Use of Spectral Information and Machine Vision for Bruise Detection on Peaches and Apricots

Zwiggelaar, Reyer; Yang, Qingsheng; Garcia-Pardo, Eva; Bull, C.R.

doi:10.1006/jaer.1996.0035

Cited by 29 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results are in agreement with findings of several authors (e.g. Geola & Pieper, 1994;Zwiggelaar, Yang, Garcia-Pardo, & Bull, 1996). The difference in reflectance between the bruised and unbruised tissue was the greatest in the NIR region, while it decreased dominantly in the visible region, and the spectral images had higher levels of noise with low reflectance especially in case of red and reddish background colors.…”

Section: Spectral Characteristics Of Normal and Bruised Surfacessupporting

confidence: 94%

Early detection of apple bruises on different background colors using hyperspectral imaging

ElMasry

Wang

Vigneault

et al. 2008

LWT - Food Science and Technology

238

View full text Add to dashboard Cite

Section: Spectral Characteristics Of Normal and Bruised Surfacessupporting

confidence: 94%

Early detection of apple bruises on different background colors using hyperspectral imaging

ElMasry

Wang

Vigneault

et al. 2008

LWT - Food Science and Technology

238

View full text Add to dashboard Cite

“…Nonetheless, bruised fruits had a lower reflectance than non-bruised samples. This result is in agreement with previous findings describing a general decrease of spectral reflectance of bruised apricots surfaces [8]. It is interesting to note that reflectance features do not depend on the post-harvest treatment, while the treatment becomes significant in case of damage where the CSPT spectra appears sensitive to the exposure to 1-MCP.…”

Section: Resultssupporting

confidence: 92%

“…These researches demonstrated since three decades ago the possibility to detect ripeness-related fruit pigment content [7] and also the presence of damages and defects, such as bruising from mechanical impacts [8,9]. Optical methods, such as VIS/NIR spectrophotometers, were also combined with other instruments and in particular, with electronic noses, showing a general improvement of both qualitative and quantitative determinations [10].…”

Section: Introductionmentioning

confidence: 99%

Sorting of apricots with computer screen photoassisted spectral reflectance analysis and electronic nose

Natale

Filippini

Pennazza

et al. 2006

Sensors and Actuators B: Chemical

View full text Add to dashboard Cite

“…An NIR imaging system can capture images under many wavelength bands in the NIR region. Such imaging systems have been used for quality control and measurement in apples (Bellon et al 1992;Upchurch et al 1994) and in peaches and apricots (Miller and Delwiche 1990;Zwiggelaar et al 1996). Sugiyama (1999) established an NIR imaging system to predict the sugar content of a cross-section of melon flesh.…”

mentioning

confidence: 99%

Development of a Near‐Infrared Imaging System for Determination of Rice Moisture

Lin

Chang

2006

Cereal Chem

View full text Add to dashboard Cite

The objective of this study was to develop a near‐infrared (NIR) imaging system to determine rice moisture content. The NIR imaging system fitted with 15 band‐pass filters (wavelengths of 870–1,014 nm) was used to capture the spectral image. In this work, calibration methods including multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural network (ANN) were used in both near‐infrared spectrometry (NIRS) and the NIR imaging system to determine the moisture content of rice. Comprehensive performance comparison among MLR, PLSR, and ANN approaches has been conducted. To reduce repetition and redundancy in the input data and obtain a more accurate network, six significant wavelengths selected by the MLR model, which had high correlation with the moisture content of rice, were used as the input data of the ANN. The performance of the developed system was evaluated through experimental tests for rice moisture content. This study adopted the coefficient of determination (rval2), the standard error of prediction (SEP), and the relative performance determinant (RPD) as the performance indices of the NIR imaging system with respect to the tests of rice moisture content. Utilizing these three models, the analysis results of rval2, SEP, and RPD for the validation set were within 0.942–0.952, 0.435–0.479%, and 4.2–4.6, respectively. From experimental results, the performance of NIR imaging system was almost the same as that of NIRS. Using the developed NIR imaging system, all of the three different calibration methods (MLR, PLSR, and ANN) provided a high prediction capacity for the determination of moisture in rice samples. These results indicated that the NIR imaging system developed in this study can be used as a device for the measurement of rice moisture content.

show abstract

Use of Spectral Information and Machine Vision for Bruise Detection on Peaches and Apricots

Cited by 29 publications

References 0 publications

Early detection of apple bruises on different background colors using hyperspectral imaging

Early detection of apple bruises on different background colors using hyperspectral imaging

Sorting of apricots with computer screen photoassisted spectral reflectance analysis and electronic nose

Development of a Near‐Infrared Imaging System for Determination of Rice Moisture

Contact Info

Product

Resources

About