Application of visible/NIR spectroscopy for the estimation of soluble solids, dry matter and flesh firmness in stone fruits

Abstract

BACKGROUND Soluble solids concentration (SSC), dry matter concentration (DMC) and flesh firmness (FF) are important fruit quality parameters in stone fruits. This study investigated the ability of a commercial visible/near‐infrared (NIR) spectrometer to determine SSC, DMC and FF in nectarine, peach, apricot and Japanese plum cultivars at harvest. The work was conducted in summer 2019/2020 on 14 stone fruit cultivars at Tatura, Australia. Two sub‐samples of 100 fruit each were collected before and after commercial maturity (± 5 days) in order to maximize sample variability. RESULTS Partial least square (PLS) regression models based on the second derivative of the absorbance in the 729–975 nm spectral region proved accurate for the prediction of SSC and DMC (R2CV > 0.750). Only the model generated for SSC in ‘Golden May’ apricot was less precise compared to other cultivars. No visible/NIR models were accurate enough to predict FF in the cultivars under study (R2CV < 0.750). CONCLUSION This study demonstrated that the visible/NIR spectrometer was a reliable tool to monitor SSC and DMC in stone fruits at harvest but proved less useful for FF estimation. These results highlight the potential of visible/NIR spectrometry to evaluate stone fruit quality both in situ pre‐harvest and in the laboratory after harvest. © 2020 Society of Chemical Industry

Maturity Prediction in Yellow Peach (Prunus persica L.) Cultivars Using a Fluorescence Spectrometer

“…Near-infrared (NIR) spectrometers have been reliably adopted for the estimation of SSC and dry matter in many different fruits—e.g., apple [ 10 , 11 , 12 ], pear [ 13 , 14 ], kiwifruit [ 15 ] and stone fruits [ 16 , 17 , 18 , 19 ]—as different wavelengths in the NIR region are very well correlated with the absorbance and reflectance of water and soluble sugars. Prediction of FF in stone fruits via NIR spectrometry is not as reliable as for SSC and dry matter, as this index is influenced by a combination of several physiological and physical factors (e.g., changes in soluble sugars and structural carbohydrates, pectins and physical damage) and does not consistently correlate with specific spectral wavelengths [ 19 ]. Other non-destructive technologies such as magnetic resonance, although very precise for the estimation of some maturity indices [ 20 ], remain too costly for wide-scale use by industry.…”

Photoactivated TiO2 Nanocomposite Delays the Postharvest Ripening Phenomenon through Ethylene Metabolism and Related Physiological Changes in Capsicum Fruit

“…This was measured in gravimetric method comparing total water content (difference of weight of the fruit before and after drying in hot air oven at 50 • C for 48 h) and the difference in weight of fruit at initial day and after 7 d of treatment taking 3 replicates from each treatment according to Scalisi and O'Connell [21].…”

Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach

“…Compared to other technologies, handheld and portable NIR spectrometers are the most advanced in many aspects, such as miniaturization, software enhancement, and expanding the operating wavelengths into the visible range. A commercial Vis/NIR spectrometer with wavelengths ranging from 310 nm to 1100 nm was recently used to assess the soluble solid concentration, dry matter, and flesh firmness in stone fruits at harvest [19]. In general, NIR spectral data are analyzed and correlated with fruit traits using multiple linear regression (MLR) and partial least squares regression (PLS).…”