Potential estimation of titratable acidity in cow milk using mid-infrared spectrometry

P t ti l

ti

ti f tit t bl

Potential estimation of titratable

acidity in cow milk using

mid-acidity in cow milk using mid

infrared spectrometry

Colinet F.G.1_{, Soyeurt H.}1,2_{, Anceau C.}3_{, Vanlierde A.}1_, 

Keyen N._y 3_{,  Dardenne P.,} 4_{, Gengler N., g} 1,2 _{and Sindic M.}3

1 Animal Science Unit, Gembloux Agro-Bio Tech, ULg, Belgium 2 National Fund for Scientific Research, Belgium

3 Food Technology Unit, Gembloux Agro-Bio Tech, ULg, Belgium 3 ood ec o ogy U t, Ge b ou g o o ec , U g, e g u 4 Valorisation of Agricultural Products Department, Walloon

Context

Context

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Interest of MIR spectrometry 

• Review of Soyeurt H ICAR 2010Review of Soyeurt H., ICAR 2010

Context

Context

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Estimation of technological abilities

• Finer and more accurate estimationsFiner and more accurate estimations

9 Better support to farmers/producers

9 Finer management of the herd

9 Finer management of the herd

Context

Context

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Cheese making process

• Global cheese yieldGlobal cheese yield

• Milk coagulation properties

9 t 40 % f i ti ( )

9 up to 40 % of variation among cows       (Ikonen et al., 2004)

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Factors influencing milk coagulation kinetics

• Coagulation enzyme

• Protein and calcium contents in milkProtein and calcium contents in milk

Context

Context

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Titratable acidity (TA)

• TA influences all phases of milk coagulationTA influences all phases of milk coagulation

• Developed acidity results from bacterial activity 9 L ti id 9 Lactic acid 9 Collection, transportation, and transformation of milk h lk • Fresh milk 9 Some components: carbondioxide, citrates, casein,  lb i / l b li d h h albumin/globulin and phosphates   9 Buffer action

Objective

Objective

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To investigate the potential use of MIR 

spectrometry in order to predict TA

p

y

p

• TA recorded as Dornic degree

• Walloon Region of Belgium

• Walloon Region of Belgium

Material and methods

Material and methods

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Sampling

• Walloon Region of BelgiumWalloon Region of Belgium

• Large variability: several criteria 9 Milk li i di id l b lk ilk 9 Milk sampling: individual or bulk milk 9 Breed: Dual Purpose Belgian Blue, Holstein, Red‐Holstein, b li d d Montbeliarde and Jersey 9 Time of sampling:  morning milking, evening milking or mix of 50 % morning & 50 % evening milk samples

Material and methods

Material and methods

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Analysis

• 225 samples225 samples • Milk Lab (Comité du Lait, Battice, Belgium) 9 MIR F Milk S FT6000 t t 9 MIR Foss MilkoScanFT6000 spectrometer 9 Analysed traits: fat, protein, free fatty acid (FFA), urea, l d ( ) i ll ( ) d lactose, dry matter (DM), somatic cell count (SCC) and pH 9 SCC Î Somatic Cell Score (SCS)

Material and methods

Material and methods

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Analysis

• Titratable acidityTitratable acidity

9 Recorded as Dornic degree (D°) 9 0 1 N NaOH solution 9 0.1 N NaOH solution 9 Consumption of NaOH to shift the pH value from 6.6 to 8.4 (phenolphthalein) (phenolphthalein) 

Material and methods

Material and methods

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Calibration procedure

• First derivative pretreatmentFirst derivative pretreatment

• Partial least square regressions 22 tli • 22 outliers • Statistical parameters 9 Mean and standard deviation (SD) 9 Standard error of calibration (SEC) 9 Calibration coefficient of determination (R²_C)

Material and methods

Material and methods

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Calibration procedure

• Cross‐validationCross validation 

9 To determine the number of factors

9 To assess the accuracy of equation

9 To assess the accuracy of equation 9 Partitioning randomly the calibration set: 102 groups St ti ti l t t th • Statistical parameters to assess the accuracy 9 Standard error of cross‐validation  (SECV) 9 Calibration coefficient of determination (R²_CV) 9 RPD = SD / SECV

Results

Results

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Characterization of the samples

Trait Mean SD Trait Mean S Fat (%) 3.88 1.03 Protein (%) 3.49 0.52

FFA (mmol/100 g of Fat) 5.63 8.62

Urea (g/100 mL) 0.023 0.011 Lactose (g/100 mL) 4 85 0 35 Lactose (g/100 mL) 4.85 0.35 DM (%) 12.66 1.25 SCS 3.31 1.90 pH 6.69 0.09

Results

Results

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Characterization of the samples

Trait Mean SD Trait Mean S Fat (%) 3.88 1.03 Protein (%) 3.49 0.52

FFA (mmol/100 g of Fat) 5.63 8.62

Urea (g/100 mL) 0.023 0.011 Lactose (g/100 mL) 4 85 0 35 Lactose (g/100 mL) 4.85 0.35 DM (%) 12.66 1.25 SCS 3.31 1.90 pH 6.69 0.09 TA (D°) 16.27 2.27 Coefficient of Variation = 14 %

Results

Results

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Observed correlations among milk components

Fat FFA Protein Urea Lactose DM SCS pHp

TA (D°) 0.04NS _0.13* _0.39*** _0.18** _0.21** _0.26*** _‐0.16* _‐0.32*** Fat ‐ 0.41*** _0.42*** _0.13* _‐0.19** _0.89*** _0.18** _‐0.18** FFA ‐ 0.68*** _0.41*** _‐0.17** _0.50*** _0.04NS _‐0.38*** Protein ‐ 0.30*** _‐0.07NS _0.69*** _0.10NS _‐0.26*** Urea ‐ 0.18** _0.25*** _‐0.18** _‐0.01NS Lactose ‐ 0.11NS _‐0.40*** _0.66*** DM ‐ 0.07NS _‐0.06NS

Results

Results

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Observed correlations among milk components

Fat FFA Protein Urea Lactose DM SCS pHp

TA (D°) 0.04NS _0.13* _0.39*** _0.18** _0.21** _0.26*** _‐0.16* _‐0.32*** Fat ‐ 0.41*** _0.42*** _0.13* _‐0.19** _0.89*** _0.18** _‐0.18** FFA ‐ 0.68*** _0.41*** _‐0.17** _0.50*** _0.04NS _‐0.38*** Protein ‐ 0.30*** _‐0.07NS _0.69*** _0.10NS _‐0.26*** Urea ‐ 0.18** _0.25*** _‐0.18** _‐0.01NS Lactose ‐ 0.11NS _‐0.40*** _0.66*** DM ‐ 0.07NS _‐0.06NS SCS ‐ ‐0.19**

Results

Results

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Observed correlations among milk components

Fat FFA Protein Urea Lactose DM SCS pHp

TA (D°) 0.04NS _0.13* _0.39*** _0.18** _0.21** _0.26*** _‐0.16* _‐0.32*** Fat ‐ 0.41*** _0.42*** _0.13* _‐0.19** _0.89*** _0.18** _‐0.18** FFA ‐ 0.68*** _0.41*** _‐0.17** _0.50*** _0.04NS _‐0.38*** Protein ‐ 0.30*** _‐0.07NS _0.69*** _0.10NS _‐0.26*** Urea ‐ 0.18** _0.25*** _‐0.18** _‐0.01NS Lactose ‐ 0.11NS _‐0.40*** _0.66*** DM ‐ 0.07NS _‐0.06NS

Results

Results

ƒ

Observed correlations among milk components

Fat FFA Protein Urea Lactose DM SCS pHp

TA (D°) 0.04NS _0.13* _0.39*** _0.18** _0.21** _0.26*** _‐0.16* _‐0.32*** Fat ‐ 0.41*** _0.42*** _0.13* _‐0.19** _0.89*** _0.18** _‐0.18** FFA ‐ 0.68*** _0.41*** _‐0.17** _0.50*** _0.04NS _‐0.38*** Protein ‐ 0.30*** _‐0.07NS _0.69*** _0.10NS _‐0.26*** Urea ‐ 0.18** _0.25*** _‐0.18** _‐0.01NS Lactose ‐ 0.11NS _‐0.40*** _0.66*** DM ‐ 0.07NS _‐0.06NS SCS ‐ ‐0.19**

Results

Results

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Statistical parameters

• 10 factors10 factors • Mean ± SD = 16.22 D° ± 2.01 (n = 203) SEC 0 56 D° • SEC = 0.56 D° • R²_C = 92.25 %

Results

Results

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Cross‐validation

SECV = 0.64 D° R²_CV = 89.88 % RPD = SD / SECV = 3.13 

Conclusions

Conclusions

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R

2_C

and R

2_CV

• High and around 90 %High and around 90 %

• Higher than observed correlations (max 39 %)

RPD 2

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RPD > 2

Î Feasibility of TA prediction in bovine milk

Î Feasibility of TA prediction in bovine milk      

from MIR spectrum

Î Calibration equation good predictor and

Î Calibration equation: good predictor and      

usable in most applications (including research)

Perspectives

Perspectives

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Validation with new set of samples

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Use of this equation

Use of this equation

• Walloon Database: 900,000 spectra

• Study of TA variability in the Walloon dairy cattle

9 Detection of potential effects of breed, season, DIM...

• Development of a genetic evaluation

• TA breeding values + others traits = a new economicTA breeding values   others traits   a new economic  index for cheese making abilities ?

Acknowledgments

Acknowledgments

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Study related to the INTERREG project 

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Acknowledgments for financial support

Acknowledgments for financial support

• European Commission p

• Walloon Regional Ministry of Agriculture

Thank you for your attention

Thank you for your attention

Corresponding author’s e‐mail: Frederic.Colinet@ulg.ac.be

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Acknowledgments for collaboration

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Acknowledgments for collaboration

• Valorisation of Agricultural Products Department,  W ll A i l l R h C Walloon Agricultural Research Center • Food Technology Unit, Gembloux Agro‐Bio Tech, ULg • Milk Committee of Battice ll d ( bl) • Walloon Breeding Association (AWE asbl) • Walloon dairy breeders