Improving the detection performance of mango firmness using a self-designed pneumatic-electromagnetic-driven impact device with the same impact force control

Authors

  • Kun Tao 1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 2. Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture, Hangzhou 310058, China 3. Key Laboratory of on-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
  • Changqing An 1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 2. Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture, Hangzhou 310058, China 3. Key Laboratory of on-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
  • Shijie Tian 4. College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
  • Huirong Xu 1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 2. Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture, Hangzhou 310058, China 3. Key Laboratory of on-Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China

DOI:

https://doi.org/10.25165/ijabe.v18i4.9645

Keywords:

mango firmness, vibration signal detection, pneumatic-electromagnetic-driven impact device, signal feature extraction, CARS-PLSR

Abstract

Mango firmness is one of the critical indicators for assessing internal quality and taste, as well as an indirect measure of maturity and freshness during ripening. Acoustic vibration technology has been widely applied for nondestructive detection of fruit firmness. However, existing detection systems face the risk of fruit damage, prediction performance limitations, and significant influence of fruit size. This study designed a nondestructive pneumatic-electromagnetic-driven impact device based on acoustic vibration technology for firmness detection of different sizes of mango with the same impact force control. Vibration signals of 156 mangoes were acquired using an embedded accelerometer, and effective vibration signals were selected by comparing the excitation vibration response signals and the free vibration response signals. The correlation between mango reference firmness and vibration signal features was then analyzed. Based on this analysis, a prediction model for mango firmness was developed using partial least squares regression based on competitive adaptive reweighted sampling (CARS-PLSR). The results showed that the energy-type and amplitude-type statistical features in the vibration signals had a good correlation with the reference firmness (|r|≥0.45), and the mango firmness prediction model based on the vibration frequency-domain signals (CARS-PLSR) had the optimal performance. The model’s prediction determination coefficient (R2P), root mean square error of prediction (RMSEP), and relative percent deviation (RPDP) were 0.95, 0.29 N/mm, and 4.20, respectively. Overall, it demonstrated that the pneumatic-electromagnetic-driven impact device integrated with an embedded accelerometer enables accurate and nondestructive detection of mango firmness. The innovative combination of pneumatic control and electromagnetic drive effectively minimizes the impact of fruit size variations and enhances prediction accuracy, demonstrating the significant potential for real-time fruit firmness sorting applications. Keywords: mango firmness, vibration signal detection, pneumatic-electromagnetic-driven impact device, signal feature extraction, CARS-PLSR DOI: 10.25165/j.ijabe.20251804.9645 Citation: Tao K, An C Q, Tian S J, Xu H R. Improving the detection performance of mango firmness using a self-designed pneumatic-electromagnetic-driven impact device with the same impact force control. Int J Agric & Biol Eng, 2025; 18(4): 282–292.

References

Goldberg T, Agra H, Ben-Arie R. Non-destructive measurement of fruit firmness to predict the shelf-life of ‘Hayward’ kiwifruit. Scientia Horticulturae, 2019; 244: 339–342.

Nouri S F, Abdanan Mehdizadeh S A. Design, construction and evaluation of a device for non-destructive measurement of firmness in fruits using vibration analysis (case study: Kiwifruit). Scientia Horticulturae, 2024; 328: 112965.

Sneddon T, Rivera S, Li M, Heyes J, East A. Non-destructive firmness assessment of ‘SunGold’ kiwifruit a three-year study. New Zealand Journal of Crop and Horticultural Science, 2024; 52(3): 195–209.

Rungpichayapichet P, Nagle M, Yuwanbun P, Khuwijitjaru P, Mahayothee B, Müller J. Prediction mapping of physicochemical properties in mango by hyperspectral imaging. Biosystems Engineering, 2017; 159: 109–120.

Li J B, Zhang H L, Zhan B S, Zhang Y F, Li R L, Li J B. Nondestructive firmness measurement of the multiple cultivars of pears by Vis-NIR spectroscopy coupled with multivariate calibration analysis and MC-UVESPA method. Infrared Physics & Technology, 2020; 104: 103154.

Nascimento P A M, Carvalho L C D, Júnior L C C, Pereira F M V, de Almeida Teixeira G H. Robust PLS models for soluble solids content and firmness determination in low chilling peach using near-infrared spectroscopy (NIR). Postharvest Biology and Technology, 2016; 111: 345–351.

Rungpichayapichet P, Chaiyarattanachote N, Khuwijitjaru P, Nakagawa K, Nagle M, Müller J, et al. Comparison of near-infrared spectroscopy and hyperspectral imaging for internal quality determination of ‘Nam Dok Mai’ mango during ripening. Journal of Food Measurement and Characterization, 2023; 17: 1501–1514.

Bhosale A A, Sundaram K K. Firmness prediction of the apple using capacitance measurement. Procedia Technol, 2014; 12: 163–167.

Bian H, Tu P, Hua‐li X, Shi P. Quality predictions for bruised apples based on dielectric properties. Journal of Food Processing and Preservation, 2019; 43(8): e14006.

Fazayeli A, Kamgar S, Nassiri S M, Fazayeli H, de la Guardia M. Dielectric spectroscopy as a potential technique for prediction of kiwifruit quality indices during storage. Information Processing in Agriculture, 2019; 6(4): 479–486.

McGlone V A, Jordan R B. Kiwifruit and apricot firmness measurement by the non-contact laser air-puff method. Postharvest Biology and Technology, 2000; 19(1): 47–54.

Kim K-B, Lee S, Kim M-S, Cho B-K. Determination of apple firmness by nondestructive ultrasonic measurement. Postharvest Biology and Technology, 2009; 52(1): 44–48.

Arai N, Miyake M, Yamamoto K, Kajiwara I, Hosoya N. Soft mango firmness assessment based on Rayleigh waves generated by a laser-induced plasma shock wave technique. Foods, 2021; 10(2): 323.

Ding C Q, Feng Z, Wang D C, Cui D, Li W H. Acoustic vibration technology: Toward a promising fruit quality detection method. Comprehensive Reviews in Food Science and Food Safety, 2021; 20(2): 1655–1680.

Fathizadeh Z, Aboonajmi M, Beygi S R H. Nondestructive firmness prediction of apple fruit using acoustic vibration response. Scientia Horticulturae, 2020; 262: 109073.

Pourkhak B, Mireei S A, Sadeghi M, Hemmat A. Multi-sensor data fusion in the nondestructive measurement of kiwifruit texture. Measurement, 2017; 101: 157–165.

Zhang H, Wu J, Zhao Z Q, Wang Z P. Nondestructive firmness measurement of differently shaped pears with a dual-frequency index based on acoustic vibration. Postharvest Biology and Technology, 2018; 138: 11–18.

Zhang W, Lv Z Z, Xiong S L. Nondestructive quality evaluation of agroproducts using acoustic vibration methods-A review. Critical Reviews in Food Science and Nutrition, 2018; 58(14): 2386–2397.

Aboonajmi M, Jahangiri M, Hassan-Beygi S R. A review on application of acoustic analysis in quality evaluation of agro-food products. Journal of Food Processing and Preservation, 2015; 39(6): 3175–3188.

Tian S J, Wang J P, Xu H R. Firmness measurement of kiwifruit using a self-designed device based on acoustic vibration technology. Postharvest Biology and Technology, 2022; 187: 111851.

Blanke M M. Non-invasive assessment of firmness and NIR sugar (TSS) measurement in apple, pear and kiwi fruit. Erwerbs-Obstbau, 2013; 55: 19–24.

Feng J, Wohlers M, Olsson S R, White A, McGlone V A, Seelye R J, et al. Comparison between an acoustic firmness sensor and a near-infrared spectrometer in segregation of kiwifruit for storage potential. XXIX International Horticultural Congress on Horticulture: Sustaining Lives, Livelihoods and Landscapes (IHC2014): 1119, 2014. doi: 10.17660/ ActaHortic.2016.1119.39.

Blanes C, Ortiz C, Mellado M, Beltrán P. Assessment of eggplant firmness with accelerometers on a pneumatic robot gripper. Computers and Electronics in Agriculture, 2015; 113: 44–50.

Cortés V, Blanes C, Blasco J, Ortíz C, Aleixos N, Mellado M, et al. Integration of simultaneous tactile sensing and visible and near-infrared reflectance spectroscopy in a robot gripper for mango quality assessment. Biosystems Engineering, 2017; 162: 112–123.

Lin H-C, Ye Y-C. Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms. Advances in Mechanical Engineering, 2019; 2019(1). doi: 10.1177/ 1687814018816751.

Khaled A Y, Ekramirad N, Parrish C A, Eberhart P S, Doyle L E, Donohue K D, et al. Non-destructive detection of codling moth infestation in apples using acoustic impulse response signals. Biosystems Engineering, 2022; 224: 68–79.

Mou Y, You X, Xu D Q, Zhou L, Zeng W, Yu S J. Regularized multivariate scatter correction. Chemometrics and Intelligent Laboratory Systems, 2014; 132: 168–174.

Chi K P, Lin J R, Chen M, Chen J J, Chen Y M, Pan T. Changeable moving window-standard normal variable transformation for Visible-NIR spectroscopic analyses. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2024; 308: 123726.

Wold S, Sjöström M, Eriksson L. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 2001; 58(2): 109–130.

Liu K, Chen X J, Li L M, Chen H L, Ruan X K, Liu W B. A consensus successive projections algorithm – multiple linear regression method for

analyzing near infrared spectra. Analytica Chimica Acta, 2015; 858: 16–23. Li H D, Liang Y Z, [31] Xu Q S, Cao D S. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 2009; 648: 77–84.

Chen N, Liu Z, Zhang T Y, Lai Q R, Zhang J S, Wei X L, et al. Research on prediction of yellow flesh peach firmness using a novel acoustic realtime detection device and Vis/NIR technology. LWT, 2024; 209: 116772.

Ding C Q, Wu H L, Feng Z, Wang D C, Li W H, et al. Online assessment of pear firmness by acoustic vibration analysis. Postharvest Biology and Technology, 2020; 160: 111042.

Downloads

Published

2025-08-21

How to Cite

Tao, K., An, C., Tian, S., & Xu, H. (2025). Improving the detection performance of mango firmness using a self-designed pneumatic-electromagnetic-driven impact device with the same impact force control. International Journal of Agricultural and Biological Engineering, 18(4), 282–292. https://doi.org/10.25165/ijabe.v18i4.9645

Issue

Section

Information Technology, Sensors and Control Systems