“La spettroscopia NIR: principi ed applicazioni in ambito ...iaa.entecra.it/WS2015/10...

“La spettroscopia NIR: principi ed applicazioni in ambito agro -alimentare”

Tiziana M.P. CATTANEO Research Unit for Food Processes

CRA-IAA, Milano - Italy

Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria

CRA – IAA Milano

Dipartimento di Trasformazione e Valorizzazione dei Prodotti Agro-Industriali

CRA-IAA, Milano - Italy

Winter School, 26-30 gennaio 2015Milano

A = log Po/P = - log10 T

P0 PLight source detector

o 10

Po= in power

P = out power

T = trasmittance: radiation trasmitted by sample

RADIATION

Log (1/R

)Log (1/R

)

1.81.8

1.01.0

Log (1/R

)Log (1/R

)

1.81.8

1.01.0

1200 1200 1600 1600 2000 2000 2400 2400 λλ (nm)(nm)

0.20.2

1200 1200 1600 1600 2000 2000 2400 2400 λλ (nm)(nm)

0.20.2

NIR spectrum

•• ““overtonesovertones””, , frequencies of multiples of the frequencies of multiples of the

frequencies of the fundamental vibrations of the frequencies of the fundamental vibrations of the

considered bondconsidered bond

•• combinationcombination bandsbands, , resulting from the resulting from the

NIRNIR

•• combinationcombination bandsbands, , resulting from the resulting from the

simultaneous transition of two or more different simultaneous transition of two or more different

vibrationalvibrational modes of a functional group having the modes of a functional group having the

same symmetrysame symmetry

•• harmonicharmonic bandsbands, , related to possible transitions related to possible transitions

between the between the vibrationalvibrational levelslevels

Most of the NIR absorption are due to

harmonic bands and the "overtones" of the

stretching and bending vibrations of the

fundamental generic XH bond.

NIR

The precise allocation of the absorption bands

is made difficult by the high number of

"overlap" of bands, as arises from NIR

spectrum. Consequently, we have a low power

of interpretation in the NIR area.

NIR information

• QUANTITATIVE

• QUALITATIVE

IDENTIFICATION PROCESS CONTROL

This region is adequate for quantitative

analysis with accuracy and precision more

comparable to UV-VIS spectroscopy than MIR

spectroscopy

NIR

UVUV--VIS > NIR > MIRVIS > NIR > MIR

FTFT--NIRNIR FTFT--IRIR

FT advantages

High ration signal/noise

accuracy and precision in identify absorption υ

high resolution > 0.1 cm-1

very fast scan (spectra collection)

no negative effect from dispersive radiation

High number of samples (NOT

replicates) > 50

CALIBRATION:

QUANTITATIVE ANALYSIS

replicates) > 50

• Samples must be selected on the

basis of specific rules

SAMPLES SELECTION

�presence of all possible combinations

between the variables under calibration

� variability in all directions but limited

in the range of interest

� the samples chosen for the calibration

must be uniformly distributed

throughout the region defined by the

variables

�NIR spectra must be collected using the

optical geometry suitable as a function of

the "state" of sample presentation

�the values of the variables to be calibrated

must be obtained also with reference

methods

�the values of repeatability and

reproducibility of the analysis of reference

measurements affect NIR accuracy

�NIR optimization measures for the

acceptability of the data : SD NIR = SD ref

Calibration set and Prediction set

(two independent set of samples )

Precision:

�Determination coefficient of calibration

�(R2 cal)�(R2 cal)

�Standard error of estimation in prediction (SEP)

Repeatability:

�Number of replicates (+DS)

Validation set(third independent set)

Cross-validation

External validation

Reproducibility:

� calibration tranfer on different

instrumentations

� 9 labs

EXPLORATIVE TECHNIQUES

QUALITATIVE ANALYSIS

LOWER NUMBER OF LOWER NUMBER OF

SAMPLES,

CHARACTERISTICS OF THE

SET (GROUP)

DISCRIMINANT TECHNIQUES

Reference methods

COMPOSITION:

Official Methods, if available

(FIL-IDF, ISO, AOAC)

QUALITATIVE ANALYSIS

Official Methods or KNOWN samples

• Few applications are now realized at the

laboratory

• Several applications are at-line, on-line,

on-line, in the field - through the use of

more robust and portable

instrumentationinstrumentation

• It is bringing the instrument to sample

more than the sample to the instrument

The spectroscopic methods can provide a

fingerprint of the product are already a valid

support as screening techniques

STANDARD METHODS

• The technology development, especiallyminiaturization, bring NIR technology closer to theoptimum point for measuring (sale, the consumer)

• Developments in chemometrics will provide usmodels more and more accurate, rapid and robustmodels more and more accurate, rapid and robust

• The instruments can improve …. But

• WE MUST CONTINUE TO THINK !!

The fundamental variables

- size and dimension

- Humidity

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- temperature

- homogeneity


- Humidity

2.500Milk

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0.000

0.500

1.000

1.500

2.000

11001300150017001900210023002500 λ(nm)

Abs[log(1/R

)]

Milk

spectrum


- temperature

Iwamoto et al. (1987)

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- size and dimension

- Humidity

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- temperature

- homogeneity

FOOD MEASUREMENTS

Quality parameters are

food specific

Greater control required by

large food processors and

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large food processors and

retailers

Control measures required

to be fast and inexpensive

FOOD MEASUREMENTS

Over 50% of applications reported

involve measurements other than

chemical composition – including:

Physical and sensory properties

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Physical and sensory properties

Process modelling

Roasting/cooking quality

Classification and authentication

Temperature effects

Food regulation

Food authenticity is still a matter of

importance to regulators, consumers

and food processors

Essentially means conformance to

labelling claim e.g. Parma ham, extra

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Essentially means conformance to

labelling claim e.g. Parma ham, extra

virgin olive oil etc.

Spectroscopic fingerprint methods

have valuable role as screening

techniques

Explosion in NIR activity!

Availability of commercial instrumentation and software

Frenetic research effort into sampling, sample presentation, calibration

development & validation, calibration transfer etc. etc.

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transfer etc. etc.

“With the coming to analytical maturity, NIR technology may be regarded by many

researchers as a tool rather than as an end in itself”

(McClure, J. Near Infrared Spectrosc., 2003, 11, 487-518)

Less applications are now confined to

laboratories

More applications are at-line, in-line, on-line, in-

field – with more robust and portable

instruments

We are taking the instrument to the sample

�The new technology

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We are taking the instrument to the sample

more than the sample to the instrument

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Optics module enables a new breed of online process

control monitors

NIR integrated into sample probe

Process Sensor as easy to use a process pH probe but

with full spectral chemical analysis capabilities

On-line NIR Sensor for Liquids

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The smallest one

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� Interesting application fields

• Models for process monitoring

• Identification of microcompounds

• Adulterations and contaminants

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• Biospectroscopy

• Classification and authenticity

Grated cheese

Qualitative analysis

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Qualitative analysis

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Composition studies

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0.06

0.08

0.1

0.12

0.14

700

nm(U

V/V

IS)

10

15

20

25

FO

RM

AG

RA

PH

UV/VIS Modello UV/VIS – UV/VIS model

GT

CT

0.06

0.08

0.1

0.12

0.14

700

nm(U

V/V

IS)

10

15

20

25

FO

RM

AG

RA

PH

UV/VIS Modello UV/VIS – UV/VIS model

GT

CT

milk coagulation process

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-0.02

0

0.02

0.04

0 500 1000 1500 2000 2500 3000

Tempo (s)Time (s)

Abs

700

-5

0

5

FO

RM

AG

RA

PH

FormagraphModello FormagraphFormagraph model

CT

ICTt coagulazionecoagulation time

-0.02

0

0.02

0.04

0 500 1000 1500 2000 2500 3000

Tempo (s)Time (s)

Abs

700

-5

0

5

FO

RM

AG

RA

PH

FormagraphModello FormagraphFormagraph model

CT

ICTt coagulazionecoagulation time

milk coagulation process

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On-line milk composition during milking

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On-line milk composition during milking

0.990.990.160.140.7 - 7.2Fat

R (V-Set)R (C-Set)V-Set

(SEP)

C-Set

(SEC)

Calibration

range

Property

0.870.950.140.122.7 - 5.0Protein

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0.820.910.170.163.4 - 5.5Lactose

0.970.980.340.318.4 - 16.2Dry matter

The same experiment has been repeated on 2010 by installing NIR

fiber in an automatic milking system with the same results

Hygienic parametersSOMATIC CELL COUNT (SCC)

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140 samples with different natural content of AFM1:SAMPLESSAMPLES

CAPRINO CRESCENZA

RICOTTAMOZZARELLA

GRANA (37)

MOZZARELLA

80

Cream and butter (23)

AFM1 AFM1 determinationdetermination

•• Immunochemical ELISAImmunochemical ELISA

Polyclonal antibodies

Std:: 00--55--1010--2525--5050--100100--250 250 pptpptDetection range: 55--22550 0 pptppt

Pre-treatment: pepsin extraction

FoodScan LAB (FOSS ITALIA)

Petri caps 90 x 15 mm

Transmittance

Range: 850 a 1050 nm (16 scan)

Data processing:

Cross validation

Calibration curves (Unscrambler 9.1):

Fresh cheese (80) – two subset

Hard cheese (37) Cream and butter (23): (Unscrambler 9.1)

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Cream and butter

100

150

200

250

R2 = 0.990NIR

-50

0

50

0 50 100 150 200

ELISA

Observation Prediction


Hard cheese

100

150

200

250

R2 = 0.957NIR

-50

0

50

0 50 100 150 200

ELISA

Obs Pred


Fresh cheese – low concentration

100

150

200

250

R2 = 0.872NIR

-50

0

50

0 50 100 150 200

ELISA

Obs Pred


600

800

1000

1200

1400

R2 = 0.823

Fresh cheese – High concentration

NIR

0

200

400

600

200 400 600 800 1000 1200ELISA

Obs Pred


• R>0.90

• 7% < RMSEC <11%

• - 12% < RMSECV <16% fresh cheese• - 12% < RMSECV <16% fresh cheese- RMSECV <20% hard cheese, cream andbutter

• Comparable to those obtained by ELISA (CV < 20%)

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Project RiProSel - CHEESE“Reproduction and selection”

Main objectives

STEP:� Cheese yield prediction

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� Cheese yield prediction� Estimation of cheese yield on individual

milk samples;� Hereditability.

SAMPLES

737 bulk milk and vat milk397 milk whey2257 individual milk

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� Fine protein composition (casein subfractions);

� Development of MonteCarlo model for fat globule size distribution;

� Cheese yield

Petri capsTransflectancePathlength: 0.3 mmRange: 4000 -10000 cm -1

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Range: 4000 -10000 cm -1

Resolution: 8 cm -1

32 scan4 replicates FT-NIR NIRFlex-500

INFORMATION

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CALIBRAZIONE αs1-cn

CRA – IAA Milano Roma, 15 novembre 2012


CALIBRAZIONE β-cn

CRA – IAA Milano Roma, 15 novembre 2012


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The use of NIR Spectroscopy for monitoring milk-whey

biotransformation processes using Lactobacillus plantarum

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PhD degree in cooperation with Kobe University – Japan

Roumiana Tsenkova team

Whey disposal is one of the main problems for dairy industry due to itscontent of nutrients such as lactose, proteins, lipids and salts.

Elements Sweet whey (%) Acid whey (%)

Solids 6,4 6,2

Proteins 0,8 0,75

Lipids 0,5 0,04

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Lipids 0,5 0,04

Lactose 4,6 4,2

Ashes 0,5 0,8

Lactic acid 0,05 0,4

Availability of high amount of lactose and the presence of other essentialnutrients for LAB growth become whey a potent raw material for theproduction of different bio-products through biotechnological means, suchas fermentation processes.

Probiotic

Lactobacillus plantarum

� Involved in many vegetable fermentations � Frequent inhabitant of the human intestinal tract

“Active microorganisms that show health benefits for the

host when consumed in adequate amounts”

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adequate amounts”

Technological characteristics to allow their production on

large scale and their incorporation into food products without losing

viability and functionality

Interest in using L. plantarumfor many applications: its

biomass and its secondary metabolites, produced during fermentation processes, could be used as probiotic carriers

in many foodstuffs

1%Lactose

4,5%

t0, t6, t8, t11, t15, t18, t24, t28, t32, t48

FT-NIR NIRFlex N-500 (BUCHI,Assago, MI)a. Transmission mode with quqrtzcuvette (pathlenght 0.2 mm)b. Trasflectance mode with an opticprobe (pathlenght 0,08 mm)

Spectra FT-NIR collection at 30°C ±1°C with NIRWare 1.2 OperatorWhole NIR range from 4000 to 10000 cm-1

Data analysis using softwareUnscrambler v. 9.2 (Camo Inc, OSLO,Norvegia)

HPLC determination of lactose and lactic acid using an ion exchanger

column in isocratic condition and

refractometer as detector

Microbial count in MRS agar and

incubation for 48 hours at 30°C ±1 °C in anaerobic

conditions

t0 t6 t8 t11 t15 t18 t24 t28 t32 t48

1 7.68 8.97 9.42 9.45 9.58 9.61 9.78 9.72 9.69 9.65

2 7.21 7.92 7.95 8.13 8.27 8.34 8.53 8.51 8.54 8.73

3 7.44 8.34 8.72 8.73 8.94 8.66 8.57 8.55 8.76 8.65

4 7.44 8.42 8.67 8.80 8.80 8.95 8.76 8.80 8.74 8.85

log CFU/mLStrain

4

5

6

7

pH

Relationship between microbial growth and fermentation trend

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Adaptability of Lactobacillus plantarum on the substrate tested: within 48 hours strains reached growth values of order 9 log CFU/mL demostrating how this

species is able to multiply even in non-optimal growth conditions.

4 7.44 8.42 8.67 8.80 8.80 8.95 8.76 8.80 8.74 8.85

5 7.19 8.23 8.53 8.91 8.99 9.13 8.97 8.98 9.01 8.99

6 7.39 8.29 8.60 8.70 9.00 8.98 9.02 9.02 9.00 9.00

3

4

0 12 24 36 48

Time (hours)

Lactose: validation and calibration curves

-1

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Measured Values

Pre

dict

ed V

alue

s

0,197SEC (g/100g)

0,985R cal

SNVPretreatment

Lactose

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3%CV% reference method

6,05RPD

24,4RER

0,97Slope

0,12Offset

0,18SEP (g/100g)

0,98R val

0,97Slope

0,1Offset

0,197SEC (g/100g)

Lactic Acid: calibration and validation curves

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Measured Values

Pre

dict

ed V

alue

s

0,980R cal

SNV, 2^ derivativePretreatment

Lactic acid

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Measured Values

3%CV% referemce

method

4,87RPD

23,15RER

0,91Slope

0,06Offset

0,154SEP (g/100g)

0,986R val

0,95Slope

0,04Offset

0,164SEC (g/100g)

0,980R cal

Good results for both parameters, after data pretreatment;

High values of R in indipendent validation using models with low number of PCs (7 for Lactose and 2 for Lactic Acid)

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Lactose Lactic Acid

RER (Ratio Error

Range)24.4 23.15 21-30

5-6,4

3,1-4,9Good for screening

tests

Reference Vaules

RPD (Ratio

Performance in

Deviation)

6.05 4.87

Good for quality

control

At line monitoring of fermentation processes

Parameters Range RRMSEC

(g/100g)

SEC

(g/100g)R

RMSEP

(g/100g)

SEP

(g/100g)

Lactose 0.24-3.56 (g/100g) 0.9885 0.0451 0.0459 0.9224 0.1802 0.1713

Lactic Acid 1.093-3.49 (g/100g) 0.9893 0.0579 0.059 0.9585 0.1245 0.127

Calibration Validation

Sample set

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Lactic Acid 1.093-3.49 (g/100g) 0.9893 0.0579 0.059 0.9585 0.1245 0.127

Biomass 7-9 (log CFU/mL) 0.8902 0.2531 0.2586 0.9222 0.207 0.213

•High correlation coefficients and good RMSEP values in validation;•In the case of biomass produced by L.plantarum SEP is lower thanSEC and this information demonstrated that the develop model wasan adequate representation of the fermentation process.

-2.00E-03

-1.50E-03

-1.00E-03

-5.00E-04

0.00E+00

5.00E-04

1.00E-03

5000 5020 5040 5060 5080 5100 5120 5140 5160 5180 5200

Abs

orba

nce

0h

15h

24h 32h

-1.00E-02

-8.00E-03

-6.00E-03

-4.00E-03

-2.00E-03

0.00E+00

2.00E-03

4.00E-03

6.00E-03

8.00E-03

4000 4800 5600 6400 7200 8000 8800 9600

Wavenumber (cm -1)

Abs

orba

nce

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Wavenumber (cm -1)

Region between 5000-5200 cm-1: relationship between bands intensity and analyte concentration

Increase of the NIR bands with the incubation time

Screening methods

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Standard methods

IDF-ISO GUIDELINES

Since 2006, the IDF/ISO has published the Internati onal Standard IDF201/ISO 21543 in order to supply QC labs with the “Guid elines for the application of near infrared spectrometry” in the r outine analysis of milk and milk products. This document is very important because it contains information for the standardization of operative conditions, sampling, calibration of instruments, selection of calibration samples, refe rence analysis, outliers,

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instruments, selection of calibration samples, refe rence analysis, outliers, statistics, and so on. The same document has been approved last year by th e Federal Democratic Republic of Ethiopia (ET-ISO 21543/ 2012 ), in assuring a common International approach for the validation of the use of NIR spectrometry applied to dairy sector.Conversely, other food sectors, such as cereals, ha ve already accepted NIR technology in a more official way.

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Since several years, instruments based on Near-Infr ared Transmission (NIT) are available; these last have f ound large application, due to the extreme simplicity of use a nd the not negligible advantage to simultaneously furnish diff erent qualitative parameters, for analyzing the cereal whole grain i n the post-harvest phase, so that to segregate the higher-quality grai n and to produce uniform quality lots. The Italian Network has been organized in 1998 with the financial

Whole grain Italian Network

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The Italian Network has been organized in 1998 with the financial support of Central and Regional Agriculture Offices and some farmer organizations. In 2003, the durum wheat Ital ian Network consisted of 90 instruments (Infratec 1229 and 1241 mod.– FossItalia, Padova) located in different sites of Central, Nort hern, Southern Italy and of Major Isles, connected by modem to the Centr al Laboratory of the CRA-QCE research centre in Rome.

Whole grain Italian Network

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Connected with the European Network

[email protected]

And now for an imaginary

journey……..

Breakfast

• Processed cereal products – lipid classes

• Cereal foods - FT-NIR/Raman & NIR for

nutritional classification

• Honey adulteration

– Fructose and glucose mixtures

– High fructose corn syrup (HFCS)

Honey Composition

• ~80% sugars, 17% water

• Sugars:

– fructose + glucose account for 85-95% of

CHO

– fructose:glucose ratio ~ 1.2:1

– sucrose ~ 1.5%

– disaccharides ~ 6.6%

– oligosaccharides ~ 1.5%

Raw Spectra

1945 nm

2120 nm

2298 nm

1490 nm

2298 nm

Expanded 2nd Derivative Plot

2402

Fructose

2276, 2322, 2354, 2376, 2420, 2448, 2484 nm

Glucose

2276, 2324 and 2454 nm

2276

2322

2352

23742402

2482

2418

2454

PCA score plot - beet invert

adulterant

H = authentic

A = 7%, B = 10%, C = 44% ....G = 70% BIS

Honey Conclusions

• Application of SIMCA models has the

potential to discriminate between

authentic honey and honey adulterated

with either beet invert syrup or high

fructose corn syrup

• Quantification of adulterant is

possible with limited accuracy

• Arabica content in coffee blends

• Caffeine and theobromine in cocoa

• Sugarcane – sugar content, quality,

FT-NIR application

Time for

lunch

• Typifying pork sausages, ham

• Chemical and physical properties of fresh pasta

(colour, cooking quality)

• Quality control of green tea (aspalathin)• Quality control of green tea (aspalathin)

• Intact fruit – sugar, portable NIR for mangoes,

sensory properties of melons, dryness defect in

mandarins, seedless grapes, time-resolved

spectoscopy (apples)

Meat Science

Volume 71, Issue 3, November 2005, Pages 490-497

Meat mixture detection in Iberian pork

sausages sausages V. Ortiz-Somovillaa, , , F. España-Españaa, E.J. De Pedro-Sanzb

and A.J. Gaitán-Jurado

Spectrochimica Acta Part A: Molecular and Biomolecular SpectroscopyVolume 72, Issue 4, May 2009, Pages 845-850

Study on discrimination of Roast green tea(Camellia sinensis L.) according togeographical origin by FT-NIRspectroscopy and supervised patternspectroscopy and supervised patternrecognition

Quansheng Chen, Jiewen Zhao and Hao Lina

J. Agric. Food Chem., 2002, 50 (20), pp 5520–5525DOI: 10.1021/jf0257188Publication Date (Web): August 28, 2002Copyright © 2002 American Chemical Society

Durum wheat adulteration detection by NIR spectroscopy multivariate calibration

Marina Cocchia, , , Caterina Durantea, Giorgia Focaa, Andrea Marchettia, Lorenzo Tassia and Alessandro Ulrici

Experiment Strategy

TARGET: Assess Quality of incoming Wheat Flour batches

• Identify Parameters to check• Identify an Analytical System able to perform quickly such checking• Define sampling methods• Collect samples and verify

5 different Wheat Flour Qualities considered (1 – 2 – 3 – 4 – 5)5 different Wheat Flour Qualities considered (1 – 2 – 3 – 4 – 5)

• Samples collected starting from January 2006

• Samples collected from different suppliers all over Italy

BÜCHI Italia Srl, Assago (MI)

BARILLA ALIMENTARE Spa, Cremona

Il controllo at -line nella produzione di omogeneizzati di carne tramite sistema FT-NIR

con sonda a fibre ottiche

PLA.D.A. Industriale S.r.l. – LatinaBÜCHI Italia S.r.l. – Assago (MI)

Presso lo stabilimento di Latina di PLA.D.A. S.r.l., produttore di babyfood, èstata valutata l’applicabilità della spettroscopia NIR per un controllo at-linedel contenuto di grassi e residuo secco in alternativa ai tradizionali metodidi controllo qualità di omogeneizzati per l’alimentazione della primainfanzia.

Riconoscimento di cultivar

1

1.2

1.4

cultivar

1

1.2

1.4

cultivar

Pink Lady

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1 3 5 7 9 11 13 15 17 19 21

23

25 27

29 31 33 35 37 39 41

43

45 47

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1 3 5 7 9 11 13 15 17 19 21

23

25 27

29 31 33 35 37 39 41

43

45 47

Golden Delicious

-0.0050

-0.0045

-0.0040

-0.0035

-0.0030

Ab

s/d

[v-1

]2

MILLED PARBOILED QUICK -

COOKING

-0.0065

-0.0060

-0.0055

-0.0050

0 400 800 1200 1600Time (s)

d2 A

bs/

d[v

M

P

QC638 940729

Ricerca rapida di melamina in

latte in polvere

Simultaneous Quantitative Determination of Melamine and Cyanuric Acid in Cow’s Milk and Milk-Based Infant Formula by Liquid Chromatography−Electrospray Ionization Tandem Mass Spectrometry

Aurlien Desmarchelier, Miriam Guillamon Cuadra, Thierry Delatour and Pascal Mottier*Nestl Research Centre, Nestec Ltd., Vers-chez-les-Blanc, 1000 Lausanne Nestl Research Centre, Nestec Ltd., Vers-chez-les-Blanc, 1000 Lausanne 26, SwitzerlandJ. Agric. Food Chem., 2009, 57 (16), pp 7186–7193DOI: 10.1021/jf901355vPublication Date (Web): July 24, 2009Copyright © 2009 American Chemical Society

A detour to

a winery

• Wine and grapes – impact of near/mid IR, vis-NIR applications,

sensory analysis, colour, identification of wine yeast strains,

alcohol content, minerals, glycerol, wine composition in the bottle,

sugar and acids in grapes by SVM, effect of freezing

Feasibility Study on the Use of Visible and Near-Infrared Spectroscopy Together withChemometrics To Discriminate betweenCommercial White Wines of Different VarietalCommercial White Wines of Different VarietalOriginsDaniel Cozzolino,*† Heather Eunice Smyth,†§ and Mark GishenJ. Agric. Food Chem., 2003, 51 (26), pp 7703–7708DOI: 10.1021/jf034959sPublication Date (Web): November 12, 2003Copyright © 2003 American Chemical Society

A nice dinner to round off the day

For Madam, the

salmon

For Sir,

the chicken

But is it

safe to

eat?

That’ s better!

• Olive oil/olives – quality control, adulteration

• Pigmentation in live farmed salmon

• Poultry – hyperspectral imaging for contaminant detection

• Rice – single kernel analysis, • Rice – single kernel analysis, quantitative & qualitative, amylose, protein, gelatinisation temperature, discriminating aromatic and non-aromatic rices, cooking kinetics

Measurement of Adulteration of Olive Oils by Near-Infrared SpectroscopyI.J. Wesley *, R.J. Barnes and A.E.J. McGillJAOCS, Vol. 72, no. 3 (1995)F. Pacheco, JAOCS, Vol. 73, no. 4 (1996)

Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra

O. Galtiera, , , N. Dupuya, Y. Le Dréaua, D. Ollivierb, C. Pinatelc, J. Kistera

and J. Artaudd

Analytica Chimica ActaVolume 595, Issues 1-2, 9 July 2007, Pages 136-144 Papers presented at the 10th International Conference on Chemometrics in Analytical Chemistry - CAC 2006

Detecting and Quantifying Sunflower Oil Adulteration in Extra Virgin Olive Oils Detecting and Quantifying Sunflower Oil Adulteration in Extra Virgin Olive Oils from the Eastern Mediterranean by Visible and Near-Infrared Spectroscopy

Gerard Downey,*† Peter McIntyre,‡ and Antony N. DaviesJ. Agric. Food Chem., 2002, 50 (20), pp 5520–5525DOI: 10.1021/jf0257188Publication Date (Web): August 28, 2002Copyright © 2002 American Chemical Society

SALMON

Usefulness of Near-Infrared Reflectance (NIR) Spectroscopy andChemometrics To Discriminate Fishmeal Batches Made withDifferent Fish Species

Daniel Cozzolino,*† A. Chree,‡ J. R. Scaife,§ and Ian Murray

J. Agric. Food Chem., 2005, 53 (11), pp 4459–4463DOI: 10.1021/jf050303iPublication Date (Web): April 26, 2005Publication Date (Web): April 26, 2005Copyright © 2005 American Chemical Society

CONCLUSIONS

NIR is now a technique of choice

rather than an end in itself.

Instrument miniaturisation will

increase and help movement of the

technique into the field.

CRA – IAA Milano


technique into the field.

Chemometrics developments will

lead to global, robust calibrations

with little or no modification by

the operator.

CONSIGLIO PER LA RICERCA IN

AGRICOLTURA E L’ANALISI

DELL’ECONOMIA AGRARIA

Unità di ricerca per i processi

dell’Industria AgroAlimentareVia Venezian 26

20133 Milano

Tel. +39 02239557217Tel. +39 02239557217

Fax +39 022365377

@ [email protected]

@ [email protected]

W www.sisnir.org

CRA – IAA Milano


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