Destructive and rapid non-invasive methods used to detect adulteration of dried powdered horticultural products: A review

Ndlovu, Phindile Faith; Magwaza, Lembe Samukelo; Tesfay, S.Z.; Mphahlele, Rebogile R.

doi:10.1016/j.foodres.2022.111198

Cited by 14 publications

(4 citation statements)

References 131 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the abovementioned calculation, the image could be divided into small cells with the size of m × m (m = 4,6,8,10,12). A local histogram is then created for each cell, which contains 9 bins and its values are distributed between (0, π).…”

Section: Feature Dataset Constructionmentioning

confidence: 99%

“…Compared with bulky foods, powdered foods are much easier for unscrupulous sellers to adulterate in order to reduce the raw material costs. Currently reported adulterants in chili powder mainly include Sudan I-IV dyes [4][5][6][7], brick powder [7,8], red beetroot [9,10], almond shell [8,11], dried tomato peel [11], and starch [11]. These adulterants pose significant health risks to consumers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Peng,

Ba,

Zhang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Driven by economic interests, adding adulterations in chili powder is a problem which threatens people’s health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed.

show abstract

Section: Feature Dataset Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Peng,

Ba,

Zhang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Unfortunately, many of these approaches are not yet exhaustive and above all are not linked to chemical, biochemical and molecular markers. This often limits their use in pre-screening contexts or as early-warning indicators rather than definitive diagnostic tools [ 31 ].…”

Section: The Authentication Frameworkmentioning

confidence: 99%

Convergent technologies to tackle challenges of modern food authentication

Frigerio,

Campone,

Giustra

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Conventional methods relying on the quantification of different quality traits such as dry matter content, oil content, and moisture content have also been reported in the study of quality parameters of fruits and vegetables; however, these methods were found to be undesirable, destructive, time-consuming, and labor-intensive ( Magwaza and Tesfay, 2015 ; Kyriacou and Rouphael, 2018 ). Therefore, the application of non-destructive bio-sensing methods as a promising alternative for evaluating the value of tropical produce has been adopted ( Ndlovu et al., 2022 ; Okere et al., 2022 ).…”

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.

View full text Add to dashboard Cite

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.

show abstract

Destructive and rapid non-invasive methods used to detect adulteration of dried powdered horticultural products: A review

Cited by 14 publications

References 131 publications

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Convergent technologies to tackle challenges of modern food authentication

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

Contact Info

Product

Resources

About