Lab. Meat Processing and Quality Control Technology

Dequan Zhang Ph.D/Professor

Lab. of Meat Science and Technology

Institute of Agro-Food Science and Technology(IAFST)

Chinese Academy of Agriculture Science(CAAS)

The Research of Meat Science and Technology in IAFST

OUTLINE

About IAFST

Introduction of meat science and

technology in IASFT

Basic situation

Founded in 2003.

Staff Composition: 98 on-the-job staff

members (72 scientists and technicians)

15 professors

20 associate professors

9 Ph.D. supervisors

17 master supervisors

About IASFT

Mission

Key technologies for

agricultural products

processing

Food science &

Technology

Food quality and

safety technology

Education and training

About IASFT

Main Areas of Activities:

Cereal & Oil Processing and Functional

Food

Vegetable & Fruit Processing and Quality

Control

Meat Processing and Quality Control

Dairy Processing and Quality Control

Food Preservation and Logistics

Food Quality and Safety

About IASFT

Introduction of meat

science and

technology

1 Eating Quality and Quality Control

2 Development of the New Products and

Utilization of offals

1 Evaluation of eating quality

and control

1.1 Prediction of eating quality by NIR

1.2 Nondestructive carcass grading technology

1.3 Influence of DPCD on the quality of meat

products


Study the relationship between the

instrumental parameters and eating

quality of meat by means of the CA

(Correlation analysis), SPA（sensory

Profile Analysis), PCA (Principal

component analysis ) and others

methods to build up the prediction

model of eating quality of meat by NIR.

Prediction of tenderness of

meat by NIR

Firstly, detect the tenderness of

meat by TPA;

Secondly, scan the meat by NIR

and acquire the NIR spectrum;

Build up the prediction model of

mutton tenderness by NIR to

TPA with difference partial

least squares (PLS) factors.

Use coefficient of determination

(R2) & root mean square error of

calibration (RMSECV) to

determine the predict ion model.

Rank:8 preprocessing method: Vector Normalization

Wave region: 11995 – 5446, 4601 – 4246cm-1

R2=0.71 RMSECV=0.46

Prediction of the tenderness of mutton (shear force ) by NIR


Prediction of color of meat

by NIR

Firstly, detect the color of meat

by color meter;

Secondly, scan the meat by NIR

and acquire the NIR spectrum;

Build up the prediction model of

mutton color by NIR with PLS

factors.

Use R2 and RMSECV to

determine the predict ion model.


Rank:6 preprocessing method: Mix-max Normalization

Wave region:11995.5 – 4597.6cm-1

R2=0.77 RMSECV=0.45

Prediction of the color of mutton by NIR

Prediction of mutton flavor by NIR

Firstly, detect the mutton flavor by sensory evaluation;

Secondly, scan the meat by NIR and acquire the NIR spectrum;

Build up the prediction model of mutton flavor by NIR with PLS factors.

Use R2 and RMSECV to determine the predict ion model.



Wave region: 11987.7-5446.2cm-1

R2=0.78 RMSECV=0.452

Prediction of the flavor of mutton by NIR

Prediction of juiciness of meat by NIR

Firstly, detect the mutton juiciness by sensory evaluation;

Secondly, scan the meat by NIR and acquire the NIR spectrum;

Build up the prediction model of mutton juiciness by NIR with PLS factors.

Use R2 and RMSECV to determine the predict ion model.



Wave region: 11987.7-4246.6cm-1

R2=0.734 RMSECV=0.435

Prediction of the juiciness of mutton by NIR

Investigate the automatic grading technology of carcass by uses of the

novel instrumental methods, such as NIR, Ultrasonic .

1.2Nondestructive carcass grading

technology by NIR

Carcass NIRsample

tenderness

texture

muscle color

fat color

marble

Quality grading

Spectra Model

Sa

mp

les

we

re

sc

an

ne

d b

y N

IR

Th

e d

ata

we

re p

ut

into

the c

om

pu

ter

Th

e p

red

ictio

n

mo

del w

ere

bu

ilt

Prediction the carcass grade

carcass weight

back-fat thickness

carcass muscle

age

Yield grading

1.2Nondestructive carcass grading

technology by NIR

Study on the grade of lamb carcass by

NIR

Yield grading

Quality grading

(1) Establish yield grade model by NIR

Screen yield grade indicators

statistic of lamb carcasscharacter indicato

r

N Max Min Mean S.D. CV% S.E.

Carcass weight x1 40 18.6000000 10.500000 14.5587500 1.8819619 12.926672 0.2975643

Leg score x2 40 12.0000000 10.000000 10.650000 0.8638020 8.1108166 0.1365791

split carcass meat weight

x3 40 6.6850000 3.0440000 5.0047000 0.7855061 15.695368 0.1241994

meat ratio x4 40 75.3400000 57.9800000 68.527250 3.1597517 4.6109419 0.4996006

Meat weight after trimming

x5 40 12.1280000 6.2640000 9.3409250 1.4638962 15.6718547 0.2314623

Back thickness x6 40 11.2500000 2.8000000 5.4145000 1.8699595 34.6361433 0.2956666

Rib thickness x7 40 26.3400000 11.030000 17.4045000 3.2844224 18.8711104 0.5193128

Body wall x8 40 25.3800000 10.540000 16.3412500 3.0797862 18.846699 0.4869570

Eye loin area x9 40 12.7000000 5.8000000 9.1372500 1.3776699 15.0775111 0.217828

correlation between nine indicators

x1 x2 x3 x4 x5 x6 x7 x8 x9

x1 1.00000

x2 0.61707 1.00000

x3 0.97256 0.61967 1.00000

x4 0.56376 0.38509 0.73673 1.00000

x5 0.91278 0.63204 0.89239 0.53220 1.00000

x6 0.55261 0.52262 0.57233 0.40026 0.49349 1.00000

x7 0.62262 0.58821 0.60029 0.34894 0.57195 0.57847 1.00000

x8 0.61494 0.59765 0.59847 0.36453 0.58080 0.56032 0.97201 1.00000

x9 0.55424 0.47427 0.56995 0.44737 0.48571 0.40115 0.64443 0.68655 1.00000


eigenvalue(λ) proportion(％) cumulative(％)

1 28.2419375 0.6614 0.6614

2 8.2389854 0.1930 0.8544

3 2.5106489 0.0588 0.9132

4 1.9283840 0.0452 0.9583

5 0.9187587 0.0215 0.9798

6 0.3965102 0.0093 0.9891

7 0.2751738 0.0064 0.9956

8 0.1879367 0.0044 1.0000

9 0.0008780 0.0000 1.0000

principal component analysis between nine indicators

Morrison considers that cumulative greater than 85% can explain the most information.


Factor 1 Factor 2

x1 0.279883 0.149989

x2 0.109546 0.006031

x3 0.120335 0.109746

x4 0.367819 0.838103

x5 0.204248 0.115483

x6 0.237468 0.028309

x7 0.576542 -.372227

x8 0.542152 -.332135

x9 0.188296 0.001307

eigenvalue 28.2419375 8.2389854

proportion（%） 0.6614 0.1930

cumulative（%） 0.6614 0.8544

0.9720

body wall= 0.91145*lib

thickness + 0.47788

（R2=0.9448）

So, lib thickness

can be used to

predict the yield

grade.


eigenvectors

model formula R2

1 y=ax+b Y=62.68465+0.33569x 0.1214

2 Y=a+bx2 Y= 65.91694+ 0.00833x2 0.0960

3 Y=a+bx3 Y= 67.05149+ 67.0514x3 0.0706

4 y=b0+b1x+b2x2 y= 44.81241+2.42034x -0.05874x2 0.2009

5 y=b0+b1*x+b2x2+b3 x3 y= 54.09022+ 0.80604 x+ 0.03164 x2 -

0.00163 x3

0.2021

6 y=1/(a+bx) Y=1/( 0.01596 -0.00007685x) 0.1301

7 y=1/(a+b*exp(-x)) y=1/( 0.01456+ 66.04731*exp(-x)) 0.0717

8 y=1/(a+bx*x) y=1/( 0.01522 -0.00000192x*x) 0.1036

9 y=1/(a+bx*x*x) y=1/( 0.01497 -5.86613*10-8x*x*x) 0.0773

10 y=a*exp(bx) y=e4.13791*exp(0.00507x) 0.1262

11 y=a*exp(bx*x) y= e4.18663*exp(0.00012615x*x) 0.1000

12 y=(a+bx)/x y=( -80.44133 + 73.35198 x)/x 0.9614

13 y=x/(a+bx) y=x/( 0.01864+ 0.01351x) 0.9485

14 y=a+b*√x y= 56.3979+2.92024^√x 0.1337

15 y=1/(a+b*√x) y=1/( 0.01739 -0.00066709*√x) 0.1422

Establish yield grade evaluation equation

Meat ratio(Y) range:57.98~75.34

Grade one：Y≥72%

Grade two：67%≤Y<72%

Grade three：62%≤Y<67%

Grade four：57%≤Y<62%

Grade five：Y<57%

-80.44133 + 73.35198*lib thickness

Meat ratio(Y) =-----------------------------------------------*100%

lib thickness



Lamb yield grade model by NIR

Rank:8, preprocessing method: No Spectral Data

Preprocessing, Wave region:11995.5 – 4597.6cm-1

R2=0.73, RMSECV=4.62

Prediction of the Lamb grade by NIR

Related coefficient

L a b ΔE

Sensory score -0.83176 0.79101 0.55469 0.91369

Correlation analysis between sensory score and color

value by color meter

(2) Establish quality grade model by NIR

⊿E=√(L-L0)2+(a-a0)

2+(b-b0)2

Make the Muscle and fat color board

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500

70.46

49.07

1 2 3 4 5 6

Muscle color board(392 data)

Fat color board (368 data)

1 2 3 4 5 6

0

5

10

15

20

25

30

35

40

0 100 200 300 400

Serie1

13.65

37.37

Marbling standard board (181data)

Take a picture for lamb marble samples and measure fat rate with Matlab.

1 2 3 4 5

6

(2) Establish quality grade model by NIR

Mode of processing

Current situation

To date, most studies focus on the inactivation of DPCD to microorganisms in liquid culture medium and foods such as juice and milk, the studies about solid food such as meat products is less.

Dense phase CO2(DPCD)

Definition

DPCD is the supercritical state (31.1 ℃,7.38MPa) or subcritical state CO2, use DPCD

to kill microorganisms in food was called DPCD sterilization technology.

1.3Influence of DPCD on the quality of meat

products

Can DPCD be used to sterilize in

low-temperature sausage?

The inactivation of bacteria by DPCDThe inactivation curve

Bacteria of low-temperature sausages were effectively

inactivated by DPCD.

The inactivation rate of bacteria increased with

increment of treatment pressure, treatment temperature

and exposure time.

The maximal drop of the total viable counts was 4.8 lg

cfu/g at 75℃, 50MPa and 30min.

Under the same temperature, the increment of pressure

can strengthen the inactivation of bacteria by DPCD

obviously, vice versa.

model R2 RMSE

linear 0.834-0.985 0.004-0.899

Weibull 0.986-0.999 0.001-0.089

Log-Logistic 0.939-0.998 0.006-0.187

The fitting effect of Linear, Weibull and Log-Logistic models to

the inactivation curves by DPCD

The inactivation of bacteria by DPCD

The non-linear model could fit to bacterial inactivation

kinetics curve by DPCD better than Linear model. Especially,

the Weibull model could fit to the bacterial inactivation

kinetics curve best.

treatment protein

(g/)

lipid

(%)

pH water-holding

capacity(%)

acid value

(mgKOH/g)

TVB-N

(mg/)

control 18.80±0.27 10.63±0.33 6.13±0.09 32.69±4.38 2.26±0.24 5.1±0.59

45℃ thermal tratment 18.76±0.22 10.38±0.24 6.15±0.11 27.16±5.09 2.33±0.22 4.3±0.64

15MPa、20min 18.74±0.21 10.21±0.25 6.14±0.09 26.86±4.12 2.28±0.19 4.2±0.78

25MPa、20min 18.71±0.27 10.01±0.19 6.13±0.18 27.75±5.20 2.18±0.27 3.2±0.72

35MPa、20min 18.76±0.25 9.87±0.33 6.13±0.10 26.24±4.71 2.17±0.35 3.7±0.67

45MPa、20min 18.73±0.24 10.08±0.24 6.12±0.11 25.87±5.16 ― ―

50MPa、20min 18.72±0.35 9.66±0.34 6.11±0.17 24.43±5.32 ― ―

control 18.79±0.24 11.16±0.27 5.98±0.17 29.24±4.04 2.38±0.23 6.2±0.64


15MPa、20min 18.78±0.17 10.26±0.31 6.00±0.13 23.46±4.36 2.23±0.28 4.4±0.73

25MPa、20min 18.70±0.26 10.14±0.42 6.00±0.21 21.78±5.11 2.17±0.37 3.6±0.79

35MPa、20min 18.72±0.21 10.02±0.33 5.98±0.16 22.24±5.43 2.14±0.26 2.8±0.56

45MPa、20min 18.67±0.33 9.82±0.28 6.01±0.12 21.92±5.78 ― ―

50MPa、20min 18.65±0.23 9.86±0.27 5.99±0.09 20.80±5.92 ― ―

Influence of DPCD on the quality of low-temperature sausage

treatment protein

(g/)

lipid

(%)

pH water-holding

capacity(%)

acid value

(mgKOH/g)

TVB-N

(mg/)

control 18.82±0.27 10.89±0.29 6.10±0.11 31.08±4.82 2.43±0.26 7.3±0.53


15MPa、20min 18.76±0.26 10.27±0.26 6.13±0.13 22.73±4.38 2.23±0.32 5.1±0.55

25MPa、20min 18.67±0.23 10.04±0.24 6.12±0.12 22.48±5.29 2.18±0.27 3.6±0.67

35MPa、20min 18.69±0.31 9.98±0.32 6.12±0.14 21.64±4.71 2.14±0.33 4.2±0.49

45MPa、20min 18.71±0.37 9.76±0.28 6.11±0.11 20.38±6.45 ― ―

50MPa、20min 18.68±0.24 9.68±0.35 6.12±0.06 19.15±4.54 ― ―

control 18.77±0.16 10.82±0.21 6.11±0.21 32.14±3.21 2.28±0.25 7.3±0.52


15MPa、20min 18.71±0.27 10.28±0.30 6.15±0.18 21.83±4.56 2.24±0.29 5.1±0.47

25MPa、20min 18.64±0.29 10.19±0.26 6.13±0.12 20.36±3.63 2.07±0.38 4.4±0.58

35MPa、20min 18.69±0.25 10.11±0.34 6.12±0.08 19.34±4.55 2.01±0.35 3.7±0.55

45MPa、20min 18.57±0.27 9.77±0.37 6.13±0.15 20.87±4.78 ― ―

50MPa、20min 18.60±0.34 9.62±0.45 6.10±0.08 18.05±5.25 ― ―


treatment protein

(g/)

lipid

(%)

pH water-holding

capacity(%)

acid value

(mgKOH/g)

TVB-N

(mg/)

control 18.91±0.28 10.13±0.18 6.11±0.18 34.58±5.33 2.26±0.27 8.1±0.48


15MPa、15min 18.83±0.22 9.21±0.21 6.14±0.07 22.41±4.65 2.15±0.32 3.4±0.54

25MPa、15min 18.78±0.31 9.01±0.27 6.13±0.11 19.35±6.34 2.00±0.28 4.2±0.65

35MPa、15min 18.67±0.26 8.86±0.38 6.13±0.15 20.17±4.77 1.97±0.37 4.4±0.71

45MPa、15min 18.74±0.25 8.95±0.24 6.12±0.13 19.23±4.46 ― ―

50MPa、15min 18.69±0.34 8.82±0.36 6.11±0.17 18.65±5.91 ― ―

control 18.75±0.22 10.29±0.26 6.14±0.20 31.44±5.12 2.43±0.25 9.4±0.57


15MPa、15min 18.63±0.24 9.22±0.33 6.15±0.17 21.51±5.61 1.79±0.38 5.1±0.45

25MPa、15min 18.59±0.25 9.08±0.28 6.16±0.18 19.10±4.72 1.73±0.34 4.6±0.56

35MPa、15min 18.56±0.28 9.05±0.34 6.15±0.17 17.39±4.34 1.61±0.29 3.6±0.62

45MPa、15min 18.51±0.28 8.76±0.39 6.14±0.15 17.88±5.13 ― ―

50MPa、15min 18.53±0.19 8.91±0.25 6.14±0.12 18.43±5.89 ― ―


• The pH value, protein content and water-holding

capacity of low-temperature sausages had not change

markedly after DPCD treatment.

• The fat content drop obviously with the increment

of treatment pressure and treatment temperature of

DPCD.

• The acid value and TVB-N all dropped markedly

after DPCD treatment.


Influence of DPCD on the flavor

SPME-GC-MS of low-temperature sausage

control Thermal treatment

HDCD处理DPCD treatment

0

10

20

30

40

50

aldehyde

hydrocarbon

alcohol

ester

acid

keton

others

Compound kind

Relative content

60℃

15 MPa

25 MPa


0

10

20

30

40

50

60

aldehyde

hydrocarbon

alcohol

ester

acid

keton

others

Compound kind

Relative content

70℃ 15 MPa

25 MPa

62 kinds of flavor compounds were detected in

low-temperature sausage treated by heat or DPCD.

The main volatile components were the aldehydes,

hydrocarbons, esters and alcohols.

DPCD has no significant effect on the main volatile

aldehydes and alcohols of low-temperature

sausages.

When the pressure of DPCD increase from 15MPa

to 25MPa, hydrocarbons in low-temperature

sausage increased significantly, the exact cause is

unclear.


Summary

Bacteria can be inactivated markedly by DPCD.

The inactivation curve of DPCD follow the non-linear

model, especially the Weibull model can fit to the

inactivation curve of DPCD in low-temperature sausage.

DPCD can hold and improve the nutrition quality and

flavor of low-temperature sausage .

It is feasible that the DPCD was used to sterilize the

low-temperature sausage.

How to inactivate the bacteria by DPCD?

Control 30MPa

Fig. SEM of E.coli ×5000

Study the influence of DPCD on the cell morphology of bacteria with

SEM/TEM. The result showed that DPCD can destroy the cell membrane

and make the inner material of cell leak out, So lead to bacteria die.

Study the influence of DPCD on the protein secondary structure of E. coli

with CD(circular dichroism) and NIR. The results showed that the α-helix

and the corner could change to β-pleated after different pressures (10-

50MPa,37℃,30min) DPCD treatment, when the pressure was up to 50MPa,

the content of β-pleated was 75.63% which increased 26.7% more than

control.

Fig. The FTIR spectra of E. coli protein powder

treated or untreated by dense phase CO2

wave number(cm-1)

tran

sm

ittan

ce(%

)

2 Development of the New Products

and Utilization of offals

Prepared meals and microwave meat

products

Fermented meat products

Chinese traditional meat products (salt,

bittern, soy sauce)

2.1 Development of the new products

Study the technology of the by-products such as skin,

discarded meat into the restructured meat products.

Study the new products from by-products such as bone

powder and extracts for flavors and dressings.

2.2 Utilization of offals

Chondroitin sulfate

羊骨源硫酸软骨素制备技术

超声辅助超滤冷冻干燥碱-酶

Interests of Cooperation

Nondestructive evaluation of eating quality by NIR or other

technology.

Non-thermal technology in meat processing such as DPCD,

HPP.

Meat fermentation technology such as salami and ham.

Our team Dr. Dequan Zhang Dr. Chunhui Zhang

Dr. Juan Li

Chunhong Li,Technician

Jie Zhu,Technician

Weili Rao, Technician

6-10 Master and Ph.D students

Thanks for

your attention!

Lab. Meat Processing and Quality Control Technology

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