A Review of Recent Studies Employing Hyperspectral Imaging for the Determination of Food Adulteration

Temiz, Havva Tümay

doi:10.3390/photochem1020008

Cited by 25 publications

(8 citation statements)

References 117 publications

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“…Hyperspectral imaging is a rapidly growing area of research in the food science sector, particularly for the determination of food quality and safety [ 163 , 164 , 165 , 166 ], but also for authentication purposes [ 167 , 168 ]. This technique can collect near-infrared spectra from each pixel in a photograph (creating a ‘hypercube’ dataset), allowing for analysis of the spatial variation of the analyte, in addition to its mean concentration in the sample.…”

Section: Scientific Effort (2016–2020)mentioning

confidence: 99%

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

et al. 2023

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Infrared spectroscopy (wavelengths ranging from 750–25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.

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Section: Scientific Effort (2016–2020)mentioning

confidence: 99%

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

et al. 2023

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“…The remaining had acceptable positive values of R 2 cv . However, there is no limitation or acceptable value for R 2 because this value can be improved by modifying the statistical model for the best performance ( Temiz and Ulaş, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…It extracts spectral and spatial information by absorbing, transmitting, reflecting, and scattering images of food products ( Cheng et al, 2017 ). HSI acquires information on the quality of fishery products in each pixel from different locations without affecting sample integrity ( Chen et al, 2021 ; Govari et al, 2021 ; Khoshnoudi-Nia and Moosavi-Nasab, 2019 ; Moosavi-Nasab et al, 2021 ; Temiz and Ulaş, 2021 ). However, none of the studies conducted thoroughly compared the physical, chemical, and biological characteristics of fish fillets using the HSI tool.…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Mackerel Fillets Stored under Different Conditions by Hyperspectral Imaging Analysis

Ismail¹,

Ryu²,

Yim³

et al. 2023

Food Sci Anim Resour

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This study was designed to compare the quality changes in mackerel fillets stored under different conditions by using hyperspectral imaging (HSI) techniques. Fillets packaged in vacuum were stored for six days under five different conditions: refrigerated at 4°C (R group); iced at 5±3°C (I group); kept at an ambient of 17±2°C (A group); frozen at −18°C for 24 h and thawed in a refrigerator at 4°C for 5 h on the sampling day (FTR group); FTR thawed in tap water instead of thawing in a refrigerator (FTW group). The FTR group had the lowest total bacterial count, drip loss, 2-thiobarbituric acid reactive substances, volatile basic nitrogen, and texture profile analysis values among groups during the entire storage period (p<0.05). Scanning electron microscopy revealed that the FTR group had less damage, while the other groups had shrunken muscle tissues. HSI integrated with the partial least squares model yielded reliable and efficient results, with high R 2 cv values, for several quality parameters of the mackerel fillets. Overall, the FTR group, involving freezing and thawing in a refrigerator, appears to be the most favorable option for maintaining the quality of mackerel fillets, which could be practically implemented in the industry. HSI is a suitable and effective technique for determining the quality of mackerel fillets stored under different conditions.

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“…Hyperspectral imaging (HSI) originally originated from remote sensing and was used by NASA for various applications with richer spectral information as well as spatial information than conventional optical images. It has been broadly applied in diverse fields of remote sensing data [1,2], agriculture [3,4], image enhancement [5], horticultural protection [6,7], disaster monitoring [8], food safety and assessment [9,10], and medicine [11][12][13], showing its great potential.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Medical Hyperspectral Images: A Review

Chen

Yu²,

Xu³

et al. 2022

Sensors

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With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.

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A Review of Recent Studies Employing Hyperspectral Imaging for the Determination of Food Adulteration

Cited by 25 publications

References 117 publications

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

Quality Evaluation of Mackerel Fillets Stored under Different Conditions by Hyperspectral Imaging Analysis

Deep Learning in Medical Hyperspectral Images: A Review

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