Non-destructive prediction of apple SSC/TAC and firmness based on multilayer autoencoder and multilayer perceptron

Abstract

The physical and biochemical indices of apple fruit serve as crucial phenotypic parameters in genomic cultivation. Among them, the soluble solids content (SSC), titratable acid content (TAC), and firmness are the three most paramount parameters that directly reflect the inner quality of apples. To achieve a more accurate prediction of the internal physicochemical indicators, a novel non-destructive detection approach fused with nonlinear and multi-features using a multilayer autoencoder (MAE) was proposed. For non-destructive detection of internal physicochemical indicators, a dielectric spectrum device was employed to gather the electrical parameters of 300 Fuji genomic sample apples. These measurements were taken at nine distinct frequencies, spanning from 0.158 to 3,980 kHz. For the normal control group for validation, to precisely detect its physical and biochemical parameters, special physicochemical analysis apparatuses were utilized to collect data on firmness, SSCs, and TACs. To predict key genomic parameters such as firmness and SSC/TAC, three classical regression models were implemented and subject to comprehensive analysis. The experimental results reveal that the nonlinear feature variable selection based on MAE and multilayer perceptron (MLP) achieved the best prediction performance. Specifically, the correlation coefficients (R²) for predicting firmness and SSC/TAC reached up to 0.88 and 0.82, respectively, with root mean square errors (RMSEs) of 0.66 and 2.08. Regarding state-of-the-art dimensionality reduction, MAE can be validated as a nonlinear feature extraction methodology for complex electrical parameters. It demonstrates robust applicability in predicting a diverse array of other genomic parameters.

Keywords

Apple / electrical parameters / multilayer autoencoder / nonlinear feature variable / firmness / SSC/TAC

References

[1] Teixeira GCM,Mattiuz B.Spraying of calcium carbonate nanoparticles on pineapple fruit reduces sunburn damage.S Afr J Bot2022;148:643-51

[2] Zeng S,Guo W,Yang S.Differences in optical properties and internal qualities of ‘Fuji’ apple produced in different areas of the Loess Plateau region.Eur J Agron2022;140:126608

[3] Serrano-Finetti E,Alejos S.Toward noninvasive monitoring of plant leaf water content by electrical impedance spectroscopy.Comput Electron Agric2023;210:107907

[4] Van Haeverbeke M, De Baets B, Stock M. Plant impedance spectroscopy: a review of modeling approaches and applications.Front Plant Sci2023;14:1187573 PMCID:PMC10426379

[5] Astashev ME,Kolik LV.Electric impedance spectroscopy in trees condition analysis: theory and experiment.Sensors2022;22:8310 PMCID:PMC9658313

[6] Cheng J,Huang Y,Lu C.Application status and prospect of impedance spectroscopy in agricultural product quality detection.Agriculture2022;12:1525

[7] Fathizadeh Z,Hassan-Beygi SR.Nondestructive methods for determining the firmness of apple fruit flesh.Inf ProcessAgric2021;8:515-27

[8] Hiruta T,Hosoya N,Kajiwara I.Firmness evaluation of postharvest pear fruit during storage based on a vibration experiment technique using a dielectric elastomer actuator.Postharvest Biol Technol2021;182:111697

[9] Feng L,Dong Z,Zhang H.Electrical impedance spectroscopy: potential non-destructive method for aflatoxin B1 in peanut.Food Meas2024;18:9353-63

[10] Tang Y,Liang Q,Che J.Non-destructive testing of the internal quality of korla fragrant pears based on dielectric properties.Horticulturae2024;10:572

[11] Yu Y.Is this pear sweeter than this apple? A universal SSC model for fruits with similar physicochemical properties.Biosyst Eng2023;226:116-31

[12] Liu D,Wang G,Wang C.Non-destructive sugar content assessment of multiple cultivars of melons by dielectric properties.J Sci Food Agric2021;101:4308-14

[13] Feng L,Sui X,Kong A.Effect of fruit ripeness on electrical impedance spectrum parameters.LWT2024;208:116751

[14] Yang Z,Zhang Y,Chen G.Design of a tomato sorting device based on the multisine-FSR composite measurement.Agronomy2023;13:1778

[15] Velásquez S,Peña N,Gutierrez N.Effect of coffee cherry maturity on the performance of the drying process of the bean: sorption isotherms and dielectric spectroscopy.Food Control2021;123:107692

[16] Ibba P,Abera BD,Petti L.Bio-impedance and circuit parameters: an analysis for tracking fruit ripening.Postharvest Biol Technol2020;159:110978

[17] An J,Xiong L,Lan H.Discrimination of inner injury of Korla fragrant pear based on multi-electrical parameters.Foods2023;12:1805 PMCID:PMC10177895

[18] Ibba P,Cantarella G.Design and validation of a portable AD5933–based impedance analyzer for smart agriculture.IEEE Access2021;9:63656-75

[19] Żywica R,Wójcik J.Application of food products electrical model parameters for evaluation of apple purée dilution.J Food Eng2005;67:413-8

[20] Bian H.The simultaneous monitoring of physiological change of apple based on dielectric parameters in static pressure.J Chin Inst Food Sci Technol2019;19:279-85

[21] Cai C,Ma H.Non-destructive detection of freshness grade for apple fruit based on bio-impedance properties.Trans Chin Soc Agric Mach2013;44:147-52

[22] Zhou Z,Gu Z.Deep learning approaches for object recognition in plant diseases: a review.Intell Robot2023;3:514-37

[23] Berahmand K,Abadeh MN,Xu Y.SDAC-DA: semi-supervised deep attributed clustering using dual autoencoder.IEEE Trans Knowl Data Eng2024;36:6989-7002

[24] Berahmand K,Xu Y.DAC-HPP: deep attributed clustering with high-order proximity preserve.Neural Comput Applic2023;35:24493-511

[25] Yongnian Z,Yihua B,Jieyu X.Tomato maturity detection based on bioelectrical impedance spectroscopy.Comput Electron Agric2024;227:109553

[26] Badfar M,Chinnam R.State-of-charge estimation across battery chemistries: a novel regression-based method and insights from unsupervised domain adaptation.J Power Sources2025;628:235760

[27] Liu D.Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy.Int J Food Prop2017;20:S2596-611

[28] Guo W,Liu D.Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy.Comput Electron Agric2015;117:226-33

[29] Guo W,Zhu X.Nondestructive detection of soluble solids content of apples from dielectric spectra with ANN and chemometric methods.Food Bioprocess Technol2015;8:1126-38

[30] Reyes, A.; Yarlequé, M.; Castro, W.; Chuquizuta, S. Determination of dielectric properties of the red delicious apple and its correlation with quality parameters. In 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL), Singapore, Nov 19-22, 2017; IEEE, 2017; pp. 19-22.

[31] Bian H,Tu P.Determination of physicochemical quality of bruised apple using dielectric properties.Food Meas2020;14:2590-9

[32] Wang D,Liang J.Nondestructive detection of kiwifruit treated with N-(2-chloro-4-pyridyl)-N’-phenylurea by electrical method.J Food Process Preserv2020;44:e14860

[33] Mohammed M,Aljabr A.Prediction of date fruit quality attributes during cold storage based on their electrical properties using artificial neural networks models.Foods2022;11:1666 PMCID:PMC9180397

[34] Wang D.Intelligent feature extraction, data fusion and detection of concrete bridge cracks: current development and challenges.Intell Robot2022;2:391-406

[35] Weng YK,Cheng CW.Use of modern regression analysis in the dielectric properties of foods.Foods2020;9:1472 PMCID:PMC7602722

[36] Niu Y,Ye L.Nondestructive detection of kiwifruit infected with Penicillium expansum based on electrical properties.Postharvest Biol Technol2023;195:112150

[37] Bian H,Hua-li X.Quality predictions for bruised apples based on dielectric properties.J Food Process Preserv2019;43:e14006

[38] Aparisi P, Fortes Sanchez E, Contat Rodrigo L, Masot Peris R, Laguarda-miro N. A rapid electrochemical impedance spectroscopy and sensor-based method for monitoring freeze-damage in tangerines.IEEE Sensors J2021;21:12009-18

[39] Guo W,Wang M.Soluble solids content detection of postharvest apples based on frequency spectrum of dielectric parameters.Trans Chin Soc Agric Mach2013;44:132-7

[40] Shang L,Nelson SO.Apple variety identification based on dielectric spectra and chemometric methods.Food Anal Methods2015;8:1042-52

[41] Wang R,Ren X.Nondestructive detection of apple watercore disease based on electric features.Trans Chin Soc Agric Eng2018;34:129-36

[42] Hinton GE.Reducing the dimensionality of data with neural networks.Science2006;313:504-7

[43] Vincent, P.; Larochelle, H.; Bengio, Y.; Manzagol, P. A. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, 2008; pp. 1096-103.

[44] Berahmand K,Salehi ES,Xu Y.Autoencoders and their applications in machine learning: a survey.Artif Intell Rev2024;57:10662

[45] Cacciarelli D.Hidden dimensions of the data: PCA vs autoencoders.Qual Eng2023;35:741-50

[46] Tsakiridis NL,Kokkas S,Tziolas NV.In situ grape ripeness estimation via hyperspectral imaging and deep autoencoders.Comput Electron Agric2023;212:108098

[47] Plakias S.A novel information processing method based on an ensemble of auto-encoders for unsupervised fault detection.Comput Ind2022;142:103743

[48] Yuan X,Wang Y.A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data.Control Eng Pract2020;104:104614

[49] Meng L,Xue Y.Research on denoising sparse autoencoder.Int J Mach Learn Cyber2017;8:1719-29

[50] Li P,Li J.A comprehensive survey on design and application of autoencoder in deep learning.Appl Soft Comput2023;138:110176

[51] Ahmed S.A software framework for predicting the maize yield using modified multi-layer perceptron.Sustainability2023;15:3017

[52] Shang L,Schäfer D.Surrogate modelling of a detailed farm-level model using deep learning.J Agric Econ2024;75:235-60

[53] Zhang H,Tang Y,Niu H.Non-destructive detection of the fruit firmness of Korla fragrant pear based on electrical properties.Int J Agric Biol Eng2022;15:216-21

[54] Cai C,Ma H.Nondestructive classification of internal quality of apple based on dielectric feature selection.Trans Chin Soc Agric Eng2013;29:279-87https://www.cabidigitallibrary.org/doi/full/10.5555/20143067192. (accessed 2025-02-21)

[55] Drezner Z,Zerom D.A modified Kolmogorov–Smirnov test for normality.Commun Stat Simul Comput2010;39:693-704

[56] Pearson ES.Tests for rank correlation coefficients: III. Distribution of the transformed Kendall coefficient.Biometrika1962;49:185-91