Detection of einkorn flour adulteration in flour and bread samples using Computer-Based Image Analysis and Near-Infrared Spectroscopy

Ayvaz, Hüseyin; Korkmaz, Fatma; Polat, Havva; Ayvaz, Zayde; Tuncel, Necati Barış

doi:10.1016/j.foodcont.2021.108162

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…Either to compensate for its weaker gluten structure or unfair economic profit, einkorn flour tends to be adulterated with bread wheat flour ( Hidalgo et al, 2016 ). Ayvaz et al (2021) assessed NIR spectroscopy to monitor bread wheat flour adulteration in einkorn flour and developed a PLSR calibration model for both flour mixtures. Highly accurate models yielded high R p and RPD values of 0.99 and 19.3, respectively, and low SECV and SEP values of 1.12 and 1.39%, respectively.…”

Section: Safety Analysis Of Wheat Flourmentioning

confidence: 99%

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Zhang

Liu

Shen

et al. 2022

Current Research in Food Science

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Section: Safety Analysis Of Wheat Flourmentioning

confidence: 99%

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Zhang

Liu

Shen

et al. 2022

Current Research in Food Science

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“…In another study, Raman spectroscopy in combination with PCA was used to discriminate cereal flour species; in this study, a PCA loading plot explained that changes in starch and protein signals allowed for the discrimination of the samples [41]. Due to the similar physical characteristics of the mentioned foods, they can be easily confused, and this can be used to create adulterated products with lower quality using cheap grains [42,43]. In another study, a hyperspectral image technique and multivariate statistical process control were useful in detecting adulterations of wheat flour in the presence of different grains [44].…”

Section: Introductionmentioning

confidence: 95%

Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics

Martínez-Julca,

Nazario-Naveda,

Gallozzo-Cárdenas

et al. 2023

Applied Sciences

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Nowadays, nutritional foods have a great impact on healthy diets. In particular, maca, oatmeal, broad bean, soybean, and algarrobo are widely used in different ways in the daily diets of many people due to their nutritional components. However, many of these foods share certain physical similarities with others of lower quality, making it difficult to identify them with certainty. Few studies have been conducted to find any differences using practical techniques with minimal preparation and in short durations. In this work, Principal Component Analysis (PCA) and Near Infrared Spectroscopy (NIR) were used to classify and distinguish samples based on their chemical properties. The spectral data were pretreated to further highlight the differences among the samples determined via PCA. The results indicate that the raw spectral data of all the samples had similar patterns, and their respective PCA analysis results could not be used to differentiate them. However, pretreated data differentiated the foods in separate clusters according to score plots. The main difference was a C-O band that corresponded to a vibration mode at 4644 cm−1 associated with protein content. PCA combined with spectral analysis can be used to differentiate and classify foods using small samples through the chemical properties on their surfaces. This study contributes new knowledge toward the more precise identification of foods, even if they are combined.

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Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy

et al. 2022

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The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1-90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400-2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively.

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Detection of einkorn flour adulteration in flour and bread samples using Computer-Based Image Analysis and Near-Infrared Spectroscopy

Cited by 9 publications

References 34 publications

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Classification of Peruvian Flours via NIR Spectroscopy Combined with Chemometrics

Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy

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