Food Chemistry & Microbiology

What changes food and what spoils it. Learn key reactions (Maillard browning, caramelisation, lipid oxidation, enzymatic browning, starch gelatinisation and retrogradation, protein denaturation), food microorganisms (bacteria, yeasts, moulds, viruses), growth factors (FAT TOM: food, acidity, temperature, time, oxygen, moisture), the bacterial growth curve, generation time and specific growth rate, pathogens (Salmonella, Listeria, E. coli O157, C. botulinum, B. cereus), spoilage, beneficial fermentation and indicator organisms — with Python for exponential growth and temperature effects.

Food chemistry explains colour, flavour and texture changes during processing and storage; food microbiology explains spoilage, foodborne illness and fermentation. Together they tell us how to process and preserve food safely.

Microbes multiply exponentially: a single bacterium doubling every 20 minutes becomes millions in a few hours at room temperature. Controlling time and temperature is therefore the core of food safety.

An analogy

Bacteria in food are like a population boom in a city with free food: given warmth, water and nutrients, numbers double at a steady rate until the food or space runs out. Refrigeration is like turning down the city's heating — life slows dramatically.

What microbes need (FAT TOM)

Growth factors:

• Food — protein-rich, moist foods support most growth
• Acidity — most pathogens stop below pH 4.6 (why acidified foods are safer)
• Temperature — danger zone ~5–60 °C; psychrophiles, mesophiles, thermophiles
• Time — exponential growth makes time critical
• Oxygen — aerobes, anaerobes (C. botulinum), facultative anaerobes
• Moisture — most bacteria need a_w > 0.91, moulds > 0.70–0.80

Growth curve and pathogens

Batch growth has lag, exponential (log), stationary and death phases. Key pathogens: Salmonella (poultry, eggs), Listeria monocytogenes (grows at fridge temperatures, ready-to-eat foods), E. coli O157:H7, Staphylococcus aureus (heat-stable toxin), Bacillus cereus (rice), Clostridium botulinum (low-acid canned foods, deadly toxin), Norovirus and Hepatitis A. Indicator organisms (coliforms, E. coli) signal hygiene failures.

Food chemistry reactions

Maillard reaction (reducing sugars + amino acids → brown colour, roasted flavour; also acrylamide), caramelisation of sugars, lipid oxidation (rancidity; controlled by antioxidants, oxygen exclusion), enzymatic browning of cut fruits (polyphenol oxidase; blanching, acids), starch gelatinisation during cooking and retrogradation (staling), protein denaturation and gelation, emulsions and foams stabilised by proteins and emulsifiers.

Fermentation and predictive microbiology

Beneficial microbes make curd, yoghurt, cheese, idli/dosa batter, bread, beer, vinegar, kimchi — producing acids, alcohol and flavours that also preserve. Predictive microbiology models (Baranyi, square-root/Ratkowsky, ComBase) forecast growth under changing conditions, supporting shelf-life and risk assessment. Quantitative microbial risk assessment (QMRA) links contamination levels to illness probability.

Formulas

• Exponential growth: N = N₀·2^(t/g) = N₀·e^(µt)
• Growth rate: µ = ln 2/g
• Log count: log N = log N₀ + (t/g)·log 2
• Temperature effect: rate(T₂) = rate(T₁)·Q₁₀^((T₂−T₁)/10)
• Danger zone: 5–60 °C: pathogens can grow

Worked example: Number of generations

Given: 4 h at g = 30 min

Find: Generations and increase.

• 240/30 = 8
• 2⁸

Answer: 256-fold

Worked example: Growth rate

Given: g = 45 min

Find: µ.

• 0.693/0.75 h

Answer: 0.92 h⁻¹

Worked example: Time to reach limit

Given: N₀ 10² CFU/g, limit 10⁶, g 30 min

Find: Time.

• log₂(10⁴) = 13.3 generations
• × 30 min

Answer: ≈ 6.6 h

Bacterial growth and refrigeration

import math
# Microbial growth: generation time and exponential growth in the lag-free phase
N0, g_min = 1e3, 20.0                     # initial CFU/g, doubling time at 37 C
for hours in (2, 4, 6):
    N = N0 * 2 ** (hours * 60 / g_min)
    print(f"after {hours} h: {N:.2e} CFU/g (log {math.log10(N):.1f})")
# Specific growth rate
mu = math.log(2) / (g_min / 60)
print(f"specific growth rate mu = {mu:.2f} per hour")
# Temperature effect: Q10 = 2 -> doubling time at 7 C (fridge) vs 37 C
g7 = g_min * 2 ** ((37 - 7) / 10)
print(f"doubling time at 7 C ~ {g7:.0f} min ({g7/60:.1f} h)")
# after 2 h: 6.40e+04 CFU/g (log 4.8)
# after 4 h: 4.10e+06 CFU/g (log 6.6)
# after 6 h: 2.62e+08 CFU/g (log 8.4)
# specific growth rate mu = 2.08 per hour
# doubling time at 7 C ~ 160 min (2.7 h)

A food left for 6 hours in warm conditions goes from 1000 to 260 million bacteria per gram — well above spoilage and infective levels. With a simple Q₁₀ = 2 model, refrigeration slows doubling 8-fold (real psychrotrophs like Listeria can still grow slowly in the fridge; many mesophiles stop almost completely).

Common mistakes

• Wrong: Cooling large pots of food slowly at room temperature. — Right: Cool rapidly (e.g. 60 → 21 °C in 2 h, → 5 °C in 4 h more).
• Wrong: Assuming refrigeration kills bacteria. — Right: It only slows growth; Listeria still grows.
• Wrong: Reheating to destroy S. aureus toxin. — Right: The toxin is heat-stable — prevent contamination.

Applications

• Cold chain design: Keeping foods out of the danger zone.
• Fermented foods: Yoghurt, cheese, idli, bread, beer.
• Shelf-life studies: Microbial challenge tests.
• Outbreak investigation: Tracing contamination sources.

Key takeaways

• FAT TOM controls microbial growth.
• N = N₀·2^(t/g); time and temperature are critical.
• Know key pathogens and their controls.
• Chemistry drives colour, flavour, texture changes.

Going further

• Predictive microbiology: Baranyi and Ratkowsky models.
• Acrylamide mitigation: Asparaginase and process control.
• Microbiome of fermented foods: Starter culture engineering.