低溫食品物流 與

projects蕭心怡

食品之特殊性Product classification for physical

distribution1. Degree of processing 2. Value of the product3. Volume and the weight of the

product4. Storage temperature5. Life-cycle6. Turnover growth, market share

食品之特殊性Storage temperature

• 12℃-18℃: 涼藏食品• 0℃-7℃: 冷藏食品• 零下 2℃- 零下 7℃: 冰溫食品• 零下 18℃: 冷凍食品 • 保存期限半年至一年

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現今低溫物流管理問題

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B2C 低溫冷藏宅配溫度記錄實證結果

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

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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

Cold Chain Temperature Monitoring

Infrared thermometer

Contact thermometer

Wireless technologies

Data Loggers

Time Temperature

Indicator

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Time Temperature

Indicator

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.

•(Taoukis, 2001)

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Low price Product unit Indirect freshness control

No digital temperature data

The food kinetic is necessary

Project 1: 開發 TTICommercially available TTIs

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TTI

Physical ChemicalBiological

EnzymaticMicrobiologicalMicrobiological

Project 1: 開發 TTI TTI OnVu™

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Project 1: 開發 TTI TTI 運用於包裝肉品實例

(Designer: Naoki Hirota)

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(Designer: Naoki Hirota)

Project 1: 開發 TTITTI 運用於包裝肉品實

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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 microbiologyOrganisms increases in number by divisionAttenuated bacteria are ‘dead’All organisms in a population have the same

characteristicsOrganisms multiply and die independentlyGrowth occurs when right conditions are met

(temp, pH, water activity) and after a lag time.Growth reduces due to depletion of nutrients,

production of toxinsMeasure of number can be absolute or density

(for a fixed volume)Growth models based on assumptions of

underlying probability distribution.

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Project 2: 利用連續溫度資訊預測架售期

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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=11584http://portal.arserrc.gov/Tutorial.aspx

Seafood spoilage (and safety) predictors

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Shelf-life

U max and shelf-life

Shelf-life

http://modelling.combase.cc/ComBase_Predictor.aspx

Seafood spoilage predictors (SSP)• The SSP was developed at the Danish Institute

for Fisheries Research (DIFRES)

• http://sssp.dtuaqua.dk/

Prediction of shelf-life

Project 2: 利用連續溫度資訊預測架售期

Bacteria growth model: Exponential

• n=count/g

• n0=count/g when t=zero,

• T=time (hr)

• μ =specific growth rate/hr

• GT=generation time

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0ln( ) ln( )n n t

Project 2: 利用連續溫度資訊預測架售期

Bacteria growth model: Modified Gompertz model

)(*

*)(MtBeeCAtN

N(t)=density at time tA=the lower asymptotic log bacterial count as t decreases indefinitelyC=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

1/20/201326

Project 2: 利用連續溫度資訊預測架售期

Findings

1/20/201327

Figure. 1. Exponential fit of B (the relative growth rate) and M (reversal point) value.

Step Time (min) Temperature N(t)

Packaging at factory (2F)

0 18.68 2.691 18.68 2.692 18.68 2.693 18.68 2.694 18.68 2.695 18.65 2.696 18.65 2.697 18.65 2.698 18.62 2.699 18.62 2.69

10 18.62 2.6911 18.58 2.6912 18.58 2.6913 18.58 2.7014 18.58 2.7015 18.55 2.7016 18.55 2.7017 18.55 2.7018 18.55 2.7019 18.52 2.7020 18.52 2.7021 18.52 2.7022 18.55 2.7023 18.49 2.7024 18.49 2.7025 18.49 2.7026 18.52 2.7027 18.52 2.7028 18.49 2.7029 18.49 2.7030 18.46 2.70

Storage

at factory (2F)

31 8.88 2.70. . .. . .

150 8.88 2.71

Transport from 2F to 1F

151 20.11 2.74. . .. . .

180 20.01 2.76

Loading to truck (1F)

181 18.57 2.78. . .. . .

210 19.47 2.80

Transport to DC

211 13.63 2.81. . .. . .

390 18.02 2.87

Table 6 Temperature data and duration time of the Day and predicted bacteria number (N(t))

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.