Application of Near Infrared Spectroscopy for Nondestructive Determination of Soluble Solids Content in Indramayu Mango Fruit

2008 ◽  
Vol 22 (2) ◽  
pp. 129-134
Author(s):  
Diding Suhandy ◽  
◽  
Rofandi Hartanto ◽  
Sulusi Prabawati ◽  
Yulianingsih Yulianingsih ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Mango Fruit ◽  
Nondestructive Determination ◽  
Solids Content

scholarly journals Determination of the Best NIR Experimental Condition for Nondestructive Soluble Solids Content Prediction in Mango Fruit Using Near Infrared Spectroscopy

2007 ◽  
Vol 21 (04) ◽  
pp. 363-372
Author(s):  
Diding Suhandy ◽  
◽  
Rofandi Hartanto ◽  
Sulusi Prabawati ◽  
Yulianingsih Yulianingsih ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Mango Fruit ◽  
Solids Content

Near-infrared spectroscopy for non-destructive determination of soluble solids content of Chinese citrus

2005 ◽  
Author(s):  
Huishan Lu ◽  
Yibin Ying ◽  
Yande Liu ◽  
Xiaping Fu ◽  
Haiyan Yu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Non Destructive

Quantitative detection of soluble solids content, pH, and total phenol in Cabernet Sauvignon grapes based on near infrared spectroscopy

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yijia Luo ◽  
Juan Dong ◽  
Xuewei Shi ◽  
Wenxia Wang ◽  
Zhuoman Li ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soluble Solids ◽  
Cabernet Sauvignon ◽  
Soluble Solids Content ◽  
Solids Content

Abstract Determination of Cabernet Sauvignon grapes quality plays an important role in commercial processing. In this research, a rapid approach based on near infrared spectroscopy was proposed to the determination of soluble solids content (SSC), pH, and total phenol content (TPC) in entire bunches of Cabernet Sauvignon grapes. Standardized normal variate (SNV) and competitive adaptive weighted sampling (CARS), genetic algorithm (GA), and synergy interval partial least squares (si-PLS) were used to optimize the spectral data. With optimal combination input, the prediction accuracy of partial least squares regression (PLSR) and support vector regression (SVR) models was compared. The results showed that these models based on variable optimization method could predict well the SSC, pH, and TPC of Cabernet Sauvignon grapes. The correlation coefficient of prediction for SSC, pH, and TPC had reached more than 0.85. This work provides an alternative to analyze the chemical parameters in whole bunch of Cabernet Sauvignon grape.

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.

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