Postharvest quality of apple predicted by NIR-spectroscopy: Study of the effect of biological variability on spectra and model performance

Bobelyn, Els; Serban, Anca-Sabina; Nicu, Mihai; Lammertyn, Jeroen; Nicolaı̈, Bart; Saeys, Wouter

doi:10.1016/j.postharvbio.2009.09.006

Cited by 242 publications

(114 citation statements)

References 51 publications

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“…However, in all the studies found in the literature, only color and moisture content were investigated with these devices. In other fields instead, these devices were successfully used (e.g., in quality and safety [15,116,150], as well as in post-harvest control of fresh fruits and vegetables using NIR [19,138,162,163] and HSI [164,165] to monitor a broad range of physicochemical changes such as: sugar content [166], protein content [167,168], starch index [135] and several related nutritional compounds [169,170]. For this reason, it seems reasonable to develop a model able to detect the same compounds and their changes during the drying process.…”

Section: Quality Control Of Vegetables During Dryingmentioning

confidence: 99%

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

et al. 2017

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An overview is given regarding the most recent use of non-destructive techniques during drying used to monitor quality changes in fruits and vegetables. Quality changes were commonly investigated in order to improve the sensory properties (i.e., appearance, texture, flavor and aroma), nutritive values, chemical constituents and mechanical properties of drying products. The application of single-point spectroscopy coupled with drying was discussed by virtue of its potentiality to improve the overall efficiency of the process. With a similar purpose, the implementation of a machine vision (MV) system used to inspect foods during drying was investigated; MV, indeed, can easily monitor physical changes (e.g., color, size, texture and shape) in fruits and vegetables during the drying process. Hyperspectral imaging spectroscopy is a sophisticated technology since it is able to combine the advantages of spectroscopy and machine vision. As a consequence, its application to drying of fruits and vegetables was reviewed. Finally, attention was focused on the implementation of sensors in an on-line process based on the technologies mentioned above. This is a necessary step in order to turn the conventional dryer into a smart dryer, which is a more sustainable way to produce high quality dried fruits and vegetables.

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Section: Quality Control Of Vegetables During Dryingmentioning

confidence: 99%

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

et al. 2017

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“…Ever since Norris (1964) first used NIRS in agriculture to measure the moisture contents in grains, it has been used to measure rapidly the moisture content, protein, and fat in agricultural products (Davies and Grant, 1987;Nicolaï et al, 2007). In addition, several different studies using NIRS have been conducted to measure physicochemical features and organoleptic properties, such as soluble solid content (SSC), acidity, and the firmness of the apples, tangerines, peaches, and pears (Kawano et al, 1993;Hwang et al, 2000;Lu et al, 2000;Lee et al, 2004;Bobelyn et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Non-destructive and Rapid Prediction of Moisture Content in Red Pepper (Capsicum annuum L.) Powder Using Near-infrared Spectroscopy and a Partial Least Squares Regression Model

Lim

Kim

et al. 2014

Journal of Biosystems Engineering

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Purpose:The aim of this study was to develop a technique for the non-destructive and rapid prediction of the moisture content in red pepper powder using near-infrared (NIR) spectroscopy and a partial least squares regression (PLSR) model. Methods: Three red pepper powder products were separated into three groups based on their particle sizes using a standard sieve. Each product was prepared, and the expected moisture content range was divided into six or seven levels from 3 to 21% wb with 3% wb intervals. The NIR reflectance spectra acquired in the wavelength range from 1,100 to 2,300 nm were used for the development of prediction models of the moisture content in red pepper powder. Results: The values of RV 2 , SEP, and RPD for the best PLSR model to predict the moisture content in red pepper powders of varying particle sizes below 1.4 mm were 0.990, ±0.487% wb, and 10.00, respectively. Conclusions: These results demonstrated that NIR spectroscopy and a PLSR model could be useful techniques for measuring rapidly and non-destructively the moisture content in red pepper powder.

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“…Trabalhos de calibrações envolvendo diferentes épocas de colheita já foram testados em culturas como melões e abacaxis (GUTHRIE; WEDDING; WALSH, 1998), maçãs (MCGLONE; KAWANO, 1998) e quivis (BOBELYN et al, 2010) e onde os autores constataram que calibrações realizadas em diferentes épocas de colheita apresentaram baixos coeficientes de determinação quando a variabilidade das frutas não são incorporadas aos modelos de calibração NIR para os parâmetros firmeza, teor de matéria seca e sólidos solúveis.…”

Section: Introductionunclassified

Espectroscopia do visível e infra-vermelho próximo para estimar sólidos solúveis e firmeza de polpa em função da época de colheita em pêssegos

Betemps

Fachinello

Galarça³

et al. 2014

Sem. Ci. Agr.

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Postharvest quality of apple predicted by NIR-spectroscopy: Study of the effect of biological variability on spectra and model performance

Cited by 242 publications

References 51 publications

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

Non-destructive and Rapid Prediction of Moisture Content in Red Pepper (Capsicum annuum L.) Powder Using Near-infrared Spectroscopy and a Partial Least Squares Regression Model

Espectroscopia do visível e infra-vermelho próximo para estimar sólidos solúveis e firmeza de polpa em função da época de colheita em pêssegos

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