Detection and Quantification of Peanut Traces in Wheat Flour by near Infrared Hyperspectral Imaging Spectroscopy Using Principal-Component Analysis

Mishra, Puneet; Herrero-Langreo, Ana; Elorza, Pilar Barreiro; Roger, Jean‐Michel; Diezma, Belén; Gorretta, Nathalie; Lleó, L.

doi:10.1255/jnirs.1141

Cited by 51 publications

(29 citation statements)

References 17 publications

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“…Similarly, the bulk density of wheat flour and ergot body particles in wheat flour were accurately determined and differentiated by using the HSI technique and PLSR‐based methods (Vermeulen and others ; Zhu and others ). In another study, the quantification of peanut traces in wheat flour products was conducted by applying PCA on hyperspectral images (1000 to 2200 nm) (Mishra and others ). The high R 2 P of 0.946 was obtained in quantifying peanut adulteration from 0.1% to 10% (w/w).…”

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

134

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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

134

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“…These indicated that for the two individual models, the limit of detection was 0.5%, while for the general model, the limit of detection was 1%. Mishra et al [3,32,33] studied the feasibility of the NIR HSI technique combined with principal component analysis (PCA), spectral band math, or independent component analysis (ICA) to detect peanut, hazelnut, and walnut particles (particle size of 1000-500 um) in wheat flour (particle size of 125-100 um and 212-160 um). The results of their Performance of the best PLSR models for (a) peanut-contaminated flour and (b) walnut-contaminated flour applied on prediction sets based on full spectra (an enlarged view of the green circle part was shown in the green pane).…”

Section: Selection Of Optimal Wavelengths and Multispectral Model Devmentioning

confidence: 99%

“…However, the most common methods for peanut and nut detection in food are on the basis of traditional protein detection methods, such as real-time polymerase chain reaction (RT-PCR) [1] and enzyme-linked immunosorbent assay (ELISA) [2]. Although these analytical methods are sensitive (0.1 mg/kg) [3], they are also destructive, time-consuming, require skilled operators, and even produce byproducts that are unfriendly to the environment. Thus, these laboratory-based detection techniques cannot meet the demand of the majority of food factories for online detection of nuts contamination.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

Zhao

Wang

et al. 2018

Applied Sciences

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Featured Application: Near-infrared hyperspectral imaging was employed for inspection of contaminant of peanut or walnut powders in whole wheat flour. A multispectral prediction model and visualization maps demonstrated the potential of the technique for the application of rapid quality control. Abstract:The general utilization of processing equipment in industry has increased the risk of foreign material contamination. For example, peanut and walnut contaminants in whole wheat flour, which typically a healthy food, are a threat to people who are allergic to nuts. The feasibility of utilizing near-infrared hyperspectral imaging to inspect peanut and walnut powder in whole wheat flour was evaluated herein. Hyperspectral images at wavelengths 950-1700 nm were acquired. A standard normal variate combined with the Savitzky-Golay first derivative spectral transformation was adopted for the development of a partial least squares regression (PLSR) model to predict contamination concentrations. A successive projection algorithm (SPA) and uninformative variable elimination (UVE) for feature wavelength selection were compared. Two individual prediction models for peanut or walnut-contaminated flour, and a general multispectral model for both peanut-contaminated flour and walnut-contaminated flour, were developed. The optimal general multispectral model had promising results, with a determination coefficient of prediction (R p 2 ) of 0.987, and a root mean square error of prediction (RMSEP) of 0.373%. Visualization maps based on multispectral PLSR models reflected the contamination concentration variations in a spatial manner. The results demonstrated that near-infrared hyperspectral imaging has the potential to inspect peanut and walnut powders in flour for rapid quality control.

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“…In our recent earlier work (Mishra et al, 2015), the hyperspectral images were analysed using PCA to detect the peanut traces in wheat flour (down to 0.01%). The interpretation of the resulting loadings vector was not very straightforward as each PCA loadings vector represented combinations of different phenomena described by the data.…”

Section: Score Images and Images After Feature Extractionmentioning

confidence: 99%

Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

Mishra

Cordella

Rutledge

et al. 2016

Journal of Food Engineering

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a b s t r a c tIn recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained.

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Detection and Quantification of Peanut Traces in Wheat Flour by near Infrared Hyperspectral Imaging Spectroscopy Using Principal-Component Analysis

Cited by 51 publications

References 17 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

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

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