Non-invasive hyperspectral imaging approach for fruit quality control application and classification: case study of apple, chikoo, guava fruits

Vetrekar, Narayan; Gad, Rajendra S.; Fernandes, Isabella M. M.; Parab, Jivan S.; Desai, A. R.; Pawar, Jyoti D.; Naik, Gourish M.; Snekhalatha, U.

doi:10.1007/s13197-015-1838-8

Cited by 22 publications

(14 citation statements)

References 28 publications

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“…Principal component analysis (PCA) was carried out to express the major information contained in the original variables with a lower number of variables, called principal components (PCs), which describe the main sources of variation in the data (Vetrekar et al 2015). Partial least squares discriminant analysis (PLS-DA) models were built to differentiate between healthy and diseased onions (Roggo et al 2007).…”

Section: Model Buildingmentioning

confidence: 99%

“…Conversely, spectral imaging allows the simultaneous collection of spatial and spectral information about the product (Lu and Chen 1999;Vetrekar et al 2015), which can be interpreted as a set of spectra on a two-dimensional area or a succession of images recorded at a number of specific wavelengths. If an application requires a real-time application, multispectral imaging (MSI) with several bands can be a practically viable solution.…”

Section: Introductionmentioning

confidence: 99%

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Novel non-destructive quality assessment techniques of onion bulbs: a comparative study

Islam¹,

Nielsen

Stærke

et al. 2018

J Food Sci Technol

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This study was designed to compare the performances of four different non-destructive methods of assessing onion quality, one of which was based on near-infrared spectroscopy, and three of which were based on spectral imaging. These methods involve a combination of wavelengths from visible to near-infrared with different acquisition systems that were applied to discriminate between pre-sorted onions by in situ measurements of the onion surface. Compared with the partial least squares discriminant analysis classification models associated with different methods, hyperspectral imaging (HSI) with both static horizontal and rotating orientation obtained a higher level of sensitivity and specificity with a lower classification error than did other methods. Moreover, models built with the reduced variables did not lower the model performances. Overall, these results demonstrate that HSI with selected wavelengths would be useful for further developing an improved real-time system for sorting onion bulbs.

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Section: Model Buildingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel non-destructive quality assessment techniques of onion bulbs: a comparative study

Islam¹,

Nielsen

Stærke

et al. 2018

J Food Sci Technol

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“…Moreover, challenges are manifested in the variability of the raw spectral data of M. micrantha in a complex field environment, the lack of prior knowledge and background interference. To address these challenges, hyperspectral preprocessing algorithms [such as FD, SD, nine-point (9P) smoothing, SG smoothing, and SNV], a feature selection algorithm (PCA), and classification algorithms [such as RF, SVM, back propagation neural network (BPNN)] (Vetrekar et al, 2015;Qi et al, 2017) have been proposed, in combination, to recognize M. micrantha in wild environments, and an accurate and fast method will be chosen.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth

Huang

Yang

et al. 2021

Front. Plant Sci.

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Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfills the above requirement. However, when working with hyperspectral images, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The spectral data of M. micrantha were collected using hyperspectral imaging in the spectral range of 450–998 nm. A different combination of preprocessing methods, principal component analysis (for dimension reduction), and three classifiers were used to analyze the collected hyperspectral images. The results showed that a combination of Savitzky-Golay (SG) smoothing, principal component analysis (PCA), and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements for specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing M. micrantha in the wild.

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“…Sensing techniques have been used in food-quality inspection such as fluorescence [ 7 , 8 , 9 , 10 ], X-ray imaging [ 11 , 12 , 13 ], near-infrared spectroscopy [ 14 , 15 , 16 , 17 ], electronic nose [ 18 , 19 ], electronic tongue [ 20 , 21 ], thermal imaging [ 22 , 23 ], and hyperspectral imaging [ 24 , 25 , 26 ]. Fluorescence sensing [ 7 , 8 ], near-infrared spectroscopy [ 14 , 15 ], and computed tomography imaging [ 11 ] have been used in particular for apple bitter pit detection.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples

Jarolmasjed

Khot

Sankaran

2018

Sensors

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Bitter pit is one of the most important disorders in apples. Some of the fresh market apple varieties are susceptible to bitter pit disorder. In this study, visible–near-infrared spectrometry-based reflectance spectral data (350–2500 nm) were acquired from 2014, 2015 and 2016 harvest produce after 63 days of storage at 5 °C. Selected spectral features from 2014 season were used to classify the healthy and bitter pit samples from three years. In addition, these spectral features were also validated using hyperspectral imagery data collected on 2016 harvest produce after storage in a commercial storage facility for 5 months. The hyperspectral images were captured from either sides of apples in the range of 550–1700 nm. These images were analyzed to extract additional set of spectral features that were effective in bitter pit detection. Based on these features, an automated spatial data analysis algorithm was developed to detect bitter pit points. The pit area was extracted, and logistic regression was used to define the categorizing threshold. This method was able to classify the healthy and bitter pit apples with an accuracy of 85%. Finally, hyperspectral imagery derived spectral features were re-evaluated on the visible–near-infrared reflectance data acquired with spectrometer. The pertinent partial least square regression classification accuracies were in the range of 90–100%. Overall, the study identified salient spectral features based on both hyperspectral spectrometry and imaging techniques that can be used to develop a sensing solution to sort the fruit on the packaging lines.

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Non-invasive hyperspectral imaging approach for fruit quality control application and classification: case study of apple, chikoo, guava fruits

Cited by 22 publications

References 28 publications

Novel non-destructive quality assessment techniques of onion bulbs: a comparative study

Novel non-destructive quality assessment techniques of onion bulbs: a comparative study

Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples

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