The Andean region is recognized today as one of the most important centers of crop origin and

diversity in the world. Quinoa, kañiwa and kiwicha are indegenous food plants of the Andean

region, dating back to 5000 years AD. The Incas appreciated their high nutritional value, and

the ease in milling these crops made it possible for the rural populations to take advantage of

their nutritional value, substituting for the lack of animal protein in these rural regions (Tapia,

1997). The protein content of quinoa, kiwicha and kañiwa is elevated, have balanced amino

acid composition and excellent nutritional value, especially when combined with other cereals

(Repo-Carrasco et al 2003; Pedersen et al 1987). These Andean grains have potential as

functional and bioactive ingredients in food products because their high dietary fiber content

and natural antioxidants such as phenolic compound (Gorinstein et al 2007). Andean grains

have potential agronomic importance across the world because they can adapt to different

environmental conditions. In particular, quinoa is a crop exhibiting a range of requirements for

humidity and temperature, with specific ecotypes adapted to diverse conditions. The crop has

been introduced to Europe, North America, Asia, Africa and Australia (Jacobsen 2003). Quinoa

was selected by FAO as one of the crops to offer food security in the current century (FAO

1998) and attention has been given to quinoa for people with celiac disease as an alternative

to the common cereals like wheat, rye, and barley, which all contain gluten (Schoenlechner et

al 2008). Because of their nutritional and economical importance there is a risk for economic

adulteration with less-expensive grains. Infrared spectroscopy is an attractive technology for

the rapid, sensitive, and high-throughput analysis of food authentication and detection of

economic adulteration.

Rapid Authentication of Andean Flours by Using a Portable Infrared Spectrometer

INTRODUCTION

METHODS

CONCLUSION SIGNIFICANCE

ACKNOWLEDGEMENTS

The authors would like to acknowledge the Ohio Agricultural Research

and Development Center and Universidad Nacional Agraria – La Molina

for their support towards this research.

RESULTS

Colleen Rossella, Gladys Tarazona Reyesb, and Luis E. Rodriguez-Saonaa

ABSTRACT

The objective of this study was to develop a rapid test combining ATR-MIR spectroscopy with

chemometrics to characterize and detect adulteration in native Andean flours

OBJECTIVE

Andean Grain Flour included Quinoa, Maca, Kiwicha,

and Cañihua. Potential adulterants were Wheat, Soy,

Barley, Faba beans & Corn

Diamond + pressure

ATR

Figure 1. Typical ATR-IR spectrum of

selected Andean grain flours

Figure 2. SIMCA classification and discriminating power based on

infrared spectra of grain flours samples using a portable system

(A,B) and a benchtop system (C,D).

Table 1. MIR Assignment of Protein Bands

x1

x2

Class 1

Class 2

Soft Independent

Modeling of Class

Analogy

Frequency

(cm-1)

Functional Group Assignment

1715 >C=O ester stretching; carboxylic acids

1695-1675 Amide I band components of proteins

1655 Amide I of α-helical structures of proteins

1637 Amide I of β-pleated sheet structures of

proteins

1550-1520 Amide II band of proteins

1515 Tyrosine band

1468 C-H deformation of >CH2 in lipids proteins

1415 C-O-H bending in CHOs, proteins

1400 C=O symmetric stretching of COO- group

1310-1240 Amide III band components of proteins

1200-1000 C–C, C–O stretching and C–O–H, C–O–C

deformation modes of carbohydrates

FT-IR spectroscopy combined with chemometrics showed

the ability to discriminate different types of commercial

flours including selected Andean grains

The most important bands explaining the discrimination

scores were in the 1000-1200 and 1500-1650 cm-1 region

associated with carbohydrates and proteins, respectively.

Interclass distances (ICD) showed the compositional

similarities between cañihua and kiwicha (ICD=2.6). It has

been reported their similar amino acid and fatty acid profiles.

Evaluation of samples obtained from several local markets

indicate some concern with adulteration of some of the

Andean grains, especially kiwicha flour.

3600 3200 2800 2400 2000 1600 1200 800

Wavenumber

Ab

so

rba

nc

e

C=

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rou

ps o

f lip

ids

Am

ide

I

Am

ide

II

CH

gro

up

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f lip

ids

CH

an

d c

arb

on

yl str

etc

hin

g

of acyl c

ha

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f tria

cylg

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rols

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gro

up

s

wa

ter

u C - O - C of

polysaccharides

3600 3200 2800 2400 2000 1600 1200 800

Wavenumber

Ab

so

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nc

e

C=

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rou

ps o

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ids

Am

ide

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Am

ide

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ids

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an

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on

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etc

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cylg

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gro

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ter

u C - O - C of

polysaccharides

3600 3200 2800 2400 2000 1600 1200 800

Wavenumber

Ab

so

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nc

e

C=

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rou

ps o

f lip

ids

Am

ide

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ids

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on

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etc

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ter

u C - O - C of

polysaccharides

Our results supports the use of IR spectroscopy

as a high-throughput technique that could be

easily adapted for in-field applications thanks to

the development of new portable devices.

We demonstrate the application of a rapid tool to

authenticate highly versatile and nutritious

Andean grains

Portable FT-IR (Agilent

Technologies)

aDepartment of Food Science and Technology, The Ohio State University, 2015 Fyffe Court, Columbus, OH 43210, USA. bDepartamento de Industrias Alimentarias, Universidad Nacional Agraria, Av. La Molina s/n. La Molina. Lima – Perú.

Andean indigenous grains such as Quinoa (Chenopodium quinoa), Cañihua (Chenopodium

pallidicaule), Kiwicha (Amaranthus caudatus L.) have high nutritional value for the Andean

region serving as principal protein sources of the region, substituting the scarce animal proteins.

The importance of these grains is based on their relatively high protein content with excellent

composition of essential amino acids, gluten-free type, good source of dietary fiber, bioactive

compounds and minerals (calcium, zinc and iron). Because of their nutritional and economical

importance there is a risk for economic adulteration with less-expensive grains. Our objective

was to develop a rapid analytical tool to characterize and detect adulteration of native Andean

indigeneous grains by combining infrared spectroscopy and pattern recognition analysis. Pure

flours produced from Andean (quinoa, cañihua, kiwicha, maca, sacha inchi) and other (maize,

soybean, wheat, linseed, algarroba, canary grass, sesame, barley, faba bean) ingredients were

provided by UNALM (Lima, Peru). In addition, commercial samples were obtained from various

local markets (Lima, Peru) and used for predictions. Unique spectral data was collected with a

portable attenuated total reflectance (ATR) mid-infrared spectrometer equipped with a diamond

crystal and analyzed by Soft independent modeling of class analogy (SIMCA). Pure flours

formed distinct clusters allowing the evaluation of commercial samples from Peruvian markets

showing some prevalence of adulteration. Spectral differences responsible for the separation of

classes were attributed to stretching vibrations of the ester (-C=O) linkage (1740 and 1780 cm-1)

of lipids and the amide (1450-1600 cm-1) region of proteins. ATR-IR spectroscopy in

combination with chemometrics was a viable tool in characterization of Andean flour samples

allowing for the rapid, “in-field”, and reliable detection of adulteration of food ingredients, making

it a great alternative to traditional testing methods.

Portable ATR-IR

Cary 630 series

PROBLEM

EXPLORATION

METHOD

SELECTION

spectra

DOE

Multivariate Analysis

Table 2. Interclass distances between flours using portable and benchtop FT-IR spectrometers

PC2

PC1

PC3

Maca

Soya

Cebada

Quinoa pred.

Kiwicha

pred. Haba

Cañihua

Algarrobo

Mashua

Linaza

Ajonjoli

Sacha Inchi

Maize

Trigo

Kiwicha std.

Alpiste Quinoa

std

B

Figure 3. SIMCA prediction plots of

market flours using a portable (A) and

a benchtop (B) FT-IR system

Flours Cañihua Cebada Haba Kiwicha Maca Quinoa Soya

Cañihua std. 3

Cebada std. 3

Haba std. 3 1

Kiwicha std. 2

Maca std. 3

Quinoa std. 2

Soya std. 3

Misclassified 2 1

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15

C1 0 9.6 8.2 3.6 17 9.6 12.3 7.5 10.6 9.8 14.8 22.5 12.8 15 6.8

C2 10.4 0 19.1 14.5 33.4 10.6 19.9 7.2 31.1 6.4 40.8 53.3 14 39.5 18.8

C3 6.2 17.3 0 13.8 28.8 19.2 20.2 18.5 23.2 15.2 31.6 38.7 23.8 37.1 15.2

C4 2.6 12 5.5 0 38.5 15.6 23.9 11 17.4 17.5 31.4 54.8 21.5 31.7 11

C5 15.7 25.2 22.4 20.3 0 35.4 19.1 39.3 34.2 37.1 46.4 24.9 55.8 48.5 21.9

C6 10.4 10 18.8 12.9 22 0 21 8.9 29.5 5.9 42.3 60.9 19 34.9 18.7

C7 14.5 11.1 21.9 17.1 8.3 11.2 0 26.8 8.9 18.5 39.6 40.2 31.9 41.4 19.3

C8 5.6 8.7 9.3 4.7 21.3 8.7 12.2 0 26.8 8.9 34.2 61 11.2 38.6 15.5

C9 8.9 24 9.3 9.1 23.7 25.3 27.8 16.5 0 34.5 31.8 39.2 38.3 17.1 14.3

C10 6 8.3 10.9 6.6 24.4 7.6 12.1 6.8 17.3 0 42.6 62.8 16.3 46.7 19

C11 5.5 12.1 5.3 5.9 12.3 12.1 13.8 9.7 3 10.2 0 50.7 46.7 38.6 16

C12 18.8 26.4 23.1 22.8 10.7 26.8 15.9 23.6 23.5 26.4 12.5 0 90.9 59.5 25.4

C13 13.8 9 19.3 13.9 19.5 10.1 13.6 5 25.8 7 14 22.6 0 51.7 20

C14 8 22.2 9.2 9.1 21.8 22.2 23.4 17.6 5.5 19.4 4.1 20.8 22.7 0 20.5

C15 11.1 31.9 7.7 11.6 32.8 31.3 32.2 21.9 5.3 26 3.3 29.4 28.2 7.4 0C1isCanihua,C2isCebada,C3isHaba,C4isKiwicha,C5isMaca,C6isMaize,C7isMashua,C8isQuinoa,C9isSoya,C10isTrigo,C11isAjonjoli,C12isAlgarrobo,C13isAlpiste,C14isLinaza,andC15isSachaInchi.

Figure 4. Prediction of market flours using a portable IR