Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation †
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. Hyperspectral Image Acquisition
2.3. Physicochemical Parameters Measurements
2.4. Data Processing
3. Results and Discussion
3.1. Quality Change of Apples during Storage
3.2. Reflectance Spectra of Control and Infested Apples during Storage
3.3. Predicting the Quality of Control and Infested Apples during Storage
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Salehi, F.; Aghajanzadeh, S. Effect of dried fruits and vegetables powder on cakes quality: A review. Trends Food Sci. Technol. 2020, 95, 162–172. [Google Scholar] [CrossRef]
- Liu, G.; Nie, R.; Liu, Y.; Mehmood, A. Combined antimicrobial effect of bacteriocins with other hurdles of physicochemic and microbiome to prolong shelf life of food: A review. Sci. Total Environ. 2022, 825, 154058. [Google Scholar] [CrossRef] [PubMed]
- Onwude, D.I.; Chen, G.; Eke-Emezie, N.; Kabutey, A.; Khaled, A.Y.; Sturm, B. Recent advances in reducing food losses in the supply chain of fresh agricultural produce. Processes 2020, 8, 1431. [Google Scholar] [CrossRef]
- Ekramirad, N.; Khaled, A.Y.; Parrish, C.A.; Donohue, K.D.; Villanueva, R.T.; Adedeji, A.A. Development of pattern recognition and classification models for the detection of vibro-acoustic emissions from codling moth infested apples. Postharvest Biol. Technol. 2021, 181, 111633. [Google Scholar] [CrossRef]
- Li, M.; Ekramirad, N.; Rady, A.; Adedeji, A. Application of acoustic emission and machine learning to detect codling moth infested apples. Trans. ASABE 2018, 61, 1157–1164. [Google Scholar] [CrossRef]
- Rady, A.; Ekramirad, N.; Adedeji, A.; Li, M.; Alimardani, R. Hyperspectral imaging for detection of codling moth infestation in GoldRush apples. Postharvest Biol. Technol. 2017, 129, 37–44. [Google Scholar] [CrossRef]
- Ekramirad, N.; Khaled, A.Y.; Donohue, K.D.; Villanueva, R.T.; Adedeji, A.A. Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning. Agriculture 2023, 13, 839. [Google Scholar] [CrossRef]
- Adedeji, A.A.; Ekramirad, N.; Khaled, A.Y.; Parrish, C. Acoustic Emission and Near-Infra Red Imaging Methods for Nondestructive Apple Quality Detection and Classification. In Nondestructive Quality Assessment Techniques for Fresh Fruits and Vegetables; Springer Nature Singapore: Singapore, 2022; pp. 301–329. [Google Scholar]
- Khaled, A.Y.; Ekramirad, N.; Parrish, C.A.; Eberhart, P.S.; Doyle, L.E.; Donohue, K.D.; Adedeji, A.A. Non-destructive detection of codling moth infestation in apples using acoustic impulse response signals. Biosyst. Eng. 2022, 224, 68–79. [Google Scholar] [CrossRef]
- Breth, D.I. Controlling Oriental Fruit Moth in Peaches Using Mating Disruption and Assessing the Problem in Apples. 2002. Available online: https://ecommons.cornell.edu/bitstream/handle/1813/45908/2002breth-NYSIPM.pdf?sequence=1&isAllowed=y (accessed on 16 April 2023).
- Hong, Y.A.; Gallardo, R.K.; Fan, X.; Atallah, S.; Gómez, M.I. Phytosanitary Regulation of Washington Apple Producers under an Apple Maggot Quarantine Program. J. Agric. Resour. Econ. 2019, 44, 646–663. [Google Scholar]
- Rozsypal, J.; Koštál, V.; Zahradníčková, H.; Šimek, P. Overwintering strategy and mechanisms of cold tolerance in the codling moth (Cydia pomonella). PLoS ONE 2013, 8, e61745. [Google Scholar] [CrossRef]
- Mditshwa, A.; Fawole, O.A.; Opara, U.L. Recent developments on dynamic controlled atmosphere storage of apples—A review. Food Packag. Shelf Life 2018, 16, 59–68. [Google Scholar] [CrossRef]
- Fathizadeh, Z.; Aboonajmi, M.; Hassan-Beygi, S.R. Nondestructive methods for determining the firmness of apple fruit flesh. Inf. Process. Agric. 2021, 8, 515–527. [Google Scholar] [CrossRef]
- Nturambirwe, J.F.I.; Opara, U.L. Machine learning applications to non-destructive defect detection in horticultural products. Biosyst. Eng. 2020, 189, 60–83. [Google Scholar] [CrossRef]
- Cárdenas-Pérez, S.; Chanona-Pérez, J.; Méndez-Méndez, J.V.; Calderón-Domínguez, G.; López-Santiago, R.; Perea-Flores, M.J.; Arzate-Vázquez, I. Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system. Biosyst. Eng. 2017, 159, 46–58. [Google Scholar] [CrossRef]
- Ma, T.; Xia, Y.; Inagaki, T.; Tsuchikawa, S. Rapid and nondestructive evaluation of soluble solids content (SSC) and firmness in apple using Vis–NIR spatially resolved spectroscopy. Postharvest Biol. Technol. 2021, 173, 111417. [Google Scholar] [CrossRef]
- Du Plessis, A.; Broeckhoven, C.; Guelpa, A.; Le Roux, S.G. Laboratory x-ray micro-computed tomography: A user guideline for biological samples. Gigascience 2017, 6, gix027. [Google Scholar] [CrossRef] [PubMed]
- Shi, J.; Zhang, F.; Wu, S.; Guo, Z.; Huang, X.; Hu, X.; Zou, X. Noise-free microbial colony counting method based on hyperspectral features of agar plates. Food Chem. 2019, 274, 925–932. [Google Scholar] [CrossRef]
- Lu, R. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sens. Instrum. Food Qual. Saf. 2007, 1, 19–27. [Google Scholar] [CrossRef]
- Ma, T.; Li, X.; Inagaki, T.; Yang, H.; Tsuchikawa, S. Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging. J. Food Eng. 2018, 224, 53–61. [Google Scholar] [CrossRef]
- Peirs, A.; Tirry, J.; Verlinden, B.; Darius, P.; Nicolaï, B.M. Effect of biological variability on the robustness of NIR models for soluble solids content of apples. Postharvest Biol. Technol. 2003, 28, 269–280. [Google Scholar] [CrossRef]
- Tian, X.; Li, J.; Wang, Q.; Fan, S.; Huang, W.; Zhao, C. A multi-region combined model for non-destructive prediction of soluble solids content in apple, based on brightness grade segmentation of hyperspectral imaging. Biosyst. Eng. 2019, 183, 110–120. [Google Scholar] [CrossRef]
- Azevedo, V.M.; Dias, M.V.; de Siqueira Elias, H.H.; Fukushima, K.L.; Silva, E.K.; Carneiro, J.D.D.S.; Borges, S.V. Effect of whey protein isolate films incorporated with montmorillonite and citric acid on the preservation of fresh-cut apples. Food Res. Int. 2018, 107, 306–313. [Google Scholar] [CrossRef]
- Vesali, F.; Gharibkhani, M.; Komarizadeh, M.H. An approach to estimate moisture content of apple with image processing method. Aust. J. Crop Sci. 2011, 5, 111–115. [Google Scholar]
- Saeys, W.; De Ketelaere, B.; Darius, P. Potential applications of functional data analysis in chemometrics. J. Chemom. A J. Chemom. Soc. 2008, 22, 335–344. [Google Scholar] [CrossRef]
- Zhang, H.; Song, T.Q.; Wang, K.L.; Wang, G.X.; Hu, H.; Zeng, F.P. Prediction of crude protein content in rice grain with canopy spectral reflectance. Plant Soil Environ. 2012, 58, 514–520. [Google Scholar] [CrossRef]
- Cui, J.; Yang, M.; Son, D.; Cho, S.I.; Kim, G. Hyperspectral imaging for tomato bruising damage assessment of simulated harvesting process impact using wavelength interval selection and multivariate analysis. Appl. Eng. Agric. 2020, 36, 533–547. [Google Scholar] [CrossRef]
- Jan, I.; Rab, A.; Sajid, M.; Ali, A.; Shah, S. Response of apple cultivars to different storage durations. Sarhad J. Agric 2012, 28, 219–225. [Google Scholar]
- Ghafir, S.A.; Gadalla, S.O.; Murajei, B.N.; El-Nady, M.F. Physiological and anatomical comparison between four different apple cultivars under cold-storage conditions. Afr. J. Plant Sci. 2009, 3, 133–138. [Google Scholar]
- Blažek, J.; Hlušičková, I.; Varga, A. Changes in quality characteristics of Golden Delicious apples under different storage conditions and correlations between them. Hortic. Sci. 2003, 30, 81–89. [Google Scholar] [CrossRef]
- Zhang, B.; Zhang, M.; Shen, M.; Li, H.; Zhang, Z.; Zhang, H.; Xing, L. Quality monitoring method for apples of different maturity under long-term cold storage. Infrared Phys. Technol. 2021, 112, 103580. [Google Scholar] [CrossRef]
- Johnston, J.W.; Hewett, E.W.; Hertog, M.L. Postharvest softening of apple (Malus domestica) fruit: A review. New Zealand J. Crop. Hortic. Sci. 2002, 30, 145–160. [Google Scholar] [CrossRef]
- Szymańska-Chargot, M.; Chylińska, M.; Pieczywek, P.M.; Rösch, P.; Schmitt, M.; Popp, J.; Zdunek, A. Raman imaging of changes in the polysaccharides distribution in the cell wall during apple fruit development and senescence. Planta 2016, 243, 935–945. [Google Scholar] [CrossRef] [PubMed]
- Kuckenberg, J.; Tartachnyk, I.; Noga, G. Evaluation of fluorescence and remission techniques for monitoring changes in peel chlorophyll and internal fruit characteristics in sunlit and shaded sides of apple fruit during shelf-life. Postharvest Biol. Technol. 2008, 48, 231–241. [Google Scholar] [CrossRef]
- Cortés, V.; Blasco, J.; Aleixos, N.; Cubero, S.; Talens, P. Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review. Trends Food Sci. Technol. 2019, 85, 138–148. [Google Scholar] [CrossRef]
- Peirs, A.; Schenk, A.; Nicolaï, B.M. Effect of natural variability among apples on the accuracy of VIS-NIR calibration models for optimal harvest date predictions. Postharvest Biol. Technol. 2005, 35, 1–13. [Google Scholar] [CrossRef]
- Nicolai, B.M.; Theron, K.I.; Lammertyn, J. Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemom. Intell. Lab. Syst. 2007, 85, 243–252. [Google Scholar] [CrossRef]
- Susič, N.; Žibrat, U.; Sinkovič, L.; Vončina, A.; Razinger, J.; Knapič, M.; Gerič Stare, B. From genome to field—Observation of the multimodal nematicidal and plant growth-promoting effects of Bacillus firmus I-1582 on tomatoes using hyperspectral remote sensing. Plants 2020, 9, 592. [Google Scholar] [CrossRef]
- Bobelyn, E.; Serban, A.-S.; Nicu, M.; Lammertyn, J.; Nicolai, B.M.; Saeys, W. Postharvest quality of apple predicted by NIR-spectroscopy: Study of the effect of biological variability on spectra and model performance. Postharvest Biol. Technol. 2010, 55, 133–143. [Google Scholar] [CrossRef]
- Guo, Z.-M.; Huang, W.-Q.; Peng, Y.-K.; Wang, X.; Li, J. Impact of region of interest selection for hyperspectral imaging and modeling of sugar content in apple. Mod. Food Sci. Technol. 2014, 30, 59–63. [Google Scholar]
- Dong, J.; Guo, W. Nondestructive determination of apple internal qualities using near-infrared hyperspectral reflectance imaging. Food Anal. Methods 2015, 8, 2635–2646. [Google Scholar] [CrossRef]
Samples Condition | Regression Model | Rc | RMSEC | Rp | RMSEP |
---|---|---|---|---|---|
Control | PLSR | 0.94 | 0.17 | 0.97 | 0.25 |
SVR | 0.93 | 0.19 | 0.93 | 0.30 | |
Infested | PLSR | 0.97 | 0.13 | 0.71 | 0.24 |
SVR | 0.65 | 0.24 | 0.44 | 0.24 | |
Combination | PLSR | 0.85 | 0.24 | 0.54 | 0.33 |
SVR | 0.89 | 0.22 | 0.49 | 0.34 |
Quality Parameter | Samples Condition | Regression Model | Rc | RMSEC | Rp | RMSEP |
---|---|---|---|---|---|---|
pH | Control, stored at 0 °C | PLSR | 0.94 | 0.17 | 0.97 | 0.25 |
SVR | 0.93 | 0.19 | 0.93 | 0.30 | ||
Firmness | Control, stored at 0 °C | PLSR | 0.95 | 1.26 | 0.93 | 1.62 |
SVR | 0.96 | 1.21 | 0.95 | 1.45 | ||
SSC | Control, stored at 0 °C | PLSR | 0.95 | 0.53 | 0.90 | 0.81 |
SVR | 0.95 | 0.56 | 0.92 | 0.89 | ||
MC | Control, stored at 0 °C | PLSR | 0.85 | 0.81 | 0.88 | 0.88 |
SVR | 0.84 | 0.82 | 0.91 | 0.82 |
Quality Parameter | Samples Condition | Regression Model | Rc | RMSEC | Rp | RMSEP |
---|---|---|---|---|---|---|
pH | Control, stored at 4 °C | PLSR | 0.92 | 0.29 | 0.89 | 0.51 |
SVR | 0.95 | 0.25 | 0.76 | 0.90 | ||
Firmness | Control, stored at 4 °C | PLSR | 0.95 | 1.37 | 0.74 | 3.19 |
SVR | 0.67 | 2.20 | 0.54 | 2.54 | ||
SSC | Control, stored at 4 °C | PLSR | 0.88 | 0.68 | 0.58 | 0.91 |
SVR | 0.99 | 0.36 | 0.77 | 0.80 | ||
MC | Control, stored at 4 °C | PLSR | 0.98 | 0.39 | 0.66 | 1.08 |
SVR | 0.96 | 0.58 | 0.95 | 0.87 |
Quality Parameter | Samples Condition | Regression Model | Rc | RMSEC | Rp | RMSEP |
---|---|---|---|---|---|---|
pH | Control, stored at 10 °C | PLSR | 0.97 | 0.18 | 0.94 | 0.40 |
SVR | 0.97 | 0.20 | 0.96 | 0.35 | ||
Firmness | Control, stored at 10 °C | PLSR | 0.98 | 0.98 | 0.95 | 0.97 |
SVR | 0.99 | 0.10 | 0.98 | 1.77 | ||
SSC | Control, stored at 10 °C | PLSR | 0.92 | 0.65 | 0.73 | 1.03 |
SVR | 0.71 | 0.92 | 0.56 | 1.19 | ||
MC | Control, stored at 10 °C | PLSR | 0.97 | 0.53 | 0.94 | 1.31 |
SVR | 0.99 | 0.10 | 0.80 | 1.16 |
Quality Parameter | Selected Wavelengths (nm) | Regression Model | Rc | RMSEC | Rp | RMSEP |
---|---|---|---|---|---|---|
pH | 950, 1161, 1221, 1224, 1271, 1274, 1334, 1378, 1381, 1471, 1474, 1477, 1481, 1584 | PLSR | 0.90 | 0.20 | 0.92 | 0.26 |
SVR | 0.98 | 0.12 | 0.92 | 0.29 | ||
SSC | 1081, 1117, 1157, 1161, 1164, 1167, 1238, 1241, 1244, 1248, 1251, 1254, 1258, 1301, 1354, 1358, 1368, 1477, 1481 | PLSR | 0.97 | 0.48 | 0.91 | 0.84 |
SVR | 0.94 | 0.57 | 0.92 | 0.85 | ||
Firmness | 957, 1164, 1184, 1248, 1321, 1324, 1477 | PLSR | 0.80 | 1.77 | 0.95 | 1.66 |
SVR | 0.87 | 1.68 | 0.94 | 1.84 | ||
MC | 953, 957, 1020, 1054, 1071, 1074, 1184, 1188, 1241, 1291, 1344, 1348 | PLSR | 0.83 | 0.83 | 0.89 | 0.80 |
SVR | 0.83 | 0.84 | 0.90 | 0.85 |
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Khaled, A.Y.; Ekramirad, N.; Donohue, K.D.; Villanueva, R.T.; Adedeji, A.A. Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation. Agriculture 2023, 13, 1086. https://doi.org/10.3390/agriculture13051086
Khaled AY, Ekramirad N, Donohue KD, Villanueva RT, Adedeji AA. Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation. Agriculture. 2023; 13(5):1086. https://doi.org/10.3390/agriculture13051086
Chicago/Turabian StyleKhaled, Alfadhl Y., Nader Ekramirad, Kevin D. Donohue, Raul T. Villanueva, and Akinbode A. Adedeji. 2023. "Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation" Agriculture 13, no. 5: 1086. https://doi.org/10.3390/agriculture13051086
APA StyleKhaled, A. Y., Ekramirad, N., Donohue, K. D., Villanueva, R. T., & Adedeji, A. A. (2023). Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation. Agriculture, 13(5), 1086. https://doi.org/10.3390/agriculture13051086