Postharvest Dry Matter and Soluble Solids Content Prediction in d’Anjou and Bartlett Pear Using Near-infrared Spectroscopy

Abstract

<jats:p>Dry matter (DM) has recently been proposed as a new quality index for apple, inspiring similar investigations in other tree fruit crops. Near-infrared spectroscopy (NIR) enables the nondestructive estimation of DM and other quality attributes, although the accuracy and reliability of this technology on North American pear varieties remain untested. In this study, predictive NIR regression models were developed for nondestructive determination of postharvest DM and soluble solids content (SSC) in d’Anjou and Bartlett pears (<jats:italic>Pyrus communis</jats:italic> L.) using a commercially available NIR spectrometer. At calibration, models performed reliably with coefficients of determination (<jats:italic>R</jats:italic><jats:sup>2</jats:sup>) of 0.940 (DM) and 0.908 (SSC) for model trained on d’Anjou pears and 0.860 (DM) and 0.839 (SSC) for model trained on Bartlett pears. Application of the models to independent validation datasets demonstrated acceptable performance with <jats:italic>R</jats:italic><jats:sup>2</jats:sup> values ranging from 0.722–0.901 and 0.651–0.844 between measured and predicted DM and SSC values, respectively. Differences in performance can be attributed to the different DM and SSC values and maturity levels between the fruit used for model calibration and those in the validation datasets. Although not all models developed in this study were accurate enough for quantitative determinations, NIR devices may be useful for orchard management decisions and fruit sorting purposes.</jats:p>

A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer

“…The most important attributes affect the NIRS are chemical bonds, such as C-H, N-H, O-H, and C-O, which are subjected to stretching or bending caused by the vibrational energy change during irradiation by NIR light [ 50 ]. Outside the selected window of wavelength regions were noisy and uninformative due to the absorbance of chlorophyll and other pigments in the visible range of 400–700 nm [ 49 ]. From Figure 2 b, the absorbance spectra were converted to the second derivative spectra and narrowed the wavelength region (729–975 nm) that could be used for the analysis of carbohydrate, sugar, and water absorbance bands in the NIR [ 51 ].…”

“…Notably, Autoscale preprocessing of SVM-R analysis increased the prediction capability with a value of 0.74, minimum RMSEP of 1.6867 and higher RPD of 1.9742. In a study using d’Anjou and Bartlett pear, the predicted R 2 of the validation dataset for SSC prediction was in the ranges of 0.651–0.844 [ 49 ]. In a further study, the standard error of laboratory (SEL) of reference method will be calculated to determine the SEP/SEL ratio that evaluates the predictive ability of equations and the precision level of models for accurate routine use.…”

Physicochemical Fingerprint of “Pera Rocha do Oeste”. A PDO Pear Native from Portugal

“…The NIR technique has been successfully used in different foods and beverages [ 11 , 12 , 13 ] to measure different parameters of pear and apple quality attributes and nutraceutical properties [ 14 , 15 , 16 ]. NIR technology has been mostly used to measure the total soluble solids of a variety of fruits [ 17 , 18 , 19 ]. Different calibration models were established to evaluate the predictive effect on the quality of different fruits [ 19 , 20 , 21 ].…”

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

“…Currently, the Australian stone fruit industry recommends the use of FF and I AD for maturity assessment, whilst SSC and fruit size are mostly used as quality parameters. 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 ].…”