Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging

Abstract:In this paper, it suggested the selection of papayas by image processing employing low-cost equipment for data acquisition. This may allow the process automation selection and classification of agricultural products for small farmers or small traders. The objective of this work was to evaluate two papaya varieties ("Solo" and "Formosa") using image programs for selection of miscellaneous format detection and fruit mapping coloration. The experiment occurred at an optic laboratory and it used a diffused light, camera, computer and papaya fruits, and for image processing, the ImageJ software, in addition to computer programs that have been developed for image processing. The detection of varied sizes and the fruit classification by mapping color were conducted using the software. The program for evaluating the two varieties format worked with 100% of the fruits and the one for mapping coloration with 80% correct results. These results showed that the program had low error and could be used for fruit selection.

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“…Also, evaluating the colors in apples, Nicolai et al [24] used hyperspectral NIR selecting the fruits with damages from the good ones.…”

Section: Mapping Colorationmentioning

confidence: 99%

Papaya (Carica papaya) Classification by Software

Fracarolli¹,

Fabbro²,

Oliveira³

2017

JAST-B

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“…The usual PLS Discriminant Analysis was proposed to overcome the multicollinearity problem of LDA and it can be defined as a straightforward extension of the PLS regression (Sjöström et al, 1986). Using a PLS-DA calibration model, Nicolaï et al (2006) discriminated between pixels of sound apple skin and bitter pit lesions. Leave-one-out cross validation was used to determine the number of latent variables based on minimisation of the root mean squared error of cross validation prior to segmentation.…”

Section: Partial Least Squaresmentioning

confidence: 99%

“…This study showed that this asymmetric second difference method provided good detection of the defective/contaminated apples, regardless of the cultivar and the colour of the apple. A hyperspectral NIR imaging system (900-1700 nm) was developed by Nicolaï et al (2006) to identify bitter pit lesions on apples. A discriminant PLS calibration model was constructed to discriminate between pixels of unaffected apple skin and bitter pit lesions.…”

Section: Estimation Of External Quality Parametersmentioning

confidence: 99%

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

Lorente

Aleixos

Gómez-Sanchís

et al. 2011

Food Bioprocess Technol

457

190

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Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time consuming for such high dimensional data sets and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, nondestructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

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“…One of the main uses of PLS is to perform discriminant analysis (PLS-DA) in order to indentify pixels that do not belong to the sample (interferences or contaminants). 32,36,37 For quantification, most of works present PLS models built using pure spectra and do not take into account possible spectral changes due to interactions of the compounds. 14,15,19,26,[38][39][40][41] PLS is employed in few works to quantify compounds by NIR imaging using a calibration set, 18,[42][43][44] and none of them presented interval PLS (iPLS) to calculate concentration maps (chemical images) in tablets.…”

Section: Introductionmentioning

confidence: 99%

A quantitative method using near infrared imaging spectroscopy for determination of surface composition of tablet dosage forms: an example of spirolactone tablets

Carneiro¹,

Poppi²

2012

J. Braz. Chem. Soc.

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Neste trabalho, a espectroscopia de imagens no infravermelho próximo (NIR) foi empregada no estudo da distribuição do princípio ativo (API) espironolactona e seus excipientes em comprimidos. As análises foram realizadas utilizando resolução espacial de 50 µm, analisando 16 mm 2 de cada comprimido padrão. Modelos de mínimos quadrados parciais por intervalo (iPLS) foram utilizados para quantificação dos excipientes e API em cada pixel afim de obter mapas de concentração para cada composto. Foram obtidos erros de quantificação entre 0,49 e 1,26% quando realizada a validação cruzada utilizando a média espectral de cada comprimido. Esses modelos de calibração foram utilizados para prever a concentração individual de cada componente em cada pixel. A concentração média de todos os pixels, para cada composto, produziu erros entre 0,05 e 1,06%. Estes resultados indicaram que os modelos estavam aptos a quantificar todos os compostos em cada pixel, individualmente. Esta aproximação necessita ser realizada uma vez que não é possível conhecer a composição real em cada pixel.In this work, near infrared (NIR) imaging spectroscopy was employed in the study of the distribution of the active pharmaceutical ingredient (API) spironolactone and its excipients in tablets. Analyses were performed using 50 µm spatial resolution to analyze 16 mm 2 of each standard tablet. Interval partial least squares models were used for API and excipients quantification in every pixel in order to obtain concentration maps for each compound. It was obtained errors of quantification between 0.49 and 1.26% when performed the cross-validation using spectral average of each tablet. These calibration models were used to predict individual concentration of each compound in the tablet, in every pixel. The average concentration of all pixels, for each compound yield errors, was between 0.05 and 1.06%. These results indicate that the models were able to quantify all compounds in each pixel. This approach is necessary since it is not possible to know the real concentration of each compound in the pixels.

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Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging

Cited by 165 publications

References 20 publications

Papaya (Carica papaya) Classification by Software

Papaya (Carica papaya) Classification by Software

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

A quantitative method using near infrared imaging spectroscopy for determination of surface composition of tablet dosage forms: an example of spirolactone tablets

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