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

Penchaiya, P.; Bobelyn, Els; Verlinden, Bert; Nicolaı̈, Bart; Saeys, Wouter

doi:10.1016/j.jfoodeng.2009.03.018

Cited by 64 publications

(38 citation statements)

References 27 publications

(33 reference statements)

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“…Penchaiya et al . used a diode‐array spectrophotometer (spectral range 780–1690 nm) to obtain models of predictive capacity (RPD cv = 2.08) slightly higher than those of this research work.…”

Section: Resultsmentioning

confidence: 80%

In situ ripening stages monitoring of Lamuyo pepper using a new‐generation near‐infrared spectroscopy sensor

et al. 2020

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BACKGROUND Near‐infrared spectroscopy (NIRS) was used as a nondestructive sensor to assess the quality of freshly harvested Lamuyo peppers. One hundred and forty‐four Lamuyo peppers, which were in a range of colors (green, chocolate, orange, and red) when harvested, were analyzed. In this study, the evolution of the main quality parameters during the harvest period was analyzed. Additionally, NIRS predictive models using a portable manual spectrophotometer to evaluate quality parameters together with color index were developed. Moreover, two procedures for taking near‐infrared spectra were tested: (1) static, where point spectral readings were taken of around the equator of the fruit; (2) dynamic, where spectra were taken by scanning the entire length of the pepper. RESULTS Green peppers and those harvested at the beginning of the campaign presented significantly lower values (P < 0.05) of dry matter, soluble solid contents, and titratable acidity, whereas those with red coloration and those harvested at the end of the campaign showed significantly higher values of these three quality parameters (P < 0.05). The predictive capacity of the NIRS models showed that the static mode proved to be the most suitable for measuring the quality of Lamuyo peppers. CONCLUSIONS The viability of NIRS for measuring dry matter content and soluble solid contents in situ, using a new‐generation NIRS sensor, was demonstrated. However, the high water content, the irregular shape of the fruit, and the fact that it is hollow inside all point to the need for using larger samples sets so as to increase the robustness of the models obtained. © 2019 Society of Chemical Industry

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Section: Resultsmentioning

confidence: 80%

In situ ripening stages monitoring of Lamuyo pepper using a new‐generation near‐infrared spectroscopy sensor

et al. 2020

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“…Los resultados obtenidos en los parámetros que definen el tamaño de fruto se encuentran dentro del rango previamente estimado por Penchaiya et al (2009) …”

Section: Resultados Y Discusión Tamaño De Frutounclassified

Influencia de la variedad, portainjerto y época de cosecha en la calidad e índices de madurez en pimiento morrón

Márquez

Chávez

2017

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Influencia de la variedad, portainjerto y época de cosecha en la calidad e índices de madurez en pimiento morrón Influence of the variety, rootstock and harvest time on the quality and maturity indices in Bell pepper Resumen IntroducciónEl uso de portainjertos para la producción de hortalizas es una técnica que ha ido en aumento en los últimos años, generando resistencia a patógenos y aumentando la producción en los cultivos.Sin embargo, hoy en día la producción no lo es todo en el rubro comercial; no basta solo con producir en grandes cantidades y en espacios más pequeños; el mercado actual está pidiendo productos de calidad. Por lo que el objetivo del presente trabajo de investigación fue estudiar el efecto de la variedad, el portainjerto y la época de cosecha en la calidad e índices de madurez en pimiento morrón. MétodoEl presente estudio se llevó a cabo en el ciclo de producción 2012, desarrollado bajo un sistema de malla sombra, ubicada en Delicias, Chihuahua, México. El diseño experimental usado fue en bloques completos al azar con tres factores estudiados: variedades (Fascinato de frutos rojos y Janette de frutos amarillos), portainjerto (con portainjerto Terrano y sin portainjerto) y tiempo de cosecha (11 de julio, 8 de agosto, 20 de agosto y 4 de septiembre). Las variables evaluadas fueron:índices de madurez y parámetros de calidad en pimiento morrón.Influencia de la variedad, portainjerto y época de cosecha en la calidad e índices de madurez en pimiento morrón ResultadosSe encontraron diferencias significativas en los parámetros de calidad e índices de madurez entre las variedades evaluadas, así mismo, el efecto del portainjerto Terrano en la calidad del fruto de las variedades Fascinato y Janette incrementó el tamaño de fruto (diámetro, longitud y peso); mientras que, en variables como firmeza, sólidos solubles totales, color (L, C, °h) y acidez titulable (% de ácido málico) el portainjerto causó un decremento. Por otra parte, la época de cosecha para las variedades estudiadas fue estrechamente relacionada con el grado de madurez del fruto. ConclusiónEl factor variedad influyó en los parámetros: diámetro, longitud, peso del fruto, firmeza, sólidos solubles totales, color y acidez; mientras que el factor portainjerto sólo afectó la firmeza del fruto.Por otra parte, el factor época de cosecha tuvo influencia en la longitud y peso del fruto, firmeza, sólidos solubles totales, color y acidez titulable. Finalmente, la interacción de los tres factores estudiados solamente fue significativa en el color del fruto. Abstract Introduction

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“…The spectral characteristics of fruits are sensitive to within plant variability (plant age, crop load, crop position within the plant, and light effect), within greenhouse variability (position in the greenhouse, nutrition, climate, and light effect), fruit maturity, fruit cultivar, and seasonal variability (Nicolai et al, 2007;Penchaiya et al, 2009). To achieve high accuracy for the prediction of fruit attributes, the calibration models should be developed at each variability condition.…”

Section: Minimum Number Of Fruit Samples For Calibration Modelsmentioning

confidence: 99%

“…Absorption property is affected by the chemical composition of the fruit and scattering property is related to the texture of the fruit. Therefore, NIR spectroscopy can be used to measure both absorption-related attributes such as soluble solids content (SSC) of fruits and texture-related attributes such as firmness (Zude et al, 2006;Nicolai et al, 2007;Penchaiya et al, 2009;Sun et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

A Melon Fruit Grading Machine Using a Miniature VIS/NIR Spectrometer: 2. Design Factors for Optimal Interactance Measurement Setup

Suh¹,

Lee²,

Yu³

et al. 2012

Journal of Biosystems Engineering

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Purpose:In near infrared spectroscopy, interactance configuration of a light source and a spectrometer probe can provide more information regarding fruit internal attributes, compared to reflectance and transmittance configuration. However, there is no through study on the parameters of interactance measurement setup. The objective of this study was to investigate the effect of the parameters on the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from greenhouses at three different harvesting seasons. The prediction models were developed at three distances of 2, 5, and 8 cm between the light source and the spectrometer probe, three measurement points of 2, 3, and 6 evenly distributed on each sample, and different number of fruit samples for calibration models. The performance of the models was compared. Results: In the test at the three distances, the best results were found at a 5 cm distance. The coefficient of determination (Rcv 2 ) values of the cross-validation were 0.717 (standard error of prediction, SEP=1.16 °Brix) and 0.504 (SEP=4.31 N) for the estimation of SSC and firmness, respectively. The minimum measurement point required to fully represent the spectral characteristics of each fruit sample was 3. The highest Rcv 2 values were 0.736 (SEP=0.87 °Brix) and 0.644 (SEP=4.16 N) for the estimation of SSC and firmness, respectively. The performance of the models began to be saturated when 60 fruit samples were used for developing calibration models. The highest Rcv 2 of 0.713 (SEP=0.88 °Brix) and 0.750 (SEP=3.30 N) for the estimation of SSC and firmness, respectively, were achieved. Conclusions: The performance of the prediction models was quite different according to the condition of interactance measurement setup. In designing a fruit grading machine with interactance configuration, the parameters for interactance measurement setup should be chosen carefully.

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

Cited by 64 publications

References 27 publications

In situ ripening stages monitoring of Lamuyo pepper using a new‐generation near‐infrared spectroscopy sensor

In situ ripening stages monitoring of Lamuyo pepper using a new‐generation near‐infrared spectroscopy sensor

Influencia de la variedad, portainjerto y época de cosecha en la calidad e índices de madurez en pimiento morrón

A Melon Fruit Grading Machine Using a Miniature VIS/NIR Spectrometer: 2. Design Factors for Optimal Interactance Measurement Setup

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