Non-destructive Measurement of Color, Firmness and Lycopene Content of Tomato using Visible and NIR Spectroscopy Limei ChenVijaya Raghavan, Ph.DDenis Charlebois, Ph.DMarie Thérèse Charles, Ph.D Clément Vigneault, Ph.D

50th Annual Conference/Conférence Annuelle13-16 July/juillet, 2008

Lonsdale Quay Hotel, 123 Carrie Cates CourtNorth Vancouver, British Columbia, Canada

Outline

Introduction Objective Materials and methods Results Conclusions

Tomato 2006 world production: 125 Tg*

Low in fat, calories and cholesterol-free; rich in vitamins A and C, lycopene, and β-carotene

Skin color and firmness - two important quality attributes for consumers**

References: *FAOSTAT online, http:// faostat.fao.org/default.aspx **Tijskens and Evelo, 1994. Postharvest Biology and Technology, 4, 85–89

Health benefits of lycopene A carotenoid imparting red color to f

ruits

Potent antioxidant: neutralize free radicals, protect body cells against oxidative damage

Possible preventive effect against several types of cancer (e.g. liver cancer, lung cancer and prostate cancer)

References: Clinton, 1998. Nutrition Reviews, 56, 35–51 Rao and Agarwal, 2000. Journal of the American College of Nutrition, 19, 563-569

Introduction of vis/NIR spectroscopyVis/NIR spectroscopy - a spectroscopic method utilising the visible and near infrared region of the electromagnetic spectrum (from about 380 to 2500 nm)

(Photo: http://www.baylor.edu/bucas/index.php?id=37025)

Principle of spectroscopy

NIR spectroscopy Large number of overtones and combination bands results in broad, poorly defined peaks

Higher signal-to-noise ratio compared to mid-IR spectroscopy

NIR application in food analysis

Good results for predicting SSC and dry matter

Grading line with NIR sensors are available from many companies (e.g. Aweta, Greefa, Mitsui-kinzoku)

Papers about fresh tomato analysis

Objective

Objective

Study the feasibility to evaluate the quality of tomatoes with non-destructive method based upon vis/NIR spectroscopy

Establish calibration models to predict color, firmness and lycopene content, simultaneously

Problem

Current methods to measure the lycopene content are laborious, destructive and organic solvents needed

Different instruments are employed for evaluation of tomato quality

Materials and methods 90 tomatoes Stored under 16ºC, 90-93% RH Spectra and quality parameters of 6 tomatoes measured

at 1, 5, 8, 12 and 16 days of ripening (DOR)

Cultivars 2 (cv. DRK 453 & cv. Trust )

Measuring times5

(breaker, turning, pink, light-red, red)

Tomatoes in each combination of

cultivar and DOR3

Collection times 3 (2 for calibration, 1 for validation)

Total No. of tomatoes = 2*5*3*3 = 90

Approach for building a spectroscopic method

Collecting spectral data (x-variables)

Determining quality parameters

(y-variables) by reference methods

Developing the calibration modelY = f (x)

Validating the model

Spectra acquisition

Spectrometer (FieldSpec® Pro FSP 350-2500P) coupled with a reflectance contact probe

6 (1 and 5 DOR) or 4 (8, 12 and 16 DOR) equidistant positions around the equator

Reflectance spectra 350-2500nm

Color and firmness measurement

Based on CIELAB system, color value L*, a*, b* measured by Minolta Chromameter

Firmness measured by a universal testing machine, expressed as peak force (N)

CIE L* a* b*

References: *Gómez et al., 2001. J. Sci. Food Agric, 81, 1101–1105 **Richardson and Hobson, 1987. J. Sci. Food Agric, 40, 245–252

(Photo: http://www.cigem.ca/pics/lab.jpg)

TCI = 2000a*/(L*(a*2+b*2)1/2)*

a*/b* ratio is a better index than a* in distinguishing varieties**

Lycopene content measurement

Homogenized and filtered to tomato juice extract

According to the reduced volumes of organic solvents method of Fish et al (2002), lycopene content determined by a spectrophotometer

Lycopene (mg/kg) = (A503 * 31.2) / (quantity of tissue used (g))

References: Fish et al., 2002. Journal of Food Composition and Analysis, 15, 309-317

Absorbance spectra560

nm

675

nm

1930 nm

980

nm

120

0

nm

1450 nm

Change of quality attributes

-0.4

0.0

0.4

0.8

1.2

1.6

0 2 4 6 8 10 12 14 16 18Day

Co

lor

a*/b

*5

10

15

20

25

30

Fir

mn

ess

(N)

color a*/b*

firmness

-20.0

-10.0

0.0

10.0

20.0

30.0

40.0

0 2 4 6 8 10 12 14 16 18

Day

TC

I

0

10

20

30

Lyc

op

ene

con

ten

t(m

g/k

g)

TCI

lycopene content

Multivariate calibration

Software package - “The Unscrambler v9.7”

Partial least squares (PLS) regression method was chosen due to the collinearity of x-variables

Full cross-validation used to determine the optimal PC number

Good PLS model: low RMSEC, RMSECV or RMSEP, high R2, SDR (SD/RMSEP)>3

Wavelength range selection

0.04

0.05

0.06

0.07

0.08

0.09

0.10

A B C D

RM

SE

CV

400

800

1,200

1,600

2,000

2,400

A B C D

Wavele

ng

th (

nm

)

For a*/b* 450-600 nm

Wavelength range selection

For TCI 430-1400 nm

2.00

2.20

2.40

2.60

A B C D

RM

SE

CV

400

800

1,200

1,600

2,000

2,400

A B C D

Wav

elen

gth

(n

m)

Wavelength range selection

For firmness 500-1100 nm

400

800

1,200

1,600

2,000

2,400

A B C D

Wav

elen

gth

(nm

)

2.00

3.00

4.00

5.00

A B C D

RM

SE

CV

(N

)

Wavelength range selection

2.00

2.50

3.00

3.50

4.00

A B C D E

RM

SE

CV

(m

g/kg

)

400

800

1,200

1,600

2,000

2,400

A B C D E

Wav

elen

gth

(nm

)

For lycopene content 450-1000 nm

Model resultsa*/b*

R2 = 0.99RMSECV=0.06SDR=8.89

-0.5

0

0.5

1

1.5

-0.5 0 0.5 1 1.5

Measured value

Pre

dic

ted

val

ue

Model results

R2=0.99 RMSECV=2.02SDR=8.84

TCI

Model resultsFirmness

R2 = 0.90RMSECV=2.24SDR=3.10

5

15

25

35

5 15 25 35

Measured value

Pre

dic

ted

val

ue

Model resultsLycopene content

R2 = 0.94RMSECV=2.57SDR=4.14

-5

5

15

25

35

45

-5 5 15 25 35 45

Measured value

Pre

dic

ted

val

ue

External validation

Parameters Wavelength range (nm)

Preprocessing method

No. of PCs

RMSEP R2 SDR

Color a*/b* 450-600 S.Golay 1st derivative

2 0.06 0.99 10.49

TCI 440-1400 - 3 1.52 0.99 11.74

Firmness 500-1100 - 4 1.44 0.97 6.12

Lycopene 450-1000 - 8 2.15 0.96 5.18

The result of firmness was superior to that reported by Shao et al. (2007) (r = 0.82, RMSEP=15.80) The result of lycopene was better than that reported by Baranska et al. (2006) using NIR (R2=0.85 and SECV=91.19)

Correlation of quality attributes

PLS2 model results

WavelengthNo. of

PCsParameters

Calibration Cross-validation External validation

RMSEC R2 RMSECV R2 SDR RMSEP R2 SDR

450-1100nm

6

Color a*/b* 0.05 0.99 0.05 0.99 10.57 0.06 0.99 8.89

TCI 2.25 0.98 2.49 0.98 7.17 1.75 0.99 11.65

Firmness 1.85 0.93 2.24 0.90 3.10 1.44 0.97 4.83

Lycopene 2.75 0.93 3.17 0.91 3.35 3.03 0.92 3.51

Conclusions It is feasible to build a fast and non-destructive

measurement of color, firmness and lycopene content of tomato fruits based on vis/NIR spectroscopy.

Calibration models with excellent performance were established to predict color value a*/b* ratio, tomato color index, firmness and lycopene content of tomato fruits.

Thank you