moving smoothly from the nir lab to the agri-food plant smoothly from the nir lab to the agri-food...

MovingsmoothlyfromtheNIRlabtotheagri-foodplant

UniversityofCórdobaFacultyofAgricultureandForestryEngineeringNon-Destruc<veSpectralSensorsUnit(NDSSU-

ETSIAM-UCO)

Prof.Dr.AnaGarrido-VaroProf.Dr.DoloresPérez-Marín

16èmesrencontresHéliospirLe3décembre2015MontpellierAgropolis

Contents1.On-siteimplementa<onofNIRSintheSpanishagri-foodindustry:twostudycases

q  Iberianporkprocessingplantsq  Compoundfeedsandmanufacturingplants

2.On-siteimplementa<onofNIRSforrawmaterialcontrolatdelivery/recep<onpoints

1ststage:analysisofmeltedfats:1992-1998

ü We started by developing calibrations after melting the fat in a microwave and using transflectance (aluminium/ golden cups)

ü Optimization of sample presentation was critical

Iberian Pork Processing Plants /1st stage

ACápsula sin

burbujas

BCápsula pocas

burbujas

CCápsula muchas

burbujas

RMSE STD A vs. B A vs. C

4.558 35.420

A B C

AIRBUBBLESDECREASEREPEATABILITY(RMSE)!!!

OpQmizaQonofsamplepresentaQon


0

0,2

0,4

0,6

0,8

1

1,2

1,4

2200

2250

2300

2350

2400

2450

2500

Longitud de onda (nm)

Lo

g (

1/R

)

Con Burbujas

Sin Burbujas

FAs CALIBRATION EQUATIONS IN MELTED FATS (n=341)

Fattty Acid

C16:0 %

C18:0 %

C18:1 %

C18:2 %

Mean SD SECV SEL

21.02

10.62

52.29

9.40

1.40

1.31

2.33

1.34

0.25

0.24

0.24

0.16

0.26

0.22

0.25

0.15

r2

0.97

0.97

0.99

0.99


Excellent calibrations but unexpected predicted data in routine analysis

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.91100

1200

1300

1400

1500

1600

1700

1800

1900

2000

2100

2200

2300

2400

2500

Wavelength (nm)

Abso

rban

ce (l

og 1

/R)

ü Spectra for the same sample of fat analysed several times over a period of 14 months in a transflectance cam-lock with a gold reflector surface

ü Loss of reflectance of the gold reflector surface used


The effect of a repeatability file 1

Palmitic Acid

Cups Reference Value

NIR predicted without rep. file

Residual without rep. file

NIR predicted with rep.

file

Residual with

rep. file

1 22.6 24.5 -1.9 22.7 -0.1 2 22.6 24.9 -2.3 22.8 -0.2 3 22.6 23.8 -1.2 22.3 0.3 4 22.6 25.0 -2.4 22.6 0.0

2

ü The repeatability file : one sample analysed weekly and every time that routine analysis of samples is done ü The repeatability file must be “refreshed” from time to time


%C16:0 %C18:0

N Val. Lab.

Val. Pred.

Master

Val. Pred. Satellite before STD

Val. Pred. Satellite

after STD

N Val. Lab.

Val. Pred.

Master



after STD

1 20.70 21.15 8.20 21.25 1 12.00 11.78 12.28 11.77 2 19.10 19.27 6.31 19.47 2 9.40 9.50 9.81 9.26 3 20.30 20.57 7.24 20.58 3 9.30 9.39 9.85 9.29 4 19.20 19.48 6.06 19.42 4 9.10 9.41 9.98 9.42 5 24.40 23.89 10.60 23.85 5 12.60 13.08 13.71 13.20 6 23.30 23.20 9.74 22.91 6 11.00 11.29 12.20 11.68 7 24.40 24.65 11.62 24.79 7 12.50 12.62 13.40 12.89 8 18.40 18.69 5.32 18.43 8 9.00 8.72 9.83 9.28 9 20.40 20.75 7.63 20.84 9 10.50 10.03 10.89 10.36 10 21.00 21.10 8.09 21.17 10 9.70 9.51 10.31 9.77

SEP 0.30 13.04 0.37 SEP 0.29 0.78 0.35 %C18:1 %C18:2

N Val. Lab.

Val. Pred. Master



after STD

N Val. Lab.

Val. Pred. Master



after STD

1 50.50 50.67 52.08 50.53 1 10.30 10.15 9.51 10.15 2 56.40 56.50 57.35 55.85 2 9.30 9.34 8.44 9.09 3 55.40 55.47 56.78 55.25 3 8.70 8.67 7.75 8.39 4 53.20 53.10 54.12 52.56 4 12.20 12.47 11.14 11.83 5 48.00 48.34 49.49 47.89 5 8.20 8.21 7.77 8.40 6 49.70 49.83 51.61 50.05 6 8.60 8.63 7.98 8.61 7 47.80 47.60 49.28 47.69 7 8.40 8.49 7.63 8.26 8 57.00 57.56 58.98 57.50 8 9.30 9.32 8.54 9.18 9 53.10 53.63 54.80 53.27 9 9.50 9.35 8.65 9.30 10 54.20 54.83 55.73 54.22 10 8.70 8.66 7.56 8.18

SEP 0.35 1.53 0.34 SEP 0.11 0.85 0.26

Calibration transfer protocol and demonstration


ü  The controls by GC onlyanalyze one fat samplerepresenta<ve of a lot ofanimals from the samefarmer. (Becauseofmoneyand<meneeded).

ü  T h e r e a r e n o t a b l edifferences among animalsinalot.

ü  AtmeanlevelNIRSandGC

produceiden<calresults.

ü  NIRS allows to obtain thefaUy acid profile of ecahindividualanimal.

1532 20.6 9.1 52.5 9.6 1533 18.9 8.4 55.5 9.6 1534 18.3 7.9 53.6 11.3 1535 19.6 8.4 54.2 9.0 1541 18.1 8.8 54.5 10.1 1542 19.5 9.1 49.5 11.9 1543 18.6 8.4 54.0 10.4 1544 19.5 9.5 52.1 10.8 1545 19.1 8.6 53.2 10.6

Muestra C16:0 C18:0 C18:1 C18:2

Individual analysis of animals from a lot

MEAN 19.4 9.0 52.8 10.6 NIRS LAB GC 20.4 8.9 53.7 10.9

NIRS allows to predict the composition of individual carcasses

Iberian Pork Processing Plants /2nd stage

Sampling at the slaughterhouse

From melted fat to intact pig tissue Iberian Pork Processing Plants /2nd stage

Quantitative Fatty Acid Analysis by Near Infrared Spectroscopy in Pig Adipose Tissues: At-Line versus In-Situ. Zamora-Rojas E.*. Pérez-Marín D.. De Pedro-Sanz E.. Guerrero-Ginel J.E. and Garrido-Varo AMeat Science 95, 503-511 (2013)

COMPARISON BETWEEN MODELS PRODUCED WITH DIFFERENT ANALYSIS MODES

Instrument / analysis mode Parameter Pre-processing

No. PLS RMSEC (%) RMSEP (%) R2

p RPDp

MEMS-NIRS Intact carcass

C16:0 SNV + DT+ 1st der. 7 0.84 1.00 0.78 2.15 C18:0 SNV + DT+ 1st der. 7 0.60 0.68 0.83 2.66 C18:1 SNV + DT+ 1st der. 7 1.47 1.30 0.84 2.63 C18:2 SNV + DT+ 1st der. 7 0.52 0.55 0.81 2.29

FNS intact

adipose tissue

C16:0 SNV + DT+ 1st der. 10 0.30 0.79 0.85 2.72 C18:0 SNV + DT+ 2nd der. 4 0.37 0.45 0.92 4.02 C18:1 SNV + DT+ 1st der. 9 0.49 1.25 0.84 2.73

C18:2 SNV + DT+ 1st der. 11 0.15 0.29 0.94 4.29

FNS / melted fat

C16:0 SNV + DT+ 1st der. 7 0.34 0.37 0-97 5.81 C18:0 SNV + DT+ 1st der. 7 0.30 0.38 0.95 4.76 C18:1 SNV + DT+ 1st der. 6 0.33 0.35 0.98 9.74 C18:2 SNV + DT+ 1st der. 9 0.13 0.19 0.97 6.55


More innovative companies implemented NIRS “ at – line” as a marketing strategy


NIRS + ICT a driving force for innovation in the Iberian Pig Industry

ü Graphical User Interface in MATLAB environment for automated NIRS predictions of the quality of the carcasse. ü IBERIUM NIRS web environment . ü Android application using the software AppInventor of MIT to access the virtual environment from mobile devices.

Iberian Pork Processing Plants /3rd stage

Server

Virtual database NIRS analysis

Automated data treatment

Android Apps to connect to the virtual data base

DOWNLOAD APPS FOR ANDROID

NIRS + ICT a driving force for innovation in the Iberian Pig Industry


Fromatlinetoon-lineNIRS1ststage:analysisoffinemilledanimalfeeds-1992

CompoundFeedsIndustry/1ststage

CalibraQonequaQonsdevelopedformanyrawmaterialsandcompoundfeeds.Suscefulltransferofthemtotheanimalfeedindustry.NANTA(NUTRECO,SP)NIRSnetwork,13instrumentscloned

CompoundFeedsIndustry/2ndstage

Fromatlinetoon-lineNIRS2ndstage:analysisofungroundfeeds-1999

Pérez-Marín D., Garrido-Varo A., Guerrero-Ginel J.E.; Gómez-Cabrera A.2004.Near Infrared reflectance Spectroscopy (NIRS) for the mandatory labeling of compound feedingstuffs: chemical composition and open declaration. Anim. Feed Sci. and Techn. 116 (3-4); p. 333-349

Mixing BeforepackagingRawmaterialrecepQon

CompoundFeedsIndustry/3rdstageMainresearchtasks:1.selecQonofcontrolpoints;2.selecQonofon-line

instrumentaQon;3.instrumentsebngintheplant.4.transferenceoflargespectrallibrariesfromat-linetoon-lineNIRSinstruments

12 samples (mixing) 12 samples (bagging)

Averaged GH = 1.972 Averaged GH = 1.294

Averaged NH = 1.223 Averaged NH = 0.924

Samples GH NH

Mixing s-1 1.439 0.968

Finished feed s-1 1.187 0.938

Mixing s-2 1.592 1.033


Mixing s-3 2.526 1.667


Mixing s-4 4.534* 3.022*

Finished feed s-4 3.412* 2.251*

CompoundFeedsIndustry/3ndstage

CalibraQonequaQonsdevelopedwithintactCFssamplescanbeusedatthemixingandpackagingstages

ThisfindingsavealotofQmeandmoney!!!

StandardizaQonalgorithms:Shenk´patentedvsPDS

Estudio 4 Compound Feeds Industry/3rd stage

RMS(c)

Instrument1vsInstrument1 6766Instrument1vs2(before) 70582STD1(patented)N=10 7524STD2(patented)N=5 7148STD3(patented)N=3 7500

STD4(PDS,w=19)N=10 7368STD5(PDS,w=19)N=5 6923STD6(PDS,w=19)N=3 6953

Focal distance 13-28 mm

Conveyor belt speed 2-8 m min-1

Sample bed 0.0625-0.50 g cm-3

Optimising factors

Instrumentsebngs:AtaskneededofpilotresearchstudiesandprocessengineeringsoluQons

Sampling to compare process and lab spectra

CompoundFeedsIndustry/3rdstage

Fine tuning of the instrument in the plant

Sampling to compare process and lab spectra

Compound Feeds Industry/3rd stage

EngineeringsoluQonstoadaptinstrumenttotheexisQngplantdesign

At last, we arrived to the plant!!!!!!

Finalconclusions

•  Tomovefromat-linetoon-line/on-siteNIRSanalysis,aclosepartnershipbetweenindustryandacademyisneeded,becauseeachindustryneedofanspecificanddedicatedresearchwhichcanaddressanumberofproblemsrelatedtothesuccessfulapplica<on.

•  Toconvertideasandindustryneedsintoreality,weneedmoreyoungpreandpostgraduatestudentstrainedinscien<fic,technologicalandbusinessskillsrelatedtoNIRSsensors,mul<and&megavariatedatamathtreatment,agri-foodengineeringandICTs.

•  TheInterna<onalVirtualPlaeormforTeaching&LearningNIRSwillofferinthenearfuture,affordablecoursesforthatincreasingdemandedprofessionalprofile.hUp://www.uco.es/nirsplaeorm/

Thankyou!!!

moving smoothly from the nir lab to the agri-food plant smoothly from the nir lab to the agri-food...

Documents