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Quality Control In The Production Process

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Gathering of Data

The following information will be discussed in relation to fruit and animal production but could be adapted to suit any desired agricultural commodity.

Data relating to product quality is required as the basis for ongoing production decisions during the course of product development and maturity. However raw data, which normally comprises the many individual measurements taken of a particular quality factor or parameter, is of limited value in that form. Data has to be converted into useful information before it can serve as a useful tool for decision-making.

Data gathering and processing are important aspects of measuring the response of production units and products to the various inputs made. Statistical analysis provides the tool for determining the significance of those responses.

In this section, we examine the process of gathering a product’s internal quality data and converting it into information upon which harvesting and packing decisions can be made. The specific example used here is therefore that for Maturity Indexing, although similar data gathering principles could be used for pest and disease monitoring to determine when critical threshold levels have been reached to justify control treatments.

Maturity Indexing is a technique used to provide information on the rate of change of fruit maturity prior to harvest, or the final development of an animal before it is ready for slaughter. Certain changes must happen in the carcass: The percentage of fat must be at an optimum to fulfil the requirement of the market etc.

The maturity of animals may differ due to a number of factors: Type of breed, feed and climate are but a view to mention. Management must take this into consideration and plan feeding, transport and marketing accordingly.

In the fresh fruit, industry maturity may differ by as much as three weeks from season to season, primarily due to the time of flowering and the weather conditions subsequent to it. This can have a major impact on management decisions relating to issues such as the contracting of labour for harvesting, the preparation of the packhouse, and logistical arrangements for transporting the packed fruit from packhouse to market.

A very advanced method to ascertain the maturity of beef and mutton before slaughter has been introduced into South Africa during the last few years. The animals are scanned with a light indicator that gives an accurate picture of fat distribution in the carcass. Although the equipment is expensive, larger enterprises are finding it more and more cost-effective and ensuring that all animals slaughtered are 100 % market-compliant.

Fruit samples are tested for acid and sugars and the ratio is calculated, and fruit colour is rated. The results of these quality factors are plotted on a graph. By doing so the data is converted into information that will begin to show clear maturity trends after a few weeks. This enables appropriate management decisions to be taken in good time.

As an example, in the table, typical average weekly fruit size, colour, sugar, acid and ratio values for Clementines are given as a reference framework for maturity indexing purposes. The minimum national standards are given lower in the table as a control. A typical Maturity Indexing information sheet template is given. The internal quality test data from each sampling is entered onto the sheet and the relevant points are connected once sufficient samples have been taken to show trends.

Below, the typical average weekly fruit size, colour, sugar, acid and ratio values for clementines is given as a reference for maturity indexing purposes.

Long-term reference framework for picking maturity and combined weekly average maturity progress (Industry)

Clementine

Week

Date

Size (mm)

Colour

Hydro TSS (%)

REFRAK TSS(%)

Acid

(%)

Ratio

10

8 Mar

43.6

8.0

10.5

9.8

2.35

4.47

11

15 Mar

43.4

7.8

10.1

9.3

2.13

4.75

12

22 Mar

42.3

7.9

10.3

9.6

2.04

5.03

13

28 Mar

45.2

7.9

10.0

9.2

1.73

5.76

14

4 Apr

45.9

7.5

10.4

9.5

1.52

6.85

15

13 Apr

50.4

7.5

10.3

9.5

1.40

7.38

16

19 Apr

49.2

7.2

10.4

9.6

1.24

8.37

17

25 Apr

53.0

6.2

10.6

9.8

1.21

8.75

18

3 May

52.1

5.2

10.9

9.9

1.11

9.78

19

10 May

52.3

4.6

10.4

9.8

1.00

10.39

20

17 May

53.1

4.2

11.0

9.9

0.98

11.21

21

23 May

56.5

3.3

11.2

10.4

0.98

11.36

22

31 May

52.5

2.9

10.6

9.9

0.92

11.52

23

7 Jun

53.1

2.6

11.1

10.4

0.85

13.14

24

14 Jun

52.6

2.5

11.5

10.8

0.87

13.31

25

21 Jun

50.6

1.1

13.5

12.2

0.90

15.08