Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics

Wu, Qianyi; Mousa, Magdi A. A.; Al‐Qurashi, Adel D.; Ibrahim, Omer H. M.; Abo-Elyousr, Kamal A. M.; Rausch, Kent D.; Aal, Ahmed M. K. Abdel; Kamruzzaman, Mohammed

doi:10.1016/j.crfs.2023.100483

Cited by 8 publications

(2 citation statements)

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“…All acquired raw hyperspectral images were then corrected into reflectance hyperspectral images using the white and dark references [ 26 ], according to the following equation:

where I = the corrected reflectance, I raw = the original reflectance, I B = the black reference, and I W = the white reference.…”

Section: Methodsmentioning

confidence: 99%

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A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries’ Pomological Traits

Amoriello,

Ciorba,

Ruggiero

et al. 2023

Sensors

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Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400−1000 nm) and short-wave infrared (SWIR) (935−1720 nm) for predicting four strawberry quality attributes (firmness—FF, total soluble solid content—TSS, titratable acidity—TA, and dry matter—DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product’s marketability.

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“…All acquired raw hyperspectral images were then corrected into reflectance hyperspectral images using the white and dark references [ 26 ], according to the following equation:

where I = the corrected reflectance, I raw = the original reflectance, I B = the black reference, and I W = the white reference.…”

Section: Methodsmentioning

confidence: 99%

“…All acquired raw hyperspectral images were then corrected into reflectance hyperspectral images using the white and dark references [26], according to the following equation:…”

Section: Hyperspectral Image Acquisitionmentioning

confidence: 99%