Analytical Solutions for Food Development

Importance of the Topic

Objective, reproducible evaluation of sensory attributes is critical in product development, quality control, and regulatory compliance. Modern analytical methods complement traditional sensory tests by providing quantitative, high-throughput data on texture, aroma, flavor, moisture, thermal behavior, color, and additive content

Objectives and Overview

This document presents an integrated suite of Shimadzu analytical solutions designed to characterize key food attributes. It outlines methodologies for measuring mechanical properties, microstructure, composition, and sensory-related compounds, and demonstrates how these data support safer, more consistent, and more palatable food products

Methodology

• Mechanical Texture Analysis: Compression, shear, penetration tests and machine learning models predict sensory hardness, crispiness, and melt behavior
• Imaging and Microstructure: X-ray CT visualizes internal porosity, filling distribution, and thickness in baked goods and pastries
• Moisture Determination: Halogen-heating moisture analyzers quantify water content across powders, liquids, and pastes with high reproducibility
• Thermal Analysis: Differential scanning calorimetry monitors melting, crystallization, glass transitions, and staling kinetics in chocolate and bread
• Particle Size and Shape: Laser diffraction and dynamic image analysis characterize particle distributions, concentrations, and shape factors in creams, chocolates, and suspensions
• Aroma and Off-Flavor Profiling: Headspace GC-MS, SPME Arrow, and specialized databases identify hundreds of volatile compounds; PCA discriminates sample varieties and detects contaminants
• Flavor Component Assays: HPLC and LC-MS/MS quantify catechins, theaflavins, amino acids, organic acids, polyphenols, sugars, organic acids, and alkaloids with high sensitivity and specificity
• Metabolomics: GC-MS/MS and LC-MS/MS with comprehensive libraries enable broad profiling of primary and secondary metabolites for functional, quality, and distribution studies
• Nutrient and Elemental Analysis: UV-Vis, ICP-OES, ICP-MS, atomic absorption, and X-ray fluorescence measure vitamins, minerals, and trace elements across broad concentration ranges
• Color Measurement: Reflectance spectrophotometry quantifies sample chromaticity (L*, a*, b*) to assess visual appeal
• Additive Testing: UV-Vis and LC-MS/MS workflows rapidly detect preservatives, sweeteners, colorants, and sulfites in compliance with food-safety regulations

Instrumentation

• Texture Analyzer EZ Test Series
• Laser Diffraction SALD-2300; Dynamic Image Analyzer iSpect DIA-10
• Bench-top X-ray CT System XSeeker 8000
• Moisture Analyzer MOC63u
• Differential Scanning Calorimeter DSC-60 Plus
• GC-MS QP2050, HS-20 NX TRAP, SPME Arrow
• Smart Aroma Database; GC/MS Off-Flavor Analysis System
• HPLC i-Series/Nexera UHPLC; Traverse MS and eMSTAT software
• LCMS-TQ RX, LCMS-8050RX, LCMS-2050
• GC-MS/MS Smart Metabolites Database; Multi-omics Analysis Package
• UV-VIS-NIR Spectrophotometer UV-3600i Plus; UV-VIS Spectrophotometer UV-1900i
• Refractive Index Detector RID-20A; ELSD-LT III; Supercritical Fluid Chromatograph Nexera UC
• FTIR IRXross; Particle and element analyzers: EDX-8100/ALTRACE; ICP-OES ICPE-9800; ICP-MS ICPMS-2040/2050; AAS AA-7800

Main Results and Discussion

Validated workflows demonstrate strong correlation between instrumental data and sensory tests. Machine learning models predict cookie hardness and crispiness from force-strain curves. X-ray CT reveals 3D pore structures in bread and filling homogeneity in pastries. Moisture analyzers deliver reproducible content across emulsions and pastes. DSC tracks cocoa butter polymorphism and staling kinetics in chocolate and bread. Particle sizing methods assess grit perception in yogurt and chocolate. GC-MS and SPME GC-MS, coupled with PCA, distinguish beer and almond aroma profiles, isolate off-flavor markers, and optimize sampling modes. HPLC and LC-MS/MS quantify catechins, theaflavins, D/L-amino acids, organic acids, vitamins, and sugars in teas, coffees, beers, yogurts, and juices. Metabolomics platforms integrate GC-MS and LC-MS data to map metabolic fingerprints in beer variants. ICP, AAS, and EDX techniques measure essential minerals and trace contaminants in wines, powdered formulas, and vegetables. UV-Vis spectrophotometry correlates juice chromaticity with ingredient profiles. Rapid LC-MS/MS protocols detect preservatives, sweeteners, colorants, and sulfites at regulatory limits

Benefits and Practical Applications

• Objective quantitative data enhances product consistency, shelf-life studies, and sensory optimization
• High throughput and automation reduce analysis time and labor
• Robust multivariate analysis (PCA) and databases accelerate sample differentiation and marker discovery
• Non-destructive imaging supports quality audits without compromising sample integrity
• Compliance support via validated methods aligned with global regulatory standards
• Flexible modular platforms accommodate a wide range of sample types and concentrations

Future Trends and Potential Applications

Integration of artificial intelligence and machine learning will further improve predictive modeling for sensory attributes. Expanded multi-omics and real-time monitoring in production lines will enable dynamic process control. Supercritical fluid chromatography and microfluidic automation promise greener, faster analyses. IoT-enabled instrumentation and cloud-based data management will support remote diagnostics and collaborative development.

Conclusion

Shimadzu’s comprehensive analytical ecosystem delivers robust, high-resolution insights into every facet of food quality—from macroscopic texture and microstructure to molecular flavor, aroma, and nutrition—empowering developers and quality teams to craft safer, more appealing products with confidence.

References

1. Y. Nakano, Y. Konya, M. Taniguchi, E. Fukusaki, Journal of Bioscience and Bioengineering, 123, 134–138 (2016)
2. N. Iwata, M. Kobayashi, Chromatography, 45, 63–72 (2024)