F1000Research · 14 authors, 13 centres
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This review summarizes technological tools (e-nose, e-tongue, colorimeter, artificial vision systems, texture analyzers, electromyography, and acoustic analysis) used to measure sensory characteristics in food. These instruments emulate human senses and provide rapid, reliable, and objective assessments of quality, authenticity, and maturity. The main limitation is that most tools measure only one sensory attribute, highlighting the need for multi-sensor systems.
**Background:** Sensory analysis is crucial for evaluating food quality and consumer acceptance. Traditional methods rely on trained panelists, which can be subjective, time-consuming, and costly. Technological tools that mimic human senses offer objective, reproducible, and rapid alternatives. This review consolidates information on various instruments used for sensory measurement in food matrices.
**Methods:** The authors conducted a narrative review of the scientific literature, documenting technological tools such as electronic noses (e-noses), electronic tongues (e-tongues), colorimeters, artificial vision systems (CVS), texture analyzers, electromyography (EMG), and acoustic analysis devices. For each tool, the review describes its internal structure, operating principles, and applications in the food industry, supported by tables summarizing relevant studies.
**Key Results:** - **E-nose:** Uses sensor arrays (e.g., MOS, CP, SAW, QCM) and pattern recognition (PCA, SVM, ANN) to detect volatile organic compounds. Applications include authenticity testing (e.g., olive oil adulteration detection with 85-87% success), classification of cheese types, and fish freshness assessment. Accuracy rates range from 71% to >99% depending on the food matrix and method. - **E-tongue:** Employs potentiometric, voltametric, or impedimetric electrodes to measure taste. Used for classification by origin (e.g., 100% accuracy for vinegar varieties), adulteration detection (e.g., 97.5% accuracy for honey), and quality assessment. Success rates vary from 65% to 100%. - **Colorimeter and CVS:** Colorimeters measure color using CIELAB space; CVS uses cameras and image processing for shape, size, color, and defect detection. CVS applications include fruit ripeness classification (e.g., 94.3% accuracy for papayas), meat freshness evaluation, and grain quality assessment. Accuracy ranges from 72.8% to 100%. - **Texture analyzer:** Based on Texture Profile Analysis (TPA), it measures hardness, cohesiveness, elasticity, etc. Used for bread, meat, cheese, and other foods. Correlations with sensory panels are reported, e.g., significant differences in 9 of 11 texture attributes for woody breast meat. - **EMG and acoustic analysis:** EMG captures jaw muscle activity during chewing; acoustic analysis records sounds from food fracture. These methods correlate with texture properties like hardness and crispness. For example, EMG variables explained 76% of variance in Indian sweets, and acoustic analysis distinguished crispness levels in biscuits. - **Cost considerations:** E-nose costs range from USD 100 to 1000; texture analyzer ~USD 23,900; colorimeter ~USD 12,000; electromyograph ~USD 10,560. Maintenance costs are 3-20% of initial value per year.
**Clinical Implications:** While this review is not a clinical study, the tools described have implications for food quality control, product development, and consumer safety. They enable objective, reproducible, and rapid assessment of sensory attributes, reducing reliance on subjective human panels. However, most tools measure only one sensory characteristic, limiting comprehensive product characterization. Future development of multi-sensor systems could enhance efficiency and drive innovation in the food industry.