Non-Destructive Measurement of Soluble Solids Content and Vitamin C in Gannan Navel Oranges by Vis-NIR Spectroscopy

2011 ◽  
Vol 9 (3) ◽  
pp. 1133-1139 ◽  
Author(s):  
Yande Liu ◽  
Xudong Sun ◽  
Xiaoling Dong ◽  
Aiguo Ouyang ◽  
Rongjie Gao ◽  
...  
2009 ◽  
Vol 94 (3-4) ◽  
pp. 267-273 ◽  
Author(s):  
Pathompong Penchaiya ◽  
Els Bobelyn ◽  
Bert E. Verlinden ◽  
Bart M. Nicolaï ◽  
Wouter Saeys

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 302
Author(s):  
Konni Biegert ◽  
Daniel Stöckeler ◽  
Roy J. McCormick ◽  
Peter Braun

Optical sensor data can be used to determine changes in anthocyanins, chlorophyll and soluble solids content (SSC) in apple production. In this study, visible and near-infrared spectra (729 to 975 nm) were transformed to SSC values by advanced multivariate calibration models i.e., partial least square regression (PLSR) in order to test the substitution of destructive chemical analyses through non-destructive optical measurements. Spectral field scans were carried out from 2016 to 2018 on marked ‘Braeburn’ apples in Southwest Germany. The study combines an in-depth statistical analyses of longitudinal SSC values with horticultural knowledge to set guidelines for further applied use of SSC predictions in the orchard to gain insights into apple carbohydrate physiology. The PLSR models were investigated with respect to sample size, seasonal variation, laboratory errors and the explanatory power of PLSR models when applied to independent samples. As a result of Monte Carlo simulations, PLSR modelled SSC only depended to a minor extent on the absolute number and accuracy of the wet chemistry laboratory calibration measurements. The comparison between non-destructive SSC determinations in the orchard with standard destructive lab testing at harvest on an independent sample showed mean differences of 0.5% SSC over all study years. SSC modelling with longitudinal linear mixed-effect models linked high crop loads to lower SSC values at harvest and higher SSC values for fruit from the top part of a tree.


Author(s):  
Izabel Camacho Nardello ◽  
Rufino Fernando Flores Cantillano ◽  
Jardel Araújo Ribeiro ◽  
Gerson Kleinick Vignolo ◽  
Marcelo Barbosa Malgarim ◽  
...  

Abstract The objective of this work was to evaluate the influence of the use of UV-C radiation at an intensity of 0.53 KJ m-2, during strawberry (Fragaria x ananassa) cultivation, on the physicochemical and phytochemical parameters of the fruits. The used experimental design was completely randomized, in a single-factor arrangement with 11, 19, 29, and 39 UV-C applications and without UV-C application. The hue of the fruits was smaller at the end of the study period. pH varied in strawberries that received 39 applications of UV-C, and the soluble solids content differed with 19 applications. Antioxidant activity was lower in fruits with 11, 19, and 29 UV-C applications, whereas vitamin C content was lower only in fruits that received 29 applications. The use of UV-C radiation at an intensity of 0.53 KJ m-2, during strawberry cultivation, affects the physicochemical and phytochemical parameters of the fruits, but does not cause losses in their quality.


2020 ◽  
Author(s):  
Bo Zhang ◽  
Mengsheng Zhang ◽  
Maosheng Shen ◽  
Hao Li ◽  
Haihui Zhang ◽  
...  

2012 ◽  
Vol 236-237 ◽  
pp. 83-88 ◽  
Author(s):  
Wei Qiang Luo ◽  
Hai Qing Yang ◽  
Wei Cheng Dai

Ultra-violet, visible and near infrared (UV-VIS-NIR) spectroscopy combined with chemometrics was investigated for fast determination of soluble solids content (SSC) of tea beverage. In this study, a total of 120 tea samples with SSC range of 4.0-9.5 ºBrix were tested. Samples were randomly divided for calibration (n=90) and independent validation (n=30). Spectra were collected by a mobile fiber-type UV-VIS-NIR spectrophotometer in transmission mode with recorded wavelength range of 203.64-1128.05 nm. Various calibration approaches, i.e., principal components analysis (PCA), partial least squares (PLS) regression, least squares support vector machine (LSSVM) and back propagation artificial neural network (BPANN), were investigated. The combinations of PCA-BPANN, PCA-LSSVM, PLS-BPANN and PLS-LSSVM were also investigated to build calibration models. Validation results indicated that all these investigated models achieved high prediction accuracy. Especially, PLS-LSSVM achieved best performance with mean coefficient of determination (R2) of 0.99, root-mean-square error of prediction (RMSEP) of 0.12 and residual prediction deviation (RPD) of 15.16. This experiment suggests that it is feasible to measure SSC of tea beverage using UV-VIS-NIR spectroscopy coupled with appropriate multivariate calibration, which may allow using the proposed method for off-line and on-line quality supervision in the production of soft drink.


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