REVIEW
Emerging nondestructive techniques to quantify the textural properties of food: A state-of-art review
Gayatri Mishra,
Prashant Sahni,
Ravi Pandiselvam,
Brajesh Kumar Panda,
Dolly Bhati,
Naveen Kumar Mahanti,
Anjineyulu Kothakota,
Manoj Kumar,
Daniel Cozzolino,
Gayatri Mishra
Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, Lethbridge, Alberta, Canada
Search for more papers by this authorPrashant Sahni
College of Dairy and Food Technology, Agriculture University, Jodhpur, Rajasthan, India
Search for more papers by this authorCorresponding Author
Ravi Pandiselvam
Division of Physiology, Biochemistry and Post-Harvest Technology, ICAR—Central Plantation Crops Research Institute (CPCRI), Kasaragod, Kerala, India
Correspondence
Ravi Pandiselvam, Division of Physiology, Biochemistry and Post-Harvest Technology, ICAR—Central Plantation Crops Research Institute (CPCRI), Kasaragod 671124, India.
Emails: [email protected]; [email protected]
Brajesh Kumar Panda, Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India.
Email: [email protected]
Search for more papers by this authorCorresponding Author
Brajesh Kumar Panda
Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
Correspondence
Ravi Pandiselvam, Division of Physiology, Biochemistry and Post-Harvest Technology, ICAR—Central Plantation Crops Research Institute (CPCRI), Kasaragod 671124, India.
Emails: [email protected]; [email protected]
Brajesh Kumar Panda, Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India.
Email: [email protected]
Search for more papers by this authorDolly Bhati
Department of Food Biosciences, Teagasc, The Agriculture and Food Development Authority, Ireland, Dublin, Ireland
Search for more papers by this authorNaveen Kumar Mahanti
Post Harvest Technology Research Station, Venkataramannagudem, Dr. Y.S.R Horticultural University, West Godavari, Andhra Pradesh, India
Search for more papers by this authorAnjineyulu Kothakota
Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum, India
Search for more papers by this authorManoj Kumar
Chemical and Biochemical Processing Division, ICAR – Central Institute for Research on Cotton Technology, Mumbai, India
Search for more papers by this authorDaniel Cozzolino
Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland, Australia
Search for more papers by this authorGayatri Mishra,
Prashant Sahni,
Ravi Pandiselvam,
Brajesh Kumar Panda,
Dolly Bhati,
Naveen Kumar Mahanti,
Anjineyulu Kothakota,
Manoj Kumar,
Daniel Cozzolino,
Gayatri Mishra
Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, Lethbridge, Alberta, Canada
Search for more papers by this authorPrashant Sahni
College of Dairy and Food Technology, Agriculture University, Jodhpur, Rajasthan, India
Search for more papers by this authorCorresponding Author
Ravi Pandiselvam
Division of Physiology, Biochemistry and Post-Harvest Technology, ICAR—Central Plantation Crops Research Institute (CPCRI), Kasaragod, Kerala, India
Correspondence
Ravi Pandiselvam, Division of Physiology, Biochemistry and Post-Harvest Technology, ICAR—Central Plantation Crops Research Institute (CPCRI), Kasaragod 671124, India.
Emails: [email protected]; [email protected]
Brajesh Kumar Panda, Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India.
Email: [email protected]
Search for more papers by this authorCorresponding Author
Brajesh Kumar Panda
Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
Correspondence
Ravi Pandiselvam, Division of Physiology, Biochemistry and Post-Harvest Technology, ICAR—Central Plantation Crops Research Institute (CPCRI), Kasaragod 671124, India.
Emails: [email protected]; [email protected]
Brajesh Kumar Panda, Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India.
Email: [email protected]
Search for more papers by this authorDolly Bhati
Department of Food Biosciences, Teagasc, The Agriculture and Food Development Authority, Ireland, Dublin, Ireland
Search for more papers by this authorNaveen Kumar Mahanti
Post Harvest Technology Research Station, Venkataramannagudem, Dr. Y.S.R Horticultural University, West Godavari, Andhra Pradesh, India
Search for more papers by this authorAnjineyulu Kothakota
Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum, India
Search for more papers by this authorManoj Kumar
Chemical and Biochemical Processing Division, ICAR – Central Institute for Research on Cotton Technology, Mumbai, India
Search for more papers by this authorDaniel Cozzolino
Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland, Australia
Search for more papers by this authorThis article was published on AA publication on: 09 February 2023
Abstract
Texture is an important sensory attribute that drives consumer acceptance of any food material. In recent times consumers' demand for high-quality food urges food industries to provide food with consistent textural properties. However, texture measurement not just requires a trained sensory panel but also a considerable amount of time and effort. On the flip side, human observation could be subjective hence repeatability of the result may not be ensured and/or relied on. Contrary to that, objective methods for texture measurement are reliable and consistent, but are not suitable for in-line application and also destructive in nature. The mentioned crisis has made industries opt for nondestructive texture analysis techniques. In the past decade, considerable research has been carried out on nondestructive texture analysis methods such as micro-deformation, and acoustic and optical techniques, showing feasibility for in-line applications. The current review focuses on the working principles and most recent applications of nondestructive techniques for texture analysis of food products. Moreover, a detailed review of contact and noncontact-type texture measurement has been presented in this article. The literature survey is concluded with future research aspects and challenges involved in the commercialization of the nondestructive texture analysis techniques.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
REFERENCES
- Abbaszadeh, R., Rajabipour, A., Ahmadi, H., Delshad, M., & Mahjoob, M. (2011). Assessment of watermelon quality using vibration spectra. Innovative Computing Technology, 241, 21–29.
10.1007/978-3-642-27337-7_3 Google Scholar
- Abbaszadeh, R., Rajabipour, A., Sadrnia, H., Mahjoob, M. J., Delshad, M., & Ahmadi, H. (2014). Application of modal analysis to the watermelon through finite element modeling for use in ripeness assessment. Journal of Food Engineering, 127, 80–84.
- Aboonajmi, M., Jahangiri, M., & Hassan-Beygi, S. R. (2015). A review on application of acoustic analysis in quality evaluation of agro-food products. Journal of Food Processing and Preservation, 39, 3175–3188.
- Adedeji, A. A., & Ngadi, M. O. (2011). Microstructural properties of deep-fat fried chicken nuggets coated with different batter formulation. International Journal of Food Properties, 14(1), 68–83.
- Akbarnejad, A., Azadbakht, M., & Asghari, A. (2017). Studies of the selected mechanical properties of banana (Cavendish Var.). International Journal of Fruit Science., 17(1), 93–101.
- Alam, T., & Takhar, P. S. (2016). Microstructural characterization of fried potato disks using X-ray micro computed tomography. Journal of Food Science, 81(3), E651–E664.
- Amigo, J. M., Del Olmo, A., Engelsen, M. M., Lundkvist, H., & Engelsen, S. B. (2019). Staling of white wheat bread crumb and effect of maltogenic α-amylases. Part 2: Monitoring the staling process by using near infrared spectroscopy and chemometrics. Food Chemistry, 297, 124946.
- Anoliefo, E., Chah, j., & Abbah, O. (2020). Consumers' perception of dried onion and okra using load adaptive drying technology in Nsukka urban, Nigeria. Journal of Agricultural Extension, 24(3), 125–137.
- Antequera, T., Caballero, D., Grassi, S., Uttaro, B., & Perez-Palacios, T. (2021). Evaluation of fresh meat quality by hyperspectral imaging (HSI), nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI): A review. Meat Science, 172, 108340.
- Arazuri, S., Arana, I., & Jaren, C. (2010). Evaluation of mechanical tomato harvesting using wireless sensors. Sensors, 10(12), 11126–11143.
- Attanasio, G., Piagnani, M. C., Chiozzotto, R., Bassi, D., Spinelli, L., & Torricelli, A. (2013). A preliminary approach to assess peach fruit texture by time-resolved spectroscopy (TRS). In Proceedings of the VIII International Peach Symposium, June 17–20, 2013, Matera, Italy (pp. 605–614).
- Auer, J., Reiter, M., Senck, S., Reiter, A., Kastner, J., & Mathmann, K. (2021). Investigation of opening eye defects and effects of different ripening profiles on eye structure in semi-hard cheese using X-ray micro-computed tomography. Food Structure, 28, 100190.
- Ávila, M., Caballero, D., Antequera, T., Durán, M. L., Caro, A., & Pérez-Palacios, T. (2018). Applying 3D texture algorithms on MRI to evaluate quality traits of loin. Journal of Food Engineering, 222, 258–266.
- Awad, T. S., Moharram, H. A., Shaltout, O. E., Asker, D. Y. M. M., & Youssef, M. M. (2012). Applications of ultrasound in analysis, processing and quality control of food: A review. Food Research International, 48(2), 410–427.
- Baiyeri, K. P., Agbo, C. U., & Ameh, G. I. (2012). Sensory evaluation response as a selection tool in African eggplant (Solanum aethiopicum) production and breeding. African Journal of Agricultural Research, 7(37), 5247–5251.
- Bajd, F., Škrlep, M., Čandek-Potokar, M., & Serša, I. (2017). MRI-aided texture analyses of compressed meat products. Journal of Food Engineering, 207, 108–118.
- Balage, J. M., e Silva, S. D. L., Gomide, C. A., de Nadai Bonin, M., & Figueira, A. C. (2015). Predicting pork quality using Vis/NIR spectroscopy. Meat Science, 108, 37–43.
- Barbin, D. F., ElMasry, G., Sun, D. W., & Allen, P. (2013). Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chemistry, 138(2–3), 1162–1171.
- Barbosa, C., Machado, T. B., Alves, M. R., & Oliveira, M. B. P. P. (2020). Fresh-cut bell peppers in modified atmosphere packaging: Improving shelf life to answer food security concerns. Molecules, 25, 2323.
- Beghi, R., Giovenzana, V., Tugnolo, A., & Guidetti, R. (2018). Application of visible/near infrared spectroscopy to quality control of fresh fruits and vegetables in large-scale mass distribution channels: A preliminary test on carrots and tomatoes. Journal of the Science of Food and Agriculture, 98(7), 2729–2734.
- Besbes, E., Jury, V., Monteau, J. Y., & Le Bail, A. (2013). Characterizing the cellular structure of bread crumb and crust as affected by heating rate using X-ray microtomography. Journal of Food Engineering, 115(3), 415–423.
- Beugnot, J. C., Lebrun, S., Pauliat, G., Maillotte, H., Laude, V., & Sylvestre, T. (2014). Brillouin light scattering from surface acoustic waves in a subwavelength-diameter optical fibre. Nature Communications, 5(1), 1–6.
- Bi, J., Li, Y., Cheng, S., Dong, X., Kamal, T., Zhou, D., … Tan, M. (2016). Changes in body wall of sea cucumber (Stichopus japonicus) during a two-step heating process assessed by rheology, LF-NMR, and texture profile analysis. Food Biophysics, 11(3), 257–265.
- Biswas, A. K., & Mandal, P. K. (2020). Current perspectives of meat quality evaluation: Techniques, technologies, and challenges. Meat quality analysis: Advanced evaluation methods, techniques, and technologies. In Meat quality analysis (pp. 3–17). Amsterdam: Elsevier Inc.
10.1016/B978-0-12-819233-7.00001-X Google Scholar
- Botosoa, E. P., Chene, C., & Karoui, R. (2013). Monitoring changes in sponge cakes during aging by front face fluorescence spectroscopy and instrumental techniques. Journal of Agricultural and Food Chemistry, 61(11), 2687–2695.
- Bruggenwirth, M., & Knoche, M. (2016). Factors affecting mechanical properties of the skin of sweet cherry fruit. Journal of the American Society for Horticultural Science, 141(1), 45–53.
- Caballero, D., Antequera, T., Caro, A., Duran, M. L., & Perez-Palacios, T. (2016). Data mining on MRI-computational texture features to predict sensory characteristics in ham. Food and Bioprocess Technology, 9(4), 699–708.
- Cafferky, J., Sweeney, T., Allen, P., Sahar, A., Downey, G., Cromie, A. R., & Hamill, R. M. (2020). Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M. longissimus thoracis et lumborum. Meat Science, 159, 107915.
- Cantre, D., East, A., Verboven, P., Araya, X. T., Herremans, E., Nicolaï, B. M., … Heyes, J. (2014). Microstructural characterisation of commercial kiwifruit cultivars using X-ray micro computed tomography. Postharvest Biology and Technology, 92, 79–86.
- Chanvrier, H., Jakubczyk, E., Gondek, E., & Gumy, J. C. (2014). Insights into the texture of extruded cereals: Structure and acoustic properties. Innovative Food Science & Emerging Technologies, 24, 61–68.
- Chanvrier, H., Pillin, C. N., Vandeputte, G., Haiduc, A., Leloup, V., & Gumy, J. C. (2015). Impact of extrusion parameters on the properties of rice products: A physicochemical and X-ray tomography study. Food Structure, 6, 29–40.
10.1016/j.foostr.2015.06.004 Google Scholar
- Chen, J., & Rosenthal, A. (2015). Food texture and structure. In Modifying food texture (pp. 3–2). Sawston, UK: Woodhead Publishing.
10.1016/B978-1-78242-333-1.00001-2 Google Scholar
- Chen, L., & Opara, U. L. (2013a). Approaches to analysis and modeling texture in fresh and processed foods—A review. Journal of Food Engineering, 119(3), 497–507.
- Chen, L., & Opara, U. L. (2013b). Texture measurement approaches in fresh and processed foods—A review. Food Research International, 51(2), 823–835.
- Cheng, J. H., Qu, J. H., Sun, D. W., & Zeng, X. A. (2014). Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage. Food Research International, 56, 190–198.
- Cheng, J. H., Sun, D. W., Han, Z., & Zeng, X. A. (2014). Texture and structure measurements and analyses for evaluation of fish and fillet freshness quality: A review. Comprehensive Reviews in Food Science and Food Safety, 13(1), 52–61.
- Civille, G. V. (2011). Food texture: Pleasure and pain. Journal of Agricultural and Food Chemistry, 59(5), 1487–1490.
- Cluff, K., Naganathan, G. K., Subbiah, J., Samal, A., & Calkins, C. R. (2013). Optical scattering with hyperspectral imaging to classify longissimus dorsi muscle based on beef tenderness using multivariate modeling. Meat Science, 95(1), 42–50.
- Contreras, M., Benedito, J., Quiles, A., Lorenzo, J. M., Fulladosa, E., Gou, P., & Garcia-Perez, J. V. (2020). Assessing the textural defect of pastiness in dry-cured pork ham using chemical, microstructural, textural and ultrasonic analyses. Journal of Food Engineering, 265, 109690.
- Cornish, A. (2019). X-ray CT imaging of food. Food Science and Technology, 33(3), 30–33.
10.1002/fsat.3303_8.x Google Scholar
- Cropotova, J., Mozuraityte, R., Standal, I. B., & Rustad, T. (2018). A non-invasive approach to assess texture changes in sous-vide cooked Atlantic mackerel during chilled storage by fluorescence imaging. Food Control, 92, 216–224.
- Cui, D., Gao, Z., Zhang, W., & Ying, Y. (2015). The use of a laser Doppler vibrometer to assess watermelon firmness. Computers and Electronics in Agriculture, 112, 116–120.
- Dănilă, D. (2015). Assessing the potato impact response using a pendulum controlled and designed by computer. Bulletin of the Transilvania University of Braşov Series II: Forestry.Wood industry. Agricultural food. Engineering, 8(57), 65–70.
- Day, L., & Golding, M. (2016). Food structure, rheology, and texture. In Encyclopedia of food chemistry (pp. 125–129). Amsterdam: Elsevier.
10.1016/B978-0-08-100596-5.03412-0 Google Scholar
- De Marchi, M., Penasa, M., Battagin, M., Zanetti, E., Pulici, C., & Cassandro, M. (2011). Feasibility of the direct application of near-infrared reflectance spectroscopy on intact chicken breasts to predict meat color and physiJacal traits. Poultry Science, 90(7), 1594–1599.
- De Souza Costa, J. D., Almeida, F. D. A. C., Neto, A. F., & Cavalcante, Í. H. L. (2017). Physical and mechanical parameters correlated to the ripening of mangoes (Mangifera indica L.) cv. ‘Tommy Atkins’. Acta Scientiarum Agronomy, 66(2), 186–192.
- Demirkesen, I., Kelkar, S., Campanella, O. H., Sumnu, G., Sahin, S., & Okos, M. (2014). Characterization of structure of gluten-free breads by using X-ray microtomography. Food Hydrocolloids, 36, 37–44.
- Ding, C., Feng, Z., Wang, D., Cui, D., & Li, W. (2021). Acoustic vibration technology: Toward a promising fruit quality detection method. Comprehensive Reviews in Food Science and Food Safety, 20(2), 1655–1680.
- Ding, C., Wang, D., Feng, Z., Li, W., & Cui, D. (2021). Integration of vibration and optical techniques for watermelon firmness assessment. Computers and Electronics in Agriculture, 187, 106307.
- Ding, C., Wu, H., Feng, Z., Wang, D., Li, W., & Cui, D. (2020). Online assessment of pear firmness by acoustic vibration analysis. Postharvest Biology and Technology, 160, 111042.
- Do Trong, N. N., Rizzolo, A., Herremans, E., Vanoli, M., Cortellino, G., Erkinbaev, C., … Saeys, W. (2014). Reprint of “optical properties–microstructure–texture relationships of dried apple slices: Spatially resolved diffuse reflectance spectroscopy as a novel technique for analysis and process control”. Innovative Food Science & Emerging Technologies, 24, 145–153.
- ElMasry, G., Sun, D. W., & Allen, P. (2012). Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering, 110(1), 127–140.
- Erkinbaev, C., Derksen, K., & Paliwal, J. (2019). Single kernel wheat hardness estimation using near infrared hyperspectral imaging. Infrared Physics & Technology, 98, 250–255.
- Fariñas, L., Sanchez-Torres, E. A., Sanchez-Jimenez, V., Diaz, R., Benedito, J., & Garcia-Perez, J. V. (2021). Assessment of avocado textural changes during ripening by using contactless air-coupled ultrasound. Journal of Food Engineering, 289, 110266.
- Fathizadeh, Z., Aboonajmi, M., & Beygi, S. R. H. (2020). Nondestructive firmness prediction of apple fruit using acoustic vibration response. Scientia Horticulturae, 262, 1–8.
- Fathizadeh, Z., Aboonajmi, M., & Hassan-Beygi, S. R. (2021). Nondestructive methods for determining the firmness of apple fruit flesh. Information Processing in Agriculture, 8(4), 515–527.
10.1016/j.inpa.2020.12.002 Google Scholar
- Feng, Y. Z., & Sun, D. W. (2012). Application of hyperspectral imaging in food safety inspection and control: A review. Critical Reviews in Food Science and Nutrition, 52(11), 1039–1058.
- Ferrer-Gallego, R., Hernández-Hierro, J. M., Rivas-Gonzalo, J. C., & Escribano-Bailón, M. T. (2013). Evaluation of sensory parameters of grapes using near infrared spectroscopy. Journal of Food Engineering, 118(3), 333–339.
- Foerster, J., Truppel, I., Bochow-Neß, O., & Geyer, M. (2013). Comparison of acoustic sensor systems for quality analysis of asparagus using scanning laser vibrometry for visualization. Computers and Electronics in Agriculture, 91, 10–18.
- Garcı́a, J. L., Ruiz-Altisent, M., & Barreiro, P. (1995). Factors influencing mechanical properties and bruise susceptibility of apples and pears. Journal of Agricultural Engineering Research, 61(1), 11–17.
10.1006/jaer.1995.1025 Google Scholar
- García-Ramos, F. J., Valero, C., Homer, I., Ortiz-Cañavate, J., & Ruiz-Altisent, M. (2005). Non-destructive fruit firmness sensors: A review. Spanish Journal of Agricultural Research, 3(1), 61–73.
10.5424/sjar/2005031-125 Google Scholar
- Geronimo, B. C., Mastelini, S. M., Carvalho, R. H., Júnior, S. B., Barbin, D. F., Shimokomaki, M., & Ida, E. I. (2019). Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization. Infrared Physics & Technology, 96, 303–310.
- Gouyo, T., Rondet, É., Mestres, C., Hofleitner, C., & Bohuon, P. (2021). Microstructure analysis of crust during deep-fat or hot-air frying to understand French fry texture. Journal of Food Engineering, 298, 110484.
- Guelpa, A., du Plessis, A., Kidd, M., & Manley, M. (2015). Non-destructive estimation of maize (Zea mays L.) kernel hardness by means of an X-ray micro-computed tomography (μCT) density calibration. Food and Bioprocess Technology, 8(7), 1419–1429.
- Guillermic, R. M., Koksel, F., Sun, X., Hatcher, D. W., Nickerson, M. T., Belev, G. S., … Scanlon, M. G. (2018). Bubbles in noodle dough: Characterization by X-ray microtomography. Food Research International, 105, 548–555.
- Guiné, R. P. F., & Barroca, M. J. (2011). Effect of drying on the textural attributes of bell pepper and pumpkin. Drying, 29(16), 1911–1919.
- Hassoun, A., & Karoui, R. (2016). Monitoring changes in whiting (Merlangius merlangus) fillets stored under modified atmosphere packaging by front face fluorescence spectroscopy and instrumental techniques. Food Chemistry, 200, 343–353.
- Hassoun, A., & Karoui, R. (2017). Quality evaluation of fish and other seafood by traditional and nondestructive instrumental methods: Advantages and limitations. Critical Reviews in Food Science and Nutrition, 57(9), 1976–1998.
- He, H. J., Wu, D., & Sun, D. W. (2014). Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets. Journal of Food Engineering, 126, 156–164.
- Hemrattrakun, P., Nakano, K., Boonyakiat, D., Ohashi, S., Maniwara, P., Theanjumpol, P., & Seehanam, P. (2021). Comparison of reflectance and interactance modes of visible and near-infrared spectroscopy for predicting persimmon fruit quality. Food Analytical Methods, 14(1), 117–126.
- Herrero-Langreo, A., Fernández-Ahumada, E., Roger, J., Palagos, B., & Lleó, L. (2012). Combination of optical and non-destructive mechanical techniques for the measurement of maturity in peach. Journal of Food Engineering, 108, 150–157.
- Homer, I., García-Ramos, F. J., Ortiz-Cañavate, J., & Ruiz-Altisent, M. (2010). Evaluation of nondestructive impact sensor to determine on-line fruit firmness. Chilean Journal of Agricultural Research, 70(1), 67–74.
- Hou, J., Hu, W., Zhang, L., Ren, Z., Sun, Q., & Wang, W. (2021). Mechanical properties of mulberry fruit under compression and nuclear magnetic resonance tests. Journal of Food Process Engineering, 44, e13856.
- Hou, J., Zhang, Y., Sun, Y., Xu, N., & Leng, Y. (2018). Prediction of firmness and pH for “Golden delicious” apple based on elasticity index from modal analysis. Journal of Food Science, 83(3), 661–669.
- Hu, J., Wang, Z., Wu, Y., Liu, Y., & Ouyang, J. (2018). Rapid determination of the texture properties of cooked cereals using near-infrared reflectance spectroscopy. Infrared Physics & Technology, 94, 165–172.
- Hu, M. H., Dong, Q. L., Liu, B. L., & Opara, U. L. (2016). Prediction of mechanical properties of blueberry using hyperspectral interactance imaging. Postharvest Biology and Technology, 115, 122–131.
- Huang, H., Liu, L., & Ngadi, M. O. (2014). Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors, 14(4), 7248–7276.
- Ignat, T., Alchanatis, V., & Schmilovitch, Z. (2014). Maturity prediction of intact bell peppers by sensor fusion. Computers and Electronics in Agriculture, 104, 9–17.
- Imanpanah, H., Kasraei, M., Raoufat, M., & Nejadi, J. (2015). Development and evaluation of a portable apparatus for bioyield detection: A case study with apple and peach fruits. International Journal of Food Properties, 18(7), 1434–1445.
- Iwatani, S., Akimoto, H., & Sakurai, N. (2013). Acoustic vibration method for food texture evaluation using an accelerometer sensor. Journal of Food Engineering, 115(1), 26–32.
- Jahanbakhshi, A., Abbaspour-Gilandeh, Y., & Gundoshmian, T. M. (2018). Determination of physical and mechanical properties of carrot in order to reduce waste during harvesting and post-harvesting. Food Science & Nutrition, 6(7), 1898–1903.
- Jahanbakhshi, A., Yeganeh, R., & Shahgoli, G. (2020). Determination of mechanical properties of Banana fruit under quasi-static loading in pressure, bending, and shearing tests. International Journal of Fruit Science, 20(3), 314–322.
- Jalilian Tabar, F., Lorestani, A. N., Gholami, R., Behzadi, A., & Fereidoni, M. (2011). Physical and mechanical properties of oak (Quercus persica) fruits. Agricultural Engineering International: CIGR Journal, 13(4), 1–7.
- Jiang, H., Yoon, S. C., Zhuang, H., Wang, W., Lawrence, K. C., & Yang, Y. (2018). Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging. Meat Science, 139, 82–90.
- Juodeikiene, G., & Basinskiene, L. (2004). Non-destructive texture analysis of cereal products. Food Research International, 37(6), 603–610.
- Kaavya, R., Pandiselvam, R., Mohammed, M., Dakshayani, R., Kothakota, A., Ramesh, S. V., … Ashokkumar, C. (2020). Application of infrared spectroscopy techniques for the assessment of quality and safety in spices: A review. Applied Spectroscopy Reviews, 55(7), 593–611.
- Kamruzzaman, M., ElMasry, G., Sun, D. W., & Allen, P. (2013). Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry, 141(1), 389–396.
- Kanchanomai, C., Ohashi, S., Naphrom, D., Nemoto, W., Maniwara, P., & Nakano, K. (2020). Non-destructive analysis of Japanese table grape qualities using near-infrared spectroscopy. Horticulture, Environment, and Biotechnology, 61, 1–9.
- Karthickumar, P., Sinija, V. R., Alagusundaram, K., & Yadav, B. K. (2018a). Evaluation of muskmelon ripening based on acoustic response. Journal of Applied Horticulture, 20(2), 125–128.
10.37855/jah.2018.v20i02.22 Google Scholar
- Karthickumar, P., Sinija, V. R., Alagusundaram, K., & Yadav, B. K. (2018b). Development of a sorting system for fruits and vegetables based on acoustic resonance technique. AMA, Agricultural Mechanization in Asia, Africa and Latin America, 49(1), 22–27.
- Kawai, T., Matsumori, F., Akimoto, H., Sakurai, N., Hirano, K., Nakano, R., & Fukuda, F. (2018). Nondestructive detection of split-pit peach fruit on trees with an acoustic vibration method. Horticulture Journal, 87(4), 499–507.
- Khoshnam, F., & Namjoo, M. (2020). The numerical simulation of rebound velocity pendulum method for ripening detection of melon. Journal of Agricultural Machinery, 10(1), 83–92.
- Khoshtaghaza, M. H., Khojastehnazhand, M., Mojaradi, B., Goodarzi, M., & Saeys, W. (2016). Texture quality analysis of rainbow trout using hyperspectral imaging method. International Journal of Food Properties, 19(5), 974–983.
- Klimkiewicz, A., Mortensen, P. P., & van den Berg, F. W. J. (2013). Spatial distribution of staling of white wheat bread as studied by texture analyser, classical NIR and hyperspectral imaging. 16th International Conference on Near Infrared Spectroscopy 2-7 June 2013, la Grande-Motte, France, 724.
- Komarnicki, P., Stopa, R., Szyjewicz, D., Kuta, Ł., & Klimza, T. (2017). Influence of contact surface type on the mechanical damages of apples under impact loads. Food and Bioprocess Technology, 10(8), 1479–1494.
- Komarnicki, P., Stopa, R., Szyjewicz, D., & Młotek, M. (2016). Evaluation of bruise resistance of pears to impact load. Postharvest Biology and Technology, 114, 36–44.
- Kowalczewski, P. Ł., Walkowiak, K., Masewicz, Ł., Smarzyński, K., Thanh-Blicharz, J. L., Kačániová, M., & Baranowska, H. M. (2021). LF NMR spectroscopy analysis of water dynamics and texture of gluten-free bread with cricket powder during storage. Food Science and Technology International, 27(8), 776–785.
- Kutlu, N., Pandiselvam, R., Saka, I., Kamiloglu, A., Sahni, P., & Kothakota, A. (2021). Impact of different microwave treatments on food texture. Journal of Texture Studies, 53, 709–736. https://doi.org/10.1111/jtxs.12635
- Laley, M. F., Moghadam, P. A., Hasanpour, A., & Rostampour, V. (2018). Detection and classification of bruises in" red delicious" apple fruits by applying piezoelectric transducers. INMATEH-Agricultural Engineering, 56(3), 211–217.
- Landahl, S., & Terry, L. A. (2020). Non-destructive discrimination of avocado fruit ripeness using laser Doppler vibrometry. Biosystems Engineering, 194, 251–260.
- Lashgari, M., Maleki, A., & Amiriparian, J. (2017). Application of acoustic impulse response in discrimination of apple storage time using neural network. International Food Research Journal, 24(3), 1075–1080.
- Laverse, J., Mastromatteo, M., Frisullo, P., Albenzio, M., Gammariello, D., & Del Nobile, M. A. (2011). Fat microstructure of yogurt as assessed by x-ray microtomography. Journal of Dairy Science, 94(2), 668–675.
- Laverse, J., Mastromatteo, M., Frisullo, P., & Del Nobile, M. A. (2011). X-ray microtomography to study the microstructure of cream cheese-type products. Journal of Dairy Science, 94(1), 43–50.
- Laverse, J., Mastromatteo, M., Frisullo, P., & Del Nobile, M. A. (2012). X-ray microtomography to study the microstructure of mayonnaise. Journal of Food Engineering, 108(1), 225–231.
- Lewis, J. A. (2014). Instrumentation. In Forensic document examination: Fundamentals and current trends (pp. 69–79). Amsterdam: Elsevier.
10.1016/B978-0-12-416693-6.00006-0 Google Scholar
- Li, H., Pidakala, P., Billing, D., & Burdon, J. (2016). Kiwifruit firmness: Measurement by penetrometer and non-destructive devices. Postharvest Biology and Technology, 120, 127–137.
- Li, J., Huang, W., Zhao, C., & Zhang, B. H. (2013). A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. Journal of Food Engineering, 116(2), 324–332.
- Li, J. L., Sun, D. W., & Cheng, J. H. (2016). Recent advances in nondestructive analytical techniques for determining the total soluble solids in fruits: A review. Comprehensive Reviews in Food Science and Food Safety, 15(5), 897–911.
- Li, Z., Li, P., & Liu, J. (2011). Physical and mechanical properties of tomato fruits as related to robot's harvesting. Journal of Food Engineering, 103(2), 170–178.
- Li, Z., Miao, F., & Andrews, J. (2017). Mechanical models of compression and impact on fresh fruits. Comprehensive Reviews in Food Science and Food Safety, 16(6), 1296–1312.
- Lille, M., Kortekangas, A., Heiniö, R. L., & Sozer, N. (2020). Structural and textural characteristics of 3D-printed protein-and dietary fibre-rich snacks made of milk powder and wholegrain rye flour. Food, 9(11), 1527.
- Lim, J., Jeong, S., Lee, J., Park, S., Lee, J., & Lee, S. (2017). Effect of shortening replacement with oleogels on the rheological and tomographic characteristics of aerated baked goods. Journal of the Science of Food and Agriculture, 97(11), 3727–3732.
- Liu, C., Liu, W., Lu, X., Ma, F., Chen, W., Yang, J., & Zheng, L. (2014). Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit. PLoS One, 9(2), 1–8.
- Liu, Q., Chen, S., Zhou, D., Ding, C., Wang, J., Zhou, H., … Li, P. (2021). Nondestructive detection of weight loss rate, surface color, vitamin C content, and firmness in mini-Chinese cabbage with Nanopackaging by Fourier transform-near infrared spectroscopy. Food, 10(10), 2309.
- Liu, Y., Cai, M., Zhang, W., Feng, W., Sun, X., Zhang, Y., & Zhou, H. (2020). Feasibility of non-destructive evaluation for apple crispness based on portable acoustic signal. International Journal of Food Science & Technology, 56(5), 2375–2383.
- Liu, Y., Zhang, Y., Jiang, X., & Liu, H. (2020). Detection of the quality of juicy peach during storage by visible/near infrared spectroscopy. Vibrational Spectroscopy, 111, 103152.
- Liu, Y. X., Cao, M. J., & Liu, G. M. (2019). Texture analyzers for food quality evaluation. In Evaluation technologies for food quality (pp. 441–463). Sawston, UK: Woodhead Publishing.
10.1016/B978-0-12-814217-2.00017-2 Google Scholar
- Liugi, R., Berardinelli, A., & Guarnieri, A. (2013). Impact device for measuring the flesh firmness of kiwifruits. Journal of Food Engineering, 96(4), 591–597.
- Lu, R. (2013). Principles of solid food texture analysis. In Instrumental assessment of food sensory quality (pp. 103–128). Sawston, UK: Woodhead Publishing.
10.1533/9780857098856.1.103 Google Scholar
- Lu, R., & Cen, H. (2013). Non-destructive methods for food texture assessment. In Instrumental assessment of food sensory quality. Woodhead Publishing Series in Food Science, Technology and Nutrition (pp. 230–255). Sawston, UK: Woodhead Publishing.
10.1533/9780857098856.2.230 Google Scholar
- Lu, R., Van Beers, R., Saeys, W., Li, C., & Cen, H. (2020). Measurement of optical properties of fruits and vegetables: A review. Postharvest Biology and Technology, 159, 111003.
- Lurie, S., Vanoli, M., Dagar, A., Weksler, A., Lovati, F., Zerbini, P. E., … Rizzolo, A. (2011). Chilling injury in stored nectarines and its detection by time-resolved reflectance spectroscopy. Postharvest Biology and Technology, 59(3), 211–218.
- Ma, J., Sun, D. W., Qu, J. H., & Pu, H. (2017). Prediction of textural changes in grass carp fillets as affected by vacuum freeze drying using hyperspectral imaging based on integrated group wavelengths. LWT- Food Science and Technology, 82, 377–385.
- Macrelli, E., Romani, A., Paganelli, R. P., Sangiorgi, E., & Tartagni, M. (2013). Piezoelectric transducers for real-time evaluation of fruit firmness. Part II: Statistical and sorting analysis. Sensors and Actuators, A: Physical, 201, 497–503.
- Mahawar, M. K., Jalgaonkar, K., Kumar, M., Meena, V. S., & Bhushan, B. (2017). Determination of some physical properties of date palm fruits (cv. Khadrawy and Medjool). Acta Agrophysica, 24(2), 217–223.
- Mayorga-Martinez, A. A., Olvera-Trejo, D., Elias-Zuniga, A., Parra-Saldivar, R., & Chuck-Hernandez, C. (2016). Non-destructive assessment of guava (Psidium guajava L.) maturity and firmness based on mechanical vibration response. Food and Bioprocess Technology, 9(9), 1471–1480.
- Mendoza, F., Lu, R., Ariana, D., Cen, H., & Bailey, B. (2011). Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 62(2), 149–160.
- Mendoza, F., Lu, R., & Cen, H. (2011). Multi-sensor data fusion for improved prediction of apple fruit firmness and soluble solids content. In M. S. Kim, S.-I. Tu, & K. Chao (Eds.), Sensing for agricutural food quality and safety III (Vol. 8027, pp. 1–14). Bellingham, WA: SPIE Proceedings.
10.1117/12.883649 Google Scholar
- Mendoza, F., Lu, R., & Cen, H. (2012). Comparison and fusion of four non-destructive sensors for predicting apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 73, 89–98.
- Mendoza, F. A., Cichy, K. A., Sprague, C., Goffnett, A., Lu, R., & Kelly, J. D. (2018). Prediction of canned black bean texture (Phaseolus vulgaris L.) from intact dry seeds using visible/near infrared spectroscopy and hyperspectral imaging data. Journal of the Science of Food and Agriculture, 98(1), 283–290.
- Miraei Ashtiani, S. H., Salarikia, A., Golzarian, M. R., & Emadi, B. (2016). Non-destructive estimation of mechanical and chemical properties of persimmons by ultrasonic spectroscopy. International Journal of Food Properties, 19(7), 1522–1534.
- Mireei, S. A., Sadeghi, M., Heidari, A., & Hemmat, A. (2015). Online firmness sensing of dates using a non-destructive impact testing device. Biosystems Engineering, 129, 288–297.
- Mishra, G., Panda, B. K., Ramirez, W. A., Jung, H., Singh, C. B., Lee, S. H., & Lee, I. (2021). Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts. Comprehensive Reviews in Food Science and Food Safety, 20(5), 4612–4651.
- Mishra, G., Panda, B. K., Ramirez, W. A., Jung, H., Singh, C. B., Lee, S. H., & Lee, I. (2022). Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of aflatoxin B1 in single kernel almonds. LWT, 155, 112954.
- Mishra, G., Srivastava, S., Panda, B. K., & Mishra, H. N. (2018). Rapid assessment of quality change and insect infestation in stored wheat grain using FT-NIR spectroscopy and chemometrics. Food Analytical Methods, 11(4), 1189–1198.
- Mishra, P., Paillart, M., Meesters, L., Woltering, E., Chauhan, A., & Polder, G. (2021). Assessing avocado firmness at different dehydration levels in a multi-sensor framework. Infrared Physics & Technology, 118, 103901.
- Moggia, C., Valdés, Y., Arancibia, A., Valdés, M., Radrigan, C., Icaza, G., … Lobos, G. A. (2022). A comparison of firmness assessment instruments for fresh blueberry fruit. HortTechnology, 32(2), 172–181.
- Mohammadi-Moghaddam, T., Razavi, S. M., Taghizadeh, M., Pradhan, B., Sazgarnia, A., & Shaker-Ardekani, A. (2018). Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels. Journal of Food Measurement and Characterization, 12(3), 1493–1502.
- Mollazade, K., Omid, M., Tab, F. A., Kalaj, Y. R., Mohtasebi, S. S., & Zude, M. (2013). Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging. Computers and Electronics in Agriculture, 98, 34–45.
- Mozafarian, M., & Kappel, N. (2020). Effect of grafting on the quality and appearance of eggplant fruit. Progress in Agricultural Engineering Sciences, 16(2), 153–116.
- Nazir, A., AlDhaheri, M., Mudgil, P., Marpu, P., & Kamal-Eldin, A. (2022). Hyperspectral imaging based kinetic approach to assess quality deterioration in fresh mushrooms (Agaricus bisporus) during postharvest storage. Food Control, 131, 108298.
- Nicolaï, B. M., Verlinden, B. E., Desmet, M., Saevels, S., Saeys, W., Theron, K., … Torricelli, A. (2008). Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biology and Technology, 47(1), 68–74.
- Nowak, K. W., Markowski, M., & Daszkiewicz, T. (2015). Ultrasonic determination of mechanical properties of meat products. Journal of Food Engineering, 147, 49–55.
- Onmankhong, J., & Sirisomboon, P. (2021). Texture evaluation of cooked parboiled rice using nondestructive milled whole grain near infrared spectroscopy. Journal of Cereal Science, 97, 103151.
- Onsawai, P., Phetpan, K., Khurnpoon, L., & Sirisomboon, P. (2021). Evaluation of physiological properties and texture traits of durian pulp using near-infrared spectra of the pulp and intact fruit. Measurement, 174, 108684.
- Oveisi, Z., Minaei, S., Rafiee, S., Eyvani, A., & Borghei, A. (2012). Application of vibration response technique for the firmness evaluation of pear fruit during storage. Journal of Food Science and Technology, 51(11), 3261–3268.
- Pan, L., Lu, R., Zhu, Q., McGrath, J. M., & Tu, K. (2015). Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy. Postharvest Biology and Technology, 102, 42–50.
- Panda, B. K., Mishra, G., Ramirez, W. A., Jung, H., Singh, C. B., Lee, S. H., & Lee, I. (2021). Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis. Journal of Food Engineering, 318, 110889.
- Pandey, P., Mishra, G., & Mishra, H. N. (2018). Development of a non-destructive method for wheat physico-chemical analysis by chemometric comparison of discrete light based near infrared and Fourier transform near infrared spectroscopy. Journal of Food Measurement and Characterization, 12(4), 2535–2544.
- Peng, J., Song, Y., Zhang, X., Pan, L., & Tu, K. (2019). Calcium absorption in asparagus during thermal processing: Different forms of calcium ion and cell integrity in relation to texture. LWT- Food Science and Technology, 111, 889–895.
- Perez-Palacios, T., Caballero, D., González-Mohíno, A., Mir-Bel, J., & Antequera, T. (2019). Near infrared reflectance spectroscopy to analyse texture related characteristics of sous vide pork loin. Journal of Food Engineering, 263, 417–423.
- Pinzer, B. R., Medebach, A., Limbach, H. J., Dubois, C., Stampanoni, M., & Schneebeli, M. (2012). 3D-characterization of three-phase systems using X-ray tomography: Tracking the microstructural evolution in ice cream. Soft Matter, 8(17), 4584–4594.
- Piovesan, A., Vancauwenberghe, V., Aregawi, W., Delele, M. A., Bongaers, E., de Schipper, M., … Nicolai, B. (2020). Designing mechanical properties of 3D printed cookies through computer aided engineering. Food, 9(12), 1804.
- Pourkhak, B., Mireei, S. A., Sadeghi, M., & Hemmat, A. (2017). Multi-sensor data fusion in the nondestructive measurement of kiwifruit texture. Measurement, 101, 157–165.
- Pozrl, T., Znidarcic, D., Kopjar, M., Hribar, J., & Simcic, M. (2010). Change of textural properties of tomatoes due to storage and storage temperatures. Journal of Food, Agriculture and Environment, Issue, 2, 292–296.
- Pu, H., Lin, L., & Sun, D. W. (2019). 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, 18(4), 853–866.
- Pullanagari, R. R., & Li, M. (2021). Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics. Journal of Food Engineering, 289, 110177.
- Qin, J., Chao, K., Kim, M. S., Lu, R., & Burks, T. F. (2013). Hyperspectral and multispectral imaging for evaluating food safety and quality. Journal of Food Engineering, 118(2), 157–171.
- Qiu, M., Jiang, H., Ren, G., Huang, J., & Wang, X. (2013). Effect of chitosan coatings on postharvest green asparagus quality. Carbohydrate Polymers, 92(2), 2027–2032.
- Radunić, M., Špika, M. J., Ban, S. G., Gadže, J., Díaz-Pérez, J. C., & MacLean, D. (2015). Physical and chemical properties of pomegranate fruit accessions from Croatia. Food Chemistry, 177, 53–60.
- Radzevičius, A., Viškelis, J., Karklelienė, R., Juškevičienė, D., & Viškelis, P. (2016). Determination of tomato quality attributes using near infrared spectroscopy and reference analysis. Zemdirbyste-Agriculture, 103(1), 91–98.
- Ragni, L., Cevoli, C., Berardinelli, A., & Silaghi, F. A. (2012). Non-destructive internal quality assessment of “Hayward” kiwifruit by waveguide spectroscopy. Journal of Food Engineering, 109, 32–37.
- Rahim, H. A., & Ghazali, R. (2012, March). The application of near-infrared spectroscopy for poultry meat grading. In Proceedings of the 2012 IEEE 8th International Colloquium on Signal Processing and its Applications (pp. 58–62). IEEE.
- Raikar, M. M., Meena, S. M., Kuchanur, C., Girraddi, S., & Benagi, P. (2020). Classification and grading of okra-ladies finger using deep learning. Procedia Computer Science, 171, 2380–2389.
10.1016/j.procs.2020.04.258 Google Scholar
- Rajkumar, P., Wang, N., EImasry, G., Raghavan, G. S. V., & Gariepy, Y. (2012). Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of Food Engineering, 108(1), 194–200.
- Ramachandran, R. P., Erkinbaev, C., Thakur, S., & Paliwal, J. (2021). Three dimensional characterization of micronized soybean seeds using X-ray microtomography. Food and Bioproducts Processing, 127, 388–397.
- Reddy, B. S., & Srinivas, P. (2017). Effect of fruit size and maturity on selected mechanical properties of tomato. International Journal of Pure Applied and Biosciences, 5(4), 1730–1737.
10.18782/2320-7051.5723 Google Scholar
- Rizzolo, A., Vanoli, M., Bianchi, G., Zanella, A., Grassi, M., Torricelli, A., & Spinelli, L. (2014). Relationship between texture sensory profiles and optical properties measured by time-resolved reflectance spectroscopy during post-storage shelf life of ‘Braeburn’ apples. Journal of Horticultural Research., 22(1), 113–121.
10.2478/johr-2014-0014 Google Scholar
- Rizzolo, A., Vanoli, M., Cortellino, G., Spinelli, L., & Torricelli, A. (2011). Quality characteristics of air-dried apple rings: Influence of storage time and fruit maturity measured by time-resolved reflectance spectroscopy. Procedia Food Science, 1, 216–223.
10.1016/j.profoo.2011.09.034 Google Scholar
- Rizzolo, A., Vanoli, M., Spinelli, L., & Torricelli, A. (2010). Sensory characteristics, quality and optical properties measured by time-resolved reflectance spectroscopy in stored apples. Postharvest Biology and Technology, 58(1), 1–12.
- Rybak, K., Wiktor, A., Witrowa-Rajchert, D., Parniakov, O., & Nowacka, M. (2021). The quality of red bell pepper subjected to freeze-drying preceded by traditional and novel pretreatment. Food, 10(2), 226.
- Saita, A., Yamamoto, K., Raevskiy, A., Takei, R., Washio, H., Shioiri, S., & Sakai, N. (2021). Crispness, the key for the palatability of “Kakinotane”: A sensory study with onomatopoeic words. Food, 10, 1724.
- Salarikia, A., Ashtiani, S. H. M., Golzarian, M. R., & Mohammadinezhad, H. (2017). Finite element analysis of the dynamic behavior of pear under impact loading. Information Processing in Agriculture, 4(1), 64–77.
10.1016/j.inpa.2016.12.003 Google Scholar
- Santos-Garcés, E., Laverse, J., Gou, P., Fulladosa, E., Frisullo, P., & Del Nobile, M. A. (2013). Feasibility of X-ray microcomputed tomography for microstructure analysis and its relationship with hardness in non-acid lean fermented sausages. Meat Science, 93(3), 639–644.
- Schoeman, L., Williams, P., du Plessis, A., & Manley, M. (2016). X-ray micro-computed tomography (μCT) for non-destructive characterisation of food microstructure. Trends in Food Science & Technology, 47, 10–24.
- Schreuders, F., Schlangen, M., Kyriakopoulou, K., Boom, R., & Goot, R. (2021). Texture methods for evaluating meat and meat analogue structures: A review. Food Control, 127, 108103.
- Shimomura, K., Horie, H., Sugiyama, M., Kawazu, Y., & Yoshioka, Y. (2016). Quantitative evaluation of cucumber fruit texture and shape traits reveals extensive diversity and differentiation. Scientia Horticulturae, 199, 133–141.
- Shirvani, M., Ghanbarian, D., & Ghasemi-Varnamkhasti, M. (2014). Measurement and evaluation of the apparent modulus of elasticity of apple based on Hooke's, Hertz's and Boussinesq's theories. Measurement, 54, 133–139.
- Shiu, J. W., Slaughter, D. C., Boyden, L. E., & Barrett, D. M. (2015). Effect of the shear-to-compressive force ratio in puncture tests quantifying watermelon mechanical properties. Journal of Food Engineering, 150, 125–131.
- Silva, L. K., Jesus, J. C., Onelli, R. R., Conceição, D. G., Santos, L. S., & Ferrão, S. P. (2021). Discriminating Coalho cheese by origin through near and middle infrared spectroscopy and analytical measures. Discrimination of Coalho cheese origin. International Journal of Dairy Technology, 74(2), 393–403.
- Siriphollakul, P., Nakano, K., Kanlayanarat, S., Ohashi, S., Sakai, R., Rittiron, R., & Maniwara, P. (2017). Eating quality evaluation of Khao Dawk Mali 105 rice using near-infrared spectroscopy. LWT- Food Science and Technology, 79, 70–77.
- Sirisomboon, P., Tanaka, M., & Kojima, T. (2012). Evaluation of tomato textural mechanical properties. Journal of Food Engineering, 111(4), 618–624.
- Sirisomboon, P., Tanaka, M., Kojima, T., & Williams, P. (2012). Nondestructive estimation of maturity and textural properties on tomato ‘Momotaro’ by near infrared spectroscopy. Journal of Food Engineering, 112(3), 218–226.
- Srivastava, R., Stieger, M., Scholten, E., Souchon, I., & Mathieu, V. (2021). Texture contrast: Ultrasonic characterization of stacked gels' deformation during compression on a biomimicking tongue. Current Research in Food Science, 4, 449–459.
- Srivastava, S., Mishra, G., & Mishra, H. N. (2018). Identification and differentiation of insect infested rice grains varieties with FTNIR spectroscopy and hierarchical cluster analysis. Food Chemistry, 268, 402–410.
- Stieger, M., & van de Velde, F. (2013). Microstructure, texture and oral processing: New ways to reduce sugar and salt in foods. Current Opinion in Colloid & Interface Science, 18(4), 334–348.
- Stopa, R., Szyjewicz, D., Komarnicki, P., & Kuta, Ł. (2018). Determining the resistance to mechanical damage of apples under impact loads. Postharvest Biology and Technology, 146, 79–89.
- Sun, M., Zhang, D., Liu, L., & Wang, Z. (2017). How to predict the sugariness and hardness of melons: A near-infrared hyperspectral imaging method. Food Chemistry, 218, 413–421.
- Sun, X., Maynard, C. J., Caldas-Cueva, J. P., Coon, C. N., & Owens, C. M. (2021). Using air deformation of raw fillet surfaces to identify severity of woody breast myopathy in broiler fillets. LWT - Food Science and Technology, 141, 110904.
- Szczesniak, A. S. (2002). Texture is a sensory property. Food Quality and Preference, 13(4), 215–225.
- Tan, M. C., Chin, N. L., Yusof, Y. A., & Abdullah, J. (2016). Novel 2D and 3D imaging of internal aerated structure of ultrasonically treated foams and cakes using X-ray tomography and X-ray microtomography. Journal of Food Engineering, 183, 9–15.
- Taniwaki, M., Sakurai, N., & Kato, H. (2010). Texture measurement of potato chips using a novel analysis technique for acoustic vibration measurements. Food Research International, 43(3), 814–818.
- Taniwaki, M., Tohro, M., & Sakurai, N. (2010). Measurement of ripening speed and determination of the optimum ripeness of melons by a nondestructive acoustic vibration method. Postharvest Biology and Technology, 56(1), 101–103.
- Tao, F., & Peng, Y. (2014). A method for nondestructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique. Journal of Food Engineering, 126, 98–106.
- Tian, B., Wang, Z., van der Goot, A. J., & Bouwman, W. G. (2018). Air bubbles in fibrous caseinate gels investigated by neutron refraction, X-ray tomography and refractive microscope. Food Hydrocolloids, 83, 287–295.
- Tian, S., & Xu, H. (2022). Mechanical-based and optical-based methods for nondestructive evaluation of fruit firmness. Food Reviews International, 1–31. https://doi.org/10.1080/87559129.2021.2015376
- Ting, V. J., Silcock, P., Bremer, P. J., & Biasioli, F. (2013). X-ray micro-computer tomographic method to visualize the microstructure of different apple cultivars. Journal of Food Science, 78(11), E1735–E1742.
- Torricelli, A., Contini, D., Dalla Mora, A., Martinenghi, E., Tamborini, D., Villa, F., … Spinelli, L. (2015). Recent advances in time-resolved NIR spectroscopy for nondestructive assessment of fruit quality. Chemical Engineering Transactions, 44(1), 43–48.
- Valdivia-Nájar, C. G., Martín-Belloso, O., & Soliva-Fortuny, R. (2017). Impact of pulsed light treatments and storage time on the texture quality of fresh-cut tomatoes. Innovative Food Science and Emerging Technologies, 45, 29–35.
- Valerga, L., Darré, M., Zaro, M. J., Vicente, A. R., Lemoine, M. L., & Concellón, A. (2020). The plant age influences eggplant fruit growth, metabolic activity, texture and shelf-life. Scientia Horticulturae, 272, 109590.
- Vangdal, E., EccherZerbini, P., Vanoli, M., Rizzolo, A., Lovati, F., Torricelli, A., & Spinelli, L. (2011, May). Detecting internal physiological disorders in stored plums (Prunus domestica L.) by time-resolved reflectance spectroscopy. In Proceedings of the IV International Conference Postharvest Unlimited, Leavenworth, WA (pp. 197–203).
- Vanoli, M., Grassi, M., Buccheri, M., Lovati, F., Sadar, N., Zanella, A., … Spinelli, L. (2016). Time-resolved reflectance spectroscopy reveals different texture characteristics in ‘Braeburn’, ‘Gala’ and ‘Kanzi®’ apples. In Proceedings of the VIII International Postharvest Symposium: Enhancing Supply Chain and Consumer Benefits-Ethical and Technological Issues 1194 (pp. 1273–1282).
- Vanoli, M., Rizzolo, A., Grassi, M., Farina, A., Pifferi, A., Spinelli, L., & Torricelli, A. (2011). Time-resolved reflectance spectroscopy nondestructively reveals structural changes in ‘pink lady®’ apples during storage. Procedia Food Science, 1, 81–89.
10.1016/j.profoo.2011.09.014 Google Scholar
- Vanoli, M., Rizzolo, A., Grassi, M., Torricelli, A., Zanella, A., & Spinelli, L. (2014). Characterizing apple texture during storage through mechanical, sensory and optical properties. In: Proceedings of the V International Conference Postharvest Unlimited 1079 (pp. 383–390).
- Vanoli, M., Rizzolo, A., Zerbini, P. E., Spinelli, L., & Torricelli, A. (2010). Non-destructive detection of internal defects in apple fruit by time-resolved reflectancespectrscopy. Proceedings of the International Conference “Environmentally friendly and safe technologies for quality of fruit and vegetables”, held in Universidade do Algarve, Faro, Portugal, on January 14-16, 2009, –20-26.
- Vasighi-Shojae, H., Gholami-Parashkouhi, M., Mohammadzamani, D., & Soheili, A. (2018). Ultrasonic based determination of apple quality as a nondestructive technology. Sensing and Bio-Sensing Research, 21, 22–26.
10.1016/j.sbsr.2018.09.002 Google Scholar
- Vursavus, K. K., Yurtlu, Y. B., Diezma-Iglesias, B., Lleo-Garcia, L., & Ruiz-Altisent, M. (2015). Classification of the firmness of peaches by sensor fusion. International Journal of Agricultural and Biological Engineering, 8, 104–115.
- Vursavusa, K. K., Kesilmisb, Z., & Oztekinc, Y. B. (2017). Nondestructive dropped fruit impact test for assessing tomato firmness. Chemical Engineering, 58, 325–330.
- Wang, L., Pu, H., Sun, D. W., Liu, D., Wang, Q., & Xiong, Z. (2015). Application of hyperspectral imaging for prediction of textural properties of maize seeds with different storage periods. Food Analytical Methods, 8(6), 1535–1545.
- Wang, Q. X., Su, L. H., Zou, J., Chen, N. X., Wu, T., & Yang, L. (2021). Research on hardness detection method of crisped grass carp based on visible-near infrared hyperspectral technology. Journal of Physics: Conference Series, 1757(1), 012002.
10.1088/1742-6596/1757/1/012002 Google Scholar
- Xiong, Z., Sun, D. W., Dai, Q., Han, Z., Zeng, X. A., & Wang, L. (2015). Application of visible hyperspectral imaging for prediction of springiness of fresh chicken meat. Food Analytical Methods, 8(2), 380–391.
- Xu, H., Wu, J., Wang, Z., Gao, Y., Wang, Z., & Zhao, Z. (2015). Characteristics of the vibration spectral response of rough peel pear among Korla pear. Science and Technology of Food Industry, 36(22), 57–60.
- Xu, X., Zhang, L., Feng, Y., ElGasim, A., Yagoub, A., Sun, Y., … Zhou, C. (2020). Vacuum pulsation drying of okra (Abelmoschus esculentus L. Moench): Better retention of the quality characteristics by flat sweep frequency and pulsed ultrasound pretreatment. Food Chemistry, 326, 127026.
- Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J., & Liu, C. (2014). Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 62, 326–343.
- Zhang, H., Wu, J., & Ma, H. (2019). Acoustic firmness measurement of differently shaped pears: Comparison of resonance indices with propagation indices. Postharvest Biology and Technology, 148, 151–157.
- Zhang, H., Wu, J., Zhao, Z., & Wang, Z. (2018). Nondestructive firmness measurement of differently shaped pears with a dual-frequency index based on acoustic vibration. Postharvest Biology and Technology, 138, 11–18.
- Zhang, J., Wang, J., Zheng, C., Guo, H., & Shan, F. (2021). Nondestructive evaluation of Chinese cabbage quality using mechanical vibration response. Computers and Electronics in Agriculture, 188, 106317.
- Zhang, W., Cui, D., & Ying, Y. (2013). Nondestructive detection of pear firmness using a laser Doppler vibrometer. In Proceedings of the 2013, Paper number 131599335, Kansas City, Missouri, July 21–24, 2013 (p. 1). American Society of Agricultural and Biological Engineers.
- Zhang, W., Cui, D., & Ying, Y. (2014). Analysis of vibration characteristic of ‘Huangguan’ pears and its relation to firmness during storage. Transactions of the ASABE, 57(5), 1407–1413.
- Zhang, W., Cui, D., & Ying, Y. (2014b). Nondestructive measurement of pear texture by acoustic vibration method. Postharvest Biology and Technology, 96, 99–105.
- Zhang, W., Cui, D., & Ying, Y. (2015a). Orthogonal test design to optimize the acoustic vibration method for pear texture measurement. Postharvest Biology and Technology, 107, 33–42.
- Zhang, W., Cui, D., & Ying, Y. (2015b). The impulse response method for pear quality evaluation using a laser Doppler vibrometer. Journal of Food Engineering, 159, 9–15.
- Zhang, W., Lv, Z., Shi, B., Xu, Z., & Zhang, L. (2021). Evaluation of quality changes and elasticity index of kiwifruit in shelf life by a nondestructive acoustic vibration method. Postharvest Biology and Technology, 173, 111398.
- Zhang, W., Wang, A., Lv, Z., & Gao, Z. (2020). Nondestructive measurement of kiwifruit firmness, soluble solid content (SSC), titratable acidity (TA), and sensory quality by vibration spectrum. Food Science & Nutrition, 8(2), 1058–1066.
- Zhao, Y., & Takhar, P. S. (2017). Micro X-ray computed tomography and image analysis of frozen potatoes subjected to freeze-thaw cycles. LWT- Food Science and Technology, 79, 278–286.
- Zhou, J., Wu, X., You, J., & Xiong, S. (2020). Rapid determination of the textural properties of silver carp (Hypophthalmichthys molitrix) using near-infrared reflectance spectroscopy and chemometrics. LWT- Food Science and Technology, 129, 109545.
- Zhu, F., Zhang, D., He, Y., Liu, F., & Sun, D. W. (2013). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food and Bioprocess Technology, 6(10), 2931–2937.
- Zhu, H., Chu, B., Fan, Y., Tao, X., Yin, W., & He, Y. (2017). Hyperspectral imaging for predicting the internal quality of kiwifruits based on variable selection algorithms and chemometric models. Scientific Reports, 7(1), 1–13.
- Zhu, X. (2012). Tutorial on hertz contact stress. OPTI, 521, 1–8.
