Predicting apple (cv. Elshof) postharvest dry matter and soluble solids content with near infrared spectroscopy

2013 ◽  
Vol 94 (5) ◽  
pp. 955-962 ◽  
Author(s):  
Sylvia Travers ◽  
Marianne G Bertelsen ◽  
Sergey V Kucheryavskiy
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content

scholarly journals Predicting pear (cv. Clara Frijs) dry matter and soluble solids content with near infrared spectroscopy

LWT ◽  
2014 ◽  
Vol 59 (2) ◽  
pp. 1107-1113 ◽  
Author(s):  
Sylvia Travers ◽  
Marianne G. Bertelsen ◽  
Karen K. Petersen ◽  
Sergey V. Kucheryavskiy
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content

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

HortScience ◽  
2018 ◽  
Vol 53 (5) ◽  
pp. 669-680 ◽  
Author(s):  
Alex Goke ◽  
Sara Serra ◽  
Stefano Musacchi
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Calibration Models ◽  
Solids Content ◽  
Fruit Sorting

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 (Pyrus communis L.) using a commercially available NIR spectrometer. At calibration, models performed reliably with coefficients of determination (R2) 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 R2 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.

scholarly journals Robust PLS models for soluble solids content and firmness determination in low chilling peach using near-infrared spectroscopy (NIR)

2016 ◽  
Vol 111 ◽  
pp. 345-351 ◽  
Author(s):  
Paloma Andrade Martins Nascimento ◽  
Lívia Cirino de Carvalho ◽  
Luis Carlos Cunha Júnior ◽  
Fabíola Manhas Verbi Pereira ◽  
Gustavo Henrique de Almeida Teixeira
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content

scholarly journals Prediction of water content and soluble solids content of ‘manalagi’ apples using near infrared spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012062
Author(s):  
K Kusumiyati ◽  
Y Hadiwijaya ◽  
D Suhandy ◽  
A A Munawar
Keyword(s):  
Infrared Spectroscopy ◽  
Water Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quality Attributes ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Soluble Solids Content ◽  
Orthogonal Signal Correction ◽  
Solids Content

Abstract The purpose of the research was to predict quality attributes of ‘manalagi’ apples using near infrared spectroscopy (NIRS). The desired quality attributes were water content and soluble solids content. Spectra data collection was performed at wavelength of 702 to 1065 nm using a Nirvana AG410 spectrometer. The original spectra were enhanced using orthogonal signal correction (OSC). The regression approaches used in the study were partial least squares regression (PLSR) and principal component regression (PCR). The results showed that water content prediction acquired coefficient of determination in calibration set (R2cal) of 0.81, coefficient of determination in prediction set (R2pred) of 0.61, root mean squares error of calibration set (RMSEC) of 0.009, root mean squares of prediction set (RMSEP) of 0.020, and ratio performance to deviation (RPD) of 1.62, while soluble solids content prediction displayed R2cal, R2pred, RMSEC, RMSEP, and RPD of 0.79, 0.85, 0.474, 0.420, and 2.69, respectively. These findings indicated that near infrared spectroscopy could be used as an alternative technique to predict water content and soluble solids content of ‘manalagi’ apples.

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