Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review
Abstract
:1. Introduction
2. E-Nose System
Principle of Odor Sensors
3. E-Eye System
Principle of Visual Sensors
4. E-Tongue Tongue System
Principle of Taste Sensors
5. Data Pretreatment
6. Chemometrics Methods
7. Application of E-Nose, E-Eye, and E-Tongue
7.1. Fresh, Refrigerated and Frozen Meat
Meat | Application | Equipment Model, Manufacturer, and Number of Sensors | Data Treatment | Main Outcomes | Ref. |
---|---|---|---|---|---|
Applications of E-nose | |||||
Pork (longissimus) | Detection of Salmonella typhimurium | PEN3 nose (Airsense, Schwerin, Germany); 10 metal-oxide semiconductor sensors | (q): PCA; (Q): SVMR and metaheuristic optimization algorithms (GA, GS, and PSO) | Clear separation of treatment in regions of PCA; model had elevated accuracy to predict the presence of Salmonella typhimurium with GA-SVMR (r2 = 0.989) followed by PSO-SVMR (r2 = 0.986) | [57] |
Pork (longissimus) | Modeling the reduction in Salmonella typhimurium and Escherichia coli with US treatment (20 kHz for 10, 20, or 30 min) | PEN3 nose (Airsense, Germany); 10 metal-oxide semiconductor sensors | (q): PCA and LDA; (Q): PLSR | Clear separation of treatment in regions of PCA and LDA; accuracy of model to predict the bacterial reduction from US treatment was elevated for both microorganisms: Salmonella typhimurium (r2 = 0.912) and Escherichia coli with (r2 = 0.932) | [58] |
Chicken breasts and thighs | Differentiate fresh and frozen-thawed samples | Author’s own E-nose system (power supply, controller, cylindrical sample holder, valves, air and vacuum pump, air filter, sensor array, data acquisition card, computer, and software for data management); 8 sensors (for alcohols, CO, CH4, C3H8, C4H10, SO2, and steam of organic solvents) | (q): F-KNN | Model had elevated accuracy for correct classification of fresh (95.2%) or frozen-thawed (94.7%) meat | [1] |
Mutton | Detection of adulteration with duck meat (10, 30, 50, 70, and 100%) | PEN3 (Win Muster Air-sense Analytic Inc, provided by AIRSENSE Company, Germany); 10 sensors | (q): LDA; MLPN | LDA indicated clear separation among adulteration levels and small overlap between 50 and 70% of duck meat; model had elevated accuracy (83–100%) for correct classification of each adulteration level | [59] |
Pork tenderloin | Quality decay during shelf life (biogenic amine content; 0, 3, 5, and 7 days at 4 °C) | Food Sniffer (ARS.LAB Inc., Redwood City, CA, USA); sensor components n.i. | (q): PCA | System correlated reasonable with Enterobacteriaceae counts (r = 0.890); poor correlation between E-nose and biogenic amine content | [87] |
Chicken breast | Quality decay during shelf life (TVB-N; 0, 1, 2, 3, 4, and 5 days at 4 °C) | Author’s own E-nose system (data acquisition, modulating, and transmitting unit; gas sensor array and chamber system; and power and gas supply unit); 8 sensors | (q): PCA | PCA had poor discrimination capacity based in TVB-N content | [88] |
Applications of E-eye | |||||
Chicken breast | Color evaluation | Doc L-Pix image system (Loccus Biotecnologia, Brazil); color system: RGB, XYZ and L*a*b* | (Q): LRA | High correlation between computer vision system and colorimeter measurement for L* (r2 = 0.99); limited correlation for a* (r2 = 0.74) and b* (r2 = 0.88) | [91] |
Center cut pork loin | Color evaluation | Digital camera (MV-VS141FM/C, Micro-vision Ltd., China); color system: RGB, HIS, and L*a*b* | (Q): LRA | Computer vision was suitable for color evaluation; correlation of E-eye with colorimeter was dependent of color space; highest correlation was obtained using L*a*b* space (r2 = 0.83) | [61] |
Pork (longissimus thoracis et lumborum) | Color evaluation and grading for color | CCD camera and 2 bars with white LED; color system: RGB, HIS, and L*a*b* | (Q): PLSR and SVMR | SVMR was more accurate (73.4%) to correctly predict color grade than PLSR (68.3%) method (data from three color systems) | [62] |
Pork and beef (longissimus thoracis) | Marbling classification | Single lens reflex camera, model Nikon SLR D7000 (Nikon Co. Ltd., Tokyo, Japan); color system: HSL | (q): K-NN | High accuracy for grading (81.6 and 76.1% in bovine and swine meat, respectively) | [64] |
Center cut pork loin | Intramuscular fat content | CCD camera, two white LED bar lights, and Computer; color system: RGB, HIS, and L*a*b* | (Q): LRA, stepwise regression model, and SVMR | SVM had higher overall accuracy (75%) than stepwise regression model (63%) to estimate pork intramuscular fat (data from three color systems) | [68] |
Pork loin | Color and marbling | Industrial camera (NI 1776C smart camera, National Instrument, Ltd., Austin, TX, USA) with a 1/1.8” F1.6/4.4–11-mm lens (LMVZ4411, Kowa, Ltd., Tokyo, Japan), a 44-in. dome light (DL180, advance illumination, Ltd., Rochester, VT, USA); color system: L*a*b* | (Q): LRA and SVMR | High prediction accuracy for color score based in L* values (92.5%) and marbling score (75.0%); LRA established poor correlations for color score (r2 = 0.64) with sensory data and marbling grade (r2 = 0.54) with intramuscular fat content | [63] |
Chicken breast (pectoralis major) | Classification according with wooden breast myopathy | Doc L-Pix image system (Loccus Biotecnologia, Brazil); color system: HSV | (q): SVM, MLP, J48 DT | The use of SVM reach accuracy of 91.8% to correctly classify samples | [65] |
Pork (longissimus lumborum) | Identification of defects | CANON EOS 350D with an EF-S 60-mm macro lens digital camera; color system: RGB, HSV, and HSL | (Q): LRA | High accuracy to with HSL system to detect PSE (91%) and DFD (73%); low capacity to differentiate red, soft, and exudative from red, firm, normal | [92] |
Pork (m. semimembranosus) | Identification of defects-classification | Digital camera Canon EOS 350D with the lens EF-S 60 mm; color system: RGB, HSV, and HSL | (Q): LRA | Low accuracy | [93] |
Chicken breast, leg, fillet, drumstick, and wing | Sorting cuts | Digital CCD camera (ace1300-200uc, Basler, Germany) and linear light-emitting diode (LED) tubes; color system: HSV and RGB | (q): PLS, LDA, and ANN | Prediction accuracy 96% at continuous measuring | [67] |
Applications of E-tongue | |||||
Cattle and buffalo meat (longissimus) | Discriminate cattle breeds | Model n.i. (Alpha M.O.S., Toulouse, France); 7 sensors | (q): LDA | Separation in three groups: Angus, Hungarian grey, and cluster composed of other breeds | [44] |
Mutton | Detection of adulteration with pork or chicken meat (0, 20, 40, 60, 80, and 100%) | α-Astree (Alpha M.O.S, Toulouse, France); 7 sensors | (q): CDA | Elevated accuracy to discriminate pork (100%) and chicken (80–90%) adulteration, regardless of adulteration level | [94] |
Beef (semitendinous) | Discrimination according to irradiation level (0, 1.5, 3.0, and 4.5 kGy) | α-Astree 2 E-tongue (Alpha M.O.S., Toulouse, France); 7 sensors | (q): PCA | Clear separation of samples according to irradiation dose | [95] |
Beef | Quality decay during shelf life (ammonia and putrescine; 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 days at 4 °C) | Author’s own E-tongue system (sensor, potentiostat/galvanostat, and computer); 3 electrodes individually coated with modified carbon screen-print, Ag and carbon | (q): PCA and PLS-DA | Differentiation of samples in four groups: days 1-2, 3-4, 5–7, and 8–10; correlation coefficient between 0.95 and 0.98 during shelf life | [70] |
7.2. Processed Meat and Meat Products
8. Advantages and Disadvantages
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Meat Product | Application | Equipment Model, Manufacturer, and Number of Sensors | Data Treatment | Main Outcomes | Ref. |
---|---|---|---|---|---|
Applications of E-nose | |||||
Dry-cured sausage | Detection of ochratoxin A-producing strains of Penicillium sp. | ISE Nose 2000 (SoaTec S.r.l., Parma, Italy), 12 metal-oxide semiconductor sensors | (q): LDA | LDA of E-nose data separated samples containing Penicillium at strain level; elevated accuracy for identification of samples containing ochratoxin A-producing strains (88%) | [99] |
Braised pork | Discrimination according to geographical origin | PEN3 E-nose (Airsense Analytics GmBh, Germany); 10 metal-oxide semiconductor sensors | (q): PCA | Better separation according to location using lean meat (overlap for half of groups) fraction than fat (overlap of almost all groups) fraction of samples | [100] |
Dry-cured ham | Quality grading (first, second and third grade) | Author’s own E-nose system (sensor chamber, control module, and wireless communication module); 12 sensors (for acetone, ammonia, butane, ethane, ethanol, hydrogen, hydrothion, isobutene, methanol, methane, methanthiol, methylbenzene, propane, trimethyl amine, and vapors of organic solvents) | (q): PCA and T-SNE; (Q) SVM, KNN, and LR | Clearest separation of samples according to grades was possible using with either PCA or T-SNE with the optimization with MIME-(SVM-BFECV) protocol; model had high accuracy (99.5%) and fast processing time (15.7 s); accuracy reduced and processing time increased in second and third grade samples | [60] |
Pasteurized sausage | Detection of adulteration with soy protein (10, 20, and 30%) | Author’s own E-nose system (compressor filter with a silica gel, sealed chamber with sensors, frequency monitoring system, and computer); 7 sensors individually coated with dicyclohexano-18-crown-6, poly(ethylene glycol) 2000, poly(ethylene glycol adipate), poly(ethylene glycol sebacate), poly(diethylene glycol succinate), Triton X-100, and polyvinylpyrrolidone | (q): PCA; PNN | PCA indicated maximum response values as relevant variable to separate groups, but poor separation was obtained from samples with 20% and 30% of soy protein; elevated accuracy for correct classification of 0, 10, 20 and 30% soy protein in sausage (96%) using non-linear strategy (PNN) | [101] |
Chicken stew | Evaluate the effect of stewing time (1, 2, and 3 at 95–99 °C) | FOX 4000 (Alpha M.O.S., Toulouse, France); 18 metal oxide semiconductor sensors | (q): PCA | Clear separation of samples with different times of stewing, especially 1 h samples from 2 and 3 h samples | [102] |
Dezhou-braised chicken | Intensity of thermal treatment (84 °C for 35 min, 95 °C for 30 min, and 121 °C for 20 min) and quality decay during shelf life (0, 7, 15, 22, 30, and 45 days at 4 °C) | FOX 4000 (Alpha M.O.S., Toulouse, France); 18 metal oxide semiconductor sensors | (q): PCA | Separation in three main groups: (1) control after 30 and 60 days, (2) 84 and 95 °C at any storage time and fresh sample, and (3) sterilized samples at any storage time | [103] |
Sugar-smoked breast and skin chicken | Characterize the effect of processing stages (pickling, air-drying, baking, and sugar-smoking) in flavor accumulation in breast and skin | PEN 3.5 E-nose (Airsense Analytics GmBh, Germany); 10 metal-oxidesemiconductor sensors | (q): PCA | Discrimination of processing stages was clearer for skin than breast (overlap of processing stage groups) samples | [104] |
Bacon | Quality decay during shelf life (0, 7, 15, 22, 30, and 45 days at 4 °C) | Fox 4000 (Alpha M.O.S., Toulouse, France); 18 metal oxide semiconductor sensors | (q): PCA | Separation in four groups: up to 15 days (overlap of groups), 22, 30, and 45 days | [105] |
Applications of E-eye | |||||
Smoked chicken thighs | Color and sensory analysis | EOS-M5, Canon, Tokyo, Japan; color system: RGB and L*a*b* | (q): K-means algorithm; (Q): LRA | Production of colorimetric cards for color evaluation; high prediction accuracy of K-mean (r2 = 0.995) and K-mean + noise (r2 = 0.952) reduction model for smoking time as function of RGB space | [106] |
Dry-cured ham | Intramuscular fat content | Digital camera (Canon EOS 50D); color system: RGB | (Q): SVM | Development of a convolutional neural network to identify intramuscular fat, accuracy of 99% and precision of 84% for intramuscular fat identification | [69] |
Meat products | Color, chemical composition and texture | Epson Perfection 4490 Photo flatbed scanner; color system: R, G, B; X, Y, Z Lab* and U, V, S | (Q): LRA | Correlation between chemical composition and image texture parameters was 0.7–0.92; high classification accuracy (83–100%) | [66] |
Chinese dry-cured hams | Color | IRIS VA 300 computer vision (Alpha M.O.S., Toulouse, France); color system: L*a*b* | (q): PCA and Cluster analysis | Clear separation of 3 groups from PC and Cluster analysis: Jinhua top-class ham; Xuanwei one-year-fermented and two-year-fermented hams; and hams from other origins and grades | [107] |
Applications of E-tongue | |||||
Sous-vide cooked beef | Discrimination according to stages (one of two), temperature (45, 60, and 70 °C) and time (3, 6, 9 and 12 h) | SA402B (Insent, Tokyo, Japan), 5 sensors (for detection of sour, bitter, astringent, umami, and salty compounds) | (Q): PLSR | Clear separation of samples according to cooking temperature in three groups: control; 45 and 60 °C, and 70 °C | [108] |
Braised pork broth | Effect of braising cycles (up to n = 10) in broth (meat + spices; only meat) or soup (only spices) | Astree (Alpha M.O.S, Toulouse, France); 7 sensors | (q): PCA | Separation in groups for meat + spices (8 groups: 1, 2, 3, 4, 5, 6, 7, 8, and 9–10), for only meat (10 groups: 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10), and for only spices (4 groups: 1, 2–5, 6, and 7–10) | [109] |
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Munekata, P.E.S.; Finardi, S.; de Souza, C.K.; Meinert, C.; Pateiro, M.; Hoffmann, T.G.; Domínguez, R.; Bertoli, S.L.; Kumar, M.; Lorenzo, J.M. Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review. Sensors 2023, 23, 672. https://doi.org/10.3390/s23020672
Munekata PES, Finardi S, de Souza CK, Meinert C, Pateiro M, Hoffmann TG, Domínguez R, Bertoli SL, Kumar M, Lorenzo JM. Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review. Sensors. 2023; 23(2):672. https://doi.org/10.3390/s23020672
Chicago/Turabian StyleMunekata, Paulo E. S., Sarah Finardi, Carolina Krebs de Souza, Caroline Meinert, Mirian Pateiro, Tuany Gabriela Hoffmann, Rubén Domínguez, Sávio Leandro Bertoli, Manoj Kumar, and José M. Lorenzo. 2023. "Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review" Sensors 23, no. 2: 672. https://doi.org/10.3390/s23020672
APA StyleMunekata, P. E. S., Finardi, S., de Souza, C. K., Meinert, C., Pateiro, M., Hoffmann, T. G., Domínguez, R., Bertoli, S. L., Kumar, M., & Lorenzo, J. M. (2023). Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review. Sensors, 23(2), 672. https://doi.org/10.3390/s23020672