低溫食品物
流與
projects
蕭心怡
食品之特殊性
Product classification for physical distribution
1. Degree of processing
2. Value of the product
3. Volume and the weight of the product
4. Storage temperature
5. Life-cycle
6. Turnover growth, market share
食品之特殊性
Storage temperature
• 12℃-18℃:
• 0℃-7℃:
• 零下2℃-零下7℃:
• 零下18℃:
• 保存期限半年至一年
3
涼藏食品
冷藏食品
冰溫食品
冷凍食品
現今低溫物流管理問題
B2C 低溫冷藏宅配溫度記錄實證結果
4
B2C 低溫冷凍宅配溫度記錄實證結果
現今低溫物流管理問題
5
三樓統皓前區冷藏庫
檢測間最高溫22.717 ℃；檢測間最低溫 -0.213 ℃；檢測間平均溫 9.049 ℃
設定溫度 7 ℃
•温度過高：（>7℃）：55 %
•冷藏温度範圍：( 7℃~0℃)：45 %
•温度稍低：（0℃~ -1℃）：0%
•温度過低：（< -5℃）：0%
四樓統皓烹調區冷凍庫4
檢測間最高溫11.334 ℃；檢測間最低溫-12.956 ℃；檢測間平均溫 -4.862 ℃
設定溫度 -18 ℃
Project 1: 開發TTI
現有冷鏈之溫度監控工具
Infrared
thermometer
Time
Temperature
Indicator
Data
Loggers
Contact
thermometer
Cold Chain
Temperature
Monitoring
Wireless
technologies
11
Project 1: 開發TTI
• 時間溫度指示劑 (Time-temperature Indicator, TTI)
A simple, inexpensive device that can show an easily
measurable, time-temperature dependent change that
reflects the full or partial temperature history of a
food product to which it is attached.
•
 Low price
 Product unit
 Indirect freshness control
12
(Taoukis, 2001)
 No digital temperature
data
 The food kinetic is
necessary
Project 1: 開發TTI
Commercially available TTIs
TTI
Physical
Biological
Microbiological
13
Chemical
Enzymatic
Project 1: 開發TTI
TTI OnVu™
14
Project 1: 開發TTI
TTI運用於包裝肉品實例
(Designer: Naoki Hirota)
(Designer: Naoki Hirota)
15
Project 1: 開發TTI
TTI運用於包裝肉品實
16
Project 2:
利用連續溫度資訊預測架售期
• 偵測工具：HOBO Data Loggers
• 偵測方式：連續監測設定每5分鐘進行
一紀錄。
• 偵測時間：2012/4/26~2012/5/02
• HOBO數：18組
Project 2:
利用連續溫度資訊預測架售期
Principles of predictive microbiology
 Organisms increases in number by division
 Attenuated bacteria are ‘dead’
 All organisms in a population have the same
characteristics
 Organisms multiply and die independently
 Growth occurs when right conditions are met (temp, pH,
water activity) and after a lag time.
 Growth reduces due to depletion of nutrients, production
of toxins
 Measure of number can be absolute or density (for a
fixed volume)
 Growth models based on assumptions of underlying
probability distribution.
18
Project 2:
利用連續溫度資訊預測架售期
19
Project 2:
利用連續溫度資訊預測架售期
Commercial software:
• Combase
http://www.combase.cc/
• Pathogen Modeling Program (PMP)
http://ars.usda.gov/Services/docs.htm?docid=11584
http://portal.arserrc.gov/Tutorial.aspx
U max and
shelf-life
Shelf-life
Seafood spoilage (and safety) predictors
Shelf-life
20
http://modelling.combase.cc/ComBase_Predictor.aspx
Prediction of shelf-life
Seafood spoilage predictors (SSP)
• The SSP was developed at the Danish Institute for
Fisheries Research (DIFRES)
• http://sssp.dtuaqua.dk/
Project 2:
利用連續溫度資訊預測架售期
Bacteria growth model: Exponential
ln(n)  ln(n0 )  t
• n=count/g
• n0=count/g when t=zero,
• T=time (hr)
• μ =specific growth rate/hr
• GT=generation time
23
Project 2:
利用連續溫度資訊預測架售期
Bacteria growth model: Modified Gompertz model
e B*( t M )
N (t )  A  C * e
N(t)=density at time t
A=the lower asymptotic log bacterial count as t decreases indefinitely
C=Nmax amd N0 differences
B=the relative maximum growth rate(/h)
M=the time at which maximum growth rate occurs
M= Tlag +1/B
Project 2:
利用連續溫度資訊預測架售期
Step by step (dynamic temperature)
1.
Deciding SSO (specific spoilage organism) or
pathogen
2.
Measuring No, Nmax, pH, Aw of your sample
3.
Obtaining B value and Tlag value (M value) from
database or published articles (at least 25).
•
Hint: choosing the most similar conditions for your
sample product
4.
Construct exponential regression for temperature-M,
and temperature-B
5.
Compute end bacteria number after experiencing
dynamic temperatures
Project 2:
利用連續溫度資訊預測架售期
Findings
26
1/20/2013
Project 2:
利用連續溫度資訊預測架售期
Findings
Figure. 1. Exponential fit of B (the relative growth rate) and M (reversal point) value.
27
1/20/2013
Table 6 Temperature data and duration time of the Day and predicted bacteria number (N(t))
Step
Packaging at factory (2F)
Storage
at factory (2F)
Transport from 2F to 1F
Loading to truck (1F)
Transport to DC
Time (min)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
.
.
150
151
.
.
180
181
.
.
210
211
.
.
390
Temperature
18.68
18.68
18.68
18.68
18.68
18.65
18.65
18.65
18.62
18.62
18.62
18.58
18.58
18.58
18.58
18.55
18.55
18.55
18.55
18.52
18.52
18.52
18.55
18.49
18.49
18.49
18.52
18.52
18.49
18.49
18.46
8.88
.
.
8.88
20.11
.
.
20.01
18.57
.
.
19.47
13.63
.
.
18.02
N(t)
2.69
2.69
2.69
2.69
2.69
2.69
2.69
2.69
2.69
2.69
2.69
2.69
2.69
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
.
.
2.71
2.74
.
.
2.76
2.78
.
.
2.80
2.81
.
.
2.87
Project 2:
利用連續溫度資訊預測架售期
Findings
• Figure 3. Predicted growth of Pseudomonas spp. on 18 oC sandwiches
through steps of 1-5 under dynamic temperature at Day3.