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Rapid and Non-Destructive Determination of Aleurone Content in Pearling Fractions of Barley by NIR and Fluorescence Spectroscopy Jens Petter Wold, Diego Airado-Rodríguez, Ann Katrin Holtekjolen, Ulla Riikka Maria Holopainen-Mantila, and Stefan Sahlstrom J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b04954 • Publication Date (Web): 07 Feb 2017 Downloaded from http://pubs.acs.org on February 7, 2017

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Journal of Agricultural and Food Chemistry

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Rapid and Non-Destructive Determination of Aleurone Content in

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Pearling Fractions of Barley by NIR and Fluorescence Spectroscopy

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Jens Petter Wold†*, Diego Airado-Rodríguez†, Ann-Katrin Holtekjølen†, Ulla Holopainen-

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Mantila‡, Stefan Sahlstrøm†

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† – Nofima AS, Norwegian Institute for Food and Fisheries Research, Tromsø, Norway

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‡ – VTT Technical Research Centre of Finland, Espoo, Finland

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Diego Airado ([email protected]); [email protected];

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[email protected]; Holopainen-Mantila Ulla ([email protected])

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*Corresponding author (Tel: +47 95979749; Fax: +47 64940333; E-mail:

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[email protected])

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Abstract

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The aim of the present work was to develop and evaluate near-infrared (NIR) and

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fluorescence spectroscopy as rapid and potential on-line methods for determination of the

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amount of aleurone in pearling dust fractions of barley. Phytic acid was used as marker for the

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aleurone layer. Different varieties of barley were pearled and dust fractions were

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progressively taken out. Sample concentration of phytic acid varied in the range 0.5 – 4.1

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g/100g and highest concentrations were obtained in dust fractions for pearling degrees in the

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range 15 - 25%. Regression models for phytic acid were developed with the same high

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correlations for NIR and fluorescence spectroscopy (R2=0.96) and prediction errors of ±0.16 –

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0.18 g/100g. The models performed well on a test set of pearling fractions from two other

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barley varieties. The techniques are rapid and non-destructive, which means that they can be

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used on-line in connection with industrial pearling systems.

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Introduction

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Currently, barley processing mainly consists of de-hulling, pearling and then milling to a

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flour. De-hulling removes the hull and also portions of the pericarp and testa. Pearling the de-

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hulled barley grains further removes the remaining hull, pericarp and testa, aleurone layers

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and germ.1 De-hulling and pearling are accomplished by abrasion, which subjects the grains

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to a rotating abrasive surface. Pearled barley typically makes up 60-70% of the total grain

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weight,2 while the byproduct, the pearling flour (30-40%), is mainly used for animal feeds.

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This process is difficult to optimize to increase process yield, that is, obtain as much flour as

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possible with a minimum or a desired amount of fractions from the outer layers. Different

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properties of the barley grains, for instance different varieties, requires different pearling

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percentages to achieve high quality flour and a minimum of byproduct. Practice is to adjust

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the pearling process according to experience or habit. Optimization of pearled barley yield

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and barley flour quality can, however, be solved by establishment of a rapid online method for

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monitoring of the pearling dust composition. Such measurements could also be used to detect

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and quantify particularly nutritional pearling fractions for better utilisation. In this study we

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focussed on the determination of aleurone content in the pearling dust.

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Barley is known to contain certain health components. The aleurone layer is composed mainly

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of arabinoxylans (67 – 71%) and beta-glucans (26%) as well as phenolic acids, vitamin E, B-

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vitamins, protein, triacylglycerol, minerals, phytate and sugars.3 The phenolic compounds

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such as ferulic acid after uptake in the gut, are transported to the blood and can possibly help

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to reduce the destructive effect of free radicals.4 Barley is also rich in beta-glucan, a

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polysaccharide building block of the cell walls in aleurone layer and the endosperm, which is

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approved by EFSA to reduce the cholesterol level and sugar in blood after a meal.5

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Holtekjølen et al.6 found that the pearling flour is a potential natural source of phytochemicals 3 ACS Paragon Plus Environment


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with antioxidant effects. Chemical characterization of different pearling fractions of barley

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was done by Blandino et al.,7 who found that the total antioxidant activity (TAA) in the 15-

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25% pearling fraction was significantly higher than in the other fractions. The inclusion of

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this fraction in preparation of bread increased TAA and dietary fibers without impairing the

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physical bread properties. For milling companies it can be important to be able to quantitate

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the amount of kernel tissues and health promoting components in their milling fractions, for

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example the content of hull, soluble fibre (β-glucan) and aleurone layer, and in that way create

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a competitive advantage in terms of diversifying milling and bakery product ranges.

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Ultimately, the consumers will benefit from a naturally produced healthier product.

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Barron et al.8 isolated barley grain tissues from both hulled and hulless barley for chemical

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analyses. They found that phytic acid is specifically localized in the aleurone layer and that it

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can be used as a marker for this tissue among other grain tissues. They also identified the

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phenolic compound p-Coumaric acid as an efficient marker of the hull and proposed that it

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could be used to monitor the dehulling process.

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In order to perform continuous product or process control, rapid analytical techniques are

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required. Traditional chemical methods typically require several hours or days, and such a

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time span is rarely applicable in modern production. Spectroscopic instrumentation has gained

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popularity in processing industries during the last 2-3 decades, being rapid, reliable and non-

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destructive analytical techniques. For online use in food production, near-infrared (NIR)

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spectroscopy is probably the most widely used spectroscopic technique. The near infrared part

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of the electromagnetic spectrum comprises overtones and combination bands of fundamental

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bond vibrations found in the infrared region. The molecular bonds that absorb in the NIR

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region are mainly -O-H, -C-H, -N-H and -S-H, reflecting molecular structural characteristics


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of proteins, carbohydrates, lipids and water. NIR is established in the milling industry for at-

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line and on-line determination of typically moisture, protein, fat and ash in flour and whole

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grains. Promising results for determination of beta-glucan has been obtained.9 It is also

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reported that phytic acid has a distinct absorption spectrum in the NIR region and that it can

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be determined in green gram seeds and cotton seeds by NIR spectroscopy.10,11 NIR is

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therefore a promising method for continuous monitoring of the pearling fractions, where the

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amount of e.g. protein, beta-glucan and phytic acid will vary systematically through the

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different kernel layers.7

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It is well known that different tissues of the grains have characteristic fluorescent properties.

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The strong bluish fluorescence from the aleurone layer comes mainly from ferulic acid.12

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Jensen et al.13 showed that endosperm emits fluorescence in the UV typical for tryptophan,

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and that pericarp has a distinct green-yellow fluorescence peaking at about 520 nm with

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unknown origin. They also demonstrated that it is possible to quantitate the amount of

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pericarp, aleurone, and endosperm in wheat milling fractions by the use of autofluorescence

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spectra and multivariate calibration. The spectroscopic measurements were performed on 20

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mg flour samples suspended in glycerol and water. Symons and Dexter14 used fluorescence

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imaging in pilot mill streams to monitor aleurone and pericarp as an estimator of flour

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refinement. Pericarp fluorescence correlated especially well with flour refinement and

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indicated promise for on-line quality evaluation in mills.

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The main aim of the present work was to develop and evaluate a rapid and preferably on-line

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method for determination of the amount of aleurone layer in pearling fractions of barley. Such

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a method could be used to control the pearling process toward maximum yield as well as

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extract fractions with high aleurone content for use in certain products. We have tested and



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compared two rapid spectroscopic techniques, which both have potential for on-line use: NIR

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and fluorescence spectroscopy. Different varieties of barley were pearled and dust fractions

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were systematically taken out over time. As reference marker for the aleurone layer we used

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phytic acid. Concentrations of different phenolic acids were also determined. Multivariate

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regression as well as spectroscopic curve resolution were used to analyse the data and make

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calibration models.

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Materials and Methods

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Materials

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For the investigations, three separate sample sets of pearling dust were produced. The sample

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sets were made as follows:

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SampleSet1: A J.E. Barley Huller (Glenn.A. Burdick, Minneapolis, MN) pearler was used to

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make 12 fractions each of two different barley varieties, 'Marigold' and 'Olve'. The pearler

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consists of a wire brush that presses small particles through a sieve. A maximum of 20 grams

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of grain could be pearled at the time. The dust fractions were made by collecting pearling dust

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systematically according to time of pearling. The fractions were collected for 0-30, 30-60, 60-

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90, and so on up to 330-360 s. A total of 24 samples were produced.

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SampleSet2: A Testing Mill TM05 (Satake, Stafford, TX) with abrasive stone #30 was used to

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make 9 fractions each from two parallels of 200 g of 'Marigold' kernels, and then 10 fractions

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each of two 200 g parallels of 'Olve' kernels. A total of 38 samples were produced.

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Progressive pearling fractions were taken out after approximate pearling degrees 9, 14, 18, 22,

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26, 30, 33, 38, 42 and 49%.


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SampleSet3: The same Satake Testing Mill TM05 was used to make 8 fractions each from two

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parallels of 200 g of 'Pihl' kernels, and then 8 fractions each of two 200 g parallels of 'Pirona'

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kernels. 'Pihl' and 'Pirona' are hulless barley varieties. A total of 32 samples were produced.

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Progressive pearling fractions were taken out after approximate pearling degrees 5, 10, 15, 21,

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26, 31, 36 and 40%.

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Spectroscopic measurements

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NIR spectra in reflection mode were obtained directly on intact dust samples with a DA7200

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spectrometer (Perten Instruments, Hägersten, Sweden). Samples were disposed in a circular

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sampling cup and measured area was approximately 30 cm2. Sampling time was set to 6 sec

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and spectra were registered by covering every fifth wavelength in the region 950-1650 nm.

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Each sample was measured in triplicate and the average spectrum was used for further

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analysis.

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Fluorescence spectra were also collected from the intact pearling dust samples. Samples were

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placed in black circular sample cups, which exposed a flat circular surface with a diameter of

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5 cm for the measurements. Measurements were carried out with a Fluoromax-4

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spectrofluorometer (Horiba Scientific, Kyoto, Japan) in front face mode via a FL-300/FM4-

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3000 bifurcated fiber-optic probe (Horiba Scientific). Distance between the probe head and

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the sample was about 5 cm and created a circular measurement area of 40 mm diameter.

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Probe and sample were covered by a black shield to avoid ambient straylight. Three different

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excitation wavelengths were employed to register emission spectra of each sample. Samples

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were sequentially illuminated with 470 nm, 350 nm and 280 nm excitation light and the

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corresponding fluorescence emission spectra were registered in the ranges 490-650 nm, 400-

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600 nm, and 300-540 nm, respectively. The three assayed excitation wavelengths were

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systematically applied in decreasing order to minimize potential sample alteration by the UV



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excitation. The selected excitation wavelengths should enable detection of pericarp, aleurone

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and endosperm, respectively.13,14

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Determination of phytic acid

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Phytic acid was used as a marker for the aleurone layer and was measured for all

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samples. It was determined by means of a K-PHYT phytic acid (phytate)/total

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phosphorus assay kit (Megazyme, Chicago, IL). This method involves acid extraction

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of inositol phosphates followed by treatment with a phytase that is specific for phytic

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acid (IP6) and the lower myo-inositol phosphate forms (i.e. IP2, IP3, IP4, IP5).

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Subsequent treatment with alkaline phosphatase ensures the release of the final

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phosphate from myo-inositol phosphate (IP1) which is relatively resistant to the action

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of phytase. The total phosphate released is measured using a modified colourimetric

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method and given as g of phosphorus/100 g of sample material.

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Determination of phenolic acids

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Total phenolic acids (ferulic acid, caffeic acid, p-coumaric acid and sinapic acid) were

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determined for four series of pearling fractions, one from each of 'Olve', 'Marigold',

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'Pihl' and 'Pirona'. Extraction of total phenolic acids was performed as described

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earlier.15 Identification and quantitation of phenolic acids were carried out using a

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UltiMate 3000 rapid separation high-performance liquid chromatography (HPLC)

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system with a 3000 RS diode array detector (Dionex, Sunnyvale, CA) and an Acquity

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UPLC BEH C8, 1.7 µm, 2.1 × 150 mm column (Waters, Milford, MA). A gradient

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elution program was used with 1% acetic acid in Milli-Q water as eluent A and 1%

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acetic acid in acetonitrile as eluent B. The injection volume was 10 µL, and the flow

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rate was 0.450 mL/min, with a column temperature of 50 °C. The gradient elution

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program was as follows: 5% B (0 min), 5% B (0.8 min), 10% B (1.2 min), 10% B (1.9 8 ACS Paragon Plus Environment

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min), 15% B (2.4 min), 15% B (3.7 min), 21% B (4.0 min), 21% B (5.2 min), 27% B

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(5.7 min), 50% B (8.0 min), 100% B (9.0 min), 100% B (12.5 min), 5% B (15.0 min),

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5% B (20.0 min). Initial conditions were used for 10 min equilibration before a new

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analysis was started. The identification and quantitation of phenolic acids were carried

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out using external standards (caffeic, p-coumaric, ferulic, and sinapic acid) and UV

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spectrum characteristics with the Chromeleon chromatography information

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management System (Dionex).16

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Microscopy of pearled barley grains

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Selected grains of cultivars 'Marigold', 'Olve', 'Pihl' and 'Pirona' representing various pearling

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times were subjected to microscopy to evaluate the effect of pearling on the aleurone layer. A

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quick staining method was developed for this examination of aleurone layer in pearled grains.

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Grains were cut in half cross-sectionally in the middle of the grain. Cross-cut surfaces of

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individual grains were stained with 0.1% (w/v) acid fuchsin (BDH Chemicals Ltd., Poole,

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UK) in 1% acetic acid for 1 min and rinsed twice with water. Stained grains were then dried

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in dark for 30 min. The cross-cut surfaces were imaged using excitation wavelength (330-

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380 nm) and emission from 420 nm with an AxioImager M.2 microscope (Carl Zeiss GmbH,

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Göttingen, Germany). Under these lightning conditions, aleurone cell walls were detected as

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bright blue due to autofluorescence of phenolic substances present in cell walls while starchy

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endosperm appeared bright red. Micrographs were obtained using a Zeiss Axiocam 506 CCD

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colour camera (Zeiss) and the Zen imaging software (Zeiss). In addition to epifluorescence

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microscopy, the grains were imaged using a SteREO Discovery.V8 stereomicroscope

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equipped with Achromat S 0.5x objective (Carl Zeiss MicroImaging GmbH) and an Olympus

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DP-25 single chip colour CCD camera (Olympus Life Science Europa GmbH, Hamburg,



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Germany). Approximately 18 – 20 grains/sample were examined and the representative

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images were chosen for publication.

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Data analysis

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Pretreatment of spectroscopic data

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NIR spectra are affected by differences in sample scattering. Differences in scattering can

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occur due to the use of different pearling systems. To reduce the effect of such phenomena the

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NIR spectra were normalized by standard normal variate (SNV)17 prior to calibration.

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The fluorescence spectra were normalised by unit area normalisation, meaning that the area

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under each spectrum equals one. This was done separately for emission spectra for the

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different excitations. This normalisation removes the main intensity variations and emphasises

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the shapes of the spectra.

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Calibration

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Partial least squares regression (PLSR)18 was used to make calibrations between spectroscopic

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data and the reference values of phytic acid. Separate and common models for samples in

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SampeSet1 and SampleSet2 were made. SampleSet3 was used as a test set.

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The optimal number of PLSR factors of the calibration models was determined by full cross

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validation. The predicted value for each sample, ‫ݕ‬ො௜ , was compared with the reference value, yi (nominal phytic acid concentration). The squared multivariate correlation coefficient (R2),

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which describes the relationship between the measured and the predicted values of the y

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variable, and the prediction error of the regression model, expressed as root-mean-square


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error of either cross-validation (RMSECV), were used to evaluate the model performance.

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RMSECV is defined as: 1 ܴ‫ = ܸܥܧܵܯ‬ඩ ෍(‫ݕ‬௜ − ‫ݕ‬ො௜ )ଶ ܰ ே

௜ୀଵ

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where i denotes the samples from i to N.

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The obtained calibration models were used to predict phytic acid concentration in the test set

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samples. Model performance was reported as the root-mean-square error of prediction

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(RMSEP), calculated exactly as RMSECV (‫ݕ‬ො௜ being result of prediction and not cross-

validation), and the multivariate correlation coefficient (R2). PLSR and spectroscopic pre-

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processing were performed with the software The Unscrambler, ver. 9.8 (Camo AS, Oslo,

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Norway).

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Multivariate curve resolution

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Fluorescence spectra lend themselves to curve resolution. Multivariate curve resolution

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(MCR), enables extraction of potential pure spectroscopic components originating from the

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different inherent fluorophores and their respective concentrations. The obtained

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concentrations for MCR-components were compared with reference analyses. A non-

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negativity constraint was applied in the spectra and concentration modes in order to obtain a

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realistic solution. The algorithm for MCR has previously been described in detail.19,20 MCR

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was carried out with the software The Unscrambler, ver. 9.8 (Camo AS)

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Results and discussion

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Detection of the effect of pearling on grain layers by microscopy 11 ACS Paragon Plus Environment


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The effect of the pearling on barley grain outer layers was verified by microscopy. For this,

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grains of four barley varieties studied with varying degree of pearling were examined. In

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Figure 1A-E, the appearance of native and pearled barley kernels of 'Olve' and the cross-cut

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surfaces of corresponding kernels stained with Acid Fuchsin are presented. Stereomicroscopy

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images showed that pearling first removed the hull and proceeded then gradually towards the

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centre parts of the grain. However, the ends of the kernels seemed to be more readily pearled

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than the sides.

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The images of the stained cross-cut surfaces revealed that aleurone appearing blue due to

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autofluorescence was first removed from the ventral side of the grain, excluding the ventral

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furrow (Figure 1B) and then from the dorsal side (Figure 1C). This was followed by the

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removal of the aleurone in the flanks of the kernels. Aleurone layer located in the ventral

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furrow was not removed even with the pearling degree of 39% (Figure 1E). In general, it was

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noticed that pearling of hulled and hulless barley kernels followed similar pattern although

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aleurone layer of hulless varieties was removed at slightly lower degree of pearling than that

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of hulled varieties (data not shown).

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Phytic acid in pearling fractions

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Figure 2 shows the concentration of phytic acid in the obtained dust fractions in the three

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sample sets. It was expected that the concentration of phytic acid should increase in the dust

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fractions from the beginning of the pearling and then decrease after passing the aleurone

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layer. In the first trial (SampleSet1) the concentration of phytic acid vs degree of pearling

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showed a quite flat appearance, which indicated that the aleurone layer was not concentrated

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in a few dust fractions. It became obvious that the pearling did not remove all the aleurone,

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since a clear decrease with pearling time was not observed. It turned out that the pearling

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system used was not very efficient. For the second trial (SampleSet2), a different pearling

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system was used, and in this case, it was observed that the concentration of phytic acid did 12 ACS Paragon Plus Environment

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indeed decrease with longer pearling times. This difference indicated that the second pearling

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system was more efficient than the first one. The aleurone was distributed more or less across

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all fractions, but highest concentrations were obtained for pearling yields in the 13-25%

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range. This result corresponds with Blandino et al.7 who found that the 15-25% pearling

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fractions of barley had the highest antioxidant activity, which most likely was due to the

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antioxidants in the aleurone layer. It should also be mentioned that the measured

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concentrations of phytic acid were higher for the variety 'Olve' than for 'Marigold'. In the third

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trial (SampleSet3) on hull-less barley, there was an immediate high concentration of phytic

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acid due to very thin layers of pericarp and testa. A gradual decrease comparable with

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SampleSet2 then occurred. The level of phytic acid in the two varieties 'Pihl' and 'Pirona' were

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quite similar.

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Phenolic acids in pearling fractions

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Concentrations of ferulic acid, caffeic acid, p-coumaric acid and sinapic acid in one pearling

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series of 'Olve' and 'Marigold' from SampleSet2 are shown in Figure 3. The evolution of the

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concentrations of caffeic and sinapic acid with pearling degree followed a pattern similar to

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the concentration of phytic acid, although the maximum concentrations were reached at

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slightly lower pearling degrees than phytic acid. Ferulic and p-coumaric acid followed

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different patterns, which are related to their distribution along the different layers of the grain.

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The concentration of ferulic acid continuously decreased at an approximately constant rate for

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pearling degrees between 10-50%. This corresponds to Ndolo et al.21 who found that the

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concentration of ferulic acid in the pericarp and aleurone in two barley varieties were high,

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but much lower in the endosperm. Thus, the more endosperm entering the pearling fraction,

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the concentration of ferulic acid will decrease. Even though ferulic acid is responsible for the

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strong bluish fluorescence in the aleurone layer, the concentrations are also high in the

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pericarp tissue, which does not exhibit this strong blue fluorescence. Regarding p-coumaric 13 ACS Paragon Plus Environment


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acid, its maximum concentration in dust is obtained at the beginning of the pearling process,

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indicating that highest concentrations are found in the pericarp. These profiles suggest that the

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concentrations of p-coumaric and ferulic acid are not the best markers for the aleurone layer.

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This is in agreement with results obtained after isolation of barley grain tissues from both

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hulled and hulless barley for chemical analyses of biomarkers.8

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Spectroscopic measurements

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Figure 4 shows typical NIR spectra from dust fractions ('Olve' from SampleSet2). For

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unprocessed absorption spectra, the highest offset was obtained for first fraction, and

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then the offset steadily decreased with increasing pearling time. This systematic

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variation in offset is probably due to a corresponding variation in light scattering in the

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samples. Colour of the samples did also vary systematically from dark to white with

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pearling time, however, colour should not affect NIR spectra much in this spectral

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region. For scatter corrected spectra the systematic offset disappeared, but clear

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systematic changes with pearling time could be observed around 1100 nm and 1200 nm.

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Figure 5 shows typical emission fluorescence spectra from dust fractions at the three assayed

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excitation wavelengths. The emission spectra for excitation at 280 nm showed a main

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maximum centred at 320 nm and a small shoulder around 438 nm. The emission spectra for

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excitation at 360 nm was less intense and had a single maximum centred at 435 nm, slightly

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broader than the main maximum of the former one. Lastly, the emission spectra for λex 470

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nm, in some cases presents a maximum at 544 nm and in other cases it presents a quite flat

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aspect with no maxima. For each sample set, the series of spectra registered at λex 280 nm,

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showed a systematic increase from first to last fraction. For λex 360 nm, there was an increase

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at start, and then a decrease; while for λex 470 nm there appeared to be a quite steady decrease


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with pearling time. For regression analysis as well as MCR we used all three emission spectra

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concatenated as shown in Figure 5.

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Calibration results

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Table 1 summarises calibration results for NIR spectroscopy for SampleSet 1 and

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SampleSet2. It is clear that quite good regression models could be established for phytic acid

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in the pearling fractions. The two sample sets behaved differently, better models were

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obtained for SampleSet2. The two sample sets were produced by different pearling machines,

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and this might be the reason for why different results were obtained, and also why the two

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sample sets were not well modelled together. Attempts to remove the main spectral variation

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due to differences in light scattering properties did not significantly improve the model where

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the two datasets were combined. The best model was obtained for SampleSet2 after SNV

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correction of the spectra. This model required one PLS factor more than the model based on

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raw absorption spectra, and this suggests that SNV enables modelling of more subtle spectral

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changes. An R2 of 0.96 and a prediction error (RMSECV) of 0.16% is acceptable for the kind

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of process application we are aiming at. Based on the regression coefficients (not shown) it is

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not easy to identify the main chemical sources of information for the model, however, several

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are probably contributing. Phytic acid has a distinct signature in the NIR region.10 The amount

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of bran, protein and beta-glucan will vary systematically in these fractions, and NIR is

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sensitive to all of them.9,22

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The results for fluorescence spectroscopy are quite similar to those of NIR (Table 1). Better

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results for SampleSet2 than for SampleSet1 were also obtained now, and the combination of

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the two data sets lead to slightly more complex models. The unit area normalisation did not

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give notably better calibration models, and for the combined data set the performance was

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worse than for raw fluorescence spectra. This suggests that the actual fluorescence intensity is



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important for the modelling. Common for NIR and fluorescence was that models for DataSet2

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required more components than did DataSet1. This indicates that SampleSet2 contained some

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spectral variation that was not present in SampleSet1. Since poorer models for phytic acid

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were obtained for SampleSet1 for both NIR and fluorescence, there might have been some

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mismatch between the spectroscopic data and the reference values. From Figure 2 is can be

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seen that phytic acid in SampleSet1 varied up and down in an unexpected way as function of

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pearling degree.

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It is reasonable that fluorescence can be used to indirectly measure the amount of phytic acid

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since it is a marker for the aleurone layer. As pointed out above, pericarp, aleurone and

353

endosperm have distinct and different fluorescent properties.13 Different phenolic acids in the

354

aleurone are fluorescent, for instance, ferulic acid, caffeic acid, sinapic acid and p-coumaric

355

acid (Figure 3).23 Phytic acid is, however, to our knowledge not fluorescent.

356

The chemical foundation for the calibrations (both NIR and fluorescence) is not completely

357

clear. It was therefore important to validate these models on independent samples. Table 2

358

summarises how the prediction models based on Sampleset2 performed on SampleSet3,

359

which were made of two other barley varieties. The models for Sampleset2 were used since

360

SampleSet2 and 3 were produced by the same pearling system. It can be seen that except for

361

the model based on raw fluorescence data, the correlations between the predicted and

362

measured values of phytic acid were high (0.95-0.97). For all the models there were unwanted

363

issues with offsets or biases compared to the reference values, but Figure 6 shows that the

364

predicted values were quite accurate and useful. All the predicted values were in the right

365

concentration range and they followed the same trend with pearling degree as did measured

366

phytic acid. On samples from 'Pihl', the NIR predicted values were very close to the reference

367

values, while for those from 'Pirona' the predicted values were systematically below. The

368

fluorescence model performed at about the same level as NIR. 16 ACS Paragon Plus Environment

Page 16 of 33

Page 17 of 33


369

There are differences between varieties of barley, and for the optical measurements described

370

here, this can probably result in systematic offsets as seen for the NIR predictions for 'Pirona'.

371

That means that for process purposes, it might be necessary to follow the progression of the

372

pearling dust during pearling rather than to rely on absolute values.

373

The model based on raw fluorescence spectra did not perform well on the test samples, while

374

the normalised spectra worked well. This indicates that the shape of the spectra are more

375

important than the actual intensities. Pre-processing of fluorescence spectra from front face

376

measurements of intact bio-samples for quantitative modelling is not a well explored topic,

377

and should be more thoroughly elucidated.

378

Multivariate curve resolution

379

MCR was attempted only on fluorescence data, since these spectra apparently consisted of

380

rather few spectral components. Normalised fluorescence spectra from SampleSet2 and

381

SampleSet3 were together subjected to MCR and three dominant spectral components were

382

found (Figure 7). Based on these components their relative concentrations could be estimated,

383

shown in Figure 8 for a set of pearling fractions for each of the varieties 'Olve', 'Marigold',

384

'Pihl' and 'Pirona'. One component shows a maximum at 335 nm for excitation 280 nm, which

385

do probably originate from tryptophan with possible contributions from tyrosine and

386

phenylalanine. The estimated concentrations for this component increases steadily with

387

pearling time and indicates the amount of endosperm in the fractions. The second component

388

has a maximum emission around 430 nm for excitation at 280 nm and at about 445 nm for

389

excitation at 350 nm. These are typical properties for phenolic acids. The concentration of this

390

component tends to follow a quite similar course to phytic acid and the aleurone layer (Figure

391

2). The third spectral component has a broad maximum around 550 nm for excitation 470 nm,



392

and it decreased quite rapidly with pearling time. The identification of this component is not

393

clear, however it has previously been detected in pericarp.13

394

The estimated concentrations for the second component correlated well with the

395

concentrations of phytic acid, ferulic acid and caffeic acid (0.95, 0.95 and 0.98, respectively)

396

for the 'Pihl' and 'Pirona' samples. For SampleSet2, 'Olve' and 'Marigold', the corresponding

397

correlations were lower (0.64, 0.88, 0.73, respectively) mainly due to mismatch with the first

398

pearling fraction in every pearling series. If these were removed from the data set, the

399

correlations increased notably (0.90. 0.90 and 0.93, respectively). It is clear that the intensity

400

of fluorescence from the outer pericarp does not correlate well with the amount of phenolic

401

acids. Even though the fluorescent ferulic acid is present in the pericarp, the fluorescence is

402

very low. A reason might me that the fluorescence is quenched or reabsorbed by other

403

molecules in this tissue.

404

Overall, the results illustrate that it is possible to decompose rather complex fluorescence

405

spectra from intact pearling fractions into approximate concentrations of aleurone, endosperm

406

and pericarp. This approach is excellent for interpretation of the spectroscopic data, and might

407

also be used for continuous and semi-quantitative process monitoring.

408

The results from this work indicate that it is possible to estimate the amount of phytic acid,

409

and therefore the amount of aleurone, in pearling dust fractions from barley by both NIR and

410

fluorescence spectroscopy. The techniques are rapid and no sample pre-treatment is required,

411

which means that they can be used on-line in connection with industrial pearling systems.

412

NIR is well established in the cereal industry and suitable equipment is available for on-line

413

use on flour streams. A system for fluorescence spectroscopy would in principle be easy to

414

make, but might not presently be ready available off the shelf. In this work we used

415

concatenated emission spectra for three different excitations, but it might be sufficient to use


Page 18 of 33

Page 19 of 33


416

two or even just one excitation. Based on modelling the 280 nm excitation was the most

417

informative, then 350 nm, and 470 nm the least informative.

418

There are systematic differences between barley varieties. For example, 'Olve' seems to

419

contain more phytic acid than 'Marigold' (Figure 2b), and 'Pirona' might contain more than

420

'Pihl'. It means that an aleurone peak in dust fractions of 'Olve' will have a higher phytic acid

421

content than the corresponding fractions from 'Marigold'. The level of phytic acid in the

422

grains at hand must probably be monitored over time in the process before process

423

optimisation can be performed.

424

An on-line system can be used to adjust the pearling time for each batch to optimise the

425

amount and flour quality and to minimise the amount of pearling dust. One can also envision

426

specially designed pearling systems where it can be possible to follow the pearling process

427

and automatically extract fractions with high concentrations of aleurone and at the same time

428

control the process towards optimal utilisation of the raw material.

429

430

Acknowledgements

431

This work was partially funded by the EU-project BarleyBoost and the Norwegian

432

Agricultural Food Research Foundation through the projects Food Imaging (project

433

number 225347/F40) and Food for Health - Dietary fibre and glycaemic carbohydrates

434

(225352/F40). Hanne Zobel is thanked for skilled technical assistance.

435

436 437



438

References

439

1.

Izydorczyk, M.S.; Symmons, S.J.; Dexter, J.E. Fractionation of wheat and barley. In

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Whole-grain foods in health and desease. Marquart, L.M., Slavin, J.L., Fulcher, R.G.,

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Eds.: American Association of Cereal Chemists, St. Paul, MN, 2002. 2. Jadhav, S.J.; Lutz, S.E.; Ghorpade, V.M.; Salunkhe, D.K. Barley: chemistry and

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value-added processing. Crit. Rev. Food Sci. Nutr. 1998, 38, 123-171.

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Hemery, Y.; Rouau, X.; Lullien-Pellerin, V.; Barron, C.; Abecassis, J. Dry processes to

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develop wheat fractions and products with enhanced nutritional quality. J. Cereal Sci.

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2007, 46, 327-347.

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Hemery, Y.; Lullien-Pellerin, V.; Rouau, X.; Abecassis, J.; Samson, M.F.; Åman, P.; von

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Reding, W.; Spoerndli, C.; Barron, C. Biochemical markers: Efficient tools for the

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assessment of wheat grain tissue proportions in milling fractions. J. Cereal Sci. 2009, 49,

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55-64.

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EFSA. 2011. Scientific Opinion on the substantiation of health claims related to beta-

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glucans from oats and barley and maintenance of normal blood LDL-cholesterol

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concentrations (ID 1236, 1299), increase in satiety leading to a reduction in energy intake

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(ID 851, 852), reduction of post-prandial glycaemic responses (ID 821, 824), and

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“digestive function” (ID 850) pursuant to Article 13(1) of Regulation (EC) No

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1924/20061. EFSA J. 2011, 9, 2207.

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6. Holtekjølen, A.K.; Sahlstrøm, S.; Knutsen, S.H. Phenolic contents and antioxidant

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activities in covered whole-grain flours of Norwegian barley varieties and in fractions

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obtained after pearling. Acta Agric. Scand., Sect. B. 2011, 61, 67-74.

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7.

Blandino, M.; Locatelli, M.; Sovrani, V.; Coïsson J.D.; Rolle, L.; Travaglia, F.; Giacosa,

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S.; Bordiga, M.; Scarpino, V.; Reyneri, A.; Arlorio, M. Progressive pearling of barley

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kernel: chemical characterization of pearling fractions and effect of their inclusion on the 20 ACS Paragon Plus Environment

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nutritional and technological properties of wheat bread. J. Agric. Food Chem. 2015, 63,

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5875–5884.

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8.

Barron, C.; Holopainen-Mantila, U; Sahlstrøm, S.; Holtekjølen, A.K.; Lullien-Pellerin, V.

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Assessment of biochemical markers identified in wheat for monitoring barley grain

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tissue. J. Cereal Sci. In press. Accepted 09.01.2017.

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9.

Schmidt, J.; Gergely, S.; Schönlechner, R.; Grausgruber, H.; Tömösközi, S; Salgó, A.;

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Berghofer E. Comparison of different types of NIR instruments in ability to measure β-

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glucan content in naked barley. Cereal Chem. 2009, 86, 398–404.

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10. Pande, R.; Mishra, H.N. Fourier transform near-infrared spectroscopy for rapid and

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simple determination of phytic acid content in green gram seeds (Vigna radiata). Food

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Chem. 2014, 172, 880–884.

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11. Parrish, F.W.; Madacsi, J.P.; Phillippy, B.Q.; Wilfred, A.G.; Buco, S.M. Determination

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of phytic acid in cottonseed by near-infrared reflectance spectroscopy. J. Agric. Food

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Chem. 1990, 38, 407-409

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12. Fulcher, R.G.; O’Brien, T.P.; Lee, J.W. Studies on the aleurone layer 1. Conventional and

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fluorescence microscopy of the cell wall with emphasis on phenol-carbohydrate

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complexes in wheat. Aust. J. Biol. Sci. 1972, 25, 23-34.

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13. Jensen, S.Å.; Munck, L.; Martens, H. The botanical constituents of wheat and wheat

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milling fractions. I. Quantification by autofluorescence. Cereal Chem. 1982, 59, 477-484.

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14. Symons, S.J.; Dexter, J.E. Computer analysis of fluorescence for the measurement of

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flour refinement as determined by flour ash content, flour grade color, and tristimulus

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color measurements. Cereal Chem. 1991, 68, 454-460.

485

15. Hole, A.S.; Grimmer, S.; Naterstad, K.; Jensen, M.R.; Paur, I.; Johansen, S.G.; Balstad,

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T.R.; Blomhoff, R.; Sahlstrom, S. Activation and inhibition of nuclear factor Kappa B



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activity by cereal extracts: Role of dietary phenolic acids. J. Agric. Food Chem. 2009, 57,

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9481-9488.

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16. Hole, A.S.; Rud, I.; Grimmer, S.; Sigl, S.; Narvhus, J.; Sahlstrom, S. Improved

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bioavailability of dietary phenolic acids in whole grain barley and oat groat following

491

fermentation with probiotic Lactobacillus acidophilus, Lactobacillus johnsonii, and

492

Lactobacillus reuteri. J. Agric. Food Chem. 2012, 60, 6369-6375.

493 494

495 496 497 498 499

17. Barnes, R.J.; Dhanoa, M.S.; Lister, S.J. Standard normal variate transformation and detrending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 1989, 43, 772-777. 18. Martens, H.; Naes, T. Multivariate Calibration; Wiley: Chichester, U.K., 1989. 19. Tauler, R. Multivariate curve resolution applied to second order data. Chemom. Intell. Lab. Syst. 1995, 30, 133–146. 20. Tauler, R.; Smilde, A.; Kowalski, B. Selectivity, local rank, 3-way data analysis and ambiguity in multivariate curve resolution. J. Chemom. 1995, 9, 31-58.

500

21. Ndolo, V.U; Beta, T.; Fulcher, R.G. Ferulic acid fluorescence intensity profiles and

501

concentration measured by HPLC in pigmented and non-pigmented cereals. Food Res.

502

Int. 2013, 52, 109-118.

503

22. Kays, S.E.; Shimizu, N.; Barton, F.E.; Ohtsubo, K. Near-infrared transmission and

504

reflectance spectroscopy for the determination of dietary fiber in barley cultivars. Crop

505

Sci. 2005, 45, 2307-2311.

506

23. Lichtenthaler, H.K.; Schweiger, J. Cell wall bound ferulic acid, the major substance of

507

the blue-green fluorescence emission of plants. J. Plant Physiol. 1998, 152, 272-282.

508 509

510


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Figure captions

512 513

Figure 1 Stereomicrographs (upper panel) and epifluorescence micrographs (lower panel) of

514

grains (cv. 'Olve') with different degree of pearling: (A) Native, unpearled grain; grains with

515

pearling degree of 14% (B), 27% (C), 32% (D) and 39% (E). In epifluorescence images,

516

aleurone layer appears bright blue surrounding starchy endosperm stained red. Scale bars

517

represent 5 mm and 500 µm in the lower and upper panel images, respectively.

518 519

Figure 2 Concentration of phytic acid in pearling fractions from (A) 'Olve' (red) and

520

'Marigold' (blue) (SampleSet1), (B) 'Olve' (red) and 'Marigold' (blue) (SampleSet2), and

521

(C) 'Pihl' (blue) and 'Pirona' (red) (SampleSet3).

522 523

Figure 3 Concentration of (A) Ferulic acid, (B) p-Coumaric acid, (C) caffeic acid, and

524

(D) Sinapic acid in pearling fractions from 'Olve' (red) and 'Marigold' (blue) from

525

SampleSet2.

526 527

Figure 4 NIR spectra from dust fractions after pearling. Absorption spectra (left), SNV

528

corrected spectra (right). Line colors indicate different pearling degrees.

529 530

Figure 5 Fluorescence spectra from dust fractions after pearling. Line colors indicate different

531

pearling degrees.

532 533

Figure 6 Predicted phytic acid in 32 pearling fractions of barley varieties 'Pihl' (upper panel)

534

and 'Pirona' (lower panel). Referenced phytic acid (black line), predicted by fluorescence

535

(blue line), predicted by NIR (red line).

536 537

Figure 7 Estimated spectral components from fluorescence spectra by MCR. Component 1

538

(green), component 2 (red), component 3 (blue).

539 540

Figure 8 Estimated concentrations of MCR components based on fluorescence spectra from

541

different barley varieties, A) 'Marigold', B) 'Olve', C) 'Pihl', D) 'Pirona'. Component 1 (green),

542

component 2 (red), component 3 (blue).

543



Page 24 of 33

544

Tables

545

Table 1 Results for Regression Models Based on NIR, Cross-Validated Models. Pearling data set Spectral data # PLSR comp. R2 RMSECV SampleSet1 NIR Absorption 2 0.86 0.23 NIR SNV 4 0.85 0.24 SampleSet2 NIR Absorption 5 0.94 0.20 NIR SNV 6 0.96 0.16 SampleSet1 + NIR Absorption 7 0.87 0.28 SampleSet2 NIR SNV 8 0.89 0.26 SampleSet1 Fluorescence 2 0.85 0.23 Fluorescence norm 3 0.85 0.24 SampleSet2 Fluorescence 5 0.95 0.18 Fluorescence norm 4 0.96 0.18 Trial 1+3 Fluorescence 5 0.92 0.22 Fluorescence norm 5 0.87 0.28

546

547

Table 2 Results for Prediction of Test Set Pearling data set Spectral data NIR Absorption SNV Fluorescence Raw fluorescence Unit area norm

R2 0.95 0.97 0.49 0.97

548 549

550

551

552


RMSEP 1.47 0.35 1.14 0.36

Offset -1.04 0.17 2.20 0.65

Page 25 of 33


553

FIGURES

A

B

C

D

Figure 1

Figure 1


E


3.5

A

3

g/100g

2.5 2 1.5 1 0.5 0 5

15

25

35

45

55

15

25

35

45

55

25

35

45

55

3.5

B

3.0

g/100g

2.5 2.0 1.5 1.0 0.5 0.0 5 4.5

C

4 3.5

g/100g

3 2.5 2 1.5 1 0.5 0 5

15

Pearling degree (% w/w)

Figure 2


Page 26 of 33

Page 27 of 33


A

B

C

D

Figure 3



Page 28 of 33

B

A

Figure 4


Page 29 of 33


Figure 5

250000

F lu o res cen ce in ten s ity

600000

40000

λex= 280 nm

λex= 360 nm

0

0

300

400

500

10 % (w/w) 14 % (w/w) 18 % (w/w) 22 % (w/w) 26 % (w/w) 30 % (w/w) 33 % (w/w) 38 % (w/w)

λex= 470 nm

0

400

450

500

550

480

520

Emission wavelength (nm)

Figure 5


560

600

640

680

4

4

3.5

3.5

3

3

2.5

2.5

g/100g

g/100g


2

2

1.5

1.5

1

1

0.5

0.5

0

0 5.0

15.0

25.0

35.0

45.0

5.0

Pearling degree (%)

15.0

25.0

35.0

45.0

Pearling degree (%)

4.5

4.5

4

4

3.5

3.5

3

3

g/100g

g/100g

Page 30 of 33

2.5 2

2.5 2

1.5

1.5

1

1

0.5

0.5

0

0 5.0

15.0

25.0

35.0

45.0

5.0

Pearling degree (%)

15.0

25.0

35.0

Pearling degree (%)

Figure 6 554


45.0

Page 31 of 33


0.1

0.08

0.1

0.07 0.08

0.08 0.06 0.05

0.06

0.06

0.04 0.04

0.04

0.03 0.02

0.02

0.02 0.01

0 300

350

400

450

500

Wavelength (nm)

0 400

0 450

500

550

600

Wavelength (nm)

Figure 7


500

550

600

Wavelength (nm)

650

MCR estimated concentrations


1.8

B

1.6

1.4

1.4

1.2

1.2

1

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0 5

MCR estimated concentrations

1.8

A

1.6

Page 32 of 33

1.8

15

25

35

45

55

5 1.8

C

1.6

1.4

1.2

1.2

1

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

25

35

45

55

D

1.6

1.4

15

0 5

15

25

35

45

5


15

25

35


Figure 8


45

Page 33 of 33


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