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Vibrational Spectroscopy and Chemometrics for Rapid, Quantitative Analysis of Bitter Acids in Hops (Humulus lupulus) Daniel Patrick Killeen, Dave H. Andersen, Ron A. Beatson, Keith C. Gordon, and Nigel B. Perry J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/jf5042728 • Publication Date (Web): 08 Dec 2014 Downloaded from http://pubs.acs.org on December 14, 2014

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Journal of Agricultural and Food Chemistry

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Vibrational Spectroscopy and Chemometrics for Rapid, Quantitative Analysis of Bitter

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Acids in Hops (Humulus lupulus)

3

Daniel P. Killeen,†,‡ David H. Andersen,§ Ron A. Beatson,§ Keith C. Gordon†,‡ and Nigel B.

4

Perry*,†,┴

5 6 7 8 9 10 11

†

Department of Chemistry, University of Otago, P. O. Box 56, Dunedin, New Zealand

‡

MacDiarmid Institute for Advanced Materials and Nanotechnology, University of Otago,

Dunedin, New Zealand §

The New Zealand Institute for Plant & Food Research Limited, 55 Old Mill, RD 3, Motueka

7198, New Zealand ┴

The New Zealand Institute for Plant & Food Research Limited, Department of Chemistry,

University of Otago, P.O. Box 56, Dunedin, New Zealand

ACS Paragon Plus Environment


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ABSTRACT: Hops, Humulus lupulus, are grown worldwide for use in the brewing industry

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to impart characteristic flavour and aroma to finished beer. Breeders produce many varietal

14

crosses with the aim of improving and diversifying commercial hops varieties. The large

15

number of crosses critical to a successful breeding program imposes high demands on the

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supporting chemical analytical laboratories. With the aim of reducing the analysis time

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associated with hops breeding, quantitative partial least squares regression (PLS-R) models

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have been produced, relating reference data, acquired by the industrial standard HPLC and

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UV methods, to vibrational spectra of the same, chemically diverse hops sample set. These

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models, produced from rapidly acquired IR, NIR and Raman spectra, were appraised using

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standard statistical metrics. Results demonstrated that all three spectroscopic methods could

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be used for screening hops for α-acid, total bitter acids and cohumulone concentrations in

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powdered hops. Models generated from Raman and IR spectra also showed potential for use

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in screening hops varieties for xanthohumol concentrations. NIR analysis was performed

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using both a standard bench-top spectrometer and a portable NIR spectrometer, with

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comparable results obtained by both instruments. Finally, some important vibrational features

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of cohumulone, colupulone and xanthohumol were assigned using DFT calculations allowing

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more insightful interpretation of PLS-R latent variable plots.

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KEYWORDS: hops, Humulus, Raman, infrared, chemometrics, α-acids


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■ INTRODUCTION

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Hops (Humulus lupulus L., Cannabaceae) are a high value crop grown worldwide for use in

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the brewing industry.1 Female plants are cultivated for their inflorescences, which produce

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large quantities of terpenes and bitter acids (Figure 1) in extracellular trichomes known as

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lupulin.1 The terpenes and their oxidation products impart aroma to finished beer while the

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bitter acids are precursors to compounds responsible for beer’s distinctive bitter taste.2 The

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trichomes also produce xanthohumol, a prenyl chalcone with potential health benefits (Figure

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1).3 Commercial hops are traditionally divided into two categories: bittering hops with high

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α-acid contents; and aroma hops which impart volatile terpenes with favourable aromas.

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Some hops varieties are used for both purposes.4

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Hops bitter acids, the focus of this work, comprise two structurally related families: α-

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acids (humulones) and β-acids (lupulones) (Figure 1). The latter exist in solution in major

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tautomeric forms (Figure 1).5,6 The α-acid concentration of a hops variety is of primary

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concern to the brewing industry because these compounds undergo thermal isomerization,

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forming the intensely bitter iso-α-acids.7 Total bitter acids (α plus β) and cohumulone

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concentrations are also important for breeding selections.8 Bitter acid concentrations of hop

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varieties are measured commercially using UV spectrophotometry and/or HPLC.9-11 UV

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analysis is rapid but requires solvent extraction and serial dilution of hops samples. HPLC

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shares these drawbacks and additionally requires longer analysis times. However, HPLC is

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more specific and can be used to quantitate cohumulone concentrations within total α-acids.

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In non-commercial settings, solvent extracted α-acids have been quantitated by 1H

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NMR spectroscopy using their distinctive intramolecularly hydrogen-bonded proton signals

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at 18 – 20 ppm.12,13 NMR has also been combined with HPLC to unambiguously identify the

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individual bitter acids.14 Several reports show the potential of near infrared (NIR)

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spectroscopy for the quantitation of bitter acids15-17 but we are not aware of any reports using



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Raman or IR spectroscopy for hops analyses. General advantages of the vibrational

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spectroscopic approach include the potential for rapid analysis, reduced sample preparation

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and portable analysis.18-20

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When relating vibrational spectra to reference data i.e. data acquired using an

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established technique, the most commonly used chemometric method is partial least squares

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regression (PLS-R).21-25 PLS-R iteratively maximizes the covariance between the spectral

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matrix and the reference data and produces highly interpretable latent variables (LVs) which

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describe diminishing quantities of spectral variance in the context of the reference data.26,27

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LVs extract problem specific data, which means that a single spectral matrix can be

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successively tuned to different reference data using PLS-R.28 This approach is particularly

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relevant to hops, where quantitation of the structurally similar α- and β-acids in addition to

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the bioactive xanthohumol is desirable.

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PLS-R has recently been successfully applied to quantitation of iridoid glycosides

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from Verbena by IR and NIR24, aspalathin in green rooibos by Raman23 and caffeine in

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roasted coffee beans by NIR.29 As suggested by these examples, studies generally use only

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one or two vibrational spectroscopy techniques, a practice that risks missing the most suitable

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technique. Analysis by all three techniques results in complementary datasets which can be

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appraised relative to one another and in the context of their performances for a given

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application. In some cases, analytical challenges have been overcome using “fused”

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spectroscopic datasets.30

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To better understand the PLS-R models, cohumulone, colupulone and xanthohumol

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were isolated, their Raman and IR spectra were measured, and the vibrational bands were

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assigned using density functional theory (DFT) calculations. These assignments allowed the

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interpretation of LVs in the generated PLS-R models, which in turn permitted a more

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insightful understanding of model performances.


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■ MATERIALS AND METHODS Plant Material. Hops were grown at Motueka, South Island, New Zealand (41° 6’ S,

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172° 58’ E) in Riwaka silt loam. Advanced selections (139 individual plants encompassing

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39 genetic varieties) from hops breeding trials were harvested in March 2013. Hops cones

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were kiln dried at ~ 60 °C for 8 h followed by determination of residual water content. Sub-

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samples, ~ 5 g of cones, were pulverized under N2 (l) and stored at –20 °C prior to analyses.

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Isolation of Hops Bitter Acids. An aliquot of the hops International Calibration

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Extract (ICE-3) was purchased from the American Society of Brewing Chemists (ASBC). A

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solution containing ~ 100 mg mL-1 of ICE-3 in MeOH was filtered before injection onto an

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Agilent 1260 Infinity semi-preparative HPLC system equipped with a Phenomenex Luna

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(5µ) C18 column (10 x 250 mm) with UV detection at 210 nm. Injections (50 µL ) were

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eluted over 18 min in 87.9:12:0.1 MeOH:H2O:formic acid, with a flow rate of 5 mL min-1.

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Fractions were collected at retention times of 7.3, 8.4, 10.8 and 12.9 min and identified by 1H

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NMR as cohumulone, a mixture of humulone/adhumulone, colupulone and a mixture of

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lupulone/adlupulone respectively by comparison to published spectra.6 Using the same

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methodology, xanthohumol was isolated from a MeOH extract of hops inflorescences. This

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compound had a retention time of 5.5 min. The compound was identified by 1H NMR by

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comparison to published spectra.31

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Quantitation of Bitter Acids by UV Spectrophotometry. Hops cones (10 g) were

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added to 100 mL of toluene for extraction in an Omni mixer. Over a 10 min period, samples

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were sequentially blended and rested for 30 s periods. Samples were filtered and 50 µL was

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diluted to 25 mL with alkaline MeOH. The UV absorbances at 275, 325 and 355 nm were

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recorded using a UV spectrophotometer, with 50 µL of toluene in alkaline MeOH (25 mL) as

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a blank. This was adapted from a standard industry method.10



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Quantitation of Bitter Acids and Xanthohumol by HPLC. Hops were extracted as

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described above and 3 mL was transferred to a 50 mL volumetric flask and made to volume

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with MeOH. 10 µL of this solution was injected onto a Shimadzu SCL10Avp HPLC system

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equipped with a 250 x 4.6 mm, 5 µm Kinetix C18 column at 30 °C at a flow rate of 1.5 mL

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min-1. The mobile phase was MeOH:H2O:phosphoric acid:0.1 M EDTA (1700:350:5:1) with

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UV detection at 270 nm. Run time was 18 min. This was adapted from a standard industry

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method.9

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IR Analysis. Hops powder (~ 10 mg) was deposited directly onto the ATR crystal

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and compressed by the anvil mechanism. Spectra were acquired using a Bruker ALPHA

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Platinum FT-IR spectrometer equipped with a diamond ATR crystal. Spectra were recorded

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from 376 – 4000 cm-1 with a spectral resolution of 4 cm-1 and were the average of 32 scans.

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Prior to acquisition of each spectrum, the crystal and anvil were thoroughly cleaned and a

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background spectrum was acquired to account for any environmental changes throughout the

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analysis. Spectral acquisition time was ~ 1 min per sample.

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Raman Analysis. Hops powder (~ 50 mg) was packed into aluminium divots for

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analysis. Spectra were acquired using a FRA 106/5 Bruker Equinox FT-Raman spectrometer

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equipped with a Nd:YAG laser emitting at 1064 nm, Equinox 55 interferometer and a

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germanium detector cooled with N2 (l). The Raman stokes shift was recorded from 200 –

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3500 cm-1 with a spectral resolution of 4 cm-1, spot size of 0.3 mm and laser power of 120

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mW. Spectra were the average of 256 scans and the acquisition time was ~ 8 min per sample.

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Raman analysis with excitation at 830 nm and 785 nm could not distinguish Raman bands

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from raised baselines due to emission. These Raman systems have previously been described

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in detail.21

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NIR Analysis. Hops powder (~ 1 g) was added to a sample cup and the surface was smoothed with gentle compression. Background spectra, consisting of diffusely reflected


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light from a reflective surface were taken prior to each sample acquisition. Diffuse

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reflectance infrared Fourier transform (DRIFT) spectra were recorded, using a FRA 106/5

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Bruker FT-NIR spectrometer equipped with an Equinox 55 interferometer and germanium

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detector cooled with N2 (l), from 4000 – 10000 cm-1 with a resolution of 8 cm-1 in Kubelka-

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Munk units. Spectral acquisition time was ~ 1 min per sample.

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Portable NIR Analysis. Hops powder (~ 1 g) were presented to the MicroNIR®

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spectrometer (JDS Uniphase Corporation) in clear, 4 mL glass vials and reflectance spectra

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were acquired through the base of the vial. A spectral acquisition time of 1 s was used and

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analysis was performed in triplicate, shaking the vial between acquisitions. Two tungsten

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filaments were used to produce polychromatic radiation and diffusely reflected light was

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dispersed with a linear variable filter onto a 128-pixel uncooled InGaAs photodiode array

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detector. Spectra were recorded from 4545 – 8696 cm-1 (1150 – 2100 nm ) with a spectral

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resolution of 32 cm-1.

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Density Functional Theory (DFT) Calculations. The molecular geometries of

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xanthohumol, cohumulone and both colupulone conformers (Figure 1) were optimized with

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DFT using the 6-31+G(d) basis set. A correction factor of 0.960 was applied to the predicted

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vibrational frequencies to correct for their systematic overestimation.32-34 The mean absolute

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deviation between the calculated and experimental vibrational bands was 13 cm-1. Quantum

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chemical calculations were performed using Gaussian 09 and, where possible, initiated from

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published crystal structures.31,35

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Regression Modeling. Quantitative IR spectroscopy. The IR spectrum of each sample

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was imported into the multivariate software “The Unscrambler®”. A Standard Normal Variate

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(SNV) transformation was applied to each spectrum to compensate for scattering effects

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arising from experimental factors such as varying optical path lengths, sample inhomogeneity

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and background effects.36 Models were generated from the spectral region from 850 – 1700



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cm-1 (the spectral region below 850 cm-1 and 1810 – 2700 cm-1 were omitted as IR spectra of

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purified bitter acids (Figure 3(a)) did not contain any distinct and/or intense vibrational bands

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in these regions, and the spectral region above 2700 cm-1 was also omitted because the C-H

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and O-H stretching vibrations of many biological compounds contribute intensity in this

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region). One third of the sample spectra were selected at random to be used as a test set for

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model validation. The remaining two thirds were used as a calibration set to generate PLS-R

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models (non-iterative, PLS127) relating the selected spectral regions to the reference values

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from the UV and HPLC analyses (Figure 2). This independent ‘test-set’ validation

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methodology is considered robust.37 Models were used to predict test set values from their IR

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spectra with selected results in Table 1. The number of LVs selected was the optimum for

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predicting values in the independent validation test-set. Data for all models are included in

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supplementary information Table S1.

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Quantitative Raman spectroscopy. The spectral region from 1050 – 1750 cm-1 was

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used to generate PLS-R models relating Raman spectra to UV and HPLC reference values

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(Figure 2). This spectral region was selected by comparison to the spectra of purified bitter

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acids (Figure 3(b)). The same validation methodology was used, with the same samples

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comprising the calibration and test sets. Selected results of the test set analyses are shown in

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Table 1. Data for all models are included in supplementary information Table S1.

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Quantitative NIR spectroscopy. PLS-R models were generated from 4000 – 7300 cm-1

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from the DRIFT NIR spectra. This spectral region was chosen based on visual inspection and

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correlation loadings, which suggested that the noisy spectral region from 7300 – 10000 cm-1

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could not effectively be related to the reference data. For the NIR spectra measured with the

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portable NIR spectrometer, regression models were generated from entire spectral range

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acquired (4545 – 8696 cm-1). In both cases, spectra were subjected to SNV and used to

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generate PLS-R models relating them to UV and HPLC reference data (Figure 2). The


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calibration and test sets were comprised of the same samples used for in the Raman and IR

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analyses and selected model performances are presented in Table 1. Data for all models are

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included in supplementary information Table S1.

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■ RESULTS AND DISCUSSION

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Thirty nine hops varieties, with 1-9 replicate samples each (139 total) were used in this study,

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including both well-known commercial varieties and genetically novel selections chosen to

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encompass broad chemotypical differences. The novel selections were designated

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sequentially based on their associated breeding trials i.e. Alpha, Aroma and Brewing (Brew)

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(Figure 2).

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Reference Analyses. Bitter acid concentrations for all 139 hops samples were

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determined by the standard industry methods of HPLC9 and UV spectrophotometry.10 The

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average α-acid and β-acid concentrations for each variety by UV are shown in Figure 2, plus

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HPLC results for the concentrations of the α-acids cohumulone and the coeluting

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adhumulone and humulone, the β-acids colupulone and the coeluting adlupulone and

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lupulones, and the chalcone xanthohumol (Figure 1). α-Acid concentrations were 2 – 17 %

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w/w and β-acids 2 – 8 %, close to the full range of values reported for wild and domesticated

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hops varieties.17,38 Xanthohumol concentrations of 0.1 – 0.8 % (Figure 2) also covered almost

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the full reported range.3

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Total α-acids and total β-acids measured by the HPLC and UV methods showed good

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agreement between the results of both analyses, with correlation coefficients of 0.99 and 0.95

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respectively. However, the absolute values for α- and β-acids by UV spectrophotometry were,

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on average, 10% larger than values from HPLC analyses. It has been reported that the UV

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analysis method is subject to interference from bitter acid oxidation products, which absorb at

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analytically relevant wavelengths and cause this discrepancy.39 Total α-acid concentrations



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did not correlate strongly with total β-acid concentrations: r2 = 0.36 (UV) and 0.39 (HPLC).

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Likens et al. found that α-acid concentrations were negatively correlated to β-acid

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concentrations in lupulin from different hops varieties but that the variation in lupulin content

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between hops varieties confounded this correlation.39 The key quality indicators for breeding

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selections i.e. total α-acids, total bitter acids (α plus β) and cohumulone concentrations, were

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strongly correlated to one another. The correlations for the HPLC results were: total α-acids

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versus total bitter acids, r2 = 0.95; total α-acids versus cohumulone, r2 = 0.82. The α-acids

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were the main bitter acids in most hops varieties, and the proportion of cohumulone in the α-

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acids was 17 – 40 % in these varieties (Figure 2). Vibrational Spectroscopic Analyses. These same 139 hops samples were analysed

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by the different vibrational spectroscopy techniques of IR, Raman and NIR (two instruments)

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to assess which was best suited to the application of rapid screening of bittering hops. PLS-R

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models were generated, relating spectral matrixes to each of the UV and HPLC reference

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data, and these models were categorized using the Ratio of Prediction to Deviation (RPD)

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methodology described by Williams (Table 1).40 “Prediction” refers to the standard error of

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prediction (SEP) of the test set values predicted by the calibration model and “Deviation”

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refers to the standard deviation of the corresponding reference data. The scale is used to

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broadly categorizing PLS-R models by their fitness for purpose and was useful for our data

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set, which contained an unwieldy number of models with a large range of performances. The

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RPD scale defines models with values of ≥3.1 as suitable for quantitative screening

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applications.40 Details of hops models which met this criterion are shown in Table 1. These

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models predicted the concentrations of the most important chemical values for breeding

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selections i.e. total α-acids, cohumulone, total bitter acids (α plus β) and the bioactive

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xanthohumol. Results from the other models can be found in supporting information (Table

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S1).


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The RPD values (Table 1) showed that spectra from all three vibrational spectroscopy

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techniques could be used to predict total α-acids and total bitter acids as determined by

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industry standard UV analyses. However, the portable NIR analysis, which had a RPD value

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of 3.0 for total α-acids, fell just short of the suitability criterion. The spectroscopic techniques

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could also be used to predict total α-acids and total bitter acids as determined by HPLC

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analyses, with the exception of the IR models, which fell short of the criterion. The IR spectra

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could be used to predict concentrations of the minor α-acid cohumulone, as could Raman

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spectra, but NIR spectral analyses failed (Tables 1 and S1).

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We had expected that xanthohumol would be readily quantifiable by Raman

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spectroscopy as the compound was predicted to show strong Raman scattering (see below).

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However, the RPD values suggested that both Raman and IR spectra would not be

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appropriate for quantitative analysis (Table 1), highlighting the difficulty of predicting which

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vibrational spectroscopic technique will be most suitable and illustrating how different

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vibrational techniques can be appropriate for different analytes. Models predicting total and

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individual β-acids failed for all spectroscopic techniques (RPD values 1.0 – 2.4), but these

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components are not considered critical for the screening of hops varieties (Table S1).

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Assignment of IR and Raman Spectra of Bitter Acids and Xanthohumol. DFT

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calculations were used to predict the optimized geometries of cohumulone, colupulone and

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xanthohumol. The predicted IR and Raman vibrational modes were compared to the

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experimental IR (Figure 3(a)) and Raman (Figure 3(b)) spectra of cohumulone and

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colupulone. Calculated values were in good agreement with the experimental, with a mean

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absolute deviation value of 13 cm-1 for the assigned bands.

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Most of the IR spectral differences occurred in the region from 1700 – 1000 cm-1

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(Figure 3(a)). The ring carbonyl band occurred at 1663 cm-1 in cohumulone and at 1652 cm-1

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in colupulone. These were in good agreement with the DFT calculations which predicted the



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bands at 1663 and 1660 cm-1 respectively. A medium intensity band was observed as a

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shoulder in cohumulone at 1579 cm-1 and an equivalent colupulone band was observed at

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1581 cm-1. These bands were assigned to the keto-enol carbonyl stretching vibration,

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predicted at 1603 and 1608 cm-1 for cohumulone and colupulone respectively. The relatively

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low frequencies of these carbonyl bands were consistent with previous vibrational

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spectroscopic and DFT studies performed on compounds bearing similar intramolecularly

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hydrogen bonded groups.41,42 The intense band at ~1525 cm-1 in both compounds was due to

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a conjugated mode, involving large displacement of the intramolecularly bonded proton.

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Lastly, a number of overlapping C-H bending modes gave rise to a strong band at ~1440 cm-1

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in both compounds.

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Selected regions of the Raman spectra of cohumulone and colupulone are shown in

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Figure 3(b). The prenyl alkene stretching vibration at 1674 ± 1 cm-1 was prominent in the

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spectra of both compounds (predicted at 1681 ± 1 cm-1). The cohumulone and colupulone

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spectra deviated from one another in the region from 1660 – 1500 cm-1 (Figure 3(b)). Four

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bands were predicted for each compound in this region with variable intensities. The

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vibrations are complex and involve displacement throughout the conjugated backbones of the

271

molecules. The bands were loosely assigned at their maxima as diene stretches. The predicted

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spectrum of the colupulone tautomer (T2) (Figure 1) had no intense vibrational modes in this

273

region, implying that the lower energy (T1) structure (Figure 1) was abundant in the solid

274

state of the compound. This was consistent with a published crystal structure.35 The other

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noteworthy bands in the Raman spectra occured at 1452, 1383 and 1352 cm-1 in both

276

cohumulone and colupulone. These were in good agreement with the calculations for

277

overlapping C-H twisting, wagging and bending modes.

278 279

IR PLS-R models predicting UV reference data. The PLS-R models predicting total α and β-acid concentrations (by UV) from IR and Raman spectral data were interpreted


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visually by comparing the first three LVs of each model to the relevant bitter acid spectra

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(Figure 4). When a LV is similar to spectra of the pure compound it implies that the model is

282

spectrally meaningful, but when the LVs no longer contain the features of the analyte, the

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model may no longer be extracting spectrally relevant information and overfitting becomes a

284

consideration.43 This approach complements the standard approach for model interpretation

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based on statistical data i.e. the validation data (Tables 1 and S1).

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In Figure 4(a), the IR spectrum of cohumulone is overlaid with the first three LVs

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from the PLS-R model for α-acids by IR spectroscopy. The first LV explained 73% of the

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reference value variance and was almost identical to the profile of cohumulone – a sure

289

indication of effective modeling. Successive LVs described less variance and deviated

290

increasingly from the spectrum of the pure compound. In Figure 4(b), LVs from the IR β-

291

acids models are overlaid with colupulone. In this case the first LV did not mimic the

292

colupulone spectrum and explained only 46% of the reference data variance. In particular,

293

one of the key lupulone bands, at 1580 cm-1, was almost absent, making the profile more like

294

cohumulone. Subsequent LVs related more specifically to β-acid concentrations and the third

295

LV, which explained 9% of the variance in the reference data, had high loading coefficients

296

around 1580 cm-1. This inspection of LVs implied that the α-acids, which were nearly always

297

present in greater concentrations (Figure 2), were interfering with chemometric quantitation

298

of β-acids, explaining the poor performance of these models (Table S1).

299

Raman PLS-R models predicting UV reference data. The Raman PLS-R LVs are

300

compared to the relevant IR and Raman spectra in Figures 4(c) and 4(d). The first LV in the

301

α-acid model had high loading coefficients around 1673, 1452, 1382 and 1352 cm-1 (Figure

302

4(c)). These bands were all observed in the spectrum of cohumulone (Figure 3(b)),

303

suggesting that the model was extracting spectrally relevant data. However, the first LV also

304

had high loading coefficients around 1608 and 1626 cm-1 which were not observed in the



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spectrum of cohumulone (Figure 3(b)). Xanthohumol, the conjugated chalcone responsible

306

for the yellow colour of lupulin (Figure 1), had intense Raman bands at both these

307

frequencies (Figure 5) and was present in the hops samples at concentrations ranging from

308

0.13 - 0.85 % (by HPLC, Figure 2). Despite its relatively low concentration, the contribution

309

of xanthohumol to the Raman spectrum of hops was pronounced due to the large and highly

310

polarizable π-conjugated system which makes the compound extremely Raman active (Figure

311

1). The relative intensities of xanthohumol and cohumulone (intensity × 10) are shown in

312

Figure 5, highlighting the extreme Raman activity of the bioactive chalcone. Figure 5 also

313

shows the vibrational displacement vectors of the most intense xanthohumol bands,

314

highlighting the complexity and delocalization of these vibrations.

315

After initial studies, it was feared that these intense xanthohumol bands would

316

interfere with PLS-R models and prohibit the use of Raman spectroscopy for the analysis of

317

hops bitter acids. This did not prove to be the case, as evidenced by the performance metrics

318

in Table 1. This was not altogether surprizing given the fact that xanthohumol concentrations

319

were correlated to α-acids (r2 = 0.68) and total bitter acids (r2 = 0.73). In the case of β-acids

320

models, xanthohumol had a more detrimental effect since concentrations of these components

321

were poorly correlated to one another (r2 = 0.46). This, in tandem with the interfering α-acids,

322

which were more concentrated in nearly all varieties, could explain the poor performance of

323

the Raman PLS-R models for β-acids (Table S1).

324

We have shown that NIR, IR and Raman spectroscopy could be used to quantitatively

325

screen for the key hops components α-acids, total bitter acids and cohumulone. It has also

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been shown that portable NIR technology could be used for this purpose. Some of the

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limitations of these techniques are also reported, including the fact that quantitation of β-

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acids is unlikely to be achieved using this approach. Although some analytical challenges

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have been overcome using “fused” spectroscopic datasets30,44, our preliminary investigation


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suggested that this approach did not improve the performance of the reported models. The

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spectroscopic techniques required less sample preparation and, in most cases, greatly reduced

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analysis times compared with traditional analyses. Solvent use was also eliminated. Some

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vibrational features of cohumulone, colupulone and xanthohumol have been assigned.

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Finally, the PLS-R LVs have been related to the pure spectra of the compounds for which

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they are predicting, allowing a more insightful appraisal of the generated PLS-R models.

336 337

■ ASSOCIATED CONTENT

338

*S Supporting Information

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Table S1: PLS-R model summaries (all models). This material is available free of charge via

340

the Internet at http://pubs.acs.org.

341

342

■ AUTHOR INFORMATION

343

Corresponding Author

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*Phone: +64 3 4798354. E-mail: [email protected].

345

Funding

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This research was supported by a University of Otago Doctoral Scholarship, in collaboration

347

with Plant & Food Research Capability Funding.

348

Notes

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The authors declare no competing financial interest.

350



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351

■ ACKNOWLEDGMENTS

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We thank R. Rushton Green, L. Graham, D. Graham and C. Sansom for their technical

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assistance and O. Watkins and E. Burgess for advice.

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Table 1. Comparison of Hops Analyses by Vibrational Spectroscopy with UV and HPLC Analyses using Partial Least Squares Regression (PLS-R)

Spectroscopic PLS-R Technique Parameters r2 (RMSEC)a IR r2 (RMSEP)b # of LVs RPDc

UV Analyses Total αTotal Acids Acids 0.92 (1.2) 0.90 (1.6) 0.91 (1.3) 0.91 (1.8) 5 5 3.4 3.3

HPLC Analyses Alpha 0.86 (1.4) 0.89 (1.4) 3 2.9

Total 0.90 (1.5) 0.89 (1.8) 5 3.0

Cohumulone 0.92 (0.3) 0.91 (0.4) 5 3.4

Xanthohumol 0.85 (0.09) 0.88 (0.07) 5 2.9

Raman

r2 (RMSEC)c r2 (RMSEP)d # of LVs RPDe

0.88 (1.5) 0.91 (1.3) 4 3.3

0.87 (1.9) 0.90 (1.8) 4 3.1

0.86 (1.5) 0.90 (1.3) 4 3.2

0.84 (1.9) 0.90 (1.7) 4 3.1

0.90 (0.4) 0.91 (0.4) 7 3.3

0.87 (0.08) 0.87 (0.08) 4 2.8

NIR DRIFT

r2 (RMSEC)c r2 (RMSEP)d # of LVs RPDe

0.87 (1.5) 0.91 (1.4) 4 3.3

0.91 (1.6) 0.93 (1.5) 4 3.7

0.86 (1.4) 0.91 (1.2) 4 3.3

0.89 (1.6) 0.92 (1.5) 4 3.6

0.82 (0.5) 0.82 (0.5) 5 2.4

0.85 (0.09) 0.78 (0.10) 6 2.1

r2 (RMSEC)c 0.82 (1.8) 0.85 (2.0) 0.81 (1.7) 0.82 (2.0) 0.76 (0.6) 0.77 (0.10) 2 d r (RMSEP) 0.88 (1.5) 0.90 (1.8) 0.90 (1.3) 0.91 (1.6) 0.76 (0.6) 0.63 (0.13) # of LVs 7 7 7 7 7 6 RPDe 3.0 2.0 1.5 3.3 3.2 3.5 a b c Root mean square error of calibration. Root mean square error of prediction. Ratio of prediction to deviation.

NIR Micro



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Figure 1. Structures of hops bitter acids and xanthohumol.

Figure 2. Concentrations of α-acids, β-acids and xanthohumol by HPLC and UV spectrophotometry.

Figure 3. IR and Raman spectral profiles of pure cohumulone and colupulone.

Figure 4. Overlay of spectral profiles of the pure bitter acids with latent variables from the Raman and IR PLS-R models predicting α-acids (a and b) and β-acids (c and d) by UV spectrophotometry. Traces are scaled for ease of comparison. Dashed lines highlight discussed bands.

Figure 5. Raman spectra of xanthohumol and cohumulone (intensity × 10) with the DFT predicted vibrational displacement vectors of the most intense xanthohumol modes inset (lengths of arrows are proportional to predicted displacements).


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Graphical Abstract


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164x101mm (96 x 96 DPI)



Structures of hops bitter acids and xanthohumol 325x251mm (72 x 72 DPI)


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Concentrations of α-acids, β-acids and xanthohumol by HPLC and UV spectrophotometry 605x1246mm (100 x 100 DPI)



107x220mm (100 x 100 DPI)


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Overlay of spectral profiles of the pure bitter acids with latent variables from the Raman and IR PLS-R models predicting α-acids (a and b) and β-acids (c and d) by UV spectrophotometry. Traces are scaled for comparison. Dashed lines highlight discussed bands. 459x323mm (300 x 300 DPI)



Raman spectra of xanthohumol and cohumulone (intensity × 10) with the DFT predicted vibrational displacement vectors of the most intense xanthohumol modes inset 632x357mm (72 x 72 DPI)


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