1. Principles of Spectrophotometry and Absorbance-Based Quantification
Spectrophotometry is a core analytical technique used to quantify biomolecules
based on light absorption. It operates under the Beer–Lambert Law:
A = εlc
- A = Absorbance
- ε = Molar absorptivity
- l = Path length
- c = Concentration
Common applications in plant biochemistry:
- Chlorophyll estimation (645 nm & 663 nm)
- Protein quantification (Bradford method)
- Sugar determination (Anthrone method)
- Phenolic content measurement
2. Enzyme Activity Assays and Kinetic Analysis
Principle
Enzyme activity is determined by measuring the rate of substrate conversion
to product under controlled conditions.
Key Parameters
- Vmax (maximum velocity)
- Km (Michaelis constant)
- Turnover number (kcat)
Common Enzyme Assays in Crops
- Nitrate reductase activity
- Peroxidase and catalase activity
- Amylase activity in germinating seeds
- Rubisco activity in photosynthesis studies
Kinetic analysis helps identify stress responses, nutrient limitations,
and varietal biochemical efficiency.
3. Pigment, Sugar, and Metabolite Quantification Methods
Chlorophyll and Carotenoids
- Acetone extraction method
- Spectrophotometric equations (Arnon method)
Total Soluble Sugars
- Anthrone reagent assay
- DNS method for reducing sugars
Proline Quantification
- Ninhydrin-based assay (stress indicator)
Total Phenolic Content
These quantification methods are essential for:
- Stress tolerance screening
- Postharvest quality assessment
- Nutrient deficiency diagnosis
- Functional crop evaluation
4. Interpretation of Biochemical Data in Crop Systems
Data Analysis Considerations
- Control vs Treatment comparison
- Replication and statistical analysis
- Standard curves and calibration
- Units consistency (mg/g FW, µmol/min/mg protein)
Applied Interpretation Examples
- High proline = drought stress adaptation
- Low chlorophyll = nitrogen deficiency
- High antioxidant enzyme activity = oxidative stress response
- Reduced enzyme Vmax = heat-induced protein instability
In applied horticulture, biochemical data must connect to:
- Yield performance
- Quality traits
- Stress tolerance
- Breeding selection criteria
5. Laboratory Integration (Applied Model)
- Weekly lab-based quantification exercises
- Mini-research project on crop stress biochemistry
- Field sample collection + laboratory analysis
- Data interpretation and report writing
6. Open Learning Resources