Application of Absorption and Scattering Properties Obtained through Image Pre-Classification Method Using a Laser Backscattering Imaging System to Detect Kiwifruit Chilling Injury

Kiwifruit chilling injury (CI) damage occurs after long-term exposure to low temperature. A non-destructive approach to detect CI injury was tested in the present study, using a laser backscattering image (LBI) technique calibrated with 56 liquid phantoms for providing absorption coefficient (µa) and reduced scattering coefficient (µs’). Calibration of LBI resulted in a true-positive (TP) classification of 91.5% and 65.6% of predicted µs’ and µa, respectively. The optical properties of ‘SunGold™’and ‘Hayward’ kiwifruit were analysed at 520 nm with a two-step protocol capturing pre-classification according to the LBI parameters used in the calibration and estimation with the Farrell equation. Severely injured kiwifruit showed white corky tissue and water soaking, reduced soluble solids content and firmness measured destructively. Non-destructive classification results for ‘SunGold™’ showed a high percentage of TP for severe CI of 92% and 75% using LBI parameters directly and predicted µa and µs’ after pre-classification, respectively. The classification accuracy for severe CI ‘Hayward’ kiwifruit with LBI parameter was low (58%) and with µa and µs’ decreased further (35%), which was assumed to be due to interference caused by the long trichomes on the fruit surface.

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Modelling Soluble Solids Content Accumulation in ‘Braeburn’ Apples

Plants ◽

10.3390/plants10020302 ◽

2021 ◽

Vol 10 (2) ◽

pp. 302

Author(s):

Konni Biegert ◽

Daniel Stöckeler ◽

Roy J. McCormick ◽

Peter Braun

Keyword(s):

Explanatory Power ◽

Partial Least Square ◽

Optical Measurements ◽

Soluble Solids ◽

Sensor Data ◽

Soluble Solids Content ◽

Mixed Effect ◽

Laboratory Errors ◽

Solids Content ◽

Non Destructive

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.

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Non-destructive measurement of firmness and soluble solids content in bell pepper using NIR spectroscopy

Journal of Food Engineering ◽

10.1016/j.jfoodeng.2009.03.018 ◽

2009 ◽

Vol 94 (3-4) ◽

pp. 267-273 ◽

Cited By ~ 39

Author(s):

Pathompong Penchaiya ◽

Els Bobelyn ◽

Bert E. Verlinden ◽

Bart M. Nicolaï ◽

Wouter Saeys

Keyword(s):

Nir Spectroscopy ◽

Bell Pepper ◽

Soluble Solids ◽

Soluble Solids Content ◽

Solids Content ◽

Destructive Measurement ◽

Non Destructive

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Using absorption and reduced scattering coefficients for non-destructive analyses of fruit flesh firmness and soluble solids content in pear ( Pyrus communis ‘Conference’)—An update when using diffusion theory

Postharvest Biology and Technology ◽

10.1016/j.postharvbio.2017.04.004 ◽

2017 ◽

Vol 130 ◽

pp. 56-63 ◽

Cited By ~ 16

Author(s):

Segun Emmanuel Adebayo ◽

Norhashila Hashim ◽

Roland Hass ◽

Oliver Reich ◽

Christian Regen ◽

...

Keyword(s):

Diffusion Theory ◽

Pyrus Communis ◽

Soluble Solids ◽

Soluble Solids Content ◽

Scattering Coefficients ◽

Flesh Firmness ◽

Fruit Flesh ◽

Solids Content ◽

Non Destructive

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Non-destructive prediction of soluble solids content of pear based on fruit surface feature classification and multivariate regression analysis

Infrared Physics & Technology ◽

10.1016/j.infrared.2018.06.019 ◽

2018 ◽

Vol 92 ◽

pp. 336-344 ◽

Cited By ~ 16

Author(s):

Xi Tian ◽

Qingyan Wang ◽

Jiangbo Li ◽

Fa Peng ◽

Wenqian Huang

Keyword(s):

Regression Analysis ◽

Multivariate Regression ◽

Multivariate Regression Analysis ◽

Surface Feature ◽

Soluble Solids ◽

Soluble Solids Content ◽

Feature Classification ◽

Solids Content ◽

Non Destructive ◽

Fruit Surface

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Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method

Postharvest Biology and Technology ◽

10.1016/j.postharvbio.2019.02.001 ◽

2019 ◽

Vol 151 ◽

pp. 79-87 ◽

Cited By ~ 33

Author(s):

Shuxiang Fan ◽

Jiangbo Li ◽

Yu Xia ◽

Xi Tian ◽

Zhiming Guo ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared ◽

Soluble Solids ◽

Soluble Solids Content ◽

Biological Variability ◽

Calibration Transfer ◽

Term Evaluation ◽

Transfer Method ◽

Solids Content

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

Sensor Letters ◽

10.1166/sl.2011.1392 ◽

2011 ◽

Vol 9 (3) ◽

pp. 1133-1139 ◽

Cited By ~ 2

Author(s):

Yande Liu ◽

Xudong Sun ◽

Xiaoling Dong ◽

Aiguo Ouyang ◽

Rongjie Gao ◽

...

Keyword(s):

Vitamin C ◽

Nir Spectroscopy ◽

Soluble Solids ◽

Soluble Solids Content ◽

Solids Content ◽

Destructive Measurement ◽

Non Destructive

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Investigation of Parameters That Affect the Acquired Near Infrared Diffuse Reflected Signals in Non-Destructive Soluble Solids Content Prediction

Engineering Journal ◽

10.4186/ej.2020.24.6.79 ◽

2020 ◽

Vol 24 (6) ◽

pp. 79-90

Author(s):

Kim Seng Chia ◽

Fan Wei Hong

Keyword(s):

Predictive Model ◽

Near Infrared ◽

Diffuse Reflection ◽

Model Performance ◽

Middle Part ◽

Soluble Solids ◽

Soluble Solids Content ◽

Geometrical Effects ◽

Solids Content ◽

Non Destructive

Near infrared spectroscopy is a susceptible technique which can be affected by various factors including the surface of samples. According to the Lambertian reflection, the uneven and matte surface of fruits will provide Lambertian light or diffuse reflectance where the light enters the sample tissues and that uniformly reflects out in all orientations. Bunch of researches were carried out using near infrared diffuse reflection mode in non-destructive soluble solids content (SSC) prediction whereas fewer of them studying about the geometrical effects of uneven surface of samples. Thus, this study aims to investigate the parameters that affect the near infrared diffuse reflection signals in non-destructive SSC prediction using intact pineapples. The relationship among the reflectance intensity, measurement positions, and the SSC value was studied. Next, three independent artificial neural networks were separately trained to investigate the geometrical effects on three different measurement positions. Results show that the concave surface of top and bottom parts of pineapples would affect the reflectance of light and consequently deteriorate the predictive model performance. The predictive model of middle part of pineapples achieved the best performance, i.e. root mean square error of prediction (RMSEP) and correlation coefficient of prediction (Rp) of 1.2104 °Brix and 0.7301 respectively.

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