Rapid Analysis of Food Raw Materials Adulteration Using Laser Direct

Food Control 113 (2020) 107114

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Food Control

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Rapid analysis of food raw materials adulteration using laser direct infrared T spectroscopy and imaging ∗ Paulo Augusto da Costa Filhoa, ,1, Leonardo Cobuccioa,1, Dipak Mainalib, Mathieu Raultb, Christophe Cavina a Nestlé Research, P.O Box 44, CH-1000, Lausanne 26, Switzerland b Agilent Technologies, United States

ARTICLE INFO ABSTRACT

Keywords: The objective of this study was to assess the application of the Laser Direct Infrared (LDIR) imaging system as a Food adulteration rapid screening technology for detection, identification, and semi-quantitation of adulterants in food ingredients. Laser direct infrared imaging Forty-five samples of skimmed milk powder, thirty-one samples of soy protein isolate, thirty-five samplesof Untargeted method chicken meat powder, thirty-two samples of pea protein isolate and six samples of wheat flour were dry blended Raw materials adulterated with nitrogen-rich compounds and bulking agents at concentrations of 1.0–15.0% (w/w). In addi- Vibrational spectroscopy tion, ten samples of skimmed milk powder were wet blended with food adulterants at 5.0% and 10.0% (w/w) to Mid-infrared check the LDIR performance when different fraudulent processes are applied. The results from this study shows that LDIR can be used as a rapid untargeted screening method that are independent of adulterants to detect, identify and semi-quantify food adulterants in dry blended samples. In most samples, the technology accurately identified all nitrogen-rich compounds and bulking agents present in the dry blended samples. In addition,the technology shows sensitivity of 82% for samples adulterated at 1% and sensitivity from 92% to 100% for samples adulterated at ≥ 5% economic adulteration. On the other hand, the detection and identification of food adulterants in samples prepared by wet blending process was more challenging than dry blended samples because mid-infrared technology may not be sensitive enough to detect adulterants if they are dissolved or if hidden within the particles.

1. Introduction effective source of adulterants in various dehydrated commodities such as meat, poultry, fish, egg and dairy. In addition to cheap plant-based Powders raw materials of animal and plant origin are prone to food protein sources, protein compensation using nitrogen rich compounds fraud (Everstine, Spink, & Kennedy, 2013; Moore, Spink, & Lipp, 2012). that can be added individually or in a mixture to increase the com- Economic adulteration may result from simple volume addition using plexity of detection. Nitrogen-rich compounds are of concern for all foreign powder materials. Many cheap natural adulterants (e.g. sugar, powders rich in proteins (e.g. flour, extract, concentrates, and isolates). maltodextrin, starch, husk powders) or chemicals (e.g. as carbonates, Numerous low molecular weight nitrogen containing chemical(s) were silicates, sulfates agents) have been identified as high risk materials to identified following the melamine crisis (Draher et al., 2016; be added to genuine food ingredients. (Hou et al., 2019; Kar, Tudu, MacMahon, Begley, Diachenko, & Stromgren, 2012) and assessed for Jana, & Bandyopadhyay, 2019; Lohumi et al., 2014; Sanvido et al., their likelihood of occurrence by various food safety agencies and FDA 2010; Wielogorska et al., 2018). as reported by MacMahon (2012). Nitrogen-rich compounds are nor- Risk of adulteration for powder ingredients, which are particularly mally used in legal commercial applications by industries (e.g. as fer- rich in proteins (e.g. lupine, pea, soy, etc), are often more sophisticated. tilizers, herbicides, non-protein nitrogen source in animal feed and Economic adulteration will require an additional compensation of the various commercial products). They are therefore easily accessible and protein content to mask the protein dilution after vol/wt increase. Such can be used by fraudsters to increase the apparent protein concentra- dual fraudulent practices will make analytical detection of food fraud tion of food which is normally measured by nitrogen test methods (De much more complex. Vegetable proteins can be easily added as cost Vries et al., 2017).

∗ Corresponding author. E-mail address: [email protected] (P.A. da Costa Filho). 1 These authors contributed equal work to this paper. https://doi.org/10.1016/j.foodcont.2020.107114 Received 29 October 2019; Received in revised form 12 January 2020; Accepted 14 January 2020 Available online 13 February 2020 0956-7135/ © 2020 Elsevier Ltd. All rights reserved. P.A. da Costa Filho, et al. Food Control 113 (2020) 107114

Several analytical methods have been developed for the detection of 2. Materials and methods nitrogenous compounds in foods. However, these methods often suffer from significant limitations for rapid control of authenticity. Someare 2.1. Raw materials and reagents rapid methods but limited to few nitrogen compounds (Abernethy & Higgs, 2013) or few food matrices (Nieuwoudt, Holroyd, McGoverin, Skimmed milk powder (SMP) (33% protein) samples were obtained Simpson, & Williams, 2016). Other methods that cover several matrices from 12 factories, widely separated geographically (USA, CH, CAN, DE, up to low limits of detection including accidental contamination require DK, AU and IN), all making the similar commercial specification of milk significant sample preparation for some nitrogen-rich compounds of powder. Commercial samples of Soy protein isolate (SPI) (92% protein, economic interest (Draher et al., 2016; Draher, Pound, & Reddy, 2014; Clarisoy 150, ADM), Pea protein isolate (PPI) (86% protein, Pisane C9, Frank, Bessaire, Tarres, Goyon, & Delatour, 2017). These methods are Cosucra), Chicken Meat Powder (CMP) (91% protein, Diana Naurals), more confirmatory and not adapted for a rapid screening analysis of Whey Protein Hydrolysate (WPH) (87% protein, Arla Foods Ingredient, powder commodities rich in protein. This is a major concern since the Denmark), Whey Protein Isolate (WPI) BiPro (95% Davisco Food Kjeldhal method remains the most applied methodology worldwide for International), gelatin 280 bloom (Foodchem International determining the protein content in foods. Corporation), Pork Liver Hydrolysate (PLH), and Wheat Flour (WF) The literature describes also several analytical methods to detect (10.50% protein, COOP, Switzerland) were used. cheap plant protein added as adulterants in food products. Enzyme Acetylenediurea (97%), allantoin (> 98%), ammonium phosphate linked immunosorbent assays (ELISAs) are sensitive but often limited to (> 98%), ammonium sulfate (> 99%), azodicarboxamide (97%), single or few plant protein sources (Kirsch et al., 2009). Indirect DNA- biuret (98%), casein from bovine milk (> 87%), creatine (> 98%), based method and high-performance liquid chromatography tandem cyanuric acid (98%), guanidine hydrochloride (> 99%), melamine mass spectrometry (HPLC-MS/MS) are complementary approaches. (99%), N-methylurea (97%), polyvinylpyrrolidone (PVP), semi- However, they are not rapid methods and requires significant sample carbazide (> 99%), thiourea (> 99%), urea, calcium carbonate (> 99), preparation and time for analysis (Bönick, Huschek, & Ravel, 2017; cellulose, maltodextrin, starch and talc were purchased from Sigma- Röder et al., 2011). Aldrich (Switzerland). N,N''-(2-Methylpropylidene)bisurea (> 91%) Vibrational spectroscopy and imaging techniques such as near-in- was purchased from Molekula GmbH (Germany). The granular and frared, mid-infrared, and Raman have been used for testing food au- coarse powders were ground into fine powder. thenticity and adulteration for several years (Lohumi, Lee, Lee, & Cho, 2015). One of the problems with traditional vibrational imaging system 2.2. Sample preparation is that they are slow and computationally intensive when it comes to analyzing larger area. This is because traditional vibrational imaging Five food raw matrices (skimmed milk powder, chicken meat powder, system requires collecting an entire spectrum on every pixel which soy protein isolate, pea protein isolate, and wheat flour) were adulterated would create a big data set, eventually slowing data analysis process to with twenty-five potential food adulterants organized into three groups get meaningful information from larger area. The other drawback is (nitrogen-rich compounds, foreign protein and bulking agents) through that the traditional imaging system requires chemometric analysis on dry and wet blends. The three groups were: nitrogen-rich compounds big data to deduce any relevant quantitative or semi-quantitative in- (acetylenediurea, allantoin, ammonium phosphate, azodicarboxamide, formation. These systems are less automated and require a spectroscopy ammonium sulfate, biuret, creatine, cyanuric acid, guanidine hydro- expert to analyze the sample and interpret the results. chloride, melamine, N-methylurea, polyvinylpyrrolidone, semicarbazide, Laser Direct Infrared (LDIR) is a new powerful infrared chemical thiourea, urea, and N,N''-(2-Methylpropylidenebisurea), foreign proteins imaging system that eliminates much of the problems associated with (wheat flour, whey protein hydrolysate, pork liver hydrolysate, andsoy other chemical imaging system. By coupling the bright quantum cas- protein isolate), and bulking agents (calcium carbonate, cellulose, mal- cade laser (QCL) source with rapidly scanning optics and using a todextrin, starch, and talc). thermoelectrically cooled single-point Mercury Cadmium Telluride detector, LDIR provides a new approach to chemical imaging and 2.2.1. Dry blending adulteration spectral analysis at an unprecedented speed without coherence arti- Samples containing various (two to five) food adulterates were facts. Because of the use of tunable laser, LDIR can rapidly acquire an prepared. These blends contain at least one nitrogen-rich compound or image over a large area with diffraction limited spatial resolution by foreign protein, and a bulking agent (see supplementary data S1, S3, S4, simply using some diagnostics wavelengths that are necessary for S5 and S6). Forty-five samples of skimmed milk powder, thirty-five analysis. This dramatically improves the speed of analysis and allows to samples of chicken meat powder, thirty-one samples of soy protein probe bigger area to obtain statistically relevant data without using any isolate, thirty-two samples of pea protein isolate and six samples of complicated and computationally intensive chemometric analysis. As a wheat flour were adulterated in terms of volume between 1.0%and result, this technological breakthrough makes it possible to scan large 15.0% (w/w). Series of 1.0–15.0% (w/w) gravimetric blends of raw areas (e.g. 25 mm × 75 mm) with varying pixel size resolution of as materials, nitrogen-rich compound or foreign protein, and bulking small as 1 μm in a few minutes. The high spatial resolution combined agent were prepared to contain the same amount of protein equivalent with the rapid acquisition of data makes this technology very inter- of authentic raw material. Afterwards, 20 g of each adulterated sample esting for analysis of powder mixtures (e.g. distribution of active in- was accurately mixed in a 50-mL plastic bottle. The mixtures were gredients and food adulteration). LDIR allows detection and identifi- homogenized by 3D shaker-mixer (Turbula®, Willy A. Bachofen, cation of chemical compounds, which is necessary for characterization Switzerland) for 30 min at 49 rpm. of homogeneity of powder mixtures and detection of foreign materials such as food adulterants. This new LDIR technology is expected to 2.2.2. Wet blending adulteration followed by spray drying provide an extended application of mid-infrared on dehydrated food Adulterated milk powder can be manufactured using the wet or dry matrices (e.g. dairy, egg, fish, meat, cereal, vegetable, spices and herbs) blending process. These practices consist of adding food adulterants (Valand, Tanna, Lawson, & Bengtström, 2019). The objective of this before or after the spray-drying process, respectively. However, the study was to assess the application of the Laser Direct Infrared (LDIR) output of these adulteration practices is different in term of homo- imaging system as a rapid screening technology for quick detection, geneity and chemical points of view. Wet blending adulteration is more identification, and semi-quantitation of food adulterants in skimmed challenging to be detected because the food adulterant is homo- milk powder, soy protein isolate, chicken meat powder, soy protein geneously distributed and it can be coated with milk protein. Moreover, isolate, chicken meat powder, pea protein isolate and wheat flour. some reactive adulterants can form molecular bonds with protein or

2 P.A. da Costa Filho, et al. Food Control 113 (2020) 107114 lactose, making detection much more challenging. Thus, it is important sample holder and the whole assembly was put into Agilent's Sample to assess if this new technology can detect both types of food adul- Planer to microtome the top layer of the sample to create a flat surface teration to ensure the safety and quality of finished products. for analysis. After microtoming, the sample was inserted into the 8700 Ten samples of skimmed milk powders were spiked with one ni- LDIR instrument for analysis. The whole process of attaching the trogen-rich compound (urea, biuret, ammonium phosphate, azodi- sample to the microscope slide and microtoming took about 5 min. carboxamide and semicarbazide) and two out of three bulking agents (calcium carbonate, maltodextrin and starch) at 5% and 10% (w/w) 2.4.2. Data collection (see supplementary data S2). The spiked samples were prepared to Library spectra of each ingredient was collected and stored in the contain the same amount of protein equivalent and total solids of un- Clarity software. In order to collect the library spectra of pure in- adulterated SMP after sample reconstitution. gredients, all ingredients (nitrogen-rich compounds, foreign protein, 30 g of each adulterated sample and 45 mL of distilled water were bulking agents and raw food matrices) were pressed in the form of transferred into 150-mL beaker and stirred until complete dissolution. pellet (7 mm diameter) individually using a pellet press (Specac, USA). The same procedure was used to prepare three control samples of au- The pellets were attached on the double-sided tape affixed in the mi- thentic SMP. croscope slide. The pellet of each ingredient was used to measure the The control and adulterated samples were re-spray dried using a Büchi pure spectrum and generate a spectral library. All the spectra were benchtop spray dryer B-290 (Büchi labortechnik AG, Flawil Switzerland). collected in direct reflectance and ATR mode in the spectral range The following instrument set up was used: flow gas (nitrogen), pump rate 975–1800 cm−1 at 0.5 cm−1 data point spacing and viewed at 8 cm−1 (30%), inlet temperature (170 °C), flowmeter (470 L/h), pump rate (30%), spectral resolution. aspirator rate (100%) and outlet temperature (90 °C). 2.5. Data analysis 2.2.3. Preparation of sample pellets The adulterated powder samples were pressed into pellets then 2.5.1. Selection of most relevant wavenumber for screening adulterants and analyzed by LDIR. To prepare the pellet, 0.8 g of mass sample was classification method weighted and introduced into an evacuable pellet die (Specac, Atlas The selection of relevant wavenumbers for adulterant screening in autotouch 40 T, Orpington, UK), pressed at 1 T for 1 min (skimmed the sample was based on the spectral features related to five food raw milk powder and meat powder) and at 9 T for 1 min (soy protein iso- matrices (skimmed milk powder, chicken meat powder, pea protein late, pea protein isolate, and wheat flour) after which the pellets isolate, soy protein isolate, and wheat flour) and suspected adulterants (13 mm in diameter and 2–3 mm thick) were obtained. The pellets were (e.g. bulking agents such as calcium carbonate and cellulosic materials). stored in a sealed aluminum foil bag under vacuum at room tempera- The spectral features (peak positions) of raw food matrices and few ture and later sent for LDIR analysis. adulterants was considered while selecting the wavenumbers to create the screening methods. The methods were based on the peak ratio (peak 2.3. ATR-FTIR instrument and baseline) and the peak combination. The peak combination method involved the addition of three peak intensity related to raw food ma- Attenuated Total Reflectance Fourier Transform mid-Infrared (ATR- trices spectra (mainly the amide peaks) divided by the spectral baseline FTIR) spectra were obtained using a Tensor 27 spectrometer (Bruker point (no peak region) intensity. Total of four screening imaging Optics) equipped with a deuterated triglycine sulfate (DTGS) detector methods were created in Clarity software to quickly scan the sample and to an ATR accessory (Smiths Detection Ltda, Warrington, England) area and acquire the infrared image contrast to inspect whether the composed of single bounce diamond crystal. The spectra were acquired sample has been contaminated and get the answer in the form of ‘yes’ or at room temperature in spectral range from 600 to 4000 cm−1, with ‘no’. First three screening methods based on the peak ratio used two resolution of 4 cm−1 and 32 scans per sample. Every 20 min, a new wavenumbers (the peak and the baseline position) related to a spectral reference spectrum was collected. The samples were placed in the ATR band and the fourth screening method based on peak combination used crystal and the sapphire anvil ensured a constant and evenly applied four wavenumbers. The peak ratio method was selected because it ac- load to the sample during the data acquisition. Duplicate spectra were counts for any topography difference on the sample surface and pro- acquired for each sample and the crystal surface was cleaned by ethanol vides contrast solely due to chemical difference on the sample. and wiped dry between two differences sample. The system was oper- For the classification of ingredients in the adulterated sample, ated using the OPUS software version 7.0 provided by Bruker Optics. Clarity software algorithm was used for method creation. Once the spectra of all the ingredients to be classified were given, Clarity soft- 2.4. Laser direct infrared system ware automatically selected the peak and the baseline wavenumber that best separated the given ingredients. The classification method All the prepared samples were analyzed as blind sample without creation took less than a minute. There is no need of chemometrics or knowing what and how many constituents were present. The mea- calibration standards for classification method creation. surements were performed using Agilent's 8700 LDIR infrared imaging system (Agilent Technologies, California, USA). 8700 LDIR uses 2.5.2. Infrared chemical maps quantum cascade laser (QCL) as the source and thermoelectrically The infrared chemical images were acquired on the sample surface of cooled single point Mercury Cadmium Telluride (MCT) as detector. The both the control sample (without adulterant) and the adulterated sample to instrument is equipped with four imaging modes: two visible imaging verify that the screening methods were able to discriminate between these mode (wide-field of view camera and high magnification camera) and two types of sample. It took about 6 min to scan the area of 13 mm diameter two infrared imaging mode (direct reflectance and ATR). The infrared sample surface with all the four screening methods at 20 μm pixel size. The chemical imaging methods were developed in the Clarity software to infrared images are the direct output from the Clarity software which re- screen for the adulterants and to classify the ingredient distribution to quires no further processing from third-party software to visualize the get the surface area concentration (semi-quantitative) information. chemical contrast. The infrared images acquired by four screening methods for control sample was compared with the infrared images acquired for 2.4.1. Sample preparation for LDIR analysis blind sample with the same four screening method. The sample would be Each sample in the form of pressed pellet (13 or 20 mm diameter) determined as abnormal (adulterated) if the blind sample image looked was affixed to the microscope slide of dimension 25 mm×75 significantly different than control sample, which would be indicated bythe using optical adhesive. The microscope slide was inserted into the presence of small black domains, as illustrated in Fig. 1. Then, by simply

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Fig. 1. Pea isolate protein screening method example: (left) control sample without adulterant & (right) sample adulterated. double clicking on the black domains, the infrared spectrum was obtained samples are represented by true negative and true positive, respectively. and compared to the library spectra to find out the adulterant identity. Once TP Sensitivity (%) = × 100 one of the adulterants was identified, the spectral band intensity (peak TP+ FN (2) ratio) from adulterant spectrum was scanned to look at its distribution. TN Then, by simply double clicking on remaining black domains, the next Specificity (%) = × 100 adulterant was found. In this manner, all the adulterant was identified. TN+ FP (3) For chemical classification image of the 13 mm diameter sample, it FP+ FN took about 1–7 min depending on the number of ingredients present in Error rate (%) = × 100 TP+ FN + TN + FP (4) the sample. For example, if there was just one contaminant present in the sample (two-constituent mixture), it took about 1 min to acquire the TN+ TP Accuracy (%) = × 100 infrared image at 20 μm pixel resolution. Similarly, for a six-constituent TP+ FN + TN + FP (5) mixture sample, it took 7 min to acquire the infrared image that showed True positive (TP), True negative (TN), False negative (FN), False positive the distribution of all ingredients. The final classification image pro- (FP). vided the semi-quantitative information in the form of area percent of each ingredient that was present in the surface that was analyzed. 3. Results and discussion 2.5.3. Quantification of food adulterants 3.1. Spectral features of authentic raw materials The surface concentration of each food adulterant in the classified infrared chemical image was estimated by using the ratio of Fig. 2 shows the mid-infrared spectra collected from samples of food the area occupied by each of the adulterant pixels to the total raw matrices (skimmed milk powder, chicken meat powder, soy protein sample area pixels as described by the following relationship area occupied by the adulterant pixels isolate, pea protein isolate and wheat flour). The spectra of soy and pea % Adulterant = × 100(1). In the classified Total Sample area pixels protein isolate are very similar. Both show strong C=O stretching band infrared chemical image, pixels representing adulterants (foreign in- (amide I) from 1600 to 1680 cm−1, strong absorption band corre- gredients) are colored differently from authentic ingredients of the sponding to a combination of C–N stretching and N–H bending (amide food matrix. The equation calculates the percentage area of pixels II) from 1500 to 1575 cm−1, weak absorption band corresponding to a occupied by the adulterant(s) within the number of pixels re- C–N stretching and N–H bending (amide III) from 1200 to 1305 cm−1, presenting the total sample area (foreign and authentic ingredients). and weak absorption band corresponding to C–O stretching at The surface concentration obtained with the areas may be com- 1061 cm−1. The spectrum of chicken meat powder shows strong broad parable to the bulk concentration. Both results may differ in cases absorption band from 1500 to 1700 cm−1 corresponding to amide I and where there is lack of homogeneity of the sample because the LDIR amide II of protein, and the absorption peak from 1400 to 1420 cm−1 measures the particles on the surface of the tablet and only reports attributed to a C–N stretching of amides. Skimmed milk powder shows surface area percent for each ingredient. the absorption bands of amide I and amide II and the characteristics absorption peaks associated to C–O, C–C, and C–O–C stretching vibra- 2.5.4. Library searching and validation tions from 900 to 1200 cm−1 of polysaccharides (mainly lactose). By The classified infrared chemical image that showed the distribution contrast, wheat flour spectrum shows weak absorption peaks of amideI of different ingredient was validated by acquiring the point spectrum and amide II of protein. In addition, the spectral region from 900 to from each ingredient representing domain and comparing those spectra 1200 cm−1 shows strong absorption peaks associated to C–O and C–C with the library spectra. The library search feature from Clarity soft- stretching (1150 cm−1), and C–O–H bending (1076 cm−1) and C–H ware was used for visualizing the match. It took about 1 s to obtain the bending (1003 cm−1) modes attributed to carbohydrates and cellular spectrum from each point of interest. polysaccharides as previously observed in other studies (Boydston- White, Gopen, Houser, Bargonetti, & Diem, 1999 .; Nwachukwu & 2.6. Evaluation of untargeted screening method performance Aluko, 2019; Wolkers, Oliver, Tablin, & Crowe, 2004; Wolpert & Hellwig, 2006; Yee, Benning, Phoenix, & Ferris, 2004). The untargeted method performance was evaluated by the ability to Fig. 3 shows the mid-infrared spectra of authentic food ingredients detect the presence of adulterants ingredients (nitrogen-rich compounds, rich in protein (grouped as foreign protein above). They show minor foreign protein and bulking agents) through the sensitivity, specificity, error differences in the intensity and peak shape of spectrum collected from rate and accuracy. In this study, the authentic sample and adulterated food ingredients of animal and vegetable origin, except pork liver

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Fig. 2. Mid-infrared spectra of five unadulterated food matrices.

hydrolysate. The spectrum of pork liver hydrolysate does not show a wide range of food ingredients. Figs. 4 and 5 show twelve out of sharp absorption peaks. The broad peaks with shoulders were observed sixteen nitrogen-rich compounds and five bulking agents spectra, re- in the spectral region attributed to amide I and amide II. Additionally, a spectively. The mid-infrared spectra of food raw materials, nitrogen- −1 −1 − band at 1040 cm and 1234 cm assigned to PO2 asymmetric and rich compounds and bulking agents show the characteristic fingerprint symmetric bending vibrations of the phospholipid phosphodiester profile and thus allowed its identification. However, maltodextrin and groups similar to that described by Liu et al. (Liu, Shaw, Man, starch were exceptions because both bulking agents have similar mid- Dembinski, & Mantsch, 2002), was observed. The spectrum of soy infrared spectra. This was not surprising because maltodextrin is a protein isolate appears to be a combination of the spectra of the major starch fragment produced by partial hydrolysis of corn, rice, potato, or milk proteins (casein and whey protein). We observed that the whey wheat starch. As a result, mid-infrared technology could detect the protein isolate and the soy protein isolate show similar intensity and presence of these two adulterants in the sample but could not dis- −1 −1 shape of absorption peaks at 1447 cm (CH2 bending) and 1392 cm criminate between starch and maltodextrin. Hence, if required, the (CH3 bending). Whereas, casein protein and soy protein isolate show proper identification of the adulterant (whether starch or maltodextrin) similar shape for the absorption peak at 1626 cm−1 (amide I) and should be done using a confirmatory method (e.g. titrimetric Lane 1069 cm−1 (C–O stretching). In addition, the spectra of intact and Eynon method with alkaline Fehling's solution for determination of hydrolyzed proteins were similar. As a result, the similarity among mid- reducing sugars (Schneider, 1980). infrared spectra suggests that the detection and identification of animal/plant-based protein from foreign source rich in protein can be challenging when added to food matrices such as skimmed milk 3.2. Analysis using laser direct infrared (LDIR) powder, plant flours and meat powders. Nevertheless, there are others analytical technologies such as DNA-based methods and protein-based In this study, we assessed the screening and the classification methods (gel electrosphoresis, immunoassays, sodium dodecyl sulfate method of LDIR to detect, identify, and semi-quantify food adulterants polyacrylamide gels, western blotting, reversed-phase high-perfor- (sixteen nitrogen-rich compounds, four foreign protein and five bulking mance liquid chromatography for amino acid analysis, etc.) can be used agents) in five vulnerable food raw materials. Table 1 summarizes the to detect this type of adulteration in foods (Montalvan, Ando, & number of unadulterated and adulterated samples of skimmed milk Echeverrigaray, 1998; Singh, Sharma, Singh, Singh, Tiwari & powder, chicken meat powder, soy protein isolate, pea protein isolate Mohapatra, 2004; Vlachos & Arvanitoyannis, 2008; Zhou et al., 2006). and wheat flour used. The number of adulterated wheat samples was By contrast, the mid-infrared spectra of nitrogen-rich compounds significantly lower because food adulterants have already beenex- and bulking agents were significantly different from food raw matrices. tensively tested for other food matrices. Therefore, it was expected that it would be relatively easy to detect and Adulterated samples were prepared to contain one foreign protein identify the presence of nitrogen-rich compounds and bulking agents in or up to four nitrogen-rich compounds with one bulking agent. In addition, the sum of all nitrogen-rich compounds/foreign protein and

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Fig. 3. Mid-infrared spectra of authentic raw materials rich in protein.

bulking agents were set at 15% economical adulteration as illustrated may allow analyzing between 3 and 10 samples per hour, which meet on Table 2. This approach allows one to use a sample to assess whether the expectation of daily analysis volume. the technology can simultaneously detect several nitrogen-rich com- Fig. 7 shows infrared chemical map of LDIR after data treatment for pounds over a wide concentration range and optimize the number of one sample representing skimmed milk powder, soy protein isolate, samples prepared. chicken meat powder and wheat flour adulterated with various ni- Fig. 6 shows the pictures of unadulterated chicken meat powder and trogen-rich compounds and one bulking agent. The green color re- adulterated chicken meat powder containing urea and cellulose. Visual presents the food matrix and the colored spots the detected adulterants. analysis of the adulterated sample suggests the presence of foreign The tiny colored spots assigned as adulterants highlight the sensitivity particles on the tablet surface after microtome. However, visual ana- of this technology in detecting very low levels of food adulterants (e.g. lysis by itself is not enough to confirm the presence of food adulterants. guanidine at 0.06%). In addition, this technology allowed identification By contrast, the infrared chemical map generated by LDIR data com- and semi-quantification of food adulterants. bined with a library searching of point spectra obtained at distinct The single measurement of sample SMP labeled as N2N-00020–26 domains allows detection and identification of macroparticles and mi- showed the presence of guanidine about 10-fold lower than the actual croparticles of food adulterants not visible to naked eyes. LDIR also concentration (Table 2). In contrast, the triplicate analysis of sample detected free amino acid (tyrosine) present in the chicken meat powder. SMP N2N-00020–26 showed the concentration of guanidine In terms of time of analysis, the screening methods to detect ab- (0.28 ± 0.19%) two-fold lower than the actual concentration. This normal samples took approximately 6 min per sample; while the result highlights the potential of the technology to verify the homo- identification of food adulterant required an additional 25 mintobe geneity of unadulterated food sample (e.g. vitamin and mineral pre- performed. For routine analysis, we expect that only a few samples will mixes) and the risk of analyzing inhomogeneous samples (adulterated be flagged as positive and require identification. Therefore, this method food samples). Nevertheless, the risk associated with inhomogeneous

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Fig. 4. Mid-infrared spectra of twelve nitrogen-rich compounds.

adulterated samples can be minimized or even resolved if LDIR analysis accuracy (5) calculated for adulteration based on nitrogen-rich com- is performed in duplicate and microtoming the sample before each re- pounds, bulking agent and taking into account both adulterants si- plicate measurement. multaneously (equation numbers 2 to 5 are described in section 2.6). Table 2 highlights that LDIR did not detect the starch added to One hundred and seventy-six samples were split in two groups (27 wheat flour because wheat contains between 70 and 75% of natural unadulterated and 149 adulterated) to assess the performance of the starch and as a result the wheat flour spectrum is very similar to starch method. For statistical analysis, starch and maltodextrin were con- spectrum. On the other hand, LDIR correctly identified 60 out of 60 sidered as the same bulking agent because they cannot be differentiated adulterated samples with maltodextrin or starch added as bulking agent by mid-infrared technology. to the other four raw materials. The method based on LDIR technology showed specificity of 100% Table 3 shows the sensitivity (2), specificity (3), error rate (4) and for nitrogen-rich compounds, bulking agents and foreign protein. On

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Fig. 5. Mid-infrared spectra of five potential bulking agents.

Table 1 sensitivity from 98% to 99% when the statistics are performed using Unadulterated and adulterated samples of raw materials. samples containing concentrations greater than or equal to 5% of

Samples SMP1 CMP2 SPI3 PPI4 WF5 equivalent protein. At 1% protein equivalent, LDIR detected 51 out of 62 (sensitivity of 82%) adulterated samples containing nitrogen-rich Dry blending Unadulterated 10 6 4 5 2 compounds, except for wheat flour. The poor sensitivity of the method Adulterated 45 35 31 32 6 for the detection of nitrogen-rich compounds at 1% protein equivalent

Wet blending Unadulterated 2 – – – – is due to the sample inhomogeneity. This is demonstrated by the results Adulterated 10 – – – – obtained for the four samples prepared from different food raw materials containing the same nitrogen-rich compounds (urea, biuret and Note: 1. Skimmed milk powder; 2. Chicken meat powder; 3. Soy protein isolate; ammonium sulfate) at 1% protein equivalent, since biuret was detected 4. Pea protein isolate; 5. Wheat flour. in 2 out of 4 samples, urea in 3 out of 4 samples and ammonium sulfate in 4 out of 4 samples. the other hand, LDIR technology can only achieve sensitivity of 100% LDIR correctly detected and identified all samples of chicken meat when a method that takes into account the presence of nitrogen-rich powder adulterated with pork liver hydrolysate at 5%, 10% and 15% compounds and bulking agent is applied. The methods developed to (w/w) and successfully identified 77% of adulterated wheat flour detect nitrogen-rich compounds and bulking agent adulterants achieved samples at 5% protein equivalent.

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Table 2 Samples of skimmed milk powder (SMP), pea protein isolate (PPI), soy protein isolate (SPI), chicken meat powder (CMP) and wheat flour (WF) adulterated with nitrogen-rich compounds and bulking agents.

Adulterants SMP1 PPI2 SPI3 CMP4 WF5 N-rich & bulking agents N2N-00020-26 N32-00005-193 N32-00006-114 N32-00010-140 L44-00034-196

Added (%) Found (%) Added (%) Found (%) Added (%) Found (%) Added (%) Found (%) Added (%) Found (%)

Guanidine 0.59 0.06 – – – – – – – – Glycoluril 0.66 0.70 – – 5.17 4.98 – – – – N-methylurea 0.68 0.35 5.10 6.40 – – – – – – Talc 13.07 4.45 8.40 8.02 – – – – – – Ammonium sulfate – – 0.80 0.83 – – – – – – Biuret – – 0.40 0.50 – – 1.75 3.11 – – Urea – – 0.30 0.45 – – 1.60 0.55 – – Semicarbazide – – – – 0.39 0.23 – – – – Calcium carbonate – – – – 9.44 5.28 – – – – Creatine – – – – – – 2.25 1.29 0.30 0.56 Maltodextrin – – – – – – 9.40 12.49 – – Cyanuric acid – – – – – – – – 0.30 0.54 N,N''-(2-Methylpropylidene) bisurea – – – – – – – – 0.30 0.76 Starch – – – – – – – – 14.10 0.0

Note: 1. Skimmed milk powder; 2. Pea protein isolate; 3. Soy protein isolate; 4. Chicken meat powder; 5. Wheat flour.

protein equivalent. Nevertheless, the method sensitivity could be im- proved if each sample was measured in duplicate and microtome the sample in between each measurement. The addition of foreign protein was assessed in samples of skimmed milk powder and chicken meat powder. Skimmed milk powder was adulterated with wheat flour, soy protein isolate or whey protein hydrolysate at 1%, 5%, 10% and 15% (w/w). LDIR successfully detected the presence of wheat flour in all samples but the adulterant was identified as starch in the sample spiked at 1%. This misclassification was expected because the spectra of starch and wheat flour are similar. In fact, the main difference between the spectra of both ingredients is the intensity of the absorption peak at 1647 cm−1. On the other hand, LDIR didn't detect samples spiked with soy protein isolate and whey protein hydrolysate. This result can be easily understood when the spectra of casein and whey protein (major milk proteins) were compared to the spectra of soy protein isolate and whey protein hydrolysate as shown on Fig. 3. The result obtained for wet blended adulterated samples was different from that of the dry blended adulterated samples. In wet blended samples, LDIR was able to detect adulterants such as calcium carbonate and starch, whereas the nitrogen-rich compounds were not detected. These results may be related to the solubility of the food adulterants such as nitrogen rich compounds and to the fact that the soluble adulterant can homogeneously disperse in particles of skimmed milk Fig. 6. A. Visible image of unadulterated meat powder in the form of tablet; B. powder or coated by lactose and milk protein making it difficult to Visible image of adulterated meat powder in the form of tablet; C. LDIR che- detect. As a result, mid-infrared technology may not be sensitive en- mical image after data treatment showing the distribution of different chemical ough to detect adulterants when it is homogeneously dispersed or ingredient. hidden within the particles.

4. Conclusion The lower method sensitivity to detect nitrogen-rich compounds in wheat flour compared to the other raw materials is due to the hetero- Overall, LDIR showed promising results for screening analysis of geneous distribution of nitrogen-rich compounds. In fact, raw materials economically motivated adulteration (> 5%) in food matrices. The containing a lower level of protein (e.g. wheat flour ± 10%) require a method developed based on LDIR technology showed high sensitivity smaller amount of nitrogen-rich compounds than protein-rich raw and selectivity for the detection of nitrogen-rich compounds and materials (e.g. soy protein isolate ± 92%) to mimic 5% protein bulking agents from dry blending adulteration. The method sensitivity equivalent. As a result, the preparation of spiked homogeneous samples and selectivity for the detection of nitrogen-rich compounds and is more challenging. For example, 0.5 g of allantoin was added into bulking agents from dry blending adulteration. The method sensitivity 19.5 g of pea protein isolate to mimic 1% protein equivalent, while and selectivity achieved 100% when the method takes into account the 0.3 g of allantoin was added into 19.7 g of wheat flour to mimic 5% presence of nitrogen-rich compounds and bulking agent. On the other

9 P.A. da Costa Filho, et al. Food Control 113 (2020) 107114

Fig. 7. LDIR chemical image after data treatment. Images showing sample of A. Skimmed milk powder adulterated with three nitrogen-rich compounds and one bulking agent; B. Pea protein isolated adulterated with four nitrogen-rich compounds and one bulking agent; C. Soy protein isolated adulterated with two nitrogen- rich compounds and one bulking agent; D. Chicken meat powder adulterated with three nitrogen-rich compounds and one bulking agent; E. Wheat flour chicken meat powder adulterated with three nitrogen-rich compounds and one bulking agent.

Table 3 Performance characteristics of untargeted method based on LDIR image.

Sensitivity (%) Specificity (%) Error rate (%) Accuracy (%)

SMP1 Nitrogen-rich 96 100 3 97 Bulking agent 94 100 5 95 Nitrogen-rich or bulking agent 100 100 0 100

CMP2 Foreign protein 100 100 0 100 Nitrogen-rich 92 100 7 93 Bulking agent 100 100 0 100 Nitrogen-rich or bulking agent 100 100 0 100

SPI3 Nitrogen-rich 96 100 3 97 Bulking agent 100 100 0 100 Nitrogen-rich or bulking agent 100 100 0 100

PPI4 Nitrogen-rich 98 100 2 98 Bulking agent 100 100 0 100 Nitrogen-rich or bulking agent 100 100 0 100

WF5 Nitrogen-rich 79 100 19 81 Bulking agent 33 100 50 50 Nitrogen-rich or bulking agent 100 100 0 100

Note: 1. Skimmed milk powder; 2. Chicken meat powder; 3. Soy protein isolate; 4 Pea protein isolate; 5. Wheat flour.

10 P.A. da Costa Filho, et al. Food Control 113 (2020) 107114 hand, the method sensitivity for detection of nitrogen-rich compounds Hou, S. W., Wei, W., Gan, J. H., Lu, Y., Tao, N. P., Wang, X. C., et al. (2019). Integrated can range from 98% to 99% when the statistics are performed using recognition and quantitative detection of starch and surimi by infrared spectroscopy and spectroscopic imaging. Spectrochimica Acta Part A: Molecular and Biomolecular samples containing concentrations greater than or equal to 5% of Spectroscopy, 215, 1–8. https://doi.org/10.1016/j.saa.2019.02.080. equivalent protein. At 1% protein equivalent, LDIR detected 51 out of Kar, S., Tudu, B., Jana, A., & Bandyopadhyay, R. (2019). FT-NIR spectroscopy coupled 62 (sensitivity of 82%) adulterated samples containing nitrogen-rich with multivariate analysis for detection of starch adulteration in turmeric powder. 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