Essential processing methods of hyperspectral images of agricultural and food products

Jia, Baoping; Wang, Wei; Ni, Xinzhi; Lawrence, Kurt C.; Zhuang, Hong; Yoon, Seung-Chul; Gao, Zhixian

doi:10.1016/j.chemolab.2020.103936

Cited by 88 publications

(47 citation statements)

References 67 publications

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“…Four regression methods (multiple linear, principal component, partial least squares, and support vector machine) with all the samples were tested to establish the best data source and regression method for the calibration model 28 . The feature selection for the optimized calibration models involved a selection on a relevant wavelength, 39 with the following steps: Data preprocessing Outlier removal Wavelength selection Outlier removal with the first optimized model New wavelength selection Results of the second optimized model …”

Section: Methodsmentioning

confidence: 99%

Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging

2020

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BACKGROUND‘Hass’ avocado consumption is increasing due to its organoleptic properties, so it is necessary to develop new technologies to guarantee export quality. Avocado fruits do not ripen on the tree, and the visual classification of its maturity is not accurate. The most commonly used fruit maturity indicator is the percentage of dry matter (DM). The aim of this research was to investigate a non‐destructive method with hyperspectral images to predict the percentage of DM of fruits across the spectral range of 400–1000 nm.RESULTSNo correlation between fruit weight and color with the percentage of DM was found in the study area. Cross‐validation efficiency of different data sources, including the spectrum extraction zone (the center, a line from the peduncle to the base, and the whole fruit) and the average of one or two fruit faces, was compared. Four linear regression models were compared. Data of the whole fruit and average of both sides per fruit using a support vector machine regression were selected for the prediction test. Following the cross‐validation concept, five sets of calibration and test data were selected and optimized for calibration. The best test prediction set comprised an R2 = 0.9, a root‐mean‐square error of 2.6 g kg−1 DM, a Pearson correlation of 0.95, and a ratio of prediction to deviation of 3.2.CONCLUSIONSThe results of the study indicate that hyperspectral images allow classifying export fruits and making harvesting decisions. © 2020 Society of Chemical Industry

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Section: Methodsmentioning

confidence: 99%

Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging

2020

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“…HSI systems operates in four modes based on the process of image acquisition mode viz., whiskbroom, staring, pushbroom and snapshot. Of the four modes, pushbroom hyperspectral imaging system, collecting spectra of line by line, is the most used for online applications in food industry ( Jia et al., 2020 ). The snapshot technology is a non-scanning technique having no moving part and records a complete three-dimensional hypercube with each video frame.…”

Section: Hyperspectral Imaging Systemsmentioning

confidence: 99%

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

262

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Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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“…The term hyperspectral imaging (HSI) refers to the blending of two different techniques, computer vision and VIS/NIR spectroscopy. With a hyperspectral camera it is possible to acquire a full spectrum for every pixel of an image, obtaining three-dimensional data called a “hyperspectral cube” [ 103 ]. After a pre-processing phase, a mean spectrum is calculated from every cube, and these are used as input for multivariate statistical analysis to resolve regression or classification problems.…”

Section: Optical Spectroscopic Techniquesmentioning

confidence: 99%

Sensing Technology for Fish Freshness and Safety: A Review

Franceschelli

Berardinelli

Dabbou

et al. 2021

Sensors

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Standard analytical methods for fish freshness assessment are based on the measurement of chemical and physical attributes related to fish appearance, color, meat elasticity or texture, odor, and taste. These methods have plenty of disadvantages, such as being destructive, expensive, and time consuming. All these techniques require highly skilled operators. In the last decade, rapid advances in the development of novel techniques for evaluating food quality attributes have led to the development of non-invasive and non-destructive instrumental techniques, such as biosensors, e-sensors, and spectroscopic methods. The available scientific reports demonstrate that all these new techniques provide a great deal of information with only one test, making them suitable for on-line and/or at-line process control. Moreover, these techniques often require little or no sample preparation and allow sample destruction to be avoided.

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Essential processing methods of hyperspectral images of agricultural and food products

Cited by 88 publications

References 67 publications

Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging

Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Sensing Technology for Fish Freshness and Safety: A Review

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