Gıda Kalite Belirlemede Elektronik Burunlar ve Makine Öğrenmesi
Özet
Bu çalışma, gıda kalitesi belirleme amacıyla elektronik burunlar ve makine öğrenmesi kullanımını ele almaktadır. Geleneksel tespit yöntemlerinin zorluklarına alternatif olarak makine öğrenmesinin, özellikle derin öğrenme algoritmalarının, gıda endüstrisindeki çeşitli alanlarda nasıl kullanıldığını vurgulamaktadır. Elektronik burun teknolojisinin temel prensipleri ve gelişimi açıklanmakta, örneklerle elektronik burunların genel gıda kalite tespitinde kullanımı gösterilmektedir. Makine öğrenimi temelleri ve çeşitli uygulama alanları ele alındıktan sonra, elektronik burunların mevcut sınırlamaları ve gelecekteki potansiyel gelişmeler tartışılmaktadır. Çeşitli elektronik burun sistemlerinin, kimyasal analiz araçları ile birleştirilerek gıda kalitesinin hızlı ve güvenilir bir şekilde belirlenmesine katkı sağladığı ve makine öğreniminin gıda endüstrisinde önemli bir rol oynadığı vurgulanmaktadır.
This study explores the use of electronic noses and machine learning for the purpose of determining food quality. It emphasizes how machine learning, particularly deep learning algorithms, serves as an alternative to the challenges posed by traditional detection methods in various areas of the food industry. The fundamental principles and development of electronic nose technology are explained, and examples illustrate their general use in food quality detection. After discussing the basics of machine learning and various application areas, the study deliberates on the current limitations of electronic noses and potential future developments. It highlights the contribution of various electronic nose systems in conjunction with chemical analysis tools to rapidly and reliably determine food quality and underscores the significant role of machine learning in the food industry.
Referanslar
Aghilinategh N, Dalvand MJ, Anvar A. Detection of ripeness grades of berries using an electronic nose. Food Science & Nutrition, 2020, 8(9), 4919-4928.
Aouadi B, Zaukuu JLZ, Vitális F, et al. Historical evolution and food control achievements of near infrared spectroscopy, electronic nose, and electronic tongue—Critical overview. Sensors, 2020, 20(19), 5479.
Arsenovic M, Sladojevic S, Anderla A, et al. (2017) FaceTime: deep learning based face recognition attendance system. In 2017 IEEE 15th International symposium on intelligent systems and informatics (SISY) New York: IEEE.
Banerjee MB, Roy RB, Tudu B, et al. Black tea classification employing feature fusion of E-Nose and E-Tongue responses. Journal of Food Engineering, 2019, 244, 55-63.
Barbosa-Pereira L, Rojo-Poveda O, Ferrocino I, et al. Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics. Food Research International, 2019, 123, 684-696.
Bouysset C, Belloir C, Antonczak S, et al. Novel scaffold of natural compound eliciting sweet taste revealed by machine learning. Food Chemistry, 2020, 324, 126864.
Chattopadhyay P, Tudu B, Bhattacharyya N, et al. Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach. Journal of Food Engineering, 2014.
Cheng L, Meng QH, Lilienthal A J, et al. Development of compact electronic noses: A review. Measurement Science and Technology, 2021, 32(6), 062002.
Colantonio V, Ferrão LFV, Tieman DM, et al. Metabolomic selection for enhanced fruit flavor. Proceedings of the National Academy of Sciences, 2022, 119(7), e2115865119.
Covington JA, Marco S, Persaud KC, et al. Artificial Olfaction in the 21 st Century. IEEE Sensors Journal, 2021, 21(11), 12969-12990.
Crasto CJ. (2013). Olfactory Receptors. Methods in molecular biologyTM. New York: Springer.
Dong W, Zhao J, Hu R, et al. Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics. Food Chemistry, 2017, 229, 743-751.
Du M, Fang Y, Shen F, et al. Multiangle discrimination of geographical origin of rice based on analysis of mineral elements and characteristic volatile components. International Journal of Food Science & Technology, 2018, 53(9), 2088-2096.
Farah JS, Cavalcanti RN, Guimarães JT, et al. Differential scanning calorimetry coupled with machine learning technique: An effective approach to determine the milk authenticity. Food Control, 2021, 121, 107585. http://dx.doi.org/10.1016/j.foodcont.2020.107585
Gonzalez Viejo C, Tongson E, Fuentes S. Integrating a low-cost electronic nose and machine learning modelling to assess coffee aroma profile and intensity. Sensors, 2021, 21(6).
Gu DC, Liu W, Yan Y, et al. A novel method for rapid quantitative evaluating formaldehyde in squid based on electronic nose. LWT, 2019, 101, 382-388.
Gu X, Karp PH, Brody SL, et al. Chemosensory functions for pulmonary neuroendocrine cells. American Journal of Respiratory Cell and Molecular Biology, 2014, 50(3), 637-646.
Ha N, Xu K, Ren G, et al. Machine learning: enabled smart sensor systems. Advanced Intelligent Systems, 2020, 2(9), 2000063. http://dx.doi.org/10.1002/aisy.202000063
Haddi Z, Alami H, El Bari N, et al. Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Research International, 2013, 54(2), 1488-1498.
Hou Y, Zhao P, Zhang F, et al. Fourier-transform infrared spectroscopy and machine learning to predict amino acid content of nine commercial insects. Food Science and Technology, 2022, 42, e100821. http://dx.doi.org/10.1590/fst.100821
Hu W, Wan L Jian Y, et al. Electronic noses: from advanced materials to sensors aided with data processing. Advanced Materials Technologies, 2019, 4(2), 1800488.
Hussain N, Sun DW, Pu H. Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications. Trends in Food Science & Technology, 2019, 91, 598-608.
Jońca J, Pawnuk M, Arsen A, et al. Electronic noses and their applications for sensory and analytical measurements in the waste management plants—A review. Sensors, 2022, 22(4), 1510.
Karakaya D, Ulucan O, Turkan M. Electronic nose and its applications: A survey. International journal of Automation and Computing, 2020, 17(2), 179-209.
Karami H, Rasekh M, Mirzaee‐Ghaleh E. Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. Journal of Food Processing and Preservation, 2020, 44(9), e14696.
Kośmider J, Cichocki K, Zamelczyk-Pajewska M, et al. Odory z produkcji kwasu fosforowego. Ochrona Powietrza I Problemy Odpadów, 1999, 33(6), 225-228.
Lee J, Song SB, Chung YK, et al. BoostSweet: Learning molecular perceptual representations of sweeteners. Food Chemistry, 2022, 383, 132435.
Li J, Wang M, Liu Q, et al. Validation of UPLC method on the determination of formaldehyde in smoked meat products. International Journal of Food Properties, 2018, 21(1), 1246-1256.
Liu Y, Pu H, Sun DW. Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends in Food Science & Technology, 2017, 69, 25-35.
Ma J, Sun DW, Pu H, et al. Advanced techniques for hyperspectral imaging in the food industry: Principles and recent applications. Annual Review of Food Science and Technology, 2019, 10, 197-220.
Moosavy MH, Kordasht HK, Khatibi SA, et al. Assessment of the chemical adulteration and hygienic quality of raw cow milk in the northwest of Iran. Quality Assurance and Safety of Crops & Foods, 2019, 11(5), 491-498.
Nagle HT, Schiffman SS. Electronic taste and smell: The case for performance standards [point of view]. Proceedings of the IEEE, 2018, 106(9), 1471-1478.
Pan L, Zhang W, Zhu N, et al. Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography-mass spectrometry. Food Research International, 2014, 62, 162-168. http://dx.doi.org/10.1016/j.foodres.2014.02.020
Pandey SK. Prevailing practices of artificial ripening of mango, banana and papaya through calcium carbide in Jharkhand. Agricultural Engineering Today, 2016, 40(3), 35-39.
Pu Hongbin, Lian Lin, and Da‐Wen Sun. "Principles of hyperspectral microscope imaging techniques and their applications in food quality and safety detection: A review." Comprehensive Reviews in Food Science and Food Safety, 2019, 18, no. 4, 853-866.
Roy M, Yadav BK. Electronic nose for detection of food adulteration: A review. Journal of Food Science and Technology, 2022, 1-13.
Rusinek R, Siger A, Gawrysiak‐Witulska M, et al. Application of an electronic nose for determination of pre‐pressing treatment of rapeseed based on the analysis of volatile compounds contained in pressed oil. International Journal of Food Science & Technology, 2020, 55(5), 2161-2170.
Sun Q, Zhang M, Mujumdar AS. Recent developments of artificial intelligence in drying of fresh food: A review. Critical Reviews in Food Science and Nutrition, 2019, 59(14), 2258-2275.
Sun Z, Zhao W, Li Y, et al. An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation. Foods, 2022, 11(20), 3248.
Tan J, Xu J. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture, 2020, 4, 104-115.
Viejo CG, Torrico DD, Dunshea FR, et al. S. Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an artificial intelligence system, Beverages 5, 2019, 33–42.
Wu D, Cheng H, Chen J, et al. Characteristics changes of Chinese bayberry (Myrica rubra) during different growth stages. Journal of Food Science and Technology, 2019, 56, 654-662.
Wu D, Luo D, Wong KY, et al. POP-CNN: Predicting odor pleasantness with convolutional neural network. IEEE Sensors Journal, 2019, 19(23), 11337-11345.
Yakubu HG, Kovacs Z, Toth T, et al. Trends in artificial aroma sensing by means of electronic nose technologies to advance dairy production–a review. Critical Reviews in Food Science and Nutrition, 2022, 63(2), 234-248.
Yang ZF, Xiao R, Xiong GL, et al. A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling. Food Chemistry, 2022, 372, 131249.
Yavuzer E, Köse M. Prediction of fish quality level with machine learning. International Journal of Food Science & Technology, 2022, 57(8), 5250-5255.
Yavuzer E. Determination of fish quality parameters with low cost electronic nose. Food Bioscience, 2021, 41, 100948.
Yavuzer E. Determination of rainbow trout quality parameters with Arduino microcontroller. Journal of Food Safety, 2020, 40(6), e12857.
Yavuzer E. Development of defective fish egg sorting machine with colour sensor for trout facilities. Aquaculture Research, 2018, 49(11), 3634-3637.
Yavuzer E. Rapid detection of sea bass quality level with machine learning and electronic nose. International Journal of Food Science & Technology, 2023, 58(5), 2355-2359.
Zakaria A, Shakaff AYM, Adom AH, et al. Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors. Sensors, 2010, 10(10), 8782-8796.
Zhang B, Huang W, Li J, et al. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 2014, 62, 326-343.
Zhang W, Lv Z, Xiong S. Nondestructive quality evaluation of agro-products using acoustic vibration methods—A review. Critical Reviews in Food Science and Nutrition, 2018, 58(14), 2386-2397.
Zohora SE, Khan AM, Hundewale N. (2013). Chemical sensors employed in electronic noses: a review. In Advances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) July 13-15, 2012, Chennai, India-Volume 3 (pp. 177-184). Springer Berlin Heidelberg.
Zou Z, Long T, Wang Q, et al. Implementation of Apple’s automatic sorting system based on machine learning. Food Science and Technology, 2022, 42, e24922. http://dx.doi.org/10.1590/fst.24922
Referanslar
Aghilinategh N, Dalvand MJ, Anvar A. Detection of ripeness grades of berries using an electronic nose. Food Science & Nutrition, 2020, 8(9), 4919-4928.
Aouadi B, Zaukuu JLZ, Vitális F, et al. Historical evolution and food control achievements of near infrared spectroscopy, electronic nose, and electronic tongue—Critical overview. Sensors, 2020, 20(19), 5479.
Arsenovic M, Sladojevic S, Anderla A, et al. (2017) FaceTime: deep learning based face recognition attendance system. In 2017 IEEE 15th International symposium on intelligent systems and informatics (SISY) New York: IEEE.
Banerjee MB, Roy RB, Tudu B, et al. Black tea classification employing feature fusion of E-Nose and E-Tongue responses. Journal of Food Engineering, 2019, 244, 55-63.
Barbosa-Pereira L, Rojo-Poveda O, Ferrocino I, et al. Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics. Food Research International, 2019, 123, 684-696.
Bouysset C, Belloir C, Antonczak S, et al. Novel scaffold of natural compound eliciting sweet taste revealed by machine learning. Food Chemistry, 2020, 324, 126864.
Chattopadhyay P, Tudu B, Bhattacharyya N, et al. Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach. Journal of Food Engineering, 2014.
Cheng L, Meng QH, Lilienthal A J, et al. Development of compact electronic noses: A review. Measurement Science and Technology, 2021, 32(6), 062002.
Colantonio V, Ferrão LFV, Tieman DM, et al. Metabolomic selection for enhanced fruit flavor. Proceedings of the National Academy of Sciences, 2022, 119(7), e2115865119.
Covington JA, Marco S, Persaud KC, et al. Artificial Olfaction in the 21 st Century. IEEE Sensors Journal, 2021, 21(11), 12969-12990.
Crasto CJ. (2013). Olfactory Receptors. Methods in molecular biologyTM. New York: Springer.
Dong W, Zhao J, Hu R, et al. Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics. Food Chemistry, 2017, 229, 743-751.
Du M, Fang Y, Shen F, et al. Multiangle discrimination of geographical origin of rice based on analysis of mineral elements and characteristic volatile components. International Journal of Food Science & Technology, 2018, 53(9), 2088-2096.
Farah JS, Cavalcanti RN, Guimarães JT, et al. Differential scanning calorimetry coupled with machine learning technique: An effective approach to determine the milk authenticity. Food Control, 2021, 121, 107585. http://dx.doi.org/10.1016/j.foodcont.2020.107585
Gonzalez Viejo C, Tongson E, Fuentes S. Integrating a low-cost electronic nose and machine learning modelling to assess coffee aroma profile and intensity. Sensors, 2021, 21(6).
Gu DC, Liu W, Yan Y, et al. A novel method for rapid quantitative evaluating formaldehyde in squid based on electronic nose. LWT, 2019, 101, 382-388.
Gu X, Karp PH, Brody SL, et al. Chemosensory functions for pulmonary neuroendocrine cells. American Journal of Respiratory Cell and Molecular Biology, 2014, 50(3), 637-646.
Ha N, Xu K, Ren G, et al. Machine learning: enabled smart sensor systems. Advanced Intelligent Systems, 2020, 2(9), 2000063. http://dx.doi.org/10.1002/aisy.202000063
Haddi Z, Alami H, El Bari N, et al. Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Research International, 2013, 54(2), 1488-1498.
Hou Y, Zhao P, Zhang F, et al. Fourier-transform infrared spectroscopy and machine learning to predict amino acid content of nine commercial insects. Food Science and Technology, 2022, 42, e100821. http://dx.doi.org/10.1590/fst.100821
Hu W, Wan L Jian Y, et al. Electronic noses: from advanced materials to sensors aided with data processing. Advanced Materials Technologies, 2019, 4(2), 1800488.
Hussain N, Sun DW, Pu H. Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications. Trends in Food Science & Technology, 2019, 91, 598-608.
Jońca J, Pawnuk M, Arsen A, et al. Electronic noses and their applications for sensory and analytical measurements in the waste management plants—A review. Sensors, 2022, 22(4), 1510.
Karakaya D, Ulucan O, Turkan M. Electronic nose and its applications: A survey. International journal of Automation and Computing, 2020, 17(2), 179-209.
Karami H, Rasekh M, Mirzaee‐Ghaleh E. Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. Journal of Food Processing and Preservation, 2020, 44(9), e14696.
Kośmider J, Cichocki K, Zamelczyk-Pajewska M, et al. Odory z produkcji kwasu fosforowego. Ochrona Powietrza I Problemy Odpadów, 1999, 33(6), 225-228.
Lee J, Song SB, Chung YK, et al. BoostSweet: Learning molecular perceptual representations of sweeteners. Food Chemistry, 2022, 383, 132435.
Li J, Wang M, Liu Q, et al. Validation of UPLC method on the determination of formaldehyde in smoked meat products. International Journal of Food Properties, 2018, 21(1), 1246-1256.
Liu Y, Pu H, Sun DW. Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends in Food Science & Technology, 2017, 69, 25-35.
Ma J, Sun DW, Pu H, et al. Advanced techniques for hyperspectral imaging in the food industry: Principles and recent applications. Annual Review of Food Science and Technology, 2019, 10, 197-220.
Moosavy MH, Kordasht HK, Khatibi SA, et al. Assessment of the chemical adulteration and hygienic quality of raw cow milk in the northwest of Iran. Quality Assurance and Safety of Crops & Foods, 2019, 11(5), 491-498.
Nagle HT, Schiffman SS. Electronic taste and smell: The case for performance standards [point of view]. Proceedings of the IEEE, 2018, 106(9), 1471-1478.
Pan L, Zhang W, Zhu N, et al. Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography-mass spectrometry. Food Research International, 2014, 62, 162-168. http://dx.doi.org/10.1016/j.foodres.2014.02.020
Pandey SK. Prevailing practices of artificial ripening of mango, banana and papaya through calcium carbide in Jharkhand. Agricultural Engineering Today, 2016, 40(3), 35-39.
Pu Hongbin, Lian Lin, and Da‐Wen Sun. "Principles of hyperspectral microscope imaging techniques and their applications in food quality and safety detection: A review." Comprehensive Reviews in Food Science and Food Safety, 2019, 18, no. 4, 853-866.
Roy M, Yadav BK. Electronic nose for detection of food adulteration: A review. Journal of Food Science and Technology, 2022, 1-13.
Rusinek R, Siger A, Gawrysiak‐Witulska M, et al. Application of an electronic nose for determination of pre‐pressing treatment of rapeseed based on the analysis of volatile compounds contained in pressed oil. International Journal of Food Science & Technology, 2020, 55(5), 2161-2170.
Sun Q, Zhang M, Mujumdar AS. Recent developments of artificial intelligence in drying of fresh food: A review. Critical Reviews in Food Science and Nutrition, 2019, 59(14), 2258-2275.
Sun Z, Zhao W, Li Y, et al. An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation. Foods, 2022, 11(20), 3248.
Tan J, Xu J. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture, 2020, 4, 104-115.
Viejo CG, Torrico DD, Dunshea FR, et al. S. Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an artificial intelligence system, Beverages 5, 2019, 33–42.
Wu D, Cheng H, Chen J, et al. Characteristics changes of Chinese bayberry (Myrica rubra) during different growth stages. Journal of Food Science and Technology, 2019, 56, 654-662.
Wu D, Luo D, Wong KY, et al. POP-CNN: Predicting odor pleasantness with convolutional neural network. IEEE Sensors Journal, 2019, 19(23), 11337-11345.
Yakubu HG, Kovacs Z, Toth T, et al. Trends in artificial aroma sensing by means of electronic nose technologies to advance dairy production–a review. Critical Reviews in Food Science and Nutrition, 2022, 63(2), 234-248.
Yang ZF, Xiao R, Xiong GL, et al. A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling. Food Chemistry, 2022, 372, 131249.
Yavuzer E, Köse M. Prediction of fish quality level with machine learning. International Journal of Food Science & Technology, 2022, 57(8), 5250-5255.
Yavuzer E. Determination of fish quality parameters with low cost electronic nose. Food Bioscience, 2021, 41, 100948.
Yavuzer E. Determination of rainbow trout quality parameters with Arduino microcontroller. Journal of Food Safety, 2020, 40(6), e12857.
Yavuzer E. Development of defective fish egg sorting machine with colour sensor for trout facilities. Aquaculture Research, 2018, 49(11), 3634-3637.
Yavuzer E. Rapid detection of sea bass quality level with machine learning and electronic nose. International Journal of Food Science & Technology, 2023, 58(5), 2355-2359.
Zakaria A, Shakaff AYM, Adom AH, et al. Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors. Sensors, 2010, 10(10), 8782-8796.
Zhang B, Huang W, Li J, et al. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 2014, 62, 326-343.
Zhang W, Lv Z, Xiong S. Nondestructive quality evaluation of agro-products using acoustic vibration methods—A review. Critical Reviews in Food Science and Nutrition, 2018, 58(14), 2386-2397.
Zohora SE, Khan AM, Hundewale N. (2013). Chemical sensors employed in electronic noses: a review. In Advances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) July 13-15, 2012, Chennai, India-Volume 3 (pp. 177-184). Springer Berlin Heidelberg.
Zou Z, Long T, Wang Q, et al. Implementation of Apple’s automatic sorting system based on machine learning. Food Science and Technology, 2022, 42, e24922. http://dx.doi.org/10.1590/fst.24922
