The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review

Xu, Huaxi; Ren, Jie; Lin, Jidong; Mao, Shijie; Xu, Zijin; Chen, Ziwei; Jie, Zhao; Wu, Yangyang; Ning, Xu; Wang, Ping

doi:10.1007/s11694-023-01822-x

Cited by 9 publications

(7 citation statements)

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“…Preprocessing of extracted hyperspectral image data is necessary to reduce artifacts (variations that are not required in the spectral data) arising due to background noise, instrumental effects or luminescence and heterogeneity in samples (shape, size and position of sample) (Magwaza et al, 2012;Xu et al, 2023). The hypothesis is that the part of the spectral signal removed represents an interference and is generally not useful for numerical analysis.…”

Section: Explorative Analysis Using Pcamentioning

confidence: 99%

Vis-NIR and SWIR hyperspectral imaging method to detect bruises in pomegranate fruit

et al. 2023

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IntroductionFresh pomegranate fruit is susceptible to bruising, a common type of mechanical damage during harvest and at all stages of postharvest handling. Accurate and early detection of such damages in pomegranate fruit plays an important role in fruit grading. This study investigated the detection of bruises in fresh pomegranate fruit using hyperspectral imaging technique.MethodsA total of 90 sample of pomegranate fruit were divided into three groups of 30 samples, each representing purposefully induced pre-scanning bruise by dropping samples from 100 cm and 60 cm height on a metal surface. The control has no pre-scanning bruise (no drop). Two hyperspectral imaging setups were examined: visible and near infrared (400 to 1000 nm) and short wavelength infrared (1000 to 2500 nm). Region of interest (ROI) averaged reflectance spectra was implemented to reduce the image data. For all hypercubes a principal components analysis (PCA) based background removal were done prior to segmenting the region of interest (ROI) using the Evince® multi-variate analysis software 2.4.0. Then the average spectrum of the ROI of each sample was computed and transferred to the MATLAB 2022a (The MathWorks, Inc., Mass., USA) for classification. A two-layer feed-forward artificial neural network (ANN) is used for classification.Results and discussionThe accuracy of bruise severity classification ranged from 80 to 96.7%. When samples from both bruise severity (Bruise damage induced from a 100cm and 60 cm drop heights respectively) cases were merged, class recognition accuracy were 88.9% and 74.4% for the SWIR and Vis-NIR, respectively. This study implemented the method of selecting out informative bands and disregarding the redundant ones to decreases the data size and dimension. The study developed a more compact classification model by the data dimensionality reduction method. This study demonstrated the potential of using hyperspectral imaging technology in sensing and classification of bruise severity in pomegranate fruit. This work provides the foundation to build a compact and fast multispectral imaging-based device for practical farm and packhouse applications.

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Section: Explorative Analysis Using Pcamentioning

confidence: 99%

Vis-NIR and SWIR hyperspectral imaging method to detect bruises in pomegranate fruit

et al. 2023

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“…Despite the potential of the HSI technique, there are several factors that must be considered when collecting food spectral images, since they can influence the final outcome. Some of these factors are linked to the spectral mode employed, sample inhomogeneity (e.g., thickness of the powder pressing [59]), specular reflections caused by the smooth surface of certain foods [60], and the selected range of bands or wavelengths [61]. As explained previously for the NIR analysis, while beverages are homogeneous samples, food products are very complex ones.…”

Section: Specific Problemsmentioning

confidence: 99%

“…However, in several food product samples, e.g., intact fruits, surfaces present irregularities by varying in color and surface conditions/roughness, which creates incident light to scatter. Although the depth of penetration of the incident light is usually negligible [61], some absorption effects are inevitable. Therefore, the light that reaches the detector carries information about the sample's composition at different locations but also different depths within the product [63].…”

Section: Specific Problemsmentioning

confidence: 99%

“…The curved surface causes a change in the distance from the detector to the sample surface, resulting in a discrepancy in the optical path length [65]. This problem can be solved during the acquisition step by rotating the sample or acquiring a panoramic image [66] or can be corrected after the acquisition by choosing adequate spectral pretreatments [61]. The other two common causes of variation in the dataset originate from specular reflection and high temperature.…”

Section: Specific Problemsmentioning

confidence: 99%

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Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

Basile,

Mallardi,

Cardone

2023

Chemosensors

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In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023.

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“…The question revolves around the practical utilization of these approaches and the challenges associated with improving data processing speed and in-line implementation ( Cortés et al., 2019 ; Si et al., 2022 ). Quick hardware and software are required to fulfill the demands of swift analysis for extensive hyperspectral datasets ( Xu et al., 2023 ) and machine learning algorithms, especially those relying on deep learning act as black boxes rather than using interpretability models for high-stakes decisions ( Caceres-Hernandez et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

Aline,

Bhattacharya,

Faqeerzada

et al. 2023

Front. Plant Sci.

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The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

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The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review

Cited by 9 publications

References 123 publications

Vis-NIR and SWIR hyperspectral imaging method to detect bruises in pomegranate fruit

Vis-NIR and SWIR hyperspectral imaging method to detect bruises in pomegranate fruit

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

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