Multi-elemental analysis of flour types and breads by using laser induced breakdown spectroscopy

Journal Pre-proof Multi-elemental analysis of flour types and breads by using laser induced breakdown spectroscopy Pervin Ari Akin, Banu Sezer, Turgay Sanal, Hakan Apaydin, Hamit Koksel, İsmail Hakkı Boyaci PII:

S0733-5210(19)30202-4

DOI:

https://doi.org/10.1016/j.jcs.2020.102920

Reference:

YJCRS 102920

To appear in:

Journal of Cereal Science

Received Date: 11 March 2019 Revised Date:

5 December 2019

Accepted Date: 18 January 2020

Please cite this article as: Akin, P.A., Sezer, B., Sanal, T., Apaydin, H., Koksel, H., Boyaci, İ.Hakkı., Multi-elemental analysis of flour types and breads by using laser induced breakdown spectroscopy, Journal of Cereal Science (2020), doi: https://doi.org/10.1016/j.jcs.2020.102920. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

CRediT Author Statement

Pervin ARI AKIN: Investigation, Validation, Visualization, Writing - Original Draft Banu SEZER: Investigation, Methodology, Data Curation, Visualization, Writing - Original Draft Turgay SANAL: Resources, Investigation, Writing - Review & Editing Hakan APAYDIN: Investigation, Validation Hamit KOKSEL: Investigation, Writing - Review & Editing İsmail Hakkı BOYACI: Investigation, Methodology, Writing - Review & Editing, Supervision

1

Title

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Multi-Elemental Analysis of Flour Types and Breads by Using Laser Induced Breakdown

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Spectroscopy

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Name of the Authors:

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Pervin ARI AKINa, Banu SEZERb, Turgay SANALa, Hakan APAYDINc, Hamit KOKSELb,

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İsmail Hakkı BOYACIb,*

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Affiliation of the Authors:

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a

Central Field Crop Research Institute, 06170, Ankara, Turkey

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a

[email protected], [email protected]

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b

Department of Food Engineering, Hacettepe University, Beytepe, 06800, Ankara, Turkey

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b

[email protected], [email protected]

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c

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Corum 19030, Turkey

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c

Hitit University Scientific Technique Application and Research Center, North Campus,

[email protected]

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*Corresponding Author:

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Prof. Dr. Ismail Hakki Boyaci

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Department of Food Engineering,

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Hacettepe University, Beytepe, 06800 Ankara, Turkey

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Phone: +90 312 297 61 46

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Fax: +90 312 299 21 23

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e-mail: [email protected]

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ABSTRACT

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Bread and flour are most commonly used products in human diet, which makes it susceptible

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to adulteration, mislabeling and addition of unpermitted amount of different flour types. The

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objective of this work was to evaluate the potential of employing laser induced breakdown

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spectroscopy to differentiate different flour types and quantify the white wheat flour addition

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to rye and oat flour and breads. In the principal component analysis, score plot represents pure

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flour types with 97.64% of the variance. In the calibration study, the measured coefficient of

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determination values was 0.989, 0.989, 0.992 and 0.991 for refined wheat flour: rye flour,

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refined wheat flour: oat flour, breads made with the blend of refined wheat: rye flour and the

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blend of refined wheat: oat flour, respectively. The limit of detection values were calculated

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as 3.82, 5.97, 4.59 and 4.92% for refined wheat flour: rye flour, refined wheat flour: oat flour,

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refined wheat: rye bread and refined wheat: oat bread, respectively.

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Keywords: Wheat flour; Bread; Laser induced breakdown spectroscopy; Partial least square.

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1. Introduction

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Throughout history, cereal based products have been a fundamental component of the

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human diet (Borneo and León, 2012; Preedy et al., 2011). Among bakery products, bread has

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been a staple food for various civilizations. Dietary guidelines of various countries

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recommend the consumption of bread and cereal based products which often form the base of

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food pyramid (Preedy et al., 2011). Cereals are important source of micronutrients; such as

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minerals and vitamins which are essential for a healthy life (Borneo and León, 2012).

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Furthermore, the risk of developing chronic diseases such as cardiovascular diseases, type 2

50

diabetes and some types of cancers can be reduced by the consumption of whole grains and

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whole grain products (Liu, 2007). Therefore, the interest of consumption of whole grain

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products has increased in recent years. Cereal grains are generally consumed after the milling

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process, especially in the Western World. During the milling process, bran and germ are

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removed from the grain due to the negative effects on bread/product quality (Borneo and

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León, 2012).

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minerals, fiber, phytochemicals and other nutrients. However, removing them from the flours

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therefore reduces the health promoting characteristics of grains. Cereal products with high

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levels of whole grains are not preferred by customers due to the deteriorative effects on

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sensory properties. High fiber content of whole grain flours dilutes and weakens the gluten

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network and decreases gas retention ability of the dough during proofing and baking (Miller

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and Bianchi, 2017). It is widely recognized that whole grain breads have lower volume,

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tougher crumb, harder crumb structure and shorter shelf life than breads made of refined

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wheat flour. However, whole grain flours are generally blended with refined wheat flour in

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order to improve the sensory and other quality characteristics of whole grain products. In

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many cases, the purpose of blending whole grain flours with refined white flour is essentially

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adulteration.

Bran and germ contain health beneficial components, such as vitamins,

3

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The definition of whole grain foods is different around the EU. For example, for a

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food to be labeled as whole grain, it must have at least 50% (based on dry matter), 90% and

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100% of whole grain in Denmark, Germany and the Netherlands, respectively. In Italy, whole

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grain bread must contain 100% of whole grain flour. Whole grain breads in France must

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contain 10% of their final weight of whole grains and 30% of final weight is required to be

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rich in whole grains (Ross et al., 2017). Due to this regulation differences, each country has

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its own limits in terms of whole grain flour content of the final product. By the reason of the

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differences in legislations, fair trade needs to be supported by monitoring the final products.

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Agricultural and Food Quality Inspection (IJHARS) in Poland reported 44 cases, between

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2010-2017, where there was a missing or incorrect description of bread type e.g. type of flour

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used in baking (wheat or rye) (Kowalska et al., 2018). It is highly important to implement

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labeling regulations for protecting customers. Therefore, identification of different types of

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flours and determination of the relative ratio of whole grain and refined wheat flour in a

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bakery product has critical importance.

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Mislabeling is an important problem faced by consumers, food processors, regulatory

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agencies, and industries (Kowalska et al., 2018). Different methods spectroscopic (Levandi et

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al., 2014), (Pasqualone et al., 2007; Verdú et al., 2016) and DNA based (Scarafoni et al.,

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2009; von Büren et al., 2001) methods have been used to characterize the flour/bread type.

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There is an urgent need for a method that can be used to analyze different cereal flours in

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wheat flour and wheat bread with rapid, simple, high accuracy and precision for quality

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control laboratories, governmental regulatory agencies and industries.

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Although elemental differences between the different types of flours were revealed

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with inductively coupled plasma optical mass spectroscopy (ICP-OES) and flame atomic

90

absorption spectroscopy (FAAS), the results have never been used to differentiate different

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flour types and quantify certain flour type in a mixture (De la Guardia and Garrigues, 2015).

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Recently a new method which is called laser induced breakdown spectroscopy (LIBS)

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become prominent in terms of elemental analysis. LIBS is an optic spectroscopic technique

94

which is used for nonintrusive, qualitative, and quantitative measurements of elemental

95

composition of gas, liquid, and solid matrices. In the LIBS technique, a beam of intense

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pulsed laser irradiation focuses on the sample to form optical plasma which atomies and

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excites samples. As it cools, it emits light of characteristic light which are record and analyzed

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spectrometer leads to formation of a strong spectrum (Muchao, 2014). Compared to other

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elemental analysis techniques, LIBS has two main advantages which are rapid sample

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preparation and measurements (Cremers et al., 2006). LIBS have been already used to

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quantify elements in different types of flours for different purposes such as ash analysis (Bilge,

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Sezer, Eseller, Berberoglu, et al., 2016), Ca addition to wheat flour (Bilge, Sezer, Eseller,

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Berberoğlu, et al., 2016), ash, protein and magnesium analysis in gluten free flour (Markiewicz-

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Keszycka et al., 2018) and determination of inorganic nutrient content (Peruchi et al., 2014).

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Thus, the objective of this work was to study the feasibility of LIBS in two different topics by

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using multivariate data analysis techniques namely principal component analysis (PCA) and

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partial least square analysis (PLS). Firstly, different flour types (such as rye, oat, bran, whole

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wheat and refined wheat flour) were differentiated based on their elemental composition.

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Secondly, refined wheat flour addition to oat and rye flour and bread were quantified.

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2. Material and Methods

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2.1.Materials

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Ten wheat (five wheat samples for refined wheat flour and five wheat samples for

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whole wheat flour samples), seven rye and five oat samples from 2017 crop year were

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evaluated. Five of the hard winter wheat samples were grown in four different locations

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(Malya, Haymana, Edirne, Altınova) of Turkey and they were milled into refined wheat flour.

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Two wheat samples were grown under irrigation in two of the locations (Edirne, Haymana)

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and the others under dryland conditions (Haymana, Malya, Altınova). Five of the hard wheat

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lines were used for whole wheat milling and were harvested from three different locations

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(Malya, Haymana, Edirne), two of which (Malya, Haymana) were dryland and the remaining

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two were irrigated. The five oat lines were grown in five different locations (Keşan, Edirne,

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Sakarya, Menemen, Bandırma) and the seven rye lines were grown at two locations (Konya,

125

Edirne).

126 127

2.2.Milling Procedure

128 129

Chaff and broken /damaged kernels were manually removed by visual inspection of

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the samples. Wheat samples were tempered to 15% moisture content overnight according to

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AACCI Approved Method 26-95.01 (AACCI, 2013a) and then milled according to AACCI

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26-50 (AACCI, 2013b) using a Buhler 202 pneumatic Laboratory Mill (Uzwil, Switzerland)..

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Firstly, tempered wheat samples were milled into straight grade flour using a Buhler 202

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pneumatic Laboratory Mill (Uzwil, Switzerland) (AACCI, 2013b). Secondly, the particle size

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of separated bran fraction was reduced by using a Laboratory Mill 3100 (Perten Instruments,

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Huddinge, Sweden) equipped with 500 µm sieve. Lastly, all of the flour streams and milling

137

fractions were combined. Oat samples were milled using a ZM 200 (Retsch GmbH, Haan,

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Germany) equipped with a 0.5 mm screen without tempering. Rye kernels were tempered to

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12.5% moisture content for 8 hours and rye after milled using a Quadrumat Jr. Flour mill (C.

140

W. Brabender Instruments, Inc., South Hackensack, NJ) according to (Ragaee et al., 2001).

141

Flour obtained from milled kernels was sieved using a 212 µm screen. Material retained on

6

142

the sieve was reduced to a particle size of 500 µm by using Laboratory Mill 3100 (Perten

143

Instruments, Huddinge, Sweden) and then mixed with the flour passing through 212 µm

144

screen.

145 146

2.3.Sample Preparation

147 148

In this study, three different sample groups were prepared. The first group was flour

149

and bran samples. The second group was blends of oat or rye flour and refined wheat flour.

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To bake oat and rye breads, blends of oat or rye flour and refined wheat flour were used.

151

Oat or rye flours were used to replace straight grade wheat flour in the dough formula

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in increment of 2.5% for the ratios between 0-5%, and in increment of 5% for the ratios

153

between 5-95%. Twenty different concentrations of each type of mixture (oat: refined wheat

154

flour or rye: rye refined wheat flour) were prepared as two replicates. In the bread making

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stage, one bread loaf was baked by using each replicate. The third group of samples prepared

156

was bread crumbs

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Bread loaves were sliced into small pieces and allowed to dry at 105 °C for 2 h, then

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the dried bread pieces were placed in a coffee grinder and ground (Bilge et al., 2015). Finally,

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3 g of flour/bran/flour blends or bread crumbs were mixed with 2 mL deionized water using a

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spatula until dough was fully developed or bread crumb mixture fully hydrated.

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Each dough/bread crumb mixture was rounded and then individually hot pressed using

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of Glutork 2020 (Perten Instruments, Huddinge, Sweden) for 4 minutes at 150 °C to obtain

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dry sheet (like a small wafer) (Sezer et al., 2017).

164 165

2.4.Bread Making Procedure

166

7

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The bread formula used was based on the AACCI Optimized Straight-Dough Bread-

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Baking Method 10-10.03 with modifications including omission of shortening sugar ,malt and

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ascorbic acid in the baking formula (AACCI, 2010). The formula for bread contained (fwb)

170

100 g of flour, 25 ml salt solution (6.0%), 25 ml yeast solution (8.0%) and 20 ml water.

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Throughout this study, the “flour” was either 100% refined wheat flour or a blend of refined

172

wheat flour and oat flour/rye flour. Oat and rye flour replaced with the refined wheat flour in

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the composite formulations.

174

Lincoln, NE) The dough was proofed for a total of 1 hour and 55 minutes. At 30 minutes the

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dough was punched, molded, panned and allowed to proof an additional 55 min. The dough

176

samples were baked in a rotary baking oven (Despatch, Minneapolis, MN, USA) at 230°C for

177

25 min. After baking, loaves were depanned and cooled for 2 h on wire racks.

Ingredients were mixed using a pin mixer (National Mfg.,

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2.5.LIBS Experiment

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The experimental set-up illustrated in the supplementary section (Figure S1. A pulsed

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Nd: YAG laser (Litron Nano SG, Litron Lasers, Cambridge, England) with a wavelength of

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1064 nm. Measurements were made between 188-900 nm wavelength range by using Applied

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Spectra 5 channel Aurora (Fremont, CA USA). The laser was operated in the Q-switched

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mode run at a repetition rate of 8 Hz, 650 ns gate delay, 1.05 ms integration time, and 36

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mJ/pulse laser energy. Flour samples were analyzed by the laser equipped with rotary system

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run at 1.33 rpm. Each sample was prepared as four replicates. For each replicate, 50 shots

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were taken from the 50 different locations and the mean value of 50 measurements of the

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spectral intensity was used. Intensities of Ca, Cu, Fe, Mg, Mn, Na, K and P of five refined

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wheat flour, five whole wheat flour, five brans, five oat flour and seven rye flour samples

191

were measured.

8

192 193

2.6.Data Analysis

194 195

LIBS spectra of samples were analyzed using PCA and PLS methods (PLS Toolbox

196

Version 7.5.2, Eigenvector Research Inc., Wenatchee, WA) for clustering pattern and

197

quantification analysis, respectively. PCA was used to investigate clustering pattern based on

198

the elemental difference between the sample types in the analyzed spectra. PLS regression is a

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recent statistical method which is especially convenient when a set of dependent variables

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need to be predicted from a large set of independent variables. In PCA analysis, five refined

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wheat flour, five whole wheat flour, five brans, five oat flour and seven rye flour samples

202

were investigated and three principal components (PCs) were chosen to build the PCA model.

203

Normalization was applied as pre-processing method. In PCA, 2/3 of the total measurement

204

were used in the calibration data set and 1/3 of them used in the validation. In the PLS

205

analysis, refined wheat flour addition to rye and oat flour samples and their bread form were

206

analyzed. Normalization and Orthogonal Signal Correction (OSC) were applied as pre-

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processing methods for oat flour model and oat bread models. Normalization and 1st

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derivative was applied as pre-processing methods for rye flour and rye bread models.

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Calibration and validation models were chosen based on low root mean square error of

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calibration (RMSEC) and root mean square error of prediction (RMSEP) values and high

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coefficient of determination value, respectively. In PLS, twelve different concentrations used

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for calibration and ten different concentrations were used for validations for each calibration

213

study. For evaluation of the system sensitivity, accuracy and precision, relative standard

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deviation (RSD) and relative error of prediction (REP) values and limit of detection (LOD),

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limit of quantitation (LOQ) values were calculated as shown in Eq. 1-4 (Gondal et al., 2010).

216

9

217

∑

(%) =

ĉ

218

Nv=number of validation spectra

219

ci =true concentration

220

ĉi =predicted concentration

221 222

∑

(%) =



ℎ !

"

=∑

$

(

$

)#



223

Nconc = number of different concentrations in the validation set

224

ρ=number of spectra per concentration

225

σ=Standard deviation

226

%& = 3.3 ×

227

%&, = 10 ×

228

S.D.: Standard deviation of the prediction values

229

S: Slope of the calibration curve

*.+. *.

*.+. *.

230 231

2.7.Elemental analysis

232

All chemicals were of analytical grade. In the sample preparation, 65% (v/v) HNO3

233

(Sigma-Aldrich Corp, St Louis, MO) and 30% (m/v) H2O2 solutions (Merck, Darmstadt,

234

Germany) were used. Multi-element (100 mg L-1) ICP QC standard solution (Chem-Lab,

235

Zedelgem, Belgium) were diluted to prepare standard solutions for Ca, Cu, Fe, Mg, Mn, P and

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Zn (Chem-Lab, Zedelgem, Belgium),Na and K (Merck, Darmstadt, Germany) standards was

237

prepared from (1000 mg mL-1) solutions.

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Total concentrations of Ca, Cu, Fe, Mg, Mn, P, Zn and Na was measured with an

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inductively coupled plasma optical emission spectrometry (ICP-OES) (Thermo Scientific

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iCap 6000 Dual view, Thermo Scientific, Cambridge, England) and an atomic absorption 10

241

spectrophotometer (AAS) (Thermo Scientific iCE 3000 Series, Thermo Scientific,

242

Cambridge, England) was used to measure total concentrations of K in all samples. Analytical

243

lines of Ca 317.9 nm, Fe 259.9 nm, Mg 279.5 nm, P 178.2 nm, Zn 213.8 nm, Na 588.9 nm,

244

Cu 324.7, Mn 257.6 and K 766.5 nm were measured. Microwave-assisted sample

245

decomposition (Berghof Instruments, Speedwave, Germany) was used.

246

In the microwave-assisted digestion, the following sample preparation procedures were

247

applied. 3 g of samples were accurately weighed into Teflon digestion vessels, and 5 mL of

248

65%, (m/v) HNO3 solution with 2 mL of a 30%, (m/v) H2O2 solution was added in to the

249

mixture carefully with a clean glass pipette then the solutions were mixed. Solution was put

250

on a rest for 10 min before closing the vessel. Samples were subjected to the 3 step

251

microwave digestion with the following procedure: 170 ºC for 5 min, 190 ºC for 15 min, 50

252

ºC for 10 min. After cooling, colorless solutions were quantitatively transferred into 10 mL

253

volumetric flasks and made up to the volume with de-ionized water.

254 255 256

3. Results and Discussion

257 258

3.1.Sample Characterization and Spectral Evaluation

259 260

In this study, rye (n=7), oats (n=5), whole wheat (n=5), wheat bran (n=5) and refined

261

wheat samples (n=5) were analyzed using LIBS. The recorded LIBS spectra contains the

262

wavelength range 188-900 nm. Identification of the emission lines in LIBS spectra were

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achieved by the National Institute of Standard and Technology (NIST), the Institute for

264

Atomic and Molecular Physics of University of Hannover and Vienna Atomic Line

265

Databases, which were commonly used online database platforms. In this study, spectral lines

11

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were characterized according to wavelength and an exploratory representation for LIBS data

267

is presented in Fig. 1. The observed neutral and ionic lines in the LIBS spectra are listed in

268

Table 1. As one can see in Fig. 1, five different type of flour samples reveal the presence of

269

both organic (C, H, N and O) and inorganic (Fe, Mg, Ca, Na, K, P) elements. The comparison

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of the LIBS spectra revealed visible differences between the five different flour types.

271

Evaluation of spectral fingerprints reveals that similar spectral patterns were observed;

272

however, spectral intensities were different in rye, oat, whole wheat and bran samples, due to

273

differences in the concentrations of the elements. Furthermore, elemental composition of

274

different flour types was obtained through ICP-OES and AAS (Table 2) (De la Guardia and

275

Garrigues, 2015). As one can see from Table 2, bran is richer in mineral composition than

276

others in terms of all the elements. Furthermore, whole wheat flour has lower mineral

277

composition than bran; however, whole wheat flour had higher mineral composition than oat,

278

rye and refined wheat flours. Furthermore, refined wheat flour has the lowest mineral content

279

than all other flour types. Similar to ICP-OES results, LIBS spectra showed that elemental

280

composition of bran and whole wheat flour samples are significantly higher than oat, rye and

281

refined wheat flour in terms of the given elements, which is similar with the data in the

282

literature (De la Guardia and Garrigues, 2015). As one can see in Fig 1, elemental variations

283

in the LIBS spectra were similar to ICP-OES results. These differences in elemental content

284

were the basis of the clustering pattern analysis in terms of Ca, Cu, Fe, Mg, Mn, Na, K and P,

285

which were observed in the LIBS spectra. The profile of the mineral content in agricultural

286

commodities vary depending on soil composition, geographical origin, agricultural practices

287

and environmental conditions (Bhattacharjee et al., 1998; Costa et al., 2010; De la Guardia

288

and Garrigues, 2015; Debastiani et al., 2014). In this study, the aim was to detect the variation

289

in the elemental composition of the ray, oats, refined wheat, whole wheat flours and wheat

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bran samples. Because of the above-mentioned reasons, samples (rye, refined wheat, whole

12

291

grain wheat, oats and bran) used in this study were collected from different locations and

292

agricultural practices. Both major and minor elemental differences between these five

293

different flour types were analyzed using chemometric methods, namely PCA and PLS. The

294

results showed that major elemental differences between the cereal species in term of both

295

concentration and LIBS intensity were observed in K, P, Mg and Ca, while relatively smaller

296

differences were seen in Fe, Cu, Mn, Zn and Na, which were consistent with the literature as

297

an average value (Collar, 2015; Pais and Jones Jr, 1997).

298 299

3.2.Clustering Pattern Analysis

300 301

In the first part of this study, clustering pattern analysis based on the LIBS spectra of

302

flour types was performed using PCA. All the flour samples were prepared as four replicates

303

in small wafer form and the spectrum of all four replicates were used in the construction of

304

both calibration and validation PCA models. The main reason behind this was to avoid the

305

difference in the spectra which may caused by shot to shot fluctuation of the laser and

306

possible inhomogeneity problems of the powder form samples. LIBS spectra of five different

307

samples having four replicates of each at different locations of samples is clustered in five

308

groups in the PCA score plot. Four replicates of each sample were close to each other, which

309

means that the spectral data with the same nature were close to each other. Simultaneous plots

310

of the scores and loadings visualized the obtained data. As can been observed, most of the

311

variations in the data set can be explained by the first three PCs. The score plot of the first two

312

PCs (PC1 and PC2), accounts for 99.18% of the total variance, which presented in the Fig. 2a

313

along with the loading plots (Fig. 2b). PC1-PC2 scatter plot clearly demonstrates the

314

clustering pattern of the identified flour types. Generally, when the PCs have more than 85%

315

cumulated reliability of the original data set, it implies that this PCs can be used to replace the

13

316

original data set. It can be seen form the Fig.2, there are five clearly separated areas which

317

represent bran, rye, oat, whole wheat and refined wheat flour samples. Each area contains

318

both calibration and validation data sets, which shows the high prediction accuracy. There are

319

only two samples which clustered in the wrong sample group. This PCA model effectively

320

classifies the unknown samples as rye, oat, whole wheat or refined wheat flour. In the

321

loadings plot, spectral lines responsible for the cluster pattern were presented in Fig. 2b. This

322

shows that the PC1 explained the variance in Na, Ca, Mg and K since high loadings values

323

were observed for peaks in the 270-300, 390-400 nm, 580-590 nm and 760-770 nm of the

324

LIBS spectrum. Moreover, PC2 explained the variance in Mg, Ca and K which has high

325

loadings value was observed for peaks in the 270-300, 390-400 and 760-770 nm.

326 327

3.3.Determination of Refined Wheat Flour Addition

328 329

In the second step of this study, quantitative analysis of refined wheat flour addition to

330

rye and oat flour and bread was achieved by using LIBS with PLS. PLS calibration and

331

validation graphs are presented in Fig. 3a, b and Fig. 3c, d for flour and bread samples,

332

respectively. Fig.3a, Fig. 3b, Fig. 3c and Fig. 3d represent the refined wheat flour addition to

333

rye and oat flour and refined wheat flour added rye and oat bread, respectively. As one can

334

see from the Fig. 3, high coefficient of determination (R2) values were obtained for both

335

calibration and validation models.

336

The results of the PLS analysis were presented in Table 3. As one can see from Table

337

3, RMSC and RMSEP values were very low. Moreover, RSD and REP values, which

338

represent the accuracy and precision of the method, are in the acceptable range. Since

339

adulteration is basically performed to make economic gain, LOD and LOQ values of the

340

developed LIBS method are found suitable to determine these types of adulterations. Different

14

341

countries have different maximum limit of refined wheat flour amount in whole grain breads.

342

However, it is obvious that mislabeling or addition of unpermitted amount of refined wheat

343

flour in bread has been a common practice (Kowalska et al., 2018). To protect the consumers,

344

satisfy the fair trade and prevent the food fraud, there is a need to develop rapid, accurate and

345

precise analytical methods. Previously, certain analytical techniques such as PCR, PCR-RFLP,

346

etc. were used to identify flour type (Scarafoni et al., 2009; von Büren et al., 2001). Despite

347

their high accuracy, these methods have certain drawbacks such as long sample preparation

348

time, use of certain hazardous chemicals, expensive equipment and laboratories, and

349

personnel equipped with these analytical skills. However, LIBS provides advantages about

350

these topics because it is relatively simple, eco-friendly, and cost-effective technique with no

351

need for sample preparation or the use of chemicals. Furthermore, it provides rapid and

352

reliable analyses of samples, which is vital for both consumers and manufacturers. Sensitivity

353

and selectivity of the LIBS method also satisfy the limitations which differ according to the

354

county regulations all over the world. In this respect, LIBS can be an alternative approach to

355

detect identification of flour type and determination of the unpermitted wheat flour addition to

356

both flour and bread samples.

357 358

4. Conclusion

359

In the present study, LIBS combined with chemometric methods were used as a rapid and

360

alternative method for classification of flour types and detection of adulteration in different

361

types of flours and breads (detection of refined wheat flour addition to oat and rye flour/ bread

362

samples above the permitted levels). Unlike the ICP and AAS, LIBS is a practical and simple

363

technique that does not require’ any chemical reagent. The basis in this method depends on

364

differences of the elemental content, and it is a promising tool both for qualitative and

365

quantitative analysis. LIBS combined with PCA provided successful classification of flour

15

366

types; namely bran, rye, oat, whole wheat, and refined wheat flours. Moreover, PLS provided

367

the quantification of unpermitted amount if refined wheat flour addition to rye and oat flour

368

and bread samples. Both PCA and PLS analysis and the obtained results are very promising to

369

propose the developed method as a routine analysis technique.

370 371

16

372

References

373

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Bilge, G., Sezer, B., Eseller, K.E., Berberoğlu, H., Köksel, H., Boyacı, İ.H., 2016. Determination of Ca

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Borneo, R., León, A.E., 2012. Whole grain cereals: functional components and health benefits. Food & function 3, 110-119.

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Collar, C., 2015. Bread and bakery products. Handbook of Mineral Elements in Food, 559-572.

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Costa, L., Toci, A., Silveira, C., Herszkowicz, N., 2010. M., Pinto, A., Farah, A. Discrimination of

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Cremers, D.A., Yueh, F.Y., Singh, J.P., Zhang, H., 2006. Laser-induced breakdown spectroscopy,

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De la Guardia, M., Garrigues, S., 2015. Handbook of mineral elements in food. John Wiley & Sons.

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Debastiani, R., Dos Santos, C., Yoneama, M., Amaral, L., Dias, J., 2014. Ion beam analysis of ground

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Gondal, M., Seddigi, Z., Nasr, M., Gondal, B., 2010. Spectroscopic detection of health hazardous

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Kowalska, A., Soon, J.M., Manning, L., 2018. A study on adulteration in cereals and bakery products from Poland including a review of definitions. Food Control 92, 348-356.

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Levandi, T., Püssa, T., Vaher, M., Ingver, A., Koppel, R., Kaljurand, M., 2014. Principal component

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Cullen, P.J., Sullivan, C., 2018. Laser-induced breakdown spectroscopy (LIBS) for rapid analysis of

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ash, potassium and magnesium in gluten free flours. Food chemistry 244, 324-330.

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Miller, R.A., Bianchi, E., 2017. Effect of RS4 Resistant Starch on Dietary Fiber Content of White Pan Bread. Cereal Chemistry 94, 185-189.

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Muchao, L., 2014. Laser Induced Breakdown Spectroscopy Data Processing Method Based on

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Wavelet Analysis, Intelligent Data analysis and its Applications, Volume I. Springer, pp. 21-30.

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Pais, I., Jones Jr, J.B., 1997. The handbook of trace elements. CRC Press.

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Pasqualone, A., Montemurro, C., Grinn-Gofron, A., Sonnante, G., Blanco, A., 2007. Detection of soft

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wheat in semolina and durum wheat bread by analysis of DNA microsatellites. Journal of

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agricultural and food chemistry 55, 3312-3318.

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Peruchi, L.C., Nunes, L.C., de Carvalho, G.G.A., Guerra, M.B.B., de Almeida, E., Rufini, I.A., Santos Jr,

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exhibiting a range of extract viscosities. 2. Rheological and baking characteristics of rye and

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rye/wheat blends and feeding value for chicks of wholemeals and breads. Journal of agricultural

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and food chemistry 49, 2446-2453.

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Ross, A.B., van der Kamp, J.W., King, R., Le, K.A., Mejborn, H., Seal, C.J., Thielecke, F., 2017.

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Healthgrain Forum. Advances in Nutrition 8, 525-531.

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Scarafoni, A., Ronchi, A., Duranti, M., 2009. A real-time PCR method for the detection and

436

quantification of lupin flour in wheat flour-based matrices. Food chemistry 115, 1088-1093.

437

Sezer, B., Bilge, G., Sanal, T., Koksel, H., Boyaci, I.H., 2017. A novel method for ash analysis in wheat

438

milling fractions by using laser-induced breakdown spectroscopy. Journal of Cereal Science 78,

439

33-38.

440

Verdú, S., Vásquez, F., Grau, R., Ivorra, E., Sánchez, A.J., Barat, J.M., 2016. Detection of adulterations

441

with different grains in wheat products based on the hyperspectral image technique: The

442

specific cases of flour and bread. Food Control 62, 373-380.

443 444

von Büren, M., Stadler, M., Lüthy, J., 2001. Detection of wheat adulteration of spelt flour and products by PCR. European Food Research and Technology 212, 234-239.

445 446

19

447

FIGURE CAPTIONS

448 449

Figure 1. Typical (a) detailed (b) LIBS spectrum of the rye, oat, bran, whole wheat and white

450

wheat flour samples.

451

Figure 2. Score plot (a) and loadings plot (b) of PCA analysis of different flour types.

452

Figure 3. PLS analysis of blends namely wheat flour: rye flour (a), wheat flour: oat flour (b),

453

wheat flour: rye bread (c) and wheat flour: oat bread (d).

454

Figure S1. Experimental setup of LIBS.

455 456

TABLE CAPTIONS

457

Table 1. Line assignment of the LIBS spectra between 186-900 nm spectral range

458

Table 2. Elemental composition of different flour types obtained through ICP-OES and AAS

459

Table 3. Statistical results of the PLS study

460 461 462 463

20

Table 3. Statistical results of the PLS study Wheat flour

Wheat flour

added rye flour

added oat flour

Parameter

Wheat flour

Wheat flour

added rye

added oat

bread

bread

R2 calibration

0.989

0.989

0.992

0.991

R2 validation

0.985

0.979

0.981

0.978

LOD (%)

3.82

5.97

4.59

4.92

LOQ (%)

12.74

19.89

15.33

16.40

RSD (%)

14.53

15.11

10.22

8.58

REP (%)

11.85

12.32

15.40

12.52

RMSEC

4.52

3.14

4.09

6.03

RMSEP

9.05

5.42

10.57

13.90

Latent variable

3

3

3

3

LOD: Limit of detection; LOQ: Limit of quantitation; RSD: RElative standard deviation; REP: RElative error of prediction; RMSEC: Root mean square error of calibration; RMSEP: Root meran square of prediction.

Table 1. Line assignment of the LIBS spectra between 186-900 nm spectral range Obtained Emission Lines (nm)

Possible Elements

213.692

P I (213.618)

214.895

P I (214.910)

247.856

Fe II (247.857)

257.607

Mn II (257.610)

259.952

Fe II (259.956)

263.080

Fe II (263.087)

279.500

Mg I (279.553)

280.204

Zn I (280.200)

285.152

Fe I (285.160)

393.360

Ca II (393.366), Ca I (393.529)

396.821

Ca II (396.8469)

422.700

Ca I (422.672), Ca II (422.815)

443.548

Ca I (443.569)

445.517

Ca I (445.478)

518.45

Mg I (518.36)

589.245

Na I (588.995)

589.806

Na I (588.995)

716.065

Ca I (714.815), Fe I (714.814)

766.783

K I (766.489)

770.107

K I (769.8965)

819.221

Na I (819.482)

821.922

Mg I (821.3034)

Table 2. Elemental composition of different rye, oat, whole wheat, bran and wheat flour samples obtained through ICP-OES and AAS Sample Name

Ca (ppm)

Cu (ppm)

Fe (ppm)

Mg (ppm)

Mn (ppm)

P (ppm)

Zn (ppm)

Na (ppm)

K (ppm)

Rye 1

186.58±4.3

3.13±1.4

21.36±1.1

691.00±2.4

21.55±3.1

2832.50±2.4

25.78±2.8

268.25±0.2

4029.86±0.6

Rye 2

217.15±2.8

3.82±1.0

42.53±1.4

777.25±2.8

26.70±1.8

3637.50±0.9

26.85±2.0

263.50±0.2

5288.30±0.8

Rye 3

219.38±0.2

4.17±0.6

40.28±0.2

808.00±0.6

26.75±0.3

3942.50±0.7

28.08±1.2

269.00±1.3

5101.67±0.5

Rye 4

228.65±1.0

5.05±0.1

41.60±0.3

810.75±0.4

27.45±0.5

4857.50±0.5

40.40±0.16

242.50±0.6

6046.92±0.7

Rye 5

120.53±1.0

4.03±0.8

25.65±0.4

773.00±1.7

15.64±0.8

3152.50±0.9

19.85±0.6

242.68±0.6

4604.95±0.5

Rye 6

136.08±1.6

4.13±0.3

28.45±0.4

776.75±1.4

15.83±0.9

3647.50±1.0

19.91±0.8

368.25±0.6

4394.62±0.4

Rye 7

131.30±1.0

3.87±0.08

25.88±0.6

806.25±0.8

16.64±0.5

3542.50±0.3

17.69±0.5

248.60±1.0

4576.98±1.0

Oat 1

357.25±0.5

5.13±0.2

41.50±0.1

847.25±0.7

45.50±0.2

6250.00±0.7

32.53±0.4

247.65±0.6

3303.47±0.3

Oat 2

388.75±0.9

3.81±0.5

34.80±0.4

830.25±2.0

40.88±0.6

6975.00±0.4

31.45±0.1

247.88±0.9

4197.43±0.6

Oat 3

332.75±1.9

4.95±0.3

47.88±1.1

797.25±2.1

35.53±1.2

7685.00±0.6

46.05±0.8

251.75±0.4

3710.46±0.2

Oat 4

334.75±1.6

3.98±0.3

37.38±0.4

814.25±0.4

34.73±0.7

6985.00±0.2

27.85±0.4

261.25±1.0

3454.23±0.9

Oat 5

292.25±0.6

6.25±0.2

47.75±0.3

774.75±1.1

28.60±0.7

5007.50±0.9

24.65±1.0

236.83±0.3

2702.09±1.0

292.25±0.6

3.71±0.5

26.75±0.5

803.25±1.4

35.98±0.6

7312.50±0.8

28.20±0.6

257.75±0.6

5204.21±0.5

213.98±0.8

4.82±0.3

24.93±0.7

777.00±0.8

32.50±0.7

6590.00±0.4

16.40±0.3

271.00±0.3

3788.12±0.3

Whole wheat 1 Whole wheat 2

Whole wheat 3 Whole wheat 4 Whole wheat 5

286.75±0.7

4.50±0.7

22.12±0.4

754.75±1.4

31.60±0.6

6945.00±0.3

20.61±1.6

292.00±0.5

4818.91±1.0

143.45±1.6

3.74±0.3

24.62±0.7

749.50±1.2

29.15±0.9

6937.50±0.2

16.22±0.6

261.00±0.7

3759.58±0.7

206.35±1.4

3.57±0.3

20.49±0.9

751.75±1.2

24.04±0.6

8402.50±0.3

20.76±0.8

297.75±0.1

4094.69±0.1

Bran 1

570.25±4.6

8.48±1.8

62.98±2.8

824.00±3.0

76.00±3.4

4575.00±1.1

57.30±1.3

226.95±0.5

9383.00±0.8

Bran 2

647.00±1.9

10.61±0.5

67.55±1.0

899.25±3.3

102.23±1.3

4715.00±0.2

45.23±0.4

289.50±0.3

12964.44±1.2

Bran 3

516.50±2.2

9.28±0.2

57.75±1.3

963.00±2.6

71.95±1.4

15572.50±0.3

60.00±0.5

396.75±0.1

12964.44±0.1

Bran 4

555.00±2.8

10.86±0.3

57.23±1.3

919.25±2.0

78.50±1.8

12895.00±0.2

48.35±1.9

347.25±0.5

11804.18±0.1

Bran 5

442.25±1.9

9.45±0.9

80.45±1.2

898.00±1.4

105.60±1.4

4805.00±0.2

42.70±0.7

277.50±0.8

9185.81±0.5

132.18±2.2

1.56±0.8

7.51±2.0

317.50±1.3

6.40±1.3

2457.50±0.5

9.45±0.3

183.68±0.1

1580.70±0.2

126.68±2.7

2.10±0.4

7.01±1.1

362.50±1.8

7.08±1.6

2632.50±0.6

6.42±0.5

218.05±0.1

1638.79±0.7

98.58±1.1

1.33±0.8

5.36±0.3

349.50±0.2

3.81±0.6

2632.50±0.5

5.63±0.2

205.00±0.1

1282.73±1.1

98.50±1.7

1.80±0.5

5.41±0.3

313.25±1.6

4.88±1.0

2725.00±0.1

5.10±0.5

211.75±0.3

1318.71±0.7

111.80±0.9

2.18±0.3

10.10±0.9

370.75±1.0

11.69±0.7

1244.25±0.1

6.58±0.9

205.25±0.8

1388.75±1.4

Refined Wheat 1 Refined Wheat 2 Refined Wheat 3 Refined Wheat 4 Refined Wheat 5

Highlights

•

Discrimination of different flour types were achieved using LIBS.

•

PCA and PLS chemometric methods were used for analysing

the LIBS

spectra. •

ICP-OES and AAS were used as reference methods for elemental analysis.

•

Refined wheat flour addition in rye/oat flour can be detected below 6% using LIBS.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: