Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flour

Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flour

Journal of Food Engineering 200 (2017) 59e69 Contents lists available at ScienceDirect Journal of Food Engineering journal homepage: www.elsevier.co...

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Journal of Food Engineering 200 (2017) 59e69

Contents lists available at ScienceDirect

Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng

Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flour Wen-Hao Su, Da-Wen Sun*, 1 Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 December 2015 Received in revised form 24 November 2016 Accepted 16 December 2016 Available online 22 December 2016

When considering food security and huge market interest, a high-efficiency method to ensure the authenticity of the food product is necessary. For this goal, spectral imaging was explored for quantitative detection of Irish organic wheat flour (OWF) adulterated with common wheat flour (WF), cassava flour (CaF) and corn flour (CoF). Hyperspectral images (900e1700 nm) of OWF samples with a series of adulteration percentages were collected. The acquired spectra were pre-processed by second derivative (2nd Der) and standard normal variate (SNV) before modelling. Then partial least squares regression (PLSR) and principal component regression (PCR) were employed for quantitative analysis of adulteration proportion of CoF, CaF and WF in OWF. To develop more effective simplified models, three groups of feature wavelengths were selected from the loading plots of principal component analysis (PCA), and first-derivative and mean centering iteration algorithm (FMCIA). The models developed using FMCIA were better than PCA. After, the corresponding feature wavelengths were further reduced based on model regression coefficients (RC). The optimal result of admixture detection was emerged by the RCFMCIA-PLSR model, with a determination coefficient of prediction (R2P) of 0.973 and a root mean square error of prediction (RMSEP) of 0.036 for OWF adulterated with CoF, R2P of 0.986 and RMSEP of 0.026 for OWF adulterated with CaF, and R2P of 0.971 and RMSEP of 0.038 for OWF adulterated with WF. Visualization maps were generated by calculating the spectral response of each pixel on flour samples. This result indicates that spectral imaging integrated with multivariate analysis has the potential to authenticate the admixtures in specific wheat flour in the range of 3e75% (w/w). © 2016 Elsevier Ltd. All rights reserved.

Keywords: Hyperspectral imaging Adulteration Authentication Chemometric analysis Visualization

1. Introduction Food fraud is becoming the big challenge for governments, industry, and standards-setting organizations as food supply chains have become more and more global and complex (Ottavian et al., 2014; Huang et al., 2015; Jha et al., 2015; Qin et al., 2016). Food adulteration is a consequence of the addition of extraneous impurities that are not normally contained within the original food substances (Zhang et al., 2013; Zhu et al., 2011). The fraudulent addition of non-authentic substances encompasses the misrepresentation or deliberate substitution of a food product without the

* Corresponding author. E-mail address: [email protected] (D.-W. Sun). 1 http://www.ucd.ie/refrig; http://www.ucd.ie/sun. http://dx.doi.org/10.1016/j.jfoodeng.2016.12.014 0260-8774/© 2016 Elsevier Ltd. All rights reserved.

purchaser's knowledge for economic gain (Spink, 2012). Thus economically motivated adulteration is a root cause of public health food risks that leads consumers into giving maximum attention to food adulteration. Abiding by green manure, crop rotation, and biological pest control laid down in organic standards of European Union (EU) law, organic foods are considered much more environmentally friendly and popular. During the past decade, the demand for organically produced food has increased significantly (David et al., 2012). Because of the motivation for economic gain, organic food adulteration has become an imperative problem. The authentication of organic staple food has been currently explored by some researchers. Based on soft independent modelling of class analogy (SIMCA) and K-nearest neighbours (KNN), Borges et al. (2015) verified organic rice adulteration by determining 20 chemical elements using inductively coupled plasma mass spectrometry

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(ICPMS). The discrimination between organic and conventional wheat has also been realized by measuring d15N and d13C of amino acids via gas chromatographyecombustioneisotope ratio mass spectrometry (GC-C-IRMS) (Paolini et al., 2015). However, these time-consuming and destructive approaches are not suitable for fast classification and detection in the food industry. The rapid detection of unknown common wheat flour (WF) mixed in organic wheat flour (OWF) should be carried out. In addition, OWF is nearly same in color to cassava flour (CaF), and similar to corn flour (CoF) as well, which makes them more difficult to be identified after adulterating. With the increasing requirement on the subtle adulterations in food, it is necessary to explore a rapid, accurate, and non-destructive method for quantitative determination of contaminants in OWF. During the past decades, some rapid and non-destructive screening techniques have been developed to evaluate food quality (Alexandrakis et al., 2012; Liu et al., 2014b; Magwaza et al., 2012; Zhu et al., 2013; Soukoulis et al., 2013; Cubero et al., 2011; Zhu et al., 2016). Among them, the machine or computer vision technique is a widely-used measurement tool to acquire and analyse the surface two-dimensional information (Sanz, 2012; Sonka et al., 2014; Wu and Sun, 2013c; Jackman et al., 2009; Du and Sun, 2005). The surface related information can be represented by an image for quality detection, classification and grading of agricultural products (ElMasry et al., 2012a; Patel et al., 2012). Although external aspects including shape, color and defects can be easily evaluated by machine vision, quality parameters related to chemical compositions are difficult to be determined with only imaging techniques (Patel et al., 2012). Recently, spectroscopy techniques such as visible/ infrared spectroscopy (Sankaran and Ehsani, 2013), Raman spectroscopy (Boyacı et al., 2014; Zheng and He, 2014; Lee and Herrman, 2016) and nuclear magnetic resonance spectroscopy (Ohtsuki et al., 2012; Wu et al., 2014; Botosoa et al., 2015) have received much research attention for raw material chemical analysis, discrimination and process monitoring, but these spectroscopic techniques cannot provide visual images of whole samples. To overcome these problems, spectral imaging developed by uniting both imaging and spectroscopic techniques can simultaneously provide spatial and spectral information of an object, which means this technique can provide both physical and chemical characteristics of an object at the same time (Su et al., 2015; Sun, 2010; Dissing et al., 2013; Xie et al., 2016; Ravikanth et al., 2016). The hyperspectral image can be decomposed into a series of two-dimensional images corresponding to numbers of specific wavelengths that reflect chemical characteristics of the object (He and Sun, 2015; Iqbal et al., 2014; Kamruzzaman et al., 2012, 2015; Zhang et al., 2016; Pan et al., 2016). Besides food products (Barbin et al., 2012b; Elmasry et al., 2012b; Feng and Sun, 2012; Wu and Sun, 2013b; Feng and Sun, 2013; Feng et al., 2013; ElMasry et al., 2013; Barbin et al., 2013), this technique has been employed in a broad range of fields such as agricultural (e.g. cereals, fruits) (Liu et al., 2013a; Mahesh et al., 2015), environmental (e.g. powder flow) (Scheibelhofer et al., 2012), pharmaceutical (Brondi et al., 2014; Muench, 2014), microbial (Gowen et al., 2015; Leroux et al., 2015), medical (e.g. disease diagnosis, image-guided surgery) (Lu and Fei, 2014), geological (e.g. regional mapping, structural interpretation) (Kurz et al., 2013; Van der Meer et al., 2012). As for fast and non-destructive analysis of staple food products, hyperspectral imaging has been implemented for evaluation of protein, starch, and amylose in rice (Liu et al., 2014a), sugar content in potatoes (Rady et al., 2015), variety classification and fungal growth in rice grains (Siripatrawan and Makino, 2015; Wang et al., 2015), Fusarium head blight in wheat kernels (Barbedo et al., 2015), aflatoxin B1 on corn cereals (Kandpal et al., 2015), classification of black beans (Jun et al., 2015), optimal cooking time of boiled

potatoes (Do Trong et al., 2011), and peanut traces in wheat flour (Mishra et al., 2015). However, to our knowledge, no research has yet been published for quantitative analysis of contaminants such as common WF, CaF and CoF in OWF based on spectral imaging. The overall objective of this research is focused on investigating the potential of hyperspectral imaging (900e1700 nm) for rapid quantitative visualization of OWF contamination. Therefore, this study was mainly performed by (1) extracting hyperspectral image data of all samples, (2) establishing partial least squares regression (PLSR) and principal component regression (PCR) models in full wavelength region, (3) selecting characteristic wavelengths that are highly linked to the intrinsic attribute of these samples, (4) identifying optimal models based on characteristic wavelengths, and (5) developing image processing algorithms to depict visual prediction results. Particularly, a new wavelength selection method for modelling was put forward and verified. 2. Materials and methods 2.1. Sample preparation and spectral image acquisition In this study, the fine and powdery OWF (variety: Avatar, origin: Ireland) samples were certified by the Organic Trust in Ireland (IE-ORG-03, EU/non-EU Agriculture), whose chemical composition was: 75.3% of carbohydrate, 10.1% of proteins, 1.4% of fat, and 3.1% of fibre. Other flour samples including common WF, CoF and CaF were produced in conventional system without following organic standards, and their compositions were 76.3%, 92.0% and 93.0% of carbohydrate, 10.3%, 0.6% and 0.8% of proteins, 0.98%, 0.7% and 0.3% of fat, and 2.7%, 0.1% and 4.7% of fibre, respectively. All these samples were collected and transported to laboratories of Food Refrigeration & Computerized Food Technology (FRCFT), University College Dublin (UCD), Ireland. Then, OWF samples were adulterated with CaF, CoF and WF in the range of 3e75% (w/w), at approximately 3% increments. Specifically, the CaF, CoF and WF were individually weighed together with OWF, thoroughly mixed and homogenized to get a total sample weight of 35 g each time at every adulterant level. Then, 150 samples (6 samples per adulterant level  25 levels) were obtained for each adulteration type. Thereinto 90 samples (4 samples per adulterant level  25 levels) were randomly selected as the calibration set and the remaining 60 samples (2 samples per adulterant level  25 levels) were selected as the prediction set. The samples were separately placed in circular transparent plastic jars one by one and imaged using the hyperspectral imaging system in the spectral range of 900e1700 nm mentioned by ElMasry et al. (2011a,b). This laboratory-based pushbroom hyperspectral imaging system scans the sample line by line, and mainly consists of a computer with a control software (SpectralCube, Spectral Imaging Ltd., Finland), a CCD camera (Xeva 992, Xenics Infrared Solutions, Belgium), a spectrograph (ImSpector N17E, Spectral Imaging Ltd., Oulu, Finland), two 500 W tungstenehalogen illuminating lamps (V-light, Lowel Light Inc., USA), and a stepper motor (GPL-DZTSA1000-X, Zolix Instrument Co., China). The reflected light from the sample is captured by the hyperspectral imaging system in spatialspectral axes, and the spectral increment between the contiguous bands is about 3.34 nm in the spectral range of 897e1753 nm yielding 256 bands. Based on this system, the images of 690 samples (60  4 for pure samples, 150  3 for adulterated samples) were collected. The spectral data corresponding to each sample was three-dimensional including both spatial and spectral information, with 256 bands (897e1753 nm) in l-direction, 320 pixels (0.578 mm/pixel) in x-direction and n-pixels (0.578 mm/ pixel) in y-direction (determined by the sample length). The different levels of flour adulteration that cannot be recognized by

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naked eyes, could be spatial visualization relying on spectral imaging technique. 2.2. Extraction of the region of interest As lots of noises were observed in regions of 897e957 nm and 1665e1753 nm in the corrected images, the remaining region from 957 to 1665 nm (212 bands) can be further used. To minimize the disturbance of instrument geometry and the dark current from camera, a white (~99% reflectance) and a dark (0% reflectance) reference images were recorded. The raw reflectance images were then calibrated into relative reflectance images based on the white and dark reference images (Firtha, 2006; ElMasry et al., 2007). The corrected images were utilized for selecting the regions of interest (ROI). A binary mask image was constructed by subtracting a lowreflectance band image (e.g. 1443 nm) from a high-reflectance band image (e.g. 1204 nm) within the same corrected hyperspectral image. After removing the background and the shadow from the corrected hyperspectral image, morphological operations such as erosion or dilation were performed on the resultant binary mask to remove the isolated parts originating from the edges of plastic jars. This resulted in a final mask containing only ROI. The mean reflectance values of all the pixels in the ROI can be extracted based on the spectral signals. The spectral data of each ROI were averaged to one spectrum to represent each sample. The same protocol was repeated for all hyperspectral images of the tested samples. 2.3. Spectral pre-treatment After acquiring the average spectral data of ROI, it was necessary to enhance the signal-to-noise ratio for a better and robust model. Prior to modelling, spectral data of samples were pre-processed by second derivative (2nd Der) (Savitsky Golay smoothing, 7 points window, 2 order polynomial) and standard normal variate (SNV). The 2nd Der approach is put forward for calculating the derivative by smoothing the spectra and taking twice the smoothed value at corresponding wavelengths (Tsai and Philpot, 1998). SNV pretreatment demands the computation of the mean and standard deviation (StdDev) of spectral values. The format of corrected spectrum is generated by this equation:

xcs

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ,v u n . uX ¼ ðx  xav Þ t ðx  xav Þ2 ðn  1Þ

(1)

1

where xcs is corrected spectral data, x is original spectrum, xav is the average of the n spectral values in the full wavelength range. 2.4. Model development and evaluation In this study, the calibration and prediction models of quantitative analysis of OWF adulteration were explored by PCR and PLSR. These models were developed by utilizing the spectra in the spectral data matrix (X) to predict proportions of adulterant in OWF in column vector (Y). The performance of these models was evaluated by determination coefficient and root mean square error of calibration (R2C, RMSEC), cross-validation (venetian blinds) (R2CV, RMSECV) and prediction (R2P, RMSEP). The relevant sources of data variability were modelled by the latent variables (LVs). The number of LVs was chosen according to the criterion of the lowest prediction error in cross-validation (venetian blinds) and the evaluation of the explained variance in the X and Y blocks (Mazivila et al., 2015). The optimal model should have higher R2C, R2CV and R2P, and the lower classification error, RMSEC, RMSECV and RMSEP.

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Generally, it is always expected to acquire RMSEs much closer to 0 and R2 more approach to 1, but a R2 of over 0.90 shows excellent performance and less than 0.82 means poor performance (Williams, 2001). 2.5. Feature wavelength selection On the other hand, the spectra containing large information volumes are not suitable for on-line detecting. Wavelength selection aims to choose several optimal bands to represent the original hyperspectral data. In this study, the optimal wavelengths for quantitative analysis of OWF adulteration were selected on basis of principal components analysis (PCA). The loadings resulting from PCA are regarded as an indication of characteristic wavelengths that does not suffer from redundancy (Su and Sun, 2016a). The wavelengths corresponding to the peak and valley of the loadings plot showed high differences in reflectance and were considered to have a great contribution to the loadings of PC. In this study, the combination of characteristic wavelengths selected by PCA method was contrasted to a new approach termed the first derivative and mean centering iteration algorithm (FMCIA) that concatenates first derivative (1st Der) and MC for spectral data treatment with standardized spectra generated by its StdDev (Su and Sun, 2016b; Su and Sun, 2016c). As a measure to quantify the extent of variation of spectral data values, the StdDev was calculated at each wavelength of all the processed spectra afterwards. Since all variables are adjusted to the same scale, the resulting StdDev coefficients show the relative importance of the spectra. The loadings plot of FMCIA are regarded as an indication of the optimized wavelengths. The feature wavelength is selected in the spectral region where there is a high difference in StdDev coefficients. Then, the selected feature wavelengths can be further reduced by regression coefficients (RC) of PLSR model (Su and Sun, 2016d). The purpose of model facilitation is to establish a very effective and simplified model with fewer characteristic wavelengths for identification and visualization of adulteration percentage. 2.6. Spectral image analysis By contrast with other spectroscopy techniques, the superiority of spectral imaging exists in transferring multivariate analysis models to each pixel of the image, generating a visualization map with adulteration proportions. Primarily, a three-dimensional spectral image at characteristic wavelengths was converted into a two-dimensional matrix. This matrix was then multiplied by regression coefficients of the optimal model. After, the resulting matrix was refolded to form a prediction map, where the levels of adulteration within all spots were exhibited and visualized by colors. All the multivariate data and image analysis were executed by house written scripts within Matlab 7.12 software (The Mathworks Inc., Natick, MA, USA). A flowchart that contains the main steps of quantitative analysis of OWF adulteration using spectral imaging is shown in Fig. 1. 3. Results and discussion 3.1. Spectral characteristics of flour samples The plotted mean spectra of each flour category and proportions of OWF adulteration from 3% to 75% were clearly displayed in Fig. 2. As is apparent from Fig. 2a, the spectral curves of different flour varieties present similar trend, peak and trough, which indicates their similarity in chemical compositions (e.g. protein, carbohydrate, fat) and characteristic wavelengths. Specifically, it was noticed that three absorption peaks were present in around

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evaluating the admixtures (CoF, CaF and WF) in OWF. The property of calibrated PCR and PLSR models was validated based on venetian blinds cross-validation followed by independent external validation. It was noticed that both PCR and PLSR possessed similar performance. For inspecting CoF adulterant, the spectra processed by 2nd Der showed better effect in PLSR model (R2P ¼ 0.986, RMSEP ¼ 0.026). With regard to the CaF admixture in OWF, cross validation results of both PCR and PLSR models pre-processed by SNV were better than that obtained using 2nd Der and raw spectra. The similarity in model performance implied the effectiveness of SNV. When the developed PCR and PLSR models were applied to the independent validation set, the levels of CaF contaminant in OWF were predicted with highest R2P of 0.988, RMSEP of 0.089, and R2P of 0.980, RMSEP of 0.143 based on SNV, respectively. However, very high values of RMSEP reduced the accuracy of both models for prediction with the P-value of 0.049 from ANOVA. Although the lowest RMSEP values were acquired by PCR and PLSR models developed using 2nd Der, the highest RMSEC and RMSECV values observed revealed that these models cannot work precisely for the development of calibration model. With only four LVs, the PCR model established using the raw spectra was survived with R2C of 0.978, RMSEC of 0.032, R2CV of 0.976, RMSECV of 0.034, and R2P of 0.976, RMSEP of 0.071 as well as the P-value of 0.045 from ANOVA. The results indicated that none of the spectral pre-processing approaches provided the advancement of model performance, which was in accord with the similar researches before (Barbin et al., 2012a; Kamruzzaman et al., 2013a,b). Compared to PCR, the better result for detection of WF adulterant was yielded by PLSR using SNV, with R2P of 0.976, RMSEP of 0.035. Overall, PLSR and SNV showed slightly stable and better performance than PCR and 2nd Der. Therefore, the SNV-PLSR model was more appropriate for detecting OWF adulteration. Fig. 1. Flow chart of the experimental design for quantitative analysis of organic wheat flour (OWF) adulteration using spectral imaging.

3.3. Selection of optimum wavelengths 980 nm, 1200 nm and 1450 nm that were respectively related to OeH stretching second overtone, CeH stretching second overtone and OeH stretching first overtones (Wu and Sun, 2013a). Compared to the absorption peak at 980 nm, the variation of spectral fingerprints of different flours at 1450 nm (OeH stretching first overtone) was more significant, followed by the absorption peak at 1200 nm. Besides, the observed spectral features of OWF were markedly different from other three. These differences in reflectance values were primarily caused by genetic and environmental factors as well as their origin sites. However, spectral characteristics of OWF were much more similar with WF. This was mainly due to their same cultivar and geographical origin. In Fig. 2(bed), we can observe that spectral differences in adulteration proportions (3%e75%) exist among various admixtures (CoF, CaF and WF) of OWF. Different colors of spectral curves from the bottom up in Fig. 2(bed) represent adulteration proportion from 3% to 75%. In addition, it is quite evident that the spectra in Fig. 2d gathered in a narrow space compared to the spectra in Fig. 2b, c. This phenomenon was possibly associated with the similar intrinsic attribute between OWF and WF. 3.2. Modelling in full spectral range It is considered that the scattering effect and random noise can be attenuated by appropriate pre-treatments. Table 1 presented main statistical parameters of PCR and PLSR models established using raw spectra in the spectral range of 957e1665 nm (212 variables) and pre-processing approaches (SNV and 2nd Der) for

In order to develop a multispectral imaging system to improve processing speed, an important precursor is to choose feature variables from the full wavelength range (212 variables). The weighted coefficients resulting from PCA were used for selection of characteristic wavelengths (Rodríguez-Pulido et al., 2013; ElMasry et al., 2011a,b). The variables corresponding to the peak and trough of regression coefficient presented higher differences in reflectance and played an important role in established models. By means of this approach, five feature wavelengths (1088, 1188, 1262, 1423 and 1658 nm) and seven feature wavelengths (987, 1094, 1205, 1285, 1423, 1625 and 1658 nm) were respectively identified from the loading plots of PC1 and PC2 as shown in Fig. 3. The differences of reflectance values were related to the spectral absorption features such as overtones and combinations of fundamental vibrations. Furthermore, another combination of feature wavelengths reflecting the spectral characteristics of all flours was chosen out via FMCIA. As described in Fig. 4a, all the flour spectra were first processed by 1st Der (7 points window, 1 order polynomial) combined with MC. Based on this procedure, the loading plot of StdDev coefficient resulting from FMCIA was used for choosing the most significant wavelengths. The variables corresponding to the peak and trough of coefficient presented higher differences and would play an important role in simplified models for developing multispectral imaging systems. As shown in Fig. 4b, eight wavelengths (1141, 1349, 1362, 1396, 1426, 1443, 1645 and 1658 nm) were selected as feature bands in the spectral range of 957e1665 nm (212 bands) based on this approach.

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Fig. 2. (a) Average spectra of different pure flours, (b) raw spectra of organic wheat flour (OWF) adulterated with corn flour (CoF), (c) raw spectra of OWF adulterated with cassava flour (CaF), and (d) raw spectra of OWF adulterated with wheat flour (WF) from adulteration proportion of 3%e75%. The different colors of spectral curves from the bottom up in (b, c, and d) represent adulteration proportion from 3% to 75%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

shown in Table 2. The obtained parameters of R2 and RMSE demonstrated that all the simplified models had reasonable ability to predict OWF contamination. It was found that the accuracy of PLSR model developed using SNV was equivalent to the simplified models without pre-treatment, especially for FMCIA-PLSR and PC2-

3.4. Facilitated PLSR model for measuring contamination degree of OWF The selected feature wavelengths were adopted to build optimized PLSR models to assess the contamination degree of OWF as

Table 1 Quantitative analysis of contaminants in organic wheat flour (OWF) based on PCR and PLSR in the full spectral range. Contaminant

Model

Pre-processing

No. LV

Calibration 2

R CoF

PCR

PLSR

CaF

PCR

PLSR

WF

PCR

PLSR

None SNV 2nd Der None SNV 2nd Der None SNV 2nd Der None SNV 2nd Der None SNV 2nd Der None SNV 2nd Der

6 7 6 5 6 5 4 5 4 5 5 3 6 5 5 5 4 7

C

0.971 0.977 0.977 0.971 0.988 0.989 0.978 0.993 0.949 0.993 0.995 0.952 0.971 0.973 0.949 0.969 0.973 0.988

Cross-validation

Prediction

RMSEC

R2CV

RMSECV

R2p

RMSEP

0.037 0.033 0.033 0.037 0.024 0.022 0.032 0.019 0.049 0.017 0.015 0.047 0.037 0.035 0.049 0.038 0.035 0.024

0.968 0.974 0.974 0.967 0.985 0.984 0.976 0.992 0.947 0.992 0.995 0.94 0.968 0.973 0.946 0.967 0.971 0.96

0.039 0.035 0.035 0.039 0.027 0.027 0.034 0.02 0.05 0.019 0.016 0.053 0.039 0.036 0.05 0.04 0.037 0.043

0.968 0.975 0.977 0.972 0.985 0.986 0.976 0.988 0.951 0.977 0.98 0.957 0.968 0.975 0.948 0.963 0.976 0.972

0.039 0.036 0.034 0.037 0.026 0.026 0.071 0.089 0.053 0.144 0.143 0.051 0.039 0.036 0.05 0.044 0.035 0.037

CoF: Corn flour, CaF: Cassava flour, WF: Wheat flour, 2nd Der: Second derivative, MSC: Multiplicative scatter correction, SNV: Standard normal variate, LV: Latent variable, R 2C: Coefficient of determination in calibration, RMSEC: Root mean square error of calibration, R 2CV: Coefficient of determination in cross-validation, RMSECV: Root mean square error of cross-validation, R 2P: Coefficient of determination in prediction, RMSEP: Root mean square error of prediction.

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Fig. 3. Loading plots for (a) PC1 and (b) PC2 in the near-infrared region.

PLSR models. However, after pre-processing with SNV, the performance of PC2-SNV-PLSR model was improved a little obvious. Besides, PC1-SNV-PLSR model also showed better accuracy for measuring WF adulterated in OWF. Based on the wavelength selection method of PCA, the optimal results for detecting all the contaminants (CoF, CaF, and WF) were collected and presented in Fig. 5(a, b, c), respectively. For feature wavelengths selected using FMCIA, their predictive abilities in PLSR models were much better without regard to spectral pre-processing, achieving high R2P of 0.975, 0.985 and 0.971 as well as low RMSEP of 0.035, 0.027 and 0.038 for assessing the CoF, CaF, and WF adulteration. The prediction results of admixtures in OWF acquired by optimal FMCIA-PLSR models were displayed in Fig. 6. These simplified models have the capacity to be used for designing more simple multispectral sensors for real-time implementation. 3.5. Further optimization of PLSR model for prediction of contaminants For PLSR analysis, the standardized RC has a great influence on the contribution of independent variables to the prediction of dependent variables. Even though FMCIA in this study was considered as the better wavelength selection method, the obtained feature wavelengths did not maintain the same significance for modelling. It is essential to further evaluate and eliminate independent variables with less significant RC. Such variables with smaller absolute values of coefficient can be identified from loading plots of RC in PLSR model. The FMCIA-PLSR coefficients in Fig. 7 respectively represent the eight feature wavelengths (1141, 1349,

1362, 1396, 1426, 1443, 1645 and 1658 nm) for prediction of contaminants including CaF, CoF, and WF in OWF. In this study, these eight feature wavelengths with absolute values of RC less than 18 were regarded as secondary feature wavelengths that should be removed. As a result, the most important feature wavelengths of four (1349, 1396, 1645 and 1658 nm), five (1396, 1426, 1443, 1645 and 1658 nm) and six (1141, 1396, 1426, 1443, 1645 and 1658 nm) were used to develop the simplest PLSR models to detect the CaF, CoF and WF contaminants, respectively. Accordingly, equations (4)e(6) were derived from the new selected feature wavelengths of four, five and six in line with the new coefficients acquired in the RC-FMCIA-PLSR model instead of utilizing original coefficient values from FMCIA-PLSR model developed using eight wavelengths. Specifically, different contaminant levels of CaF was detected based on the regression equation as follow:

YCaF ¼ 0:17  16:43X1349 nm þ 24:91X1396 nm  25:63X1645 nm þ 18:89X1658 nm (2) Likewise, a formula obtained using the coefficients of RCFMCIA-PLSR model to predict CoF can be expressed as follow:

YCoF ¼ 1:88 þ 7:26X1396 nm  56:21X1426 nm þ 61:93X1443 nm  48:61X1645 nm þ 36:62X1658 nm (3) In addition, another formula was shown below to determine WF using the RC-FMCIA-PLSR model:

Fig. 4. (a) Spectra processed by 1st Der and mean centring (MC), and (b) selection of sensitive wavelengths based on FMCIA.

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Table 2 Model performance of PLSR based on characteristic wavelengths for quantitative analysis of contaminants in OWF. Band selection method

FMCIA

Feature wavelengths (nm)

1141, 1349, 1362, 1396, 1426, 1443, 1645, 1658

Pre-processing

None

SNV

PC1

1088, 1188, 1262, 1423,1658

None

SNV

PC2

987, 1094, 1205, 1285, 1423, 1625, 1658

None

SNV

No. LV

8 8 8 7 7 7 3 5 4 4 4 4 7 6 6 6 5 5

Contaminant

CoF CaF WF CoF CaF WF CoF CaF WF CoF CaF WF CoF CaF WF CoF CaF WF

Calibration

Cross-validation

Prediction

R2C

RMSEC

R2CV

RMSECV

R2p

RMSEP

0.964 0.986 0.970 0.969 0.986 0.959 0.886 0.970 0.909 0.915 0.972 0.922 0.962 0.985 0.907 0.967 0.983 0.914

0.041 0.026 0.038 0.038 0.025 0.044 0.073 0.037 0.065 0.063 0.036 0.060 0.042 0.027 0.066 0.039 0.028 0.064

0.958 0.984 0.964 0.965 0.984 0.956 0.881 0.968 0.901 0.911 0.970 0.920 0.957 0.983 0.896 0.962 0.981 0.908

0.044 0.028 0.041 0.040 0.027 0.045 0.075 0.039 0.068 0.064 0.037 0.061 0.045 0.028 0.070 0.042 0.030 0.066

0.975 0.985 0.971 0.977 0.985 0.962 0.908 0.976 0.913 0.922 0.974 0.923 0.968 0.981 0.898 0.973 0.983 0.914

0.035 0.027 0.038 0.034 0.227 0.042 0.067 0.036 0.067 0.061 0.036 0.062 0.044 0.263 0.073 0.042 0.299 0.068

CoF: Corn flour, CaF: Cassava flour, WF: Wheat flour, SNV: Standard normal variate, LV: Latent variable, FMCIA: first derivative and mean centering iteration algorithm, PC: Principal component, R 2C: Coefficient of determination in calibration, RMSEC: Root mean square error of calibration, R 2CV: Coefficient of determination in cross-validation, RMSECV: Root mean square error of cross-validation, R 2P: Coefficient of determination in prediction, RMSEP: Root mean square error of prediction.

YWF ¼ 1:85 þ 14:08X1141 nm  20:64X1396 nm  42:28X1426 nm þ 61:49X1443 nm  36:45X1645 nm þ 29:92X1658 nm (4) Based on these three groups of most important feature wavelengths, the performance of RC-FMCIA-PLSR models for quantitative detection of CaF, CoF and WF in OWF was summarized in Table 3. The higher R2P of 0.986, 0.973 and 0.971 with lower RMSEP of 0.026, 0.036 and 0.038 were severally achieved by the optimized RC-FMCIA-PLSR model to evaluate CaF, CoF and WF adulteration.

Compared with original full-wavelength models as well as FMCIAPLSR models, parallel or higher accuracies were acquired by RCFMCIA-PLSR models using fewer central wavelengths. Specifically, the performance of model developed using five wavelengths was very near to the model using eight wavelengths for assessing CoF adulteration. In contrast, the better accuracy for detecting OWF adulterated with CaF was collected by RC-FMCIA-PLSR using only four feature wavelengths (1349, 1396, 1645 and 1658 nm), rather than the FMCIA-PLSR model established using eight wavelengths (1141, 1349, 1362, 1396, 1426, 1443, 1645 and 1658 nm). This result demonstrated that the rest of four wavelengths (1141, 1362, 1426

Fig. 5. Optimal prediction results of adulterants using wavelength selection method of PCA based on (a, b) PC2-SNV-PLSR models and (c) PC1-SNV-PLSR model.

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Fig. 6. Optimal prediction results of adulterants using wavelength selection method of FMCIA based on FMCIA-PLSR models.

and 1443 nm) was useless for CaF detection. However, the threeeighth wavelengths (1141, 1426 and 1443 nm) played important roles in the measurement of WF adulteration. However, by utilizing

these six feature wavelengths (1141, 1396, 1426, 1443, 1645, 1658 nm), the accuracy of detecting WF adulteration was unexpectedly lower than CoF and CaF adulteration, which meant it was more difficult to distinguish the WF in OWF. One of the main reasons is probably that variety differences between OWF and CaF as well as OWF and CoF are larger than that of OWF and WF. Moreover, it was noticed that differences among R2C, R2CV, and R2P were very small in the RC-FMCIA-PLSR model, which could indicate the robustness of this final model. Therefore, it was feasible to assume that feature wavelengths extracted using RC-FMCIA can be used as the most representative wavelengths to develop multispectral systems for on-line applications. 3.6. Visual detection

Fig. 7. FMCIA-PLSR coefficients derived from eight feature wavelengths (1141, 1349, 1362, 1396, 1426, 1443, 1645 and 1658 nm) for prediction of contaminants in terms of (a) CaF, (b) CoF, and (c) WF. The bands from 1 to 8 represent corresponding wavelengths from 1141 to 1658 nm.

The adulterants in OWF were not only detected based on the developed models using spectral data but also can be visualized in the spatial dimension from spectral images. This technique has an obvious advantage to identify both the gradients and spatial distributions of specific samples by spatial visualization in each pixel of the image based on their spectral characteristics. The simplified RC-FMCIA-PLSR model was transferred in each pixel to compute the dot product between the optimal regression coefficients and the spectral values of all pixels in the image for spatial visualization. Fig. 8 presents the resulting false-color images of some samples (the proportion of WF adulterant in OWF in this case). These distribution maps were generated by predicting variations in the proportion of adulteration along with a linear color scale from low (blue) to high (red). The spectral differences of pixels indicated the concentration of admixture in OWF. This kind of visual identification cannot be accomplished using naked eyes but would be realized by multispectral imaging. It was distinct to discriminate the

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Table 3 Model performance of PLSR using fewest wavelengths for quantitative analysis of contaminants in OWF. Contaminant

CaF CoF WF

Band selection method

RC-FMCIA RC-FMCIA RC-FMCIA

Feature wavelengths (nm)

1349, 1396, 1645, 1658 1396, 1426, 1443, 1645, 1658 1141, 1396, 1426, 1443, 1645, 1658

No. LV

4 5 6

Calibration

Cross-validation

Prediction

R2C

RMSEC

R2CV

RMSECV

R2p

RMSEP

0.985 0.961 0.970

0.027 0.043 0.038

0.983 0.958 0.966

0.028 0.044 0.040

0.986 0.973 0.971

0.026 0.036 0.038

CaF: Cassava flour, CoF: Corn flour, WF: Wheat flour, LV: Latent variable, RC: Regression coefficient, FMCIA: first-derivative and mean centering iteration algorithm, R 2C: Coefficient of determination in calibration, RMSEC: Root mean square error of calibration, R 2CV: Coefficient of determination in cross-validation, RMSECV: Root mean square error of cross-validation, R 2P: Coefficient of determination in prediction, RMSEP: Root mean square error of prediction.

Fig. 8. Prediction maps of WF adulterated in OWF based on optimal PLSR model.

adulteration proportion varying from sample to sample and even within the same sample. Accordingly, the results were very graphic and optimistic to present the potential of the spectral imaging technique for rapid and non-destructive prediction of adulterants in OWF.

3.7. Discussion The categories of different flours and proportions of adulterants (CoF, CaF and WF) in Avatar wheat flour were accurately evaluated based on the spectral imaging technique using selected feature wavelengths. In this research, the specific samples of Avatar wheat flour (origin: Ireland) that were certified as organic flour by the Organic Trust in Ireland (IE-ORG-03, EU/non-EU Agriculture) were adulterated with three other cheaper flours. Whether the classifier was really influenced by specific spectral features related to organic farming remains unknown because any two samples from different producers would differ spectrally for a number of various reasons (flour processing, handling, storage, soil type, weather, etc). Therefore, this study did not involve the research of distinguishing between flour originated from farms practicing organic farming and traditional farming. It was also noticed that very high accuracies (R2p ¼ 0.971e0.986) for detecting impurities (3e75%) in OWF were achieved by the optimal PLSR model. The prediction results found in this study were similar to those mentioned by Liu et al. (2013a,b) for detecting lotus root powder adulterated with potato and sweet potato starch (R2P ¼ 0.959e0.990). Rady et al. (2015) reported that appropriate pre-treatment and modelling approaches can improve the accuracy of the result, which indicated that the proposed methods in our research was acceptable based on the comparison of two spectral pre-processing methods (SNV and 2nd Der) and two modelling methods (PCR and PLSR). Besides, big variations of genic or chemical compositions among these flours made the effective detection of adulterants possible. It was found that the R2 decreased dramatically when the adulteration

proportion was lower than 3% (Liu et al., 2013a,b). This meant that the higher was the proportion of adulterant the better was the detection result. As the proportions of impurities in OWF were from 3% to 75%, this provided a reasonable probability of the higher accuracy obtained in our study. Moreover, although there were three categories of impurities in OWF, these impurities were respectively and separately mixed with OWF and each impurity only contained one flour variety. The single source of adulterants may also give rise to higher accuracy rates. Additionally, a number of studies have highlighted the higher prediction results of spectroscopic techniques coupled with multivariate analysis in detection of food adulteration. For example, based on Fourier transform infrared (FTIR) and PLSR model, the highest R2p of 0.99 was acquired to determine the starch adulteration (1e35 wt % starch) in onion powder (Lohumi et al., 2014). Better evaluation results of food adulteration were not only achieved by detecting powdery foods but also presented in the determination of foreign matters in minced meats. Specifically, the highest R2p of 0.97, 0.99 and 0.99 were respectively obtained by the PLSR model to detect adulterants including minced chicken, pork and horsemeat in fresh minced beef or lamb based on hyperspectral imaging (Kamruzzaman et al., 2016, 2015, 2013). In their studies, minced beef or lamb samples were adulterated with these adulterants in the range 0e40% or 50% (w/w) at approximately 2% intervals. Therefore, the higher detection accuracy of flour adulteration in this study is perfectly logical and reasonable. 4. Conclusions With globalisation and rapid distribution systems, food adulteration incidents can have international repercussions with farreaching consequences. Thus, there is a growing requirement for the development of fast, low-cost and effective analytical approaches to test for adulteration. This study has emphasized the potential of hyperspectral imaging in tandem with multivariate analyses for real-time detection of admixtures (CoF, CaF and WF) in

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specific OWF (variety: Avatar) grown in Ireland. It was found that the wavelength selection method based on FMCIA was better than PCA. Moreover, the RC-FMCIA-PLSR model using fewer wavelengths showed analogous or even better accuracies for detection OWF adulterated with CaF, CoF and WF, yielding higher R2P of 0.986, 0.973 and 0.971 with lower RMSEP of 0.026, 0.036 and 0.038, respectively. The results demonstrated that the optimized PLSR model can be efficiently used to evaluate contaminants (CoF, CaF and WF) in OWF. It also revealed that the specific OWF contaminated by CaF and CoF can be detected more accurately than the OWF mixed with common WF. Besides, the simultaneous representation of both spectral and spatial image information is exclusively involved in spectral imaging. Even though the contaminants (WF, CaF and CoF) investigated in this study are just three categories, it is possible to detect the presence of other unknown adulterated flour categories or exotic matters based on the methodology used. To our knowledge, this is the first time for spectral imaging applied to detection of contaminants in specific organic wheat products. 5. Future work As there are so many influence factors, it is impossible to immediately recognize the origin of the differences. Hence, in the beginning, it is better to investigate the same wheat category not only simultaneously grown in both organic farm and traditional farm by the same climatic environment but also harvested at the same time period and processed in a single manufacturer. In the future research, a very wide variety of OWF samples and impurities from multiple manufacturers, stored in multiple conditions, and measured in presence of various environmental factors (temperature, humidity, etc.) need to be gradually investigated to follow up the current study. With more representative and many more samples to be evaluated, it will be more helpful to develop robust classifiers to estimate whether future samples deviate from the model. Acknowledgments The authors would like to acknowledge the UCD-CSC Scholarship Scheme supported by University College Dublin and China Scholarship Council. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jfoodeng.2016.12.014. References Alexandrakis, D., Downey, G., Scannell, A.G.M., 2012. Rapid non-destructive detection of spoilage of intact chicken breast muscle using near-infrared and Fourier transform mid-infrared spectroscopy and multivariate statistics. Food Bioprocess Technol. 5 (1), 338e347. Barbedo, J.G., Tibola, C.S., Fernandes, J.M., 2015. Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosyst. Eng. 131, 65e76. Barbin, D.F., ElMasry, G., Sun, D.-W., Allen, P., 2012a. Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal. Chim. Acta 719, 30e42. Barbin, Douglas, Elmasry, Gamal, Sun, Da-Wen, Allen, Paul, 2012b. Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci. 90 (1), 259e268. Barbin, Douglas F., ElMasry, Gamal, Sun, Da-Wen, Allen, Paul, 2013. Non-destructive determination of chemical composition in intact and minced pork using nearinfrared hyperspectral imaging. Food Chem. 138 (2e3), 1162e1171. Borges, E.M., Gelinski, J.M.L.N., de Oliveira Souza, V.C., Barbosa Jr., F., Batista, B.L., 2015. Monitoring the authenticity of organic rice via chemometric analysis of elemental data. Food Res. Int.  ne , C., Blecker, C., et al., 2015. Nuclear Magnetic resonance, Botosoa, E.P., Che

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