Three works have been found in the literature, proposed by Bizzani et al [20], Uwadaira et al [35] and Huang et al [36]. Table 15 describes the main features of these works and the obtained values of the coefficient of determination, R 2 .…”

“…Type of Fruit Method (Spectrum Unit: nm) R 2 Proposed method Apple 868 to 884 nm 0.803 Bizzani et al [20] Orange 1000 to 2500 nm 0.92 Uwadaira et al [35] Peach 500 to 1000 nm 0.8 Huang et al [36] Tomato 300 to 550 nm 0.894…”

Use of non-destructive instrumental techniques to evaluate effects of gamma irradiation, quality and sensory attributes in orange juice

“…However, the viscosity of orange juice is related to the presence of molecules such as pectin that alter the distribution of particle size in the suspension, consequently increasing the viscosity of the juice (Aghajanzadeh, Kashaninejad, & Ziaiifar, 2017;Croak & Corredig, 2006). TD-NMR also was used to predict the total pectin in intact orange where showed a good performed in the PLS models (r= 076 and error of prediction 5.76%) (Bizzani, Flores, Colnago, & Ferreira, 2017). The presence of paramagnetic ions, such as the iron contained in orange juice (Ladaniya, 2008), may have played an important role in achieving this result, as it may reduce the relaxation time of the water in some oxidation states (Ribeiro et al, 2010).…”