Detection of adulterations with different grains in wheat products based on the hyperspectral image technique: The specific cases of flour and bread

Verdú, Samuel; Vásquez, Francisco; Grau, Raúl; Ivorra, Eugenio; Sanchez, Alberto; Baviera, José Manuel Barat

doi:10.1016/j.foodcont.2015.11.002

Cited by 65 publications

(26 citation statements)

References 20 publications

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“…For the determination of wheat flour adulteration, the superior ability of HSI has been extensively investigated. Based on the VIS/NIR HSI technique (400 to 1000 nm), adulterants (such as sorghum flour, oat flour, and corn flour), and attenuation characters that took place during heat treatments with wheat flour, were evaluated with higher correlation significance that was achieved by applying a multivariate statistical process control (MSPC) method (Verdú and others , ). In another study, Su and Sun () revealed that NIR HSI technique (897 to 1753 nm) in tandem with multivariate analyses was capable of measuring and visualizing admixtures, including common wheat flour, cassava flour, and corn flour into organic wheat flour in the range of 3% to 75% (w/w) (Figure ).…”

Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

138

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Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

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Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

138

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“…Thus, consumers, governments and industries must invest in preventing food adulteration (Su & Sun, 2017;Verdú et al, 2016). Food adulteration is not new, and a great number of cases were reported in diverse products: fresh or preserved meat and fish, edible oils, processed fruits and vegetables, dairy products, sauces, soups, syrups and honey, spices, or even cereal grains and flours (Collins, 1993;Knödler, Most, Schieber, & Carle, 2010;Verdú et al, 2016;Ziegler et al, 2016). Several efforts have been made to assess the quality of food products by the detection of possible exogenous added ingredients.…”

Section: Introductionmentioning

confidence: 99%

Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods

2019

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Quinoa flour has been receiving an increasing attention as a substitute for wheat flour in bread formulations due to immuno-nutritional features. This growing interest in quinoa has increased the demand and consequently the prices, being a target for possible adulterations with cheaper cereals. Fourier transform Mid-infrared spectroscopy (FT-MIR) was used in the present work as a fingerprinting technique to detect the presence of three adulterants (soybean, maize and wheat flours). Partial least squares discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) models were used to classify pure from adulterated samples. 414 samples were measured, including pure quinoa flour, pure adulterant flours and adulterated quinoa flours using three different proportions (10, 5 and 1% w/w). PLS-DA showed better classification results than SIMCA, with error rates from 2 to 8% for the three strategies used to detect the presence of adulterants.

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“…With such advantages and flexibility, NIR hyperspectral imaging technique was developed to analyze various food adulteration to determine adulterants including duck or pork in minced lamb (Kamruzzaman, Sun, ElMasry, & Allen, 2013;Zheng, Li, Wei, & Peng, 2019), adulteration of shrimp with gelatine (Wu, Shi, He, Yu, & Bao, 2013), starch adulteration of fresh cheese (Barreto, Cruz-Tirado, Siche, & Quevedo, 2018), herbals in tea samples (Sandasi, Chen, Vermaak, & Viljoen, 2018), and papaya seed, buckwheat or millet in black pepper (McGoverin, September, Geladia, & Manley, 2012;Orrillo et al, 2019). Furthermore, NIR hyperspectral imaging has also been successfully performed the detection and analysis of adulteration in powdered food, involving milk powder adulterated with melamine (Fu et al, 2014), adulterated organic Avatar wheat flour with common wheat flour, cassava flour, and corn flour (Su & Sun, 2017), detection of adulterations with different grains in wheat products (Verdú et al, 2016), purity detection of organic spelt flour (Su & Sun, 2016), ergot particle quantification in cereal flour (Vermeulen, Ebeneb, Orlandoc, Pierna, & Baeten, 2017), whole wheat flour blended with walnut and peanut powders (Zhao et al, 2018), and identification of adulterated cooked millet flour (Shao, Xuan, Hu, & Gao, 2018). However, to the best of our knowledge, detection of adulterants and authenticity discrimination for raw coarse grain flours has been rarely discussed using NIR hyperspectral imaging.…”

Section: Introductionmentioning

confidence: 99%

Detection of adulterants and authenticity discrimination for coarse grain flours using NIR hyperspectral imaging

Shao

Xuan

et al. 2019

J Food Process Engineering

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Near‐infrared (NIR) hyperspectral imaging combined with multivariate analyses were used to execute adulterants detection and authenticity discrimination for coarse grain flours including millet, corn, and soybean. Spectral data in the range of 865–1,711 nm were first extracted and preprocessed from a total of 1,080 samples of six categories of pure and adulterated flours. Principal component analysis was employed to generate scores scatter plots for differentiating specific grouping of samples. Successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were adopted to select the effective wavelengths. Then identification models were developed by partial least squares discriminant analysis (PLS‐DA) using effective wavelengths and full spectra (FS) as inputs, respectively. The results indicated all of models provided the satisfactory identification rate, over 94.8% for adulterated flours, and even achieved 100% for pure flours. Considering computational cost reduction and model simplification, PLS‐DA‐CARS and PLS‐DA‐SPA models were more effective in practice within an acceptable range in comparison with PLS‐DA‐FS model. Especially, PLS‐DA‐SPA model could achieve higher identification accuracy than PLS‐DA‐CARS model. It can be concluded that NIR hyperspectral imaging technology as a noncontact and quick analytical method has promising application prospects for quality control of flour products. Practical Applications Coarse grain flours and its products are more popular with people in recent years because they contain abundant vitamins, dietary fiber, microelements, and other nutrients. Driven by huge profits, some coarse grain flours such as millet, corn, and soybean are often sold in term of adulterated flours, which not only disrupts market order, but also results in quality degradation and great economic losses. As a noncontact and quick analytical technology, NIR hyperspectral imaging was used for detecting the adulteration of coarse grain flours to eliminate fraudulent or unfair practices. Multivariate analysis methods were employed to speed the detection process and improve operation efficiency. The results indicated that NIR hyperspectral imaging technology was feasible and effective in discriminating the authenticity of coarse grain flours, laying a foundation for designing a portable inspection device with the optical filters.

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Detection of adulterations with different grains in wheat products based on the hyperspectral image technique: The specific cases of flour and bread

Cited by 65 publications

References 20 publications

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods

Detection of adulterants and authenticity discrimination for coarse grain flours using NIR hyperspectral imaging

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