Download PDF
Planta Med 2009; 75(7): 746-756
DOI: 10.1055/s-0028-1112220
Plant Analysis
Mini-Review
© Georg Thieme Verlag KG Stuttgart · New York

Near Infrared Spectroscopy in Natural Products Analysis

Daniel Cozzolino1
  • 1The Australian Wine Research Institute, Waite Campus, Urrbrae, Australia
Further Information

Publication History

Received: July 22, 2008 Revised: November 23, 2008

Accepted: November 27, 2008

Publication Date:
22 January 2009 (online)

   Permissions and Reprints

Abstract

Several medicinal and herbal plants properties are related to individual compounds such as essential oils, terpenoids, flavonoids, which are present in natural products in low concentrations (e. g., ppm or ppb). For many years, the use of classical separation and chromatographic and spectrometric techniques such as high performance liquid chromatography (HPLC), gas chromatography (GC), liquid chromatography (LC) and mass spectrometry (MS) were initially used for the elucidation of isolated compounds from different plant matrices. Spectroscopic techniques in the infrared (IR) wavelength region of the electromagnetic spectrum have been used in the food industry to monitor and evaluate the composition of foods. Although Herschel discovered light in the near-infrared (NIR) region as early as 1800, most spectroscopists of the first half of the last century ignored it, in the belief that it lacked any analytical interest. However, during the last 40 years NIR spectroscopy has become one of the most attractive and used methods for analysis. This mini-review highlights recent applications of NIR spectroscopy to the qualitative and quantitative analysis of plant natural products.

Abbreviations

ANN:artificial neural network

AOTF:acousto-optic tunable filter

ATR:attenuated total reflection

CP:crude protein

DA:discriminant analysis

DM:dry matter

DOSC:direct orthogonal signal correction

dw:dry weight

FT-NIR:Fourier transform near infrared

GC:gas chromatography

G-G:glycosylated glucose

GSL:glucosinolates

InGaAs:indium gallium arsenide

iPLS:interval partial least squares

IR:infrared

LC:liquid chromatography

LCTF:liquid crystal tunable filters

MEMS:micro electro mechanical systems

MIR:mid infrared

MLR:multiple linear regression

MPLS:modified PLS

MSC:multiplicative scatter correction

MS:mass spectrometry

NIR:near infrared reflectance

NOCH:NIR on combine harvest

PCA:principal component analysis

PDA:photodiode array

PLS:partial least squares

SECV:standard error of cross validation

SEP:standard error of prediction

Si:silicon

siPLS:synergy interval partial least squares

SVM:support vector machine

SIMCA:soft independent modelling of class analogies

R:correlation coefficient

R2:coefficient of determination

RPD:residual predictive deviation

RMSEC:root mean square error of calibration

RMSECV:root mean square error of cross validation

RMSEP:root mean square rrror of prediction

REAC:trolox equivalent antioxidant capacity

TLC:thin layer chromatography

THC:tetrahydrocannabinol

UV:ultraviolet

VIS:visible

WT:wavelet transformation

Key words

near infrared spectroscopy - natural products - chemometrics - quantitative analysis

References

  • 1 Krüger H, Schulz H. Analytical techniques for medicinal and aromatic plants.  Stewart Post Harvest Rev. 2007;  3 1-12
    PubMedGoogle Scholar
  • 2 Schulz H. Analysis of coffee, tea, cocoa, tobacco, spices, medicinal and aromatic plants, and related products. In: Roberts CA, Workman J, Reeves III JB, editors Near infrared spectroscopy in agriculture. Madison; ASA, CSSA, SSSA 2004: 345-7
    Google Scholar
  • 3 Hounsome N, Hounsome B, Tomos D, Edward-Jones G. Plant metabolites and nutritional quality of vegetables.  J Food Sci. 2008;  73 R48-R65
    CrossrefPubMedGoogle Scholar
  • 4 McClure F W. 204 years of near infrared technology: 1800 – 2003.  J Near Infrared Spectrosc. 2004;  11 487-8
    PubMedGoogle Scholar
  • 5 Batten G. Plant analysis using near infrared reflectance spectroscopy: the potential and limitations.  Aus J Exp Agric. 1998;  38 697-706
    PubMedGoogle Scholar
  • 6 Blanco M, Villaroya I. NIR spectroscopy: a rapid-response analytical tool.  Trends Anal Chem. 2002;  21 40-50
    CrossrefPubMedGoogle Scholar
  • 7 Deaville E R, Flinn P C. Near infrared (NIR) spectroscopy: an alternative approach for the estimation of forage quality and voluntary intake. In: Givens DI, Owen E, Axford RFE, Omedi HM, editors. Forage evaluation in ruminant nutrition.  Wallingford: CABI. Publishing;  2002 301-10
    PubMedGoogle Scholar
  • 8 Miller C hE. Chemical principles of near infrared technology. In: Williams PC, Norris KH, editors. Near infrared technology in the agricultural and food industries. St.  Paul: American Association of Cereal. Chemist;  2001 9-29
    PubMedGoogle Scholar
  • 9 Osborne B G, Fearn T, Hindle P H. Practical near infrared spectroscopy with applications in food and beverage analysis, 2nd edition.  Harlow: Longman Scientific and. Technical;  1993 227
    PubMedGoogle Scholar
  • 10 Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies.  J Pharm Biomed Anal. 2007;  44 683-90
    CrossrefPubMedGoogle Scholar
  • 11 Huang H, Yu H, Xu H, Ying Y. Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review.  J Food Eng. 2008;  87 303-13
    PubMedGoogle Scholar
  • 12 Williams P C. Implementation of near infrared technology, 2nd edition.  St Paul: American Association of Cereal. Chemist;  2001 145-51
    PubMedGoogle Scholar
  • 13 Cozzolino D, Cynkar W, Janik L, Dambergs R G, Gishen M. Analysis of grape and wine by near infrared spectroscopy – a review.  J Near Infrared Spectrosc. 2006;  14 279-89
    CrossrefPubMedGoogle Scholar
  • 14 Nicolai B M, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron K I. Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review.  Post Harvest Biol Technol. 2007;  46 99-108
    PubMedGoogle Scholar
  • 15 Workman J J r. Near infrared spectrophotometers. In: Roberts CA, Workman J, Reeves III JB, editors. Near infrared spectroscopy in agriculture. Madison: ASA, CSSA, SSSA; 2004: 11-3. 
    Google Scholar
  • 16 Stratis D N, Eland K L, Carter J C, Tomlinson S J, Angel S M. Comparison of acoustic-optic and liquid crystal tunable filters for laser induced breakdown spectroscopy.  Appl Spectrosc. 2001;  55 999-1004
    CrossrefPubMedGoogle Scholar
  • 17 Crocombe R A. MEMS technology moves process spectroscopy into a new dimension.  Spectrosc Europe. 2004;  3 16-9
    PubMedGoogle Scholar
  • 18 Roggo Y, Edmond A, Chalus P, Ulmschneider M. Infrared hyperspectral imaging for quality analysis of pharmaceutical solid forms.  Anal Chim Acta. 2005;  535 79-87
    CrossrefPubMedGoogle Scholar
  • 19 Upchurch B L, Throop J A. Effects of storage duration on detecting water core in apples using machine vision.  Trans ASAE. 1994;  37 482-6
    PubMedGoogle Scholar
  • 20 Kim M S, Chen Y R, Mehl P M. Hyperspectral reflectance and fluorescence imaging systems for food quality and safety.  Trans ASAE. 2001;  45 2027-37
    PubMedGoogle Scholar
  • 21 Bearman G, Levenson R. Biological imaging spectroscopy. Available at http://trs-new.jpl.nasa.gov/dspace/handle/2014/11 729
    Google Scholar
  • 22 Naes T, Isaksson T, Fearn T, Davies T. A User-friendly guide to multivariate calibration and classification. Chichester; NIR Publications 2002: 420
    Google Scholar
  • 23 Adams M J. Chemometrics in analytical spectroscopy. In: Barnett NW, editor. RSC Spectroscopy monographs. Letchworth: The Royal Society of Chemistry; 1995: 216. 
    Google Scholar
  • 24 Siebert K J. Chemometrics in brewing: a review.  J Am Soc Brew Chem. 2001;  59 147-56
    PubMedGoogle Scholar
  • 25 Arvantoyannis I, Katsota M N, Psarra P, Soufleros E, Kallinthraka S. Application of quality control methods for assessing wine authenticity: Use of multivariate analysis (chemometrics).  Trends Food Sci Technol. 1999;  10 321-6
    CrossrefPubMedGoogle Scholar
  • 26 Murray I, Cowe I. Sample preparation. In: Roberts CA, Workman J, Reeves III JB, editors. Near infrared spectroscopy in agriculture. Madison: ASA, CSSA, SSSA; 2004: 75-85. 
    Google Scholar
  • 27 Pizarro C, Esteban-Diez I, Gonzalez-Saiz J M, Forina M. Use of near-infrared spectroscopy and feature selection techniques for predicting the caffeine content and roasting color in roasted coffees.  J Agric Food Chem. 2007;  55 7477-8
    CrossrefPubMedGoogle Scholar
  • 28 Huck C W, Guggenbichler W, Bonn G K. Analysis of caffeine, theobromine and theophylline in coffee by near infrared (NIRS) compared to high-performance liquid chromatography (HPLC) coupled with mass spectrometry.  Anal Chim Acta. 2005;  538 195-203
    CrossrefPubMedGoogle Scholar
  • 29 Chan C O, Chu C C, Mok D KW, Chau F T. Analysis of berberine and total alkaloid content in Cortex Phellodendri by near infrared spectroscopy (NIRS) compared with high performance liquid chromatography coupled with ultra visible spectrometric detection.  Anal Chim Acta. 2007;  592 121-31
    CrossrefPubMedGoogle Scholar
  • 30 Qu H B, Liu Q, Cheng Y Y. Determination of the Coptis extracts alkaloids using near infrared diffuse reflectance spectroscopy.  Chin J Anal Chem. 2004;  32 477-80
    PubMedGoogle Scholar
  • 31 Gonzalez-Martin I, Hernandez-Hierro J M, Bustamante-Rangel M, Barros-Ferreiro N. Near infrared spectroscopy (NIRS) reflectance technology for the determination of tocopherols in alfalfa.  Anal Bional Chem. 2006;  386 1553-8
    CrossrefPubMedGoogle Scholar
  • 32 Szlyk E, Szydlowsha-Czerniak A, Kowalczyk-Marzec A. NIR determination and partial least squares regression for determination of natural alfa tocopherols in vegetable oils.  J Agric Food Chem. 2005;  53 6980-7
    CrossrefPubMedGoogle Scholar
  • 33 Luypaert J, Zhang M H, Massart D L. Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea Camellia sinensis (L).  Anal Chim Acta. 2003;  478 303-12
    CrossrefPubMedGoogle Scholar
  • 34 Chen Q, Zhao J, Liu M, Cai J, Liu J. Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms.  J Pharm Biomed Anal. 2008;  46 568-73
    CrossrefPubMedGoogle Scholar
  • 35 Chen Q, Zhao J, Xingyi H, Haidong Z, Liu M. Simultaneous determination of total polyphenols and caffeine contents of green tea by near infrared reflectance spectroscopy.  Microchem J. 2006;  83 42-7
    CrossrefPubMedGoogle Scholar
  • 36 Chen Q, Zhao J, Haidong Z, Xynyu W. Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration.  Anal Chim Acta. 2006;  572 7-84
    PubMedGoogle Scholar
  • 37 Yan S H. Evaluation of the composition and sensory properties of tea using near infrared spectroscopy and principal component analysis.  J Near Infrared Spectrosc. 2005;  13 313-5
    CrossrefPubMedGoogle Scholar
  • 38 Yu C Y, Tong Z K, Huang H H, Zhe Y Q. Quantification of phenolic compound in Magnolia officinalis by near infrared reflectance spectroscopy.  J Zhejiang Forestry College. 2007;  24 544-9
    PubMedGoogle Scholar
  • 39 Rankine B. A manual of winemaking practice for Australia and New Zealand. Sydney; Pan Macmillan Australia 1989: 374
    Google Scholar
  • 40 Gishen M, Dambergs R G, Cozzolino D. Grape and wine analysis – enhancing the power of spectroscopy with chemometrics. A review of some applications in the Australian wine industry.  Aust J Grape Wine Res. 2005;  11 296-305
    CrossrefPubMedGoogle Scholar
  • 41 Manley M, Joubert E, Botha M. Quantification of the major phenolic compound, soluble solid content and total antioxidant activity of green rooibos (Aspalathus linearis) by means of near infrared spectroscopy.  J Near Infrared Spectrosc. 2006;  14 213-22
    CrossrefPubMedGoogle Scholar
  • 42 Joubert E, Manley M, Botha M. Use of NIRS for quantification of mangigerin and hesperidin contents of dried honeybush (Cyclopia genistoides) plant material.  J Agric Food Chem. 2006;  54 5279-83
    CrossrefPubMedGoogle Scholar
  • 43 Zhou X, Xiang B, Wang Z, Zhang M. Determination of quercetin in extracts of Ginkgo biloba L leaves by near infrared reflectance spectroscopy based on interval partial least squares (iPLS) model.  Anal Lett. 2007;  40 3383-91
    CrossrefPubMedGoogle Scholar
  • 44 Gaub M, Roeseler C H, Roos G, Kovar K A. Analysis of plant extracts by NIRS: simultaneous determination of kavapyrones and water in dry extracts of Piper methysticum Forst.  J Pharm Biomed Anal. 2004;  36 859-64
    CrossrefPubMedGoogle Scholar
  • 45 Gautz L D, Kaufusi P, Jackson M C, Bittenbender H C, Tang C S. Determination of kavalcatones in dried kava (Piper methysticum) powder using near infrared reflectance spectroscopy and partial least squares.  J Agric Food Chem. 2006;  54 6147-52
    CrossrefPubMedGoogle Scholar
  • 46 Font R, Del Rio-Celestino M, Rosa E, Aires A, De Haro-Bailon A. Glucosinolate assessment in Brassica olerace leaves by near infrared spectroscopy.  J Agric Sci. 2005;  143 65-73
    CrossrefPubMedGoogle Scholar
  • 47 Montes J M, Paul C, Melchinger A E. Quality assessment of rapeseed accessions by means of near infrared spectroscopy on combine harvesters.  Plant Breed. 2007;  126 329-30
    CrossrefPubMedGoogle Scholar
  • 48 Montes J M, Melchinger A E, Reif C J. Novel throughput phenotyping platforms in plant genetic studies.  Trends Plant Sci. 2007;  12 433-6
    CrossrefPubMedGoogle Scholar
  • 49 Font R, Del Rio-Celestino M, Fernandez-Martinez J M, De Haro-Bailon A. Use of near infrared spectroscopy for screening the individual and total glucosinolate contents in Indian mustard seed (Brassica juncea L Czern &Coss).  J Agric Food Chem. 2004;  52 3563-9
    CrossrefPubMedGoogle Scholar
  • 50 Hom N H, Backer H C, Mollers C. Non-destructive analysis of rapeseed quality by NIRS of small seed samples and single seeds.  Euphytica. 2007;  153 27-34
    PubMedGoogle Scholar
  • 51 Olejniczak J, Adamczak M, Wojciechowski A, Feide T Z, Mollers C. Near infrared reflectance (NIRS) as a useful method for analysis of induced mutations and quantitative traits in seeds of winter oilseed rape (Brassica napus L).  Prace z Zakresu Nauk Rolniczych Lesnych. 2005;  98 – 99 197-203
    PubMedGoogle Scholar
  • 52 Font R, Del Rio-Celestino M, Cartea E, De Haro-Bailon A. Quantification of glucosinolates in leaves of leaf rape (Brassica napus ssp pabularia) by near infrared spectroscopy.  Phytochemistry. 2005;  66 175-85
    CrossrefPubMedGoogle Scholar
  • 53 Quilitzsch R, Schulz H, Schütze W. Evaluation of glucosinolates in leaves and stems of various Brassica species by near infrared spectroscopy. Proceedings 12th Near Infrared Conference Hamilton, New Zealand; 2007
    Google Scholar
  • 54 Wu Y W, Sun S Q, Zhou Q, Leung H W. Fourier transform mid infrared (MIR) and near infrared (NIR) spectroscopy for rapid quality assessment of Chinese medicine preparation Honghua oil.  J Pharm Biomed Anal. 2008;  46 498-504
    CrossrefPubMedGoogle Scholar
  • 55 Schulz H, Barinanska M, Quilitzsch R, Schütze W, Lösing G. Characterisation of peppercorn, pepper oil and pepper oleoresin by vibrational spectroscopy methods.  J Agric Food Chem. 2005;  53 3358-63
    CrossrefPubMedGoogle Scholar
  • 56 Schulz H, Quilitzsch R, Schütze W, Krüger H. Near infrared spectroscopy measurement of pungency and flavour in white and black peppercorn. Proceedings 12th Near Infrared Conference Hamilton, New Zealand; 2007
    Google Scholar
  • 57 Juliani H R, Kapteyn J, Jones D, Koroch A R, Wang M, Charles D. Application of near infrared spectroscopy in quality control and determination of adulteration of African essential oils.  Phytochem Anal. 2006;  17 121-8
    CrossrefPubMedGoogle Scholar
  • 58 Yap K YL, Chan S Y, Chan Y W, Lim C S. Overview on the analytical tools for quality control of natural based supplements: A case study of ginseng.  Assay Drug Dev Technolo. 2005;  3 383-9
    PubMedGoogle Scholar
  • 59 Fuzzati N. Analysis methods of ginsenosides: Review.  J Chromatogr B. 2004;  812 119-23
    PubMedGoogle Scholar
  • 60 Cynkar W U, Cozzolino D, Dambergs R G, Janik L, Gishen M. Effect of variety, vintage and winery on the prediction of glycosylated compounds (G-G) in white grape juice by visible and near infrared spectroscopy.  Aust J Grape Wine Res. 2007;  13 101-5
    CrossrefPubMedGoogle Scholar
  • 61 Kim K S, Park S H, Shim K B, Ryu S N. Use of near infrared spectroscopy for estimating lignan glycosides contents in intact sesame seeds.  J Crop Sci Biotechnol. 2007;  10 185-92
    PubMedGoogle Scholar
  • 62 Zalacain A, Ordoudi S A, Diaz-Plaza E M, Carmona M, Blazquez I, Tsimidou M Z. Near infrared spectroscopy in saffron quality control; determination of chemical composition and geographical origin.  J Agric Food Chem. 2005;  53 9337-41
    CrossrefPubMedGoogle Scholar
  • 63 Joubert E, Manley M, Gray B R, Schulz H. Rapid measurement and evaluation of the effect of drying conditions on harpagosne cinetent in Harpagophytum procumbens (devils claw) root.  J Agric Food Chem. 2005;  53 3493-502
    CrossrefPubMedGoogle Scholar
  • 64 Yang N, Guixing R. Application of near infrared reflectance spectroscopy to the evaluation of rutin and d-chiro-inositol contents in tartary buckwheat.  J Agric Food Chem. 2008;  56 761-4
    CrossrefPubMedGoogle Scholar
  • 65 Smyth H, Cozzolino D, Cynkar W U, Dambergs R G, Sefton M, Gishen M. Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wines. Possibilities and limits.  Anal Bioanal Chem. 2008;  390 1911-6
    CrossrefPubMedGoogle Scholar
  • 66 Garaude-Verdier Y. Use of near infrared spectroscopy to measure carnosic acid content in rosemary leaves. Proceedings 12th Near Infrared Conference Hamilton, New Zealand; 2007
    Google Scholar
  • 67 Brereton R G. Chemometrics. Data analysis for the laboratory and chemical plant. Oxford; John Wiley and Sons 2003: 489
    Google Scholar
  • 68 Cordella C, Moussa I, Martel A C, Sbirrazzuoli N, Lizzani-Cuvelier L. Recent developments in food characterisation and adulteration detection: technique-oriented perspective.  J Agric Food Chem. 2002;  50 1751-4
    CrossrefPubMedGoogle Scholar
  • 69 Xiaoli L, Yong H. Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks.  Biosyst Eng. 2008;  99 313-21
    CrossrefPubMedGoogle Scholar
  • 70 Chen Q, Zhao J, Fang C H, Dongmei W. Feasibility study on identification of green, black and Oolong tea using near infrared reflectance spectroscopy based on support vector machines (SVM)with multivariate calibration.  Spectrosc Acta Part A. 2007;  66 568-74
    PubMedGoogle Scholar
  • 71 Esteban-Diez I, Gonzalez-Saiz J M, Saenz-Gonzales C, Pizarro C. Coffee varietal differentiation based on near infrared spectroscopy.  Talanta. 2007;  71 221-9
    CrossrefPubMedGoogle Scholar
  • 72 Wang L, Lee F CS, Wang X. Near infrared spectroscopy for classification of licorice (Glycyrrhizia uralensis Fisch) and prediction of the glycyrrhizic acid (GA) content.  LWT. 2007;  40 83-8
    CrossrefPubMedGoogle Scholar
  • 73 Wilson N, Heinrich M. The use of near infrared spectroscopy to discriminate between THC-rich and hemp forms of Cannabis.  Planta Med. 2006;  72: DOI 10.1055/s-2006 – 950 060
    PubMedGoogle Scholar
  • 74 Chen Y, Xie M Y, Yan Y, Zhu S B, Nie S P, Li C. et al . Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques.  Anal Chim Acta. 2008;  618 121-30
    CrossrefPubMedGoogle Scholar
  • 75 Rosa S S, Barata P A, Martins J M, Menezes J C. Near infrared reflectance spectroscopy as a process analytical technology tool in Ginkgo biloba extract qualification.  J Pharm Biomed Anal. 2008;  47 320-7
    CrossrefPubMedGoogle Scholar
  • 76 Ikeda T, Kanaya S, Yonetani T, Kobayashi A, Fukusaki E. Prediction of Japanese green tea ranking by Fourier transform near infrared reflectance spectroscopy.  J Agric Food Chem. 2007;  55 9908-12
    CrossrefPubMedGoogle Scholar
  • 77 He Y, Li X L, Deng X F. Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model.  J Food Eng. 2007;  79 1238-42
    CrossrefPubMedGoogle Scholar
  • 78 Davrieux F. Genotype characterisation of cocoa into genetic groups through caffeine and theobromine content predicted by near infrared spectroscopy. Proceedings 12th Near Infrared Conference Hamilton, New Zealand; 2007
    Google Scholar

Daniel Cozzolino

The Australian Wine Research Institute

Waite Campus

PO Box 197

Urrbrae – 5064

Australia

Fax: +61-8-8303-6601

Email: daniel.cozzolino@awri.com.au

      >