Experimental IA on lactose spoilage PDF

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Experimental IA on lactose spoilage...

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Investigating the effect of lactose percentages in various milk types when exposed to Lactobacillales and the consequent extent of spoilage at room temperature

Background On average, the world consumes almost 560 million tonnes of milk a year (ChartsBin). Comprised of water, basic sugars, fats, proteins, vitamins and minerals, it is an important dietary component of many cultures and greatly beneficial to growing children; however, these conditions are also ideal for bacterial growth. As living cells, bacteria undergo metabolic functions, of which they will require a source of energy. In refrigerated milk, psychrophilic (cold-tolerant) bacteria produce lipases and proteinases that break down the fats and proteins in milk, which cause curdling (Society for General Microbiology). When unrefrigerated, Lactobacillales u ses the enzyme lactase to break down lactose sugars and produce lactic acid. The subsequent increase in acidity causes curdling and the characteristic sour smell and taste of spoilt milk. In order to increase shelf life, manufacturers process milk either by means of pasteurization or through Ultra High Temperature-treatment (UHT), both of which use an increase in temperature to kill off any microorganisms that may cause potential harm to the consumers. People who are lactose intolerant are not capable of digesting lactose, as their bodies are lactase-deficient. To accommodate them, milk can be processed through immobilized lactase to break down the lactose into galactose and glucose. Even though this lactose-free milk no longer contains lactose for Lactobacillales  to break down, if left unrefrigerated, it will still spoil like regular milk. Which then begs the question of: What is the relationship between changing the percentage of lactose present in 5 types of milk (Non-fat, 1% Low-fat, Whole, Soy and UHT Whole) exposed to Lactobacillales  and the consequent extent of spoilage at room temperature (22ºC), as measured by the change in pH, over a period of 48 hours? (Note: As fat content decreases in milk, lactose content increases; under this assumption, milk of varying fat content can be used to investigate changes in lactose content) Hypothesis: As lactose content increases, the extent of spoilage will increase, as measured by a decrease in pH. This is due to the greater amount of lactose available to Lactobacillales, allowing for metabolism and reproduction, and therefore even greater amounts of lactic acid produced to reduce pH. As the various types of milk vary in initial pH, comparisons will be made based on average percentage change. Independent Variable: Percentage of lactose present in milk (0%, 4.8%, 5.0%, 5.2%) Dependent Variable: Extent of spoilage as measured by changes in pH over a period of 48 hours

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Controlled Variables: Table 1: The methods for controlling variables and justifications for doing so. Variable and Method of controlling

25mL of milk per trial in the same standard 50mL beakers

pH readings taken within a 15 minute time span at each time of data recording

Same standard (Vernier) pH probes & data recording (Logger Pro) software used

Temperature will be assumed as constant throughout the 48 hours

Justification Potentially, spoilage could be affected by the surface area to volume ratio of milk exposed to the environment for contamination by bacteria, and therefore variations in the volume of milk or in beaker size may influence the rate of spoilage. Additionally as the impact of this variable is not known and thus not taken into account in the calculations of the rate of spoilage, it must be kept constant to increase the precision of the trials. As the data being recorded is a change in pH over time in a series of intervals, it is important that the readings are taken within a short time span of one another to prevent extraneous deviations as a result of extended spoilage. However the rate of spoilage is not very fast, and therefore a limited range of 15 minutes can be allowed as the extent of uncertainty between trials. Though pH probes and data recording software are generally standardized and similarly calibrated, there are slight variations between manufacturers that impact the error and accuracy of the apparatus. In order to increase precision between trials, data recording apparatus should be kept consistent. As temperature increases, the rate of pH change will increase as a result of an increased rate of metabolic activity in the bacteria causing the spoilage. However as room temperature naturally fluctuates throughout the day, it will be assumed that the averages of these temperatures can be taken as a constant over the 48-hour experimental period.

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Limitations in control variables: Due to a limitation in the types of milk each brand offers, it was not possible to obtain all 5 lactose percentage increments of milk within the same brand. In this experiment, Non-fat, Low fat and Whole milk were obtained from the same brand, whilst Soymilk and UHT processed milk were of different brands. Though the percentages of lactose content in each type of milk were sourced from researched literature values that were averages calculated from a meta-analysis of various manufacturers and industry standards (Carper), it was not possible to discern the extent of variability of the samples in this experiment from these averages. Additionally as lactose percentages may differ across manufacturers, there is further uncertainty in using different brands as samples. The biggest limitation and uncontrolled variable is in the type and amount of bacteria present in each sample. For one, as the experiment was carried out in an open system, there was no method of determining which species of bacteria were present, or the extent of contamination in each sample. It can be assumed that Lactobacillales i s naturally present as a result of transfer throughout the manufacturing process; however, there may also be other unaccounted microorganisms present that haven’t been taken into account. This has the potential to skew the results of this experiment, especially when considering that UHT milk samples are processed such that they are predominantly sterilized of Lactobacillales , meaning that the other unaccounted microorganisms contaminating the sample after exposure to the environment would have a much greater impact on the rate of spoilage.

Materials and Apparatus: Table 2: Apparatus and materials used, as well as corresponding amounts Amount Materials / Apparatus (Brand used) Non-fat Milk (Pure & Best) Low Fat Milk (Pure & Best) 125 mL Whole Cream Milk (Pure & Best) Soy Milk (Vitamilk) UHT Whole cream Milk (Nestlé) x30

50mL Beakers (Pyrex) Standard pH probes (Vernier pH sensors) and data recording software (Logger-Pro)

x5

25mL graduated cylinders (Pyrex) x1

Funnel Distilled Water

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Procedure: 1. Wash apparatus with distilled water to limit uneven microbial contamination 2. Measure out 25 mL of each type of milk using a graduated cylinder. Funnel may be needed. 3. Transfer 25 mL of milk into separate 50mL beakers. Label as necessary 4. Take initial pH reading using a standard pH probe and recording software (6 subsequent readings were taken at the same time over the 48-hour period) Safety: While no harmful chemicals are used in this experiment, method of disposal is important, as spoilt milk contains various microorganisms and bacterial species. Wash apparatus thoroughly. Milk should not be ingested.

Data Collection and Processing Table 3: Sample data collection table showing the pH readings of five different types of milk, taken within a 48-hour period. (Trial 1) Milk Type (% Lactose)

Time Elapsed (hr)

0

3

Non-Fat (5.2%)

7.31

7.26

1% Low (5.0%)

7.19

Whole (4.8%) Soymilk (0%) UHT (4.8%)

pH (±0.01) 20

24

27

44

48

7.08

7.10

6.99

6.13

6.00

7.12

6.91

6.86

6.91

6.81

6.55

7.06

7.02

7.07

7.16

7.26

7.47

7.53

6.96

6.96

6.66

6.17

6.09

5.83

5.74

6.90

6.92

6.98

6.90

6.83

6.77

6.76

Qualitative Observations: Due to the limited time constraints, all 5 trials were run simultaneously in the same vicinity; cross contamination may have been possible. It was also not possible to take pH readings during certain times of the day, and because of limited lab access during the evenings, readings were only taken in the mornings and afternoons, leaving a large period of time in which pH readings are not known. However it is assumed that the general trend can still be accurately deduced using the data collected. There were also discrepancies in the initial pH readings of the different types of milk, thus it was decided that percentage change would be measured rather than absolute change, and the effects of discrepancies between trials will be considered negligible once averages are taken. It was observed that the different types of milk clearly curdle at different rates. For this experiment, curdling was determined by a visible loss of suspension such that the denatured protein would coagulate and form a foam-like layer separate of the liquid 5

sample (See Figure 1 below). Initial curdling of whole milk and low fat milk can be observed within 27 hours of being unrefrigerated, and it is predominantly around the rim of the meniscus of the sample. At 44 hours, all non-fat milk samples have visibly curdled, and UHT processed milk samples do not show any visible signs of curdling. Whole milk though curdled from the day before does not seem to have changed in the 17-hour period. Due to its dilute nature, non-fat milk loses suspension when spoiled, such that the curdled protein floats on top of a slightly opaque yellow-coloured liquid. The denatured protein is easily broken up and slight effervescence was observed as it degraded. This can be presumed to be carbon dioxide gas produced as a result of cell respiration. At the end of the 48-hour period, it was observed that the soymilk samples were completely denatured into a yoghurt-like consistency, clumped together in the middle of the beaker. Contrastingly, the other spoiled milk samples remained around the sides of the beaker, leaving the non-curdled milk in the center. Figure 1: Photograph demonstrating completely curdled samples of Non-fat milk

Table 4: Processed data table showing the average pH readings of 5 different types of milk, taken within a 48-hour period. Milk Type (% lactose)

Average pH (±0.01) 0

3

20

24

27

44

48

Non-Fat (5.2%)

Time Elapsed (hr)

7.32

7.26

7.06

7.05

7.00

6.10

6.03

1% Low (5.0%)

7.17

7.13

6.94

6.86

5.67

6.33

6.05

Whole (4.8%)

7.07

7.02

7.02

7.10

7.19

6.95

6.88

Soymilk (0%)

7.00

6.97

6.66

6.20

6.05

5.74

5.64

UHT (4.8%)

6.92

6.94

7.01

6.92

6.84

6.45

6.33

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Table 5: Processed data table showing initial and final average pH readings of 5 different types of milk. Average pH change and average percentage change is also shown. Average pH Milk Type (% Lactose)

Initial

Final

Change

Average Percentage Change (%)

Non-Fat (5.2%)

7.32

6.03

1.29

17.62

1% Low (5.0%)

7.17

6.05

1.12

15.62

Whole (4.8%)

7.07

6.88

0.18

2.55

Soy (0%)

7.00

5.64

1.36

19.43

UHT (4.8%)

6.92

6.33

0.58

8.38

Sample Calculations: Average – (Taken from initial pH readings of Soymilk samples) x=

x=

x1 +x2 +x3 +…+xn n

6.96+7.00+7.00+7.01+7.01 5

x = 6.996≈ 7.00 (3sf)

Average Percentage change - (Taken from Soymilk samples) % change = (

Change Initial )×

100

1.36 % change = ( 7.00 )× 100

% change = 19.4285714…%≈ 19.43 (2dp)

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Figures 2-6: Graphs showing the change in average pH of milk over time (hr) of 5 different types of milk, 2 R values, equations of the trend lines, and key for type of milk (% lactose) are also shown.

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9

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Figure 7: Graph showing average percentage change in pH for five different types of milk over a period of 48 hours. Error bars representing ±1 standard deviations are shown.

All graphs show a general decrease in pH over time, though the extent of the change and the strength of the correlation, as demonstrated by the correlation coefficient (R2 value), vary in each type of milk. In figure 2, showing the change in pH of non-fat milk over time, the decreasing trend is relatively steep, with an overall approximate change in pH of 1.32. This is quite significant as pH is a logarithmic scale representing the concentration of hydrogen ions (H+ ), and a single value change in the scale is equivalent to a hydrogen ion concentration change by a factor of 10. The correlation itself is strong with an R2 value of 0.868 (3sf). This is further supported in figure 7 as the non-fat milk samples had among the highest average percent change in pH (17.62%) and the lowest standard deviation (SD= 0.34). This could potentially be due to the dilute and predominantly protein based nature of non-fat milk. As previously noted, lower concentrations of fat in milk is supplemented by higher concentrations of lactose, therefore the Lactobacillales t hat contaminate the exposed samples will be able to produce more lactic acid, thus leading to a greater change in pH. The higher levels of acidity denatures the proteins to a much greater extent than if there were fats present as well, resulting in the observed spoilage. Contrastingly, in figure 4, showing the change in pH of whole milk over time, the slope of the trend is relatively shallow, with an overall approximate change of 0.12 and a weak correlation (R2 = 0.239 to 3sf). In only looking at the data points, the trend seems to fluctuate in a sinusoidal manner, initially decreasing in pH on the first two readings, increasing in the next three, before decreasing again for the final pH. This is reflected in figure 7 as the lowest average percentage change (2.55%) and largest standard deviation 11

(SD = 7.02). The correlation between the limited change in pH and the low lactose concentrations in whole milk supports the hypothesis made that an increase in lactose concentration will result in a greater extent of spoilage. However, due to the large standard deviation and low R2 value, it can’t be said that the results are conclusive in supporting or falsifying the hypothesis. In just looking at the values for non-fat, low fat, and whole milk, it can be seen that there is general decline in percentage pH change and therefore extent of spoilage as lactose concentration decreases. Despite this trend, soymilk has the greatest average percentage change (19.43%) supported by a strong trend correlation and low standard deviations (R2 =0.948 to 3sf; SD = 2.27) even though it does not contain lactose at all. This can be attributed to the difference in protein composition of soymilk in comparison to the caseins found in cow’s milk. Additionally, just because it does not contain lactose to be broken down by Lactobacillales, does not mean that soymilk is not susceptible to spoilage by other microorganisms that use alternative lipases and proteinases. As these microbes will break down the fats present in soymilk as well as the soy-proteins, and feed on the more easily digested glucose and galactose, it can be said that the extent of spoilage will be even greater, as all components of the soymilk samples are being fully digested leading to what is observed as “spoiling”. Furthermore, this explains the manner in which the soymilk samples spoiled in contrast to that of the different types of cow’s milk (See Qualitative Observations), as the soy-proteins are being broken down into simpler polypeptide chains that are more easily denatured by acidic conditions. UHT processed milk, though still cow’s milk, is processed and in a different manner from the types of milk that require refrigeration. The process uses high temperatures (130ºC) to kill microorganisms, which slightly denatures the caseins and results in a more acidic initial pH reading ( x = 6.92). Additionally, this greatly reduces the amount of bacteria already existing in the UHT samples, which could potentially mean that it is less vulnerable to contamination and the denaturing effects of pH change than regular whole milk even though they have generally the same percentage lactose concentration. In regards to the trend, it was similar to the whole milk samples in that there was an initial rise in pH before a decrease towards the final pH reading, and the average percentage change remained low (8.38%). Though the R2 value suggests a moderate strength in correlation, large standard deviations (SD = 6.03) indicates a lack of precision in the values collected, which hinders the reliability of the data set. It is also important to note that the rates at which pH decreased in all samples varied and that data values more accurately show a sigmoidal trend, with a steep drop in pH midway through the experiment that plateaus towards the end. However, as there were large gaps in the data, especially between 3 to 20 and 27 to 44 hours of elapsed time due to limitations of laboratory access, this trend cannot be fully supported. Therefore a linear trend was chosen for data comparison, as it was clear that pH decreased over time, even as the rates of decrease varied. These variations could be a result of differing rates in microbial reproduction and metabolism, as well as the fact that microorganisms and the enzymes they secrete have optimum pH values, such that an increase in enzyme activity to 12

increase pH will eventually inhibit further spoilage. In order to more accurately determine the shape of the trend, further trials should be conducted with data collection taken at more consistent time ranges, or with continuous data collection using automated, digital data-logging software. Conclusion and Evaluation In conclusion, the results of the experiment supports the hypothesis made that as lactose concentration in a sample of milk increases, the extent of spoilage as determined by a change in pH levels would increase as well. However, this is only within the parameters of the samples being cow’s milk and similarly processed. The results are limited in this manner, as results regarding UHT and soymilk do not follow the general trend, due to their manufacturing process or composition. More specifically, as the microorganisms causing spoilage in soymilk differ from the Lactobacillales  in the other milk samples, it can be said that these results are a systematic anomaly of the experiment, cause by the limitations in the availability of lactose-free milk. Additiona...